With the rapid increase of user generated data on digital platforms, the task of categorizing and classifying theses huge data has become difficult. Topic modeling is an unsupervised machine learning technique that can be used to get a summary from a large collection of documents. Topic modeling has been widely used in English content, yet the application of topic modeling in Arabic language is limited. Therefore, the aim of this paper is to provide a systematic review of the application of topic modeling algorithms in Arabic content. Using a well-known and trusted databases including ScienceDirect, IEEE Xplore, Springer Link, and Google Scholar. Considering the publication date from 2012 to 2022, we got 60 papers. After refining the papers based on predefined criteria, we resulted in 32 papers. Our result show that unfortunately the application of topic modeling techniques in Arabic content is limited.

This study aims to explore the status of e-learning in the public sector institutes of the Sukkur region in Pakistan. A survey was conducted to collect data from students and teachers regarding their awareness, access, and use of e-learning resources. The results showed that although there is a widespread use of the internet and mobile devices for accessing information, there is a lack of awareness and access to e-learning resources. Barriers to accessing e-learning content and a lack of familiarity with e-learning content development technologies were also identified. The study concludes that there is a need for improved e-learning facilities and curriculum in the public sector institutes of the Sukkur region in Pakistan. Recommendations are provided for developing a correctness-centered e-learning based curriculum that is tailored to the specific needs of the students in the region. It is hoped that the findings of this study will inform efforts to improve the teaching and learning process in the region and provide students with greater flexibility and access to study materials.

Blockchain technology has emerged as one of the most crucial solutions in numerous industries, including healthcare. The combination of blockchain technology and cloud computing results in improving access to high-quality telemedicine and healthcare services. In addition to developments in healthcare, the operational strategy outlined in Vision 2030 is extremely essential to the improvement of the standard of healthcare in Saudi Arabia. The purpose of this survey is to give a thorough analysis of the current state of healthcare technologies that are based on blockchain and cloud computing. We highlight some of the unanswered research questions in this rapidly expanding area and provide some context for them. Furthermore, we demonstrate how blockchain technology can completely alter the medical field and keep health records private; how medical jobs can detect the most critical, dangerous errors with blockchain industries. As it contributes to develop concerns about data manipulation and allows for a new kind of secure data storage pattern to be implemented in healthcare especially in telemedicine fields is discussed diagrammatically.

This paper proposed an Inter-Carrier-Interference (ICI) Canceling Orthogonal Frequency Division Multiplexing (OFDM) receiver for 5G mobile system to support 500 km/h linear motor high speed terrestrial transportation service. A receiver in such high-speed train sees the transmission channel which is composed of multiple Doppler-shifted propagation paths. Then, a loss of sub-carrier orthogonality due to Doppler-spread channels causes ICI. The ICI Canceler is realized by the following three steps. First, using the Demodulation Reference Symbol (DMRS) pilot signals, it analyzes three parameters such as attenuation, relative delay, and Doppler-shift of each multi-path component. Secondly, based on the sets of three parameters, Channel Transfer Function (CTF) of sender sub-carrier number 𝒏 to receiver sub-carrier number 𝒍 is generated. In case of 𝒏≠𝒍, the CTF corresponds to ICI factor. Thirdly, since ICI factor is obtained, by applying ICI reverse operation by Multi-Tap Equalizer, ICI canceling can be realized. ICI canceling performance has been simulated assuming severe channel condition such as 500 km/h, 2 path reverse Doppler Shift for QPSK, 16QAM, 64QAM and 256QAM modulations. In particular, for modulation schemes below 16QAM, we confirmed that the difference between BER in a 2 path reverse Doppler shift environment and stationary environment at a moving speed of 500 km/h was very small when the number of taps in the multi-tap equalizer was set to 31 taps or more. We also confirmed that the BER performance in high-speed mobile communications for multi-level modulation schemes above 64QAM is dramatically improved by the use of a multi-tap equalizer.

The study aimed to provide a proposed conception of technical solutions to address the educational loss in mathematics in the fifth grade of primary school, and the study adopted the descriptive approach. male and female teachers, and to achieve the study objective, the researcher built a tool for the study, a "questionnaire", in which he used the comprehensive inventory method, and the results of the study showed: identifying the learning outcomes that represent an educational loss, and identifying the learning outcomes that are considered essential in teaching and learning mathematics for the fifth grade of primary school. In the event that it is not achieved by students, it is considered an educational loss that may affect the future of students' education and learning. Because it is a basis for later experiences in mathematics in other classes, and the study also found the effectiveness of the proposed visualization of technical solutions provided to address the educational loss in mathematics for the fifth grade: (short electronic tests, YouTube channel, homework, educational platform, electronic worksheets, and communication channels).

Renewable energy is not a new terminology. One of the fastest growing renewable energies is solar energy. The implementation of solar energy provides several advantages including the reduction of some of the environmental risks of fossil fuel consumption. This research elaborated the importance of the adaption of solar energy by developing a spatial decision support system (SDSS), while the Residential Solar Energy Adoption (RSEA) is an instantiation artifact in the form of an SDSS. As a GIS web-based application, RSEA allows stakeholders (e.g., utility companies, policymakers, service providers homeowners, and researchers) to navigate through locations on a map interactively. The maps highlight locations with high and low solar energy adoption potential that enables decision-makers (e.g., policymakers, solar firms, utility companies, and nonprofit organizations) to make decisions. A combined qualitative and quantitative methodological approach was used to evaluate the application's usability and user experience, and results affirmed the ability of the factors of utility, usefulness, and a positive user experience of the residential solar energy adoption of spatial decision support system (RSEA-SDSS). RSEA-SDSS in improving the decision-making process for potential various stakeholders, in utility, solar installations, policy making, and non-profit renewable energy domains.

The healthcare industry continues to adopt and integrate smart technology in its operations, from medical devices to managing operations. However, the adoption curve has not been smooth, and the historical record of technology adoption in the Kingdom of Saudi Arabia reveals the existence of both known and unknown issues. This review paper is aimed to explain the influences and barriers present in the Saudi healthcare sector affecting IoT technology adoption. A comprehensive discussion of the literature illustrated that Vision 2030, the privatisation trend, transformation in disease patterns and ageing, issues in management and increasing public awareness are the key drivers that may influence the need for the medical Internet of Things (mIoT) in Saudi healthcare. However, based on the past trend, the introduction and adoption of mIoT will likely experience issues such as noncompliance from doctors and nurses due to negative beliefs, lack of knowledge and inadequate perception of effort requirements. Thus, in-depth research of the factors associated with mIoT technology adoption is suggested for a smooth transition.

Nowadays, the power of data analytics in general and visual data analytics, in particular, have been proven to be an important area that would help development in any domain. Many well-known IT services best practices have touched on the importance of data analytics and visualization and what it can offer to information technology service management. Yet, little research exists that summarises what is already there and what can be done to utilise further the power of data analytics and visualization in this domain. This paper is divided into two main parts. First, a number of IT service management tools have been summarised with a focus on the data analytics and visualization features in each of them. Second, interviews with five senior IT managers have been conducted to further understand the usage of these features in their organisations and the barriers to fully benefit from them. It was found that the main barriers include a lack of good understanding of some visualization design principles, poor data quality, and limited application of the technology and shortage in data analytics and visualization expertise.

This research is conducted to minimize the potential security risks of conducting online exams to an acceptable level as vulnerabilities and threats to this type of exam are presented. This paper provides a general structure for the risk management process and some recommendations for increasing the level of security.

In this article the problem of computing floating-point reciprocal cube root functions is considered. Our new algorithms for this task decrease the number of arithmetic operations used for computing $1/{\sqrt[3]{x}}$. A new approach for selection of magic constants is presented in order to minimize the computation time for reciprocal cube roots of arguments with movable decimal point. The underlying theory enables partitioning of the base argument range x∈[1,8) into 3 segments, what in turn increases accuracy of initial function approximation and decreases the number of iterations to one. Three best algorithms were implemented and carefully tested on 32-bit microcontroller with ARM core. Their custom C implementations were favourable compared with the algorithm based on cbrtf(x) function taken from C ＜math.h＞ library on three different hardware platforms. As a result, the new fast approximation algorithm for the function $1/{\sqrt[3]{x}}$ was determined that outperforms all other algorithms in terms of computation time and cycle count.

The rapid increase in the use of new technology known as 'blockchain technologies' has addressed many challenges in different areas and provided benefits to users, in this paper we discuss the field of supply chains, improve confidence and transparency between participants and stakeholders significantly also in this paper we Compare between different blockchain frameworks focusing on most popular frameworks. Moreover, we proposed a model in the supply chain using a blockchain framework, the proposed supply chain model included many different resources that help to exchange information over the network. The proposed model also includes smart contracts that maintain all rules for transactions. using blockchain technology information such as transaction details, time and money are recorded and stored within the system from the beginning of the transaction entry.

Now a days, large volumes of data is accumulating in every field due to increase in capacity of storage devices. These large volumes of data can be applied with data mining for finding useful patterns which can be used for business growth, improving services, improving health conditions etc. Data from different sources can be combined before applying data mining. The data thus gathered can be misused for identity theft, fake credit/debit card transactions, etc. To overcome this, data mining techniques which provide privacy are required. There are several privacy preserving data mining techniques available in literature like randomization, perturbation, anonymization etc. This paper proposes an Enhanced Hybrid Privacy Preserving Data Mining(EHPPDM) technique. The proposed technique provides more privacy of data than existing techniques while providing better classification accuracy. The experimental results show that classification accuracies have increased using EHPPDM technique.

Successful implementations of DevOps practices significantly improvise software efficiency, collaboration and security. Most of the organizations are adopting DevOps for faster and quality software delivery. DevOps brings development and operation teams together to overcome all kind of communication gaps responsible for software failures. It relies on different sets of alternative tools to automate the tasks of continuous integration, testing, delivery, deployment and monitoring. Although DevOps is followed for being very reliable and responsible environment for quality software delivery yet it lacks many quantifiable aspects to prove it on the top of other traditional and agile development methods. This research evaluates quantitative performance of DevOps and traditional/ agile development methods based on software metrics. This research includes three sample projects or code repositories to quantify the results and for DevOps integrated selective tool chain; current research considers our earlier proposed and implemented DevOps hybrid model of integrated automation tools. For result discussion and validation, tabular and graphical comparisons have also been included to retrieve best performer model. This comparative and evaluative research will be of much advantage to our young researchers/ students to get well versed with automotive environment of DevOps, latest emerging buzzword of development industries.

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

Many organizations are looking for digital innovation to apply in business process management and this information revolution leaves its effect on the businesses and anticipate competitors. In this article, investigates the strength of the relationship between business process management (BMP) and Digital Innovations (DI) since it has been underdeveloped. The results and findings are extracted from international survey with explanations of expert panel to generalized a positive and moderate link of multiple factors that are affecting the strategic decision-making in business process management. It is extended to the Technology Organization Environment (TOE) framework and contour organizations along their Digital Process Innovation (DPI).

Numerous genuine issues, for example, financial exchange expectation, climate determining and so forth has inalienable arbitrariness related with them. Receiving a probabilistic system for forecast can oblige this dubious connection among past and future. Commonly the interest is in the contingent likelihood thickness of the arbitrary variable included. One methodology for expectation is with time arrangement and auto relapse models. In this work, liner expectation technique and approach for computation of forecast coefficient are given and likelihood of blunder for various assessors is determined. The current methods all need in some regard assessing a boundary of some accepted arrangement. In this way, an elective methodology is proposed. The elective methodology is to gauge the restrictive thickness of the irregular variable included. The methodology proposed in this theory includes assessing the (discretized) restrictive thickness utilizing a Markovian definition when two arbitrary factors are genuinely needy, knowing the estimation of one of them allows us to improve gauge of the estimation of the other one. The restrictive thickness is assessed as the proportion of the two dimensional joint thickness to the one-dimensional thickness of irregular variable at whatever point the later is positive. Markov models are utilized in the issues of settling on an arrangement of choices and issue that have an innate transience that comprises of an interaction that unfurls on schedule on schedule. In the nonstop time Markov chain models the time stretches between two successive changes may likewise be a ceaseless irregular variable. The Markovian methodology is especially basic and quick for practically all classes of classes of issues requiring the assessment of contingent densities.

A detailed survey, applications and challenges of video encoding-decoding systems is discussed in this paper. A novel architecture has also been set aside for future work in the same direction. The literature reviews span the years 1960 to the present, highlighting the benchmark methods proposed by notable academics in the field of video compression. The timeline used to illustrate the review is divided into three sections. Classical methods, conventional heuristic methods, and current deep learning algorithms are all used for video compression in these categories. The milestone contributions are discussed for each category. The methods are summarized in various tables, along with their benefits and drawbacks. The summary also includes some comments regarding specific approaches. Existing studies' shortcomings are thoroughly described, allowing potential researchers to plot a course for future research. Finally, a closing note is made, as well as future work in the same direction.

Network intrusion refers to any unauthorized penetration or activity on a computer network. This upsets the confidentiality, integrity, and availability of the network system. One of the major threats to any system's availability is a Denial-of-Service (DoS) attack, which is intended to deny a legitimate user access to resources. Therefore, due to the complexity of DoS attacks, it is increasingly important to abstract and describe these attacks in a way that will be effectively detected. The automaton theory is used in this paper to implement a SYN Flood detection system based on Time-Dependent Finite Automata (TDFA).

In the process of remarkable progress in the medical and technical field and activating the role of technology in health care services and applications, and since the safety of medical data and its protection from security violations plays a major role in assessing the security of health facilities and the safety of medical servers Thus, it is necessary to know the cyber vulnerabilities in health information systems and other related services to prevent and address them in addition to obtaining the best solutions and practices to reach a high level of cybersecurity against attackers, especially due to the digital transformation of health care systems and the rest of the dealings. This research is about what cyberattacks are and the purpose of them, in addition to the methods of penetration. Then challenges, solutions and some of the security issues will be discussed in general, and a special highlight will be given to obtaining a safe infrastructure to enjoy safe systems in return.

This article presents an examination of the major cognitive-semantic theories in linguistics (Langacker, Lakoff, Fillmore, Croft). The CST's foundations are discussed concerning the educational policy changes, which are necessary to improve the linguistic disciplines in the changing context of higher education, as well as the empowerment and development of the industry. It is relevant in the light of the linguistic specialists' quality training and the development of effective methods of language learning. Consideration of the theories content, tools, and methods of language teaching, which are an important component of quality teaching and the formation of a set of knowledge and skills of students of linguistic specialties, remains crucial. This study aims to establish the main theoretical positions and directions of cognitive-semantic theory in linguistics, determine the usefulness of teaching the basics of cognitive linguistics, the feasibility of using methods of cognitive-semantic nature in the learning process. During the research, the methods of linguistic description and observation, analysis, and synthesis were applied. The result of the study is to establish the need to study basic linguistic theories, as well as general theoretical precepts of cognitive linguistics, which remains one of the effective directions in the postmodern mainstream. It also clarifies the place of the main cognitive-semantic theories in the teaching linguistics' practice of the XXI century.

Ethical hackers are using different tools and techniques to encounter malicious cyber-attacks generated by bad hackers. During the software development process, development teams typically bypass or ignore the security parameters of the software. Whereas, with the advent of online web-based software, security is an essential part of the software development process for implementing secure software. Security features cannot be added as additional at the end of the software deployment process, but they need to be paid attention throughout the SDLC. In that view, this paper presents a new, Ethical Hacking - Software Development Life Cycle (EH-SDLC) introducing ethical hacking processes and phases to be followed during the SDLC. Adopting these techniques in SDLC ensures that consumers find the end-product safe, secure and stable. Having a team of penetration testers as part of the SDLC process will help you avoid incurring unnecessary costs that come up after the data breach. This research work aims to discuss different operating systems and tools in order to facilitate the secure execution of the penetration tests during SDLC. Thus, it helps to improve the confidentiality, integrity, and availability of the software products.

Aim of this study to examine the mediating role of network service between perceived quality, retailer service and customer satisfaction during COVID-19. Primary data gathered through adopted questionnaire from previous studies and 200 university students were asked to fill online questionnaire during COVID-19 situation in country. Structural Equation Modelling technique applied in order to test the proposed hypothesis generated from existing literature review. Findings revealed full mediation effect of network service for both perceived quality and retailer service on customer satisfaction during COVID-19. New insights of this study are key role of network services is identified and university students' satisfaction is measured for online classes in developing country, Pakistan. In future serial mediation is suggested for validity of existing results in developed and developing countries.

Blockchain system brought innovation in the area of accounting, credit monitoring and trade secrets. Consensus algorithm that considered the central component of blockchain, significantly influences performance and security of blockchain system. In this paper we presented four consensus protocols specifically as Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS) and Practical Byzantine Fault-Tolerance (PBFT), we also reviewed different security threats that affect the performance of Consensus Protocols and precisely enlist their counter measures. Further we evaluated the performance of these Consensus Protocols in tabular form based on different parameters. At the end we discussed a comprehensive comparison of Consensus protocols in terms of Throughput, Latency and Scalability. We presume that our results can be beneficial to blockchain system and token economists, practitioners and researchers.

In this paper, we consider the maximum scatter traveling salesman problem (MSTSP), a travelling salesman problem (TSP) variant. The problem aims to maximize the minimum length edge in a salesman's tour that travels each city only once in a network. It is a very complicated NP-hard problem, and hence, exact solutions can be found for small sized problems only. For large-sized problems, heuristic algorithms must be applied, and genetic algorithms (GAs) are found to be very successfully to deal with such problems. So, this paper develops a hybrid GA (HGA) for solving the problem. Our proposed HGA uses sequential sampling algorithm along with 2-opt search for initial population generation, sequential constructive crossover, adaptive mutation, randomly selected one of three local search approaches, and the partially mapped crossover along with swap mutation for perturbation procedure to find better quality solution to the MSTSP. Finally, the suggested HGA is compared with a state-of-art algorithm by solving some TSPLIB symmetric instances of many sizes. Our computational experience reveals that the suggested HGA is better. Further, we provide solutions to some asymmetric TSPLIB instances of many sizes.

Today, social media has become a vital tool. The world communicates and reaches the news and each other's opinions through social media accounts. Recently, considerable research has been done on analyzing social media due to its rich data content. At the same time, since the beginning of the COVID-19 pandemic, which has afflicted so many around the world, the search for a vaccine has been intense. There have been many studies analyzing people's feelings during a crisis. This study aims to understand people's opinions about available Coronavirus vaccines through a learning model that was developed for this purpose. The dataset was collected using Twitter's streaming Application Programming Interface (API) , then combined with another dataset that had already been collected. The final dataset was cleaned, then analyzed using Python. Polarity and subjectivity functions were used to obtain the results. The results showed that most people had positive opinions toward vaccines in general and toward the Pfizer one. Our study should help governments and decision-makers dispel people's fears and discover new symptoms linked to those listed by the World Health Organization.