Editor IJATCA

The rapid pace of technological advancement has driven the emergence of innovative payment platforms catering to consumers' demands for adaptable, user-friendly, cost-effective, and time-efficient transaction solutions. Among these cutting-edge innovations, decentralized digital currencies, commonly known as cryptocurrencies, along with their underlying blockchain technology, have emerged as highly promising disruptors in the financial landscape. This comprehensive review article delves into the intricate effects of cryptocurrencies as a decentralized form of money on contemporary society. Leveraging the inherent accessibility and cost-efficiency of cryptocurrencies, these digital assets empower marginalized populations, facilitating seamless cross-border remittances and expanding financial opportunities. Nonetheless, the volatile nature of cryptocurrency prices and the prevalence of fraudulent activities have sparked concerns among investors and regulators. The lack of robust regulatory oversight and the veil of anonymity shrouding transactions have rendered cryptocurrencies susceptible to scams and money laundering, necessitating unwavering vigilance and innovative strategies to combat these issues. Additionally, the energy-intensive nature of cryptocurrency mining has sparked environmental apprehensions, underscoring the imperative of adopting sustainable practices in the industry. Prudent management of digital assets and the implementation of robust security measures are vital in safeguarding users from potential risks, including fund loss resulting from accidents or hacking incidents. The future of cryptocurrencies promises further advancements, with various countries exploring the concept of Central Bank Digital Currencies (CBDCs) to enhance payment systems and financial services. Scalable solutions, such as layer 2 protocols and sharding techniques, hold the potential to address scalability challenges and optimize transaction speeds. Moreover, the convergence of Artificial Intelligence (AI) and cryptocurrencies opens up an exciting frontier where AI models and strategies may unveil novel insights and approaches in cryptocurrency markets. While cryptocurrencies present promising opportunities for financial inclusion and innovation, they also pose challenges and risks that demand careful consideration. By embracing innovation, nurturing financial inclusion, and proactively addressing potential risks, the future of cryptocurrencies is poised for sustained growth and evolution, shaping the way we interact with money and finance in the years to come.

Interoperability stands as a significant challenge for the healthcare industry in the digital era. Electronic health records (EHRs) encompassing vital patient information such as medical history, lab tests, demographics, medications, allergies, immunization records, radiology images, and vital signs are confined within isolated databases, incompatible systems, and proprietary software. This predicament poses substantial barriers to data exchange, analysis, and interpretation. Recognizing the need for improved access, analysis, and communication between healthcare systems, medical devices, and applications at both local and crossorganizational levels, the healthcare sector seeks a solution. Application Programming Interface (API) integration has emerged as the preferred method to facilitate data flow between internal applications, EHRs, and other data exchange tools within the healthcare industry. APIs enable a secure and seamless exchange of data and functionalities, making them a vital component in managing the data exchange process. This paper emphasizes the indispensability of interoperability for future medical advancements. APIs are the most useful tool among many developing technologies, such as IoT, SaaS, and cloud computing, for maximizing performance, boosting revenue, and bettering consumer comprehension. The paper highlights the advantages of APIs and proposes an API-led integration framework to enhance the interoperability of patient health information among healthcare organizations while ensuring data privacy and security.

The main issue in image forensics is to discover whether an image is authentic or forged and, if forged, to locate which regions have been manipulated. The simple accessibility of image manipulation software have proliferated the possibility of image forgery. Detection of Splicing forgery is targeted in this paper. Noise component of a color image have been utilized to extract features from suspected image. As the consistency of noise between RGB color channel of forged and authentic image are different, so it leaves the clues of forgery. First digit features are extracted using Benfords’ law and provided to the SVM classifier Columbia uncompressed image splicing detection evaluation dataset and CASIA v1.0 dataset are used to test the proposed technique. Our technique outperforms various previous techniques of image splicing detection.

Differential Evolution (DE) is an evolutionary optimization technique that is very simple, fast, and robust at numerical optimization. It has mainly three advantages; finding the true global minimum regardless of the initial parameter values, fast convergence, and using few control parameters. The main advantage of the DE over other methods is its stability. DE algorithm is a population based algorithm like genetic algorithms using similar operators; crossover, mutation and selection. DE becomes impressive because of the parameters; crossover ratio (CR) and mutation factor (F) do not require the same tuning which is necessary in many other Evolutionary Algorithms. In the present study, DE has been used to solve the two chemical engineering problems from the literature. The comparison is made with some other well-known conventional and non-conventional optimization methods. From the results, it was observed that the convergence speed of DE is significantly better than the other techniques. Therefore, DE algorithm seems to be a promising approach for engineering optimization problems.

Organisations and individuals have embraced new practices such as social distancing and remote working as a result of the COVID-19 virus which was declared a pandemic by the World Health Organisation. Cyber criminals around the world capitalised on the crisis while the entire world focused on the pandemic, in particular the health and economic threats posed by COVID-19.Humans have always been the weakest link in cybersecurity, they are either carrying out the attack or the target of the attack. Individuals are increasingly the targets of two types of attacks: social engineering which seeks to circumvent an existing process and exposes an individual's lack of security awareness, while exposing obsolete/vulnerable software in a system or technology are the targets of logical engineering. Recent trends, according to cybersecurity statistics and which are also side effects of the global pandemic, reveal a huge increase in hacked and breached data from sources that are increasingly common in the workplace, like mobile and smart devices. Additionally, remote workforce greatly increased, giving room for cyberattacks. This paper concludes that the pandemic introduced a new variation in cyber-attacks which majorly focused on; security risk from remote working/learning, malicious websites, mobile threats, malicious social media messaging and business email compromise, which might lead to the chances of downloading adware, spyware, ransomware and other malicious software. This paper also recommends that proper awareness on the new gimmicks of cybercriminals is essential inextinguishing the chances of a cyber-attack and possible information breach.

All over the world, the COVID-19 health crisis has had an impact on the teaching-learning process. Since the closure of schools and the suspension of face-to-face classes, preventive and urgent actions have been put in place to guarantee pedagogical continuity. In Morocco, from September 7, 2020, the Ministry of National Education has declared the adaptation of distance education as a pedagogical form, with the possibility of covering face-to-face teaching, while allowing parents of students to choose the mode of teaching that suits their children. Faced with the experience of distance learning and technical problems related to internet access and the inadequacy of digital tools experienced the school year 2019-2020 most parents have chosen the face-to-face mode. Faced with this situation and in order to preserve the health security of students, the Ministry has implemented a teaching mode that consists of alternating groups of students in each class to attend face-to-face classes. From then on, this change emerged from new evaluation practices. The main objective of this study is to analyze the evaluation practices of school mathematics learning in Morocco during the COVID-19 pandemic, and to identify the different forms taken by formative evaluation in such circumstances. For this purpose, we opted for a qualitative approach based on a content analysis through the analysis of the continuous tests of 10 teachers of mathematics of the qualifying secondary cycle, addressed to their students of the scientific common core level during the first semester of the school year 2020-2021; as well as the realization of an interview with the same teachers, in order to explain the obtained results. The results demonstrate a certain deviation from the formative evaluation process, as well as a decline in the evaluation practices considered in COVID-19.

Road traffic injuries are one of the highest public health hazards and in order to bring down the mishaps, one should be well aware of the road safety rules. Most highway users ignore road safety rules probably because they believe it is perfectly okay to violate them, or they get a feeling of accomplishment from being able to violate them and not get caught. The helmet is the main safety equipment for motorcyclists, but many riders do not use it. In order to enhance the enforcement of obeying road safety rule of wearing a suitable helmet for every motorcycle user on the highway, an automated system that captures highway users' state and report defaulters to appropriate authorities is important. Deep learning method known as transfer learning with Convolutional Neural Networks can be highly efficient in expediting detection of helmet on the highway.

In this modern era, the misuse of computers and tape recorders results in the widespread use of telephones, cell phones and tape recorders. This makes them an effective tool for the commission of criminal offences, where certain forms of crime are often used by criminals. Any criminal minded individuals might use some strategies and tricks to conceal the voice, assuming they would remain undercover, that some would not identify them. The misuse of voice can be evaluated by using the individualization character of audio. The manipulation or alteration of the speech of individuals is regarded as voice disguise. It can be done intentionally or non-intentionally. Luckily, it is not so easy. Because everyone has their own different and unique voice. By examining the parameters like pitch, frequency, way of talking, focusing on vowels can help to identify the disguised voice produced by someone by changing their voice. Pitch shift is a common method of mask introduced by perpetrators When comparing forensic voices, there is a lot of variation in acoustic properties leads to weaker detection in speaker performance. In most cases, the perpetrator tries to cover his voice until an anonymous or diverse call is sent. That is why it is necessary, before naming a speaker, to research the possibilities for covering the voice. The review paper deals with the studies reported on to analyse and compare genuine with disguised voices using various techniques and softwares.

Nowadays everyone has the ability to capture, store and transfer the digital image. In forensics, unlike other physical evidences, image shows the authenticity of real objects and scene. Sometimes in criminal activities photographs are taken by any witness but due to their blurred appearance they become neglected as evidence. These days many sophisticated image-processing software's are available that has led not only to increase the cases of manipulated or faked photographs but also emerged as problem solver to the similar images. Various softwares which works as image enhancement improves the quality of digital image that can help to retrieve the useful information from blurred image. The current paper deals with review of literature reported related to image enhancement or de-blurring of digital images of both types i.e. motion blur and out-of-focus blur using Adobe Photoshop Software.

Another technique for building and tracking networks has been created by software-defined networking (SDN), but it has also updated the assault surface formed by the organization.SDN offers numerous designs that permit straightforward moderation of particular sorts of assaults, for example, DoS, and permit further work to alleviate certain assaults. In any case, SDN regularly presents new imperfections that are absent in customary organizations, for example, a nonappearance of correspondence between the control plane and the information plane. A few new advancements and strategies have been recommended to conquer shortcomings in SDN security and some extra work may likewise be applied to fix them. Current SDN work explores many measurable patterns that contribute to the state of SDN technology implementation. Because Open Flow is SDN's most common implementation is currently being used in production environments, and IOT of research has been done to use and develop the protocol. There is anyway another exploration pattern that has work that is for the most part pertinent to SDN, including designs that give more adaptability than Open Flow. The expected study will probably follow these patterns by enhancing Open Flow protocol and suggesting more general alternatives, and this research will include further development of network design testing tools and research into Open Flow enhancements when used in production environments. This work presents a study survey review of current SDN security research and other work done in the field of SDNs that is relevant to security and a forecast of future SDN security research directions.

Size reduction mechanism in real life data sets are very important and an essential factor in healthcare based machine learning (ML) analysis due to high dimension in nature. ML based feature selection aims in determining a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. Rough Set (RS) theory provides the mechanism of discovery of data dependencies in the data set and the novel reducts facilitates, the reduction of the number of conditional attributes and the set of associated objects contained in a dataset in preserving the information of the original dataset. The process use the data alone and does not need any additional information. This paper presents the fundamental concepts of RS and Tolerance RS approaches and adapts the related feature selection for two relevant healthcare applications. Firstly, the TRS based feature selection method is used in latest developments of three medical dataset classification analysis, secondly the method is used in Chest X-Ray image analysis for nCOVID19 diagnose or test classifications and non-invasive thermal imaging process to detect inflammation and vascular dysfunction for sensitive screening of nCOVID19 cases.

The object of the present research paper is to develop Artificial Neural Network Simulation and by using five and three independent π-term from five independent pi terms. (Aspect ratio, aggregate-cement ratio, water-cement ratio, percentage of fibre and control strength)) for prediction of SFRC strength. From three independent pie terms control strength, percentage of fibre and Aspect ratio. The output of this network can be evaluated by comparing it with experimental strength and the predicted ANN simulation strength. The study becomes more fruitful when most influencing π-term is calculated for the prediction of SFRC strength. The beauty of both the models is that we can predict compressive strength, flexural strength and split tensile strength by using same model.

Cloud Computing (CC) is a model that allows shared and configurable computing resources positioned in the cloud with little management effort from the CloudServicesProviders (CSP). However, security, reliability, cost, virtualization, need, on demand service, maintenance, integration, user friendliness legislation and regulations are top priority in the adoption of cloud computing concepts with security being the most important. This paper highlights the challenges of adoption of cloud computing paradigm, examines threats in cloud computing as highlighted by several authors with proposed solutions. The burdens on Cloud Services Providers and the future of this technological shift are also discussed.

The recent influx in the deployment of cloud computing can be attributed to large, medium, small enterprises and individuals' quest to decrease IT cost and overcome economic recession. However, the cloud is still faced with challenges such as data breaches, data loss, malicious insider and denial of service attacks and all point to security of the cloud. This makes security an important discuss in cloud computing, which led to the objectives of this research, which are toevaluate security protocols or measures employed by major cloud service providers and offer recommendations to both Cloud Service Providers (CSPs) and the user.The research employed comparing techniques to evaluate the security measures and protocols employed by the top three cloud service providers which are Microsoft, Amazon and Google. It is worthy to note that, the compute service of CSPs are claimed to be secured as well as their storage services. It is recommended that CSPs provide a 2-factor authentication for users, this ensures that cloud users use very strong password or keys. While cloud users should ensure proper cloud logging and authentication, they should also avoid weak and generic passwords, as weak and generic password makes the cloud susceptible to breaches.

Innovative advancements in the Telecommunication sector in form of 5G and 6G technologies have raised the issues pertaining to human health impacts by the electromagnetic waves (EMW) delivered by cell phone base transmitter stations (BTS). Present paper highlights the health issues, which might arise from EMW from the cell phone BTS. Because of super-fast speed and potential of lower latency of connectivity even in the far-flung areas, 5G and 6G technology will be exclusively relied upon for implementation in different industrial fields, research and development in the near future and will further satisfy the thorough need for bandwidth through positioning of huge number of heavily dense sited base stations working in the millimeter-wave extend. Presentation of new emission sources, working in correspondence to previously existing 2G/3G/4G versatile innovations, raises worries about surpassing the acceptable EMF exposure limits. Due to these services, cell phone clients and individuals living inside short proximity of the cell phone base stations have become progressively worried over the possible harmful effects of radiofrequency radiation generated by these devices to their health The way that this radiation is concealed, vague, and enters and leaves our bodies without our insight makes it considerably alarming.

The paper comprises of several interfaces in between several layers of SDN architecture. There are four interfaces defined eastbound westbound northbound southbound each. Different kinds of methods & techniques, as well as targeted audiences, have been displayed concerning literature review is also defined. The components and its pros cons are well signifying its noted value. The major challenges encountered thereafter a reflected in SDN techniques and report cards through it curb its challenges and benefits discussed thereafter. The various segment of SDN is defined including its controllers those are centralized structures, distributed structure and multilayer structure. Therefore, we can conclude by abstracting a graph scale through which an audience can easily simplify its implementation thereon.

Machine learning is an application of artificial intelligence in which the machines learn themselves and then work accordingly to the instructions. Basically, machine learning works on the data sets. Data is unprocessed raw facts and figures. The machine works on the data, tries to understand and correlate with different fields and then give output. In this paper, we will be discussing the basic knowledge required to build up the machine learning models, the hypes and reality related to machine learning and most importantly how machine learning and interrelated fields are used in various platforms. This is one of the fast-growing fields in the present world, as it is reducing the load of computation and helping companies to strategies accordingly. But as every coin has two sides, machine learning also has its own positive and negative views as day by day it is reducing human efforts. Almost every multinational company is using this technology for solving the problems of society and people. Machine learning is also linked to other branches like artificial intelligence, data science, computational statistics and probability. These all fields are linked with one another, machine learning is all about the mathematics mainly probability and statistics. Analyzing the data depending upon the various factors and then work according to them is a part of machine learning.