The Internet of things (IoT) is the main advancement in data processing and communication technologies. In IoT, intelligent devices play an exciting role in wireless communication. Although, sensor nodes are low-cost devices for communication and data gathering. However, sensor nodes are more vulnerable to different security threats because these nodes have continuous access to the internet. Therefore, the multiparty security credential-based key generation mechanism provides effective security against several attacks. The key generation-based methods are implemented at sensor nodes, edge nodes, and also at server nodes for secure communication. The main challenging issue in a collaborative key generation scheme is the extensive multiplication. When the number of parties increased the multiplications are more complex. Thus, the computational cost of batch key and multiparty key-based schemes is high. This paper presents a Secure Multipart Key Distribution scheme (SMKD) that provides secure communication among the nodes by generating a multiparty secure key for communication. In this paper, we provide node authentication and session key generation mechanism among mobile nodes, head nodes, and trusted servers. We analyzed the achievements of the SMKD scheme against SPPDA, PPDAS, and PFDA schemes. Thus, the simulation environment is established by employing an NS 2. Simulation results prove that the performance of SMKD is better in terms of communication cost, computational cost, and energy consumption.

The Proportional Integral Derivative (PID) controller is the most popular industrial controller and more than 90% process industries use this controller. During the past 50 years, numerous good tuning methods have been proposed for Single Input Single Output Systems. However, design of PI/PID controllers for multivariable processes is a challenge for the researchers. A comparative study of three PID controllers design methods has been carried-out. These methods include the DS (Direct Synthesis) method, IMC (Internal model Control) method and ETF (Effective Transfer Function) method. MIMO PID controllers are designed for a number of 2×2, 3×3 and 4×4 process models with multiple delays. The performance of the three methods has been evaluated through simulation studies in Matlab/Simulink environment. After extensive simulation studies, it is found that the Effective Transfer Function (ETF) Method produces better output responses among two methods. In this work, only decentralized methods of PID controllers have been studied and investigated.

Diabetes Mellitus (DM) is one of common chronic diseases leading to severe health complications that may cause death. The disease influences individuals, community, and the government due to the continuous monitoring, lifelong commitment, and the cost of treatment. The World Health Organization (WHO) considers Saudi Arabia as one of the top 10 countries in diabetes prevalence across the world. Since most of the medical services are provided by the government, the cost of the treatment in terms of hospitals and clinical visits and lab tests represents a real burden due to the large scale of the disease. The ability to predict the diabetic status of a patient without the laboratory tests by performing screening based on some personal features can lessen the health and economic burden caused by diabetes alone. The goal of this paper is to investigate the prediction of diabetic and prediabetic patients by considering factors other than the laboratory tests, as required by physicians in general. With the data obtained from local hospitals, medical records were processed to obtain a dataset that classified patients into three classes: diabetic, prediabetic, and non-diabetic. After applying three machine learning algorithms, we established good performance for accuracy, precision, and recall of the models on the dataset. Further analysis was performed on the data to identify important non-laboratory variables related to the patients for diabetes classification. The importance of five variables (gender, physical activity level, hypertension, BMI, and age) from the person's basic health data were investigated to find their contribution to the state of a patient being diabetic, prediabetic or normal. Our analysis presented great agreement with the risk factors of diabetes and prediabetes stated by the American Diabetes Association (ADA) and other health institutions worldwide. We conclude that by performing class-specific analysis of the disease, important factors specific to Saudi population can be identified, whose management can result in controlling the disease. We also provide some recommendations learnt from this research.

The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.

Cloud computing is one of the most expanding technologies nowadays; it offers many benefits that make it more cost-effective and more reliable in the business. This paper highlights the various benefits of cloud computing and discusses different cryptography algorithms being used to secure communications in cloud computing environments. Moreover, this thesis aims to propose some improvements to enhance the security and safety of cloud computing technologies.

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

The technological advancement in computer vision has made system like grab-and-go grocery a reality. Now all the shoppers have to do now is to walk in grab the items and go out without having to wait in the long queues. This paper presents an intelligent retail environment system that is capable of monitoring and tracking customer's activity during shopping based on their interaction with the shelf. It aims to develop a system that is low cost, easy to mount and exhibit adequate performance in real environment.

The research aims to identify the role of administrative leaders working in universities in the success of organizational development and change, and the extent of the relationship and ability of administrative leaders at all levels of leadership within universities to the effectiveness of the process of development and organizational change at Northern Border University. In addition to presenting some recommendations and suggestions that can contribute to identifying the best leadership styles that contribute to the success of the development process and positive organizational change. Where leadership, whether in the private sector or the public sector, is one of the main functions concerned with the processes of direction, development, and modernization in the performance of the facility and an important element to activate the organizations' ability to perform their role and achieve their goals. The behavior and trends of leaders represent an important indicator in knowing the type of efforts made by them to improve performance and develop organizations and human resources. The research reached many results, perhaps the most important of which is that the dominant leadership style in universities is the democratic style, followed by the bureaucratic leadership style. The results also showed that there is a significant role for administrative leaders in bringing about development and positive change at Northern Border University at the level of individuals, groups, and organizations. And it became clear that there is an availability of leadership capabilities to an acceptable degree in the administrative leaders. The results of the statistical analysis showed a positive relationship between administrative leadership ability and democratic style. In addition to the existence of a negative relationship between the administrative leadership ability and the bureaucratic style and the freestyle. It was also clear that there were no differences in dealing between males and females, as well as age, educational qualification, experience, and job grade, but there were differences in dealing with the job title.

The locating-chromatic number denote by 𝛘_𝐿(G), is the smallest t such that G has a locating t-coloring. In this research, we determined locating-chromatic number for subdivision of certain barbell operation of origami graphs.

The article assesses the level of navigation safety, in theoretical terms, defines the complexity of managing navigational risks in practice. The issues of assessing the navigational safety have been studied due to the importance and relevance of the issue in question, however, due to the great complexity of the problem under consideration, the article considers and indicates the directions for the development of the solution of the given direction, where, first of all, it became necessary to analyze the issue of assessing the levels of navigation risks when navigating vessels of various types in difficult navigation conditions.

Peer to Peer Networks play an increasing role in today's networks, also it's expected that this type of communication networks evolves more in the future. Since the number of users that is involved in Peer to Peer Networks is huge and will be increased more in the future, security issues will appear and increase as well. Thus, providing a sustainable solution is needed to ensure the security of Peer to Peer Networks. This paper is presenting a new protocol called Malicious Trust Managers Identification (MTMI). This protocol is used to ensure anonymity of trust manager, that computes and stores the trust value for another peer. The proposed protocol builds a secure connection between trust managers by using public key infrastructure. As well as experimental testing has been conducted to validate the proposed protocol.

The article considers one of the areas of development of artificial intelligence where there is the development of computer intelligent systems capable of performing functions traditionally considered intelligent - language comprehension, inference, use of accumulated knowledge, learning, pattern recognition, as well as learn and explain their decisions. It is found that informational intellectual systems are promising in their development. The article is devoted to intelligent information systems and technologies in educational activities, ie issues of organization, design, development and application of systems designed for information processing, which are based on the use of artificial intelligence methods.

The emergence of new technologies and their implementation by different manufacturers of electronic devices are experiencing an ascending trend. Most of the time, these protocols are expected to reach a certain degree of maturity, and electronic equipment manufacturers use simplified communication standards and interfaces that have already reached maturity in terms of their development such as ModBUS, KNX or CAN. This paper proposes an IoT solution of the Smart Home type based on an Analysis and Prediction System. A data acquisition component was implemented and there was defined an algorithm for the analysis and prediction of actions based on the values collected from the data update component and the data logger records.

The article considers the state of legislation that regulates the use of public methods of obtaining information by authorized state bodies. The correlation of public investigative (search) actions with operative-search measures as concepts denoting the application of public methods of obtaining information has been studied. In addition, it argues the need for more detailed delineation and legislative regulation of public investigative (search) actions and operational and investigative measures at the legislative and departmental levels. The purpose of the article is to analyze certain provisions of the Law of Ukraine "On operational and investigative activities" to identify inconsistencies in the content of the text of the law in order to correct and prevent ambiguity in the theory and practice of law enforcement.

The article defines the basic concepts: "election campaign", "social capital", "conversion of social capital"; the principles and methods of research of social capital conversion in election campaigns are studied; the process of using social capital in politics is defined; ways of converting social capital into politics are considered; the possibilities of converting social capital in election campaigns are described. Election campaigns have been found to be a successful form of social capital conversion. The ability to use social capital in the election campaign speaks of its high potential. Election campaigns are not an effective use of social capital.

The article examines the formation of a competitive paradigm of economic security of industrial enterprises in the formation of a circular economy. The basic laws of industrial enterprises are formed, which determined the characteristics of competitive positions and threats. The basic competitive concepts and their application at maintenance of economic safety of the industrial enterprises in the conditions of formation of circular economy are described. Thus, the technological approach to the formation of a competitive paradigm is based on production technologies, opportunities for technological modernization and development of infrastructure and intellectual competencies. The institutional concept reveals the regulatory mechanisms for ensuring competitiveness through the protectionism of national industrial enterprises, standardization and regulation of market imbalances. The innovation-investment approach within the competitive paradigm is also manifested in the creation of competitive advantages due to the presence of active innovative developments and their commercialization, knowledge and competencies of staff, capitalization of intelligence and communications, constant updating of infrastructure and technologies. Collectively, innovation and investment effects on the level of economic security allow industrial enterprises to ensure resilience to increasing competition, the emergence of new market challenges in the formation of a circular economy. A strategic approach to the application of a competitive paradigm to ensure the economic security of industrial enterprises allows you to justify the prospects for development and design behavioral models to predict and assess potential threats. The concept of system management is based on the complexity of threat analysis, the integrity of the economic security system, system-forming functions and patterns of implementation of industrial development tasks in the formation of a circular economy. The application of the described concepts is formalized by the authors through the definition of the basic patterns, directions and characteristics of their impact on the elements of the security system of industrial enterprises in the formation of a circular economy.

Detecting fraudulent insurance claims is difficult due to small and unbalanced data. Some research has been carried out to better cope with various types of fraudulent claims. Nowadays, technology for detecting fraudulent insurance claims has been increasingly utilized in insurance and technology fields, thanks to the use of artificial intelligence (AI) methods in addition to traditional statistical detection and rule-based methods. This study obtained meaningful results for a fraudulent insurance claim detection model based on machine learning (ML) and deep learning (DL) technologies, using fraudulent insurance claim data from previous research. In our search for a method to enhance the detection of fraudulent insurance claims, we investigated the reinforcement learning (RL) method. We examined how we could apply the RL method to the detection of fraudulent insurance claims. There are limited previous cases of applying the RL method. Thus, we first had to define the RL essential elements based on previous research on detecting anomalies. We applied the deep Q-network (DQN) and double deep Q-network (DDQN) in the learning fraudulent insurance claim detection model. By doing so, we confirmed that our model demonstrated better performance than previous machine learning models.

Under the mobile ad-hoc network system, the main reason for causing congestion is because of the limited availability of resources. On the other hand, the standardised TCP based congestion controlling mechanism is unable to control and handle the major properties associated with the shared system of wireless channels. It creates an effect on the design associated with suitable protocols along with protocol stacks through the process of determining the mechanisms of congestion on a complete basis. Moreover, when bringing a comparison with standard TCP systems the major environment associated with mobile ad hoc network is regraded to be more problematic on a complete basis. On the other hand, an agent-based mobile technique for congestion is designed and developed for the part of avoiding any mode of congestion under the ad-hoc network systems.

The article assesses the level of navigation safety, in theoretical terms, defines the complexity of managing navigational risks in practice. The issues of assessing the navigational safety have been studied due to the importance and relevance of the issue in question, however, due to the great complexity of the problem under consideration, the article considers and indicates the directions for the development of the solution of the given direction, where, first of all, it became necessary to analyze the issue of assessing the levels of navigation risks when navigating vessels of various types in difficult navigation conditions.

State information policy is an important component of foreign and domestic policy of the country and covers all spheres of society. The rapid development of the information sphere is accompanied by the emergence of fundamentally new threats to the interests of the individual, society, state and its national security. The article considers the components of the state information policy to ensure information security of the country and identifies the main activities of public authorities in this area. Internal and external information threats to the national security of Ukraine and ways to guarantee the information security of the country are analyzed. Information security is seen as a component of national security, as well as a global problem of information protection, information space, information sovereignty of the country and information support of government decisions. Approaches to ensure the process of continuity of the information security system of the state in order to monitor new threats, identify risks and levels of their intensity are proposed.

The expansion of the world wide web has led to a huge amount of user generated content over different forums and social media platforms, these rich data resources offer the opportunity to reflect, and track changing public sentiments and help to develop proactive reactions strategies for decision and policy makers. Analysis of public emotions and opinions towards events and sentimental trends can help to address unforeseen areas of public concerns. The need of developing systems to analyze these sentiments and the topics behind them has emerged tremendously. While most existing works reported in the literature have been carried out in English, this paper, in contrast, aims to review recent research works in Arabic language in the field of thematic sentiment analysis and which techniques they have utilized to accomplish this task. The findings show that the prevailing techniques in Arabic topic-based sentiment analysis are based on traditional approaches and machine learning methods. In addition, it has been found that considerably limited recent studies have utilized deep learning approaches to build high performance models.

The article discusses the use of the concept of digitalization in the science of education. The influence of information technologies on the ability to self-study is analyzed. Various technologies that are used in science and education are shown. The issues of the advantages of using IT as a tool for creating conditions for the implementation of the problem-activity approach and the organization of project activities are considered. The possibilities are shown, which gives the opportunities that the use of ICT of distance educational resources in the educational process gives. Shown is their auxiliary form of transmission, information retrieval; working out skills and consolidating what has been learned. Based on the analysis of the presented material of the article, you can see what problems can be solved using IT and remote resources.

The article discusses the results of a quantitative analysis of open educational resources in the field of information technology. Study is based on a study of the content of ten platforms that provide access to open resources (OPs). To achieve this goal, we used the following methods: theoretical analysis and generalization of Internet sources to determine the popularity of educational platforms and resources on them; quantitative data analysis to determine the relative proportion of IT courses in various parameters: the relative weight of courses in the IT field in general and on each platform in particular, the language of instruction, the quantitative content by thematic areas. The following platforms providing access to open educational resources were subjected to quantitative analysis: Coursera, Edx, Udemy, MIT OpenCourseWare, OpenLearn, Intuit, Prometheus, UoPeople, Open Learning Initiative, Maidan Open University.

The article reveals the essence of the concept of "pedagogical innovation" and identifies trends in the development of a modern educational institution. A qualitative analysis of the scientific literature in the field of innovation science has been carried out. The essence of the concept of "pedagogical innovation" is revealed. The modern classification of pedagogical innovations is given. The factors of the success of the introduction of pedagogical innovations are determined. The main trends in the development of modern educational institutions are outlined.

In our study we performed a sentiments analysis from the images. For this purpose, we used 153 images that contain: people, animals, buildings, landscapes, cakes and objects that we divided into two categories: images that suggesting a positive or a negative emotion. In order to classify the images using the two categories, we created two models. The SAI-G model was created with Google's AutoML Vision service. The SAI-C model was created on the Clarifai platform. The data were labeled in a preprocessing stage, and for the SAI-C model we created the concepts POSITIVE (POZITIV) AND NEGATIVE (NEGATIV). In order to evaluate the performances of the two models, we used a series of evaluation metrics such as: Precision, Recall, ROC (Receiver Operating Characteristic) curve, Precision-Recall curve, Confusion Matrix, Accuracy Score and Average precision. Precision and Recall for the SAI-G model is 0.875, at a confidence threshold of 0.5, while for the SAI-C model we obtained much lower scores, respectively Precision = 0.727 and Recall = 0.571 for the same confidence threshold. The results indicate a lower classification performance of the SAI-C model compared to the SAI-G model. The exception is the value of Precision for the POSITIVE concept, which is 1,000.

The article is assigned to a prelude to the joke of effective pedagogical technologies in professional education in the minds of її continuity. Analyzed the definitions "innovation", "technology", "pedagogical technology", "Innovation pedagogical technology". Blocks have been added, which are stored in innovation processes in the light. The significant role of innovation in education is stated. Presented warehouses pedagogical technologies: conceptual, conceptual-processual and professional. The purpose of introducing innovative technologies into the educational process of vocational education institutions has been clarified. The reasons for finding and implementing innovative pedagogical technologies in education are analyzed.

This research was conducted with the aim to appraise the level of satisfaction of students and faculty members with the services of the Deanship of e-Learning and Distance Education at Umm Al-Qura University. In addition, it investigated any differences arising between the evaluation of students and faculty members for these services owing to their gender..To achieve these goals, a descriptive analysis methodology was used in this research. The sample comprised 1357 students (704 male and 653 female) and 372 faculty members (208 male and 164 female) from Umm Al-Qura University in the academic year 2020-2021. To collect the requisite data, the study participants were asked to complete a 5-point Likert scale questionnaire, and the validity and reliability of the data were then assessed. The findings revealed the existence of a high level of satisfaction of students and faculty members with the services of Deanship of e-Learning and Distance Education at Umm Al-Qura University. There are no statistically significant differences between the mean scores of students (male/female) at Umm Al-Qura University in evaluating the said services. Furthermore, there are no statistically significant differences between the mean scores of faculty members (male/female) at Umm Al-Qura University in evaluating these. There exist statistically significant differences between the mean scores of faculty members and students in the evaluation of the services of the Deanship for the benefit of faculty members.

Image tampering detection and localization have become an active area of research in the field of digital image forensics in recent times. This is due to the widespread of malicious image tampering. This study presents a new method for image tampering detection and localization that combines the advantages of dilated convolution, residual network, and UNET Architecture. Using the UNET architecture as a backbone, we built the proposed network from two kinds of residual units, one for the encoder path and the other for the decoder path. The residual units help to speed up the training process and facilitate information propagation between the lower layers and the higher layers which are often difficult to train. To capture global image tampering artifacts and reduce the computational burden of the proposed method, we enlarge the receptive field size of the convolutional kernels by adopting dilated convolutions in the residual units used in building the proposed network. In contrast to existing deep learning methods, having a large number of layers, many network parameters, and often difficult to train, the proposed method can achieve excellent performance with a fewer number of parameters and less computational cost. To test the performance of the proposed method, we evaluate its performance in the context of four benchmark image forensics datasets. Experimental results show that the proposed method outperforms existing methods and could be potentially used to enhance image tampering detection and localization.

Multiple waves of COVID-19 highlighted one crucial aspect of this pandemic worldwide that factors affecting the spread of COVID-19 infection are evolving based on various regional and local practices and events. The introduction of vaccines since early 2021 is expected to significantly control and reduce the cases. However, virus mutations and its new variant has challenged these expectations. Several countries, which contained the COVID-19 pandemic successfully in the first wave, failed to repeat the same in the second and third waves. This work focuses on COVID-19 pandemic control and management in Saudi Arabia. This work aims to predict new cases using deep learning using various important factors. The proposed method is called Deep Learning and Dynamic Weighing-based (DLDW) COVID-19 cases prediction method. Special consideration has been given to the evolving factors that are responsible for recent surges in the pandemic. For this purpose, two weights are assigned to data instance which are based on feature importance and dynamic weight-based time. Older data is given fewer weights and vice-versa. Feature selection identifies the factors affecting the rate of new cases evolved over the period. The DLDW method produced 80.39% prediction accuracy, 6.54%, 9.15%, and 7.19% higher than the three other classifiers, Deep learning (DL), Random Forest (RF), and Gradient Boosting Machine (GBM). Further in Saudi Arabia, our study implicitly concluded that lockdowns, vaccination, and self-aware restricted mobility of residents are effective tools in controlling and managing the COVID-19 pandemic.

Classification of different blood cell types is an essential task for human's medical treatment. The white blood cells have different types of cells. Counting total White Blood Cells (WBC) and differential of the WBC types are required by the physicians to diagnose the disease correctly. This paper used transfer learning methods to the pre-trained deep learning models to classify different WBCs. The best pre-trained model was Inception ResNetV2 with Adam optimizer that produced classification accuracy of 98.4% for the dataset comprising four types of WBCs.

A sensor network is made up of many sensors deployed in different areas to be monitored. They communicate with each other through a wireless medium. The routing of collected data in the wireless network consumes most of the energy of the network. In the literature, several routing approaches have been proposed to conserve the energy at the sensor level and overcome the challenges inherent in its limitations. In this paper, we propose a new low-energy routing protocol for power grids sensors based on an unsupervised clustering approach. Our protocol equitably harnesses the energy of the selected cluster-head nodes and conserves the energy dissipated when routing the captured data at the Base Station (BS). The simulation results show that our protocol reduces the energy dissipation and prolongs the network lifetime.

This article substantiates the scientific provisions for modelling the level of Ukraine's public financial security taking into account the impact of budget policy, in the process of which identified indicators of budget policy that significantly affect the public financial security and the factors of budget policy based on regression analysis do not interact closely with each other. A seven-factor regression equation is constructed, which is statistically significant, reliable, economically logical, and devoid of autocorrelation. The objective function of maximizing the level of public financial security is constructed and strategic guidelines of budget policy in the context of Ukraine's public financial security are developed, in particular: optimization of the structure of budget revenues through the expansion of the resource base; reduction of the budget deficit while ensuring faster growth rates of state and local budget revenues compared to their expenditures; optimization of debt serviced from the budget through raising funds from the sale of domestic government bonds, mainly on a long-term basis; minimization of budgetary risks and existing threats to the public financial security by ensuring long-term stability of budgets etc.

Data modeling is a process of developing a model to design and develop a data system that supports an organization's various business processes. A conceptual data model represents a technology-independent specification of structure of data to be stored within a database. The model aims to provide richer expressiveness and incorporate a set of semantics to (a) support the design, control, and integrity parts of the data stored in data management structures and (b) coordinate the viewing of connections and ideas on a database. The described structure of the data is often represented in an entity–relationship (ER) model, which was one of the first data-modeling techniques and is likely to continue to be a popular way of characterizing entity classes, attributes, and relationships. This paper attempts to examine the basic ER modeling notions in order to analyze the concepts to which they refer as well as ways to represent them. In such a mission, we apply a new modeling methodology (thinging machine; TM) to ER in terms of its fundamental building constructs, representation entities, relationships, and attributes. The goal of this venture is to further the understanding of data models and enrich their semantics. Three specific contributions to modeling in this context are incorporated: (a) using the TM model's five generic actions to inject processing in the ER structure; (b) relating the single ontological element of TM modeling (i.e., a thing/machine or thimac) to ER entities and relationships; and (c) proposing a high-level integrated, extended ER model that includes structural and time-oriented notions (e.g., events or behavior).

The spread of cloud computing, digital computing, and the popular social media platforms have led to increased growth of data. That growth of data results in what is known as big data (BD), which seen as one of the most strategic resources. The analysis of these BD has allowed generating value from massive raw data that helps in making effective decisions and providing quality of service. With Vision 2030, Saudi Arabia seeks to invest in BD technologies, but many challenges and barriers have led to delays in adopting BD. This research paper aims to search in the state of Big Data Analytics (BDA) in Saudi higher education sector, identify the barriers by reviewing the literature, and then to apply the design-reality gap model to assess these barriers that prevent effective use of big data and highlights priority areas for action to accelerate the application of BD to comply with Vision 2030.

The article deals with problems of innovation development on a network basis, which require effective mechanisms of innovation communications. In research the organizational aspects of ICT infrastructure development for innovation networks sustainable development based on cooperative marketing principles is considered. The proposed research idea is based on the idea that ICT implementation is based not only on the operational approach for innovation management as a factor of efficiency of internal communications, but also on knowledge economy and post-industrial economy trends. Therefore, the purpose of study is to develop an ICT model of innovation infrastructure to improve its effectiveness (strategic character) and efficiency (operative character) through increasing the efficiency of network communication interactions. Creation of information space and communication tools to support innovation network sustainable development and cooperation activities in research is proposed to be solved with the help of specialized ICT platform. It is shown, that ICT platform of innovation cooperation innovation network is important tool for common work of participants. ICT platform is considered as an integrated information system designed to automate business processes related to the sustainable development of innovation network, segment management and integration with HEI information systems and industrial cooperation. The main factors that determine the need to use a special ICT platform for innovation network cooperation were considered. The main issues of concurrent engineering (C-technology) application in high-technology industries and innovation cooperation for integrated product development were studied.

With the development of communication networks, the processes of exchanging and transmitting information rapidly developed. As millions of images are sent via social media every day, also wireless sensor networks are now used in all applications to capture images such as those used in traffic lights, roads and malls. Therefore, there is a need to reduce the size of these images while maintaining an acceptable degree of quality. In this paper, we use Python software to apply K-mean Clustering algorithm to compress RGB images. The PSNR, MSE, and SSIM are utilized to measure the image quality after image compression. The results of compression reduced the image size to nearly half the size of the original images using k = 64. In the SSIM measure, the higher the K, the greater the similarity between the two images which is a good indicator to a significant reduction in image size. Our proposed compression technique powered by the K-Mean clustering algorithm is useful for compressing images and reducing the size of images.

The Islamic jurisprudential legal system represents an essential component of the Islamic religion, that governs many aspects of Muslims' daily lives. This creates many questions that require interpretations by qualified specialists, or Muftis according to the main sources of legislation in Islam. The Islamic jurisprudence is usually classified into branches, according to which the questions can be categorized and classified. Such categorization has many applications in automated question-answering systems, and in manual systems in routing the questions to a specialized Mufti to answer specific topics. In this work we tackle the problem of automatic categorisation of Islamic jurisprudential legal questions using deep learning techniques. In this paper, we build a hierarchical deep learning model that first extracts the question text features at two levels: word and sentence representation, followed by a text classifier that acts upon the question representation. To evaluate our model, we build and release the largest publicly available dataset of Islamic questions and answers, along with their topics, for 52 topic categories. We evaluate different state-of-the art deep learning models, both for word and sentence embeddings, comparing recurrent and transformer-based techniques, and performing extensive ablation studies to show the effect of each model choice. Our hierarchical model is based on pre-trained models, taking advantage of the recent advancement of transfer learning techniques, focused on Arabic language.

The purpose of this article is to explore the modern branding - its nature, history, originality, spirit and value to consumers. In particular, graphic design of the brand is an important component in terms of the modern European culture. The scientific novelty of the work implies the study of the contemporary design, which widely embraces the sphere of cultural and social life and can contribute to the development of progressive phenomena in society, as the design can literally be defined as a rethinking, a search for new meanings. The design itself is a versatile means of communication at the level of feelings and meanings. One of its main goals is the harmonization, and even formation of perception of the world, creation of new images or rethinking the essence of ordinary things. Hence, the research in the field of the design development is topical in the worldwide cultural space..

The development of various phishing websites enables hackers to access confidential personal or financial data, thus, decreasing the trust in e-business. This paper compared the detection techniques utilizing URL-based features. To analyze and compare the performance of supervised machine learning classifiers, the machine learning classifiers were trained by using more than 11,005 phishing and legitimate URLs. 30 features were extracted from the URLs to detect a phishing or legitimate URL. Logistic Regression, Random Forest, and CatBoost classifiers were then analyzed and their performances were evaluated. The results yielded that CatBoost was much better classifier than Random Forest and Logistic Regression with up to 96% of detection accuracy.

Digital content protection has recently become an important requirement in biometrics-based authentication systems due to the challenges involved in designing a feasible and effective user authentication method. Biometric approaches are more effective than traditional methods, and simultaneously, they cannot be considered entirely reliable. This study develops a reliable and trustworthy method for verifying that the owner of the biometric traits is the actual user and not an impostor. Watermarking-based approaches are developed using a combination of a color face image of the user and a mobile equipment identifier (MEID). Employing watermark techniques that cannot be easily removed or destroyed, a blind image watermarking scheme based on fast discrete curvelet transform (FDCuT) and discrete cosine transform (DCT) is proposed. FDCuT is applied to the color face image to obtain various frequency coefficients of the image curvelet decomposition, and for high frequency curvelet coefficients DCT is applied to obtain various frequency coefficients. Furthermore, mid-band frequency coefficients are modified using two uncorrelated noise sequences with the MEID watermark bits to obtain a watermarked image. An analysis is carried out to verify the performance of the proposed schema using conventional performance metrics. Compared with an existing approach, the proposed approach is better able to protect multimedia data from unauthorized access and will effectively prevent anyone other than the actual user from using the identity or images.

The modern technological era has brought about the Semantic Web. Ontologies are essential to achieve the vision of the Semantic Web. Ontologies enable machines to understand data. The Arabic Language currently does not have a significant presence on the Web. To achieve a comparable level of Arabic access to other important languages, further work is needed to build Arabic ontologies. A goal is to design and create a robust Arabic ontology that represents the concepts from a large and significant subset of Arabic. We use a source of Hadiths (prophet saying and deeds) from Riyadh As-Saliheen. Preliminary results are very promising.

The relevance of the study implies the need to explain the main determinants of environmental policy, allowing countries to converge on a common working basis. The purpose of the research is to explore ways in which the environmental aspects of EU regions and territories can be shaped to apply to domestic environmental policy. A total of 997 representatives from the Ukrainian UTCs, who are involved in local environmental policy, participated in the survey. Results of the research. A hierarchy of regional environmental policy objectives has been identified. Three key principles of eco-policy development have been distinguished. The means of the innovative approach strategy implementation have been outlined (formation of the regional market of environmental services; organization of interaction between environmental agencies and market structures; establishment of environmental funds which finance environmental activities; implementation of the "collateral return" system; formation of a system of benefits and loans to enterprises that successfully implement the environmental policy). The means of the prognostic approach implementation strategy to the development of the region's environmental policy have been determined (the use of an orderly and successful long-term strategy of economic development of the region; obligatory consideration in the mechanisms strategy aimed at improvement of environmental management system of the region, interbranch impact of the projects implemented on the region's ecological situation; taking into consideration the economic and geographical peculiarities of the region, the necessary infrastructure, issues of the territorial location of the large enterprises in the region and their impact on the environment). The means of the traditional approach strategy implementation have been outlined (activity programs focused on solution of specific problems, rather than on the implementation of measures for environmental restoration and enhancement; nature conservation problems have interbranch, interdepartmental character due to unity of the region's ecosystem; tracking complexity of changing conditions (water basins, woodlands, air-mass transport), regional boundaries generally do not coincide with natural ecosystems boundaries). The practical significance of the study lies in providing recommendations for the implementation of certain aspects of Ukrainian UTC's environmental policy.

Civil aviation cybersecurity challenges are global in nature and must be addressed using global best practices and the combined efforts of all stakeholders. This requires the development of comprehensive international strategies and detailed plans for their implementation, with appropriate resources. It is important to build such strategies on a common methodology that can be applied to civil aviation and other interrelated critical infrastructure sectors. The goal of the study was to determine the methodological basis for developing an international civil aviation cybersecurity strategy, taking into account existing experience in strategic planning at the level of international specialized organizations. The research was conducted using general scientific and theoretical research methods: observation, description, formalization, analysis, synthesis, generalization, explanation As a result of the study, it was established the specifics of the approach to formulating strategic goals in civil aviation cybersecurity programs in the documents of intergovernmental and international non-governmental organizations in the aviation sphere, generally based on a comprehensive vision of cybersecurity management. A comparative analysis of strategic priorities, objectives, and planned activities for their implementation revealed common characteristics based on a single methodological sense of cybersecurity as a symbiosis of five components: human capacity, processes, technologies, communications, and its regulatory support. It was found that additional branching and detailing of priority areas in the strategic documents of international civil aviation organizations (by the example of Cybersecurity Strategy and Cybersecurity Action Plan) does not always contribute to compliance with a unified methodological framework. It is argued that to develop an international civil aviation cybersecurity strategy, it is advisable to use the methodological basis of the Cyber Security Index.

The rapid development of various applications in networking system, business, medical, education, and other domains that use basic data access operations such as insert, edit, delete and search makes data structure venerable and crucial in providing an efficient method for day to day operations of those numerous applications. One of the major problems of those applications is achieving constant time to search a key from a collection. A number of different methods which attempt to achieve that have been discovered by researchers over the years with different performance behaviors. This work evaluated these methods, and found out that almost all the existing methods have non-constant time for adding and searching a key. In this work, we designed a multi-indexes hashing algorithm that handles a collision in a hash table T efficiently and achieved constant time O(1) for searching and adding a key. Our method employed two-level of hashing which uses pattern extraction h₁(key) and h₂(key). The second hash function h₂(key) is use for handling collision in T. Here, we eliminated the wasted slots in the search space T which is another problem associated with the existing methods.

In this proposal, we aim to enhance the security of systems accounts by improving the authentication techniques. We mainly intend to enhance the accuracy of the one-time passwords via including voice biometric and recognition techniques. The recognition will be performed on the server to avoid redirecting voice signatures by hackers. Further, to enhance the privacy of data and to ensure that the active user is legitimate, we propose to periodically update the activated sessions using a user-selected biometric factor. Finally, we recommend adding a pre-transaction re-authentication which will guarantee enhanced security for sensitive operations. The main novelty of this proposal is the use of the voice factor in the verification of the one-time password and the various levels of authentications for a full-security guarantee. The improvement provided by this proposal is mainly designed for sensitive applications. From conducted simulations, findings prove the efficiency of the proposed scheme in reducing the probability of hacking users' sessions.

Software vulnerabilities are becoming more and more increasing, their role is to harm the computer systems of companies, governmental organizations and agencies. The main objective of this paper is to propose a method that will cluster future software vulnerabilities that may spread. This method is developed by combining the Multiple Correspondence Analysis (MCA), the Elbow procedure and the Kmeans Algorithm. A simulation was done on a dataset of 15713 observations. This simulation allowed us to identify families of future vulnerabilities. This model was evaluated using the silhouette index.

In this paper, we have simulated a rectangular microstrip patch antenna for aerospace applications based on graphen as a conductor and a multilayer substrate .as a result of the use of the graphen patch we obtained a reconfigurable antenna on the frequency range (0.6-0.7 terahertz) with a gain up to 12 db. The simulation of this antenna has been performed by using CST Microwave Studio, which is a commercially available finite integral based electromagnetic simulator.