All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller quad skate variant of hockey team sports, it is of great interest to automatically track player’s movements and positions, player’s sticks and, also, making other judgments, such as being able to locate the ball. In this work, we introduce a real-time pipeline composed by an object detection model, created specifically for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and quick motions, our deep learning object detection model effectively identifies and tracks, in real-time, important visual elements such as: ball; players; sticks; referees; crowd; goalkeeper; and goal. Using a curated dataset composed by a collection of videos of rink hockey, comprising 2525 annotated frames, we trained and evaluated the algorithm performance and compare it to state of the art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80%, and presents a good performance in terms of accuracy and speed, according to our results, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected one important event type in rink hockey games, the occurrence of penalties.

In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.

This dossier is aimed at individuals involved in the development, implementation, and application of AI-supported systems in the field of education, particularly those who are dealing with metadata in this context. The insights and recommendations described in this dossier are based on interviews with five experts from the USA and UK, research on metadata standards, and our personal experiences in the context of the INVITE competition. This dossier provides an overview of the current development of learning metadata and existing standards. It shows the efforts and obstacles to introducing those metadata standards from an international perspective based on interviews with five experts in the field. Furthermore, this dossier shows the international experiences in selecting and using metadata standards and how these findings can inspire discussions in the German-speaking discourse regarding the use of metadata in education.

Vertical Indoor Farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use Deep Learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s eye and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering and fruiting classes. All trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.

Aiming at the problem of UAV cluster configuration reconstruction, in order to obtain the cluster reconstruction method, the theory and simulation of cluster reconstruction control strategy is studied by simplifying the model，deducing the theoretical formula and analyzing the example. Taking 10 UAVs cluster as an example, the distribution of UAVs in different configurations was analyzed. According to the change of cluster configuration, the principle of formation reconstruction and the execution ability of configuration, the cluster capability was analyzed. The group behavior is modeled by group behavior rules, reconfiguration topology analysis and reconfiguration cost measurement. Based on the cluster parameters and configuration characteristics, simulation experiments were carried out to obtain the change curve of the reconstruction cost and time and the trajectory movement diagram of the UAV in different configurations.

This paper proposed a three-layer distributed simulation network architecture: 1. Distributed virtual simulation network, 2. Perception and control sub-network, and 3. Collaborative communication service network. This simulation architecture runs on a distributed platform. The platform can provide a unique virtual scenario and several simulation services to verify the individual robot system's basic perception, control, and planning algorithms and the distributed collaborative algorithms of heterogeneous multi-robot systems. Furthermore, we designed a simulation experiment scenario for classical heterogeneous robot systems such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). At last, We gave a rough estimation model of the bandwidth.

As technology and multimedia production have advanced, there has been a significant rise in attacks on digital media, resulting in duplicated, fraudulent, and altered data and the infringement of copyright laws. This paper presents a robust and secure digital image watermarking technique that has been implemented in the spatial domain and exploits the erratic and chaotic behaviour of the powerful elementary cellular automata rule-30. The crucial characteristics of the watermarking system, i.e., imperceptibility, capacity, and robustness, have been perfectly balanced by the suggested blind watermarking technique. In this approach, prior to embedding, the grayscale watermark image is downsized to its two Most Significant Bits (MSBs). Then, the 2-MSBs watermark is encrypted using an ECA rule-30 so as to level up the security attribute of the system. Then, the host image is scrambled using ECA rule-30 to distribute the watermark pixels throughout the host image and thus achieve the highest robustness against geometrical attacks. Finally, the encrypted watermark data is embedded into the scrambled host image using the ECA rule-30-based embedding key. The proposed method performs better in terms of imperceptibility, capacity, and robustness when compared to several systems with similar competencies. The simulation's findings demonstrate strong imperceptibility as evaluated by the Peak Signal-to-Noise Ratio (PSNR), which has an average value of 58.3735 dB and a high payload. The experimental outcomes, observed across a diverse range of standardized attack scenarios, unequivocally establish the ascendancy of the proposed algorithm over competing methodologies in the realm of image watermarking.

The Vu Gia - Thu Bon River Basin (VG-TB) is confronting numerous challenges related to water security (WS), particularly in the face of the impacts of climate change. These challenges manifest in salinity intrusion, altered rainfall patterns, and reduced water supply in downstream areas. To assess the current state of water security in the basin, the study employed statistical analysis methods, Process Analysis Method (PAM), SMART, and Analytic Hierarchy Process (AHP) to develop a comprehensive Assessment Framework for Water Security (AFWS) in the VG-TB. The AFWS encompasses five key dimensions, 17 indicators, and 28 variables, aligning with the UN-Water definition of water security and addressing the Sustainable Development Goal 6 (SDG6) criteria for global water sustainability. It also adheres to the Asian Development Bank's (ADB) approach to water security assessment as outlined in the Asian Water Development Outlook (AWDO) reports. This allows for the computation of a comprehensive Water Security Index (WSI) for specific regions, serving as a foundation for decision-making and policy formulation aimed at enhancing water security in the context of climate change and facilitating sustainable basin development in the future.

Background: Circulating rare cells participate in breast cancer evolution as systemic components of the disease and thus, are a source of theranostic information. Exploration of cancer-associated rare cells is in its infancy. We determined the validity of a rare-cell detection platform and demonstrated the usefulness of a certain rare cell subset as a novel approach to characterize the breast cancer system. Methods: Linearity of the Rarmax® platform was established using a spike-in approach. The platform includes red blood cell lysis, leukocyte depletion and high-resolution fluorescence image recording. Rare cell analysis was conducted on 28 samples (before and after surgery) from 14 patients with breast cancer, 20 healthy volunteers and 9 non-cancer control volunteers. In-depth identification of rare cells, including circulating tumor cells, endothelial-like cells, erythroblasts and inflammation-associated cells, was performed using a phenotype and morphology-based classification system. Results: The platform performed linearly over a range of 5-950 spiked cells, with an average recovery of 84.6%. Circulating epithelial and endothelial-like cell subsets have been demonstrated to be associated with or independent of cancer with tumor presence. Furthermore, certain cell patterns may be associated with treatment-related adverse effects. The sensitivity in detecting tumor-presence and cancer-associated abnormality before surgery was 50% and 85.7%, respectively and the specificity was 100% and 96.6%, respectively. Conclusion: The present study supports the idea of a cancer-associated rare cell abnormality to represent tumor entities as well as systemic cancer. The latter is independent of the apparent clinical cancer.

Artificial general intelligence (AGI) progression metrics indicate AGI will occur within decades. No proof exists that AGI will benefit humans and not harm or eliminate humans. I propose a set of logically distinct conceptual components that are necessary and sufficient to 1) ensure various AGI scenarios will not harm humanity and 2) robustly align AGI and human values and goals. By systematically addressing pathways to malevolent AI we can induce the methods/axioms required to redress them. Distributed ledger technology (DLT, ‘blockchain’) is integral to this proposal, e.g. ‘smart contracts’ are necessary to address evolution of AI that will be too fast for human monitoring and intervention. The proposed axioms: 1) Access to technology by market license. 2) Transparent ethics embodied in DLT. 3) Morality encrypted via DLT. 4) Behavior control structure with values at roots. 5) Individual bar-code identification of critical components. 6) Configuration Item (from business continuity/disaster recovery planning). 7) Identity verification secured via DLT. 8) ‘Smart’ automated contracts based on DLT. 9) Decentralized applications - AI software modules encrypted via DLT. 10) Audit trail of component usage stored via DLT. 11) Social ostracism (denial of resources) augmented by DLT petitions. 12) Game theory and mechanism design.

Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer’s disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcel-lular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.

Originally developed to image the shadow region of the central black hole in Sagittarius A* and in the nearby galaxy M87, the Event Horizon Telescope (EHT) provides deep, very high angular resolution data on other AGN sources too. The challenges of working with EHT data have spurred the development of new image reconstruction algorithms. This work briefly reviews the status of the EHT and its utility for observing AGN sources, with emphasis on novel imaging techniques that offer the promise of better reconstructions at 1.3 mm and other wavelengths.

This article is an effort to scrutinize the role of Information Technology development in the chronological transformation of Urban Planning domain using the exploratory research approach. In this research, it is argued that the theoretical and practical understanding of Urban Planning should absorb and integrate the bright outcome of the rise of Information and Technology to foster congruent future urban development. The article addresses the trends of transformation in the urban planning domain through the myopic lens of the expansion of information and communications technology era followed by investigating the key drivers shaping the interaction between modern-day urban planning and information technology considering both the dark and bright sides into account.

Background: The research is conducted against the backdrop of the rising incidence of diabetes and the demand for novel approaches to improve patient outcomes. With the goal of revolutionizing conventional methods and addressing the changing healthcare landscape, the study concentrated on the application of AI technology in diabetes treatment. Objective: Examining the various ways AI can be used in diabetes care and assessing how it can enhance automated screenings, tailored treatment recommendations, predictive modeling, and patient self-management were the main goals of the study. Result: The findings demonstrated how well AI works to anticipate a patient's unique reaction to a therapy, assist medical personnel in making decisions, and give patients more control over their own care. Artificial intelligence (AI)-powered solutions showed promise in improving real-time monitoring and encouraging patient adherence, supporting a comprehensive strategy for managing diabetes. Conclusion: The research indicates that artificial intelligence (AI) has great potential to change the way diabetes is treated. However, it also highlights the need for more research, ethical issues, and validation of AI. The results advocate for joint efforts to ensure responsible and patient-centered technology innovation in diabetes care and encourage the integration of AI with new technologies.

Continuum robots play the role of end-effector in various surgical robots and endoscopic devices. The robots may be categorized as rigid or soft continuum robots (SCRs). While the SCRs have proven advantages such as safety and compliance, more research and development are required to enhance their capability for specific medical scenarios. This research aims at designing a soft robot considering concepts of geometric and kinematic similarities. The chosen application is a semi-invasive medical application known as Transesophageal Echocardiography (TEE), but the methodology can be implemented in other similar applications. Since established rigid carrier construction make use of principles we find as well embodied in vertebral spines, the paragon for soft robot solution can be the phylogenetic precursor of the spine, the Chorda dorsalis. The feasibility of fabrication of a soft endoscopic device derived from that paragon within a robotics laboratory was shown empirically by producing a three-segment pneumatic SCR. The kinematic model was proposed based on the method of transformation matrices, and an algorithm based on a self-organizing map (SOM) was proposed and applied to realize kinematic similarity. The simulation results indicate that the algorithm acting as an open-loop controller forces the soft robot tip to follow the desired path which in this case is the path of the rigid probe. It is concluded that the solution provides a soft robot that can surrogate and succeed the traditional rigid counterpart owing to size, workspace, and kinematics.

This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset.

According to the World Health Organization, about 15% of the world’s population has some form of disability. Assistive Technology, in this context, contributes directly to the overcoming of difficulties encountered by people with disabilities in their daily lives, allowing them to receive education and become part of the labor market and society in a worthy manner. Assistive Technology has made great advances in its integration with Artificial Intelligence of Things (AIoT) devices. AIoT processes and analyzes the large amount of data generated by IoT devices and applies Artificial Intelligence models, specifically Machine Learning, to discover patterns for generating insights and assisting in decision making. Based on a systematic literature review, this article aims at identifying the Machine Learning models used in multiple different research about Artificial Intelligence of Things applied to Assistive Technology. The survey of the topics approached in this article also highlights the context of such research, their application, IoT devices used, and gaps and opportunities for further development. Survey results show that 50% of the analyzed research address visual impairment, and for this reason, most of the topics cover issues related to computational vision. Portable devices, wearables, and smartphones constituted the majority of IoT devices. Deep Neural Networks represent 81% of the Machine Learning models applied in the reviewed research.

Ideas about Artificial Intelligence appeared about half a century ago, but only now is it becoming an essential element of everyday life. The data provided is getting bigger and bigger and, in this case, we need artificial intelligence that helps us with superhuman powers. The interaction with medicine is getting closer and closer, medicine being a field that continues to be perfected. Machine learning, deep learning, neural networks, computer vision are some of the mechanisms that are already applied in medicine, becoming a trend in healthcare worldwide. Artificial intelligence will be the thing that will change contemporary medicine. Developed countries such as Japan, France, Germany have already implemented artificial intelligence in their medical systems. The help is in medical diagnosis, patient monitoring, personalized therapy, and workflow optimization. Artificial intelligence will help surgeons to perfect their skills, to standardize techniques, to choose the best surgical technique. The goal is to predict complications, reduce diagnostic time, diagnose complex pathologies, guide the surgeon intraoperatively and reduce medical errors. We are at the beginning but the potential is enormous.

The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores into cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

This article describes the transition of an undirected active network, used for numerical and logical analysis, and made up of communicating operators and nodes, and being self-extensible, into a network of semantic structures, and the extensions necessary to handle language. The network, called Active Structure, was infused with knowledge in the form of tens of thousands of definitions taken from a human-readable dictionary. Where the dictionary fell short, from circularity, lack of a hierarchical form for its senses and sub senses, or inadequate detail around verbs, the entries were extensively curated. The result is a machine which has access to every meaning of every word in its vocabulary, and needs to manage the right meaning for every use of a word, a task that our unconscious minds now handle. To demonstrate that such a network can aspire to human-level tasks, we have begun loading an Anti-Money Laundering Act so that the machine can devise and test strategies against money launderers. There is a higher goal being aimed at, the breaking of the Four Pieces Limit. That is, the network is not intended to save money on human cognitive labor, but to raise an absurdly low limit on it.

The scientific fields of complexity, artificial life (ALife), and artificial intelligence (A.I.) share several commonalities: historic, conceptual, methodological, and philosophical. It was possible to develop them only because of information technology, while their origins can be traced back to cybernetics. In this perspective, I'll revise the expectations and limitations of these fields, some of which have their roots in the limits of formal systems. I will use interactions, self-organization, emergence, and balance to compare different aspects of complexity, ALife, and A.I. The paper poses more questions than answers, but hopefully it will be useful to align efforts in these fields towards overcoming --- or accepting --- their limits.

The essence of cross-modal generation and retrieval modeling for image-text interactions lies in semantic consistency. Among these, cross-modal representation alignment serves as the foundational requirement for achieving cross-modal semantic consistency. The crux of our proposed method entails the collaborative training of an image captioning model(referred to as the ’captioner’) and a pair of contrastive encoders for image-text matching(referred to as the ’concoder’). This synergistic approach yields concurrent enhancements in concoder’s performance, captioner’s proficiency, and cross-modal semantic coherence. We coin the proposed method as ‘ReCap’. To begin, we initialize the concoder through knowledge distillation from a pre-trained model. During the training process, we use the output of the initialized concoder as input to a residual attention network, jointly training it with the captioner to achieve semantic consistency. Subsequently, we iteratively update the concoder during the image-text momentum encoding phase using the residual attention network, creating a closed-loop for semantic consistency resolution. Upon completion of training, the concoder and captioner modules can be used independently to perform tasks related to cross-modal retrieval and cross-modal generation of text and images. In order to substantiate the efficacy of our proposed method, we initially conducted empirical experiments encompassing image-text retrieval and image captioning tasks, employing a widely recognized benchmark dataset, MSCOCO. Subsequently, we assessed the fine-grained cross-modal alignment performance of the concoder through an image classification task on the iNaturalist 2018 dataset. The achieved performance metrics notably surpassed those achieved by several incumbent state-of-the-art models, thus validating the proficiency of our method. Finally, motivated by the practical requirements of handling the nature conservation image data, we performed caption annotation for the iNaturalist 2018 dataset. Subsequently, we trained the ReCap on this annotated data. Experimental results demonstrate that our proposed method can maintain semantic consistency between cross-modal retrieval and image caption generation for species with similar visual features but distinct semantics. This achievement signifies a meaningful exploration in the field of cross-modal semantic consistency representation, holding significant practical value in the domain of biological research.

Earth observation satellites can capture images of specific areas in Earth's orbit based on user requirements. Despite the high demand for satellite imagery, satellites remain a scarce resource. When there are many observation tasks that are large and dense, they may interfere with each other due to satellite mobility. To address this issue, a multi-satellite scheduling algorithm based on a task merging mechanism has been proposed. This paper proposes a two-stage approach to the problem: task merging phase and merging task scheduling phase. The task merging phase introduces two methods based on Mean Shift and Complete Graph to generate the merging task set. In the merging task scheduling phase, a multi-satellite scheduling algorithm based on merging tasks is employed to solve the problem. Finally, certain measurement experiments suggest the effectiveness of the algorithm.

The article focuses on breast cancer, mammography, and artificial intelligence. First, breast cancer is a widespread health problem that affects millions of people worldwide, and mammography is a widely adopted screening method. Then it introduced the advantages of mammography after AI participation, and introduced the importance of early detection of breast cancer and the application of artificial intelligence in breast cancer detection and treatment. From these aspects extend to the entire medical field of artificial intelligence issues. Several times throughout the paper, ethical issues that could arise from applying AI to healthcare are highlighted. At the end of the article, the paper describes the continued development of AI-based mammography analysis over the next period of time.

Chronic non-communicable diseases (NCDs) are major public health problems and a significant financial burden on public health systems. By 2030, mortality due to NCDs, such as cardiovascular diseases, cancer, respiratory diseases, and diabetes, is predicted to increase in Brazil. A peculiar aspect of NCDs involves their long-term and integrated care management. This paper proposes a chronic disease management platform based on artificial intelligence to deliver digital health services everywhere. The proposed platform is anchored and built with healthcare 4.0 technologies, such as wearable devices, the internet of medical things, and artificial intelligence cloud-based solutions that allow the deployment of a smart healthcare system. In addition, the paper presents the feasibility of the platform in a diabetes prediction study case. For the study case, an initial dataset was established with bio-impedance, oxygen concentration, pulse rate, skin impedance, and skin temperature attributes. A baseline was implemented with ten regression models to assess the prediction performance of the mean squared error, root mean squared error, and r-squared score to compare predictive findings with capillary blood glucose measurements. Results evidence that the decision tree regressor and three ensemble methods (bagging decision tree regressor, random forest regressor, and AdaBoost regressor) yielded improvement over the other models. Moreover, a comparison among those models revealed that the decision tree regressor outperforms them and presents promissory outcomes.

Educational institutions must identify students who are academically struggling to provide them with necessary support to improve their performance. In this context, recommendation systems powered by deep learning techniques are vital for detecting and categorizing such students. These systems help students plan their future by uncovering patterns in their historical academic data. This study introduces a new deep learning model designed to classify academically underperforming students in educational settings. The model incorporates a Gated Recurrent Neural Network (GRU) and includes specific neural network features like a dense layer, max-pooling layer, and the ADAM optimization algorithm. The model's training and evaluation were conducted using a dataset comprising 15,165 student assessment records from various academic institutions. The performance of the developed GRU model was benchmarked against other educational recommendation systems, including Recurrent Neural Network models, AdaBoost, and the Artificial Immune Recognition System v2. The proposed GRU model demonstrated remarkable accuracy, achieving an overall rate of 99.70%.

In contemporary construction, the prevalence of vibration serviceability issues in lightweight and slender structures has become increasingly common, owing to advancements in building materials and construction methods. While these structures often meet the criteria for ultimate limit states, they can still elicit complaints due to excessive vibrations induced by human activity. To address this challenge, the Integral Resonant Control (IRC) technique has emerged as a favored approach for actively damping vibrations in various systems. This study introduces a fresh perspective on implementing a multi-input multi-output (MIMO) IRC scheme for active vibration control (AVC) specifically tailored for pedestrian structures utilizing inertial mass actuators. Building upon a common framework and design methodology outlined in previous research, this work presents a novel application of MIMO IRC for AVC. The designed controller is rigorously tested and implemented on a laboratory floor structure to validate its efficacy.

Electricity price forecasting has become a crucial element for both private and public decision-making. This importance has been growing since the wave of deregulation and liberalization of energy sector worldwide late 1990s. Given these facts, this paper tries to come up with a precise and flexible forecasting model for the wholesale electricity price for the Italian power market on an hourly basis. We utilize artificial intelligence models such as neural networks and bagged regression trees that are rarely used to forecast electricity prices. After model calibration, our final model is bagged regression trees with exogenous variables. The selected model outperformed neural network and bagged regression with single price used in this paper, it also outperformed other statistical and non-statistical models used in other studies. We also confirm some theoretical specifications of the model. As a policy implication, this model might be used by energy traders, transmission system operators and energy regulators for an enhanced decision-making process.

In response to the need to address the safety challenges in the use of artificial intelligence (AI), this research aimed to develop a framework for a safety controlling system (SCS) to address the AI black-box mystery in the healthcare industry. The main objective was to propose safety guidelines for implementing AI black-box models to reduce the risk of potential healthcare-related incidents and accidents. The system was developed by adopting the multi-attribute value model approach (MAVT), which comprises four symmetrical parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. On the basis of the MAVT approach, three layers of attributes were created. The first level contained 6 key dimensions, the second level included 14 attributes, and the third level comprised 78 attributes. The key first level dimensions of the SCS included safety policies, incentives for clinicians, clinician and patient training, communication and interaction, planning of actions, and control of such actions. The proposed system may provide a basis for detecting AI utilization risks, preventing incidents from occurring, and developing emergency plans for AI-related risks. This approach could also guide and control the implementation of AI systems in the healthcare industry.

In this work, a general framework for the analysis of governance of artificial intelligence is presented. A dashboard is presented for this analysis from the perspective of political theory. The dashboard allows eventual comparisons between democratic and non democratic regimes, useful for countries in the global south and western countries. The dashboard allows us to assess the key features that determine the governance model for artificial intelligence at the national level, for local governments and for other participant actors. The dashboard allows us to appraise aims of AI strategy and aims left aside in alternative strategies. The work shows the theoretical framework could be of use to advance case studies globally, and comparative endeavors. Applying the theoretical dashboard, the case study on China has been undertaken. These are the main points the study tackles in the final discussion, conclusion and further recommendations: 1) the complex reality of AI command and control 2) uncertainties about future society and the polity against AI development and 3) particular cultural values enshrined in countries' AI development.

Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws to create investment models or predict changes in financial markets. As such, artificial intelligence technology is being used in various fields. So, it is very important to know exactly what factors have an important influence on each field of artificial intelligence technology and how the relationship between each field is connected. Therefore, it is necessary to analyze artificial intelligence technology in each field. In this paper, we analyze patent documents related to artificial intelligence technology. We propose a method for keyword analysis within factors using artificial intelligence patent data sets for artificial intelligence technology analysis. This is a model that relies on feature engineering based on deep learning model named KeyBERT, and using vector space model. A case study of collecting and analyzing artificial intelligence patent data was conducted to show how the proposed model can be applied to real-world problems.

The artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopaedic-specific problems. One particular area of investigation focuses on the shoulder pathology, which is a range of disorders or abnormalities of shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate the current AI applications in the shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI are also discussed.

Generative artificial intelligence tools are flourishing across various sciences, particularly in the educational domain. ChatGPT, with its versions 3.5 and 4.0, stands out for its multilingual capabilities, catering to both students and educators. This research aimed to evaluate the performance differences between these versions, and importantly, to compare the accuracy of ChatGPT's responses in English versus Arabic. In our study, we presented ChatGPT with 39 chemistry problems related to 6th-7th grade curriculum, including twenty-five open questions and fourteen multiple-choice questions. Each response was categorized as accurate, partially accurate, or inaccurate. Our analysis, focused on trends across versions and languages, revealed significant enhancements in version 4.0, particularly in its ability to process Arabic. However, despite these improvements, our findings indicate that responses in English consistently outperformed those in Arabic in terms of accuracy. In light of this discrepancy, we suggest that ChatGPT either incorporates additional Arabic databases into its training regimen or develops a code that adopts the following strategy to ensure more accurate responses in science education: questions should first be translated from Arabic to English, addressed, and then retranslated into Arabic before being presented to the user. This approach takes advantage of the greater accuracy of responses in English to benefit Arabic-speaking users, and it may also improve outcomes for speakers of other languages who experience less precise answers in the fields of chemistry or science education.

This study presents the synthesis and electrochemical evaluation of nitrogen-doped vanadium oxide (N-V2O3/C) as a cathode material for aqueous zinc-ion batteries (AZIBs), using a hydrothermal method. Compared to undoped V2O3/C, N-V2O3/C exhibits enhanced electrical conductivity, capacity, and electrochemical kinetics, attributed to the incorporation of pyridinic and pyrrolic nitrogen. Initial charge-discharge cycles indicate phase transitions to amorphous vanadium oxides, enhancing conductivity. N-V2O3/C shows a high specific capacity of 168.4 mAh g−1 at 10 A g−1 and remarkable reversibility, highlighted by the transient existence of intermediate species during cycling. Optimal electrochemical performance is achieved with a vanadium to nitrogen molar ratio of 2:3, indicating the significant impact of nitrogen doping concentration on the material’s efficiency. This work underscores the potential of N-V2O3/C as a superior cathode material for AZIBs.

Using density functional theory (DFT), the density of states of NH4V3O8 (NVO) was analyzed pre and post-oxygen defects (Od) formation. The findings revealed a reduced bandgap in NVO after Od introduction, emphasizing the role of Od in enhancing conductivity of the material, thus improving its electrochemical attributes. Through the water bath method, both NVO and its oxygen-deficient counterpart, (NH4)2V10O25⸱8H2O (NVOd), were synthesized as potential cathode materials for aqueous zinc-ion batteries (AZIBs). Experimental outcomes resonated with DFT predictions, highlighting the beneficial role of oxygen defects in boosting electrical conductivity. Notably, the refined material displayed a remarkable capacity of 479.3 mAh g-1 at 0.1 A g-1, underscoring its promise for advanced energy storage solutions.

Artificial intelligence (AI) is increasingly being used to improve the quality control of point-of-care diagnostics. This is caused by a number of factors, including the following: 1. AI can accelerate and improve testing accuracy. In comparison to humans, AI technology can review data more quickly and precisely, reducing errors and improving overall quality assurance. 2. When it comes to improving POC test findings on healthcare issues such as infectious diseases or medical crises such as heart attacks, AI can be used for sophisticated predictive analytics and modeling that aid in better decision-making. 3. Artificial intelligence facilitates process automation, increasing productivity and lowering labor costs in labor-intensive tasks such as testing and analyzing samples collected at point-of-care facilities. 4. The use of AI enables organizations implementing these solutions to gain insights from large volumes of raw diagnostic data generated faster and more accurately, allowing them to build solid frameworks around preventive care initiatives and significantly influence public health outcomes.5. Artificial intelligence (AI) has been demonstrated to be a useful tool for real-time monitoring systems that identify any problems with test results early so that they can be corrected before affected patients receive inaccurate diagnoses or treatment plans based on false information provided by diagnostic tests performed at points of care such as clinics or hospitals.Using these technologies would allow healthcare organizations to spend less on labor while still receiving exact diagnoses and rapid treatment delivery at a fraction of the cost that manual approaches required earlier.

The term artificial intelligence refers to the field of computer science that deals with the design and implementation of computer systems that mimic elements of human behavior that imply basic intelligence: learning, adaptability, inference, contextual understanding, and problem solving among others. Artificial intelligence is a point of intersection between multiple sciences such as computer science, psychology, philosophy, neurology, linguistics and engineering science, with the aim of synthesizing intelligent behavior, with elements of reasoning, learning and adaptation to the environment, while it is usually applied to specially made machines or computers.

Lead optimization in drug discovery is a crucial phase where initial hits are refined into compounds with improved pharmacological properties. While traditional methods rely on manual experimentation and modifications, AI-driven techniques have revolutionized this process by leveraging big data and predictive modeling. This review explores how AI-driven approaches accelerate lead optimization, showcasing examples like deep neural networks and reinforcement learning. Integration of multi-omic data and experimental validation further enhances AI-driven strategies. The future lies in refining these AI methods, democratizing tools, and interdisciplinary collaboration to streamline drug discovery and address medical needs efficiently.

Human action recognition (HAR) based on skeleton data is a challenging yet important technique because of its wide range of applications in many fields, including patient monitoring, security surveillance, and observing human-machine interactions. Many algorithms that attempt to distinguish between many types of activities have been proposed. However, most practical applications require highly accurate detection of specific types of activities. In this study, a novel and highly accurate spatiotemporal graph autoencoder network for HAR based on skeleton data is proposed. Furthermore, an extensive study was conducted using different modalities. For this purpose, a spatiotemporal graph autoencoder that automatically learns spatial as well as temporal patterns from human skeleton datasets was built. The powerful graph convolutional network named GA-GCN, developed in this study, notably outperforms most of the existing state-of-the-art methods based on two common datasets, namely NTU RGB+D and NTU RGB+D 120 . On the first dataset, we achieved an accuracy of 92.3% and 96.7% based on the cross-subject and cross-view evaluations, respectively. On the other more challenging dataset (i.e. NTU RGB+D 120), GA-GCN achieved 88.8% and 90.4% based on the cross-subject and cross-set evaluation, respectively.

Along with the rapid development and wide application of Artificial Intelligence (AI), discussions regarding AI Sustainability have emerged. Employing a systematic mapping study, this paper reviews research about AI Sustainability from diverse fields and viewpoints, including three dimensions economic, social, and environmental. Our analysis indicates a transformative shift in research focus, evolving from fragmented approaches to a more holistic understanding of AI Sustainability. This maturation is reflected in the interplay between multiple dimensions and the integration of both "AI as a tool for sustainability" and "Sustainability of AI" perspectives. As the field continues to mature, researchers should embrace a comprehensive and unbiased exploration of AI Sustainability across all dimensions and approaches, ensuring a well-rounded foundation for future advancements in this rapidly evolving domain.

Artificial intelligence (AI) has emerged as a powerful tool for addressing the challenges posed by climate change and their impact on public health. By leveraging its capacity to analyze and predict climatic patterns, AI offers opportunities to enhance resource management and develop effective strategies for climate change mitigation. Moreover, AI can contribute to generating sustainable solutions that address the complex and interconnected nature of climate change. For example, AI can enable the optimization of energy consumption and facilitate the integration of renewable energy sources into existing systems. It can also support the development of climate models that provide timely and accurate predictions, enabling policymakers to implement proactive measures for disaster preparedness and response. Furthermore, AI-powered disease surveillance and mitigation techniques can improve public health outcomes by identifying patterns and trends in the spread of diseases in relation to climatic factors.However, the widespread adoption of AI-based solutions is not without challenges. Ethical concerns surrounding privacy and data ownership must be addressed, as the use of AI requires access to large datasets, raising potential privacy risks. Technical constraints, such as limited computational power and the need for sophisticated algorithms, also pose obstacles to the implementation of AI in climate change mitigation strategies. Furthermore, issues related to the accessibility and affordability of AI technologies must be resolved to ensure equitable distribution and maximize its potential impact on public health.To fully harness the power of AI in addressing climate change and improving public health outcomes, it is crucial to promote innovation, multidisciplinary collaboration, and open data science. Innovation can drive the development of new AI algorithms and technologies specifically tailored to address climate change challenges. Multidisciplinary approaches that bring together experts from diverse fields, including climate science, public health, and computer science, can foster a holistic understanding of complex systems and enable the design of comprehensive solutions. Finally, open data science practices, such as sharing data and algorithms, can facilitate collaboration and accelerate progress in mitigating climate change and its public health impacts.In conclusion, AI offers promising opportunities for effectively addressing climate change and its impact on public health. However, to fully realize its potential, it is essential to tackle ethical and privacy concerns, overcome technical constraints, and ensure accessibility and affordability. By promoting innovation, multidisciplinary collaboration, and open data science, we can unlock the transformative power of AI in mitigating climate change and improving public health outcomes.

The application of smartphone-based color evaluation of passive sampling devices for gases has only been sparsely reported. The present review aims to compile available publications with respect to the configuration of the passive samplers, conditions of smartphone photographing, analytical procedure for color detection and quantification (including calibration processes), and their application to different target gases. The performance of the methods - whenever available - is presented regarding the analytical specifications selectivity, sensitivity, and limit of detection in comparison with other color evaluation methods of passive samplers. Practical aspects like requirements of instrumentation and ease of use will be outlined in view of the potential employment in education and citizen science projects. In one section of the review the inconsistent terminology of passive and diffusive sampling is discussed in order to clarify the distinction of information obtained from the uptake of the passive samplers between gas-phase concentration and the accumulated deposition flux of gaseous analytes. Colorimetric gas sensors are included in the review when applied in passive sampling configurations and evaluation is performed with smartphone-based color evaluation. Differences in the analytical procedures employed after the passive sampling step and prior to the detection of the colored compounds are also presented.

The transport sector is pivotal and indispensable in our daily existence, being the exclusive conduit for the intercontinental conveyance of commodities and individuals. Hence, comprehending the phenomenon of business failure within the transport industry assumes paramount significance in delineating an effective competitive policy for this sector. The primary objective of this research paper is to execute a comparative investigation between statistical models forecasting business failure and models founded on artificial intelligence, within the context of the transport sector. This analysis spans the temporal expanse from 2014 to 2021 and encompasses the nations of Portugal, Spain, France, and Italy, aiming to ascertain superior efficacy among these models. The dataset employed for this endeavor encompassed a final cohort of 4866 companies, comprising 2881 that endured as going concerns and 1985 that succumbed to business failure. The models developed for this study exhibited the capacity to accurately categorize a proportion of companies ranging from 71% to 73%. Nonetheless, upon comparative scrutiny of these outcomes with those derived from the statistical models dedicated to business failure prediction, it becomes evident that the latter demonstrate an enhanced predictive prowess, manifesting fewer errors in the classification of the scrutinized companies.

This paper proposes an AI-based approach to overcome the limitations of the SHM system in measuring global stress with limited sensors. Feature elements are selected based on correlation analysis among finite elements and used as stress-measured points. An ANN is used to establish the solution relationship between the feature and correlation elements. The proposed method is applied to the connector structure of an offshore platform, and an optimal ANN is established to optimize accuracy by considering factors like the number of sensors, neural network framework, and convergence criteria. The accuracy of the ANN is verified through a real-scale model test, demonstrating 93.6% accuracy. This technology represents a significant advancement, enhancing the practicality of the structural health monitoring (SHM) system from “point monitoring" to “field monitoring".

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

Artificial Intelligence (AI) has emerged as a disruptive technology in various areas, and medicine is no exception. In the last decade, there has been rapid progress in the application of AI in healthcare, from diagnosing and prognosticating diseases to improving operational efficiency in hospitals. This literature review explores the advances of artificial intelligence in the field of medicine, discusses its benefits, and discusses the ethical and regulatory challenges that need to be addressed to maximize its potential.

Lane detection is a foundational technology for autonomous driving systems. It involves identifying the boundaries of lanes on the road and ensuring the vehicle stays within these boundaries. Accurate lane detection is crucial for safety, navigation and traffic management. This paper presents an artificial intelligent based autonomous road lane detection and navigation system for vehicles. The system can detect and analyze road lanes accurately and efficiently in real-time, with frame rates of up to 17 FPS. By utilizing image processing techniques, the system can identify the location and boundaries of road lanes and obstacles on the road and provide accurate and reliable navigation guidance to the driver. The system is integrated with sensors and actuators to provide comprehensive autonomous navigation. The efficiency of the proposed system is demonstrated using accuracy of lane detection on straight and curve lanes covering frames per second and motor of the speed as parameters for assessment. Results show that the proposed system detects lane as entire lane detection, partial lane detection and no detection status on different settings.

In this paper we designed system for smart university building based on artificial intelligence (AI) and internet of things (IOT). Our idea can be summarized in smart security system that has different sensors to detect the surrounding environment of the class room in campus which keep everyone and everything on campus safer. By using (IOT), (AI) technologies and applications and by using microcontroller programming we can make the university building safer, secure and more energy saves.

Accurate lithium battery diagnosis and prognosis is critical to increase penetration of electric vehicles and grid-tied storage systems. They are both complex due to the intricate, nonlinear, and path-dependent nature of battery degradation. Data-driven models are anticipated to play a significant role in the behavioral prediction of dynamical systems such as batteries. However, they are often limited by the amount of training data available. In this work, we generated the first big data comprehensive synthetic datasets to train diagnosis and prognosis algorithms. The proof-of-concept datasets are over three orders of magnitude larger than what is currently available in the literature. With benchmark datasets, results from different studies could be easily equated, and the performance of different algorithms can be compared, enhanced, and analyzed extensively. This will expend critical capabilities of current AI algorithms, tools, and techniques to predict scientific data.

Recent research emphasises the importance of Artificial Intelligence applications as supporting tools for students in higher education. Simultaneously, an intensive exchange of views has started in the public debate in the international educational community. However, for a more proper use of these applications, it is necessary to investigate the factors that explain their intention and actual use in the future. With the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, this work analyses the factors influencing students' use and intention to use artificial intelligence technology. For this purpose, a sample of 199 Greek students at the School of Humanities and Social Sciences from the University of Patras participated in a survey. The findings highlight that expected performance, habit, and enjoyment of these Artificial Intelligence applications are key determinants influencing teachers' intentions to use them. Moreover, behavioural intention, habit, and facilitating conditions explain the usage of these Artificial Intelligence applications. The study did not reveal any moderating effects. The limitations, practical implications, and proposed directions for future research based on these results are discussed.

Merkel cell carcinoma (MCC) is a rare, highly aggressive skin cancer characterized by two etiologies: the first one, observed in most cases, is attributed to Merkel cell polyomavirus (MCPyV), a DNA tumor virus, while the second one is due to ultraviolet (UV) radiation exposure. Currently, there are limited diagnostic, prognostic, and therapeutic biomarkers available for MCC. MicroRNAs (miRNAs) are small RNA molecules that do not code for proteins but regulate various cellular processes, including cell cycle progression, proliferation, differentiation, and apoptosis. The dysregulation of miRNAs has been associated with a wide range of cancers since they can act as either tumor suppressors or oncogenes. This paper aims to collect, summarize and re-organize the recent discoveries concerning miRNAs, whose dysregulation in the pathogenesis and progression of MCC is slowly emerging. Furthermore, the potential clinical applications of miRNAs as diagnostic biomarkers, possible differentiation between MCPyV-related and unrelated MCC, predictors of response to therapy and prognostics are also discussed. From this review, it emerges that miRNAs may have, in the near future, the potential to become clinically significant in improving the diagnostic accuracy, prognostic evaluation, and therapeutic options of MCCs. Finally, the new perspectives on the poorly explored field of artificial intelligence in MCC are provided.

This manuscript examines the role of artificial intelligence in cancer imaging. It throws light on how artificial intelligence (AI) can significantly cut the wait time of patients and their clinicians in getting cancer diagnoses and assist health care providers by highlighting areas of an image where the interpreter needs to focus more for better result analysis and quality output. It also provides details on how the rise of AI attempts to standardize imaging results across providers by eliminating inter- and intra-observer variations among health care providers. Finally, it exposes the numerous limitations associated with AI use in cancer imaging, such as the need to digitize pathology laboratories and workflows, as well as transform our century-old tissue processing methods to fit into modern technological standards. In the field of radiology, the challenge of curating the enormous amount of data generated through MRIs, CT scans, PET scans, etc., poses a significant challenge to the ability of researchers to train relevant AI models to high levels of accuracy and reliability. In addition, over-reliance on AI by clinicians can deprive them of a common-sense approach to the health care issues of their patients and negatively impact doctor-patient relationships and confidentiality.

The medical & the dental field is a never ending field of innovations & developments and each time the reasearchers come up with something new. One such new dimension in the fields of medicine being the incorporation of Artificial intelligence assisted technologies improving diagnosis, treatmemt plan and treatment stategies. This review focusses on the application of different technologies of AI in different fields of medicine.

The huge advancement of Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques enables the scientific community to gain access to biological datasets, clinical data, and several databases containing billions of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as Machine Learning (ML)/Deep Learning (DL) and overall Artificial Intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, the translational research is benefitting from computer-based approaches, transforming the design and execution of the translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

Artificial Intelligence (AI) tools and techniques are comparatively recent advances of present-day globalized world. With their easy availability and friendly user interface AI has affected every sphere of human being. These tools are increasingly becoming popular because of the numerous benefits occurred to the society in many ways. However, there are not only the positive side of these technologies, but also many negative sides. Along with the development and adoption of AI, cyber-attacks are increased and disrupting the information system. Threat actors may misuse artificial intelligence. Hence, these tool designers are working and applying the ability of generative AI can tip the scales in cybersecurity. So, in the age of AI, there is always tension between the cyber attackers and the defenders to make a balanced information system. It is a general rule that defenders must be well in advance in their favor and keep ahead of adversaries. The United National and several governments for example the US, UK, and the big AI giants like Microsoft along with OpenAI, are working on emerging cyberattacks. This research paper explores the concept of information resilience in ML and AI systems, focusing on strategies to enhance their ability to withstand uncertainties, adversarial attacks, and data perturbations.

The recent remarkable progress in Artificial Intelligence (AI) has renewed the discussion on the possibility of human-level AI. Despite the difficulty of the problem and the presence of many large gaps and challenges, most people working in the field think that human-level AI is achievable, but they disagree on the date. In this paper, I briefly discuss the possibility of human-level AI from the perspective of two traditions in the Islamic world, namely Kalam and Sufism, and despite their different mode of investigation, they converge to the same conclusion on this topic.

Historically the use of artificial intelligence (AI) in paediatrics dates back to 1984 with the intro-duction of a computer-assisted medical decision-making system called SHELP. Since then, research on the use of AI in pediatrics has become much more popular, and the number of reviewed publications largely increased. Consequently, a need for a holistic research landscape enabling researchers and other interested parties to gain insights into, deepen their knowledge about or inform them about the use of AI in pediatrics arised. To fill this gap a novel methodology named synthetic knowledge synthesis was applied. Using SKS we identified most prolific countries, in-stitutions, source titles, funding agencies, research themes, research gaps and hot spots.

In this paper, we look at how Artificial Swarm Intelligence can evolve using evolutionary algorithms that try to minimize the sensory surprise of the system. We will show how to apply the free-energy principle, borrowed from statistical physics, to quantitatively describe the optimization method (sensory surprise minimization), which can be used to support lifelong learning. We provide our ideas about how to combine this optimization method with evolutionary algorithms in order to boost the development of specialized Artificial Neural Networks, which define the proprioceptive configuration of particular robotic units that are part of a swarm. We consider how optimization of the free-energy can promote the homeostasis of the swarm system, i.e. ensures that the system remains within its sensory boundaries throughout its active lifetime. We will show how complex distributed cognitive systems can be build in the form of hierarchical modular system, which consists of specialized micro-intelligent agents connected through information channels. We will also consider the co-evolution of various robotic swarm units, which can result in development of proprioception and a comprehensive awareness of the properties of the environment. And finally, we will give a brief outline of how this system can be implemented in practice and of our progress in this area.

Obstacle-avoidance robots have become an essential field of study in recent years. This paper analyzes two cases that extend reactive systems focused on obstacle detection and its avoidance. The scenarios explored get data from their environments through sensors and generate information for the models based on artificial intelligence to obtain a reactive decision. The main contribution is focused on the discussion of aspects that allow comparing both approaches, such as the heuristic approach implemented, requirements, restrictions, response time, and performance. The first case presents a mobile robot that applies fuzzy logic to achieve soft turning basing its decision on depth image information. The second case introduces a mobile robot based on multi-layer perceptron and ultrasonic sensors to decide how to move in an uncontrolled environment. The analysis of both options offers perspectives to choose between reactive obstacle-avoidance systems based on ultrasonic or Kinect sensors, models that infer optimal decisions applying fuzzy logic or artificial neural networks, with key elements and methods to design mobile robots with wheels. Therefore, we show how AI or Fuzzy Logic techniques allow us to design mobile robots that learn from their “ experience ” by making them safe and adjustable for new tasks, unlike traditional robots that use large programs to perform a specific task.

Recently, Artificial Intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies as well as increasing the effectiveness of the training process. Thus, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in the medical image scan segmentation, and intelligent visual-content generation, i.e. computer-generated three-dimensional (3D) images in the context of Extended Reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper also attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. Finally, this paper concludes with possible developments and open challenges in AI application in Extended Reality-based solutions. Finally, the future lines of research and development directions of Artificial Intelligence applications both in medical image segmentation and Extended reality-based medical solutions are discussed.

The potential of digital twin technology is yet to be fully realised due to its diversity and untapped potential. Digital twins enable systems’ analysis, design, optimisation, and evolution to be performed digitally or in conjunction with a cyber-physical approach to improve speed, accuracy, and efficiency over traditional engineering methods. Industry 4.0, factories of the future, and digital twins continue to benefit from the technology and provide enhanced efficiency within existing systems. Due to the lack of information and security standards associated with the transition to cyber digitisation, cybercriminals have been able to take advantage of the situation. Access to a digital twin of a product or service is equivalent to threatening the entire collection. There is a robust interaction between digital twins and artificial intelligence tools, which leads to strong interaction between these technologies, so it can be used to improve the cybersecurity of these digital platforms based on their integration with these technologies. This study aims to investigate the role of artificial intelligence in providing cybersecurity for digital twin versions of various industries, as well as the risks associated with these versions. In addition, this research serves as a road map for researchers and others interested in cybersecurity and digital security.

This review article focuses on the role of Artificial Intelligence (AI) in transforming healthcare in Africa, specifically in combatting infectious diseases, neglected tropical diseases (NTDs), and antimicrobial resistance. We provide a comprehensive overview of the significance of AI in the healthcare industry, highlighting its urgency and importance in addressing these specific health challenges in Africa. We begin by discussing the role of AI in infectious disease surveillance and outbreak detection. We explore how AI technology can be employed for real-time tracking and prediction of outbreaks, providing examples of successful AI applications in infectious disease surveillance within the African context.Next, we examine the potential of AI-enabled diagnosis and treatment for faster and more accurate diagnoses of infectious diseases and NTDs. We highlight specific examples of AI applications in diagnosing and treating these diseases in Africa, showcasing the potential of AI to improve clinical outcomes and save lives.Furthermore, we focus on how AI-driven drug discovery and development can expedite the search for new treatments for infectious diseases and combat antimicrobial resistance. We present examples of AI applications in drug discovery within the African context, illustrating the potential for AI to revolutionize the development of effective therapeutics.In addition, we delve into how AI-powered public health interventions can enhance the design and implementation of targeted interventions. We explore how AI can optimize resource allocation and facilitate data-driven decision-making processes, providing examples of AI applications in public health in Africa.Finally, we address the challenges and limitations of implementing AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. We discuss potential barriers and ethical concerns surrounding AI applications in healthcare, aiming to encourage informed and responsible utilization of AI technologies.Overall, this review emphasizes the importance and potential of AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. It positions AI as a catalyst for revolutionizing healthcare in the region, leading to more effective disease surveillance, diagnosis, treatment, drug discovery, and public health interventions.

Neurology is a quickly evolving specialty that requires clinicians to make precise and prompt diagnoses and clinical decisions based on the latest evidence-based medicine practices. In all Neurology subspecialties, Stroke and Epilepsy in particular, clinical decisions affecting patient outcomes depend on neurologists accurately assessing patient disability. Artificial intelligence (AI) can predict the expected neurological impairment from an AIS (Acute Ischemic Stroke), the possibility of ICH (IntraCranial Hemorrhage) expansion, and the clinical outcomes of comatose patients. This review article informs readers of artificial intelligence principles and methods. The article introduces the basic terminology of artificial intelligence before reviewing current and developing AI applications in neurology practice. AI holds promise as a tool to ease a neurologist’s daily workflow and supply unique diagnostic insights by analyzing data simultaneously from several sources, including neurological history and examination, blood and CSF laboratory testing, CNS electrophysiologic evaluations, and CNS imaging studies. AI-based methods are poised to complement the other tools neurologists use to make prompt and precise decisions that lead to favorable patient outcomes.

This paper explores the integration of multiple intelligences and artificial intelligence (AI) in language learning, focusing on its potential to enhance personalization and engagement. Drawing from existing research and studies conducted in various contexts, including the Philippines, this study aims to contribute to the understanding of the benefits, challenges, and effectiveness of this integration. The paper begins with an introduction that highlights the background and significance of integrating multiple intelligences and AI in language learning, identifying research gaps, objectives, research questions, and the theoretical framework. A literature review provides an overview of multiple intelligences theory by Howard Gardner, the role of AI in language learning, and identifies gaps in the existing literature. The methodology section outlines the research design and approach, participant selection, data collection methods, validity and reliability measures, and ethical considerations. Findings and results are presented through the analysis of qualitative data, exploring emergent themes and patterns. The discussion section critically examines the identified research gaps, discusses the validity and reliability of the study, addresses the scope and limitations, and explores the implications of the findings for theory, practice, and future research. The conclusion summarizes the key findings and contributions of the study, reflects on the achievement of research objectives, offers recommendations for further research, and provides final remarks tying together the main points of the study. This paper contributes to the existing body of knowledge by providing insights into the integration of multiple intelligences and AI in language learning and its impact on personalization, engagement, and language learning outcomes.

This study reports on the development of bio-based hydrophobic coatings for packaging papers through deposition of polyhydroxybutyrate (PHB) particles in combination with nanofibrillated cellulose (NFC) and plant wax. In a first approach, PHB particles in micrometer range (PHB-MP) were prepared through a phase-separation technique providing internally nanosized structures. The particles were transferred as a coating by dip-coating filter papers in the particle suspension, followed by sizing with a carnauba wax solution. This approach allowed partial to almost full surface coverage of PHB-MP over the paper surface resulting in static water contact angles of 105° to 122° and 129° to 144° after additional wax coating. In a second approach, PHB particles with submicron sizes (PHB-SP) were synthesized by an oil-in-emulsion (o/w) solvent evaporation method, and mixed in aqueous suspensions with 0 to 7 wt% NFC. After dip-coating filter papers in PHB-SP/NFC suspensions and sizing with a carnauba wax solution, static water contact angles of 112° to 152° were obtained. The intrinsic properties of the particles were analyzed by scanning electron microscopy, thermal analysis and infrared spectroscopy, indicating higher crystallinity for PHB-SP than PHB-MP. The chemical interactions between the more amorphous PHB-MP particles and paper fibers were identified as an esterification reaction, while the morphology of the NFC fibrillar network was playing a key role as binding agent in the retention of more crystalline PHB-SP at the paper surface, hence contributing to higher hydrophobicity.

To ensure consumers can purchase high-quality rice, accurate identification of rice varieties is particularly important. Methods: This article conducts research based on machine learning algorithms from the perspectives of image, spectrum, and spectrogram fusion. Six types of hyperspectral image data of rice were preprocessed using convolutional smoothing (SG) and multiple scatter correction (MSC). Texture information of the images was extracted using gray-level co-occurrence matrix. Spectra, texture, and spectrogram data were fused into a new matrix. Spectral, texture, and spectrogram fusion data were used as inputs for the model, and support vector machines (SVM), logistic regression (LR), and K-nearest neighbors (KNN) classification models were constructed and compared. Results: From the classification results, the spectrogram fusion classification performance was better than classification models using only spectra or texture. Conclusion: The research results showed that the accuracy of SVM and LR classification models exceeded 90%, and the LR model performed the best, effectively classifying rice varieties.

In the wake of increasing digital fraud, this paper introduces an innovative application of Artificial Intelligence (AI) in detecting fraudulent activities across finance, healthcare, and e-commerce sectors. It presents a detailed analysis of machine learning methodologies, specifically focusing on the advantages of supervised, unsupervised, and deep learning techniques. The paper addresses the challenges such as data imbalance, model interpretability, and ethical implications in AI-based fraud detection. It also discusses the necessity of high-quality datasets and advocates for the integration of traditional and advanced machine learning methods to enhance accuracy and adaptability in fraud identification. However, it acknowledges the limitations including computational demands and overfitting risks. The study underscores the importance of collaborative efforts between AI experts and industry professionals to develop ethical, efficient, and reliable AI solutions for fraud detection.

Recent developments, begun by the ascending spiral of the anticipated endless prospects of ChatGPT, promote artificial intelligence (AI) as an indispensable tool and commodity whose time has come. Yet the sinister specter of a technology that has hidden and unmanageable attributes that might be harmful to society looms in the background. As well as the likelihood that it will never deliver on the purported promise of artificial general intelligence (AGI). Currently the prospects for the development of AI and AGI are more a matter of opinion than based on a consistent methodological approach. Thus the need to take a step back to develop a general framework from which to evaluate current AI efforts, which also permits the determination of the limits to its future prospects as AGI. To gain insight into the development of a general framework, a key question that needs resolution is, what is the connection between human intelligence and machine intelligence? This is the question that needs a response because humans are at the center of AI creation and realize that without an understanding of how we become what we become, we have no chance of finding a solution. This work proposes infoautopoiesis, the self-referential, recursive and interactive process of self-production of information, as the needed general framework. Infoautopoiesis shows how the key ingredient of information is fundamental to an insightful resolution to this crucial question and allows predictions as to the present and future of AGI.

Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks and deep learning. AI can be applied in many different areas, such as econometrics, biometry, e-commerce and the automotive industry. In recent years, AI has found its way into healthcare as well, helping doctors to make better decisions (‘clinical decision support’), localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a ‘black box’, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans. In this narrative review, we will have a look at the current status of XAI in healthcare, describe several issues around XAI, and discuss whether it can really help healthcare to advance, for example by increasing understanding and trust. Finally, alternatives to increase trust in AI are discussed, as well as future research possibilities in the area of XAI.

This manuscript compares deterministic artificial intelligence to model following control applied to DC motor control, including evaluation of the threshold computation rate to let unmanned underwater vehicles correctly follow the challenging discontinuous square wave command signal. The approaches presented in the main text are validated in MATLAB®, where the motor process is discretized at multiple step sizes, which is inversely proportional to the computation rate. The performance is evaluated by the error mean and standard deviation. With a large step size, discrete deterministic artificial intelligence shows a larger error mean than the model-following self-turning regulator approach (benchmark selection). However, the performance gets optimized with the step size decreased. The error mean is close to the continuous deterministic artificial intelligence when the step size is reduced to 0.2 seconds, which means that the computation rate and the sampling period restrict discrete deterministic artificial intelligence. In that case, the continuous deterministic artificial intelligence is the most feasible and reliable selection for future applications on unmanned underwater vehicles since it is superior to all the approaches with multiple computation rates.

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stages of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

This article conducts a literature review of current and future challenges in the use of artificial intelligence (AI) in cyber physical systems. The literature review is focused on identifying a conceptual framework for increasing resilience with AI through automation supporting both, a technical and human level. The methodology applied resembled a literature review and taxonomic analysis of complex internet of things (IoT) interconnected and coupled cyber physical systems. There is an increased attention on propositions on models, infrastructures and frameworks of IoT in both academic and technical papers. These reports and publications frequently represent a juxtaposition of other related systems and technologies (e.g. Industrial Internet of Things, Cyber Physical Systems, Industry 4.0 etc.). We review academic and industry papers published between 2010 and 2020. The results determine a new hierarchical cascading conceptual framework for analysing the evolution of AI decision-making in cyber physical systems. We argue that such evolution is inevitable and autonomous because of the increased integration of connected devices (IoT) in cyber physical systems. To support this argument, taxonomic methodol- ogy is adapted and applied for transparency and justifications of concepts selection decisions through building summary maps that are applied for designing the hierarchical cascading conceptual framework.

Luminescence-based sensors for measuring oxygen concentration are widely used both in industry and research due to the practical advantages and sensitivity of this type of sensing. The measuring principle is the luminescence quenching by oxygen molecules, which results in a change of the luminescence decay time and intensity. In the standard approach, this change is related to an oxygen concentration using the Stern–Volmer equation. This equation, which in most of the cases is non-linear, is parametrized through device-specific constants. Therefore, to determine these parameters every sensor needs to be precisely calibrated at one or more known concentrations. This work explores an entirely new artificial intelligence approach and demonstrates the feasibility of oxygen sensing through machine learning. The specifically developed neural network learns very efficiently to relate the input quantities to the oxygen concentration. The results show a mean deviation of the predicted from the measured concentration of 0.5 % air, comparable to many commercial and low-cost sensors. Since the network was trained using synthetically generated data, the accuracy of the model predictions is limited by the ability of the generated data to describe the measured data, opening up future possibilities for significant improvement by using a large number of experimental measurements for training. The approach described in this work demonstrates the applicability of artificial intelligence to sensing technology and paves the road for the next generation of sensors.

Over the years, artificial intelligence (AI) is spreading its roots in different areas by utilizing the concept of making the computers learn and handle complex tasks that previously require substantial laborious tasks by human beings. With better accuracy and speed, AI is helping lawyers to streamline work processing. New legal AI software tools like Catalyst, Ross intelligence, and Matlab along with natural language processing provide effective quarrel resolution, better legal clearness, and superior admittance to justice and fresh challenges to conventional law firms providing legal services using leveraged cohort correlate model. This paper discusses current applications of legal AI and suggests deep learning and machine learning techniques that can be applied in future to simplify the cumbersome legal tasks.

The recent advancement of Artificial Intelligence (AI) and Machine Learning (ML) tools has had a pervasive effect on every sphere of human existence. Libraries and information centres are not immune to these technological advances. These disruptive technologies have transformed the traditional face of libraries and their operations. There are many available AI tools, for example, chatbots, literature review tools, plagiarism detection tools, and several other developments in the field that have perhaps made the functioning of libraries easier. However, there is growing concern and uncertainty among both traditional library and modern professionals regarding the possible job loss and security issues related to these new technologies, which the professionals must deal with. Librarians and information professionals must be ready to handle the unforeseen situation. However, it is to be observed how libraries cope with these uncertainties, and threats to the integrity, availability of data, and normal library housekeeping functions.

The body's billions of bacteria—the microbiota—influence health and illness. The microbiome—the genetic material of these microbes—affects digestion, immunological function, and mental health. Due to bacterial variety, complicated relationships, and research technique constraints, investigating the microbiota is difficult. AI and machine learning have expanded microbiome research. Artificial intelligence (AI) systems can efficiently scan large microbiome datasets to help researchers understand microbial populations and their functions. AI-based predictive algorithms can analyze food impacts on microbial communities and forecast illness risks based on gut microbiota composition. AI helps find microbial biomarkers linked to specific health disorders, enabling early illness identification and targeted therapy. AI-driven medication development platforms also modulate microbiome to treat microbiota-related diseases. Understanding microbial populations' involvement in health and illness requires understanding complicated microbial interactions within the microbiome and between it and the host. AI systems improve human health by comprehending these complex relationships. AI in microbiota study must overcome various obstacles. Data quality and AI model interpretability are essential for accurate findings. Diverse and representative datasets avoid biases and strengthen AI-driven conclusions. AI in microbiota research requires data protection, informed permission, and appropriate use of sensitive data. AI is improving our understanding of the human microbiome and its health effects, changing microbiota research. Overcoming difficulties and adhering to ethical norms will enable the proper use of AI-driven findings, leading to microbiome insights and precision medical breakthroughs.

Globally, an approximate of 380,000 patients succumbed to death due to the pandemic COVID-19 which also infected more than six million people since December 2019. Not sparing anyone, COVID-19 infections are widely reported among healthcare professionals, sanitation workers and researchers too while global leaders and various governments are providing their best in defending their citizens against this airborne and contact spread virus. In order to contain the virus and protect millions of lives from this deadly coronavirus, there is a need to have a combination of advanced engineering technology and medical facilities. Application of applied science, engineering and technology diffuse almost every aspect of contemporary living. Grasping the fundamentals to determine humanity's most imperative and forthcoming challenges is essential. Artificial Intelligence, the technology that learns, adapts and reciprocates the actions according to the situations, finds optimum position in the fight against corona virus and acts as a powerful tool against this pandemic. In this research article, the authors discusses how Artificial Intelligence (AI) can be leveraged to fight the deadly virus. The research paper further discusses the efficient utilization of AI across the globe to help in testing, treating and serving the population in these hard times. This manuscript focuses on the potential impact of the process in which AI can be implemented to prevent, test and treat.

Recent advancements in cognitive neuroscience, particularly in Electroencephalogram (EEG) signal processing, image generation, and brain-computer interfaces (BCI), have opened up new avenues for research. This study introduces a novel framework, Bridging Artificial Intelligence and Neurological Signals (BRAINS), which leverages the power of AI to extract meaningful information from EEG signals and generate images. The BRAINS framework addresses the limitations of traditional EEG analysis techniques, which struggle with nonstationary signals, spectral estimation, and noise sensitivity. Instead, BRAINS employs Long Short-Term Memory (LSTM) networks and contrastive learning, which effectively handle time-series EEG data and recognize intrinsic connections and patterns. The study utilizes the MNIST dataset of handwritten digits as stimuli in EEG experiments, allowing for diverse yet controlled stimuli. The data collected is then processed through an LSTM-based network employing contrastive learning and extracting complex features from EEG data. These features are fed into an image generator model, producing images as close to the original stimuli as possible. This study demonstrates the potential of integrating AI and EEG technology, offering promising implications for the future of brain-computer interfaces.

The aim of this research is to apply artificial intelligence (AI) methods, specifically artificial immune systems (AIS), in order to design an optimal control strategy for a multivariable control plant. Two specific industrial control approaches are investigated: I-PD (Integral-Proportional Derivative) and PI-D (Proportional-Integral Derivative) control. The study results in a novel solution to the control synthesis problem for the industrial system. In particular, the research addresses the synthesis of I-P control for a two-loop system in the technological process of a distillation column. This synthesis is accomplished using the AIS algorithm, making it the first application of this technique to this specific context. Methodological approaches are proposed to enhance the performance of industrial multivariable control systems through effective utilization of optimization algorithms by establishing modified quality criteria. The problem of synthesis of multi-input multi-output (MIMO) control system is solved, taking into account the interconnections due to the decoupling procedure.

Quantitative structure-activity relationship (QSAR) model is adopted to study the relationship between the chemical and physical properties of various substances and the structure. Through QSAR studies, the internal relationship between the invisible structure and the activity can be obtained. In this paper, a novel chaos-enhanced accelerate particle swarm algorithm (CAPSO) is proposed, which is used to molecular descriptors screening and optimization of the weights of back propagation artificial neural network (BP ANN). Then, the QSAR model based on CAPSO and BP ANN is put forward, hereinafter referred to as CAPSO BP ANN model. The prediction experiment showed that the CAPSO algorithm is a reliable method for screening molecular descriptors and the five molecular descriptors obtained by CAPSO algorithm could well characterize the molecular structure of each compound in pKa prediction. The experimental results also showed the CAPSO BP ANN model has a good performance in predicting the pKa values of various compounds, the absolute mean relative error, root mean square error, and square correlation coefficient are respectively 0.5364, 0.0632, and 0.9438, indicating the higher prediction accuracy and correlation. The proposed hybrid intelligent model can be applied in all kinds of engineering design, prediction of physical and chemical properties and intelligent calculation.

The vehicular ad hoc network (VANET) when augmented with internet is called internet of vehicles (IoV). In the IoV, vehicles have on board units (OBUs), use communication technologies and customized software to communicate with infrastructure around them. The vehicles can have permanent connectivity and global network awareness based on cloud computing. IoV technologies are still being assessed to ensure that the network is protected from attacks, supports a dynamic vehicular mobility scenarios and meets the low latency communication requirements between the vehicles and the surrounding infrastructure. To meet these strict Quality of Service (QoS) demands, Unmanned Aerial Vehicles (UAVs) play a vital role in IoV ecosystems. An UAV can serve as a mobile roadside unit (mRSU) to transmit data between vehicles, stationery RSUs and other UAVs. The UAVs have better line of sight (LOS) connections which result in better channel states and lower path loss than fixed RSUs. Although the inclusion of UAVs in vehicular networks has improved the system performance by providing better resource management and routing solutions, the traditional optimization techniques with their limitations are often not applicable. Therefore, in recent years, research community is integrating Artificial Intelligence (AI) and Machine Learning (ML) into UAV based IoV ecosystem to better manage resource allocation, routing and mobility management issues to optimize the overall network performance. In this survey paper we review the existing research done in AI/ML based UAV-IoV networks and communications with focus on resource management and routing from year 2019 to 2023. We have studied different AI techniques, their training attributes and architectures in various research works. The limitations of AI-based approaches in terms of required computational resources, availability of real world data and AI models’ complexity for the UAV-IoV environment are also discussed. Finally, the future directions for UAV-IoV research opportunities that can leverage the full potential of AI are presented.

Recently, the application of Artificial Intelligence (AI) in many areas of life has allowed raising the efficiency of systems and converting them into smart ones, especially in the field of energy. Integrating AI with power systems allows electrical grids to be smart enough to predict the future load, which is known as Intelligent Load Forecasting (ILF). Hence, suitable decisions for power system planning and operation procedures can be taken accordingly. Moreover, ILF can play a vital role in electrical demand response, which guarantees a reliable transitioning of power systems. This paper introduces a Perfect Load Forecasting Strategy (PLFS) for predicting future load in smart electrical grids based on AI techniques. The proposed PLFS consists of two sequential phases, which are; Data Preprocessing Phase (DPP) and Load Forecasting Phase (LFP). In the former phase, input electrical load dataset is prepared before the actual forecasting takes place through two essential tasks, namely feature selection and outlier rejection. Feature selec-tion is done using Advanced Leopard Seal Optimization (ALSO) as a new natural inspired opti-mization technique, Citation: To be added by editorial staff during production. Academic Editor: Firstname Last-name Received: date Revised: date Accepted: date Published: date Copyright: © 2023 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). While outlier rejection is accomplished through Interquartile Range (IQR) as a measure of statis-tical dispersion. On the other hand, actual load forecasting takes place in LFP using a new pre-dictor called; Weighted K-Nearest Neighbor (WKNN) algorithm. The proposed PLFS has been tested through excessive experiments. Results have shown that PLFS outperforms recent load forecasting techniques as it introduces the maximum prediction accuracy with the minimum root mean square error.

Dementia is a major issue for healthcare systems worldwide, necessitating the development of creative and effective strategies for its management. This paper examines the use of Artificial Intelligence (AI) technologies in caring for and managing people with dementia. AI-driven solutions can improve diagnosis, personalize care, optimize medication management, and reduce the burden on caregivers. This paper discusses the implementation of an AI-based framework for creating videos related to people’s memories to support train-therapy or travel- therapy, a non-pharmacological intervention for Alzheimer’s disease patients

Malaria remains a primary public health challenge in Africa, and there is growing interest in leveraging artificial intelligence (AI) to raise malaria interventions. This research examines the possible influence, challenges, and recommendations for implementing AI-based personalized malaria interventions in Africa. AI offers several opportunities in malaria management, including early detection and prediction of outbreaks, improved diagnosis, personalized interventions, optimal treatment recommendations, surveillance and response, resource optimization, and research innovation. However, the implementation of AI in malaria interventions faces various challenges. These include data availability and quality, infrastructure and resource constraints, contextual relevance and generalizability, ethical and privacy considerations, integration into healthcare workflows, and the need to build trust and acceptance among stakeholders. To address these challenges, it is recommended to strengthen data infrastructure, build local capacity in AI technologies, contextualize AI models to local settings, address ethical considerations, establish monitoring and evaluation frameworks, promote collaboration and knowledge sharing, and secure sustainable funding and long-term commitment. By considering these recommendations, stakeholders can work towards implementing AI-based personalized malaria interventions in Africa that can contribute to improving malaria control outcomes, reducing the weight of the disease, and advancing public health in the region.

Agricultural greenhouse is largely answered in the agricultural sphere, despite the shortcomings it has, including overheating during the day and night cooling which sometimes results in the thermal inversion mainly due to its low inertia. The glasshouse dressed chapel is relatively more efficient than the conventional tunnel greenhouse. Its proliferation on the ground is more or less timid because of its relatively high cost[14-22]. Agricultural greenhouse aims to create a favorable microclimate to the requirements of growth and development of culture, from the surrounding weather conditions, produce according to the cropping calendars fruits, vegetables and flower species out of season and widely available along the year. It is defined by its structural and functional architecture, the quality thermal, mechanical and optical of its wall, with its sealing level and the technical and technological accompanying[12-13]. The greenhouse is a very confined environment, where multiple components are exchanged between key stakeholders and them factors are light, temperature and relative humidity[8]. This state of thermal evolution is the level sealing of the cover of its physical characteristics to be transparent to solar, absorbent and reflective of infrared radiation emitted by the enclosure where the solar radiation trapping effect otherwise called "greenhouse effect" and its technical and technological means of air that accompany. The socio-economic analysis of populations in the world leaves appear especially the last two decades of rapid and profound transformations These changes are accompanied by changes in eating habits, mainly characterized by rising consumption spread along the year[14]. To effectively meet this demand, greenhouse-systems have evolved, particularly towards greater control of production conditions (climate, irrigation, ventilation techniques, CO2 supply, etc ...). Technological progress has allowed the development of greenhouses so that they become increasingly sophisticated and of an industrial nature (heating, air conditioning, control, computer, regulation, etc ...). New climate driving techniques have emerged, including the use of control devices from the classic to the use of artificial intelligence[10-11] such as neural networks and / or fuzzy logic, etc... As a result, the greenhouse growers prefer these new technologies while optimizing the investment in the field to effectively meet the supply and demand of these fresh products cheaply and widely available throughout the year.

The main purpose of this article is to identify brain diseases such as Alzheimer's and Parkinson's in an intelligent way using Python software and processing the function of neurons in the neural network areas of the brain, which are classified into different components, and the data is based on the information obtained from FMRI images using algorithms artificial intelligence can intelligently diagnose the type of disease. Therefore, by using spatial clustering based on the deep learning method with noise algorithm (DBSCAN), fMRI brain image and fMRI brain surface complexity are classified, and then image preprocessing will be applied to fMRI images by using artificial intelligence neural networks (ANN) algorithm. became. Then, by using the ANN algorithm and Deep CNN topology of the brain neural network, the type of disease in the brain can be diagnosed by simulating the performance of an artificial neural network (ANN) in Python software. Deep machine learning-based methods, especially algorithms based on neural networks, have shown great success in medical image analysis for various tasks including brain tumor detection, segmentation, and classification, and Alzheimer's and epilepsy. In this research, we propose an integrated model based on a convolutional neural network for simultaneous recognition and classification of brain diseases for intelligent diagnosis of tumors and epilepsy. The average accuracy obtained from our proposed model is 97.89 percent accuracy and 91.66 percent specificity.

Pain is a multifaceted multisystem disorder that adversely affects neuro-psychological processes. This study compares the effectiveness of central-stimulation (tDCS) and peripheral-stimulation (TENS) in pain-inhibition during a cognitive-task in healthy volunteers and to observe potential neuro-cognitive improvements. Healthy eighty volunteers went through a complete experimental protocol comprising pain-induction (CPT), administration of tDCS/TENS, EEG-data acquisition, and cognitive assessments. The protocol for this study is categorized into 04-groups: G1(Control), G2(TENS), G3(Anodal-tDCS), and G4(Cathodal-tDCS). Paired t-tests (p

This paper presents the development of a novel anti-violence device titled "no pAIn" (an acronym for Never Oppressed Protected by Artificial Intelligence Nonviolence system), which harnesses the power of artificial intelligence (AI). Primarily designed to combat violence against women, the device offers personal safety benefits for individuals across diverse demographics. Operating autonomously, it necessitates no user interaction post-activation. The AI engine conducts real-time speech recognition and effectively discerns genuine instances of aggression from non-violent disputes or conversations. Facilitated by its Internet connectivity, in the event of detected aggression, the device promptly issues assistance requests with real-time precise geolocation tracking to predetermined recipients, for immediate assistance. Its compact size enables discreet concealment within commonplace items like candy wrappers, or within purpose-built casings, or as wearable accessories. The device is battery-operated. The prototype was developed using a microcontroller board (Arduino Nano RP2040 Connect), incorporating an omnidirectional microphone and Wi-Fi module, all at a remarkably low cost. Subsequent functionality testing, performed in debug mode using the Arduino IDE serial monitor, yielded successful results. The AI engine exhibited exceptional accuracy in word recognition, complemented by a robust logic implementation, rendering the device highly reliable in discerning genuine instances of aggression from non-violent scenarios

Smart grids represent the evolution of traditional electrical grids through the integration of digital and communication technologies. These grids are capable of monitoring and managing the distribution of electricity efficiently and resiliently. Artificial Intelligence (AI) plays a crucial role in this context, offering advanced solutions for optimization, forecasting, and energy management. This paper explores the main AI technologies applied to smart grids, highlighting their advantages, challenges, and impacts on energy efficiency and sustainability. The methodology employed includes a comprehensive literature review, analysis of current AI applications in smart grids, and examination of case studies to illustrate the practical implementation and benefits of AI technologies in this field.

Artificial Intelligence (AI) in healthcare has ushered in a revolutionary era by transforming the way we provide care and reduce adverse consequences. This paper explores the field of AI applications in healthcare, including side effect management, streamlined administrative procedures, and customized treatment programs.

Background: Artificial intelligence can help improve the quality of healthcare by analyzing vast amounts of data and providing more effective and personalized treatment plans. Researchers are working on developing AI-powered solutions that can help improve the outcomes of patients. Objective: To explore the potential of AI in improving healthcare outcomes and patient experience. Results: The study suggests that AI can improve healthcare efficiency and patient outcomes but cannot fully replace human healthcare professionals. AI can assist healthcare professionals in their work, leading to better resource utilization and improved patient care. However, there is still a need for human healthcare professionals to oversee AI systems and provide empathy and personalized care to patients. Conclusion: While there is immense potential for AI in healthcare, it is not yet feasible to replace human healthcare workers. Instead, it should be viewed as a tool that can help improve the efficiency and effectiveness of human healthcare.

Globally, zoonotic diseases have been on the rise in recent years. Predictive modelling approaches have been successfully used in the literature to identify the underlying causes of these zoonotic diseases. We examine the latest research in the field of predictive modeling that verifies the growth of zoonotic pathogens and assesses the factors associated with their spread. The results of our survey indicate that popular mathematical models can successfully be used in modeling the growth rate of these pathogens under varying storage temperatures. Additionally, some of them are used for the assessment of the inactivation of these pathogens based on various conditions. Based on the results of our study, machine learning models and deep learning are commonly used to detect pathogens within food items and to predict the factors associated with the presence of the pathogens.

ackground: The swift assimilation of Artificial Intelligence (AI) across multiple industries is altering the worldwide economic terrain. Unprecedented opportunities and difficulties are brought about by this revolutionary technology, which has an impact on innovation, trade, labor markets, and security. Objective: The goal of this study is to thoroughly investigate the economic effects of AI by exploring how it may affect labor markets, innovation, trade internationally, and security. The goal is to offer insights that support strategic decision-making and policy formation by examining current trends, opportunities, and difficulties. Results: The research highlights the dual character of artificial intelligence's influence, highlighting both its potential to spur economic expansion and its drawbacks, including the loss of jobs, a lack of standards, and security issues. The study highlights how crucial cooperation is to overcoming these obstacles and realizing AI's full potential. Conclusion: The balancing opportunities and risks becomes critical as AI continues to change the economic landscape. The report promotes proactive steps including ethical AI design guidelines, ongoing worker retraining, and flexible regulatory strategies. As we shape an AI future that puts inclusion, creativity, and responsible governance first, collaboration emerges as a key theme.

Artificial Intelligence has transformed the cyber security industry by enabling organizations to systematize and enlarge outdated safety procedures. AI can provide more effective threat detection and response capabilities, enhance vulnerability management, and improve compliance and governance. AI technologies such as machine learning, natural language processing, behavioral analytics, and deep learning can enhance cyber security defenses and protect against a wide range of cyber threats, including malware, phishing attacks, and insider threats.Theoretical underpinnings of AI in cyber security, such as machine learning, natural language processing, behavioral analytics, and deep learning, are discussed. The advantages of using AI in cyber security are discussed including speed and accuracy, continuous learning and adaptation, and efficiency and scalability. It's important to note that AI is not a silver bullet for cyber security and should be used in conjunction with other security measures to provide a comprehensive defense strategy.AI has transformed the way cyber security operates in today's digital age. By analyzing vast amounts of data quickly and accurately it has become a valuable tool for organizations looking to protect their assets from cyber threats.

AI is extraordinarily useful technology but it is becoming increasingly powerful with rapidly growing capabilities to disrupt and harm human society. We call on international and national organizations and individuals to join forces in banning the development of superintelligent AI and introducing regulations to prevent and mitigate AI-caused harm.

Artificial Intelligence models are increasingly used in manufacturing to inform decision-making. Responsible decision-making requires accurate forecasts and an understanding of the models’ behavior. Furthermore, the insights into models’ rationale can be enriched with domain knowledge. This research builds explanations considering feature rankings for a particular forecast, enriching them with media news entries, datasets’ metadata, and entries from the Google Knowledge Graph. We compare two approaches (embeddings-based and semantic-based) on a real-world use case regarding demand forecasting.

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning, deep learning and neural networks. AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of ‘big data’ and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-5 of diseases studied using AI; the United States, China, United Kingdom, South Korea and Canada are publishing the most articles in AI research; MIT is the world’s leading university in AI research; and convolutional neural networks are by far the most popular deep learning algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision-making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of data sets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

This is a short introductory review on quantum-like modeling of cognition with applications to decision making and rationality. The aim of the review is twofold: a) to present briefly the apparatus of quantum information and probability theory useful for such modeling; b) to motivate applications of this apparatus in cognitive studies and artifical intelligence, psychology, decision making, social and political sciences. We define quantum rationality as decision making that is based on quantum information processing. Quantumly and classically rational agents behaves differently. A quantum-like agent can violate the Savage Sure Thing Principle, the Aumann theorem on impossibility of agreeing to disagree.Such an agent violates the basic laws of classical probability, e.g., the law of total probability and the Bayesian probability inference. In some contexts, ``irrational behavior'' (from the viewpoint of classical theory of rationality) can be profitable, especially for agents who are overloaded by a variety of information flows. Quantumly rational agents can save a lot of information processing resources. At the same time, this sort of rationality is the basis for quantum-like socio-political engineering, e.g., social laser. This rationality plays the important role in the process of decision making not only by biosystems, but even by AI-systems. The latter equipped with quantum(-like) information processors would behave irrationally, from the classical viewpoint. As for biosystems, quantum rational behavior of AI-systems has its advantages and disadvantages. Finally, we point out that quantum-like information processing in AI-systems can be based on classical physical devices, e.g., classical digital or analog computers.

Open-vocabulary learning has recently gained prominence as a means to enable image segmentation for arbitrary categories based on textual descriptions. This advancement has extended the applicability of segmentation systems to a broader range of generally purpose scenarios. However, current methods often revolve around specialized architectures and parameters tailored to specific segmentation tasks, resulting in a fragmented landscape of segmentation models. In response to these challenges, we introduce OVAMTSeg, a versatile framework designed for Open-Vocabulary and Multitask Image Segmentation. OVAMTSeg harnesses adaptive prompt learning to empower the model to capture category-sensitive concepts, enhancing its robustness across diverse multi-task and scenario contexts. Text prompts are employed to effectively capture semantic and contextual features of the text, while cross-attention and cross-modal interactions enable the fusion of image and text features. Furthermore, a transformer-based decoder is incorporated for dense prediction. Extensive experimental results underscore the effectiveness of OVAMTSeg, showcasing its state-of-the-art performance and superior generalization capabilities across three segmentation tasks. Notable achievements include a 47.5 mIoU in referring expression segmentation, 51.6 mIoU on Pascal-VOC with four unseen classes, 46.6 mIoU on Pascal-Context in zero-shot segmentation, 65.9 mIoU on Pascal-5i, and 35.7 mIoU on COCO-20i datasets for one-shot segmentation.