In the current Internet system, there are many problems using anonymity of the network communication such as personal information leaks and crimes using the Internet system. This is why TCP/IP protocol used in Internet system does not have the user identification information on the communication data, and it is difficult to supervise the user performing the above acts immediately. As a study for solving the above problem, there is the study of Policy Based Network Management (PBNM). This is the scheme for managing a whole Local Area Network (LAN) through communication control for every user. In this PBNM, two types of schemes exist. As one scheme, we have studied theoretically about the Destination Addressing Control System (DACS) Scheme with affinity with existing internet. By applying this DACS Scheme to Internet system management, we will realize the policy-based Internet system management. In this paper, basic system design for PBNM scheme for multi-domain management utilizing data science and AI is proposed.

The current study aims to determine digital transformation (organizational, technical, and human resources) requirements at Saudi universities from Umm Al-Qura University faculty members' perspectives. The researcher used a quantitative approach based on the descriptive analytical design. To answer the questions of the study, the researcher used the questionnaire as a data collection tool. The questionnaire was sent electronically to faculty members working in colleges and institutes affiliated with Umm Al-Qura University in Makkah Al-Mukarramah, Saudi Arabia. The questionnaire consisted of the three dimensions of digital transformation: organizational; technical; and human resources requirements. The results showed that requirements related to human resources came first with an average of 2.25 then the organizational requirements with an average of 1.95, and in the last, technical requirements came with an average of 1.64. In addition, some suggestions were given by the participants (faculty members) related to the mechanism that could contribute to implementing digital transformation at Saudi universities. Likewise, at the end of the study, the researcher has given some suggestions related to the implementation of digital transformation requirements at Saudi universities.

In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.

The paper describes the new approaches to the automated analysis of curricula according to the higher education standard. The analysis process is proposed to carry out in two ways: (a) the analysis of completeness and sufficiency of curricula according to the standard of higher education; (b) the comparison of curricula of the same qualification and specialty. The problem of improving the quality of university students' training launches the process of monitoring and analyzing educational curricula and their correspondence to the higher education standard. We developed the rules and methods to compare curricula. In addition, we implemented the automated system of curricula comparison. The paper reveals the use of these methods based on the analysis of the curriculum bachelor level of higher education "Informatics", specialty "Computer science", at the Faculty of Computer Science and Cybernetics of the Taras Shevchenko National University of Kyiv. The findings put towards the idea that the implementation of developed methods as well as the automated system of curricula analysis will improve the educational services by higher education institutions.

The paper aims to clarify how to make a decision using geographic information systems and how to choose the best route between two cities to suit the expectations of the driver and his sense of safety and comfort on the road. Use a special model for network analysis, where the network analysis tool relied on the following data (maximum speed of the road - number of intersections - road width - peak period) in choosing the best safe path. The paper concluded that the best safe route for refugees between the cities of Khartoum - Arqin crossing is ( Khartoum - Shendi - Atbara - Meroe - Abu Hamad - Wadi Halfa). We advise all GIS users to use the theories of spatial analysis when creating a new model.

The trend of satisfying consumer needs (payment for mobile communication, music services, cab ordering, banking products, and food delivery) on a unified online platform has shaped a digital ecosystem, an instrument creating a unified space of economic interaction. Representatives of e-commerce are major stakeholders in the development of such tools. In particular, subscription services (multiservice subscriptions) allow users to create their own ecosystems based on their personal preferences. The rate of subscription service use is growing around the world, yet understanding of the peculiarities of development of this e-commerce sphere is limited due to insufficient research.The study aims to determine the motives and barriers to the use of subscription services (multiservice subscriptions) by consumers and their relationship with consumer characteristics.Proceeding from an online survey of 200 users, the study determines the relationship between the gender and income of consumers and their use of subscription services, motives and motivators for using subscription services, and barriers to the choice of a particular subscription service. The obtained results may serve as a basis for managerial decisions in e-commerce and for improving the effectiveness of marketing solutions.

Text mining (TM) is most widely used to find patterns from various text documents. Cyber-bullying is the term that is used to abuse a person online or offline platform. Nowadays cyber-bullying becomes more dangerous to people who are using social networking sites (SNS). Cyber-bullying is of many types such as text messaging, morphed images, morphed videos, etc. It is a very difficult task to prevent this type of abuse of the person in online SNS. Finding accurate text mining patterns gives better results in detecting cyber-bullying on any platform. Cyber-bullying is developed with the online SNS to send defamatory statements or orally bully other persons or by using the online platform to abuse in front of SNS users. Deep Learning (DL) is one of the significant domains which are used to extract and learn the quality features dynamically from the low-level text inclusions. In this scenario, Convolutional neural networks (CNN) are used for training the text data, images, and videos. CNN is a very powerful approach to training on these types of data and achieved better text classification. In this paper, an Ensemble model is introduced with the integration of Term Frequency (TF)-Inverse document frequency (IDF) and Deep Neural Network (DNN) with advanced feature-extracting techniques to classify the bullying text, images, and videos. The proposed approach also focused on reducing the training time and memory usage which helps the classification improvement.

In the past decade, Autonomous Vehicle Systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the classification of objects at short and long distances. This paper presents classification of objects using CNN based vision and Light Detection and Ranging (LIDAR) fusion in autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image up sampling theory. By creating a point cloud of LIDAR data up sampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data and fed into a deep CNN. The proposed method can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data. This method is adopted to guarantee both object classification accuracy and minimal loss. Experimental results show the effectiveness and efficiency of presented approach for objects classification.

We introduce DCNN and DRAE appraoches for compression of medical videos, in order to decrease file size and storage requirements, there is an increasing need for medical video compression nowadays. Using a lossy compression technique, a higher compression ratio can be attained, but information will be lost and possible diagnostic mistakes may follow. The requirement to store medical video in lossless format results from this. The aim of utilizing a lossless compression tool is to maximize compression because the traditional lossless compression technique yields a poor compression ratio. The temporal and spatial redundancy seen in video sequences can be successfully utilized by the proposed DCNN and DRAE encoding. This paper describes the lossless encoding mode and shows how a compression ratio greater than 2 (2:1) can be achieved.

The article considers online discussion as an interactive learning method in the conditions of distance learning. The essence of discussion and the stages of its organization are described. The main objective of discussion in distance learning is defined as the stimulation of interest in learning and the involvement of various viewpoints in an active discussion of the stated problems. The key role in ensuring the efficiency of a discussion is identified. The article develops a model for organizing asynchronous online discussions on the Moodle platform, highlighting the sequence of stages and their content. An experimental study of the use of the discussion method in the training of students in distance learning conditions is carried out. Based on the results of the methodological experiment, conclusions are drawn about student interest in online discussions. The authors conclude that the interest of students of different specialties in asynchronous online discussions varies, and the greatest interest is demonstrated by linguistics students. Nevertheless, the differences in student interest in online discussions by groups (specialties) are more likely attributable to subjective factors, which do not affect the overall picture in a major way.

The majority of product users rely on the reviews that are posted on the appropriate website. Both users and the product's manufacturer could benefit from these reviews. Daily, thousands of reviews are submitted; how is it possible to read them all? Sentiment analysis has become a critical field of research as posting reviews become more and more common. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM, CNN, RNN, and GRU. Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. According to experimental results utilizing a publicly accessible dataset with reviews for all of the models, both positive and negative, and CNN, the best model for the dataset was identified in comparison to the other models, with an accuracy rate of 81%.

Instagram is one of the fastest-growing online photo social web services where users share their life images and videos with other users. Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. Hashtags can be used on just about any social media platform, but they're most popular on Twitter and Instagram. Using hashtags is essentially a way to group together conversations or content around a certain topic, making it easy for people to find content that interests them. Practically on average, 20% of the Instagram hashtags are related to the actual visual content of the image they accompany, i.e., they are descriptive hashtags, while there are many irrelevant hashtags, i.e., stophashtags, that are used across totally different images just for gathering clicks and for search ability enhancement. Hence in this work, Sorting instagram hashtags all the way through mass tagging using HITS (Hyperlink-Induced Topic Search) algorithm is presented. The hashtags can sorted to several groups according to Jensen-Shannon divergence between any two hashtags. This approach provides an effective and consistent way for finding pairs of Instagram images and hashtags, which lead to representative and noise-free training sets for content-based image retrieval. The HITS algorithm is first used to rank the annotators in terms of their effectiveness in the crowd tagging task and then to identify the right hashtags per image.

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

Data confidentiality refers to the characteristic that information kept undisclosed or hidden from unauthorized parties. It considered a key security requirement in current supply chain management (SCM) systems. Currently, academia and industry tend to adopt blockchain and IoT technologies in order to develop efficient and secure SCM systems. However, providing confidential data sharing among these technologies is quite challenging due to the limitations associated with blockchain and IoT devices. This review paper illustrates the importance of preserving data confidentiality in SCM systems by highlighting the state of the art on confidentiality-preserving methodologies in the context of blockchain based IoT-SCM systems and the challenges associated with it.

In many training institutions, the major advancement of Information Technology is having a profound impact on the way in which instructors teach and students learn, as well as how the two interact. The training process is continuing with the goal of enhancing the calibre of instruction and engagement. Top colleges and institutions have more recently developed a variety of Massive Open Online Courses (MOOC) systems centred on the development of new educational offering ways. These have not only captured the interest of students and scholars in the field of higher education, but also that of staff members in the private and public sectors. This study uses a Unified Theory of Acceptance and Use of Technology (UTAUT) model to assess the top MOOC providers and pinpoint the key elements influencing learner acceptance of MOOCs in Saudi Arabian training. A total of 382 government trainees in Saudi Arabia participated in an online survey, the results of which underwent analysis using structural equation modelling. This study identifies the key elements influencing Saudi government employee trainees' intentions to use MOOCs, with the findings indicating that the suggested model can account for 86.2% of user behaviour and 88.5% of user intentions.

Investment authorities are broad financial institutions that carefully manage investments on behalf of the national government using a long-term value development approach. To provide a stronger structure or framework for In-vestment Authorities to govern the distribution of funds to public and private markets, we've started research to create a blockchain-based prototype for managing and tracking numerous finances of such authorities. We have taken the case study of Oman Investment Authority (OIA) of Sultanate of Oman. Oman's wealth is held in OIA. It is an organization that oversees and utilizes the additional capital generated by oil and gas profits in public and private markets. Unlike other Omani funds, this one focus primarily on assets outside the Sultanate. The operation of the OIA entails a huge number of transactions, necessitating a high level of transparency and administration among the parties involved. Currently, OIA relies on various manuals to achieve its goals, such as the Authorities and Responsibilities manual, the In-vestment Manual, and the Code of Business Conduct, among others. In this paper, we propose a Blockchain based framework to manage the operations of OIA. Blockchain is a part of the Fourth Industrial Revolution, and it is re-shaping every industry. The main components of every blockchain are assets and participants. The funds are the major assets in the proposed study, and the participants are the various fund shareholders/recipients. The block-chain's transactions are all safe, secure, and immutable, and it's part of a trustless network. The transactions are simple to follow and verify. By replacing intermediary firms with smart contracts, blockchain-based solutions eliminate any middlemen in the fund allocation process.

This work take deeper analysis of Adaptive Moment Estimation (Adam) and Adam with Weight Decay (AdamW) implementation in real world text classification problem (DGA Malware Detection). AdamW is introduced by decoupling weight decay from L2 regularization and implemented as improved optimizer. This work introduces a novel implementation of AdamW variant as AdamW+ by further simplifying weight decay implementation in AdamW. DGA malware detection LSTM models results for Adam, AdamW and AdamW+ are evaluated on various DGA families/ groups as multiclass text classification. Proposed AdamW+ optimizer results has shown improvement in all standard performance metrics over Adam and AdamW. Analysis of outcome has shown that novel optimizer has outperformed both Adam and AdamW text classification based problems.

The National Agency for Quality Assurance in Higher Education plays a crucial role in education in Ukraine, as an independent entity creates and ensures quality standards of higher education, which allow to properly implement the educational policy of the state, develop the economy and society as a whole.The purpose of the article: to reveal the crucial role of the National Agency for Quality Assurance in Higher Education to create quality management of higher education institutions, to show its mechanism as an independent entity that creates and ensures quality standards of higher education. and society as a whole. The mission of the National Agency for Quality Assurance in Higher Education is to become a catalyst for positive changes in higher education and the formation of a culture of its quality. The strategic goals of the National Agency are implemented in three main areas: the quality of educational services, recognition of the quality of scientific results, ensuring the systemic impact of the National Agency. The National Agency for Quality Assurance in Higher Education exercises various powers, which can be divided into: regulatory, analytical, accreditation, control, communication.The effectiveness of the work of the National Agency for Quality Assurance in Higher Education for 2020 has been proved. The results of a survey conducted by 183 higher education institutions of Ukraine conducted by the National Agency for Quality Assurance in Higher Education are shown. Emphasis was placed on the development of "Recommendations of the National Agency for Quality Assurance in Higher Education regarding the introduction of an internal quality assurance system." The international activity and international recognition of the National Agency for Quality Assurance in Higher Education are shown.

Internet of Things (IoT) is a relatively new concept that has gained immense popularity in a short period of time due to its wide applicability in making human life more convenient and automated. As an illustration: the development of smart homes, smart cities, etc. However, it is also accompanied by a substantial number of risks and flaws. IoT makes use of low-powered devices, so secure, less time-consuming and energy-intensive transmission (routing) of messages due to the limited availability of energy is one of the many and most significant concerns for IoT developers. The following paper presents a trust-based routing scenario for the Internet of Things (IoT) that exploits the past transmission record from the cupcarbon simulator's log files. Artificial Neural Network is used to quantify knowledge of trust, calculate the value of trust, and share this information with other network devices. As a human behavioural pattern, trust provides a superior method for making routing decisions. If there is a tie in the trust values and no other path is available, the remaining battery power is used to break the tie and make a forwarding decision; this is also seen as a more efficient use of the available resources. The proposed algorithm is observed to have superior energy consumption and routing decisions compared to conventional routing algorithms, and it improves the communication pattern.

As the old saying goes "a picture is worth a thousand words" data visualization is essential in almost every industry. Companies make Data-driven decisions and gain insights from visual data. However, there is a need to investigate the role of data visualization in human resource management. This review aims to highlight the power of data visualization in the field of human resources. In addition, visualize the latest trends in the research area of human resource and data visualization by conducting a bibliometric analysis. The study adopted a literature review on recent publications from 2017 to 2022 to address research questions.

Network security is now more crucial than ever for consumers, companies, and military clients. Security has elevated to the top of the priority list since the Internet's creation. The evolution of security technology is now better understood. The area of community protection as a whole is broad and dynamic. News from the days before the internet and more recent advancements in community protection are both included in the topic of observation. Recognize current research techniques, previous Defence strategies that were significant, and network attack techniques that have been used before. The security of various domain names is the subject of this article's description of bibliographic research.

The main purpose of the study is to analyze the main features of the pedagogical culture management system in the context of a post-pandemic. Education is the main social institution in which they are professionally engaged in the transfer of the cultural experience of mankind to the next generations. The cultural foundations of management in this area are extremely important. Therefore, the management of pedagogical culture is relevant. Methodology implies the use of modern research methods. Based on the results of the study, key aspects of the pedagogical culture management system in the post-pandemic conditions were identified.

A stroke is a medical disease where a blood vessel in the brain ruptures, causes damage to the brain. If the flow of blood and different nutrients to the brain is intermittent, symptoms may occur. Stroke is other reason for loss of life and widespread disorder. The prevalence of stroke is high in growing countries, with ischemic stroke being the high usual category. Many of the forewarning signs of stroke can be recognized the seriousness of a stroke can be reduced. Most of the earlier stroke detections and prediction models uses image examination tools like CT (Computed Tomography) scan or MRI (Magnetic Resonance Imaging) which are costly and difficult to use for actual-time recognition. Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. In recent times ML algorithms have gained lot of attention due to their accurate results in medical fields. Hence in this work, Stroke disease identification system by using Machine Learning algorithm is presented. The ML algorithm used in this work is Artificial Neural Network (ANN). The result analysis of presented ML algorithm is compared with different ML algorithms. The performance of the presented approach is compared to find the better algorithm for stroke identification.

By looking the importance of communication, data delivery and access in various sectors including governmental, business and individual for any kind of data, it becomes mandatory to identify faults and flaws during cyber communication. To protect personal, governmental and business data from being misused from numerous advanced attacks, there is the need of cyber security. The information security provides massive protection to both the host machine as well as network. The learning methods are used for analyzing as well as preventing various attacks. Machine learning is one of the branch of Artificial Intelligence that plays a potential learning techniques to detect the cyber-attacks. In the proposed methodology, the Decision Tree (DT) which is also a kind of supervised learning model, is combined with the different cross-validation method to determine the accuracy and the execution time to identify the cyber-attacks from a very recent dataset of different network attack activities of network traffic in the UNSW-NB15 dataset. It is a hybrid method in which different types of attributes including Gini Index and Entropy of DT model has been implemented separately to identify the most accurate procedure to detect intrusion with respect to the execution time. The different DT methodologies including DT using Gini Index, DT using train-split method and DT using information entropy along with their respective subdivision such as using K-Fold validation, using Stratified K-Fold validation are implemented.

The main purpose of the article is to study the peculiarities of the work of the Ukrainian language in the upper grades of the lyceum based on the activity approach. Despite the fact that a number of scientific studies and applied developments on teaching Ukrainian as a foreign language have recently appeared in Ukrainian linguistics, significant problems in this area should be recognized (organization of the educational process when learning a language as a foreign language, general methodological principles, psycho- and sociolinguistic foundations, communicative approaches), the non-resolution of which leads to methodologically unreasonable teaching of the Ukrainian language as a foreign language, the use of methods of teaching the language as a native language or the study of the language as a subject (linguistic aspect). In addition, due attention is not paid to the development of communication skills, which, firstly, worsens the quality of teaching and learning. Based on the results of the analysis, the key aspects of the work on the Ukrainian language in the senior classes of the lyceum were analyzed on the basis of an activity approach.