bluuebunny/crossref_metadata_2025_split huggingface dataset:
A new application for fast prediction and protection of electrical drive wheel speed using machine learning methodology
Authors: Khessam Medjdoub , Lousdad Abdelkader , Hazzab Abdeldjebar , Rezkallah Miloud , Chandra Ambrish 
Published: June 2022

This paper introduces <span>a non-linear implementation of the speed control technique of permanent magnetic synchronous motors (PMSM) using electronic differential (ED) command. Artificial neural network (ANN) coupled with particles swarm optimization (ANN-PSO) are implemented to control wheel speed and steering angle. The main purpose of the PMSM system and its application is the command of electric vehicles (EV). In the controller design, three-phase currents and rotor speed shall be measurable and eligible for feedback. Our propulsion platform consists of two PMSM in the back. The study with implemented ANN-PSO is performed after collecting the data from the ED to manage the control of speed EV, Left and right of steering angle and steering ahead. Based on this strategy, a new application can be provided in the GPS application to give the information as input (curved path angle) to ANN-PSO. Next, the application of ANN-PSO can estimate the parameters of ED to avoid the slip, as well as improves better performance and dynamic stability of electric vehicle drive systems.</span>
Optimum control for dynamic voltage restorer based on particle swarm optimization algorithm
Authors: Salman Saddam Subhi , Humod Abdulrahim Thiab , Hasan Fadhil A. 
Published: June 2022

This article <span>addresses a variety of power quality concerns, including voltage sag and swell, surges, harmonics, and so on, utilizing a dynamic voltage restorer (DVR). The proposed controller for DVR is proportional plus integral (PI) controller. Two methods are used for tuning the parameters of PI controller, trial and error and intelligent optimal method. The utilized optimal method is particle swarm optimization (PSO) method. Results depicted that DVR using PI controller tuned by PSO has improved performance than PI controller tuned by trial and error in term of rise time, maximum overshoot and settling time, as well as total harmonic distortion (THD). These improvements are applicable for voltage sag and swell conditions.</span>
Challenges and recent developments in solar tracking strategies for concentrated solar parabolic dish
Authors: Lorilla Franch Maverick A. , Barroca Renyl 
Published: June 2022

This contribution <span>presents an overview of control strategies for parabolic dish concentrated solar power (PD-CSP) sun tracker technologies from the literature on different implementations. This paper also highlights the practical challenges in designing sun trackers for CSP applications. Solar radiation undergoes seasonal changes and transients from the cloud that is exceptionally challenging to manipulate. This paper may provide control engineers with technical information on high-precision solar dynamic tracking and optical accuracy by emphasizing the features, advantages, and limitations of different control techniques and algorithms. Implementation of efficient control strategies addresses concerns in amending dynamics and nonlinearities, thus improving the system responses and extending the operational timeline of CSP plants, which results in a reduced cost per kWh produced.</span>
Word recognition and automated epenthesis removal for Indonesian sign system sentence gestures
Authors: Rakun Erdefi , Widhinugraha I Gusti Bagus Hadi , Putra Setyono Noer Fitria 
Published: June 2022

This research <span>focuses on building a system to translate continuous Indonesian sign system (SIBI) gestures into text. In a continuous gesture, a signer will add an epenthesis (transitional) gesture, which is hand movement with no meaning but needed to connect the hand movement of one word with the next word in a continuous gesture. Reducing the number of irrelevant inputs to the model through automated epenthesis removal can improve the system's ability to recognize the words in continuous gestures. We implemented threshold conditional random fields (TCRF) to identify epenthesis gestures. The dataset consists of 2,255 videos representing 28 common sentences in SIBI. The translation system consists of MobileNetV2 as a feature extraction technique, removing epenthesis gestures found by the TCRF, and a long short-term memory (LSTM) for the classifier. With the MobileNetV2-TCRF-bidirectional LSTM model, the best word error rate (WER) and sentence accuracy (SAcc) were 33.4% and 16.2%, respectively. Intermediate-stage processing steps consisting of sandwiched majority voting of the TCRF and the removal of word labels whose number of frames is less than two frames, along with LSTM output grouping, were able to reduce WER from 33.4% to 3.4% and increase SAcc from 16.2% to 80.2%.</span>
Challenges of IEEE 802.11a technology and improving its performance using diversity technique
Authors: Mohammed Najat Shyaa Jasim , Awad Buraq Abdulhadi , Abdullah Zainab B. , Shneen Salam W. 
Published: June 2022

In this technology, <span>information can be transmitted through the air without the need for cables, wires, or other electronic connectors, using electromagnetic waves such as infrared, radio and satellite frequencies. In this research, the most important challenges and problems faced by wireless communication systems were identified and how to solve and address them. (IEEE 802.11aWLAN) was also simulated and tested using Mat lab program, where the signal generated in (IEEE 802.11aWLAN) was examined in the form of orthogonal frequency division multiplexing (OFDM) modulation by examining the performance of the bit error rate of this system using diversity technology to improve the performance of this system where we adopted the reception diversity technology, In the case of line of sight (LOS) channel and multipath channel using maximal ratio combining (MRC) method. We notice that the use of the MRC receiving diversity technique in the system reduces the bit error rate and increases the efficiency of the system and the performance improve with increase the number of receiving antenna with gain 6 dB.</span>
Data-driven optimal antenna planning for enhanced 4G mobile networks under realistic environment
Authors: Zeleke Seifu Girma , Haile Beneyam Berehanu , Bekele Ephrem Teshale 
Published: June 2022

Recently different capacity boosting technologies, <span>including 3D beamforming and expanding operating bandwidth have been investigated and included in the enhanced fourth generation (4G) and fifth-generation standards. For mobile operators to enhance their network performance, applying these advanced technologies is vital. To achieve that, planning and optimization work need to consider the spatiotemporal distribution of users. Although the capacity impact of advanced antenna technologies is investigated well, deployment options to exploit their benefits in a cost-effective manner considering a realistic network environment are seldom reported. This work presents a data driven and multi-objective based optimal vertical beams placement for an enhanced 4G mobile network with the newly introduced C-band. The method is utilized for an area located in Addis Ababa, Ethiopia, taking into account the existing 4G mobile network as a reference configuration. Users are distributed based on the data collected by ethio-telecom network management system. Findings indicate that optimal vertical beams placement achieved for gradual deployment with consideration cell edge and aggregate throughput performances while reducing network cost. While being cost-effective, up to 69.2% and 73.8% cell edge performance gain is achieved at 20 and 30 pareto points relative to an existing macro mobile network.</span>
Highly efficient microstrip patch antenna for wireless gigabit alliance applications
Authors: Barua Soumen , Md. Imran Abu Zafar , Hoque Bhuiyan Md. Emdadul , Barua Subrata , Zahidur Rashid Syed , Gafur Abdul , Ahmed Md. Razu 
Published: June 2022

A <span>wireless gigabit alliance (WiGig) is widely known for higher bandwidth which operates at 60GHz unlicensed spectrum for super-fast data speed over short distances for millimeter-wave band application. This study offers an efficient, compact microstrip patch antenna for WiGig with a lesser return loss -50.45 dB (60 GHz). The gain and directivity of the proposed antenna are 13.01 dB and 13.42 dBi. It provides higher efficiency of around 91%. The proposed antenna also covers another mm-wave band that is at 95 GHz. The U.S. military uses electromagnetic radiation of 95 GHz as a non-lethal weapon. So, the anticipated antenna can be used for a miniaturized active denial system. The proposed antenna has a good gain, directivity, and efficiency for the multigigabits/s data rate. The aforesaid properties imply that it can be used for wireless personal area networks (WPAN) and wireless local area network (WLAN) and fifth-generation (5G) applications too.</span>
Joint polar with physical layer network coding and massive MIMO: performance analysis
Authors: Mahmood Alza Abduljabbar , Kadhim Abdulkareem Abdulrahman 
Published: June 2022

Large spectral efficiency, reliability, coverage, and energy efficiency are all major requirements to meet the targets of fifth-generation (5G) and beyond communication networks. The transmitted signal is usually susceptible to errors that reduced reliability and throughput of the system. Physical layer network coding (PLNC) is a promising technology to achieve better throughput, low latency, and high transmission rate. This paper considers the combination of PLNC with polar coding using massive multi-input multi-output (MIMO) system to enhance the transmission reliability by reducing the bit error rate (BER) and improve system reliability. This arrangement is investigated in two-way wireless relay transmission over millimetre wave (mmWave) band channel model. The results of the extensive simulation tests demonstrated improvements in throughput and BER achieved using polar code with PLNC and a sufficient number of antenna elements in the mMIMO system. The BER performance of polar-coded PLNC arrangement outperformed PLNC without coding for 128 and 256 receiving antenna elements in the relay node regardless the number of antenna elements in the user equipment side. The tests showed that the combination of the polar code with PLNC and MIMO system is not encouraging at low Signal-to-noise ratio (SNR).
Development and performance evaluation of object and traffic light recognition model by way of deep learning
Authors: Bali Shweta , Kumar Tapas , Tyagi Shyam Sunder 
Published: June 2022

Deep <span>learning models have shown incredible achievement in the field of autonomous driving, covering different aspects ranging from recognizing traffic signs and traffic lighs, vehicle detection, license plate detection, pedestrian detection. Most of the algorithms perrform better when the traffic lights are bigger in size, but the performance degrades in case of small-sized traffic lights. In this paper, the main emphasis is on evaluating two most promising deep learning architectures: single shot detector (SSD) and faster region convolutinal network (Faster R-CNN) on “la route automatisée (LaRA) traffic light dataset” which contains small traffic lights as objects. The strengths and weaknesses are evaluated based on different parameters. The performance is compared in terms of mean average Precision (mAP@0.50) and average recall. The impact of data augmentation on the two architectures is also analyzed. ResNet50 V1 as feature extractor for Faster R-CNN achieved 96% mAP (mean average precision) which performed better than Original ResNet50 V1 Faster R-CNN pipeline. Also, different parameters such as batch size, learning rate and optimizer are tuned for detecting and classifying small traffic lights into different categories. </span>
Traffic sign recognition based color model
Authors: Younus Majid Dherar , Al-Sabawi Emad A. 
Published: June 2022

The main <span>problems that encounter the traffic light detection algorithm have become a handicap to the performance of the algorithms. Problems associated with the change of sign color due to bad weather and illumination changes of sunlight make the detection hard task. In the current work, we discuss these problems and propose a new idea of an efficient real time color sign recognition that relies only on color information. The proposed approach is based on building a red-model in hypothetical red, green, blue (RGB) cube using a large database of traffic signs. The segmentation has been implemented on the traffic signs that hold red color only as an example to illustrate the proposed approach. Results showed that the proposed algorithm is accurate as well as the computational cost is reduced.</span>
Load balancing in cloud computing: an analytical review and proposal
Authors: Maurya Santosh Kumar , Sinha Garima 
Published: June 2022

Task scheduling <span>or mapping of requests to machines with specified requirements becomes a very important problem to get optimized results. With the requests of many users, the machine can go under different conditions such as heavy load and minimum load. These various aspects can lead to system failure and loss of data. Hence the load must be balanced to beat the losses of the system. A load balancing concept described how different types of loads could be balanced with each machine. The load can be of memory, computation, and network. The paper mainly focuses on all kinds of load to provide a big view for load balancing in cloud computing. Along with a review of all load balancing techniques, the paper is also presenting a proposal to balance the load with decision theory. The proposed paper introduces the basic techniques and types for load balancing and their implications for innovation.</span>
Toward mobile learning at Jordanian higher education institutions
Authors: Noor Ahmad Shukri Mohd , Atoom Marwan Nasser Yousef , Jalil Masita Abdul 
Published: June 2022

Globally, <span>teaching methods and tools in higher education institutions (HEIs) have changed nowadays. Many attempts have been made in Jordanian higher education institutions (JHEIs) in order to improve and continuity of the educational process, especially during coronavirus pandemic. The outbreak of this virus has become a major disruption where all Jordanian universities cancelled classes and moved toward online learning, and mobile learning (ML) has appeared as one of the possible solutions. ML is in its early stages at JHEIs, and it is academically unexplored enough. So, this study explores the ML experience at JHEIs during coronavirus disease 2019 (COVID-19) crisis. The data were collected using a web survey where 272 students in JHEIs participated. The results revealed that the smartphone is the most widely used mobile device for ML ML is easy to use, ML increases the interaction between the instructor and the students and among the students themselves, ML has a positive impact on students’ performance, and also students are willing to use ML in the future. The outcomes of the study support policy makers at JHEIs to make educational decisions relating ML phenomenon.</span>
Diagnosis of hepatitis disease using machine learning techniques
Authors: Ahmed Ibraheem I. , Mohammed Duraid Y. , Zidan Khamis A. 
Published: June 2022

Hepatitis is <span>an infection that causes inflammation of liver tissue. Many studies have developed machine learning models for hepatitis disease diagnosis. However, there has been little discussion about the relationship between hepatitis symptoms. The first objective of this study is to provide a brief description of a real-world hepatitis disease symptom dataset. Furthermore, the authors proposed a stand-alone classification platform using random forest, decision tree, and support vector machine into healthy people or hepatitis patients using adaptive wrapper feature selection. It was discovered that there is a strong link between certain characteristics and hepatitis diagnosis. The work presented here may help improve hepatitis diagnosis in the early stages, which may lead to a reduction in the acute effects of hepatitis on human life. It is worth noting that random forest (RF) gave the highest accuracy and stayed slightly consistent through all sets of features in comparison to decision tree (DT) and support vector machines (SVM).</span>
Overlapping clustering with k-median extension algorithm: An effective approach for overlapping clustering
Authors: Danganan Alvincent E. , Arceo Regina P. 
Published: June 2022

Most natural <span>world data involves overlapping communities where an object may belong to one or more clusters, referred to as overlapping clustering. However, it is worth mentioning that these algorithms have a significant drawback. Since some of the algorithm uses k-means, it also inherits the characteristics of being noise sensitive due to the arithmetic mean value which noisy data can considerably influence and affects the clustering algorithm by biasing the structure of obtained clusters. This paper proposed a new overlapping clustering algorithm named OCKMEx, which uses k-median to identify overlapping clusters in the presence of outliers. This new method aims to determine the insensitivity of the OCKMEx algorithm in locating data points that overlap even with outliers. An experimental evaluation of the algorithm was conducted wherein synthetic datasets served as a data source, and the F1 measure criterion was applied to assess the OCKMEx algorithm performance. Results indicate that the OCKMEx algorithm implementing the use of k-median performed a higher accuracy rate of 100% in identifying data points that overlap even with outliers compared to the existing k-means algorithm. The algorithm exhibited a promising performance in identifying overlapping clusters and was resistant to outliers.</span>
Artificial neural network modeling for predicting the quality of water in the Sabak Bernam River
Authors: Affandi Faqihah , Rahman Mohamad Faizal Abd , Che Ani Adi Izhar , Sulaiman Mohd Suhaimi 
Published: June 2022

Water <span>quality prediction is aided by environmental monitoring, ecological sustainability, and aquaculture. Traditional prediction approaches capture the nonlinearity and non-stationarity of water quality well. Due to their rapid progress, artificial neural networks (ANNs) have become a hotspot in water quality prediction in recent years. ANNs are utilised in this study to predict water quality using soft computing techniques. The feedforward network and the standard back-propagation method of Levenberg-Marquardt and scaled conjugate gradient learning algorithm were employed in this research. One hidden layer has been recommended for the modelling, with the number of hidden neurons set at 3, 24, and 49. For this analysis, six different testing percentages were used, and the output data can be categorised as '0' for clean water and '1' for polluted water. From the results, it can be shown that the most optimised model was from the model of trainlm with a testing percentage of 18% and with 3 number of neurons. This most optimised model obtains an accuracy of 91.7%, the best validation performance of 0.073346 with 24 epochs, and having a receiver operating characteristic (ROC) curve that is closer to the true positive rate compared to other samples.</span>
Implementation of eigenface method and support vector machine for face recognition absence information system
Authors: Annubaha Chakim , Widodo Aris Puji , Adi Kusworo 
Published: June 2022

The student <span>attendance system is what is needed in the process of recording attendance in learning and the development of student achievement. Currently several modern educational institutions have implemented a student attendance system using QR codes or fingerprints, but many still use the traditional system by calculating the number of students attending class. Based on these problems, the solution that can be given is to implement a student attendance system through face matching in the Android mobile application with Eigenface algorithm and support vector machine (SVM) algorithm. Eigenface using the principal component analysis (PCA) method can be used to reduce the dimensions of facial images so that they produce fewer variables and are easier to handle. The results obtained are then entered into a pattern classifier to determine the identity of the owner of the face. This study used 100 facial data as test data and training data. The system test results show that the use of Eigenface with SVM as a classifier can provide a fairly high level of accuracy. For facial images that were included in the training, 91% of the identification was correct.</span>
Recommendation method based on learner profile and demonstrated knowledge
Authors: Bourkoukou Outmane , El Bachari Essaid , Lachgar Mohamed 
Published: June 2022

The COVID-19 <span>pandemic is increasingly gaining popularity when discussing e-learning in the context of institutional and organizational learning because of its numerous benefits which make it possible for learners to learn regardless of the circumstances and/or the timing. Therefore, the expanding dominion of online learning has caused problem in terms of determining adequate learning activities for the learner in this context, and it relatively becomes a widely used learning technique for learners. Several studies in online learning focused mainly on increasing student achievements based on recommendation systems. An ideal recommender system in e-learning environment should be built with both accurate and pedagogical goals. To address this challenge, we propose a recommendation method based on learner preferences and knowledge level using machine learning technique. The learning approach is designed based on this technology to build a personalized e-learning scenario by selecting the most adequate learning activities for the learner. Moreover, several experiences were conducted in the real environment to evaluate our system. The results show the quality of learning and the learner's satisfaction.</span>
Cancerous brain tumor detection using hybrid deep learning framework
Authors: Kothari Sonali , Chiwhane Shwetambari , Jain Shruti , Baghel Malti 
Published: June 2022

Computational <span>models based on deep learning (DL) algorithms have multiple processing layers representing data at multiple levels of abstraction. Deep learning has exploded in popularity in recent years, particularly in medical image processing, medical image analysis, and bioinformatics. As a result, deep learning has effectively modified and strengthened the means of identification, prediction, and diagnosis in several healthcare fields, including pathology, brain tumours, lung cancer, the abdomen, cardiac, and retina. In general, brain tumours are among the most common and aggressive malignant tumour diseases, with a limited life span if diagnosed at a higher grade. After identifying the tumour, brain tumour grading is a crucial step in evaluating a successful treatment strategy. This research aims to propose a cancerous brain tumor detection and classification using deep learning. In this paper, numerous soft computing techniques and a deep learning model to summarise the pathophysiology of brain cancer, imaging modalities for brain cancer, and automated computer-assisted methods for brain cancer characterization is used. In the sense of machine learning and the deep learning model, paper has highlighted the association between brain cancer and other brain disorders such as epilepsy, stroke, Alzheimer's, Parkinson's, and Wilson's disease, leukoaraiosis, and other neurological disorders.</span>