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Dependability Assessment of a Dual-Axis Solar Tracking Prototype Using a Maintenance-Oriented Metric System
Exploring the Intersection of Ergonomics, Design Thinking, and AI/ML in Design Innovation
E-Marketplace State of the Art and Trends: VR-ZOCO—An Architectural Proposal for the Future

Journal Description

Applied System Innovation

Applied System Innovation is an international, peer-reviewed, open access journal on integrated engineering and technology. The journal is owned by the International Institute of Knowledge Innovation and Invention (IIKII) and is published bimonthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), Inspec, Ei Compendex and other databases.
Journal Rank: JCR - Q2 (Computer Science, Information Systems) / CiteScore - Q1 (Applied Mathematics)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 27.7 days after submission; acceptance to publication is undertaken in 4.9 days (median values for papers published in this journal in the first half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 3.8 (2023); 5-Year Impact Factor: 3.2 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2571-5577

Latest Articles

24 pages, 931 KiB

Open AccessReview

Machine Learning and Deep Learning Models for Demand Forecasting in Supply Chain Management: A Critical Review

by Kaoutar Douaioui, Rachid Oucheikh, Othmane Benmoussa and Charif Mabrouki

Appl. Syst. Innov. 2024, 7(5), 93; https://doi.org/10.3390/asi7050093 - 26 Sep 2024

This paper presents a comprehensive review of machine learning (ML) and deep learning (DL) models used for demand forecasting in supply chain management. By analyzing 119 papers from the Scopus database covering the period from 2015 to 2024, this study provides both macro- [...] Read more.

This paper presents a comprehensive review of machine learning (ML) and deep learning (DL) models used for demand forecasting in supply chain management. By analyzing 119 papers from the Scopus database covering the period from 2015 to 2024, this study provides both macro- and micro-level insights into the effectiveness of AI-based methodologies. The macro-level analysis illustrates the overall trajectory and trends in ML and DL applications, while the micro-level analysis explores the specific distinctions and advantages of these models. This review aims to serve as a valuable resource for improving demand forecasting in supply chain management using ML and DL techniques. Full article

(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)

25 pages, 2362 KiB

Open AccessArticle

The E(G)TL Model: A Novel Approach for Efficient Data Handling and Extraction in Multivariate Systems

by Aleksejs Vesjolijs

Appl. Syst. Innov. 2024, 7(5), 92; https://doi.org/10.3390/asi7050092 - 26 Sep 2024

This paper introduces the EGTL (extract, generate, transfer, load) model, a theoretical framework designed to enhance the traditional ETL processes by integrating a novel ‘generate’ step utilizing generative artificial intelligence (GenAI). This enhancement optimizes data extraction and processing, presenting a high-level solution architecture [...] Read more.

This paper introduces the EGTL (extract, generate, transfer, load) model, a theoretical framework designed to enhance the traditional ETL processes by integrating a novel ‘generate’ step utilizing generative artificial intelligence (GenAI). This enhancement optimizes data extraction and processing, presenting a high-level solution architecture that includes innovative data storage concepts: the Fusion and Alliance stores. The Fusion store acts as a virtual space for immediate data cleaning and profiling post-extraction, facilitated by GenAI, while the Alliance store serves as a collaborative data warehouse for both business users and AI processes. EGTL was developed to facilitate advanced data handling and integration within digital ecosystems. This study defines the EGTL solution design, setting the groundwork for future practical implementations and exploring the integration of best practices from data engineering, including DataOps principles and data mesh architecture. This research underscores how EGTL can improve the data engineering pipeline, illustrating the interactions between its components. The EGTL model was tested in the prototype web-based Hyperloop Decision-Making Ecosystem with tasks ranging from data extraction to code generation. Experiments demonstrated an overall success rate of 93% across five difficulty levels. Additionally, the study highlights key risks associated with EGTL implementation and offers comprehensive mitigation strategies. Full article

(This article belongs to the Section Information Systems)

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49 pages, 5929 KiB

Open AccessReview

Innovating Patent Retrieval: A Comprehensive Review of Techniques, Trends, and Challenges in Prior Art Searches

by Amna Ali, Ali Tufail, Liyanage Chandratilak De Silva and Pg Emeroylariffion Abas

Appl. Syst. Innov. 2024, 7(5), 91; https://doi.org/10.3390/asi7050091 - 26 Sep 2024

As the patent landscape continues to grow, so does the complexity of retrieving relevant “prior art”, “background art”, or “state of the art” from an expanding pool of publicly available patent data, a critical step in establishing novelty. However, retrieving this information presents [...] Read more.

As the patent landscape continues to grow, so does the complexity of retrieving relevant “prior art”, “background art”, or “state of the art” from an expanding pool of publicly available patent data, a critical step in establishing novelty. However, retrieving this information presents significant challenges due to its volume and complexity. This systematic literature review surveys patent retrieval techniques over the past decade, focusing on ‘prior art’ and ‘novelty’ searches. Adhering to the PRISMA 2020 guidelines, our research includes 78 pertinent articles selected from a corpus of 1441, providing an in-depth overview of recent advancements, emerging trends, challenges, and future directions in the field of patent prior art retrieval. The review addresses six research questions: defining the current state of the art, evaluating the efficacy of various approaches, examining commonly used patent data collections, exploring the impact of semantic search and natural language processing (NLP) technologies, identifying frequently used components of patent documents, and discussing ongoing challenges in the domain of patent prior art search and retrieval. Our findings highlight the growing use of NLP to enhance the precision and comprehensiveness of patent searches, particularly on the Cross-Language Evaluation Forum for Intellectual Property (CLEF-IP) and the United States Patent and Trademark Office (USPTO) databases. Despite advancements, the specialized and technical nature of patent language continues to pose significant challenges in achieving high accuracy in patent retrieval. Full article

(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)

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16 pages, 4769 KiB

Open AccessArticle

Digital Forensics Readiness in Big Data Networks: A Novel Framework and Incident Response Script for Linux–Hadoop Environments

by Cephas Mpungu, Carlisle George and Glenford Mapp

Appl. Syst. Innov. 2024, 7(5), 90; https://doi.org/10.3390/asi7050090 - 25 Sep 2024

The surge in big data and analytics has catalysed the proliferation of cybercrime, largely driven by organisations’ intensified focus on gathering and processing personal data for profit while often overlooking security considerations. Hadoop and its derivatives are prominent platforms for managing big data; [...] Read more.

The surge in big data and analytics has catalysed the proliferation of cybercrime, largely driven by organisations’ intensified focus on gathering and processing personal data for profit while often overlooking security considerations. Hadoop and its derivatives are prominent platforms for managing big data; however, investigating security incidents within Hadoop environments poses intricate challenges due to scale, distribution, data diversity, replication, component complexity, and dynamicity. This paper proposes a big data digital forensics readiness framework and an incident response script for Linux–Hadoop environments, streamlining preliminary investigations. The framework offers a novel approach to digital forensics in the domains of big data and Hadoop environments. A prototype of the incident response script for Linux–Hadoop environments was developed and evaluated through comprehensive functionality and usability testing. The results demonstrated robust performance and efficacy. Full article

(This article belongs to the Section Information Systems)

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17 pages, 7936 KiB

Open AccessArticle

Industrial Environmental Impact Assessment Method Based on Detection of Complex Anomalies in Time Series

by Elena Safonova, Alla Kravets, Maxim Shcherbakov, Alexey Kizim, Mohammad Al-Gunaid and Alexander Echin

Appl. Syst. Innov. 2024, 7(5), 89; https://doi.org/10.3390/asi7050089 - 24 Sep 2024

To minimize the environmental impact of energy enterprises, it is important to promptly identify cases of possible changes in the quality of wastewater generated at power plants, that is, cases of exceeding the maximum permissible concentrations of contamination in wastewater. The goal of [...] Read more.

To minimize the environmental impact of energy enterprises, it is important to promptly identify cases of possible changes in the quality of wastewater generated at power plants, that is, cases of exceeding the maximum permissible concentrations of contamination in wastewater. The goal of the method for detecting complex anomalies in multidimensional time series obtained from smart energy stations’ sensor channels is to improve the accuracy of detecting contamination levels in industrial wastewater. To achieve this goal, the following tasks were addressed: methods for detecting time series anomalies were analyzed, the method for detecting complex anomalies was developed, software implementation of the algorithm was carried out, and experiments were conducted. The developed method is recommended for use in a smart energy monitoring system. Full article

(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)

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23 pages, 4841 KiB

Open AccessArticle

Neural Network System for Predicting Anomalous Data in Applied Sensor Systems

by Serhii Vladov, Victoria Vysotska, Valerii Sokurenko, Oleksandr Muzychuk, Mariia Nazarkevych and Vasyl Lytvyn

Appl. Syst. Innov. 2024, 7(5), 88; https://doi.org/10.3390/asi7050088 - 23 Sep 2024

This article advances the research on the intelligent monitoring and control of helicopter turboshaft engines in onboard conditions. The proposed neural network system for anomaly prediction functions as a module within the helicopter turboshaft engine monitoring and control expert system. A SARIMAX-based preprocessor [...] Read more.

This article advances the research on the intelligent monitoring and control of helicopter turboshaft engines in onboard conditions. The proposed neural network system for anomaly prediction functions as a module within the helicopter turboshaft engine monitoring and control expert system. A SARIMAX-based preprocessor model was developed to determine autocorrelation and partial autocorrelation in training data, accounting for dynamic changes and external factors, achieving a prediction accuracy of up to 97.9%. A modified LSTM-based predictor model with Dropout and Dense layers predicted sensor data, with a tested error margin of 0.218% for predicting the TV3-117 aircraft engine gas temperature values before the compressor turbine during one minute of helicopter flight. A reconstructor model restored missing time series values and replaced outliers with synthetic values, achieving up to 98.73% accuracy. An anomaly detector model using the concept of dissonance successfully identified two anomalies: a sensor malfunction and a sharp temperature drop within two minutes of sensor activity, with type I and II errors below 1.12 and 1.01% and a detection time under 1.611 s. The system’s AUC-ROC value of 0.818 confirms its strong ability to differentiate between normal and anomalous data, ensuring reliable and accurate anomaly detection. The limitations involve the dependency on the quality of data from onboard sensors, affected by malfunctions or noise, with the LSTM network’s accuracy (up to 97.9%) varying with helicopter conditions, and the model’s high computational demand potentially limiting real-time use in resource-constrained environments. Full article

(This article belongs to the Section Information Systems)

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17 pages, 1686 KiB

Open AccessArticle

A Study on Operator Allocation in Consideration of Fatigue in Cell Manufacturing System

by Moe Endo and Harumi Haraguchi

Appl. Syst. Innov. 2024, 7(5), 87; https://doi.org/10.3390/asi7050087 - 23 Sep 2024

In a labor-intensive cell production system, it is important to train operators effectively because their skills are essential for productivity. Our previous study proposed a method to classify these skills according to a “skill index” based on the time required for each task [...] Read more.

In a labor-intensive cell production system, it is important to train operators effectively because their skills are essential for productivity. Our previous study proposed a method to classify these skills according to a “skill index” based on the time required for each task and the allocated operators based on this method. However, in actual workplaces, it is assumed that operators accumulate fatigue due to the repetition of work, which affects the assembly time. In this study, we propose an operator allocation method that considers the effect of fatigue and verify its effectiveness compared with the results of the previous study by computer experiments. In addition, an assembly experiment with a toy is conducted based on the operator allocation method derived from the computer experiments. The experimental results show that the proposed method is effective and indicate that appropriate parameter setting is crucial when applying it in real-world scenarios. Full article

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20 pages, 3749 KiB

Open AccessArticle

Buffer with N Policy and Active Management

by Andrzej Chydzinski

Appl. Syst. Innov. 2024, 7(5), 86; https://doi.org/10.3390/asi7050086 - 17 Sep 2024

The N policy is a buffer and transmission management scheme proposed for nodes in wireless sensor networks to save energy. It exploits the concept that the output radio of a node is initially switched off until a critical queue of packets is built [...] Read more.

The N policy is a buffer and transmission management scheme proposed for nodes in wireless sensor networks to save energy. It exploits the concept that the output radio of a node is initially switched off until a critical queue of packets is built up. Then, the output transmission begins and continues until the buffer is completely flushed. The cycle then repeats. In this study, we analyze a buffer with the N policy, equipped additionally with active queue management, which allows for dropping some packets depending on the current buffer occupancy. This extension enables controlling the performance of the node to a much greater extent than in the original N policy. The main contribution is the formulae for the key performance characteristics of the extended policy: the queue size distribution, throughput, and energy efficiency. These formulae are proven for a model with a general distribution of service time and general parameterizations of active management during the energy-saving and transmission phases. Theoretical results are followed by sample numerical calculations, demonstrating how the system’s performance can be controlled using active management in the transmission phase, the energy-saving phase, or both combined. The influence of the threshold value in an actively managed buffer is then shown and compared with its passive counterpart. Finally, solutions to some optimization problems, with the cost function based on the trade-off between the queue length and throughput, are presented. Full article

(This article belongs to the Section Applied Mathematics)

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22 pages, 1609 KiB

Open AccessArticle

Evaluation of the Performance of Neural and Non-Neural Methods to Classify the Severity of Work Accidents Occurring in the Footwear Industry Complex

by Jonhatan Magno Norte da Silva, Maria Luiza da Silva Braz, Joel Gomes da Silva, Lucas Gomes Miranda Bispo, Wilza Karla dos Santos Leite and Elamara Marama de Araujo Vieira

Appl. Syst. Innov. 2024, 7(5), 85; https://doi.org/10.3390/asi7050085 - 15 Sep 2024

In the footwear industry, occupational risks are significant, and work accidents are frequent. Professionals in the field prepare documents and reports about these accidents, but the need for more time and resources limits learning based on past incidents. Machine learning (ML) and deep [...] Read more.

In the footwear industry, occupational risks are significant, and work accidents are frequent. Professionals in the field prepare documents and reports about these accidents, but the need for more time and resources limits learning based on past incidents. Machine learning (ML) and deep learning (DL) methods have been applied to analyze data from these documents, identifying accident patterns and classifying the damage’s severity. However, evaluating the performance of these methods in different economic sectors is crucial. This study examined neural and non-neural methods for classifying the severity of workplace accidents in the footwear industry complex. The random forest (RF) and extreme gradient boosting (XGBoost) methods were the most effective non-neural methods. The neural methods 1D convolutional neural networks (1D-CNN) and bidirectional long short-term memory (Bi-LSTM) showed superior performance, with parameters above 98% and 99%, respectively, although with a longer training time. It is concluded that using these methods is viable for classifying accidents in the footwear industry. The methods can classify new accidents and simulate scenarios, demonstrating their adaptability and reliability in different economic sectors for accident prevention. Full article

(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)

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15 pages, 4554 KiB

Open AccessArticle

Curvature Sensing and Control of Soft Continuum Robots Using e-Textile Sensors

by Eric Vincent Galeta, Ayman A. Nada, Ibrahim Hameed and Haitham El-Hussieny

Appl. Syst. Innov. 2024, 7(5), 84; https://doi.org/10.3390/asi7050084 - 13 Sep 2024

Soft continuum robots, with their flexible and deformable structures, excel in tasks requiring delicate manipulation and navigation through complex environments. Accurate shape sensing is vital to enhance their performance, safety, and adaptability. Unlike rigid sensors, soft sensors conform to the robot’s flexible surfaces, [...] Read more.

Soft continuum robots, with their flexible and deformable structures, excel in tasks requiring delicate manipulation and navigation through complex environments. Accurate shape sensing is vital to enhance their performance, safety, and adaptability. Unlike rigid sensors, soft sensors conform to the robot’s flexible surfaces, ensuring consistent measurement of shape and motion. This paper introduces a new approach using soft e-textile resistive sensors, which integrate seamlessly with the robot’s structure. These sensors adjust their resistance in response to movements, capturing multidimensional force data. A deep Convolutional Neural Network (CNN) decodes the sensor signals, enabling precise shape estimation and control. Our findings indicate that soft e-textile sensors may surpass traditional rigid sensors in shape sensing and control, significantly improving the functionality of soft continuum robots in challenging applications. Full article

(This article belongs to the Section Control and Systems Engineering)

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20 pages, 5589 KiB

Open AccessArticle

Advanced Control Strategies for Securing UAV Systems: A Cyber-Physical Approach

by Mohammad Sadeq Ale Isaac, Pablo Flores Peña, Daniela Gîfu and Ahmed Refaat Ragab

Appl. Syst. Innov. 2024, 7(5), 83; https://doi.org/10.3390/asi7050083 - 6 Sep 2024

This paper explores the application of sliding mode control (SMC) as a robust security enhancement strategy for unmanned aerial vehicle (UAV) systems. The study proposes integrating advanced SMC techniques with security protocols to develop a dual-purpose system that improves UAV control and fortifies [...] Read more.

This paper explores the application of sliding mode control (SMC) as a robust security enhancement strategy for unmanned aerial vehicle (UAV) systems. The study proposes integrating advanced SMC techniques with security protocols to develop a dual-purpose system that improves UAV control and fortifies against adversarial actions. The strategy includes dynamic reconfiguration capabilities within the SMC framework, allowing adaptive responses to threats by adjusting control laws and operational parameters. This is complemented by anomaly detection algorithms that monitor deviations in control signals and system states, providing early warnings of potential cyber-intrusions or physical tampering. Additionally, fault-tolerant SMC mechanisms are designed to maintain control and system stability even when parts of the UAV are compromised. The methodology involves simulation and real-world testing to validate the effectiveness of the SMC-based security enhancements. Simulations assess how the UAV handles attack scenarios, such as GPS spoofing and control signal jamming, with SMC adapting in real-time to mitigate these threats. Field tests further confirm the system’s capability to operate under varied conditions, proving the feasibility of SMC for enhancing UAV security. This integration of sliding mode control into UAV security protocols leverages control theory for security purposes, offering a significant advancement in the robust, adaptive control of UAVs in hostile environments. Full article

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13 pages, 1440 KiB

Open AccessArticle

The Use of Fuzzy Modelling Based on Expert Knowledge to Determine Poland’s Energy Security Index Taking into Account Political Conditions

by Jarosław Joostberens, Aurelia Rybak and Aleksandra Rybak

Appl. Syst. Innov. 2024, 7(5), 82; https://doi.org/10.3390/asi7050082 - 5 Sep 2024

This article presents the results of research on the energy security index in Poland. Since the development of renewable energy sources forced by the transformation of the national energy system will require an increased supply of rare earth elements, the level of demand [...] Read more.

This article presents the results of research on the energy security index in Poland. Since the development of renewable energy sources forced by the transformation of the national energy system will require an increased supply of rare earth elements, the level of demand for these metals was taken into account when determining the energy security index. Furthermore, the development of renewable energy sources in Poland will directly depend on the volume of energy demand and, as the events of previous years have shown, on political and legal conditions, especially in the case of wind energy. Since some of these factors are qualitative, it was impossible to use a quantitative method. Therefore, fuzzy sets were used. Fuzzy modelling is based on expert knowledge. Using this method, the authors created three alternative scenarios for the development of Poland’s energy security index: pessimistic, optimistic, and the most probable. Full article

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19 pages, 4995 KiB

Open AccessArticle

Intelligent Procurement Scheduling System for Items Involving Public Procurement

by Nadanakumar Muniswamy and Parthiban Palanisamy

Appl. Syst. Innov. 2024, 7(5), 81; https://doi.org/10.3390/asi7050081 - 5 Sep 2024

The procurement of goods is considered a critical part in supply chain management, and it often has several unprecedented barriers leading to failure of the project. Uncertainties in availability, cost and demand-supply matching combined with stringent government norms andprocurement policies of various organizations [...] Read more.

The procurement of goods is considered a critical part in supply chain management, and it often has several unprecedented barriers leading to failure of the project. Uncertainties in availability, cost and demand-supply matching combined with stringent government norms andprocurement policies of various organizations need a thorough study in the present-day environment to develop sustainable and lean supplychain management. In this paper, use of a fuzzy logic system to estimate the tender finalization period of engineering items that involve public procurement is discussed. The tender finalization period is normally based on key parameters, such as criticality of the requirement of an item for the project, the number of variants available in a supply, competition amongst bidders, frequency of buying the item and the tender value. The methodology to arrive at the membership functions of the key parameters and the logic used to arrive at the tender finalization period estimation are well discussed in this paper. The proposed fuzzy logic approach was applied to an industrial case and the results show good agreement between expert opinion and the fuzzy logic system output. This paper will definitely help procurement managers in any organization to plan their activities in an effective manner. Full article

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17 pages, 2748 KiB

Open AccessReview

Application of Three-Dimensional Vision Technology in Dance

by Yixun Zhong, Xiao Fu, Zhihao Liang and Honglong Ning

Appl. Syst. Innov. 2024, 7(5), 80; https://doi.org/10.3390/asi7050080 - 31 Aug 2024

The development of science and technology constantly injects new vitality into dance performance and creation. Among them, three-dimensional (3D) vision technology provides novel ideas for the innovation and artistry of dance performances, expands the forms of dance performances and the way to present [...] Read more.

The development of science and technology constantly injects new vitality into dance performance and creation. Among them, three-dimensional (3D) vision technology provides novel ideas for the innovation and artistry of dance performances, expands the forms of dance performances and the way to present dance works, and brings a brand-new viewing experience to the audience. Nowadays, 3D vision technology in dance has been widely researched and applied. This review presents the background of the 3D vision technology application in the dance field, analyzes the main types of technology and working principles for realizing 3D vision, summarizes the research and application of the 3D vision technology in dance creation, perception, enhancement, and dance teaching, and finally looks forward to the development prospect of the 3D vision technology in the dance. Full article

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12 pages, 736 KiB

Open AccessArticle

Perceived Quality in the Automotive Industry: Do Car Exterior and Interior Color Combinations Have an Impact?

by Giuseppina Tovillo, Mariachiara Rapuano, Alessandro Milite and Gennaro Ruggiero

Appl. Syst. Innov. 2024, 7(5), 79; https://doi.org/10.3390/asi7050079 - 30 Aug 2024

Since in the automotive field colors play an important role, the present study tried to answer the following questions: is the perceived quality (PQ) of the vehicle interior color different after visually exploring the car body color? If so, how? Here, exploiting immersive [...] Read more.

Since in the automotive field colors play an important role, the present study tried to answer the following questions: is the perceived quality (PQ) of the vehicle interior color different after visually exploring the car body color? If so, how? Here, exploiting immersive virtual reality simulations and eye-tracking technology, participants were asked to visually explore an unbranded car in different exterior/interior color combinations and rate its PQ. Fixation duration (time eyes are fixed on a target) was considered as an implicit measure of visual attention allocation while PQ evaluations were considered as explicit measures of individual preferences for car colors. As for eye-tracking data, the results showed that white and red car exteriors affected the attention to interiors with the fixation duration being longer for gray than black interiors. Moreover, the subjective evaluations of car PQ predicted eye-tracking patterns: as the negative evaluation increased, the fixation duration on car interiors also increased. Overall, these preliminary results suggested the need to further explore the relationship between PQ and attentional/motivational processing as well as the role of subjective aesthetic preferences for color combinations in the automotive field. Full article

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11 pages, 3172 KiB

Open AccessArticle

AI Asthma Guard: Predictive Wearable Technology for Asthma Management in Vulnerable Populations

by Hajar Almuhanna, Manayer Alenezi, Mariam Abualhasan, Shouq Alajmi, Raghad Alfadhli and Abdullah S. Karar

Appl. Syst. Innov. 2024, 7(5), 78; https://doi.org/10.3390/asi7050078 - 30 Aug 2024

This paper presents AI Asthma Guard, a novel wearable device designed to predict and alert users of impending asthma attacks using artificial intelligence. The system integrates physiological and environmental sensors to monitor health metrics such as the heart rate, oxygen saturation, and exposure [...] Read more.

This paper presents AI Asthma Guard, a novel wearable device designed to predict and alert users of impending asthma attacks using artificial intelligence. The system integrates physiological and environmental sensors to monitor health metrics such as the heart rate, oxygen saturation, and exposure to specific air pollutants, which are crucial in managing asthma in children and individuals with mental disabilities. Utilizing machine learning models, including support vector machines and random forest, AI Asthma Guard classifies the risk levels of asthma attacks and provides timely notifications. This study details the device’s design, implementation, and preliminary testing results, underscoring its potential to improve health outcomes by enabling proactive asthma management. The implications of this technology reflect its alignment with the Sustainable Development Goals by enhancing individual health and well-being. The integration of a companion app leveraging large language models like ChatGPT facilitates user interaction, providing personalized advice and educational content about asthma management. Full article

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14 pages, 7195 KiB

Open AccessFeature PaperArticle

RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction

by Alaa Eleyan, Ebrahim AlBoghbaish, Abdulwahab AlShatti, Ahmad AlSultan and Darbi AlDarbi

Appl. Syst. Innov. 2024, 7(5), 77; https://doi.org/10.3390/asi7050077 - 29 Aug 2024

Heart disease, a global killer with many variations like arrhythmia and heart failure, remains a major health concern. Traditional risk factors include age, cholesterol, diabetes, and blood pressure. Fortunately, artificial intelligence (AI) offers a promising solution. We have harnessed the power of AI, [...] Read more.

Heart disease, a global killer with many variations like arrhythmia and heart failure, remains a major health concern. Traditional risk factors include age, cholesterol, diabetes, and blood pressure. Fortunately, artificial intelligence (AI) offers a promising solution. We have harnessed the power of AI, specifically deep learning and convolutional neural networks (CNNs), to develop Rhythmi, an innovative mobile ECG diagnosis device for heart disease detection. Rhythmi leverages extensive medical data from databases like MIT-BIH and BIDMC. These data empower the training and testing of the developed deep learning model to analyze ECG signals with accuracy, precision, sensitivity, specificity, and F1-score in identifying arrhythmias and other heart conditions, with performances reaching 98.52%, 98.55%, 98.52%, 99.26%, and 98.52%, respectively. Moreover, we tested Rhythmi in real time using a mobile device with a single-lead ECG sensor. This user-friendly prototype captures the ECG signal, transmits it to Rhythmi’s dedicated website, and provides instant diagnosis and feedback on the patient’s heart health. The developed mobile ECG diagnosis device addresses the main problems of traditional ECG diagnostic devices such as accessibility, cost, mobility, complexity, and data integration. However, we believe that despite the promising results, our system will still need intensive clinical validation in the future. Full article

(This article belongs to the Special Issue Advancing Healthcare Through Intelligent Clinical Decision Support Systems: Techniques, Applications, and Future Directions)

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25 pages, 16100 KiB

Open AccessArticle

E-Marketplace State of the Art and Trends: VR-ZOCO—An Architectural Proposal for the Future

by José Jesús Castro-Schez, Rubén Grande, Vanesa Herrera, Santiago Schez-Sobrino, David Vallejo and Javier Albusac

Appl. Syst. Innov. 2024, 7(5), 76; https://doi.org/10.3390/asi7050076 - 29 Aug 2024

E-commerce has become uniquely relevant to small- and medium-sized enterprises (SMEs) as an essential catalyst for their growth and sustainability. SMEs see e-commerce portals as a strategic way to engage in digital business activities without having to implement costly proprietary e-commerce solutions. In [...] Read more.

E-commerce has become uniquely relevant to small- and medium-sized enterprises (SMEs) as an essential catalyst for their growth and sustainability. SMEs see e-commerce portals as a strategic way to engage in digital business activities without having to implement costly proprietary e-commerce solutions. In addition, partnering with these portals frees them from complex tasks such as positioning, portal maintenance, and adapting the portal to new technologies and trends. This multifaceted advantage positions e-commerce portals as invaluable partners, streamlining operations and allowing SMEs to focus more on their core business competencies. However, e-commerce portals or e-marketplaces are not without their challenges. Today, they face increasing pressure to reduce their environmental impact and to empower local commercial businesses, as well as local businesses in the entertainment and culture industry. To address these challenges, there is a pressing need to propose new types of e-marketplaces that support the concept of the 15-minute city and in which virtual and augmented reality play a key role. These marketplaces would not only boost environmental sustainability but also strengthen the connection between local businesses and the community, creating a stronger and more collaborative network that benefits both businesses and consumers. Full article

(This article belongs to the Section Information Systems)

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19 pages, 5833 KiB

Open AccessArticle

Identification of Transformer Parameters Using Dandelion Algorithm

by Mahmoud A. El-Dabah and Ahmed M. Agwa

Appl. Syst. Innov. 2024, 7(5), 75; https://doi.org/10.3390/asi7050075 - 29 Aug 2024

Researchers tackled the challenge of finding the right parameters for a transformer-equivalent circuit. They achieved this by minimizing the difference between actual measurements (currents, powers, secondary voltage) during a transformer load test and the values predicted by the model using different parameter settings. [...] Read more.

Researchers tackled the challenge of finding the right parameters for a transformer-equivalent circuit. They achieved this by minimizing the difference between actual measurements (currents, powers, secondary voltage) during a transformer load test and the values predicted by the model using different parameter settings. This process considers limitations on what values the parameters can have. This research introduces the application of a new and effective optimization algorithm called the dandelion algorithm (DA) to determine these transformer parameters. Information from real-time tests (single- and three-phase transformers) is fed into a computer program that uses the DA to find the best parameters by minimizing the aforementioned difference. Tests confirm that the DA is a reliable and accurate tool for estimating the transformer parameters. It achieves excellent performance and stability in finding the optimal values that precisely reflect how a transformer behaves. The DA achieved a significantly lower best fitness function value of 0.0136101 for the three-phase transformer case, while for the single-phase case it reached 0.601764. This indicates a substantially improved match between estimated and measured electrical parameters for the three-phase transformer model. By comparing DA with six competitive algorithms to prove how well each method minimized the difference between measurements and predictions, it could be shown that the DA outperforms these other techniques. Full article

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12 pages, 1186 KiB

Open AccessArticle

An Expert Usability Evaluation of a Specialized Platform for Designing and Producing Online Educational Talking Books

by Mohamed Elsayed Ahmed and Shinobu Hasegawa

Appl. Syst. Innov. 2024, 7(5), 74; https://doi.org/10.3390/asi7050074 - 26 Aug 2024

Educational institutions are increasingly using audio-based learning resources and technologies nowadays, especially for students who are auditory learners and visually impaired. The ability to design and create online educational talking books with a pedagogical foundation has become essential for students studying instructional and [...] Read more.

Educational institutions are increasingly using audio-based learning resources and technologies nowadays, especially for students who are auditory learners and visually impaired. The ability to design and create online educational talking books with a pedagogical foundation has become essential for students studying instructional and information technology in the age of digital learning. With the need to enhance such skills to target students in higher educational institutions, instructional and information technology students have no specialized platform for designing and producing an online educational talking book without web programming challenges. This study suggests a new specialized, web-based platform that can assist students in developing online educational talking books. In this study, fourteen instructional technology experts evaluated the proposed platform’s usability. An online questionnaire was utilized to gather data, applying qualitative and quantitative methodologies. The results show that the proposed platform is appropriate for creating and developing an online educational talking book for the intended audience of students. Furthermore, the suggested platform’s current version had a workable design that was appropriate for helping students acquire the necessary abilities. Full article

(This article belongs to the Special Issue Feature Papers in the ‘Applied Systems on Educational Innovations and Emerging Technologies’ Section)

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