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Optical dromions for complex Ginzburg Landau model with nonlinear media

applied Mathematics(A Journal of Chinese Universities) 2023年第1期38卷 111-125页

作者： Syed T.R.Rizvi Aly R.Seadawy Muhammad Younis S.O.Abbas Abdul Khaliq Department of Mathematics COMSATS University IslamabadLahore CampusPakistan Mathematics Department Faculty of ScienceTaibah UniversityAl-Madinah Al-Munawarah 41411Kingdom of Saudi Arabia Punjab University College of Information Technology University of the PunjabLahore Pakistan Mathematics Department Riphah Institute of Computing and Applied SciencesRiphah International UniversityLahorePakistan

This manuscript studies the optical dromions with beta derivative(BD)applied to the Complex Ginzburg Landau equation(CGLE)with Kerr law,parabolic law,cubic quintic septic law and quadratic cubic *** obtain bright dromians by using the sine-cosine method(SCM).We will also obtain domain walls with the assistance of Bernoulli equation approach(BEA).Constraint conditions are also listed.

关键词： CGLE optical dromians non-Kerr laws

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Classification and Comprehension of Software Requirements Using Ensemble Learning

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Computers, Materials & Continua 2024年第8期80卷 2839-2855页

作者： Jalil Abbas Arshad Ahmad Syed Muqsit Shaheed Rubia Fatima Sajid Shah Mohammad Elaffendi Gauhar Ali School of Computer Science and Technology Anhui UniversityHefei230039China School of Computing Sciences Pak Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science and IT University of LahoreLahore55150Pakistan Department of Computer Science Emerson UniversityPunjabMultan60000Pakistan EIAS(Emerging Intelligent Autonomous Systems)Data Science Lab Prince Sultan UniversityRiyad12435Saudi Arabia

The software development process mostly depends on accurately identifying both essential and optional ***,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional *** address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human ***,existing techniques often struggle with complex instructions and large-scale *** our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer *** results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT *** datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse *** findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

关键词： Ensemble learning machine learning non-functional requirements requirement engineering accuracy sliding window

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A Comparative Study of Recent 2D Human Pose Estimation Methods 6

A Comparative Study of Recent 2D Human Pose Estimation Metho...

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6th International Conference on computing and Informatics, ICCI 2024

作者： Elshami, Nada E. Salah, Ahmad Mohsen, Heba Department of Computer Science Faculty of Computers and Information Technology Future University in Egypt New Cairo Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computers and Informatics Zagazig University Zagazig Egypt

ISBN: (纸本)9798350373875

Human Pose Estimation is a computer vision technique utilized in various fields such as healthcare, security, and sports to detect the pose of single or multi-person utilizing various machine learning and deep learning methodologies. These methodologies can be applied in a variety of ways, including top-down and bottom-up approaches. Furthermore, this technique can be used with images or videos, as well as in 2D or 3D domains. This paper delves into recent advancements in deep learning methods used for estimating the pose, covering commonly employed datasets, evaluation metrics, and the challenges encountered in this field. It aims to assist new researchers in getting familiar with this technique based on work published between 2019 and 2023, with a spotlight on 2D and omitting 3D. © 2024 IEEE.

关键词： Deep learning

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IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： Farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI Cybersecurity

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Spider Inspired Hexapod Mobile Robot With Ultrasonic SLAM Exploration In Hazards Environment

Spider Inspired Hexapod Mobile Robot With Ultrasonic SLAM Ex...

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Advances in technology and computing (ICATC), International Conference on

作者： Thisal Induwara Thisara Pathirana Thushani Mallikarathne Department of Applied Computing Faculty of Computing and Technology University of Kelaniya Kelaniya Sri Lanka Department of Mechanical and Manufacturing Faculty of Technology Wayamba University of Sri Lanka

ISBN: (数字)9798331518219

ISBN: (纸本)9798331518226

This research starts with issues of human operations in hazardous environments and the need for robots capable of handling such issues, then introduces the main concept a six legged robot, inspired by the biological structure and movement of spiders, designed and fabricated using 3d printing technology. This research is focused on the development and evaluation of a bio inspired hexapod mobile robot for mapping and exploration in hazardous environments. It incorporates slam technology, with a view to creating an accurate map in complex and hazardous terrains using the hector slam mapping tool. The results obtained show that the walking speed of the robot on flat surfaces is 4.8 cm/s and on uneven terrains, it walks at a speed of 4.2 cm/s with mapping accuracy of about 83%. Additionally, the robot's stability was confirmed through torque tests, showing that the motors could handle the required load, maintaining balance even under additional weight. This study concludes that the spider inspired hexapod robot is a viable solution for autonomous exploration in hazardous environments, offering significant potential for applications in search and rescue operations, disaster recovery, and inspection of dangerous sites. The study concludes that the spider inspired robot is a promising solution for navigating and mapping hazardous environments, with potential applications in disaster management and exploration.

关键词： Legged locomotion Simultaneous localization and mapping Accuracy Torque Navigation Inspection Three-dimensional printing Motors Hazards Acoustics

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Optimized Vaccine Selection Using Machine Learning and Genetic Algorithms: A Study on Side Effects of COVID-19 Vaccines 12th

Optimized Vaccine Selection Using Machine Learning and Genet...

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12th International Conference on Soft computing for Problem Solving, SocProS 2023

作者： Soni, Vishal Joshi, Shubham Deep, Kusum Pant, Millie Department of Mechatronics Indian Institute of Information Technology Bihar Bhagalpur813210 India Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India Department of Mathematics Indian Institute of Technology Uttarakhand Roorkee247667 India Mehta Family School for Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India

ISBN: (纸本)9789819732913

The COVID-19 pandemic has necessitated large-scale vaccination campaigns to control the virus's spread. Given the scale of these efforts, it's crucial to scrutinize potential adverse effects, especially in vulnerable population groups. This study combines machine learning and Genetic Algorithms (GA) to analyze the patterns of patient recovery post-vaccination. We use a range of classifiers, including the Gradient Boosting Decision Tree (GBDT), to identify key features and symptom categories that influence patient recovery. We also use Principal Component Analysis (PCA) to reduce dimensionality and uncover patterns within high-dimensional data. The data used in this study have taken from the Vaccine Adverse Event Reporting System (VAERS). With the help of GA, we aim to guide optimal vaccine selection for susceptible demographics, minimizing the risk and severity of side effects. Our research paves the way for more personalized vaccination strategies, promoting healthier outcomes and reducing risks associated with mass vaccination. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Effect of vertical rotation and salt on the stability of a couple stress fluid layer

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Chinese Journal of Physics 2025年 96卷 245-257页

作者： Choudhary, Shalu Devi, Reeta Mahajan, Amit Sunil Chandel, Vishal Sharma, Manoj Sarita Yadav, Dhananjay Department of Mathematics Uttaranchal University Uttarakhand Dehradun India Department of Mathematics Govt. Post Degree College Nagrota Bagwan H.P. Kangra India Department of Applied Sciences National Institute of Technology Delhi India Department of Mathematics and Scientific Computing NIT Hamirpur India Department of Mathematics and Physical Sciences University of Nizwa Oman

The study investigates the stability characteristics of the effect of rotation and salt on convection in a couple stress fluid subject to different conducting boundary conditions. The stability analysis, conducted through linear stability theory and energy theory, confirms that the critical conditions for the onset of instability remain identical for both approaches. The results indicate that couple stresses significantly hinder fluid flow, reducing heat transfer through convection. Furthermore, increased rotation and salt concentration exhibit stabilizing effects, delaying the onset of convection. Among the boundary conditions examined, the free-free (FF) system exhibits an earlier onset of convection, while the rigid-rigid (RR) system demonstrates enhanced stability at lower Taylor numbers. However, beyond a certain threshold of the Taylor number, the FF system becomes more stable than the RR system. These findings underscore the crucial role of boundary conditions and physical parameters in modulating convection in a rotating couple stress fluid layer. © 2025 The Physical Society of the Republic of China (Taiwan)

关键词： Couple-stress fluid Rotation Solute Stability analysis

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The Smart Assistive technology in Education for Students with Disabilities in Oman

The Smart Assistive Technology in Education for Students wit...

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computing and Data Analytics (ICCDA), International Conference on

作者： Junath Naseer Ahamed Laryam Nasser Albalushi Hind Maktoom Almamari Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

The research in Oman assessed smart assistive technology's impact on students with disabilities, focusing on academic improvement and user satisfaction. Using a mixedmethods approach, it found significant gains in student engagement and academic outcomes. Recommendations include expanding assistive tools and training educators. Limitations were a small sample size and uneven tech access. The study concluded that such technology greatly enhances inclusive education in Oman.

关键词： Training Data analysis Focusing Assistive technologies

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A Study on the Importance of Cybersecurity and its Significance in Omani Society

A Study on the Importance of Cybersecurity and its Significa...

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computing and Data Analytics (ICCDA), International Conference on

作者： Junath Naseer Ahamed Aisha Abdullah Mohammed Hamda Khamis Al Ghaithi Rawya Khamis Al Salty Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This study investigates the critical role of cybersecurity in Omani society, analyzing its significance in safeguarding digital assets and maintaining societal well-being. By examining the current cybersecurity landscape in Oman, including challenges and vulnerabilities, the study highlights the pressing need for robust cybersecurity measures. Additionally, it explores the implications of cyber threats on various sectors, such as government, finance, healthcare, and education, underscoring the far-reaching consequences of inadequate cybersecurity practices. Furthermore, the study assesses existing cybersecurity initiatives and policies in Oman and identifies areas for improvement to enhance overall cyber resilience. Through empirical research and data analysis, this study contributes valuable insights into the importance of cybersecurity in protecting national interests, promoting economic growth, and preserving social stability in Oman.

关键词： Privacy Data analysis Pressing Passwords Stability analysis Loss measurement Computer security Computer crime Protection Resilience

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