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Photovoltaic (PV) system has gradually become a research focus in the field of renewable energy power generation, and the output efficiency of PV system is the major concern of researchers. There are obvious non-linear characteristics in the output of PV system, and it will be greatly affected by the external environment. For achieving the maximum output power, PV system must operate under the guidance of maximum power point tracking (MPPT) methods. The tracking time and accuracy of these methods need to be improved. Therefore, this study contributes to increasing the output efficiency of PV system by improving the tracking time and accuracy with using Fuzzy Gain Scheduling of Proportional-Integral-Derivative (FGS-PID) regulator in the grid-connected mode. The suggested adaptive FGS-PID scheme is implemented using two-level control system architecture, combining the benefits of fuzzy logic as well as conventional PID regulator. Ziegler–Nichols tuning method is employed to select the initial values of PID gains. In the simulations of PV system under the varying irradiance and temperature, the tracking speed and tracking accuracy of FGS-PID (scaling factors) were higher than those of ANN, ANFIS, Fuzzy, INC, P&O, PID, and FGS-PID methods. In summary, FGS-PID (scaling factors) has better performance in tracking time and accuracy than other comparison algorithms. It can improve the output efficiency of PV system in practical application. [ABSTRACT FROM AUTHOR]

Blockchain is a new technology that provides services of immutability, trust, disintermediation, collaboration, transparency. Nowadays, the use of this new technology is mostly used for Bitcoin and other cryptocurrencies, but apart from this, blockchain technology improved the performance level of other areas of life i.e. Higher Education Institution (HEI) and stakeholders, certificate verification and many other domains. Students and educational institutions' important data are mostly shared via different networks. The data integrity, privacy and security are the major issues for these in the blockchain technology which cannot be avoided. This paper is a documented plan for to conduct or a protocol based on which a systematic literature review would be conducted focusing the issues, challenges and major strengths of blockchain and its educational paradigms. The result of this review will be highly helpful for the new researchers to overcome the proposed issues and challenges, exploring educational paradigms in Blockchain, elaborate the major strengths helping the educational institutes in adapting process and increase the level of satisfaction. This study plans to explore some educational types with some issues and challenges such as scalability, immutability, and easy adaptation. [ABSTRACT FROM AUTHOR]

Software defect prediction (SDP) in the initial period of the software development life cycle (SDLC) remains a critical and important assignment. SDP is essentially studied during few last decades as it leads to assure the quality of software systems. The quick forecast of defective or imperfect artifacts in software development may serve the development team to use the existing assets competently and more effectively to provide extraordinary software products in the given or narrow time. Previously, several canvassers have industrialized models for defect prediction utilizing machine learning (ML) and statistical techniques. ML methods are considered as an operative and operational approach to pinpoint the defective modules, in which moving parts through mining concealed patterns amid software metrics (attributes). ML techniques are also utilized by several researchers on healthcare datasets. This study utilizes different ML techniques software defect prediction using seven broadly used datasets. The ML techniques include the multilayer perceptron (MLP), support vector machine (SVM), decision tree (J48), radial basis function (RBF), random forest (RF), hidden Markov model (HMM), credal decision tree (CDT), K-nearest neighbor (KNN), average one dependency estimator (A1DE), and Naïve Bayes (NB). The performance of each technique is evaluated using different measures, for instance, relative absolute error (RAE), mean absolute error (MAE), root mean squared error (RMSE), root relative squared error (RRSE), recall, and accuracy. The inclusive outcome shows the best performance of RF with 88.32% average accuracy and 2.96 rank value, second-best performance is achieved by SVM with 87.99% average accuracy and 3.83 rank values. Moreover, CDT also shows 87.88% average accuracy and 3.62 rank values, placed on the third position. The comprehensive outcomes of research can be utilized as a reference point for new research in the SDP domain, and therefore, any assertion concerning the enhancement in prediction over any new technique or model can be benchmarked and proved. [ABSTRACT FROM AUTHOR]

In the recent era, a liver syndrome that causes any damage in life capacity is exceptionally normal everywhere throughout the world. It has been found that liver disease is exposed more in young people as a comparison with other aged people. At the point when liver capacity ends up, life endures just up to 1 or 2 days scarcely, and it is very hard to predict such illness in the early stage. Researchers are trying to project a model for early prediction of liver disease utilizing various machine learning approaches. However, this study compares ten classifiers including A1DE, NB, MLP, SVM, KNN, CHIRP, CDT, Forest-PA, J48, and RF to find the optimal solution for early and accurate prediction of liver disease. The datasets utilized in this study are taken from the UCI ML repository and the GitHub repository. The outcomes are assessed via RMSE, RRSE, recall, specificity, precision, G-measure, F-measure, MCC, and accuracy. The exploratory outcomes show a better consequence of RF utilizing the UCI dataset. Assessing RF using RMSE and RRSE, the outcomes are 0.4328 and 87.6766, while the accuracy of RF is 72.1739% that is also better than other employed classifiers. However, utilizing the GitHub dataset, SVM beats other employed techniques in terms of increasing accuracy up to 71.3551%. Moreover, the comprehensive outcomes of this exploration can be utilized as a reference point for further research studies that slight assertion concerning the enhancement in extrapolation through any new technique, model, or framework can be benchmarked and confirmed. [ABSTRACT FROM AUTHOR]

In the recent era, a liver syndrome that causes any damage in life capacity is exceptionally normal everywhere throughout the world. It has been found that liver disease is exposed more in young people as a comparison with other aged people. At the point when liver capacity ends up, life endures just up to 1 or 2 days scarcely, and it is very hard to predict such illness in the early stage. Researchers are trying to project a model for early prediction of liver disease utilizing various machine learning approaches. However, this study compares ten classifiers including A1DE, NB, MLP, SVM, KNN, CHIRP, CDT, Forest-PA, J48, and RF to find the optimal solution for early and accurate prediction of liver disease. The datasets utilized in this study are taken from the UCI ML repository and the GitHub repository. The outcomes are assessed via RMSE, RRSE, recall, specificity, precision, G-measure, F-measure, MCC, and accuracy. The exploratory outcomes show a better consequence of RF utilizing the UCI dataset. Assessing RF using RMSE and RRSE, the outcomes are 0.4328 and 87.6766, while the accuracy of RF is 72.1739% that is also better than other employed classifiers. However, utilizing the GitHub dataset, SVM beats other employed techniques in terms of increasing accuracy up to 71.3551%. Moreover, the comprehensive outcomes of this exploration can be utilized as a reference point for further research studies that slight assertion concerning the enhancement in extrapolation through any new technique, model, or framework can be benchmarked and confirmed. [ABSTRACT FROM AUTHOR]

This study predicts the investigation of surface eco-protection techniques for cohesive soil slopes along the selected Guthrie Corridor Expressway stretch by way of analyzing a new set of probabilistic models using a hybrid technique of artificial neural network and fuzzy inference system namely adaptive neuro-fuzzy inference system (ANFIS). Soil erosion and mass movement which induce landslides have become one of the disasters faced in Selangor, Malaysia causing enormous loss affecting human lives, destruction of property and the environment. Establishing and maintaining slope stability using mechanical structures are costly. Hence, biotechnical slope protection offers an alternative which is not only cost effective but also aesthetically pleasing. A parametric study was carried out to discover the relationship between various eco-protection techniques, i.e., application of grasses, shrubs and trees with different soil properties as well as slope angles. Then the data have been used to develop a new hybrid ANFIS technique for prediction of factor of safety (FOS) of slopes. Four inputs were considered in relation to the different vegetation types, i.e., slope angle (θ), unit weight (γ), effective cohesion (c′), effective friction angle (ø′). Then, many hybrid ANFIS models were constructed, trained and tested using various parametric studies. Eventually, a hybrid ANFIS model with a high performance prediction and a low system error was developed and introduced for solving problem of slope stability. [ABSTRACT FROM AUTHOR]

E‐learning, as a complex system, includes distance learning, teaching materials in various forms and shapes, group and individual learning processes, as well as interactive and tutorial work. To increase the effectiveness and efficiency of the e‐learning system, it is necessary first of all to consider the characteristics of students and their learning styles. Based on the data collected in various ways, in surveys, using the distance learning dystem (DLS) Moodle, based on the subjective assessment of the subject teachers, as well as based on data from the business information system, the student preferences are determined. Then, based on these data, an adaptation is made, a process that adapts the work of the DLS based on student knowledge. The primary goals that can be achieved through the adjustment of the e‐learning system are to improve the design and usefulness of the course, help with finding information on the course, more efficient searching and placement of search results in the context of students' interest, as well as increasing students' loyalty to a higher education institution. [ABSTRACT FROM AUTHOR]

An infrastructure development in landscape and clearing of more vegetated areas have provided huge changes in Malaysia gradually leading to slope instabilities accompanied by enormous environmental effects such as properties and destructions. Thus, prudent practices through vegetation incorporating to use slope stability is an option to the general stabilized technique. Few researches have investigated the effectiveness of vegetative coverings related to slope and soil parameters. The main goal of this study is to provide an intelligent soft computing model to predict the safety factor (FOS) of a slope using support vector regression (SVR). In the other words, SVR has investigated the surface eco-protection techniques for cohesive soil slopes in Guthrie Corridor Expressway stretch through the probabilistic models analysis to highlight the main parameters. The aforementioned analysis has been performed to predict the FOS of a slope, also the estimator's function has been confirmed by the simulative outcome compared to artificial neural network and genetic programing resulting in a drastic accurate estimation by SVR. Using new analyzing methods like SVR are more purposeful than achieving a starting point by trial and error embedding multiple factors into one in ordinary low-technique software. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to examine the impact of ICT and digital knowledge on students' thoughts and beliefs. Using Information and Communication Technology (ICT) in learning and teaching processes can improve the interpretation of knowledge, not only in the learning process but also for thoughts and beliefs. Beliefs and thoughts as propositional content are understood to be a subjective manner of knowing and becoming a focal point of education process. In addition, ICT plays a vital role in enhancing the efficiency of the teaching process which can change the thoughts of learners. So, in this paper, the usage of ICT in education was considered as a key factor for improving students' thoughts and beliefs. In addition, a conceptual model was proposed to evaluate this impact. Design/methodology/approach: Data were collected from 384 students from secondary schools in Iran. For assessing the elements of the model, a complete questionnaire was designed. For statistical analysis of questionnaires, SPSS 22 and SMART-PLS 3.2 software package was used. Findings: The obtained results showed the high strength of the proposed model. The outcomes indicated that digital technology acceptance positively affects students' thoughts and beliefs. In addition, the findings showed that the role of digital knowledge, digital training facilities and digital education content on students' thoughts and beliefs was significant. Research limitations/implications: The authors deal with one experiment and so the results cannot be generalized. The trail should be repeated with many groups and in diverse contexts. Originality/value: Despite the importance of the investigating the impact of ICT and digital knowledge on the students' thoughts and beliefs, the relationship among these factors was not examined well in previous research. Thus, the investigation of the impact of ICT and digital knowledge on the students' thoughts and beliefs is the main originality of this research. For this goal, a new conceptual model is proposed, which has 11 sub-indicators within four variables: digital technology acceptance, digital knowledge, digital training facilities and digital education content. [ABSTRACT FROM AUTHOR]

Purpose: Despite the importance of investigating the impact of cloud computing on the e-learning process, the relationship development between the two is not examined well. Thus, the main goal of this research is to assess how to improve the e-learning process by using cloud services. This paper aims to attempt to investigate the impact of cloud computing on e-learning development. Design/methodology/approach: The paper is based on both quantitative and qualitative methodologies. For review-related work and statistical analysis of questionnaires, the SPSS 22 and SMART-PLS 3.2 software package are used. Findings: The results from the study show that e-learning development is significantly influenced by the quality of services, cloud features, university readiness and users' readiness. Originality/value: The adoption of cloud technology within an instructional environment has the capacity of offering new opportunities for improvement and innovation for gaining knowledge of the process. [ABSTRACT FROM AUTHOR]

Web engineering is a systematic approach to develop web applications and numerous web engineering methods have been proposed. These methods were extended through defining new models by using different mechanisms to capture the web application concepts. Due to the complexity rising of web applications, the web engineering methods cannot provide web solutions anymore. Even though Interaction Flow Modeling Language (IFML) is recently proposed as a new method for developing web applications, it has limitations. Therefore these methods need to be improved. In this paper, we present the ability of IFML extensibility to support new concerns from web applications. Moreover, we extend IFML through UML mechanisms to support new concerns from the context to the user interface. The new IFML solves the lack of context web application through defining a new model and becomes a new direction to develop concerns modern web applications. [ABSTRACT FROM AUTHOR]

A Robin problem is a mixed problem with a linear combination of Dirichlet and Neumann D-N conditions. The aim of this paper are presents a new boundary integral equation BIE method for the solution of unbounded Robin boundary value problem BVP in the simply connected domain. The method show how to reformulate the Robin boundary value problem BVP as Riemann-Hilbert problem RHP which lead to the system of integral equation and the related differential equations are also created that give rise to unique solutions. Numerical results on several tests regions by the Nyström method NM with the trapezoidal rule TR are presented to clarify the solution technique for the Robin problem when the boundaries are sufficiently smooth. [ABSTRACT FROM AUTHOR]

Plagiarism is generally defined as literary theft and academic dishonesty. This considered as the serious issue in an academic documents and texts. There are numerous of plagiarism detection techniques have been developed for various natural languages, mainly English. In this paper we investigate and review the plagiarism detection techniques and algorithms which have been developed for Arabic Script Languages (ASL) and providing a literature review of the utilized methods in terms of techniques and outcomes. The result of this paper will help the researchers who are going to commence their development and extend their researches in ASL like Arabic, Persian, Urdu and Kurdish. [ABSTRACT FROM AUTHOR]

QoS-aware composition of web services is a business process to achieve functional goals by considering QoS criteria simultaneously. Although there are many composition algorithms taking either a single criterion or multiple criteria into account, a composite service can presumably consist of redundant services. Our proposed algorithm on Web service composition focuses on multiple QoS criteria as well as redundancy of component services at the time of composition. This work aims to reduce execution time, cost, and the number of component services in the composition process. To fulfill this functional goal, we use linked list and hash table data structures to provide the proposed algorithm with a better time complexity. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing as a new model of delivering IT services on the Internet has attained high attention recently. In this new paradigm, efficient service management causes the high quality of provided services. Scheduling as one of the most important duties of service management is a key problem in cloud computing that affects the total system performance. In most cases, the meta‐heuristic methods are used for optimizing the scheduling issues instead of traditional methods. One of the influential evolutionary algorithms for optimizing the complicated problems is a non‐dominated sorting particle swarm optimization (NSPSO) technique. In this paper, we propose a meta‐heuristic technique using the NSPSO model for decreasing total cost and consumed total time. Furthermore, fuzzy set theory is applied to select the best solution. Simulation results have indicated that the efficiency of NSPSO is improved. In the many types of experiment, the proposed NSPSO algorithm was appropriate to keep a good spread of solutions and good converge. In addition, the diversity preserving mechanism applied in NSPSO has improvement against the other two investigated algorithms. [ABSTRACT FROM AUTHOR]