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Explainable Artificial Intelligence (XAI) is a branch of AI that mainly focuses on developing systems that provide understandable and clear explanations for their decisions. In the context of cancer diagnoses on medical imaging, an XAI technology uses advanced image analysis methods like deep learning (DL) to make a diagnosis and analyze medical images, as well as provide a clear explanation for how it arrived at its diagnoses. This includes highlighting specific areas of the image that the system recognized as indicative of cancer while also providing data on the fundamental AI algorithm and decision-making process used. The objective of XAI is to provide patients and doctors with a better understanding of the system’s decision-making process and to increase transparency and trust in the diagnosis method. Therefore, this study develops an Adaptive Aquila Optimizer with Explainable Artificial Intelligence Enabled Cancer Diagnosis (AAOXAI-CD) technique on Medical Imaging. The proposed AAOXAI-CD technique intends to accomplish the effectual colorectal and osteosarcoma cancer classification process. To achieve this, the AAOXAI-CD technique initially employs the Faster SqueezeNet model for feature vector generation. As well, the hyperparameter tuning of the Faster SqueezeNet model takes place with the use of the AAO algorithm. For cancer classification, the majority weighted voting ensemble model with three DL classifiers, namely recurrent neural network (RNN), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM). Furthermore, the AAOXAI-CD technique combines the XAI approach LIME for better understanding and explainability of the black-box method for accurate cancer detection. The simulation evaluation of the AAOXAI-CD methodology can be tested on medical cancer imaging databases, and the outcomes ensured the auspicious outcome of the AAOXAI-CD methodology than other current approaches.

In this study, three-dimensional (3D) turbulent core annular flow (CAF) regime is investigated numerically. The proposed model is based on the 3D Reynolds average Navier–Stokes (RANS) equations combined with a pure convective transport equation of the volume of fluid (VOF) to predict the interface between the oil and water phases. The k-ω turbulence model is adopted to better reproduce the oil and water flow characteristics. The two-phase (CAF) regime can be predicted by two inlet configurations: the T-junction (3D-T) and the straight pipe (3D-S). These two configurations are simulated and compared for pipe diameter D = 0.026 m and pipe length L = 4 m . For these two inlet configurations, the computed mixture velocity profile and the water volume fraction at a test section z = 100 D were compared to experimental measurements. The 3D-T configuration gives more appropriate results. The 3D-S slightly overestimates the maximum velocity at the test section and the lower and upper water layer of the (CAF) flow is shifted in the upward direction. For the 3D-T, the relative error in the pressure drop is 3.3%. However, for the 3D-S, this error is 13.0%.

The main purpose of this study is to compare two-dimensional (2D) and three-dimensional (3D) two-phase models for both stratified and slug flows. These two flow regimes interest mainly the petroleum and chemical industries. The volume of fluid (VOF) approach is used to predict the interface between the two-phase flows. The stratified turbulent flow corresponds to the oil-water phases through a cylindrical pipe. To simulate the turbulent stratified flow, the k − ω turbulence model is used. The slug laminar flow concerns the kerosene-water phases through a rectangular microchannel. The simulated results are validated using the previous experimental results available in the literature. For the stratified flow, the axial velocity and the water volume fraction profiles obtained by 2D and 3D models approximate the measurement profiles at the same test section. Also, the T-junction in a 2D model affects only the inlet vicinity. For downstream, the 2D and 3D models lead to the same axial velocity and water volume distribution. For the slug flow, the simulated results show that the 3D model predicts the thin film wall contrary to the 2D model. Moreover, the 2D model underestimates the slug length.

A Stable and highly reliable DC link voltage represents an important factor for efficient power transfer in high voltage direct current (HVDC) networks. In this framework, this paper investigates the control and stability analysis of voltage source converter (VSC) for DC link voltage regulation. To separately achieve the independent active and reactive power control, the system voltages and currents are represented in the synchronous reference frame. In order to optimally design the parameters of proportional-integral (PI) controller, the inner and outer loops’ transfer functions are thoroughly derived/developed. In addition,/moreover, in order to attain satisfactory/certain system performance, modulus optimum, symmetrical optimum pole placement control approaches are studied and implemented for the purpose of tuning the voltage/current controller gain parameters. In particular for the symmetrical optimum control method, the gain parameters of the DC-bus voltage are determined under different values of network impedance. Furthermore, the impact/influence of changing gain parameters on the DC-link voltage and poles/zeros movement are investigated. MATLAB/Simulink model is built and simulation studies are carried out to verify the introduced concepts.

Stochastic nature of load demand has a great impact on the performance of electrical power system. As a result, planning of electrical power system considering load uncertainties became inevitable. This paper presents Monte Carlo simulation based different bio-inspired algorithms, grey wolf optimization (GWO), manta ray foraging optimization (MRFO), satin bower bird optimization (SBO) and whale optimization (WOA) to optimize locations of three DG units under load uncertainties considering 500 scenarios. Each scenario includes 50 iterations which means that for each run we have 25,000 iterations and 500 characteristics for different load value. Two objectives are achieved. Firstly, statistically finding the optimal probabilistic location of three DG units under load uncertainties in IEEE 33-bus and IEEE 69-bus radial distribution system based on Monte Carlo simulation integrated with different bio-inspired algorithms. Secondly, comparing between the performances of four different bio-inspired algorithms. Three objective functions are considered, minimizing active power loss, minimizing voltage deviation and maximizing voltage stability index. The active and reactive power demand are normally distributed using normal distribution function. The optimal probabilistic location is investigated considering two cases under load uncertainties, optimizing location of three DG units generally and optimizing location of one DG unit assuming two optimum locations for the other two units extracted from case I. The obtained results (after placing DG units) are compared to the base case (DG units are not connected) and compared to each other according to the optimization technique. The results show that, SBO algorithm superiors other algorithms almost in all cases. Comes next GWO which provide good results generally. However, the good performance obtained by MRFO, it consumes twice the time of other algorithms. WOA however fast convergence, it provides results worse than other algorithms. The system is applied to the well-known IEEE 33-bus and IEEE 69-bus radial distribution system.

In this work, we present the dynamics of the one dimension fractional-order Rulkov map of biological neurons. The one-dimensional neuron map shows all the dynamical behaviors observed in the real-time experiment. The integer order one-dimensional Rulkov map exhibits chaotic dynamics in the presence of time-dependent external stimuli like periodic sinusoidal force or random Gaussian process. When we construct a large complex network of neurons, the higher system dimension, as well as the external forcing, is always an obstacle. Interestingly, our study shows even with constant external stimuli, the fractional-order one-dimensional neuron shows a rich variety of complex dynamics including chaotic dynamics. We present our results based on the Lyapunov exponent of the fractional-order systems.

Electrical power networks are expanded regularly to meet growing energy requirements. Reactive power dispatch (RPD) optimization is a powerful tool to enhance a system’s efficiency, reliability, and security. RPD optimization is classified as a non-linear and non-convex problem. In this paper, the RPD optimization problem is solved based on novel hybrid genetic algorithms—equilibrium optimizer (GAEO) optimization algorithms. The control variables are determined in such a way that optimizes RPD and minimizes power losses. The efficiency of the proposed optimization algorithms is compared to other techniques that have been used recently to solve the RPD problem. The proposed algorithm has been tested for optimization RPD for three test systems, IEEE14-bus, IEEE-30bus, and IEEE57-bus. The obtained results show the superiority of GAEO over other techniques for small test systems, IEEE14-bus and IEEE-30bus. GAEO shows good results for large system, IEEE 57-bus.

Peer-to-Peer (P2P) networks are prominent in the Internet-of-things-assisted industrial environments for distributed computing and smart control systems. The problem arises with the independence and peer systems security due to anonymous access and security measures. In this paper, an innovative control-driven autonomous authentication scheme is proposed for improving the access security of P2P industrial systems. The proposed scheme provides authentication based on P2P system control requirements within its access time. The P2P control systems and their functionalities are provided with classified security measures for administering autonomous security. The advantage of offering autonomous protection is to prevent the sequence of security breaches and control sabotage. In this process, the control system requirements and authentications are paired by identifying the machines' operating time and access time. For identification and grouping-based classification, support vector machines are used. It learns the sabotage and control requirements based on access and control time for providing a rupture-less industrial process. It helps to leverage the detection of autonomous adversaries in P2P industrial control systems. Besides, a less complex and latent-free security measure is achievable using the proposed scheme.

Because of the heath measures taken during the outbreak of Covid-19, the lack of educational methods has become the primary concern among educational professionals who have been using technology as a motivational tool. Gamification is very important because it helps students to represent their study contents and enrich their experiences of higher education when learning in-person is unavailable during the Covid-19 period. This study seeks to present an Android-based gamification app to evaluate the effect of using gamification and e-quizzes on college students’ learning. We used the visual blocks language from the MIT App Inventor platform to develop an application, available at (https://play.google.com/store/apps/details?id=appinventor.ai_mekomerofofo.projectGamification). The participants were students from level 2 who used digital lessons for learning MATLAB. The study included gamified learning and non-gamified learning, both integrated into lesson plans, to investigate the differences in learners’ performance. Two types of quizzes were used for instruction: gamified e-quizzes and paper-based quizzes. The outcomes plainly showed that using the new gamified e-quiz was more effective than using paper-based quizzes. They are better for assessing the learning performance of the students in question, specifically in terms of formative assessment. It is very important for instructors to apply games as a modern and innovation-oriented tool through which students can be engaged in an attractive, competitive experience.

The interaction of the solitary wave with an oil platform composed of four vertical circular cylinders is investigated for two attack angle of the solitary wave β = 0 ° (square arrangement) and β = 45 ° (diamond arrangement). The solitary wave is generated using an internal source line as proposed by Hafsia et al. (2009). This generation method is extended to three-dimensional wave flow and is integrated into the PHOENICS code. The volume of fluid approach is used to capture the free surface evolution. The present model is validated in the case of a solitary wave propagating on a flat bottom for H / h = 0.25 where H is the wave height and h is the water depth. Compared to the analytical solution, the pseudowavelength and the wave crest are well reproduced. For a solitary wave interacting with square and diamond cylinders, the simulated results show that the maximum run-ups are well reproduced. For the diamond arrangements, the diffraction process seems to not affect the maximum run-ups, which approached the isolated cylinder. For the square arrangement, the shielding effect leads to a maximum wave force more pronounced for the upstream cylinder array.

The endless problem of energy supplies are always floating on the surface. As a result, there are a daily improvement to optimize power generators, networks and system configuration. Renewable distributed generators (RDG) are in the heart of these developments. The size of RDG is increasing daily so, it must be optimized to maximize benefits and eliminate drawbacks. Optimization algorithms are one of the fast growing techniques. In this study the Manta Ray Foraging optimization algorithm (MRFO) is applied to minimize power losses through sizing and allocation of DG type I integrated into radial distribution network (RDN). The proposed technique was tested on three different networks, IEEE 33, 69 and 85 test systems. Also, three cases were assumed to evaluate the effectiveness of MRFO algorithm. The results were compared to recent applied techniques.

The most important behavior for evolution system is the blow-up phenomena because of its wide applications in modern science. The article discusses the finite time blowup that arise under an appropriate conditions. The nonsolvability of boundary value problem for damped pseudoparabolic differential equations with variable exponents is investigated. Such problem has been previously studied in the case if p and q are constants. New here is the case of variables of nonlinearity p and q which make the problem has a scientific interest.

This study investigates the three-dimensional (3-D) solitary wave interaction with two cylinders in tandem and side-by-side arrangements for two wave heights. The solitary wave generation and propagation are predicted using the volume of fluid method (VOF) coupled with the NavierStokes transport equations. The PHOENICS code is used to solve these transport equations. The solitary wave generation based on the source line developed by Hafsia et al. (2009) is extended in three-dimensional wave flow and is firstly validated for solitary waves propagating on a flat bottom. The comparison between numerical results and analytical solution for small wave height H / h = 0.1 and 0.2 shows good agreements. The wave crest and the pseudo-wavelength are well reproduced. Excellent agreements were found in terms of maximum run-up and wave forces by comparison with the present model and analytical studies. The present model can be tested for the extreme solitary wave to extend its application to a more realistic case study as the solitary wave diffraction with an offshore oil platform.

Power loss is a bottleneck in every power system and it has been in focus of majority of the researchers and industry. This paper proposes a new method for determining the power loss in wind-solar power system based on deep learning. The main idea of the proposed scheme is to freeze the feature extraction layer of the deep Boltzmann network and deploy deep learning training model as the source model. The sample data with closer distribution with the data under consideration is selected by defining the maximum mean discrepancy contribution coefficient. The power loss calculation model is developed by configuring the deep neural network through the sample data. The deep learning model is deployed to simulate the non-linear mapping relationship between the load data, power supply data, bus voltage data and the grid loss rate during power grid operation. The proposed algorithm is applied to an actual power grid to evaluate its effectiveness. Simulation results show that the proposed algorithm effectively improved the system performance in terms of accuracy, fault tolerance, nonlinear fitting and timeliness as compared with existing schemes.

This paper is focused on Intel Advanced Vector Extension (AVX) which has been borne of the modern developments in AMD processors and Intel itself. Said prescript processes a chunk of data both individually and altogether. AVX is supporting variety of applications such as image processing. Our goal is to accelerate and optimize square single-precision matrix multiplication from 2080 to 4512, i.e. big size ranges. Our optimization is designed by using AVX instruction sets, OpenMP parallelization, and memory access optimization to overcome bandwidth limitations. This paper is different from other papers by concentrating on several main technique and the results therein. Making parallel implementation guidelines of said algorithms, where the target architecture’s characteristics need to be taken into consideration when said algorithms are applied are presented. This work has a comparative study of using most popular compilers: Intel C++ compiler 17.0 over Microsoft Visual Studio C++ compiler 2015. Additionally, a comparative study between single-core and multicore platforms has been examined. The obtained results of the proposed optimized algorithms are achieved a performance improvement of 71%, 59%, and 56% for C = A.B, C = A.BT, and C = AT.B separately compared with results that are achieved by implementing the latest Intel Math Kernel Library 2017 SGEMV subroutines.

This paper proposes an explicit adaptive controller to damp oscillations and to enhance the single machine infinite bus SMIB stability. Owing to the increasing requests for renewable energy and operating conditions, the identification for power systems has been increased recently. Changes in the power system parameters cause to use an explicit self-tuning control. The controller structure consists of combined on-line identifier and a feedback controller as PID and a radial basis function neural network (RBFNN) which acts as an adaptive power system stabilizer for SMIB. An adaptive linear neural network (ADALINE) depending on the input and output of open loop system is employed as on-line model identification to mimic on-line the SMIB output. The difference between SMIB and the identified model responses is used to adjust the ADALANN model weights on-line depending on a recursive least squares principle RLS and a recursive least square with adaptive directional forgetting RLSMadf. The particles swarm optimization (PSO) beside RLS and RLSMadf assess the weights of (RBFNN) and coefficients of PID controllers depending on the on-line ADALINE model weights. The proposed controller is validated with several operating conditions under various disturbances. The simulation results show the proposed controller whose parameters depend on on-line tuning techniques provides better performance than a conventional PID controller.

Recently, an explosive growth in the potential use of natural metaphors in modelling and solving large-scale non-linear optimization problems. Artificial neural network (ANN) is a potent gadget broadly utilized in many data classification tasks. Fundamentally, nature-inspired algorithms have demonstrated their effectiveness and ability over traditional algorithms for generating the optimal ANN parameters, rules and topology that provide the best classification performance with regarding to the quality of the solution, computational cost and avoiding local minima. The literature is vast and growing. This study provides a review on the basic theories and main recent algorithms for optimizing the ANN. Different types of nature-inspired meta-heuristic algorithms are presented; outlining the concepts and components that are used in order to give a summary and ease of the state-of-the-arts to find suitable methods in real world applications for the readers. Additionally, this survey covers the most used type of neural networks, feed-forward neural network (FFNN) in several optimized applications. The performances of FFNNs designed by nature-inspired algorithms have been explored in single and multi-dimensional optimization space; highlighting their models, features, objectives, constraints, etc. to analyse their differences and similarities. A comprehensive survey of the earliest works and recent modified in the last decade in addition to expect approaches has been investigated in details.

In primary education, a variety of teaching methods, such as enhanced and discovery learning, have had a significant influence on student achievement, particularly in mathematics. Several studies have discussed the positive effects of the appropriate use of technology in the classroom on student achievement. The main purpose of the current study was to determine the effects of a mobile application on student achievement in a primary school mathematics course in Saudi Arabia. Java was used in the development of the application. The study adopted a quasi-experimental design. The sample comprised 40 students from the Unaizah International School. The data collection instrument was a test in a mathematics course. The test had a reliability of >0.84. The pre- and post-test scores were analyzed with a t-test, which was used to examine the two null hypotheses at the 0.05 level of significance. The findings revealed that mobile applications are more effective than traditional methods for improving student outcomes in mathematics. This indicates the need for support to be provided for such activities in primary school classes. The results further indicate the effectiveness of this current application in developing students’ cognitive skills and improving their mathematical abilities.

Data mining optimization received much attention in the last decades due to introducing new optimization techniques, which were applied successfully to solve such stochastic mining problems. This paper addresses implementation of evolutionary optimization algorithms (EOAs) for mining two famous data sets in machine learning by implementing four different optimization techniques. The selected data sets used for evaluating the proposed optimization algorithms are Iris dataset and Breast Cancer dataset. In the classification problem of this paper, the neural network (NN) is used with four optimization techniques, which are whale optimization algorithm (WOA), dragonfly algorithm (DA), multiverse optimization (MVA), and grey wolf optimization (GWO). Different control parameters were considered for accurate judgments of the suggested optimization techniques. The comparitive study proves that, the GWO, and MVO provide accurate results over both WO, and DA in terms of convergence, runtime, classification rate, and MSE.

Maximizing the classification accuracy and minimizing the number of selected features are the two main incompatible objectives for using feature selection to overcome the curse of dimensionality. “Classification accuracy highly dependents on the nature of the features in a dataset which may contain irrelevant or redundant data. The main aim of feature selection is to eliminate these types of features to enhance the classification accuracy.” This work presents a new meta-heuristic optimization approach, called Parasitism-Predation Algorithm (PPA), which mimics the interaction between the predator (cats), the parasite (cuckoos) and the host (crows) in the crow–cuckoo–cat system model to overcome the problems of low convergence and the curse of dimensionality of large data. The proposed hybrid framework combines the relative advantages of cat swarm optimization (CSO), cuckoo search (CS) and crow search algorithm (CSA) to attain a combinatorial set of features to boost up the classification accuracy. Nesting, parasitism, and predation phases are supposed to help exploration ability and balance in the context of solving classification problems. In addition, Levy flight distribution is applied to help better diversity of conventional CSA and improve ability of exploration. Meanwhile, an effective fitness function is utilized to enable the proposed PPA-based feature selector using K-Nearest Neighbors algorithm (KNN) to attain a combinatorial set of features. The proposed PPA and four standard heuristic search algorithms are looked at to gauge how efficient the proposed option is. Additionally, eighteen classification datasets are deployed to gauges its efficacy. The results highlight that the algorithm proposed is both effective and competitive in terms of performance of classification and dimensionality reduction as opposed to other heuristic options.

In this paper, by using subsuper solutions method, we study the existence of weak positive solutions for a new class of p,q Laplacian nonlinear elliptic system in bounded domains, when ax, bx,αx, and βx are sign-changing functions that maybe negative near the boundary, without assuming sign conditions on f0,g0,h0, and γ0.

The current paper introduces a realistic solution for energy demand in Makadi Bay, Red-Sea, Hurgada, Egypt using energy system crossbred of Renewable Wind Energy System (WES) and Photovoltaic System (PVS) in the presence of Battery Energy Storage (BES). A real measurement for wind speed was recorded through a year of 2017. Also, the sun irradiance and temperature were recorded through the same period, to be considered for the output power calculations from the proposed crossbred renewable energy system. The demand load data for the city was recoded as well as through the same period for evaluating the feasibility of the system if it can cover the city loads. Linear TORSCHE optimization technique has utilized to reach an optimum solution of the proposed crossbred renewable energy system. Individual configuration of PVS & WES in presence of BES have been studied and compared with the hybrid PV/WT. Furthermore, economic analysis has presented to prove the best economical system. The obtained results show that installing such hybrid system consists of WES, PVS and BES is cheaper than installing each one individually.

Hybrid optimization techniques have been extensively utilized for solving optimal power flow problems in distribution systems integrated with or without renewable energy systems, with load uncertainty. Particle swarm optimization (PSO) is integrated with Gray wolf optimizer (GWO) to create a hybrid algorithm, HPSOGWO. HPSOGWO is implemented to augment the optimal power flow solutions of IEEE-30 bus and IEEE-62 bus systems. Five objective functions are considered to investigate the power quality of the hybrid algorithm. The proposed algorithm strength is justified by a comparative study with each individual algorithm. The suggested algorithms provide different accuracy results in small and large scale distributed systems, which indicates their drawbacks in certain systems. The system is solved using MATLAB.

The Saudi Arabian government is committed to updating and improving its education system. Thus, in March 2017, a project was declared to convert the existing book-based methodology to modern, mobile technology in the K-12 education space by 2021. As part of this process, a deep-dive literature review of student acceptance of mobile learning confirmed that there was limited research into what elements had an effect on how much students were likely to accept learning with mobile applications in the five to 18-year-old demographic of K-12. The conclusion of the literature review was that the Saudi Arabian Education Ministry must acquire an understanding of these elements in order to strategize the implementation of the new technology. This study approached high school students, aged 16 – 18, in Saudi Arabia, to examine the elements which would influence their acceptance of mobile learning technology. The research consolidated known elements of education, namely learning self-management, system quality, and hedonic motivation with the Unified Theory of Acceptance and Use of Technology (UTAUT) to create a significant theoretical model for the new technology in a high school setting. Conclusions were drawn that societal influence did not affect the student’s approach to mobile learning, but that learning self-management, the expectancy of effort and performance, hedonic motivation and the quality of the system did affect the acceptance behaviour of the students. It was also noted that gender was not a significant factor in the study

Electronic government (E-government) systems are becoming an integral component of government service delivery systems. Because of the rapid growth of Internet and Information System (IS) technologies in Malaysia, E-government systems are becoming more and more necessary. This study examined user attitudes, usage intensions, and satisfaction with E-government systems using the Information System (IS) success model and the Technology Acceptance Model (TAM). This study deployed a questionnaire to 714 E-government users. The questionnaire results were analysed using the structural equation model (SEM). This study found that E-government perceived ease of use and perceived usefulness were strongly influenced by IS success model constructs and perceived trust. This study also found that user attitudes, usage intensions, and satisfaction were strongly influenced by TAM factors.

Hybrid micro-grid systems (HMGS) are small scale power system where the energy sources are installed to supply local customers. These systems may be considered as promising energy solution to meet the increased in energy demand and traditional sources depletion. Cost of electricity, system reliability, and environmental impacts of the system are three design criteria that must be considered in obtaining the accurate parameters of hybrid renewable energy system components. In this paper, hybrid micro-grid renewable energy system includes photovoltaic system, (PV) wind energy system, (WES) battery bank, (BB) and conventional diesel generator (DG) are proposed to meet the energy requirements in remote area, located in Red Sea called city of Bernice, Egypt, at 23° 54′ 31″ N, 35° 28′ 21″ E. Optimization of Cost of Electricity (COE), Renewable Factor (RF), and Loss of Power Supply Probability (LPSP) are main objective of the designing process of the hybrid system considered as the objective functions. Then, Multi-objective multi-verse optimization (MOMVO) algorithm is used with considering two scenarios, the first one is renewable sources and the second is renewable/diesel energy source. All the possible HMGS configurations namely: PV/battery, wind/battery, PV/wind/battery and PV/battery/diesel, wind/battery/diesel, PV/wind/battery/diesel are studied and analyzed. Moreover, one year hourly meteorological weather data for case study are recorded. Reverse osmosis desalination (ROD) is considered in conjunction with the residential load. The proposed power management strategy is used to manage the system operation when supplying the load. A linear fuzzy membership function is used for purpose of decision making. The simulation results show that MOMVO produces appropriate components size and the PV/wind/battery/diesel is the optimum configuration with values of COE = 0.2720$/KWh, LPSP = 0.1397, and RF = 92.37% at w1 = 0.5, w2 = 0.3, and w3 = 0.2. Sensitivity analysis is performed to show the effect of changing system parameters on the objective functions. It is also shown that the techno-economic feasibility of using HMGS for rural electrification systems and enhance energy access.

This study aims to investigate the perceptions of near-field communication (NFC) usage for mobile payments in Saudi Arabia. In order to develop a mathematical framework for the acceptance of NFC quality of information for mobile payments, researchers have combined the technological acceptance model (TAM) and the idea of perceived risk. An online and physical study of 1217 NFC portable credit card holders in Saudi Arabia was conducted. Exploratory and confirmatory analyses were utilized to analyze the factor structure of the measurement items, and Smart PLS 2.0 from structural equation modeling (SEM) was used to assess the theories and hypotheses that had been put forth. The results show that (1) social influence, perceived element of risk, and subjective norms each have a negative influence on preconceptions of trust in online payment methods using NFC; (2) social influence, perceived element of risk, and social norms all have a positive effect on satisfaction with the security of electronic payment using NFC; (3) perceived ease of use has a negative effect on perceived confidence in digital payment using NFC; and (4) perceived ease of use has a negative effect on perceived trust in online payment using NFC. As a consequence of these findings, users’ attitudes regarding the use of NFC and behavioral intentions to utilize NFC mobile payment can be revealed. This study created a unique approach for assessing perceptions, perceived trust, and NFC information quality in mobile payment uptake in Saudi Arabia. As a consequence, banks may find this research useful as they implement new strategies to attract more customers, such as perceived security, brand trust, and NFC information quality in mobile payment adaption.

connected. Online machine learning is developing day by day. Many systems have been completely modified thanks to this development to help achieve more correct results. The Internet of Things allows us to develop a system that is able to operate without human intervention. In other words, the Internet of Things is a technology that has the ability to transmit data on the Internet without human-computer cooperation. The Internet of Things is used in different ways in most regions. This study aims to develop an automated attendance management system where attendance is recorded via mobile devices and the student's presence is limited to a specific place based on a specific network. Recent advances in wireless technologies have led to the development and growth of smart systems in everyday life. Nowadays, the Wi-Fi localization mechanism can cover a specific area so that any user connected to the Wi-Fi station can be recognized. We developed the design and implementation of class attendance monitoring system through Wi-Fi signal. The developed software system will be able to store, retrieve and deliver student information, such as attendance or absence, through a mobile device with server knowledge.

A vehicular ad-hoc network (VANET) was employed in commercial, road-safety, and entertainment applications due to its accessibility. Applications and services were shared with the service providers (SP) over mobile nodes to any destination without special infrastructure. The mobility pattern of the nodes was independent, and the acceleration remained unpredictable, which led to service failures and information drop-outs. Resuming communication requires the flooding of additional control messages, which exploits the network resource in a shorter period. This paper introduces the neighbor predictive adaptive handoff (NPAH) algorithm for ensuring seamless communication, regardless of the application service time. NPAH discovers weak communication links in the service, which persist through the least resource dependent distance-based neighbor discovery. The selected neighbors are characterized by distance and minimum cost exploitation using the Q-learning technique. The process of learning decides the handoff of a vehicle based on storage utilization and cost factors. The results demonstrated the effectiveness of the NPAH algorithm by achieving less packet loss, shorter outage times, and improving the delivery factor.

The development of emotional intelligence robotics in the learning environment plays valuable support for social interaction among students. Emotional intelligence robots should be scalable to recognize emotions, appear empathetic in learning situations, and enrich the confidence with students for active interaction. This paper presents some related issues about integrating emotional intelligence robotics in E-learning such as its role and outcomes to motivate interaction during education and discover the main aspects of the emotional intelligence between humans and robots. This paper aims to determine the design requirements of emotional robots. Besides, this paper proposed a framework of educational Robotics with Emotional Intelligence in Learning(EREIL). EREIL consists of three main units; student emotions discovery, student emotions representation, and EREIL-Student Communication (RSC). In addition, it introduces a perception of EREIL working. In the future, this paper tries to merge more sensor devices and machine learning algorithms to integrate face analysis with speech recognition. Besides, it can add a persuasion unit in the EREIL robot to convince students with better learning choices to their abilities.

Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (ε constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.