Scene change detection is an important topic because of the wide and growing range of its applications. Streaming services from many providers are increasing their capacity which causes the industry growth. The method for the scene change detection is described here and compared with the State-of-the-Art methods over the Open Video Scene Detection (OVSD) - an open dataset of Creative Commons licensed videos freely available for download and use to evaluate video scene detection algorithms. The proposed method is based on scene analysis using threshold values and smooth scene changes. A comparison of the presented method was conducted in this research. The obtained results demonstrated the high efficiency of the scene cut localization method proposed by authors, because its efficiency measured in terms of precision, recall, accuracy, and F-metrics score exceeds the best previously known results.

The modern stage of society's development is characterized by the rapid penetration of information technologies into all spheres of life. Their use contributes to improving the quality of tourism services, as well as the competitiveness of tourism industry enterprises. The role of information technology in tourism is growing more and more every year, which determines the relevance of the study of modern trends in the use of information technology in the tourism sector. The purpose of the study is to determine the possibilities of using information technologies to improve the quality of services provided to the tourism industry under the COVID-19 restrictions. The article systematizes the main approaches to the "cluster" category and provides an original definition of the "regional tourist cluster" concept. Based on an expert survey, the main trends in the introduction of information technologies in the tourism industry under the COVID-19 restrictions have been identified, which include virtual reality and augmented reality, speech recognition technologies, photo, video, audio (contactless control technologies), mobile IT applications and Big Data technologies. It has been concluded that the vast majority of improvements in the organization of tourism services under restrictions will be based on the organization of virtual solutions and online activities. The types of tourism services will also change, and information technology will help their development and dissemination.

Mankind has been living in the third millennium of a new era for 15 years. Today he realized that he was on a tiny planet with not much space. The 20th century laid bare its global consequences in all its horror.. According to the Stockholm International Peace Research Institute, more than 30 armed conflicts are recorded on the planet every year, leading to the death of more than 1,000 people during the year. Over the past 15 years, the total number of deaths as a result of military actions in such conflicts has also increased: more than 17 thousand people - in 2002 to more than 22.5 thousand - in 2011. 2022, in turn, became the beginning of a new round of military history, bringing changes in the social, environmental, agro-industrial, economic and other spheres not only in Ukraine, but also in all countries of the civilized world. As a result of the study, the most significant impact of the negative factors of the war, namely the economic, legal, regional and environmental aspects, was identified and analyzed.

The main purpose of the study is to analyze the current state of the organization's information security system in the context of COVID-19 on the basis of public-private partnership. The development of public-private interaction in information security is one of the priorities of the state policy of many estates. Among the priorities of public-private partnership in cybersecurity and information security, there is an expansion of interaction between government agencies and private scientific institutions, public associations and volunteer organizations, including in training, as well as increasing the digital literacy of citizens and the security culture in cyberspace. As a result of the study, the foundations of the organization's information security system in the context of COVID 19 were formed on the basis of public-private partnership.

The hypothesis of the study of the article is that the use of elements of artificial intelligence will increase the effectiveness of the educational process of the university if: a set of pedagogical conditions for the construction and use of an expert system with elements of artificial intelligence in the educational process of the university is revealed; a model for preparing a future teacher of vocational training for the use of elements of artificial intelligence has been developed; a special course has been developed that contributes to the implementation of the professional orientation of education. In accordance with this, the following tasks were studied in the article: An analysis of scientific and methodological research in the field of the current state, prospects for the development and use of elements of artificial intelligence in the preparation of a future teacher of vocational training and to determine the dynamics of the introduction of intelligent expert systems in education; A set of pedagogical conditions for the construction and use of an expert system with elements of artificial intelligence in the educational process of a university is revealed; It is substantiated to develop a model for preparing a teacher of vocational training to use elements of artificial intelligence.

The sowing campaign is taking place with great difficulty as a result of active military actions in the eastern and southern parts of the country, specializing in the cultivation of grain crops. Seaports are blocked, which creates new threats to global food security. The generalization of analytical data is aimed at characterizing the food security of Ukraine before and during military actions, followed by the designation of possible consequences, including on global food security. The generalizations made prove the need to consolidate the efforts of Ukraine, as one of the world's largest food producers, and international organizations in order to avoid the greatest catastrophe of mankind in its modern history, which will be caused by famine.

The advent, course, and possible consequences of the COVID-19 pandemic are now the focus of global attention. From whichever side the geopolitical centers of influence might view it, the problem of the coronavirus concerns all world leaders and the representatives of all branches of science, especially physicians, economists, and politicians - virtually the entire population of the planet. The uniqueness of the COVID-19 phenomenon lies in the uncertainty of the problem itself, the peculiarities and specifics of the course of the biological processes in modern conditions, as well as the sharp confrontation of the main political players on the world stage. Based on an analysis of scientific research, the article describes the profile of the emotional concept of "anxiety" in Russian linguoculture. Through monitoring the headlines of Russian media reports in the "COVID-19" section of Google News and Mail News news aggregators dated August 4-6, 2021, the study establishes the quantitative and qualitative characteristics of the alarm-generating news products on coronavirus in the Russian segment of the Internet and interprets the specifics of media information about COVID-19. The level of mass media criticism in Russia is determined through a phone survey. It is concluded that coronavirus reports in online media conceptualize anxiety about the SARS virus and the COVID-19 disease as a complex cognitive structure. The media abuse the trick of "magic numbers" and emotionally expressive words in news headlines, which are perceived by mass information consumers first and typically uncritically.

The article substantiates the feasibility of using and actively implementing innovative technologies in the practice of organizing the educational process. The need for the use of telecommunication technologies, which provide constant communication between students and the teacher outside the classroom, has been identified. Particular attention is paid to the latest approaches to the use of various forms of multimedia technologies in student education, which intensify the process of acceptance and assimilation of educational material by foreign students. The advantages of using innovative means of distance education are determined, which thanks to modern electronic educational systems allow students to receive quality higher education. Innovative technologies promote the development of cognitive interest in students, they learn to systematize and summarize the material studied, discuss and debate. In this regard, the reorientation of the system of higher education in Europe towards innovation is becoming the most important tool in ensuring the competitiveness of graduates in the labor market. In addition, the investment attractiveness of a university often depends on the innovative nature of the development of scientific, educational and practical activities of the subjects of the educational process, their inclusion in the national innovation system. The article analyzes that in the universities of the European Union in the training of specialists in the management of basic interactive methods, forms and tools are binary lecture, briefing, webinar, video conference, video lecture, virtual consultation, virtual tutorial, slide lecture, comp. utheric tests. Various classes on slide technology took active forms during the training of management specialists.

It is relevant and timely for the existence and prosperity of today's tourism to build up a stock of new abilities and a set of innovations. At present, the tourism industry is experiencing a new stage in its digital transformation. The newest technologies, which are now spreading en masse and one of which is rightfully considered to be blockchain technology, enable tourists to receive tourist services directly from the producers, which not only gives the consumer the opportunity to enjoy higher quality and inexpensive products but also increases the responsibility of the producer. The article analyzes research literature on the possibility of using blockchain technology in the tourism industry. Based on an expert survey, the main problems, prospects, and advantages of the implementation of blockchain technology in the tourism industry are identified. The paper proposes and analyzes an option for the use of blockchain technology on the basis of a blockchain project with a mobile app for users and a dedicated website and public API for travel service providers.

The recent past has seen a tremendous amount of advancement in the field of Internet of Things (IoT), allowing the influx of a variety of devices into the market. IoT devices are present in almost every aspect of our daily lives. While this increase in usage has many advantages, it also comes with many problems, including and not limited to, the problem of security. There is a need for better measures to be put in place to ensure that the users' data is protected. In particular, fitness trackers used by a vast number of people, transmit important data regarding the health and location of the user. This data is transmitted from the fitness device to the phone and from the phone onto a cloud server. The transmission from device to phone is done over Bluetooth and the latest version of Bluetooth Light Energy (BLE) is fairly advanced in terms of security, it is susceptible to attacks such as Man-in-the-Middle attack and Denial of Service attack. Additionally, the data must be stored in an encrypted form on the cloud server; however, this proves to be a problem when the data must be decrypted to use for running computations. In order to ensure protection of data, measures such as end-to-end encryption may be used. Homomorphic encryption is a class of encryption schemes that allow computations on encrypted data. This paper explores the application of homomorphic encryption for fitness trackers.

The article discusses the application of the IoT concept in the sensitive field of medical devices on the example of a developed prototype of a mechanotherapeutic simulator. Mechanotherapy is a complex of therapeutic, preventive, and restorative exercises conducted using simulators, specially designed for developing movements in individual joints. Mechanotherapy is used for the early and painless restoration of joint mobility, to prevent complications associated with prolonged immobilization of the injured area of extremities. Using the mechanotherapy simulator allows developing the joint painlessly, which accelerates the metabolism in the injured area, and soft tissues are restored to normal. The article provides information about the electronic components that ensure the wireless operation of the device and describes in detail the applied software as well as the client application for a mobile device.

One type of network security breach is the availability breach, which deprives legitimate users of their right to access services. The Denial of Service (DoS) attack is one way to have this breach, whereas using the Intrusion Detection System (IDS) is the trending way to detect a DoS attack. However, building IDS has two challenges: reducing the false alert and picking up the right dataset to train the IDS model. The survey concluded, in the end, that using a real dataset such as MAWILab or some tools like ID2T that give the researcher the ability to create a custom dataset may enhance the IDS model to handle the network threats, including DoS attacks. In addition to minimizing the rate of the false alert.

Medical data is one of the data that must be kept in safe containers, far from intrusion, viewing and modification. With the technological developments in hospital systems and the use of cloud computing, it has become necessary to save, encrypt and even hide data from the eyes of attackers. Medical data includes medical images, whether they are x-ray images of patients or others, or even documents that have been saved in the image format. In this review, we review the latest research and the latest tools and algorithms that are used to protect, encrypt and hide these images, and discuss the most important challenges facing these areas.

Traditional Cloud Computing would be unable to safely host IoT data due to its high latency as the number of IoT sensors and physical devices accommodated on the Internet grows by the day. Because of the difficulty of processing all IoT large data on Cloud facilities, there hasn't been enough research done on automating the security of all components in the IoT-Cloud ecosystem that deal with big data and real-time jobs. It's difficult, for example, to build an automatic, secure data transfer from the IoT layer to the cloud layer, which incorporates a large number of scattered devices. Addressing this issue this article presents an intelligent algorithm that deals with enhancing security aspects in IoT cloud ecosystem using butterfly optimization algorithm.

The photocatalytic degradation of salicylic acid takes place in several stages involving coupled phenomena, such as the transport of molecules and the chemical reaction. The systems of transport equations and the photocatalytic reaction are numerically solved using COMSOL Mutiphysics (CM) simulation software. CM will make it possible to couple the phenomena of flow, the transport of pollutants (salicylic acid) by convection and diffusion, and the chemical reaction to the catalytic area (bauxite or TiO2 doped by nanoparticles). The simulation of the conversion rate allows to correctly fit the experimental results. The temporal simulation shows that the reaction reaches equilibrium after a transitional stage lasting over one minute. The outcomes of the study highlight the importance of diffusion in the boundary layer and the usefulness of injecting micro-agitation into the microchannel flow. Under such conditions, salicylic acid degrades completely.

The video-assisted human action recognition [1] field is one of the most active ones in computer vision research. Since the depth data [2] obtained by Kinect cameras has more benefits than traditional RGB data, research on human action detection has recently increased because of the Kinect camera. We conducted a systematic study of strategies for recognizing human activity based on deep data in this article. All methods are grouped into deep map tactics and skeleton tactics. A comparison of some of the more traditional strategies is also covered. We then examined the specifics of different depth behavior databases and provided a straightforward distinction between them. We address the advantages and disadvantages of depth and skeleton-based techniques in this discussion.

The article is devoted to analyzing the police influence on road safety formation. The globalization processes confirm the study's relevance, provoking population mobility and the need to increase freight traffic. The study aims to identify the factors that affect road safety and the activities of the police and the EU transport safety units. An empirical analysis using factor analysis, correlation analysis, and general scientific methods of cognition were carried out to achieve the goal. The analysis results found that the number of police officers affects road safety, but not in all countries, which confirms the importance of other factors. Based on the analysis results of the scientific literature, the factors affecting transport safety are legislative regulation, the use of innovative technologies, transport infrastructure, geography, and psycho-physical and emotional drivers factors. It has been proved that the police authorities and units cannot fully ensure transport safety in the European Union because the safety is formed by a complex of actions by the state and road users.

The goal of this research is to look into different techniques to solve the problem of authorship verification for Arabic short writings. Despite the widespread usage of Twitter among Arabs, short text research has so far focused on authorship verification in languages other than Arabic, such as English, Spanish, and Greek. To the best of the researcher's knowledge, no study has looked into the task of verifying Arabic-language Twitter texts. The impact of Stylometric and TF-IDF features of very brief texts (Arabic Twitter postings) on user verification was explored in this study. In addition, an analytical analysis was done to see how meta-data from Twitter tweets, such as time and source, can help to verify users perform better. This research is significant on the subject of cyber security in Arabic countries.

The number of published journals, conferences, and research papers in computer science is increasing rapidly, which has led to a challenge in coming up with new and unique ideas for research. To alleviate the issue, this paper uses artificial neural networks (ANNs) to generate new computer science research ideas. It does so by using a dataset collected from IEEE published journals and conferences to train an ANN model. The results reveal that the model has a 14% success rate in generating usable ideas. The outcome of this paper has implications for helping both new and experienced researchers come up with novel research topics.

Novel Coronavirus (COVID-19) is viewed as one of the main general wellbeing theaters on the worldwide level all over the planet. Because of the abrupt idea of the flare-up and the irresistible force of the infection, it causes individuals tension, melancholy, and other pressure responses. The avoidance and control of the novel Covid pneumonia have moved into an imperative stage. It is fundamental to early foresee and figure of infection episode during this troublesome opportunity to control of its grimness and mortality. The entire world is investing unimaginable amounts of energy to fight against the spread of this lethal infection. In this paper, we utilized machine learning and deep learning techniques for analyzing what is going on utilizing countries shared information and for detecting the climate factors that effect on spreading Covid-19, such as humidity, sunny hours, temperature and wind speed for understanding its regular dramatic way of behaving alongside the forecast of future reachability of the COVID-2019 around the world. We utilized data collected and produced by Kaggle and the Johns Hopkins Center for Systems Science. The dataset has 25 attributes and 9566 objects. Our Experiment consists of two phases. In phase one, we preprocessed dataset for DL model and features were decreased to four features humidity, sunny hours, temperature and wind speed by utilized the Pearson Correlation Coefficient technique (correlation attributes feature selection). In phase two, we utilized the traditional famous six machine learning techniques for numerical datasets, and Dense Net deep learning model to predict and detect the climatic factor that aide to disease outbreak. We validated the model by using confusion matrix (CM) and measured the performance by four different metrics: accuracy, f-measure, recall, and precision.

Technological advancements taken the health care industry by a storm by embedding sensors in human body to measure their vitals. These smart solutions provide better and flexible health care to patients, and also easy monitoring for the medical practitioners. However, these innovative solutions provide their own set of challenges. The major challenge faced by embedding sensors in body is the issue of lack of infinite energy source. This work presents a meta-heuristic based routing model using modified PSO, and adopts an energy harvesting scheme to improve the network lifetime. The routing process is governed by modifying the fitness function of PSO to include charge, temperature and other vital factors required for node selection. A reactive routing model is adopted to ensure reliable packet delivery. Experiments have been performed and comparisons indicate that the proposed Energy Harvesting and Modified PSO (EHMP) model demonstrates low overhead, higher network lifetime and better network stability.

Children are the foundation and future of this society and understanding their impressions and behaviors is very important and the child's behavioral problems are a burden on the family and society as well as have a bad impact on the development of the child, and the early diagnosis of these problems helps to solve or mitigate them, and in this research project we aim to understand and know the behaviors of children, through artificial intelligence algorithms that helped solve many complex problems in an automated system, By using this technique to read and analyze the behaviors and feelings of the child by reading the features of the child's face, the movement of the child's body, the method of the child's session and nervous emotions, and by analyzing these factors we can predict the feelings and behaviors of children from grief, tension, happiness and anger as well as determine whether this child has the autism spectrum or not. The scarcity of studies and the privacy of data and its scarcity on these behaviors and feelings limited researchers in the process of analysis and training to the model presented in a set of images, videos and audio recordings that can be connected, this model results in understanding the feelings of children and their behaviors and helps doctors and specialists to understand and know these behaviors and feelings.

In reason of the high capacity and low latency, the 5G wireless networks used nowadays in many of life applications such as: remote surgery and guiding vehicle. The high requirements of 5G networks makes it more vulnerable for security threats and attacks. This paper presents some challenges faced by 5G networks and presets some of the security solutions.

Online services are widely spread, and their use increases day by day. As COVID-19 spread and people spent much time online, fraud scams have risen unexpectedly. Manipulation techniques have become more effective at swindling those lacking basic technological knowledge. Unfortunately, a user needs a quorum. The interest in preventing scammers from obtaining effective quality service has become the most significant obstacle, increasing the variety of daily Internet platforms. This paper is concerned with analyzing purchase data and extracting provided results. In addition, after examining relevant documents presenting research discussing them, the recommendation was made that future work avoids them; this would save a lot of effort, money, and time. This research highlights many problems a person may face in dealing with online institutions and possible solutions to the epidemic through theft operations on the Internet.

Crypto-mining malware (known as crypto-jacking) is a novel cyber-attack that exploits the victim's computing resources such as CPU and GPU to generate illegal cryptocurrency. The attacker get benefit from crypto-jacking by using someone else's mining hardware and their electricity power. This research focused on the possibility of detecting the potential crypto-mining malware in an environment by analyzing both static and dynamic approaches of deep learning. The Program Executable (PE) files were utilized with deep learning methods which are Long Short-Term Memory (LSTM). The finding revealed that LTSM outperformed both SVM and RF in static and dynamic approaches with percentage of 98% and 96%, respectively. Future studies will focus on detecting the malware using larger dataset to have more accurate and realistic results.

Cloud computing is the latest approach that is developed for reducing the storage of space to store the data and helps the quick sharing of the data. An increase in the cloud computing users is observed that is also making the users be prone to hacker's attacks. To increase the efficiency of cloud storage encryption mechanisms are used. The encryption techniques that are discussed in this survey paper are searchable encryption, attribute-based, Identity-based encryption, homomorphic encryption, and cloud DES algorithms. There are several limitations and disadvantages of each of the given techniques and they are discussed in this survey paper. Techniques are found to be effective and they can increase the security of cloud storage systems.

The study deals with the problem of transformation of the teacher's role in the information society. A comparative analysis of the competencies of the teacher, declared in the Pedagogical Constitution of Europe, the documents of the New Ukrainian School, the scientific research of contemporary scholars was conducted. The correlation analysis of the survey results for teachers and students' parents on their expectations of contemporary teachers was presented. It was noted that the analyzed views of scientists, legislative documents, and the results of sociological research help to modify the educational process of competitive and effective prospective teachers' training. Based on the survey results the acmeograms of the teacher's main role positions as an orientation for training of prospective teachers were developed.

The use of public Wi-Fi has increased in recent years with many people like to spend their time outside in malls or café shops which provide public Wi-Fi for their customers. However, since the public Wi-Fi can be accessed from any device the security of public Wi-Fi plays a big role to prevent the stealing of information by an attacker with methods and techniques such as WPA, WPA2 and WPA3. However, it is well known to the attackers that these methods are not difficult to get hacked by the attacker device to take the client precious information. Some researches were done in increasing the security of public Wi-Fi each with their own different technique or algorithm to provide more secure connection to the public Wi-Fi and prevent any unauthorized user to connect to avoid stealing the data of another legal user. Theses research paper are evaluated to learn which method excel in protecting the public Wi-Fi security by giving an analysis to the methods provided by the research paper with comparing the pros and cons of each algorithm. Moreover, the research displays that there are methods to actually provide security to the public Wi-Fi with each being very different in implementation.

Multi-objective optimization problems (MOPs) arise in many real-world applications. MOPs involve two or more objectives with the aim to be optimized. With these problems improvement of one objective may led to deterioration of another. The primary goal of most multi-objective evolutionary algorithms (MOEA) is to generate a set of solutions for approximating the whole or part of the Pareto optimal front, which could provide decision makers a good insight to the problem. Over the last decades or so, several different and remarkable multi-objective evolutionary algorithms, have been developed with successful applications. However, MOEAs are still in their infancy. The objective of this research is to study how to use and apply machine learning (ML) to improve evolutionary multi-objective optimization (EMO). The EMO method is the multi-objective evolutionary algorithm based on decomposition (MOEA/D). The MOEA/D has become one of the most widely used algorithmic frameworks in the area of multi-objective evolutionary computation and won has won an international algorithm contest.

There has been a rapid increase in the use of cloud email services. As a result, email encryption has become more commonplace as concerns about cloud privacy and security grow. Nevertheless, this increase in usage is creating the challenge of how to effectively be searching and filtering the encrypted emails. They are popular technologies of solving the issue of the encrypted emails searching through searchable public key encryption. However, the problem of encrypted email filtering remains to be solved. As a new approach to finding and filtering encrypted emails in the cloud, we propose a ciphertext-based encrypted policy attribute-based encryption scheme and keyword search procedure based on hidden policy ciphertext. This feature allows the user of searching using some encrypted emails keywords in the cloud as well as allowing the emails filter-based server toward filter the content of the encrypted emails, similar to the traditional email keyword filtering service. By utilizing composite order bilinear groups, a hidden policy system has been successfully demonstrated to be secure by our dual system encryption process. Proposed system can be used with other scenarios such as searching and filtering files as an applicable method.

Early detection can help prevent heart disease, which is one of the most common reasons for death. This paper provides a clinical support model for predicting cardiac disease. The model is built using two publicly available data sets. The admissibility and application of the the model are justified by a sequence of tests. Implementation of the model and testing are also discussed

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

The main purpose of the study is to determine the main factors influencing the economic environment of the region and its financial and economic security. Ukraine aspires to become a full member of the European Union, but the events of 2022 have changed everything. Full-scale military operations on the territory of Ukraine significantly affect its socio-economic situation Today, the issue of studying the negative impact of military operations on the economic environment is very relevant. Based on the results of the study, we have identified the main negative factors of the military impact on the economic environment of the region and its financial and economic security.

The article analyzes the regulations of current criminal law and current issues, combating drug crime on the Internet, as well as measures to combat drug crime in the field of modern information technology. In connection with the growth of crimes in the field of drug trafficking committed with the use of information and telecommunications technologies, the urgent task of the state is to find effective ways to reduce drug crime. The article considers criminologically significant aspects of the mechanism of illicit drug trafficking, which is carried out with the use of information and telecommunication technologies and means of remote communication.

The article considers the state and trends of distance learning in the world and Ukraine, identifies the main species differences between distance education and other forms of education, analyzes the state of the global market for educational services provided via the Internet. Important features and characteristics of distance learning, examples of its organization in higher education, as well as statistics on the development of distance learning in our country. The main problematic points on the way to the implementation of the distance education system in Ukraine and the factors that hinder the development of this promising form of education are outlined.

The continuous information technology and telecommunication (ICT) developments inspire several Saudi Arabia citizens to transact and interact online. However, when using online platforms, several people are likely to lose their personal information to cybercriminals. In the survey, 553 Saudi Arabia citizens and 103 information technology (IT) specialists confirm the expansion of digital economy and the need for smart cities with various services, including e-commerce and solid cyber security. 96.6% of the participants believe Saudi Arabia is digitalizing its economy; yet, 33.3% of the participants believe that residents are uninformed about living and operating in smart cities. Several people (47.29%) with medium internet speed are more aware about smart cities than those with fastest internet speed (34%). Besides, online transactions via credit cards subjected 55.5% of the participants to privacy and security issues. These findings validate the essence of cyber security awareness programs among Saudi Arabia citizens and IT professionals to boost public trust and acceptance of cybersecurity frameworks.

As a result, for the increasing expansion by the university faculties in the field of postgraduate studies, The Deanship of Graduate Studies at the university has been established in 1430 AH/2009 CE to specifically address the needs of the current and prospective graduate population to supervise postgraduate studies programs in coordination with the concerned faculties. This comes as a result for the university being certain of the importance of providing postgraduate studies opportunities that follow the bachelor's degree to qualify our ambitious youth appropriately. The University offers 72 different Graduate programs, awarding doctoral and master's degrees along with fellowships and diplomas in various disciplines like health, engineering, science, literary, and educational. Currently, the financial model for admission and students' payment is manual and paper based. This paper proposes to provide a user interface for Financial Management in Deanship of Graduate studies The basic purpose of the system was to minimize human interference and reduce mistakes placed by human interference, also to have efficient and a fast performance, and perform Electronic Data Interchange (EDI) for various tasks such as billing and scheduling details.

Side-channel attacks are a quiet mighty type of attack that targets specific physical implementations vulnerabilities. Even though several researchers have examined diverse means and methods of detecting side-channel attacks, at the present time a systematic review of these approaches does not exist. The purposes of this paper are to give an extensive analysis of literature on side-channel attack detection and offer intuitiveness from past research studies. In this study, a literature survey is conducted on articles related to side-channel attack detection between 2020 and 2022 from ACM and IEEE digital libraries. From the 10 publications included in the study, it appears they target either a single type of side-channel attacks or multiple types of side-channel attacks. Therefore, a vital review of each of the two categories is provided, as well as possible prospective research in this field of study.

Taking into account the globalization of the economy and the intensification of European integration processes, it has been proved that an important part of evaluating the effectiveness of activity of public sector enterprises should be a comparative analysis of the overall operation environment, namely public procurement, economic environment and tax policy, with the relevant systems of other countries of the world. The indicators and components of international ratings "Doing Business", "Paying Taxes", "Benchmarking Public Procurement" have been studied. Different groups of indicators forming "Doing Business" and "Paying Taxes" in the context of the impact on public sector enterprises have been identified: those which fully comply with the need to evaluate the operating environment of public sector enterprises and those which should be used with restrictions, regarding the peculiarities of creating and functioning of public sector enterprises. A comparative analysis of the place of Ukraine, Lithuania and Slovenia among other countries of the world in accordance with the international rankings of "Doing Business", "Paying Taxes", "Benchmarking Public Procurement" have been made. It has been substantiated that the results of such comparative analysis will allow identifying risk areas, and relevant information can be used in developing of recommendations for improving the effectiveness of activity of public sector enterprises.

Job offers have become more widespread and it has become easier and faster to apply for jobs through electronic recruitment platforms. In order to increase the protection of the data that is attached to the recruitment platforms. In this research, a proposed model was created through the use of hybrid encryption, which is used through the following algorithms: AES,Twofish,. This proposed model proved the effectiveness of using hybrid encryption in protecting personal data.

The latest global COVID-19 pandemic has made the use of facial masks an important aspect of our lives. People are advised to cover their faces in public spaces to discourage illness from spreading. Using these face masks posed a significant concern about the exactness of the face identification method used to search and unlock telephones at the school/office. Many companies have already built the requisite data in-house to incorporate such a scheme, using face recognition as an authentication. Unfortunately, veiled faces hinder the detection and acknowledgment of these facial identity schemes and seek to invalidate the internal data collection. Biometric systems that use the face as authentication cause problems with detection or recognition (face or persons). In this research, a novel model has been developed to detect and recognize faces and persons for authentication using scale invariant features (SIFT) for the whole segmented face with an efficient local binary texture features (DLBP) in region of eyes in the masked face. The Fuzzy C means is utilized to segment the image. These mixed features are trained significantly in a convolution neural network (CNN) model. The main advantage of this model is that can detect and recognizing faces by assigning weights to the selected features aimed to grant or provoke permissions with high accuracy.

The article is dedicated to the phenomenon of modern ex-libris created with the help of computer graphics. The process of evolution in the use of various techniques of book plate creating is analyzed, the role of computer technologies in the popularization of the modern book plate is emphasized. The debatable nature of the issue of the danger of displacement, replacement of traditional techniques of printed graphics with computer technologies is emphasized. Computer graphics are positioned as an effective way to popularize the Ukrainian ex-libris in the foreign art space. The characteristic features of the Ukrainian computer ex-libris are highlighted, a parallel with the book plate created with the help of computer graphics by masters of other countries (Belgium, China, the Netherlands, Poland, Turkey) is drawn

Outcome-based education (OBE) is a tried-and-true teaching technique based on a set of predetermined goals. Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs) are the components of OBE. At the end of each year, the Program Outcomes are evaluated, and faculty members can submit many recommended measures which dependent on the relationship between the program outcomes and its courses outcomes to improve the quality of program and hence the overall educational program. When a vast number of courses are considered, bad actions may be proposed, resulting in unwanted and incorrect decisions. In this paper, a recommender system, using collaborative filtering and association rules algorithms, is proposed for predicting the best relationship between the program outcomes and its courses in order to improve the attributes of the graduates. First, a parallel algorithm is used for Collaborative Filtering on Data Model, which is designed to increase the efficiency of processing big data. Then, a parallel similar learning outcomes discovery method based on matrix correlation is proposed by mining association rules. As a case study, the proposed recommender system is applied to the Computer Information Systems program, College of Computer Sciences and Information Technology, Al-Baha University, Saudi Arabia for helping Program Quality Administration improving the quality of program outcomes. The obtained results revealed that the suggested recommender system provides more actions for boosting Graduate Attributes quality.

The jurisprudential legal rules govern the way Muslims react and interact to daily life. This creates a huge stream of questions, that require highly qualified and well-educated individuals, called Muftis. With Muslims representing almost 25% of the planet population, and the scarcity of qualified Muftis, this creates a demand supply problem calling for Automation solutions. This motivates the application of Artificial Intelligence (AI) to solve this problem, which requires a well-designed Question-Answering (QA) system to solve it. In this work, we propose a QA system, based on retrieval augmented generative transformer model for jurisprudential legal question. The main idea in the proposed architecture is the leverage of both state-of-the art transformer models, and the existing knowledge base of legal sources and question-answers. With the sensitivity of the domain in mind, due to its importance in Muslims daily lives, our design balances between exploitation of knowledge bases, and exploration provided by the generative transformer models. We collect a custom data set of 850,000 entries, that includes the question, answer, and category of the question. Our evaluation methodology is based on both quantitative and qualitative methods. We use metrics like BERTScore and METEOR to evaluate the precision and recall of the system. We also provide many qualitative results that show the quality of the generated answers, and how relevant they are to the asked questions.

Kinship is a permanent relationship which is likely to impact the regulations adopted by the legislator to deal with crimes involving close relatives. Accordingly, the title "Impact of Kinship on a Criminal Case" is intended to highlight the legal provisions related kinship, regarding such case, and the procedures associated therewith; as the legislator takes kinship into account, particularly with respect to the procedures related to crimes between relatives; and it is the legislator's aim to protect such relation.

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

The article identifies and theoretically substantiates the trends of national resilience in the context of establishing the security of the country and its civilizational subjectivity. The strategy of development of the pedagogical university in the conditions of European integration into the European educational and scientific space based on certain characterological features of the personality of the volunteer in the context of allocation of personal resilience is developed. The analysis of both external and internal challenges and threats to the civilization of the country needs to be understood in the context of economic, socio-political, legal, military-political, spiritual-cultural, educational-scientific and network-information resilience. The concepts of "national resilience" and "national security" are quite close - at first glance, even identical. However, a deeper understanding clarifies the differences: national security is a state of protection of the country identity and its very existence, the realization of its national interests. In turn, resilience is a fairly effective strategy and a fundamental guarantee of national security. At the same time, it is extremely important to understand that both national security as a state and national resilience as a strategy are only means of achieving and developing a strong and humanistic civilizational subjectivity of the country. After all, such subjectivity opens for citizens the opportunity for development, dignified self-realization and a proper life. The restructuring of the volunteer's motivational sphere is due to the dominance of such leading motives, which are focused mainly on maintaining and restoring health, which leads to distorted meaningful life goals: isolation, alienation, passivity, inertia, reduced activity, limited communication, etc. The characteristics of relatively stable human behavior include several primary and secondary properties. The primary (relevant) properties include patience, trust, hope, faith, confidence, determination, perseverance, and love; the secondary - punctuality, neatness, obedience, honesty, loyalty, justice, diligence, thrift, accuracy, conscientiousness, obligation, etc. The restructuring of the volunteer's motivational sphere is due to the dominance of such leading motives, which are focused mainly on maintaining and restoring health, which leads to distorted meaningful life goals: isolation, alienation, passivity, inertia, reduced activity, limited communication, etc. The characteristics of relatively stable human behavior include several primary and secondary properties. The primary (relevant) properties include patience, trust, hope, faith, confidence, determination, perseverance, and love; the secondary - punctuality, neatness, obedience, honesty, loyalty, justice, diligence, thrift, accuracy, conscientiousness, obligation, etc. The use of information and communication technologies in volunteering will contribute to the formation of resilience traits in the structure of personality formation. Directly to the personal traits of resilience should be included methodological competencies, which include methodological knowledge, skills and abilities (ability to define ultimate and intermediate goals, plan, conduct and analyze knowledge, establish and implement interdisciplinary links with disciplines of medical-psychological-pedagogical cycles, etc.). All these competencies form the professional resilience of the volunteer.

The article is devoted to the research and search for effective pedagogical technologies. The definitions of "innovation", "technologicalization", "pedagogical technology" are analyzed. "Innovative pedagogical technology". The blocks that make up the innovative processes in education are studied. The importance and role of innovations in education (vocational education) are clarified. The components of pedagogical technology are studied: conceptual, content-procedural and professional. The signs of classifications of pedagogical technologies are investigated. The stages of evolution of the definition of "pedagogical technology", as well as the levels of its application in pedagogical science are highlighted. Criteria of manufacturability and signs of pedagogical technology are investigated.The algorithm for the analysis of pedagogical technology is investigated: identification of technology; name of technology; conceptual principles; the content of education; activity characteristics; organizational and methodological support of the educational process. Pedagogical technologies that should be implemented in vocational education institutions are analyzed.

Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.

Innovations significantly affect the efficiency of the socioeconomic systems of the regions, acting as a system-forming element of their development. Modern models of economic development also consider innovation activity, intellectual potential, knowledge as the basic factors for stimulating the economic growth of the region. The purpose of the study is to develop methodological foundations for evaluating the effectiveness of a regional innovation system based on a multidimensional analysis of its effects. To further study the effectiveness of RIS, we have used one of the methods of multidimensional statistical analysis - canonical analysis. The next approach allows adding another important requirement to the methodological provision of evaluation of the level of innovation development of industries and regions, namely - the time factor, the formalization of which is realized in autoregressive dynamic economic and mathematical models and can be used in our research. Multidimensional Statistical Analysis for RIS effectiveness estimation was used to model RIS by typological regression. Based on it, multiple regression models were built in groups of regions with low and relatively high innovation potential. To solve the methodological problem of RIS research, we can also use the approach to the system as a "box" with inputs and outputs.