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information technology (ACIT)

作者： Md Sakib Ullah Sourav Mohammad Sultan Mahmud Hua Zheng Mohammad Aljaidi Md. Simul Hasan Talukder Rejwan Bin Sulaiman Abdullah Hafez Nur Ahmad Al-Qerem School of Management Science and Engineering Shandong University of Finance and Economics Jinan China College of Computer Science and Software Engineering Shenzhen University Senzhen China Department of Software Engineering Software Engineering Institute of Guangzhou Guangzhou China Department of Computer Science Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Dhaka School of Computer Science & Technology Northumbria University Newcastle Tayne United Kingdom Northumbria UK Department of Electronic and Telecommunication Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Due to its importance in studying people’s thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language, with little work on low-resource languages. Though sentiment analysis, particularly emotion classification in English, has received increasing attention in recent years, little study has been done in the context of Bangla, one of the world’s most widely spoken languages. In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes, i.e., anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labelled datasets of Bangla comments on six emotion classes with fresh manually labelled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available.

关键词： Measurement Human computer interaction Sentiment analysis Analytical models Codes Accuracy Web 2.0 Text categorization Transformers information technology

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A Logical Evaluation of Predicting Immune System Strength by using Enhanced Deep Learning Strategy

A Logical Evaluation of Predicting Immune System Strength by...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： K. Malathi M. Ramkumar E D Kanmani Ruby G.B. Santhi Vivek Korde Hamzeh J Aljawawdeh Department of CSE Saveetha School of Engineering Saveetha Institute of Science and Technology (SIMATS) Saveetha University Chennai Department of ECE Veltech Rangarajan Dr Sagunthala R & D Institute of Science & Technology Chennai Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Department of software engineering Faculty of information technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The immune system plays a crucial role in defending the body against diseases, making it vital to develop models that can accurately predict immune system strength. This study presents an Enhanced Deep Learning Strategy (EDLS) designed to predict immune system strength based on immune-related biomarkers such as White Blood Cell (WBC) count and Immunoglobulins. A dataset consisting of 23,654 records with 12 features was collected from clinical sources. The data underwent comprehensive preprocessing, including noise reduction using a Median Filter, feature selection with Random Forest, and data fusion using Kalman Filters. The EDLS model integrates AutoEncoder for dimensionality reduction and Cascaded Long Short-Term Memory (LSTM) for capturing temporal dependencies. The proposed model outperforms nine traditional and advanced machine learning models, including Logistic Regression, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), with an accuracy of 94.72%. Evaluation metrics such as Precision (92.01%), Recall (93.77 % ), and F1-Score (92.88 % ) highlight the model's effectiveness in predicting immune system strength. Feature importance analysis reveals that WBC count and Immunoglobulins are the most influential predictors. This study demonstrates the potential of EDLS for enhancing immune strength prediction, which can have significant implications for personalized healthcare and disease prevention.

关键词： Deep learning Support vector machines Biological system modeling Autoencoders Predictive models Biomarkers Convolutional neural networks Long short term memory Immune system Diseases

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Vision-based one-dimensional barcode detection 20

Vision-based one-dimensional barcode detection

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6th International Conference on engineering and MIS, ICEMIS 2020

作者： Ishtewi, Nada Salem Abdalla, Mohamed A. E. Benghazi Libya Software Engineering Faculty of Information Technology University of Benghazi Benghazi Libya

ISBN: (纸本)9781450377362

The correct barcode detection is an important task to identify items and products from images. It is the cornerstone of the barcode decoding stage. Simply, incorrect detection leads to incorrect recognition. There are several studies which presented various level of accuracy based on the utilizing image processing or artificial neural networks. This paper proposes an image processing-based method to detect the one-dimensional barcode. The method proposed has succeeded when examined two published datasets of 215 and 1055 barcode images to reach accuracy rates of 86% and 85.8%, respectively. The yielded results of this paper have exceeded the results of the previous studies of the publishers of the datasets. © 2020 ACM.

关键词： Feature extraction

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Improved Urban AODV Routing with Distributed Semi-Clustering Protocol for Vehicular Adhoc Network

Improved Urban AODV Routing with Distributed Semi-Clustering...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Ch. Bhanu S. Sugumaran M. Senthil A S Hussein Alok Narkhede K. Kausalya Department of MBA QIS College of Engineering and Technology (QISCET) Ongole Department of ECE Sreenivasa Institute of Technology and Management Studies Chittoor Andhara Pradesh Department of CSE QIS College of Engineering and Technology Ongole India Department of Software Engineering Faculty of Information Technology Jordan Zarqa University Zarqa University of Business and Technology - (UBT) Jeddah SA Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

The proposed UADSC protocol improves urban Ad hoc on-demand remote vector routing (AODV) routing with integrated routing and clustering protocol improvements for efficient edge computing networks. Improved reliability and connectivity are achieved through the integration of GPS-based weight estimation for next-hop selection. Like LEACH, the protocol uses Distributed Semi-Clustering Protocol (DSCP) with multi-hop data aggregation to save energy. The use of head selection that considers both distance and residual energy helps to optimize data routing back to the base station and enhances network stability. The protocol workflow facilitates route finding and maintenance in real-time, which ensures robust connectivity in a dynamic urban environment. This comprehensive model aims to improve network efficiency, energy efficiency, and overall reliability, which are critical for applications as diverse as the Internet of Things, autonomous vehicles, and smart city infrastructure. The following parameters are used to showcase that the UADSC model is more efficient. The parameters are data success rate, throughput, routing overhead, data loss rate, and average delay are compared and show more efficiency with NRHC, QLBC, and BCSA with our proposed UADSC model.

关键词： Smart cities Computational modeling Estimation Routing Throughput Real-time systems Routing protocols Internet of Things Vehicle dynamics AODV

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Accessibility Metadata: A Proposal for the OER's Evaluation Considering Quality Models 5

Accessibility Metadata: A Proposal for the OER's Evaluation ...

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5th IEEE Ecuador Technical Chapters Meeting, ETCM 2021

作者： Chimbo, Bryan Ingavélez-Guerra, Paola Otón-Tortosa, Salvador Salesian Polytechnic University Research Group on Artificial Intelligence and Assistive Technologies Cuenca Ecuador University of Alcalá Faculty of Computer Science Information Technology Department of Software Engineering Madrid Spain

ISBN: (纸本)9781665441414

The collaborative approach which are born OER (Open Educational Resource) is the joint search for pedagogical and technological challenges to achieve improved quality reconstruction. It is necessary to demonstrate defined metrics that support processes and provide guidelines or instructions related to design for all. The open feature of the OERs must demonstrate accessibility, guaranteeing their interoperability. The incorporation of intelligent systems based on the efficient publication of accessibility information, contributes to the evaluation of accessible resources, knowledge modeling and future trends based on the feedback of profiles, and personalization from the user experience through. This paper presents the proposal for an expert system based on the ISO 40500, ISO 24751 and UDL standards. © 2021 IEEE.

关键词： Metadata

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A Bio-Medical Snake Optimizer System Driven by Logarithmic Surviving Global Search for Optimizing Feature Selection and its application for Disorder Recognition

arXiv

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arXiv 2024年

作者： Khurma, Ruba Abu Alhenawi, Esraa Braik, Malik Hashim, Fatma A. Chhabra, Amit Castillo, Pedro A. MEU Research Unit Faculty of Information Technology Middle East University Amman11831 Jordan Computer Science Department Al- Ahliyya Amman University Amman Jordan Software Engineering Department Al- Ahliyya Amman University Amman Jordan Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan Faculty of Engineering Helwan University Egypt Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar143005 India Department of Computer Engineering Automatics and Robotics University of Granada Granada Spain

It is of paramount importance to enhance medical practices, given how important it is to protect human life. Medical therapy can be accelerated by automating patient prediction using machine learning techniques. To double the efficiency of classifiers, several preprocessing strategies must be adopted for their crucial duty in this field. Feature selection (FS) is one tool that has been used frequently to modify data and enhance classification outcomes by lowering the dimensionality of datasets. Excluded features are those that have a poor correlation coefficient with the label class, that is, they have no meaningful correlation with classification and do not indicate where the instance belongs. Along with the recurring features, which show a strong association with the remainder of the features. Contrarily, the model being produced during training is harmed, and the classifier is misled by their presence. This causes overfitting and increases algorithm complexity and processing time. The pattern is made clearer by FS, which also creates a broader classification model with a lower chance of overfitting in an acceptable amount of time and algorithmic complexity. To optimize the FS process, building wrappers must employ metaheuristic algorithms (MAs) as search algorithms. The best solution, which reflects the best subset of features within a particular medical dataset that aids in patient diagnosis, is sought in this study using the Snake Optimizer (SO). The swarm-based approaches that SO is founded on have left it with several general flaws, like local minimum trapping, early convergence, uneven exploration and exploitation, and early convergence. By employing the cosine function to calculate the separation between the present solution and the ideal solution, the logarithm operator was paired with SO to better the exploitation process and get over these restrictions. In order to get the best overall answer, this forces the solutions to spiral downward. Additionally, S

关键词： Evolutionary algorithms

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Virtual Reality Semiconductor Laboratory: Usability Testing to Improve User Experience (UX) in Virtual Environments

Virtual Reality Semiconductor Laboratory: Usability Testing ...

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Sciences and Techniques of Automatic Control and Computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Ahmed Jamah Ahmed Alnagrat Muhannad Ismail Alkhallas Abdalslam S Imhmed Mohamed Rizalafande Che Ismail Syed Zulkarnain Syed Idrus Halifah Salem Saed Department of Communication Faculty of Business & Communication (FPK) Department of Computer Science and Information Technology Higher Institute of Science and Technology Wadi al-Shati Universiti Malaysia Perlis(UniMAP) Wadi al-Shati Fezzan Libya Kangar Perlis Malaysia Department of Software Engineering Faculty of Engineering Technology in Zuwarah Zuwarah Libya Faculty of Information Technology Aljufra University Aljufra Libya Faculty of Electronic Engineering Technology (FTKEN) Universiti Malaysia Perlis (UniMAP) Arau Malaysia Department of Communication Faculty of Business & Communication (FPK) Universiti Malaysia Perlis(UniMAP) Kangar Perlis Malaysia Department of Computer Science and Information Technology Higher Institute of Science and Technology Wadi al-Shati Wadi al-Shati Fezzan Libya

The Virtual Reality (VR) Semiconductor Laboratory, a computer-based virtual lab designed for training engineering students in semiconductor concepts, was evaluated to enhance user experience (UX) in virtual environments. A sample of 97 participants, including undergraduate, Master's, and PhD students, participated in the study. The User Experience Questionnaire (UEQ) was developed to collect data from users to evaluate their experience with the VR lab. The results of the study showed that the participants had a positive experience with the VR learning environment. They found the experience to be enjoyable, easy to understand, and helpful in completing learning tasks. The majority of participants rated the experience as very easy or easy in terms of learning and understanding the material, enjoyment, ease of using the interface, comfort with body movement during learning, and assistance with tasks. Additionally, most participants rated the experience as very easy in terms of task completion and comfort with the VR experiment. The study found high reliability in terms of usability, indicating that the UEQ is a valid and reliable tool for assessing virtual laboratory usability. These findings suggest that virtual laboratories can be effective learning and training tools in various fields, such as science and engineering. This research highlights the potential of VR technology in improving the training and learning experience for students.

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Fine-grained Graph Learning for Multi-view Subspace Clustering

arXiv

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arXiv 2022年

作者： Wang, Yidi Pei, Xiaobing Zhan, Haoxi The School of Software Huazhong University of Science and Technology Wuhan430074 China The Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116000 China

Multi-view subspace clustering (MSC) is a popular unsupervised method by integrating heterogeneous information to reveal the intrinsic clustering structure hidden across views. Usually, MSC methods use graphs (or affinity matrices) fusion to learn a common structure, and further apply graph-based approaches to clustering. Despite progress, most of the methods do not establish the connection between graph learning and clustering. Meanwhile, conventional graph fusion strategies assign coarse-grained weights to combine multi-graph, ignoring the importance of local structure. In this paper, we propose a fine-grained graph learning framework for multi-view subspace clustering (FGL-MSC) to address these issues. To utilize the multi-view information sufficiently, we design a specific graph learning method by introducing graph regularization and a local structure fusion pattern. The main challenge is how to optimize the fine-grained fusion weights while generating the learned graph that fits the clustering task, thus making the clustering representation meaningful and competitive. Accordingly, an iterative algorithm is proposed to solve the above joint optimization problem, which obtains the learned graph, the clustering representation, and the fusion weights simultaneously. Extensive experiments on eight real-world datasets show that the proposed framework has comparable performance to the state-of-the-art methods. The source code of the proposed method is available at https://***/siriuslay/FGL-MSC. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

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COVID TCL:A Joint Metric Loss Function for Diagnosing COVID-19 Patient in the Early and Incubation Period

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Computer Systems Science & engineering 2023年第10期47卷 187-204页

作者： Rui Wen Jie Zhou Zhongliang Shen Xiaorui Zhang Sunil Kumar Jha Department of Electromechanical Engineering Zhejiang Business Technology InstituteNingbo315012China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China Department of Physics Faculty of ScienceAdani UniversityAhmedabadIndia

Convolution Neural Networks(CNN)can quickly diagnose COVID-19 patients by analyzing computed tomography(CT)images of the lung,thereby effectively preventing the spread of ***,the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 *** reduces the model’s classification sensitivity,resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 *** solve the problem,this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image classification,combining softmax loss to design a jointly supervised metric loss function COVID Triplet-Center Loss(COVID-TCL).Triplet loss can increase inter-class discreteness,and center loss can improve intra-class ***,COVID-TCL can help the CNN-based model to extract more discriminative features and strengthen the diagnostic capacity of COVID-19 patients in the early stage and incubation ***,we use the extreme gradient boosting(XGBoost)as a classifier to design a COVID-19 images classification model of CNN-XGBoost architecture,to further improve the CNN-based model’s classification effect and operation *** experiment shows that the classification accuracy of the model proposed in this paper is 97.41%,and the sensitivity is 97.61%,which is higher than the other 7 reference *** COVID-TCL can effectively improve the classification sensitivity of the CNN-based model,the CNN-XGBoost architecture can further improve the CNN-based model’s classification effect.

关键词： Covid-19 diagnose convolutional neural networks XGBoost COVID triplet-center loss early and incubation COVID-19 patients

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Comparative study for different types of mppt algorithms using direct control method 1

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2nd International Conference on Electrical and Electronics engineering, ICEEE 2021

作者： Shams, Immad Mohammed, Karam Khairullah Mekhilef, Saad Tey, Kok Soon Power Electronics and Renewable Energy Research Laboratory Department of Electrical Engineering Faculty of Engineering Kuala Lumpur50603 Malaysia Control and Mechatronics Engineering Department School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

ISBN: (数字)9789811607493

ISBN: (纸本)9789811607486

Maximum power point tracking (MPPT) controllers achieve higher importance due to the high expansion rate of photovoltaic systems. The probability of partial shading conditions (PSCs) is also increased which drastically reduced the maximum power extraction capability from PV systems. In this paper, different types of MPPT algorithms using direct control method have been examined under the same test conditions. The current research trend on MPPT controllers has also been highlighted. This paper gives a brief description to engineers and practitioners working in the area of MPPT controllers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Controllers

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