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A three-phase induction motor is an electrical machine that can be used for many industrial applications. A three-phase driver circuit, also known as a three-wire series circuit or three-wire delta circuit is an electrical power system. These motors typically rely on power supplied using electric cables, which carry alternating currents. The three-phase motor inverter driver circuit is a simple circuit consisting of three half-wave rectifiers, which are connected in a bridge. When the input voltage level of the DC power supply to the inverter is high enough, this arrangement can provide a large current through the induction motor. This paper will show how to build a three-phase inverter driver circuit from scratch for a threephase induction motor by using (transistors and diodes) for photovoltaic application. The paper will guide people through each step with diagrams and schematics that make it easy for anyone to understand every part of this project and ensure people's safety by learning how these components work before putting them into their own design. The focus of this project will be on the use of STM32F103C8T6 as microcontrollers to generate the pulse width modulation (PWM) signals.

1226 articles on privacy and COVID-19 were published by authors from 69 countries in this year's issue. COVID 19's privacy is now the focus of many researchers' attention. The present body of knowledge on privacy for COVID-19 digital technologies has been thoroughly analyzed, and a concise overview of research status and future developments can be gleaned. This paper conducted a bibliometric examination of privacy using the Scopus dataset. Utilizing VOSviewer software, the relevant literature papers published on this topic were examined to determine the field's development history, research hotspots, and future directions. Over time, there has been a rise in the number of studies published in privacy for COVID-19, particularly after 2020, and the growth rate has been steadily increasing. Regarding published research, the United States and China lead the pack. These articles appeared in primarily English-language journals and conference proceedings. Privacy and COVID-19 research was mostly computer science. The most used terms in privacy and COVID-19 were data privacy and humans. This paper examines the evolution of privacy and COVID-19 research and indicates current research priorities and future research goals. Furthermore, the privacy and COVID-19 study seem to be a promising sphere as this study identifies 26 domains.

Detecting anomalous events in videos is one of the most popular computer vision topics. It is considered a challenging task in video analysis due to its definition, which is subjective or context-dependent. Various approaches have been proposed to address the anomaly detection problems. These approaches vary from hand-crafted to deep learning. Many researchers have gone into determining the best approach for effectively detecting anomalies in video streams while maintaining a low false alarm rate. The results proved that approaches based on deep learning offer very interesting results in this field. In this paper, we review a family of video anomaly detection approaches based on deep learning techniques, which are compared in terms of their algorithms and models. Moreover, we have grouped state-of-the-art methods into different categories based on the approach adopted to differentiate between normal and abnormal events, and the underlying assumptions. Furthermore, we also present publicly available datasets and evaluation metrics used in existing works. Finally, we provide a comparison and discussion on the results of various approaches according to different datasets. This paper can be a good starting point for such researchers to understand this field and review existing work related to this topic.

Peer to peer lending is famous for easy and fast loans from complicated traditional lending institutions. Therefore, big data and machine learning are needed for credit risk analysis, especially for potential defaulters. However, data imbalance and high computation have a terrible effect on machine learning prediction performance. This paper proposes a stacking ensemble learning with features selection based on embedded techniques (gradient boosted trees (GBDT), random forest (RF), adaptive boosting (AdaBoost), extra gradient boosting (XGBoost), light gradient boosting machine (LGBM), and decision tree (DT)) to predict the credit risk of individual borrowers on peer to peer (P2P) lending. The stacking ensemble model is created from a stack of meta-learners used in feature selection. The feature selection+ stacking model produces an average of 94.54% accuracy and 69.10 s execution time. RF meta-learner+Stacking ensemble is the best classification model, and the LGBM meta-learner+stacking ensemble is the fastest execution time. Based on experimental results, this paper showed that the credit risk prediction for P2P lending could be improved using the stacking ensemble model in addition to proper feature selection.

The purpose of this paper is to use the Rasch model software in analyzing the identified influencing factors of internet banking towards improving adoption usage and to find the validity and reliability of the instrument. An initial conceptual model was developed based on the previous of Literature Review. About 17 factors have been identified from the previous models and frameworks and they have been improved as influencing factors. Those factors are the website design (WD), the ease of use (EOU), the security, the quality system, the social influence, the trust, the electronic word of mouth (eWOM), the rewards, the perceived usefulness (PU), the perceived ease of use (PEOU), the intention to use (IU), the sustainability, the commitment, the user experience/generation, the knowledge, the profession, and the income. The questionnaire survey was successfully conducted with 51 respondents and pilot data verification of 30 respondents. The results showed that person reliability was 0.93 spread of person respondent was 54.06 and person separation was 3.66 However, item reliability result was 0.88, spread of item was 45.98 and item separation equal to 2.70 was fair. This paper was proven to be a significant contribution to the validity and reliability of using the Rasch model.

Microwave is the most popular sensor in industrial applications for detecting material characterization. Over the past decade, microwave sensor is high demand, especially in medical for detecting cancer in the human body, agriculture for detecting moisture of soil, and freshness in the food industry. The previous study has shown that the high demands of the microwave sensor in industrial applications make researchers always think of new ideas to design microwave sensors to improves accuracy and sensitivity. This paper reviews an investigation of material characterization of recent developments of a planar sensor for various contaminants and parameter value of solid, liquid, and powder as material under test (MUT). Planar resonator sensor enhances the weakness of conventional sensors in bulky size, required a large volume of samples, and high cost. This planar sensor will differentiate MUT properties based on scattering parameters at various operating frequencies. The framework presented in this review paper includes new developments in resonator structure as well as advanced design of potential future research work. Previous studies will be objectively analysed and compared in order to gain a better understanding of microwave resonant sensors and to develop innovative concepts to further enhance application research involving material characterization.

The evolution of wireless communication networks has introduced various applications that require massive device connectivity and high spectral efficiency. Non-orthogonal multiple access (NOMA) technique is one of the most promising technologies to perform efficiently data transmission. The NOMA technique can allocate the same resource block for two users by super-imposing signals. At the receiver, the signals are separated by performing successive interference cancellation (SIC) technique. For efficient data transmission, the fading and shadowing effects of channels also play a pivotal role. Many researches have considered Rayleigh, Rician, Nakagami-m, and other fading channels in various perspectives. In our paper, a system model based on a NOMA network with two users over log-normal fading distribution in the presence of channel estimation errors and SIC imperfections is proposed. The performance is analyzed in terms of outage probability and simulations are performed with the assistance of Monte-Carlo simulations. The obtained results shown the effectiveness in comparison with the traditionally used fading distributions. The same analysis is also performed in various scenarios of power allocation levels, target rates, and imperfections. The transmit SNR and power allocation of the users are important for efficient communication in any fading distribution as shown in this paper.

Day to day to electrical power demand increases very rapidly with linear and non-linear load demands. Especially the nonlinear loads are creating the harmonics in the current and voltage signals. The current and voltage signal values are measured with the phasor measurement unit (PMU) for the proper magnitude and phase angle calculation even in the presence of harmonic components in the signals. The performance of the PMU is depending upon the phasor calculation technique. Different technique/methods are available for the phasor calculation, from the method to method there is difference in the accuracy, phasor computation time and complexity. In this paper various techniques for phasor calculation are presented. For better performance of PMU, more accurate and less computation time for phasor calculation technique is required. But in real time, accuracy and speed both may not satisfied with single technique. Need to find a satisfactory technique, which satisfies the speed of phasor computation and accuracy. In this paper it is proposed that direct phasor estimation technique, which gives the better results in terms of accuracy and time and this method, satisfies the requirements for the dynamic monitoring of power system according to the IEEE std. C37.118.1-2011.

This paper deals with an improved backstepping control strategy for sensorless control of a permanent magnet synchronous motor (PMSM) based on field-oriented control (FOC), using a backstepping controller to improve its performances. However, this control requires the precise knowledge of some machine’s variables which could not be available. In electric drives control, sensors are generally used as the main devices for feedback information. Some practical constraints could affect the system performances, due to the lack of measurement material or maintenance difficulties caused by the dysfunction or faults of the used sensors such as: encoder or resolver sensors of speed-position. In this paper, a sensorless control is proposed based on a dynamic backstepping method and an extended Kalman filter (EKF) which uses the state space formulation with a set of mathematical equations to recursively estimate future observations and minimizes the mean square error of the estimated variables (rotor speed position and torque) to design controllers for nonlinear systems. The proposed control scheme achieves the asymptotically uniformed stability. The effectiveness of this method is illustrated by the stabilization and tracking numerical examples and the obtained simulation results show the effectiveness and the feasibility of the proposed controller using Lyapunov approach.

Some of the benefits of fifth-generation (5G) mobile communications include low latency, fast data rates, and increased amount of perceived service quality of users and base station capacity. The purpose of this paper is to solve some of the problems in the traditional mobile system by increasing the channel capacity, non-orthogonal multiple access (NOMA), has a chance of winning the race, power-domain NOMA (PD-NOMA) is widely used in but it requires a large power imbalance between the signals allocated to various users to work. This paper also proposes an improved mobile system model and compares it with a traditional mobile system, then evaluates the effect of channel coding types on the spectrum efficiency performance. A proposed mobile system relied on increasing the number of users as well as increasing the frequency spectrum and is also proposed to improve the error rate, which is incorporated into NOMA and orthogonal frequency division multiplexing (OFDM) schemes at the same time to provide great flexibility and compatibility with other services, such as the 5G and sixth-generation (6G) systems. The mobile gully system (MGS) system is compared to a traditional system, the result is demonstrated that the proposed outperforms the orthogonal multiple access (OMA) system in terms of sum-rate capacity, and bit error rate (BER) performance.

An unbalanced electric power system can occur due to unequal lines and/or loads in each phase of the system. For a system with a high level of unbalance, the assumption that the system is balanced becomes incorrect. For a case like this, the system must be analyzed using three-phase approaches because single-phase analysis techniques can no longer be used to obtain accurate results. Therefore, the development of three-phase models of all components of the electric power system, including the wind turbine generating system (WTGS), is very important so that the system can properly be analyzed and its performance can correctly be evaluated. This paper proposes a method to incorporate WTGS in a three-phase load flow analysis of an unbalanced distribution system. The proposed method is based on the previously published single-phase two-port network model. In this paper, the single-phase two-port network model is modified and extended so that it can be applied to unbalanced systems. The model is then included in the three-phase distribution system load flow (DSLF) analysis. The proposed method is validated using 12-node and 19-node distribution systems.

The use of information and communication technology is very beneficial in the education sector because it can enhance the quality of education. However, the implementation of ICT in the education sector of developed and developing countries is a challenging task. This paper explains the comparative study of ICT issues in the education sector of developed and developing countries. In particular, we compare issues between Pakistan and high-tech countries. Our study reveals the fact that the education sector is facing numerous ICT problems that are based on culture, finance, management, infrastructure, lack of training, lack of equipment, teacher’s refusal, and ethical issues. At the end of this paper, various issues faced by the implementation of ICT in the education sector of Pakistan have been categorized into various types, namely, infrastructure, lack of IT professionals, lack of high-speed internet and equipment. Our research is based on five key research questions related to ICT issues. We used a mixed approach where the results of this study can be used as a set of guidelines to help make the learning environment technology-oriented, fast, planned, and productive. Future directions are also given at the end of this paper.

Advancements in information technology is contributing to the excessive rate of big data generation recently. Big data refers to datasets that are huge in volume and consumes much time and space to process and transmit using the available resources. Big data also covers data with unstructured and structured formats. Many agencies are currently subscribing to research on big data analytics owing to the failure of the existing data processing techniques to handle the rate at which big data is generated. This paper presents an efficient classification and reduction technique for big data based on parallel generalized Hebbian algorithm (GHA) which is one of the commonly used principal component analysis (PCA) neural network (NN) learning algorithms. The new method proposed in this study was compared to the existing methods to demonstrate its capabilities in reducing the dimensionality of big data. The proposed method in this paper is implemented using Spark Radoop platform.

Biometric authentication is a technology that has become significant in the high level of personal identity security. This paper provides a signature recognition system. This paper provides a static signature recognition system (SSRS). We have classified the signature in two ways. The first method uses the genetic algorithm (GA), considering that the signature is the chromosome with 35 genes, and each feature is a gene. With applying the processes of the GA between chromosomes and the formation of generations in sequence until we reach the optimal solution by finding the chromosome closest to the chromosome that enters the system. In the second method, we have classified the signature by calculating the Euclidean Distance between the query signature and the signatures stored in the database. The signature closest to a confirmed threshold is considered the desired goal. The database uses 25 handwritten signatures (15 signatures for training and five original signatures, and five fake signatures written by other people for testing), so we have a database of 500 signatures. With a 94% discrimination rate, the genetic recognition system (GRS) was able to access the solutions, and with a (91% rate) the euclidean recognition system (ERS) was done. The application uses MATLAB.

Recent healthcare reports indicate clearly an increasing mortality rates worldwide which puts a significant burden on the healthcare sector due to different diseases. Coronary artery diseases (CAD) is one of the main reasons of these uprising death rates since it affects the heart directly. For early diagnosis and treatment of CADs, a swiftly growing technology called data mining has been used to collect and categorize necessary data from patients; age, blood sugar and pressure, a type of thorax pain, cholesterol, and so on. Therefore, this paper adopted four data mining methods; decision tree (DT), logistic regression (LR), random forest (RF), and Naïve Bayes (NB) to achieve the goal. The paper utilized the Cleveland dataset along with Python programming language to compare among the four data mining methods in terms of precision, accuracy, recall, and area under the curve. The results illustrated that NB method has the best accuracy of 89.47% compared with previous studies which will help with accurate, fast and inexpensive diagnosis of CADs.

An effective teaching contributes to the students’ performance and this depends on qualified-trained teachers, school administration, parents and conducive environment for teaching and learning. It is proven that students are more successful in their academic achievements, in the societies where teachers perceive their profession as their job. This paper explores the impact of the society’s perception on teachers’ professionalism. The method employed in this paper is qualitative by using interview technique and used audio-recorded to ensure a complete transcript. The result indicated that in some developing countries including Cameroon, Kenya and Fiji has various reasons for their job. Teaching profession was affected by how the society considers teachers whereby they are perceived as disadvantaged population who cannot fully take part in the social and economic activities due to the fact that their salary is very low compared other public servants. This low salary and loss of respect of teachers by the society lead to the job dissatisfaction and made the teaching profession as a transitional job before waiting to move to another different professional field.

This paper advances video analytics with a focus on crowd analysis for Hajj and Umrah pilgrimages. In recent years, there has been an increased interest in the advancement of video analytics and visible surveillance to improve the safety and security of pilgrims during their stay in Makkah. It is mainly because Hajj is an entirely special event that involve hundreds of thousands of people being clustered in a small area. This paper proposed a convolutional neural network (CNN) system for performing multitude analysis, in particular for crowd counting. In addition, it also proposes a new algorithm for applications in Hajj and Umrah. We create a new dataset based on the Hajj pilgrimage scenario in order to address this challenge. The proposed algorithm outperforms the state-of-the-art approach with a significant reduction of the mean absolute error (MAE) result: 240.0 (177.5 improvement) and the mean square error (MSE) result: 260.5 (280.1 improvement) when used with the latest dataset (HAJJ-Crowd dataset). We present density map and prediction of traditional approach in our novel HAJJ-crowd dataset for the purpose of evaluation with our proposed method.

In recent years, internet of things (IoT) represents a giant and a promoter area in innovation and engineering fields. IoT devices are spread in various fields and offer advanced services which assist their users to monitor and control objects remotely. IoT has a set of special characteristics such as dynamic, variety of data and huge scale which introduces a great challenge in the field of retrieval technologies, more precisely real-time retrieval. This paper addresses the issue of real-time retrieval of connected things and tries to propose an innovative solution which allows the retrieval of these things and their descriptive data. The paper proposes an on-line tool for real-time retrieval of connected things and their descriptive data based on network port scanning technique. The performance of this tool proves to be powerful under normal conditions, however more tests must be implemented in the aim to improve the proposed solution. The tool resulted from this work appears to be promising and can be used as a reference by network administrators and IT security managers for the development of new security mechanisms and security reinforcement.

The world is currently facing a strong epidemic and pandemic of coronavirus. This motivates establishing our paper, where this virus pushes researchers to study, investigate, observe, analyse and try solving its related issues. In this work, an artificial neural network (ANN) model of backpropagation neural network (BNN) with two hidden layers is proposed for expecting confirmed cases and death cases of coronavirus disease 2019 (covid-19). As a field of study, Iraq country has been considered in this paper. Covid-19 dataset from our world in data (OWID) is used here. Promising result is achieved where a very small error value of 0.0035 is reported in overall the evaluations. This paper may implicate establishing further researches that consider other parameters and other countries over the world. It is worth mentioning that the suggested ANN model may help decision maker people in taking quarantine movements against the strong epidemic and pandemic of covid-19.

The acute shortage of trained and experienced sonographers causes the detection of congenital heart defects (CHDs) extremely difficult. In order to minimize this difficulty, an accurate fetal heart segmentation to the early location of such structural heart abnormalities prior to delivery is essential. However, the segmentation process is not an easy task due to the small size of the fetal heart structure. Moreover, the manual task for identifying the standard cardiac planes, primarily based on a four-chamber view, requires a well-trained clinician and experience. In this paper, a CNN method using U-Net architecture was proposed to automate fetal cardiac standard planes segmentation from ultrasound images. A total of 519 fetal cardiac images was obtained from three videos. All data is divided into training and testing data. The testing data consist of 106 slices of the four-chamber segmentation tasks, i.e. atrial septal defect (ASD), ventricular septal defect (VSD), and normal. The segmentation of the post-processing method is needed to enhanced the segmentation result. In this paper, a combination technique with U-Net and Otsu thresholding gives the best performances with 99.48%-pixel accuracy, 96.73% mean accuracy, 94.92% mean intersection over union, and 0.21% segmentation error. In the future, the implementation of Deep Learning in the study of CHDs holds significant potential for identifying novel CHDs in heterogeneous fetal hearts.

The main goal of a battery management system (BMS) is to estimate parameters descriptive of the battery pack operating conditions in real-time. One of the most critical aspects of BMS systems is estimating the battery's state of charge (SOC). However, in the case of a lithium-ion battery, it is not easy to provide an accurate estimate of the state of charge. In the present paper we propose a mechanism based on an extended kalman filter (EKF) to improve the state-of-charge estimation accuracy on lithium-ion cells. The paper covers the cell modeling and the system parameters identification requirements, the experimental tests, and results analysis. We first established a mathematical model representing the dynamics of a cell. We adopted a model that comprehends terms that describe the dynamic parameters like SOC, open-circuit voltage, transfer resistance, ohmic loss, diffusion capacitance, and resistance. Then, we performed the appropriate battery discharge tests to identify the parameters of the model. Finally, the EKF filter applied to the cell test data has shown high precision in SOC estimation, even in a noisy system.

Class imbalance and overlapping on multi-class can reduce the performance and accuracy of the classification. Noise must also be considered because it can reduce the performance of classification. With a resampling algorithm and feature selection, this paper proposes a method for improving the performance of hybrid approach redefinition-multi class (HAR-MI). Resampling algorithm can overcome the problem of noise but cannot handle overlapping well. Feature selection is good at dealing with overlapping but can experience a decrease in quality if there is a noise. The HAR-MI approach is a way to deal with multi-class imbalance issues, but it has some drawbacks when dealing with overlapping. The contribution of this paper is to suggest a new approach for dealing with class imbalance, overlapping, and noise in multi-class. This is accomplished by employing minimizing overlapping selection (MOSS) as an ensemble learning algorithm and a preprocessing technique in HAR-MI, as well as employing multi-class combination cleaning and resampling (MC-CCR) as a resampling algorithm at the processing stage. When subjected to overlapping and classifier performance, it is discovered that the proposed method produces good results, as evidenced by higher augmented r-value, class average accuracy, class balance accuracy, multi class g-mean, and confusion entropy.

Predicting the reading difficulty level of English texts is a critical process for second language education and assessment. Reading difficulty level is concerned with the problem of matching a reader’s proficiency and the appropriate text. The reading difficulty level or readability assessment is the process for predicting the reading grade level required from an input text or document, which corresponds to the reader and to the materials. Students in Jordan at their academic levels find obstacles in finding relevant readable data for any subject at their levels. This paper is intended to introduce a model that foretells the reading difficulty level of a given text in terms of a student's ability to read and understand English as a non-native English speaker in Jordanian schools. In this paper, Jordanian students were classified into four categories according to their knowledge of English. The prediction of the reading difficulty level is achieved by using a modern statistical model that is situated on the Bayes model. The model compares the given text with some standard predefined text that strongly reflects the ability to read and understand English text. The accuracy of the proposed model was tested using the hold-out method. The overall prediction accuracy was 75.9%.

The video codecs are focusing on a smart transition in this era. A future area of research that has not yet been fully investigated is the effect of deep learning on video compression. The paper’s goal is to reduce the ringing and artifacts that loop filtering causes when high-efficiency video compression is used. Even though there is a lot of research being done to lessen this effect, there are still many improvements that can be made. In This paper we have focused on an intelligent solution for improvising in-loop filtering in high efficiency video coding (HEVC) using a deep convolutional neural network (CNN). The paper proposes the design and implementation of deep CNN-based loop filtering using a series of 15 CNN networks followed by a combine and squeeze network that improves feature extraction. The resultant output is free from double enhancement and the peak signal-to-noise ratio is improved by 0.5 dB compared to existing techniques. The experiments then demonstrate that improving the coding efficiency by pipelining this network to the current network and using it for higher quantization parameters (QP) is more effective than using it separately. Coding efficiency is improved by an average of 8.3% with the switching based deep CNN in-loop filtering.