In order to overcome the power fluctuation issues in photovoltaic (PV) smart grid-connected systems and the inverter nonlinearity model problem, an adaptive backstepping command-filter and a double second order generalized Integrators (DSOGI) controller are designed in order to tune the AC current and the DC-link voltage from the DC side. Firstly, we propose to present the filter mathematical model throughout the PV system, at that juncture the backstepping control law is applied in order to control it, Moreover the command filter is bounded to the controller aiming to exclude the backstepping controller differential increase. Additionally, The adaptive law uses Lyapunov stability criterion. Its task is to estimate the uncertain parameters in the smart grid-connected inverter. A DSOGI is added to stabilize the grid currents and eliminate undesirable harmonics meanwhile feeding maximum power generated from PV to the point of common coupling (PCC). Then, guaranteeing a dynamic effective response even under very unbalanced loads and/or intermittent climate changes. Finally, the simulation results will be established using MATLAB/SIMULINK proving that the presented approach can control surely the smart grid-connected system.

This paper proposes using micro-learning at Saudi universities. It commences with an account of the concept of micro-learning and the difference between micro-learning and electronic learning. Then it touches on the significance, principles, and examples of micro-learning, followed by some micro-learning applications and pitfalls. The paper closes with a proposal for using this learning mode at Saudi universities.

Sensor Nodes play a major role to monitor and sense the variations in physical space in various real-time application scenarios. These nodes are powered by limited battery resources and replacing those resource is highly tedious task along with this it increases implementation cost. Thus, maintaining a good network lifespan is amongst the utmost important challenge in this field of WSN. Currently, energy efficient routing techniques are considered as promising solution to prolong the network lifespan where multi-hop communications are performed by identifying the most energy efficient path. However, the existing scheme suffer from performance related issues. To solve the issues of existing techniques, a novel hybrid technique by merging particle swarm optimization and game theory model is presented. The PSO helps to obtain the efficient number of cluster and Cluster Head selection whereas game theory aids in finding the best optimized path from source to destination by utilizing a path selection probability approach. This probability is obtained by using conditional probability to compute payoff for agents. When compared to current strategies, the experimental study demonstrates that the proposed GTPSO strategy outperforms them.

The fifth generation (5G) mobile communication technology is designed to meet all communication needs. Heterogeneous networks (HetNets) are a new emerging network structure. HetNets have greater potential for radio resource reuse and better service quality than homogeneous networks since they can evolve small cells into macrocells. Effective resource allocation techniques reduce inter-user interference while optimizing the utilization of limited spectrum resources in HetNets. This article discusses resource allocation in 5G HetNets. This paper explains HetNets and how they work. Typical cell types in HetNets are summarized. Also, HetNets models are explained in the third section. The fourth component addresses radio resource control and mobility management. Moreover, future study in this subject may benefit from this article's significant insights on how HetNets function.

The main purpose of the study is to determine the key aspects of the mechanisms of state management of the exchange of information about cyberattacks, cyber incidents, and information security incidents. The methodology includes a set of theoretical methods. Modern government, on the one hand, must take into account the emergence of such a new weapon as cyber, which can break various information systems, can be used in hybrid wars, influence political events, pose a threat to the national security of any state. As a result of the study, key elements of the mechanisms of state management of the exchange of information about cyberattacks, cyber incidents, and information security incidents were identified.

The spread of Omicron, a mutated version of COVID-19 across several countries is leading to the discussion of lockdown once again for curbing the spread of the new virus. In this context, this research is showing the impact of lockdown for the successful control of the COVID-19 pandemic in Saudi Arabia. The outbreak of the COVID-19 pandemic around the globe has affected Saudi Arabia with around 2,37,803 confirmed cases within the initial 4 months of transmission. Saudi Arabia has announced a 21-day lockdown from March 23, 2020, to reduce the transmission of the COVID-19 pandemic. Machine Learning-based, Multinomial logistic regression was applied to understand the relationship between daily COVID-19 confirmed cases and lockdown in the 17 most-affected cities of KSA. We used secondary published data from the Ministry of Health, KSA daily dataset of COVID-19 confirmed case counts. These 17 cities were categorized into 4 classes based on lockdown dates. A total of three scenarios such as night lockdown, full lockdown, and no lockdown have been analyzed with the total number of confirmed cases with 4 classes. 15 out of 17 cities have shown a strong correlation with a confidence interval of 95%. These findings provide evidence that the COVID-19 pandemic may be partially suppressed with lockdown measures.

The current study investigates how student-teacher interaction can be developed through task-based teaching in undergraduate students' Saudi teaching and learning context. An experiment was conducted for five weeks on 85 male undergraduate students at a Saudi public university based in Jeddah, Saudi Arabia. The study investigated different types of student-teacher interaction through task-based teaching (speaking activities). The results revealed that the experimental group (43 students) evinced much more enthusiasm, willingness, engagement and readiness in their inclass participation than their peers in the control group (42 students). The student-teacher interaction also helped students to be more responsive to general and specific topics in speaking activities. The study recommends that decision-makers in education make student-teacher interaction part of the student's monthly assessment. It also recommends that more efforts be made to foster the awareness of students, teachers, and parents awareness of the academic and non-academic importance of interaction. One final recommendation of the research is that student-teacher interaction should be more emphasized and integrated into the school curriculum and adopted as a critical teaching strategy.

Pneumonia is a form of acute respiratory infection that affects the lungs. According to the World Health Organization, pneumonia is the leading cause of death for children worldwide. As a result, pneumonia was the top killer of children under the age of five years old in 2015, which is 15% of all deaths worldwide. In this paper, we used CNN model architectures to compare between the result of proposed a CNN method with VGG based model architecture. The model's performance in detecting pneumonia shows that the proposed model based on VGG can classify normal and abnormal X-rays effectively and more accurately than the proposed model used in this paper.

Students can successfully connect with one another thanks to the introduction of Web 2.0 and the tools and technology linked with it. The fact that rising digital tools are systematically influencing the education system is not a secret. The purpose of the research article efficiently evaluates the influence of incorporation of media in the activities of the scientific library of the higher education institution. The research Methodology is the Concepts, techniques, and procedures to effectively inculcate primary and secondary data to conduct the research effortlessly. It's worth noting that in this case, quantitative primary research was provided in the form of a survey. The researchers have proposed a survey in order to successfully instil a comprehensive view on the "incorporation of media in the operations of the scientific library of higher education institutions." As a result, fifty-one higher education institution principals were asked to attend this session. This is necessary to understand that they are both well-educated and cognizant of the impact of technology innovation on schooling. As a result, the researchers were able to gain a comprehensive view of this situation thanks to this survey. The results effectively showed that most of the participants believe that social media plays a vital role in shaping up higher education and at the same time they believe that the libraries of famous educational institutions must adapt as per the new educational trend so that teachers and students both can tap into its benefit.The practical significance of the result is manoeuvred by the efficient survey analysis and at the same time, peer-reviewed journals have been employed to put forward authentic information. Therefore, efficient insight regarding this topic has been gathered by the researchers.

The main purpose of the study is to analyze the main aspects of state regulation of the banking business in the context of social and digital transformation. One of the key elements of the functioning of the economy of any country are banks that ensure the redistribution of financial resources and stimulate economic growth. However, the banking sector, like other activities, is dynamic and depends on the pace of development and forms of technological progress that affect the forms and types of information and digital technologies, as well as the globalization and remoteness of banking services. Accordingly, the need for effective implementation of the latest technologies becomes relevant, which will not only help increase consumer satisfaction with the banking product, but also ensure the development of the country's financial sector. As a result of the study, trends in the development of state regulation of the banking sector in the digital economy were identified.

This paper shows the hiding process of unlimited secret text size in an image using three methods: the first method is the traditional method in steganography that based on the concealing the binary value of the text using the least significant bits method, the second method is a new method to hide the data in an image based on Exclusive OR process and the third one is a new method for hiding the binary data of the text into an image (that may be grayscale or RGB images) using Exclusive and Huffman Coding. The new methods shows the hiding process of unlimited text size (data) in an image. Peak Signal to Noise Ratio (PSNR) is applied in the research to simulate the results.

Applied Behaviour Analysis (ABA) is the applied science where strategies are derived from the principles of behaviour and are applied to improve meaningful social behaviours [3]. This study investigates the possible inclusive value of the use ABA in schools in Saudi Arabia. Interviews were conducted with two ABA therapists and a support teacher in order to address this possibility. From the research findings, it emerged how ABA is one of the cognitive-behavioural intervention models and therefore, can be generalised to other disorders or diagnoses and the applied behavioural analysis does have an inclusive value as it structures individualized activities for the increase of both personal and social skills and these activities allow the subject to work on his or her skills, which are, however, absolutely preparatory to the inclusion of the subject in the classroom context.

With the advancement in the Internet of things Technology (IoT) cloud computing, billions of physical devices have been interconnected for sharing and collecting data in different applications. Despite many advancements, some latency - specific application in the real world is not feasible due to existing constraints of IoT devices and distance between cloud and IoT devices. In order to address issues of latency sensitive applications, fog computing has been developed that involves the availability of computing and storage resources at the edge of the network near the IoT devices. However, fog computing suffers from many limitations such as heterogeneity, storage capabilities, processing capability, memory limitations etc. Therefore, it requires an adequate task scheduling method for utilizing computing resources optimally at the fog layer. This work presents a comprehensive review of different task scheduling methods in fog computing. It analyses different task scheduling methods developed for a fog computing environment in multiple dimensions and compares them to highlight the advantages and disadvantages of methods. Finally, it presents promising research directions for fellow researchers in the fog computing environment.

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.

This research article presents the work that is related to the application of different machine learning based similarity techniques on religious text for identifying similarities and differences among its various translations. The dataset includes 10 different English translations of verses (Arabic: Ayah) of two Surahs (chapters) namely, Al-Humazah and An-Nasr. The quantitative similarity values for different translations for the same verse were calculated by using the cosine similarity and semantic similarity. The corpus went through two series of experiments: before pre-processing and after pre-processing. In order to determine the performance of machine learning based similarities, human annotated similarities between translations of two Surahs (chapters) namely Al-Humazah and An-Nasr were recorded to construct the ground truth. The average difference between the human annotated similarity and the cosine similarity for Surah (chapter) Al-Humazah was found to be 1.38 per verse (ayah) per pair of translation. After pre-processing, the average difference increased to 2.24. Moreover, the average difference between human annotated similarity and semantic similarity for Surah (chapter) Al-Humazah was found to be 0.09 per verse (Ayah) per pair of translation. After pre-processing, it increased to 0.78. For the Surah (chapter) An-Nasr, before preprocessing, the average difference between human annotated similarity and cosine similarity was found to be 1.93 per verse (Ayah), per pair of translation. And. After pre-processing, the average difference further increased to 2.47. The average difference between the human annotated similarity and the semantic similarity for Surah An-Nasr before preprocessing was found to be 0.93 and after pre-processing, it was reduced to 0.87 per verse (ayah) per pair of translation. The results showed that as expected, the semantic similarity was proven to be better measurement indicator for calculation of the word meaning.

Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is the main challenge that organizations face when building this type of predictive models. Federated learning (FL) is a promising approach that overcomes data security and privacy challenges, as it enables organizations to build a machine learning model based on distributed datasets without sharing raw data or violating data privacy. In this paper, a federated long short- term memory (LSTM) model is proposed and compared with a traditional LSTM model. Proposed model is developed using TensorFlow Federated (TFF) and the Keras API to predict the number of crimes. The proposed model is applied on the Boston crime dataset. The proposed model's parameters are fine tuned to obtain minimum loss and maximum accuracy. The proposed federated LSTM model is compared with the traditional LSTM model and found that the federated LSTM model achieved lower loss, better accuracy, and higher training time than the traditional LSTM model.

A Mobile Ad-hoc Network (MANET) is a collection of moving nodes that communicate and collaborate without relying on a pre-existing infrastructure. In this type of network, nodes can freely move in any direction. Routing in this sort of network has always been problematic because of the mobility of nodes. Most existing protocols use simple routing algorithms and criteria, while another important criterion is path selection. The existing protocols should be optimized to resolve these deficiencies. 'Particle Swarm Optimization (PSO)' is an influenced method as it resembles the social behavior of a flock of birds. Genetic algorithms (GA) are search algorithms that use natural selection and genetic principles. This paper applies these optimization models to the OLSR routing protocol and compares their performances across different metrics and varying node sizes. The experimental analysis shows that the Genetic Algorithm is better compared to PSO. The comparison was carried out with the help of the simulation tool NS2, NAM (Network Animator), and xgraph, which was used to create the graphs from the trace files.

Wireless access technologies are emerging to enable high data rates for mobile users and novel applications that encompass both human and machine-type interactions. An essential approach to meet the rising demands on network capacity and offer high coverage for wireless users on upcoming fifth generation (5G) networks is heterogeneous networks (HetNets), which are generated by combining the installation of macro cells with a large number of densely distributed small cells Deployment in 5G architecture has several issues because to the rising complexity of network topology in 5G HetNets with many distinct base station types. Aside from the numerous benefits that dense small cell deployment delivers, it also introduces key mobility management issues such as frequent handover (HO), failures, delays and pingpong HO. This article investigates 5G HetNet mobility management in terms of radio resource control. This article also discusses the key challenges for 5G mobility management.

Cloud Computing offers flexible, on demand, ubiquitous resources for cloud users. Cloud users are provided computing resources in a virtualized environment. In order to meet the growing demands for computing resources, data centres contain a large number of physical machines accommodating multiple virtual machines. However, cloud data centres cannot utilize their computing resources to their total capacity. Several policies have been proposed for improving energy proficiency and computing resource utilization in cloud data centres. Virtual machine placement is an effective method involving efficient mapping of virtual machines to physical machines. However, the availability of many physical machines accommodating multiple virtual machines in a data centre has made the virtual machine placement problem a non deterministic polynomial time hard (NP hard) problem. Metaheuristic algorithms have been widely used to solve the NP hard problems of multiple and conflicting objectives, such as the virtual machine placement problem. In this context, we presented essential concepts regarding virtual machine placement and objective functions for optimizing different parameters. This paper provides a taxonomy of metaheuristic algorithms for the virtual machine placement method. It is followed by a review of prominent research of virtual machine placement methods using meta heuristic algorithms and comparing them. Finally, this paper provides a conclusion and future research directions in virtual machine placement of cloud computing.

The recent outbreak of COVID-19 pandemic cases around the globe has affected Saudi Arabia with around 15, 00,000 confirmed cases within the initial 4 months of transmission. The present investigation analyzed the relationship between daily COVID-19 confirmed cases and meteorological parameters in seventeen cities of KSA. We used secondary published data from the Ministry of Health, KSA daily dataset of COVID-19 confirmed case counts. The meteorological parameters used in the present investigation are temperature, humidity, dew point, and wind speed. Pearson correlation and Spearman rank correlation tests were utilized for data analysis. The incubation period of COVID-19 varies from 1 day to 14 days as per available information. Therefore, an attempt has been made to analyze the effects of meteorological factors with bins of 1, 3, 7, and 14 days. The results suggested that the highest number of correlations (15 cities) was observed for temperature (maximum, minimum, and average) and humidity (12 cities) (minimum and average). The dew point showed relationships for 7 cities and wind showed moderate correlations only for 2 cities. The study results might be useful for authorities and stakeholders in taking specific measures to combat the Covid-19 pandemic.

The study aims to discover the scope of pre-service special education teachers' knowledge and perceptions of using computer technology in teaching students with disabilities from a pre-service teacher (PST) perspective in light of the gender and sub-major variables. The sample consisted of 84 MEd students/pre-service teachers at the Department of Special Education, Faculty of Education, Umm Al-Qura University. The descriptive analytical approach is used due to its relevance to the study. A survey consisting of the participant's basic information section and 12 statements was sent to a set of pre-service teachers. Findings showed that pre-service special education teachers had an overall high knowledge of using computer technology (M=3.93). Findings also indicated that there were no gender- or major-related statistically significant differences (α = 0.05), in pre-service special education students' knowledge and perceptions of using computer technology.

The new commercial maritime law in the Kingdom came comprehensive and detailed for all topics related to commercial maritime navigation, thus responding to most of the problems that arise in the field, specifically regarding the ship as the focus of the rules of maritime law. This system defines the ship in law, regulates its civil status, determines how to name it, determine its domicile, and the conditions for acquiring Saudi nationality. It also contained a regulation of the rights granted to ships by ownership, as well as their lease and mortgage, the mechanism of attachment to them to settle debts and the rights in kind dependent on them and controlling the rights of third parties on ships and the procedures for forcibly selling them from precautionary seizure and executive seizure and then forced sale in public auction. Until this research was an effort to present a clear picture about the legal system of the ship in the new Saudi commercial maritime system and confirming the extent of the success of the Saudi legislator with the ship system in highlighting the legal frameworks for this facility prepared for maritime navigation.

The role that English currently plays is undeniable. It has become the most common means of communication among native speakers of several languages around the world. English penetrates into all areas of people's daily lives. In the field of Information Technology (IT), English has taken a dominant position, as many of the terms used on a daily basis are written in English. The purpose of the article is to analyze the linguistic features of anglicisms in the field of Information Technology. Methods. The research is based on systematic and comparative analysis, dialectical method, as well as methods of classification and generalization. Results. This study presents the results of compiling a multilingual glossary with anglicisms used in the GitHub and 3D Slicer fields. Despite the limited number of terms included in the glossary, the article provides a lot of evidence for the influence of the English language in the areas of Information Technology, GitHub and 3D Slicer under consideration. The types of anglicisms used in the 3D Slicer area seem to be more diverse than in the GitHub area. This study found that five European languages use language strategies to solve any communication problem. The multilingual glossary showed that in some cases there is a coexistence between Anglicism and the native term. In other cases, the English term is the only one used in different languages. There are cases when only the native language is used. Conclusions. This study is a useful tool that helps to improve the efficiency of communication between engineers and technicians who speak different native languages. The ultimate goal of this research will be to create a multilingual glossary that is still under development and is likely to cover other IT areas such as Python and VTK.

The classification of hyperspectral imagery (HSI) is essential in the surface of earth observation. Due to the continuous large number of bands, HSI data provide rich information about the object of study; however, it suffers from the curse of dimensionality. Dimensionality reduction is an essential aspect of Machine learning classification. The algorithms based on feature extraction can overcome the data dimensionality issue, thereby allowing the classifiers to utilize comprehensive models to reduce computational costs. This paper assesses and compares two HSI classification techniques. The first is based on the Joint Spatial-Spectral Stacked Autoencoder (JSSSA) method, the second is based on a shallow Artificial Neural Network (SNN), and the third is used the SVM model. The performance of the JSSSA technique is better than the SNN classification technique based on the overall accuracy and Kappa coefficient values. We observed that the JSSSA based method surpasses the SNN technique with an overall accuracy of 96.13% and Kappa coefficient value of 0.95. SNN also achieved a good accuracy of 92.40% and a Kappa coefficient value of 0.90, and SVM achieved an accuracy of 82.87%. The current study suggests that both JSSSA and SNN based techniques prove to be efficient methods for hyperspectral classification of snow features. This work classified the labeled/ground-truth datasets of snow in multiple classes. The labeled/ground-truth data can be valuable for applying deep neural networks such as CNN, hybrid CNN, RNN for glaciology, and snow-related hazard applications.

The occurrence of Type 2 Diabetes Mellitus (T2DM) is hoarding globally. All kinds of Diabetes Mellitus is controlled to disrupt over 415 million grownups worldwide. It was the seventh prime cause of demise widespread with a measured 1.6 million deaths right prompted by diabetes during 2016. Over 90% of diabetes cases are T2DM, with the utmost persons having at smallest one other chronic condition in UK. In valuation of contemporary applications of Big Data (BD) to Diabetes Medicare by sighted its upcoming abilities, it is compulsory to transmit out a bottomless revision over foremost theoretical literatures. The long-term growth in medicine and, in explicit, in the field of "Diabetology", is powerfully encroached to a sequence of differences and inventions. The medical and healthcare data from varied bases like analysis and treatment tactics which assistances healthcare workers to guess the actual perceptions about the development of Diabetes Medicare measures accessible by them. Apache Spark extracts "Resilient Distributed Dataset (RDD)", a vital data structure distributed finished a cluster on machines. Machine Learning (ML) deals a note-worthy method for building elegant and automatic algorithms. ML library involving of communal ML algorithms like Support Vector Classification and Random Forest are investigated in this projected work by using Jupiter Notebook - Python code, where significant quantity of result (Accuracy) is carried out by the models.

The modern development of higher education in Ukraine is the result of two main factors. One of them - the factor of social progress - reflects the transformations inherent in modern Ukrainian society. These include, first of all, the processes of democratization and the development of civic responsibility. European the choice of Ukraine, the integration of our state into the European space determine accordingly, the second factor influencing the development of domestic higher education - the trends that guide the progress of the European Higher Education Area (European Higher Education Area (hereinafter - EHEA) and the European Research Area Research Area, hereinafter - ERA). The strategy of information support of the educational process (approved by the European Commission in 2010) recognizes the leading role of higher education as a driver of social progress, accordingly states the priority - the development of free economic education and identifies indicators of such progress - the achievement and international attractiveness of European free economic education. The information support of modernization challenges in higher education are aimed at the educational process, the leadership position of students, in particular through promotion and implementation of leading achievements and best practices in the context of globalization.

The main purpose of the article is to study the features of the impact of social and digital changes on the system of government regulation of banking acitivity. The digital economy sets the vector along which socio-economic systems of micro-, meso-, macro-levels will develop in the long term, which necessitates research and a comprehensive analysis of digital transformation processes. Once a priority for individual innovative companies, today digital transformation has become a mass phenomenon, and the corresponding projects are vital for the success of not only individual companies, but also regions and countries. At the same time, this transformation itself is closely connected with the trend of servicing socio-economic systems and is largely implemented on its basis. Moreover, this relationship and the mechanism for its implementation remain insufficiently studied, which necessitates the development of tools for its identification, assessment and management. As a result of the analysis, the key aspects of the impact of social and digital changes on the system of state regulation of banking activities were identified.

Femtocells are being incorporated into heterogeneous networks in order to increase the network capacity. However, intensive deployment of femtocells results in undesired interference, which lowers the system's performance. Controlling the femtocell transmission power is one of of the aspects that can be addressed in order to mitigate the negative effects of the interference. It may also be utilized to facilitate the auto-configuration of the network's conductance, if necessary. This paper proposes the use of an auto-configuration technique for transmission power. The suggested technique is based on the transmission power of macrocells and the coverage provided by femtocells. The simulation findings show that the network's capacity has increased, and the amount of interference has decreased.

This study investigates the impact of flipped classroom strategy in developing students' achievement and acquisition of life skills. The study employed a quasi-experimental design where students were divided into two groups: an experimental (N=22) and a control (N=22). The randomly selected and assigned sample consisted of sixth-year elementary school students studying English as a basic course. The findings revealed statistically significant differences between the two group's means in both achievement and life skills tests in favor of the experimental group. Students of the experimental group who studied using the flipped classroom strategy outperformed the control group who studied in the standard way in achieving the English language and in the life situations test, where the effect size of the use of the strategy was large in both dependent variables. The study is concluded with some recommendations to facilitate the use of flipped classroom strategy for EFL teachers. This can be achieved by training teachers on using the strategy and providing technological resources at schools to implement the strategy efficiently.

Eco-driving of vehicles today presents an advantage that aims to reduce energy consumption and limit CO2 emissions. The application for this option is possible to older vehicles. In this paper, we propose an efficient implementation for IoT (Internet of Things) system for controlling vehicle components that affect the quality of driving (acceleration, braking, clutch, gear change) via Smartphone using Wi-Fi and BLE as communication protocol. The user can see in real-time data from sensors that control driver action on vehicle driving systems such as acceleration, braking, and vehicle shifting through a web interface. Thanks to this communication, the user can control his driving quality and, hence, eco-driving can be achieved

Commenced in 1954 by IBM, machine translation has expanded immensely, particularly in this period. Machine translation can be broken into seven main steps namely- token generation, analyzing morphology, lexeme, tagging Part of Speech, chunking, parsing, and disambiguation in words. Morphological analysis plays a major role when translating Indian languages to develop accurate parts of speech taggers and word sense. The paper presents various machine translation methods used by different researchers for Indian languages along with their performance and drawbacks. Further, the paper concentrates on parts of speech (POS) tagging in Marathi dialect using various methods such as rule-based tagging, unigram, bigram, and more. After careful study, it is concluded that for machine translation, parts of speech tagging is a major step. Also, for the Marathi language, the Hidden Markov Model gives the best results for parts of speech tagging with an accuracy of 93% which can be further improved according to the dataset.

The purpose of this study was to identify the obstacles facing gifted students with learning disabilities (GSLDs) from the point of view of their teachers in the Makkah region and to find suggested solutions to overcome these obstacles. The study covered Makkah, Jeddah and Taif and used semi-structured interviews which included open-ended questions. The study findings indicated that there were several educational obstacles including the absence of adapted courses or specialized teachers for GSLDs category and the insufficient time for the students to express their talents. According to the findings, there were also societal obstacles including the society's failure to expect the presence of talents along with disabilities, or its denial or rejection of their talents in addition to ridiculing them. The findings also confirmed the existence of administrative obstacles including the lack of community partnership. There were also family obstacles such as the family's lack of encouragement for the students, and ignorance of the nature of GSLDs. The study came up with a number of solutions and proposals related to awareness, educational institutions, education and competitions for talented people with learning disabilities.

Over the last decade, the scientific community has been actively developing technologies for automated software bug fixes called Automated Program Repair (APR). Several APR techniques have recently been proposed to effectively address multiple classroom programming errors. However, little attention has been paid to the advances in effective APR techniques for software bugs that are widely occurring during the software life cycle maintenance phase. To further enhance the concept of software testing and debugging, we recommend an optimized automated software repair approach based on hybrid technology (OAPR-HOML'1). The first contribution of the proposed OAPR-HOML'1 technique is to introduce an improved grasshopper optimization (IGO) algorithm for fault location identification in the given test projects. Then, we illustrate an opposition learning based artificial neural network (OL-ANN) technique to select AST node-level transformation schemas to create the sketches which provide automated program repair for those faulty projects. Finally, the OAPR-HOML'1 is evaluated using Defects4J benchmark and the performance is compared with the modern technologies number of bugs fixed, accuracy, precession, recall and F-measure.

By explaining the essence of corporate governance as well as disclosure and transparency, the study examined the guarantees of applying disclosure and transparency to firms listed on the Saudi stock exchange. The research also addressed the disclosure and transparency duties of firms listed on the Saudi stock exchange. Finance to prepare a prospectus, as the Capital Market Authority's regulations required that the prospectus includes information that enables the investor in securities to make his investment decision based on real foundations based on the issuing company's financial position and to ensure that companies fulfill that disclosure in the prospectus. Firms who fail to disclose are required by law to do so, and the Capital Market Authority's laws mandate companies listed on the financial market to regularly report fundamental events linked to the issuer or the securities issued by it. The Capital Market Authority must make it available to the public dealing with the business issuing the securities, and The Capital Market Authority's Law and Regulations have imposed fines on corporations that do not comply with disclosure and make the Board of Director's report available. The research focused on activities that the legislator deemed to be a breach of the obligation of openness, such as the danger of many measures aimed at ensuring the impartiality and transparency of trading in the Saudi financial market, as well as the absence of conflicts of interest. The research also addressed the sanctions imposed on The source for failing to meet the obligation of disclosure and openness, as well as the mechanisms of compensating persons harmed by the failure to meet that responsibility.

With the growing exchange of data between individuals and institutions through various electronic communication, valuable data protection is in high demand to ensure that it is not hacked and that privacy is protected. Many security techniques, such as encryption and steganography, have emerged to prevent security breaches. The purpose of this research is to integrate cryptographic and steganography techniques to secure text message sending. The Rijndael algorithm was used to encrypt the text message, and the Least Significant Bit algorithm was also used to hide the encrypted message in a color image. Experiments on the suggested method have proven that it can improve the security of sent messages due to the human eye's inability to identify the original image from the image after it has been covered, as well as the encryption of the message using a password.

Femtocell networks can be a potential method for increasing the capacity of LTE networks, especially in indoor areas. However, unplanned deployment of femtocells results in co-tier interference and cross-tier interference problems. The interference reduces the advantages of implementing femtocell networks to a certain extent. The notion of Fractional Frequency Reuse (FFR) is proposed in order to reduce the impact of interference on the system's performance. In this paper, a dynamic approach for efficiently partitioning the spectrum is suggested. The goal is to enhance the capacity of femtocells, which will improve the performance of the system. The suggested strategy allocates less resources to the macrocell portion of the network, which has a greater number of femtocells deployed to maximize the utilization of available resources for femtocell users. The spectrum division would be dynamic. The proposed strategy is evaluated through a simulation using MATLAB tool. In conclusion, the results showed that the proposed scheme has the potential to boost the system's capacity.

Human factor represents a very challenging issue to organizations. Human factor is responsible for many cybersecurity incidents by noncompliance with the organization security policies. In this paper we conduct a comprehensive review of the literature to identify strategies to address human factor. Security awareness, training and education program is the main strategy to address human factor. Scholars have consistently argued that importance of security awareness to prevent incidents from human behavior.

Internet of things (IoT) has emerged as the most popular technique that facilitates enhancing humans' quality of life. However, most time sensitive IoT applications require quick response time. So, processing these IoT applications in cloud servers may not be effective. Therefore, fog computing has emerged as a promising solution that addresses the problem of managing large data bandwidth requirements of devices and quick response time. This technology has resulted in processing a large amount of data near the data source compared to the cloud. However, efficient management of computing resources involving balancing workload, allocating resources, provisioning resources, and scheduling tasks is one primary consideration for effective computing-based solutions, specifically for time-sensitive applications. This paper provides a comprehensive review of the source management strategies considering resource limitations, heterogeneity, unpredicted traffic in the fog computing environment. It presents recent developments in the resource management field of the fog computing environment. It also presents significant management issues such as resource allocation, resource provisioning, resource scheduling, task offloading, etc. Related studies are compared indifferent mentions to provide promising directions of future research by fellow researchers in the field.

Small cells, particularly femtocells, are regarded a promising solution for limited resources required to handle the increasing data demand. They usually boost wireless network capacity. While widespread usage of femtocells increases network gain, it also raises several challenges. Interference is one of such concerns. Interference management is also seen as a main obstacle in the adoption of two-tier networks. For example, placing femtocells in a traditional macrocell's geographic area. Interference comes in two forms: cross-tier and co-tier. There have been previous studies conducted on the topic of interference management. This study investigates the principle of categorization of interference management systems. Many methods exist in the literature to reduce or eliminate the impacts of co-tier, cross-tier, or a combination of the two forms of interference. Following are some of the ways provided to manage interference: FFR, Cognitive Femtocell and Cooperative Resource Scheduling, Beamforming Strategy, Transmission Power Control, and Clustering/Graph-Based. Approaches, which were proposed to solve the interference problem, had been presented for each category in this work.

The big data term refers to the great volume of data and complicated data structure with difficulties in collecting, storing, processing, and analyzing these data. Big data analytics refers to the operation of disclosing hidden patterns through big data. This information and data set cloud to be useful and provide advanced services. However, analyzing and processing this information could cause revealing and disclosing some sensitive and personal information when the information is contained in applications that are correlated to users such as location-based services, but concerns are diminished if the applications are correlated to general information such as scientific results. In this work, a survey has been done over security and privacy challenges and approaches in big data. The challenges included here are in each of the following areas: privacy, access control, encryption, and authentication in big data. Likewise, the approaches presented here are privacy-preserving approaches in big data, access control approaches in big data, encryption approaches in big data, and authentication approaches in big data.

LTE (Long-Term Evolution, sometimes known as 4G LTE) is a wireless high-speed data communication technology for mobile phones and data terminals. The Packet Scheduler (PS) is an important component in improving network performance. Physical Resource Blocks (PRBs) are assigned to associated User Equipment by the packet scheduler (UEs). The primary contribution of this study is a comparison of the eNodeB throughput between a suggested method and the Round Robin (RR) Algorithm. The RR Algorithm distributes PRBs among all associated UEs without taking channel circumstances into account. In this research, we present a new scheduling method that takes into account the number of PRBs and associated UEs and produces higher throughput than the RR algorithm.

Wireless sensor networks (WSN) are becoming widely used in collecting and sensing information in different fields such as in the medical area, smart phone industry and military environment. The main concern here is reducing the power consumption because it effects in the lifetime of wireless sensor during commutation because it may be work in some environment like sensor in the battlefields where is not easy to change the battery for a node and that may decrease the efficiency of that node and that may affect the network traffic may be interrupted because one or more nodes stop working. In this paper we implement, simulate, and investigate S-MAC protocol with mobility support and show the sequence of events the sender and receiver go through. We tested some parameters and their impacts of on the performance including System throughput, number of packets successfully delivered per second, packet delay, average packet delay before successful transmission.

Femtocells have recently been recognized for their potential to boost network capacity, improve end-user QoS and throughput, and do so at a cheap cost and with ease of implementation. The use of femtocells in indoor environments, such as residential buildings with neighboring homes, is becoming more popular. Femtocells are subject to interference from other femtocells, and the unwanted effects of interference are amplified when femtocells are deployed in close proximity to one another. As a consequence, the network's overall performance is degraded to a significant degree. One of the strategies that is thought to be effective in reducing the impact of interference is altering the transmission power of the femtocells. In this paper, a dynamic downlink transmission power of femtocells is suggested. In accordance with the observed cost function unit, each femtocell automatically changes its transmission power. If a femtocell causes too much interference for its neighbors, its transmission power level will be limited by that interference's rate. A simulation experiment is conducted to validate the effectiveness of the suggested system when compared with other schemes. When compared to previous schemes, which are addressed in this study, the numerical results show that the proposed strategy could provide more capacity while also ideally mitigating the influence of interference among co-channel deployed femtocells.

Nowadays, research in deep learning leveraged automated computing and networking paradigm evidenced rapid contributions in terms of Software Defined Networking (SDN) and its diverse security applications while handling cybercrimes. SDN plays a vital role in sniffing information related to network usage in large-scale data centers that simultaneously support an improved algorithm design for automated detection of network intrusions. Despite its security protocols, SDN is considered contradictory towards DDoS attacks (Distributed Denial of Service). Several research studies developed machine learning-based network intrusion detection systems addressing detection and mitigation of DDoS attacks in SDN-based networks due to dynamic changes in various features and behavioral patterns. Addressing this problem, this research study focuses on effectively designing a multistage hybrid and intelligent deep learning classifier based on modified deep forest classification to detect DDoS attacks in SDN networks. Experimental results depict that the performance accuracy of the proposed classifier is improved when evaluated with standard parameters.

The article reflects short-term perspectives on the use of information and communication technologies in the training of teachers for higher education. Education is characterized by conservatism, so aspects of systematic development of the industry are relevant to this cluster of social activity. Therefore, forecasting the introduction of innovative elements of ICT training is in demand for the educational environment. Forecasting educational trends are most relevant exactly in the issues of training future teachers of higher education because these specialists are actually the first to implement the acquired professional skills in pedagogical activities. The article aims to consider the existing potential of ICT-based learning, its implementation in the coming years, and promising innovative educational elements that may become relevant for the educational space in the future. The tasks of scientific exploration are to show the optimal formats of synergy between traditional and innovative models of learning. Based on already existing experience, extrapolation of conditions of educational process organization with modeling realities of using information and communication technologies in various learning dimensions should be carried out. Educational trends for the next 3 years are a rather tentative forecast because, as demonstrated by the events associated with the COVID-19 pandemic, the socio-cultural space is very changeable. Consequently, the dynamism of the educational environment dictates the need for a value-based awareness of the information society and the practical use of technological advances. Thus, information and communication technologies are a manifestation of innovative educational strategies of today and become an important component along with traditional aspects of educational process organization. Future higher education teachers should develop a training strategy taking into account the expediency of the ICT component.

The radio resources available in a wireless network system are limited. Therefor, job of managing resources is not easy task. Because the resources are shared among the UEs that are connected, the process of assigning resources must be carefully controlled. The packet scheduler in an LTE network is in charge of allocating resources to the user equipment (UE). Femtocells networks are being considered as a promising solution for poor channel performance for mulitple environments. The implementation of femtocells into a macrocell (traditional base station) would boost the capacities of the cellular network. To increase femtocells network capacity, a reliable Packet Scheduler mechanism should be implemented. The Packet Scheduler technique is introduced in this paper to maximize capacity of the network while maintaining fairness among UEs. The proposed solution operates in a manner consistent with this principle. An analysis of the proposed scheme's performance is conducted using a computer simulation. The results reveal that it outperforms the well-known PF scheduler in terms of cell throughput and average throughput of UEs.

Electronic media are an integral part of modern civilization; educational practices are no exception, which should change the content orientations, structures and methodological approaches in accordance with the requirements of the educational market. This makes it relevant to find effective and successful configurations in the process of implementing modern educational practices. The purpose of the research lies in determining the basic principles of electronic media and their place in modern education, identifying the effectiveness of teaching disciplines with application of electronic media, as well as establishing the level of assessment by students of the need to involve different types of electronic media in the educational process and professional practice. The research methodology is complex; the descriptive method and methods of observation, analysis and synthesis have been used in the academic paper. The method of pedagogical experiment has become the principal one; the method of questionnaires and statistical methods have been also used. The hypothesis of the academic paper lies in the fact that the involvement of electronic media in the educational process makes it more effective and requires conceptual changes in educational practices. The result of the research manifests in the identification of new opportunities for the use of electronic media, leading to conceptual shifts in the framework of modern educational policies. In the future, it will be appropriate to consider the theoretical aspects of changing worldview models in education and the use of new media in the educational process, their effectiveness and relevance.

The new millennium is characterized by an unprecedented breakthrough in knowledge and information and communication technologies, and the challenges of the XXI century require modernized paradigms of interaction in all spheres of life. Education continues to play a key role in national and global growth. The key role of education and its leadership in developing creative potential, as the main paradigm of the countries' stability, have significantly influenced educational centers. The developers of educational programs use information technologies as an incentive to develop creative potential of educational process. Professional training of the educational candidate is enhanced by the use of information technologies, so the educational applicants should develop technological skills to be productive members of society. Using the latest achievements in the field of information technologies for the organization of the educational process helps to form the operational style of education applicants' thinking, which provides the ability to acquire skills of processing information, that is presented in the text, graphic, tabular form, and increase the level of general and informational culture necessary for better orientation in the modern information space. The purpose of the research is to determine the effectiveness of information technologies as an incentive to develop creative potential of educational process on the basis of the survey, to establish advantages and ability to provide high-quality education in the context of using information technologies. Methods of research: comparative analysis; systematization; generalization, survey. Results. Based on the survey conducted among students and teachers, it has been found out that the teachers use the following information technologies for the development of creative potential of the educational process: to provide video and audio communication process (100%), Moodle (95,6%), Duolingo (89,7%), LinguaLeo (89%), Google Forms (88%) and Adobe Captivate Prime (80,6%). It is determined that modular digital learning environments (97,9%), interactive exercises tools (96,3%), ICT for video and audio communication (96%) and interactive exercises tools (95,1%) are most conducive to the development of creative potential of the educational process. As a result of the research, it was revealed that implementation of information technologies for the development of creative potential of educational process in educational institutions is a complex process due to a large number of variables, which should be taken into account both on the educational course and on the individual level. It has been determined that the using the model of implementation information technologies for the development of creative potential in educational process, which is stimulated due to this model, benefits both students and teachers by establishing a reliable bilateral connection between teacher and education applicant.

This paper undertakes educational games and gamification, their features, importance, and integration into the educational process. Besides outlining features, benefits, and difficulties, it highlights the difference between gaming, gamification, and game-based learning. The article contends that game-based learning and gamification elements such as reward, completion, and cooperation develop students' positive attitudes toward the curriculum and boost their learning motivation.

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.