Payment systems are evolving, and this study examines how blockchain and AI improve online transactional security and service quality. The study examines micro and macro payment systems, compares online, and offline methods all over the world. The study also examines how blockchain and AI affect payment system security, privacy, and efficiency globally and rapidly digitizing economy. Digital payment methods are growing all over the world with high literacy and digital engagement, but they face challenges. The research highlights cybersecurity threats and the need to balance user convenience and security. It suggests blockchain and AI improve online payment services, supporting the policies for different countries. In this extensive research survey, we compare and evaluate the strengths and weaknesses of various payment systems, their practicality, and their robustness. This study also examines how technological innovations and payment systems interact to reveal how blockchain and AI could transform the financial sector. It seeks to understand how technology-enhancing service quality can boost customer satisfaction and financial stability in the digital age. The findings should help policymakers, financial institutions, and technology developers optimize online payment systems for a more secure and efficient digital economy.

The travelling salesman problem is very famous and very difficult combinatorial optimization problem that has several applications in operations research, computer science and industrial engineering. As the problem is difficult, finding its optimal solution is computationally very difficult. Thus, several researchers have developed heuristic/metaheuristic algorithms for finding heuristic solutions to the problem instances. In this present study, a new hybrid genetic algorithm (HGA) is suggested to find heuristic solution to the problem. In our HGA we used comprehensive sequential constructive crossover, adaptive mutation, 2-opt search and a new local search algorithm along with a replacement method, then executed our HGA on some standard TSPLIB problem instances, and finally, we compared our HGA with simple genetic algorithm and an existing state-of-the-art method. The experimental studies show the effectiveness of our proposed HGA for the problem.

The multi-tenancy and high scalability of the cloud have inspired businesses and organizations across various sectors to adopt and deploy cloud computing. Cloud computing provides cost-effective, reliable, and convenient access to pooled resources, including storage, servers, and networking. Cloud service models, SaaS, PaaS, and IaaS, enable organizations, developers, and end users to access resources, develop and deploy applications, and provide access to pooled computing infrastructure. Despite the benefits, cloud service models are vulnerable to multiple security and privacy attacks and threats. The SaaS layer is on top of the PaaS, and the IaaS is the bottom layer of the model. The software is hosted by a platform offered as a service through an infrastructure provided by a cloud computing provider. The Hypertext Transfer Protocol (HTTP) delivers cloud-based apps through a web browser. The stateless nature of HTTP facilitates session hijacking and related attacks. The Open Web Applications Security Project identifies web apps' most critical security risks as SQL injections, cross-site scripting, sensitive data leakage, lack of functional access control, and broken authentication. The systematic literature review reveals that data security, application-level security, and authentication are the primary security threats in the SaaS model. The recommended solutions to enhance security in SaaS include Elliptic-curve cryptography and Identity-based encryption. Integration and security challenges in PaaS and IaaS can be effectively addressed using well-defined APIs, implementing Service Level Agreements (SLAs), and standard syntax for cloud provisioning.

Resorting to electronic arbitration to resolve disputes in international trade contracts is the most important reflection of technological progress on the reality of international commercial arbitration. Electronic is a modern image of traditional arbitration, and this type of arbitration provides many advantages that are not provided by any legal system for resolving disputes, including speed, effectiveness and lower costs. What will this development produce? Through technical progress in the means of communication, it has become conceivable that international trade dealers agree to arbitration via electronic means of communication, followed by the completion of the arbitration process via the Internet, leading to the issuance of the electronic arbitration award in an electronic manner as well.

In this paper, we analyze the security of a self-recovery fragile watermarking scheme proposed by C. Wang et al. An attack against C. Wang et al.'s scheme is demonstrated. The theoretical and experimental results show that the proposed scheme is not secure against attacks.

Health Monitoring System is a sophisticating technology and another way to the normal/regular management of the health of the patient. This Health Monitoring Mobile Application is a contribution from our side to the public and to the overall health industry in Pakistan. With the help of Health mobile application, the users will be able to store their medical records, prescriptions and retrieve them later. The users can store and keep track of their vital readings (heart rate, blood pressure, fasting glucose, random glucose). The mobile application also shows hospitals that are nearby in case the user wants to avail of any medical help. An important feature of the application is the symptoms-based disease prediction, the user selects the symptoms which he has and then the application will name certain diseases that match those symptoms based on relevant algorithms. The major advances and issues have been discussed, and as well as potential tasks to health monitoring will be identified and evaluated.

The Internet of Things (IoT) enables the connection of millions of disparate devices to the World Wide Web. To perform the task, a lot of smart gadgets must work together. The gadgets recognize other devices as part of their network service. Keeping participating devices safe is a crucial component of the internet of things. When gadgets communicate with one another, they require a promise of confidence. Trust provides certainty that the gadgets or objects will function as expected. Trust management is more difficult than security management. This review includes a thorough examination of trust management in a variety of situations.

Background: The COVID-19 pandemic (the form of coronaviruses) developed at the end of 2019 and spread rapidly to almost every corner of the world. It has infected around 25,334,339 of the world population by the end of September 1, 2020 [1] . It has been spreading ever since, and the peak specific to every country has been rising and falling and does not seem to be over yet. Currently, the conventional RT-PCR testing is required to detect COVID-19, but the alternative method for data archiving purposes is certainly another choice for public departments to make. Researchers are trying to use medical images such as X-ray and Computed Tomography (CT) to easily diagnose the virus with the aid of Artificial Intelligence (AI)-based software. Method: This review paper provides an investigation of a newly emerging machine-learning method used to detect COVID-19 from X-ray images instead of using other methods of tests performed by medical experts. The facilities of computer vision enable us to develop an automated model that has clinical abilities of early detection of the disease. We have explored the researchers' focus on the modalities, images of datasets for use by the machine learning methods, and output metrics used to test the research in this field. Finally, the paper concludes by referring to the key problems posed by identifying COVID-19 using machine learning and future work studies. Result: This review's findings can be useful for public and private sectors to utilize the X-ray images and deployment of resources before the pandemic can reach its peaks, enabling the healthcare system with cushion time to bear the impact of the unfavorable circumstances of the pandemic is sure to cause

This paper proposes a new communication system for e-learning applications to mitigate the negative impacts of COVID-19 where the online massive demands impact the current commu-nications systems infrastructures and capabilities. The proposed system utilizes high-altitude platforms (HAPs) for fast and efficient connectivity provision to bridge the communication in-frastructure gap in the current pandemic. The system model is investigated, and its performance is analyzed using adaptive antenna arrays to achieve high quality and high transmission data rates at the student premises. In addition, the single beam and multibeam HAP radio coverage scenarios are examined using tapered uniform concentric circular arrays to achieve feasible communication link requirements.

It is a common observation that whenever any patient arrives at the front desk of a hospital, outpatient clinic, or other health-associated centers, they have to first queue up in a line and wait to fill in their registration form to get admitted. The long waiting time without any status updates is the most common complaint, worrying health officials. In this paper, UrNext, a location-aware mobile-based solution using Bluetooth low-energy (BLE) technology, is presented to solve the problem. Recently, a technology-oriented method has been gaining popularity in solving the healthcare sector's problems, namely the Internet of Things (IoT). The implementation of this solution could be explained through a simple example that when a patient arrives at a clinic for her consultation. There, instead of having to wait in long lines, she will be greeted automatically, receive a push notification telling her that she has been admitted along with an estimated waiting time for her consultation session. This will not only provide the patients with a sense of freedom but would also reduce uncertainty levels that are generally observed, thus saving both time and money. This work aimed to improve clinics' quality of services and organize queues and minimize waiting times in clinics, leading to patient comfortability and reducing the burden on nurses and receptionists. The results demonstrated that the presented system was successful in its performance and helped achieve high usability.

In this paper, we propose a novel robust blind crypto-watermarking method for medical images security based on hiding of DICOM patient information (patient name, age...) in the medical imaging. The DICOM patient information is encrypted using the AES standard algorithm before its insertion in the medical image. The cover image is divided in blocks of 8x8, in each we insert 1-bit of the encrypted watermark in the hybrid transform domain by applying respectively the 2D-LWT (Lifting wavelet transforms), the 2D-DCT (discrete cosine transforms), and the SVD (singular value decomposition). The scheme is tested by applying various attacks such as noise, filtering and compression. Experimental results show that no visible difference between the watermarked images and the original images and the test against attack shows the good robustness of the proposed algorithm.

This article aims to compare the accuracy of the Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) approaches in recognizing Sundanese characters. Based on experiments, the level of accuracy that has been obtained by the BPNN technique is 95.23% and the LVQ technique is 66.66%. Meanwhile, the learning time that has been required by the BPNN technique is 2 minutes 45 seconds and then the LVQ method is 17 minutes 22 seconds. The results indicated that the BPNN technique was better than the LVQ technique in recognizing Sundanese characters in accuracy and learning time.

Nowadays the deadly virus famous as COVID-19 spread all over the world starts from the Wuhan China in 2019. This disease COVID-19 Virus effect millions of people in very short time. There are so many symptoms of COVID19 perhaps the Identification of a person infected with COVID-19 virus is really a difficult task. Moreover it's a challenging task to identify whether a person or individual have covid test positive or negative. We are developing a framework in which we used machine learning techniques..The proposed method uses DecisionTree, KNearestNeighbors, GaussianNB, LogisticRegression, BernoulliNB , RandomForest , Machine Learning methods as the classifier for diagnosis of covid ,however, 5-fold and 10-fold cross-validations were applied through the classification process. The experimental results showed that the best accuracy obtained from Decision Tree classifiers. The data preprocessing techniques have been applied for improving the classification performance. Recall, accuracy, precision, and F-score metrics were used to evaluate the classification performance. In future we will improve model accuracy more than we achieved now that is 93 percent by applying different techniques

One of the goals of the Saudi Arabia 2030 vision is to ensure full employment of its citizens. Recruitment of graduates depends on the quality of skills that they may have gained during their study. Hence, the quality of education and ensuring that graduates have sufficient knowledge about the in-demand skills of the market are necessary. However, IT graduates are usually not aware of whether they are suitable for recruitment or not. This study builds a prediction model that can be deployed on the web, where users can input variables to generate predictions. Furthermore, it provides data-driven recommendations of the in-demand skills in the Saudi IT labor market to overcome the unemployment problem. Data were collected from two online job portals: LinkedIn and Bayt.com. Three machine learning algorithms, namely, Support Vector Machine, k-Nearest Neighbor, and Naïve Bayes were used to build the model. Furthermore, descriptive and data analysis methods were employed herein to evaluate the existing gap. Results showed that there existed a gap between labor market employers' expectations of Saudi workers and the skills that the workers were equipped with from their educational institutions. Planned collaboration between industry and education providers is required to narrow down this gap.

The novel coronavirus 2019 is called COVID-19 has outspread swiftly worldwide. An early diagnosis is more important to control its quick spread. Medical imaging mechanics, chest calculated tomography or chest X-ray, are playing a vital character in the identification and testing of COVID-19 in this present epidemic. Chest X-ray is cost effective method for Covid-19 detection however the manual process of x-ray analysis is time consuming given that the number of infected individuals keep growing rapidly. For this reason, it is very important to develop an automated COVID-19 detection process to control this pandemic. In this study, we address the task of automatic detection of Covid-19 by using a popular deep learning model namely the VGG19 model. We used 1300 healthy and 1300 confirmed COVID-19 chest X-ray images in this experiment. We performed three experiments by freezing different blocks and layers of VGG19 and finally, we used a machine learning classifier SVM for detecting COVID-19. In every experiment, we used a five-fold cross-validation method to train and validated the model and finally achieved 98.1% overall classification accuracy. Experimental results show that our proposed method using the deep learning-based VGG19 model can be used as a tool to aid radiologists and play a crucial role in the timely diagnosis of Covid-19.

The article examines the key constants of reengineering the modern educational cluster, associated with the processes of digital transformation of all spheres of modern socio-cultural space. The first constant is the strategic rethinking of the educational process organization and awareness of the new roles of all participants (tutors, applicants, controlling elements, etc.). The other constant involves practical re-design of the system of educational services, which consists in the reorientation from the traditional model of education functioning for society to the implementation of the educational format in the form of new projects (structural, target, business). Consequently, the purpose of the study is to highlight the attitudes relevant to the modern realities of information and technological support of education in the context of socio-economic interactions of society. The criteria for the reengineering of educational concepts and the structural organization of the educational sphere are defined. The modern world is going through a period of complete digital transformation of all spheres of public activity. The scientific intelligence notes that education is no exception in these processes, as the dependence of educational realities on information and computer technologies is now noted. The COVID-19 pandemic, for all its tragedy, was also a kind of trigger, clearly marking the new components that have become defined in the organization of the educational process. The conclusion is made that the use of digital technologies in the organization of the educational institution or in the organization of the educational process has become not an auxiliary element, but a dominant factor. Mobility, dynamism, interdisciplinarity, synergy - all these aspects are relevant for socio-economic interactions of society and should be provided by educational programs. The results of the study can be used in the reorganization processes of educational institutions and institutions. Further research requires aspects of the analysis of the foreign experience of reengineering in education, carried out taking into account digital transformations of modern sociocultural space.

A conceptual model represents an understanding of a system that is going to be developed, which in this research, a driving simulation software to study driver behavior at signalised intersections. Therefore, video observation was conducted to study driver compliance behaviour within the dilemma zone at signalised intersection, with regards to driver's distance from the stop line during yellow light interval. The video was analysed using Thematic Analysis and the data extracted from it was analysed using Chi-Square Independent Test. The Thematic Analysis revealed two major themes which were traffic situation and driver compliance behaviour. Traffic situation is defined as traffic surrounding the driver, such as no car in front and behind, car in front, and car behind. Meanwhile, the Chi-Square Test result indicates that within the dilemma zone, there was a significant relationship between driver compliance behaviour and driver's distance from the stop line during yellow light interval. The closer the drivers were to the stop line, the more likely they were going to comply. In contrast, drivers showed higher non-compliant behavior when further away from stop line. This finding could help in the development of conceptual model of driving simulation with purpose in studying driver behavior.

Twin clutch model enables the power-shifts as conventional planetary automatic transmission and eradicates the disadvantages of single clutch trans- mission. The automatic control of the dual clutches is a problem. Particularly to control the clutching component that engages when running in one direction of revolution and disengages when running the other direction, which exchange the torque smoothly during torque phase of the gearshifts on planetary-type automatic transmissions, seemed for quite a while hard to compensate through clutch control. Another problem is to skip gears during multiple gearshifts. However, the twin clutch gear control described in ["M Goetz, M C Levesley and D A Crolla. Dynamics and control of gearshifts on twin clutch transmissions, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 2005"], a significant improvement in twin clutch gear control system is discussed. In this research our objective is to formally specify the twin clutch gear control system and verify it with the help of formal methods. Formal methods have a high potential to give correctness estimating techniques. We use UPPAAL for formal specification and verification. Our results show that the twin clutch gear control model partially fulfills its functional requirements.

The characteristic aspects of learning a foreign language require special resources and tools for online learning. Criteria for choosing educational platforms depend on key elements of an academic subject area. Microsoft Teams (hereafter, MT) educational platform is competitive one because it meets most of the needs that arise during the formation of a secondary linguistic persona. Due to the large number of corporate programs, there are a successful acquisition of language skills and the implementation of all types of oral activities of students. A significant MT advantage is the constant analysis and monitoring of the platform of participants' needs in the educational process by developers. The article highlights MT advantages and disadvantages. The attention is drawn to individual programs, which, in the authors' opinion, are the most successful to learn writing, reading, speaking, listening, as well as organize classes that meet needs of modern foreign students.

The healthy products dedicated for young people are qualified as a solution to protect the future generation, especially that most commercial deals do not consider the consumer's health and environment. Therefore, it is crucial to define the antecedent of healthy purchases and to examine their impact on teenagers. This research aims to explore the antecedents and the consequences of the consumption of Saudis teenagers. Therefore, we develop a research model in the conceptual framework and the hypotheses to test. The empirical analysis required two samples from Saudis youth consumers. The first sample was utilized in the exploratory study with SPSS software. Then, the second was employed to the confirmatory part with the Amos software, as well as the validation of the hypotheses, and model with Fuzzy Set approach. The findings of this study have significant insights into the Saudi consumption and implications for both practitioners and researchers. Then, we have particularly strenuous on intention purchase antecedents of organic foods, and their consume habit moderation.

Hardware components are an integral part of Hardware Define Radio (HDR) for seamless operations and optimal performance. On the other hand, Software Define Radio (SDR) is a program that does not rely on any hardware components for its performance. Both of the latter radio programmers utilize modulation functions to make their core components from signal processing viewpoint. The following paper concentrates on SDR based modulation and their performance under different modulations. The bit error rate (BER) of modulations such as PSK, QAM, and PSAM were used as indicators to test channel quality estimation in planar Rayleigh fading. Though it is not commonly used for channel fading, the method of the adder determines the regionally segmented channel fading. Thus, the estimation error of the channel change substantially reduces the performance of the signal, hence, proving to be an effective option. Moreover, this paper also elaborates that BER is calculated as a function of the sample size (signal length) with an average of 20 decibels. Consequently, the size of the results for different modulation schemes has been explored. The analytical results through derivations have been verified through computer simulation. The results focused on parameters of amplitude estimation error for 1dB reduction in the average signal-to-noise ratio, while the combined amplitude deviation estimation error results are obtained for a 3.5 dB reduction

Road transport occupies the largest share in domestic and international transport. It is of key importance for the development of the economy, forasmuch as it provides the livelihood of the population, the development of the national economy, the possibility of establishing foreign economic relations. The purpose of the research is as follows: analysis of the current state of functioning of the road transport sector in Eastern Europe and identification of key problems and trends in its development. Research methods: Methods of grouping, comparison and generalization, correlation analisys have been used to identify the dynamics of the main indicators of road transport in Eastern Europe. The method of correlation-regression analysis has been applied to determine the impact of increasing the length of roads on the turnover of the road freight transport and the number of employed population in this area. Results. It has been found that the increase in the employed population by 96% and increase in revenues from transportation and storage of goods, postal and courier services (turnover of the road freight transport - in the original language) in the field of road transport by 82% is explained by the change in transport infrastructure capacity by increasing length of highways. According to the correlation analysis, it has been revealed that there is a high direct dependence between the length of roads and increased revenues from transportation and storage of goods in the field of road transport, as well as between the length of roads and increasing employment in this area.

Weather forecasting is considered to be of utmost important among various important sectors such as flood management and hydro-electricity generation. Although there are various numerical methods for weather forecasting but majority of them are reported to be Mechanistic computationally demanding due to their complexities. Therefore, it is necessary to develop and build models for accurately predicting the weather conditions which are faster as well as efficient in comparison to the prevalent meteorological models. The study has been undertaken to forecast various atmospheric parameters in the city of Bangalore using Naïve Bayes algorithms. The individual parameters analyzed in the study consisted of wind speed (WS), wind direction (WD), relative humidity (RH), solar radiation (SR), black carbon (BC), radiative forcing (RF), air temperature (AT), bar pressure (BP), PM₁₀ and PM_2.5 of the Bangalore city collected from Air Quality Monitoring Station for a period of 5 years from January 2015 to May 2019. The study concluded that Naive Bayes is an easy and efficient classifier that is centered on Bayes theorem, is quite efficient in forecasting the various air pollution parameters of the city of Bangalore.

In empirical software engineering research, there is an increased use of questionnaires and surveys to collect information from practitioners. Typically, such data is then analyzed based on overall, descriptive statistics. Overall, they consider the whole survey population as a single group with some sampling techniques to extract varieties. In some cases, the population is also partitioned into sub-groups based on some background information. However, this does not reveal opinion diversity properly as similar opinions can exist in different segments of the population, whereas people within the same group might have different opinions. Even though existing approach can capture the general trends there is a risk that the opinions of different sub-groups are lost. The problem becomes more complex in case of longitudinal studies where minority opinions might fade or resolute over time. Survey based longitudinal data may have some potential patterns which can be extracted through a clustering process. It may reveal new information and attract attention to alternative perspectives. We suggest using a data mining approach to finding the diversity among the different groups in longitudinal studies (agile skeptics). In our study, we show that diversity can be revealed and tracked over time with the use of clustering approach, and the minorities have an opportunity to be heard.

Chatbots have become very popular in our times and are used in several fields. The emergence of chatbots has created a new way of communicating between human and computer interaction. A Chatbot also called a "Chatter Robot," or conversational agent CA is a software application that mimics human conversations in its natural format, which contains textual material and oral communication with artificial intelligence AI techniques. Generally, there are two types of chatbots rule-based and smart machine-based. Over the years, several chatbots designed in many languages for serving various fields such as medicine, entertainment, and education. Unfortunately, in the Arabic chatbots area, little work has been done. In this paper, we developed a beneficial tool (chatBot) in the Arabic language which contributes to educating people about the Prophet's biography providing them with useful information by using Natural Language Processing.