An operation of an organization is currently using a digital environment which opens to potential cyber-attacks. These phenomena become worst as the cyberattack landscape is changing rapidly. The impact of cyber-attacks varies depending on the scope of the organization and the value of assets that need to be protected. It is difficult to assess the damage to an organization from cyberattacks due to a lack of understanding of tools, metrics, and knowledge on the type of attacks and their impacts. Hence, this paper aims to identify domains and sub-domains of cyber-attack taxonomy to facilitate the understanding of cyber-attacks. Four phases are carried in this research: identify existing cyber-attack taxonomy, determine and classify domains and sub-domains of cyber-attack, and construct the enhanced cyber-attack taxonomy. The existing cyber-attack taxonomies are analyzed, domains and sub-domains are selected based on the focus and objectives of the research, and the proposed taxonomy named AVOIDITALS Cyber-attack Taxonomy is constructed. AVOIDITALS consists of 8 domains, 105 sub-domains, 142 sub-sub-domains, and 90 other sub-sub-domains that act as a guideline to assist administrators in determining cyber-attacks through cyber-attacks pattern identification that commonly occurred on digital infrastructure and provide the best prevention method to minimize impact. This research can be further developed in line with the emergence of new types and categories of current cyberattacks and the future.

The article defines the hypothesis of the research, it is an assumption that the process of forming ICT competence will be effective if: theoretically substantiated, developed and practically implemented a structural and functional model for the formation of ICT competence, taking into account interdisciplinary integration, the pedagogical conditions that contribute to the formation of ICT competence have been determined: interdisciplinary integration as the basis for building an interdisciplinary course, the content of which is aimed at the formation of ICT competence.

Non-abelian group based cryptosystems are a latest research inspiration, since they offer better security due to their non-abelian properties. In this paper, we propose a novel approach to non-abelian group based public-key cryptographic protocols using semidirect products of finite groups. An intractable problem of determining automorphisms and generating elements of a group is introduced as the underlying mathematical problem for the suggested protocols. Then, we show that the difficult problem of determining paths and cycles of Cayley graphs including Hamiltonian paths and cycles could be reduced to this intractable problem. The applicability of Hamiltonian paths, and in fact any random path in Cayley graphs in the above cryptographic schemes and an application of the same concept to two previous cryptographic protocols based on a Generalized Discrete Logarithm Problem is discussed. Moreover, an alternative method of improving the security is also presented.

Internet Service Providers (ISPs) constantly endeavor to resolve network congestion, in order to provide fast and cheap services to the customers. This study suggests two models based on Markov chain, using three and four access attempts to complete the call. It involves a comparative study of four models to check the relationship between Internet Access sharing traffic, and the possibility of network jamming. The first model is a Markov chain, based on call-by-call attempt, whereas the second is based on two attempts. Models III&IV suggested by the authors are based on the assumption of three and four attempts. The assessment reveals that sometimes by increasing the number of attempts for the same operator, the chances for the customers to complete the call, is also increased due to blocking probabilities. Three and four attempts express the actual relationship between traffic sharing and blocking probability based on Markov using MATLAB tools with initial probability values. The study reflects shouting results compared to I&II models using one and two attempts. The success ratio of the first model is 84.5%, and that of the second is 90.6% to complete the call, whereas models using three and four attempts have 94.95% and 95.12% respectively to complete the call.

In the current context of digitalization of education, the use of modern methods and techniques of data analysis and processing in order to improve students' school results has a very important role. In our paper, we aimed to perform a comparative study of the classification performances of AdaBoost, SVM, Naive Bayes, Neural Network and kNN algorithms to classify the results obtained at the Baccalaureate by students from a college in Suceava, during 2012-2019. To evaluate the results we used the metrics: AUC, CA, F₁, Precision and Recall. The AdaBoost algorithm achieves incredible performance for classifying the results into two categories: promoted / rejected. Next in terms of performance is Naive Bayes with a score of 0.999 for the AUC metric. The Neural Network and kNN algorithms obtain scores of 0.998 and 0.996 for AUC, respectively. SVM shows poorer performance with the score 0.987 for AUC. With the help of the HeatMap and DataTable visualization tools we identified possible correlations between classification results and some characteristics of data.

For the implementation of innovative products or services companies use different innovation strategies, in particular, outsourcing is quite widespread, given the high level of specialization and professionalism of companies that provide such services. The aim of this article was to evaluate the link between outsourcing of innovative activities and indicators of companies' efficiency. The methodology of the research includes the methods of correlation analysis and regression analysis to reveal the correlation between the use of outsourcing of innovative products and services and the turnover of the EU enterprises. The results show a high level of innovative activity of EU enterprises (50.3% innovatively active). Innovative business processes are the most widespread form of innovation activity (41% of enterprises implement business processes, and the indicator is higher than 41% in the most developed EU countries). The turnover of enterprises with new or substantially improved products was found to grow by 10.92% for the period 2016-2018. The correlation analysis shows weak direct links between the indicator of turnover growth and the number of enterprises that have independently implemented innovations, or have implemented innovations jointly with third-party companies, or using outsourcing services. The highest level of direct connection was found between the growth of turnover of enterprises that launched a new product for the market and the growth of the number of enterprises that implemented innovations with the involvement of organizations (correlation 0.273). Based on the regression analysis it was found that with a 1% change in the growth rate of the number of innovation-active enterprises, the growth rate of turnover from new or improved products increased by 5.67%. At the same time, with a 1% change in the growth rate of the number of enterprises that implement innovation through outsourcing, the growth rate of turnover from new or improved products will increase by 7.15%.

The article is devoted to the study of the features of administrative liability for offenses in the informational sphere, the definition of the concept and features. Based on the examples of implementation of instruments of European legislation into the national legal system and examples of national legal practice, the authors have identified the features of informational and legal sanctions aimed at restricting the rights of access of subjects to information, prohibiting them to disseminate certain information, restricting the rights to disseminate certain information, and suspending informational activities. It has been substantiated that the administrative liability for informational offenses as a protective legal institution is created to contribute to the solution of such acute problems of legal support of human and society interests in the new informational dimensions.

In the last few years, the massive development in wireless networks, high internet speeds and improvement in car manufacturing has shifted research focus to Vehicular Ad-HOC Networks (VANETs). Consequently, many related frameworks are explored, and it is found that security is the primary issue for VANETs. Despite that, a small number of research studies have taken into consideration the identification of performance standards and parameters. In this paper, VANET security frameworks are explored, studied and analysed which resulted in the identification of a list of performance evaluation parameters. These parameters are defined and categorized based on the nature of parameter (security or general context). These parameters are identified to be used by future researchers to evaluate their proposed VANET security frameworks. The implementation paradigms of security frameworks are also identified, which revealed that almost all research studies used simulation for implementation and testing. The simulators used in the simulation processes are also analysed. The results of this study showed that most of the surveyed studies used NS-2 simulator with a percentage of 54.4%. The type of scenario (urban, highway, rural) is also evaluated and it is found that 50% studies used highway urban scenario in simulation.

Waste accumulation is becoming a significant challenge in most urban areas and if it continues unchecked, is poised to have severe repercussions on our environment and health. The massive industrialisation in our cities has been followed by a commensurate waste creation that has become a bottleneck for even waste management systems. While recycling is a viable solution for waste management, it can be daunting to classify waste material for recycling accurately. In this study, transfer learning models were proposed to automatically classify wastes based on six materials (cardboard, glass, metal, paper, plastic, and trash). The tested pre-trained models were ResNet50, VGG16, InceptionV3, and Xception. Data augmentation was done using a Generative Adversarial Network (GAN) with various image generation percentages. It was found that models based on Xception and VGG16 were more robust. In contrast, models based on ResNet50 and InceptionV3 were sensitive to the added machine-generated images as the accuracy degrades significantly compared to training with no artificial data.

The relevance of research provides the necessity to identify the basic problems in the public governance sphere and information technology relations, forasmuch as understanding such interconnections can indicate the consequences of the development and spreading information technologies. The purpose of the research is to outline the issues of applying information technologies in public governance sphere. 500 civil servants took part in the survey (Ukraine). A two-stage study was conducted in order to obtain practical results of the research. The first stage involved collecting and analyzing the responses of civil servants on the Mentimeter online platform. In the second stage, the administrator used the SWOT-analysis system. The tendencies in using information technologies have been determined as follows: the institutional support development; creation of analytical portals for ensuring public control; level of accountability, transparency, activity of civil servants; implementation of e-government projects; changing the philosophy of electronic services development. Considering the threats and risks to the public governance system in the context of applying information technologies, the following aspects generated by societal requirements have been identified, namely: creation of the digital bureaucracy system; preservation of information and digital inequality; insufficient level of knowledge and skills in the field of digital technologies, reducing the publicity of the state and municipal governance system. Weaknesses of modern public governance in the context of IT implementation have been highlighted, namely: "digitization for digitalization"; lack of necessary legal regulation; inefficiency of electronic document management (issues caused by the imperfection of the interface of reporting interactive forms, frequent changes in the composition of indicators in reporting forms, the desire of higher authorities to solve the problem of their introduction); lack of data analysis infrastructure (due to imperfections in the organization of interaction between departments and poor capacity of information resources; lack of analytical databases), lack of necessary digital competencies for civil servants. Based on the results of SWOT-analysis, the strengths have been identified as follows: (possibility of continuous communication; constant self-learning); weaknesses (age restrictions for civil servants; insufficient acquisition of knowledge); threats (system errors in the provision of services through automation); opportunities for the introduction of IT in the public governance system (broad global trends; facilitation of the document management system). The practical significance of the research lies in providing recommendations for eliminating the problems of IT implementation in the public governance sphere outlined by civil servants..

Alzheimer's is a chronic progressive disease which exhibits varied symptoms and behavioural traits from person to person. The deterioration in cognitive abilities is more noticeable through their Activities and Instrumental Activities of Daily Living rather than biological markers. This information discussed in social media communities was collected and features were extracted by using the proposed fuzzy logic based algorithm to address the uncertainties and imprecision in the data reported. The data thus obtained is used to train machine learning models in order to predict the longevity of the patients. Models built on features extracted using the proposed algorithm performs better than models trained on full set of features. Important findings are discussed and Support Vector Regressor with RBF kernel is identified as the best performing model in predicting the longevity of Alzheimer's patients. The results would prove to be of high value for healthcare practitioners and palliative care providers to design interventions that can alleviate the trauma faced by patients and caregivers due to chronic diseases.

The demand for e-learning through video lectures is rapidly increasing due to its diverse advantages over the traditional learning methods. This led to massive volumes of web-based lecture videos. Indexing and retrieval of a lecture video or a lecture video topic has thus proved to be an exceptionally challenging problem. Many techniques listed by literature were either visual or audio based, but not both. Since the effects of both the visual and audio components are equally important for the content-based indexing and retrieval, the current work is focused on both these components. A framework for automatic topic-based indexing and search depending on the innate content of the lecture videos is presented. The text from the slides is extracted using the proposed Merged Bounding Box (MBB) text detector. The audio component text extraction is done using Google Speech Recognition (GSR) technology. This hybrid approach generates the indexing keywords from the merged transcripts of both the video and audio component extractors. The search within the indexed documents is optimized based on the Naïve Bayes (NB) Classification and K-Means Clustering models. This optimized search retrieves results by searching only the relevant document cluster in the predefined categories and not the whole lecture video corpus. The work is carried out on the dataset generated by assigning categories to the lecture video transcripts gathered from e-learning portals. The performance of search is assessed based on the accuracy and time taken. Further the improved accuracy of the proposed indexing technique is compared with the accepted chain indexing technique.

The key challenges and problematic aspects of the formation of intellectually and innovation-oriented strategies of regional entities at the present stage of their development are considered. The main tasks that arise in the process of strategizing the potential-forming development of regional economic systems in the context of digitalization are identified. The list of key organizational and economic directions of strategic character of providing intellectual and innovative development of regional economic systems is formed, which includes clustering of centers of high-tech development of regions, creation of creative hubs, development of knowledge infrastructure and improvement of interregional cooperation; a brief description of each of the presented strategic organizational and economic directions is given. Based on the analysis, the key strategic guidelines for the development of regional economic entities in the context of digitalization under the influence of potential-forming determinants, which form the content basis for further processes of strategizing qualitative aspects of development of specific regional entities.

The article presents the idea that in the conditions of modernization of higher professional education, there is an obvious contradiction between the practical need of higher education for a scientifically grounded theory and methodology of distance learning for students and the lack of the necessary didactic and content-methodological guidelines for its implementation. In theoretical research and applied development, as a rule, the technocratic approach prevails. The emphasis is on the technical capabilities of modern computers and telecommunications. Productively, from a pedagogical point of view, this contradiction can be resolved on the basis of modeling distance learning management for students, developing and implementing a corporate information educational environment in universities, increasing the effectiveness of the methodology of pedagogical interaction between students and teachers during the implementation of distance learning.

Banking systems are sensitive to data privacy since users' data, if not well protected, may be used to perform fake transactions. Blockchains, public and private, are frequently used in such systems thanks to their efficiency and high security. Public blockchains fail to fully protect users' data, despite their power in the accuracy of the transactions. The private blockchain is better used to protect the privacy of the sensitive data. They are not open and they apply authorization to login into the blockchain. However, they have a lower security compared to public blockchain. We propose in this paper a hybrid public-private architecture that profits from network virtualization. The main novelty of this proposal is the use of network virtualization that helps to reduce the complexity and efficiency of the computations. Simulations have been conducted to evaluate the performance of the proposed solution. Findings prove the efficiency of the scheme in reducing complexity and enhancing data privacy by guarantee high security. The contribution conducted by this proposal is that the results are verified by the centralized controller that ensures a correct validation of the resulted blockchains. In addition, computation complexity is to be reduced by profiting from the cooperation performed by the virtual agents.

The study develops methodological aspects for modeling the determining impact of modernization on the enterprise's economic security in development competitive conditions using the model of speed, stability and spaciousness of modernization. Modeling the determining impact of modernization on the enterprise's economic security in a competitive conditions involves: firstly, the formation of estimated modeling indicators in accordance with the speed, stability and spaciousness of the enterprise's modernization; secondly, establishing the weight of indicators in the assessment system using the tools of cognitive judgment; thirdly, the establishment of reference values of sound evaluation indicators; fourthly, the calculations of the integrated impact assessment of the modernization's determining impact modeling on the enterprise's ensuring economic security in a competitive conditions; fifthly, conducting calculations and analytical summarization of the results. To determine a comprehensive integrated indicator of the modernization changes impact on the competitiveness and economic security of enterprises, we use the correlation method of the calculated value with the reference value, as well as use weights for groups of calculations. Approbation of modeling of determining influence of modernization on maintenance of economic safety of the enterprise in competitive conditions of development by authors was carried out concerning such enterprises, as: JSC "Ukrzaliznytsia", SE "Ukraerorukh", SE IA "Boryspil", SE "Ukrposhta", KP "Kyivpastrans".

Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.

The article is devoted to the study of the issue of liability for the dissemination of inaccurate information on the Internet. The article emphasizes that now the public opinion of socially active segments of the population is mainly formed on the basis of not critical but subjective ("consumer") analysis of the received information. The modern realities show that the main source of its origin is the Internet: electronic media and, above all, the relevant social networks. The problem of dissemination by the mass media of inaccurate information obtained via the Internet is solved; it is seen in bringing the laws of Ukraine, which regulate public relations regarding the dissemination of information, into compliance with the Civil Code of Ukraine. The issues of development and implementation of new legislative acts aimed at regulating the status of subjects of informational relations on the Internet and establishing the grounds for their liability are also investigated.

Project management is a current trend of management in the public sphere, based on different principles, methods and tools. The tools include information technologies providing control over time, cost, quality and planning process in order to ensure accountability to interested parties. The goal of the research was to examine the impact of the integration of information systems in project management of the public sphere on the quality of public governance and administration using the example of infrastructure projects involving the private sector in developing countries. The methodology of the research is based on the concepts of "digital-era governance" (DEG), "Information governance" and "project governance" to determine the effectiveness of information systems and technologies in the management of infrastructure projects in the public sphere. The data from the countries with Lower middle income (India, Indonesia, Philippines, Ukraine, Vietnam) and Upper middle income (Argentina, Brazil, China, Colombia, Mexico, Peru, Romania, Russian Federation, Thailand, Turkey) for 1996-2020 were used to study the effects of DEG. The results show two main trends in the countries with Lower middle income and Upper middle income. The first trend is the development of digital governance, the concept of "digital-era governance" through information systems and performance measurement of the governance system, forecasting of investment flows of infrastructure projects, measurement of payback and effectiveness parameters for investment management in the public sector, decision support. The second trend is the existence of systemic challenges related to corruption, social and institutional factors through the development of democracy in developing countries and the integration of NPM similar to developed countries. The confidence of interested parties, especially private investors, in public authorities is determined by other factors - the level of return on investment, risks and assignment of responsibility, probability of successful completion of the project. These data still remain limited for a wide range of project participants, including citizens.

One of the most important inventions and developments in the digital world today is the healthcare system based on blockchain technology. Healthcare is an important field that requires the application of security mechanisms due to the sensitivity of patient data. The association of blockchain with healthcare contributed to achieving better security mechanisms than the traditional approach. The new approach operates in a decentralized system, which in turn, improves security in the healthcare environment. Consequently, blockchain technology has emerged as one of the most crucial solutions to security violations and challenges in the healthcare industry. This paper provides a comprehensive review of several experts' recent protection and detection approaches in this domain. It is also imperative to note that the paper focuses only on the recent techniques that have been published during 2017-2020. The sophisticated procedures have been investigated and discussed in terms of similarities and differences to highlight the significance of the protection needed to secure the healthcare environment.

The term parking management system usually refers to the custom built hardware intensive systems installed in building and malls. However, there are many places where such expensive solutions cannot be installed due to various reasons, like cost and urgent/temporary setup requirements. This project focuses on developing a parking management system based on image processing to detect vacant parking slot in an area where automated systems are not installed. Camera images of the parking area are subjected to image processing algorithm which marks virtual slots in the area and extracts occupancy information to guide the incoming drivers about availability and position of vacant spaces. The application consists of two interfaces: one for the guidance of the incoming drivers and the other one for the administrator. The later interface also informs the administrator if a car is not parked properly in the virtual slot. This parking system would reduce the stress and time wastage associated with car parking and would make the management of such areas less costly.

This is the world of computer science and innovations. In this modern era, every day new apps, webs and software are being introduced. As well as new apps and software are being introduced, similarly threats and vulnerable security matters are also increasing. Web apps are software that can be used by customers for numerous useful tasks, and because of the developer experience of good programming standards, web applications that can be used by an attacker also have multiple sides. Web applications Security is expected to protect the content of critical web and to ensure secure data transmission. Application safety must therefore be enforced across all infrastructure, including the web application itself, that supports the web applications. Many organizations currently have a type of web application protection scheme or attempt to build/develop, but the bulk of these schemes are incapable of generating value consistently and effectively, and therefore do not improve developers' attitude in building/designing stable Web applications. This article aims to analyze the attacks on the website and address security scanners of web applications to help us resolve web application security challenges.

With the recent advances in information and communication technology, online management of students' learning data has become the norm. Research on learning analysis that predicts the near future (in a few years) of students' careers using machine learning methods and state transition models has been widely conducted. It is important for educators to evaluate the comprehension stability of students to prevent a decrease in their comprehension rate and dropouts in the class. In this study, we measured the comprehension process of university students in different types of lectures. Herein, we report on the results of data analysis using time series and data statistics, and consider several educational approaches.

The market for smart phones has been booming in the past few years. There are now over 400,000 applications on the Android market. Over 10 billion Android applications have been downloaded from the Android market. Due to the Android popularity, there are now a large number of malicious vendors targeting the platform. Many honest end users are being successfully hacked on a regular basis. In this work, a cloud based reputation security model has been proposed as a solution which greatly mitigates the malicious attacks targeting the Android market. Our security solution takes advantage of the fact that each application in the android platform is assigned a unique user id (UID). Our solution stores the reputation of Android applications in an anti-malware providers' cloud (AM Cloud). The experimental results witness that the proposed model could well identify the reputation index of a given application and hence its potential of being risky or not.

The intensification of the processes of the digital economy development is leading to the transformation of the higher education system. Universities are forced to digitalize their own educational, research, international, marketing, financial and economic activities in order to maintain a competitive position in the global market of educational services. The purpose of the article is to study the role of information and communication technologies in the development of the higher education system and to ensure its adaptability to modern challenges of digital economy. To achieve this goal, methods of content analysis, logical generalization, systematization and a structural-functional method are used. In the article, the authors substantiate the urgency of forming a holistic strategy to ensure the adaptability of higher education to the challenges of digital economy. In the structure of this strategy, the information-technological block is singled out and described. The authors specified a set of positive synergetic effects from the introduction of modern information and communication technologies in the activities of universities. The main information threats to the digitalization of higher education related to the protection of personal data and university systems from cyberattacks and fraudulent schemes are identified. In conclusion, the authors detail the measures for the strategy implementation to ensure the adaptability of higher education to digital economy.

DNA sequencing provides fundamental data in genomics, bioinformatics, biology and many other research areas. With the emergent evolution in DNA sequencing technology, a massive amount of genomic data is produced every day, mainly DNA sequences, craving for more storage and bandwidth. Unfortunately, managing, analyzing and specifically storing these large amounts of data become a major scientific challenge for bioinformatics. Those large volumes of data also require a fast transmission, effective storage, superior functionality and provision of quick access to any record. Data storage costs have a considerable proportion of total cost in the formation and analysis of DNA sequences. In particular, there is a need of highly control of disk storage capacity of DNA sequences but the standard compression techniques unsuccessful to compress these sequences. Several specialized techniques were introduced for this purpose. Therefore, to overcome all these above challenges, lossless compression techniques have become necessary. In this paper, it is described a new DNA compression mechanism of pattern matching extended Compression algorithm that read the input sequence as segments and find the matching pattern and store it in a permanent or temporary table based on number of bases. The remaining unmatched sequence is been converted into the binary form and then it is been grouped into binary bits i.e. of seven bits and gain these bits are been converted into an ASCII form. Finally, the proposed algorithm dynamically calculates the compression ratio. Thus the results show that pattern matching extended Compression algorithm outperforms cutting-edge compressors and proves its efficiency in terms of compression ratio regardless of the file size of the data.

The article studies the discourse and a legal uncertainty of the popular and generally understandable concept of cybercrime. The authors reveal the doctrinal approaches to the definition of cybercrime, cyberspace, computer crime. The analysis of international legal acts and legislation of Ukraine in fighting cybercrime is carried out. The conclusion is made about the need to improve national legislation and establish international cooperation to develop the tools for countering cybercrime and minimizing its negative outcomes. The phenomenon of nicknames is studied as a semantic source, which potentially generates a number of threats and troubles - the crisis of traditional anthroponymic culture, identity crisis, hidden sociality, and indefinite institutionalization, incognito style, a range of manifestations of loneliness - from voluntary solitude to traumatic isolation and forced detachment. The core idea is that it is the phenomenon of incognito and hidden name (nickname and other alternatives) that is the motivational stimulus for the fact of information trouble or crime.

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.

The article discusses the pedagogical support for the formation of ICT competence assumes the individualization of students' training, due to their personal and professional needs and interests, and the provision of the necessary conditions and means to implement this process. A theoretical analysis is carried out and an analysis of experimental work on the implementation of the developed model for the formation of ICT competence is generalized specialists on the basis of pedagogical support.

The article is devoted to the problems and prospects of integration of informational technologies in the administration of justice as a necessary component of the development of the informational society in Ukraine. In general, informational technologies make it possible to create new forms of organization and interaction of bodies of public authorities with society, to introduce innovative solutions for legal regulation and organization of public relations. More and more services for citizens are moving to electronic format. Judicial reform is aimed at ensuring more comfortable and convenient interaction with the courts. The need for the usage of informational technology in the proceedings is preconditioned by the global informatization of modern society, the development of new forms of interaction in the civil sphere with the usage of electronic means of communication: the global Internet, mobile, and satellite communication systems and more. "Electronic justice" involves the use of information and communication technologies in the implementation of procedural law.

Cloud of things (CoTs) is a newer idea which combines cloud computing (CC) with the Internet of Things (IoT). IoT capable of comprehensively producing data, and cloud computing can be presented pathways that allow for the progression towards specific destinations. Integrating these technologies leads to the formation of a separate element referred to as the Cloud of Things (CoTs). It helps implement ideas that make businesses more efficient. This technology is useful for monitoring a device or a machine and managing or connecting them. Since there are a substantial amount of machines that can run the IoT, there is now more data available from the IoT that would have to be stored on a local basis for a provisional period, and this is impossible. CoTs is used to help manage and analyze data to additionally create usable information by permitting and applying the development of advanced technology. However, combining these elements has a few drawbacks in terms of how secure the process is. This investigation aims to recent study literature from the past 3 years that talk about how secure the technology is in terms of protecting by authentication, reliability, availability, confidentiality, and access control. Additionally, this investigation includes a discussion regarding some kinds of potential attacks when using Cloud of Things. It will also cover what the various authors recommend and conclude with as well as how the situation can be approached to prevent an attack.

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

The outbreak of the deadly virus COVID-19 is said to infect 17.3Cr people around the globe since 2019. This outbreak is continuously affecting a lot of new people till this day and, most of it is said to under control. However, vaccines introduced around the world can help mitigate the risk of the virus. Apart from medical professionals, prediction models are also said to combinedly help predict the risk of infection based on given datasets. This paper is based on publication of a machine learning approach using regression models to predict the output based on dataset which have indictors grouped based on active, tested, recovered and critical cases along with regions and cities covering most of it from Dubai. Hence, the active cases are tested based on the other indicators and other attributes. The coefficient of the determination (r²) is 0.96, which is considered promising. This model can be used as an frame work, among others, to predict the resources related to the dangerous outbreak.

The Internet of Medical Things (IoMTs) are to be considered an investment and an improvement to respond effectively and efficiently to patient needs, as it reduces healthcare costs, provides the timely attendance of medical responses, and increases the quality of medical treatment. However, IoMT devices face exposure from several security threats that defer in function and thus can pose a significant risk to how private and safe a patient's data is. This document works as a comprehensive review of modern approaches to achieving security within the Internet of Things. Most of the papers cited here are used been carefully selected based on how recently it has been published. The paper highlights some common attacks on IoMTs. Also, highlighting the process by which secure authentication mechanisms can be achieved on IoMTs, we present several means to detect different attacks in IoMTs

Smart application is developed in this paper by using an android-based platform to automatically determine the human emergency state (Lifesaver) by using different technology sensors of the mobile. In practice, this Lifesaver has many applications, and it can be easily combined with other applications as well to determine the emergency of humans. For example, if an old human falls due to some medical reasons, then this application is automatically determining the human state and then calls a person from this emergency contact list. Moreover, if the car accidentally crashes due to an accident, then the Lifesaver application is also helping to call a person who is on the emergency contact list to save human life. Therefore, the main objective of this project is to develop an application that can save human life. As a result, the proposed Lifesaver application is utilized to assist the person to get immediate attention in case of absence of help in four different situations. To develop the Lifesaver system, the GPS is also integrated to get the exact location of a human in case of emergency. Moreover, the emergency list of friends and authorities is also maintained to develop this application. To test and evaluate the Lifesaver system, the 50 different human data are collected with different age groups in the range of (40-70) and the performance of the Lifesaver application is also evaluated and compared with other state-of-the-art applications. On average, the Lifesaver system is achieved 95.5% detection accuracy and the value of 91.5 based on emergency index metric, which is outperformed compared to other applications in this domain.

The article presents the concept of ICT - competence, which is considered as the most important characteristic of professional competence, which includes a combination of the following components: motivational-value (orientation of the individual to the development of his ITC-competence in future professional activities); technological (complex of skills and abilities of ICT activities); cognitive (a system of knowledge of modern technologies of future professional activity); it is determined that the pedagogical support of the formation of ICT competence of future specialists is the individualization of the process training, due to their personal and professional needs and the specifics of a regional university, providing the necessary conditions for the implementation of this process.

Bioinformatics is formed with a blend of biology and informatics technologies and it employs the statistical methods and approaches for attending the concerning issues in the domains of nutrition, medical research and towards reviewing the living environment. The ceaseless growth of DNA sequencing technologies has resulted in the production of voluminous genomic data especially the DNA sequences thus calling out for increased storage and bandwidth. As of now, the bioinformatics confronts the major hurdle of management, interpretation and accurately preserving of this hefty information. Compression tends to be a beacon of hope towards resolving the aforementioned issues. Keeping the storage efficiently, a methodology has been recommended which for attending the same. In addition, there is introduction of a competent algorithm that aids in exact matching of small pattern. The DNA representation sequence is then implemented subsequently for determining 2 bases to 6 bases matching with the remaining input sequence. This process involves transforming of DNA sequence into an ASCII symbols in the first level and compress by using LZ77 compression method in the second level and after that form the grid variables with size 3 to hold the 100 characters. In the third level of compression, the compressed output is in the grid variables. Hence, the proposed algorithm S_Pattern DNA gives an average better compression ratio of 93% when compared to the existing compression algorithms for the datasets from the UCI repository.

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

In the vibrant environment, documentation and managing systems are maintained autonomously through education foundations, book materials and libraries at the same time as information are not voluntarily accessible in a centralized location. At the moment Libraries are providing online resources and services for education activities. Moreover, libraries are applying outlets of social media such as Facebook as well as Instagrams to preview their services and procedures. Librarians with the assistance of promising tools and technology like analytics software are capable to accumulate more online information, analyse them for incorporating worth to their services. Thus Libraries can employ big data to construct enhanced decisions concerning collection developments, updating public spaces and tracking the purpose of library book materials. Big data is being produced due to library digitations and this has forced restrictions to academicians, researchers and policy creator's efforts in enhancing the quality and effectiveness. Accordingly, helping the library clients with research articles and book materials that are in line with the users interest is a big challenge and dispute based on Taibah university in Saudi Arabia. The issues of this domain brings the numerous sources of data from various institutions and sources into single place in real time which can be time consuming. The most important aim is to reduce the time that lapses among the authentic book reading and searching the specific study material.

During the past two years, the education systems in the world witnessed unprecedented turmoil due to the coronavirus (Covid-19) pandemic, as most schools and universities in the world closed their doors to more than 1.5 billion students, or more than 90% of the total learners, according to recent figures issued by the UNESCO Institute for Statistics. Education experts have agreed that post- coronavirus education will not be the same as before, especially with the increasing use of modern technology in education. One of the most important new patterns with a structure digital in education is distance education, this style has been used, in many countries of the world, as an alternative to traditional education, since the beginning of the pandemic. In Saudi Arabia, this type of education has been used in all educational institutions, starting from kindergarten until the postgraduate level, as an alternative to face-to-face education to preserve the health and safety of students and workers in educational institutions. This study aimed to explore the level of satisfaction of preparatory year students on distance learning in their first year of study at Umm Al-Qura University. The findings of this study showed that students in the preparatory year were satisfied with their online learning experience. In addition, the results revealed that there was no effect for gender and location of study on students' level of satisfaction. Saudi universities should continue to work to create a suitable learning environment for students at the e-learning level.

This study aims to examine the factors that affect the intention of merchants to adopt mobile payments in Saudi Arabia. The underlying model used in the study is the technology acceptance model, which includes two factors: perceived ease of use and perceived usefulness. The effects of trust, cost, and compatibility are also examined because these are among the common factors used based on the literature. An online questionnaire was completed by 242 merchants in Saudi Arabia who had already adopted mobile payment services. A significant relationship was identified between perceived usefulness and compatibility in terms of merchants' intention to adopt mobile payment systems, while an insignificant relationship was identified with perceived ease of use, trust, and cost.

The knowledge management was considered as the inevitable result of the rule of knowledge in this era, and its importance became clear in being the main source for achieving success, the need to consider and manage knowledge as an independent field that must be addressed with a clear scientific methodology has become intangible - they are very valuable and a strategic asset. On the other hand, the innovation process relates to all parts of the organization, and helps to improve the behavioral patterns of individuals and their attitudes towards adopting modern and innovative ideas, it is a purposeful process adopted by the senior management and works to provide the capabilities and requirements for embodying the innovative behavior in it. In the field of dealing with the market, it is a product of the organization's innovative approach, which aims at advancement, change, and intended and organized renewal. The main objective of this article is to determine the most appropriate ways to integrate knowledge management mechanisms to employ innovation factors within universities based on the role of universities in supporting innovation. This was achieved through reviewing many relevant research and listing the most prominent concepts of knowledge management, its importance, objectives, and processes determining the stages of knowledge management application, the requirements for applying knowledge management, and the obstacles that impede its application; Then the statement "Innovation in universities, through which it addressed the concept of innovation, its importance, stages, and requirements for its application, as well as identifying the most prominent models of innovation, and obstacles to innovation, in addition to that the role of universities in supporting innovation will be identified. From the surveyed study done in this article, we concluded that the relationship among organizational culture, knowledge management and innovation capability can provide useful insights for managers regarding developing a strong culture, promote knowledge management practices effectively and eventually enhance the whole organization's innovation capability. Also, we found that different components of Knowledge Management as Knowledge activities, Knowledge types, transformation of knowledge and technology have a significant positive effect in bringing innovation through transformation of knowledge into knowledge assets in universities.

Multicast Routings is a big challenge due to limitations such as node power and bandwidth Mobile Ad-hoc Network (MANET). The path to be chosen from the source to the destination node requires protocols. Multicast protocols support group-oriented operations in a bandwidth-efficient way. While several protocols for multi-cast MANETs have been evolved, security remains a challenging problem. Consequently, MANET is required for high quality of service measures (QoS) such infrastructure and application to be identified. The goal of a MANETs QoS-aware protocol is to discover more optimal pathways between the network source/destination nodes and hence the QoS demands. It works by employing the optimization method to pick the route path with the emphasis on several QoS metrics. In this paper safe routing is guaranteed using the Secured Multicast Routing offered in MANET by utilizing the Ant Colony Optimization (ACO) technique to integrate the QOS-conscious route setup into the route selection. This implies that only the data transmission may select the way to meet the QoS limitations from source to destination. Furthermore, the track reliability is considered when selecting the best path between the source and destination nodes. For the optimization of the best path and its performance, the optimized algorithm called the micro artificial bee colony approach is chosen about the probabilistic ant routing technique.

Information technology and the need for timely and effective communication during the Covid-19 have made most governments adopt technological approaches to provide their services. E-government services have been adopted by most governments especially in developed countries to quickly and effectively share information. This study discusses the reasons why governments in the Gulf region should develop a new model for information technology knowledge management practices. To achieve this, the author identified possible benefits of adopting information technology knowledge management practices and why most governments in the Gulf find it hard to adopt them. Knowledge management allows for learning, transfer as well as sharing of information between government organizations and citizens and with the development of technology, the effectiveness of electronic services can easily be achieved. Also, effective adoption of information technology can improve knowledge management with the help of techniques that enhance capture, storage, retrieval as well as sharing of information. The author used systematic literature review to select 28 journals and articles published post 2019. IEEE, Google Scholar and Science Direct were used to select potential studies from which 722 journals and articles were selected. Through screening and eligibility assessment, 21 articles were retained while the back and forward search had 7 more articles which were also included in the study. Using information gathered from these articles and journals a new conceptual model was developed to help improve information technology knowledge management for governments in the Gulf region to effectively deliver e-services during Covid-19. This model was developed based on the process of KM, Theory of Planned Behavior and Unified Theory of Acceptance and Use of Technology. Based on the developed model. From UTAUT model, performance expectancy, effort expectancy as well as social influence had a great impact.

This study aims at knowing both the level of information security awareness in the use of social media among female secondary school students in Makkah Al-Mukarramah, and the procedures that students follow when exposed to hacking or other security problems. The study relied on the descriptive survey approach. The results showed a high percentage of social media use among the study sample, and the most used applications by the students are snapchat and Instagram applications successively. In fact, 48% of the study sample have awareness of information security, the majority of the students memorize the password in the devices, most of them do not change them, and they have knowledge of fake gates and social engineering. However, their knowledge of electronic hacking is weak, and students do not share passwords with anyone at a rate of 67%. At the same time, they do not update passwords. Moreover, most of the procedures followed by students when exposed to theft and hacking is to change the e-mail data and the password, and the results varied apart from that, which reflects the weak awareness of the students and the weakness of procedures related to information security. The study recommends the necessity to raise awareness and education of the importance of information security and safety, especially in light of what the world faces from data electronic attacks and hackings of electronic applications.

One of the most active areas of research in the field of affective computing and signal processing is emotion recognition. This paper proposes emotion recognition of low-resource (Sindhi) language. This work's uniqueness is that it examines the emotions of languages for which there is currently no publicly accessible dataset. The proposed effort has provided a dataset named MAVDESS (Mehran Audio-Visual Dataset Mehran Audio-Visual Database of Emotional Speech in Sindhi) for the academic community of a significant Sindhi language that is mainly spoken in Pakistan; however, no generic data for such languages is accessible in machine learning except few. Furthermore, the analysis of various emotions of Sindhi language in MAVDESS has been carried out to annotate the emotions using line features such as pitch, volume, and base, as well as toolkits such as OpenSmile, Scikit-Learn, and some important classification schemes such as LR, SVC, DT, and KNN, which will be further classified and computed to the machine via Python language for training a machine. Meanwhile, the dataset can be accessed in future via https://doi.org/10.5281/zenodo.5213073.