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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

Computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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Avian Hazard Mitigation: A Classification Approach to Bird Strike Analysis

Avian Hazard Mitigation: A Classification Approach to Bird S...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Najiba Said Hamed Al-Zadjali Charles Savarimuthu B. Sudaravadivazhagan Emanuel O. Rances College of Computing and Information Sciences University of Technology and Applied Sciences-Muscat Muscat Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences-Al Musanah Musanah Sultanate of Oman College of Engineering and Technology University of Technology and Applied Sciences-Al Musanah Musanah Sultanate of Oman

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Bird strikes during landing or takeoff can result in fatalities and catastrophic accidents, raising concerns about loss of life for the aviation sector. This study explored the potential of employing machine-learning algorithms to mitigate bird strikes in aviation. Bird strike data were collected, and the Boruta algorithm feature-selection technique was applied to identify relevant features for an experiment. Irrelevant features were omitted, and relevant features related to bird strikes were identified for analysis. The multinomial Naive Bayes algorithm is then used to segregate objects based on their features. The experimental results illustrate the power of using machine learning techniques to segregate and analyze bird strike data, thereby aiding in determining the causes and effects of damage.

关键词： Machine learning algorithms Accuracy Prevention and mitigation Machine learning Birds Prediction algorithms Hazards Classification algorithms Trajectory Naive Bayes methods

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Reported Challenges in Deep Learning-Based Human Pose Estimation: A Systematic Review

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IEEE Access 2025年 13卷 80520-80539页

作者： Fisal, Nagwa R. Fathalla, Ahmed Elmanakhly, Dina Salah, Ahmad Suez Canal University Faculty of Science Department of Mathematics Ismailia Egypt University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Faculty of Computers and Informatics Zagazig University Department of Computer Science Egypt

Human Pose Estimation (HPE) is the technique of precisely estimating the locations of human joints in images and videos. Deep learning has been frequently applied recently to improve HPE performance. However, a number of challenges, including occlusions, crowded environments, and hidden joints, make it difficult to create an effective HPE model. This systematic review provides a comprehensive analysis of HPE reported challenges, focusing on hidden joint issues due to their significant impact on performance. This study systematically examines a number of HPE models, choosing 74 papers on deep learning-based HPE that have been published since 2018 based on specific criteria. The reported challenges are listed along with their frequency and related references, the review also covers datasets, loss functions, evaluation metrics, and pre-trained models used in HPE. Our analysis demonstrates that occlusion and crowded scenes are still the main issues that affect the model performance, highlighting the importance of addressing hidden joint problem. The review presents a number of findings, such as the most widely utilized architecture in HPE literature is the residual network, the mean square error is the most frequently used loss function. Furthermore, MPII and Human3.6M are the most used datasets for 2D and 3D pose estimation, respectively. Finally, this study highlights the gaps in the field, which could lead to new research opportunities. © 2013 IEEE.

关键词： Challenges deep learning hidden joints human pose estimation

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Wavelet-Transformed K-NN Pipeline for EEG-Based Eye Blink Classification with Time Wrapping 2nd

Wavelet-Transformed K-NN Pipeline for EEG-Based Eye Blink C...

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2nd International Conference on Data Science and Network Engineering, ICDSNE 2024

作者： Priyadharshini Jayadurga, N. Chandralekha, M. Saleem, Kashif Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Sciences and Engineering College of Applied Studies and Community Service King Saud University Riyadh Saudi Arabia

ISBN: (纸本)9789819783359

An innovative pipeline integrating wavelet transform, k-nearest neighbors (k-NN), and temporal wrapping approaches is presented in this work for the classification of eye blinks in electroencephalogram (EEG) recordings. The proposed process commences with raw EEG data preprocessing followed by wavelet treatment to extract significant time and frequency domain properties. These characteristics are subsequently utilized by a k-NN classifier to enhance the accuracy of eye blink recognition by using spatial relationships within EEG signals. Notably, the integration of a novel time wrapping technique deals with temporal dynamics, supplying the model with resilience to fluctuations in eye blink patterns across time. The pipeline’s enhanced efficiency can be observed through validation using a customized EEG dataset, particularly when navigating the time complexity associated with eye blink events. Evaluations performed against current techniques demonstrate the efficacy of the proposed solution. The outcomes not only demonstrate the methodology’s accuracy, but also its possible use in real-world settings featuring clinical diagnostics and human-computer interactions. This work conveys a strong basis for the discipline of EEG-based eye blink classification, having implications to enhance neuroscience and facilitating beneficial uses in healthcare and assistive technologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wavelet transforms

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Artificial Intelligence and Machine Learning for Accessibility: Smart Reader as an Assistive Tool for the Visually Impaired

Artificial Intelligence and Machine Learning for Accessibili...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Suresh Palarimath Venkateswaran Radhakrishnan Devarajan Veerasamy Prajoona Valsalan Vimbi Viswan Muni Balaji Thumu College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Electrical and Computer Engineering Department College of Engineering Dhofar Univesity Salalah Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Sohar Sultanate of Oman

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The amalgamation of artificial intelligence (AI) and machine learning (ML) has greatly improved accessibility for visually impaired individuals, fostering increased autonomy through pioneering assistive technology. This study introduces the Smart Reader, a device that integrates optical character recognition (OCR) and text-to-speech (TTS) technologies to enhance accessibility to printed documents. The Smart Reader utilizes a camera to take text images, translates them into digital representation through OCR, and converts the text into audible speech via TTS. The system has an intuitive user interface, an automatic page-turning mechanism, and a dictionary lookup function, guaranteeing a seamless reading experience. The findings indicate that the Smart Reader significantly improves access to written information, hence enhancing the quality of life for visually impaired individuals, especially in educational environments such as schools and libraries. The device is engineered for user-friendliness, enhancing accessibility and autonomy. The Smart Reader utilizes AI and ML, marking a significant progression in assistive technology, promoting an inclusive society where visually impaired individuals can engage with printed material with assurance. In addition to functionality, it seeks to empower people by connecting capability with opportunity, guaranteeing equitable access to information for everyone.

关键词： Visualization Translation Optical character recognition Visual impairment Machine learning Assistive technologies User interfaces Turning Libraries Text to speech

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Passive on-chip isolators based on the thin-film lithium niobate platform

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Chinese Physics B 2025年第3期34卷 408-413页

作者： Jiacheng Liu Gongyu Xia Qilin Hong Pingyu Zhu Kai-Kai Zhang Keyu Xia Ping Xu Shiqiao Qin Zhihong Zhu College of Advanced Interdisciplinary Studies&Hunan Provincial Key Laboratory of Novel Nano Optoelectronic Information Materials and Devices National University of Defense TechnologyChangsha 410073China Institute for Quantum Information and State Key Laboratory of High-Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China College of Engineering and Applied Sciences National Laboratory of Solid State Microstructuresand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210023China

Optical isolators,the photonic analogs of electronic diodes,are essential for ensuring the unidirectional flow of light in optical systems,thereby mitigating the destabilizing effects of back ***-film lithium niobate(TFLN),hailed as“the silicon of photonics,”has emerged as a pivotal material in the realm of chip-scale nonlinear optics,propelling the demand for compact optical *** report a breakthrough in the development of a fully passive,integrated optical isolator on the TFLN platform,leveraging the Kerr effect to achieve an impressive 10.3 dB of isolation with a minimal insertion loss of 1.87 *** theoretical simulations have demonstrated that our design,when applied to a microring resonator with a Q factor of 5×10^(6),can achieve 20 dB of isolation with an input power of merely 8 *** advancement underscores the immense potential of lithium niobate-based Kerr-effect isolators in propelling the integration and application of high-performance on-chip lasers,heralding a new era in integrated photonics.

关键词： thin-film lithium niobate Kerr effect optical isolator

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A novel U-net model for brain tumor segmentation from MRI images

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Alexandria Engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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Enhanced Bloom’s Educational Taxonomy for Fostering Information Literacy in the Era of Large Language Models

arXiv

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arXiv 2025年

作者： Luo, Yiming Liu, Ting Pang, Patrick Cheong-Iao McKay, Dana Chen, Ziqi Buchanan, George Chang, Shanton Faculty of Applied Sciences Macao Polytechnic University China School of Computing Technologies RMIT University Australia School of Computing and Information Systems The University of Melbourne Australia

The advent of Large Language Models (LLMs) has profoundly transformed the paradigms of information retrieval and problem-solving, enabling students to access information acquisition more efficiently to support learning. However, there is currently a lack of standardized evaluation frameworks that guide learners in effectively leveraging LLMs. This paper proposes an LLM-driven Bloom’s Educational Taxonomy that aims to recognize and evaluate students’ information literacy (IL) with LLMs, and to formalize and guide students practice-based activities of using LLMs to solve complex problems. The framework delineates the IL corresponding to the cognitive abilities required to use LLM into two distinct stages: Exploration & Action and Creation & Metacognition. It further subdivides these into seven phases: Perceiving, Searching, Reasoning, Interacting, Evaluating, Organizing, and Curating. Through the case presentation, the analysis demonstrates the framework’s applicability and feasibility, supporting its role in fostering IL among students with varying levels of prior knowledge. This framework fills the existing gap in the analysis of LLM usage frameworks and provides theoretical support for guiding learners to improve IL. Copyright © 2025, The Authors. All rights reserved.

关键词： Taxonomies

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Revolutionizing healthcare: An IoT-driven approach to remote patient health monitoring and management during the pandemic and beyond

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Alexandria Engineering Journal 2025年 126卷 358-367页

作者： Al-Wesabi, Fahd N. Elhessewi, Ghada Moh. Samir Alqahtani, Mohammed Alshuhail, Asma Althobaiti, Turke Aljehane, Nojood O. Alzahrani, Mohammed Yahya Almansour, Hamad Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Health Sciences College of Health and Rehabilitation Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Department of Computer Science Faculty of Science Northern Border University Arar73222 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Saudi Electronic University Saudi Arabia Applied Collage Najran University Najran Saudi Arabia

The integration of Internet of Things (IoT) technologies into the healthcare sector has witnessed substantial growth, particularly during global health emergencies such as the COVID-19 pandemic. This research introduces a Smart Health Monitoring System (SHMS) that enables real-time patient health tracking and environmental monitoring using IoT and machine learning (ML). The primary goal of the system is to provide continuous and remote monitoring of vital signs, including Body Temperature (BT), Heart Rate (HR), Environmental Temperature (ET), and Environmental Humidity (EH). Sensor-based data acquisition is managed through microcontroller hardware, with data transmitted via Wi-Fi to cloud-based platforms accessible through smartphones, laptops, and other internet-enabled devices. The system processes data using machine learning models—Logistic Regression and Decision Tree—to predict the patient's health status with high accuracy. Results indicate that the Logistic Regression model outperforms the Decision Tree model, achieving an accuracy rate of 88.89 %. Additionally, the SHMS architecture is scalable, supporting multi-user environments and large numbers of connected devices, making it suitable for real-world healthcare deployment. To enhance pandemic responsiveness, the system can be extended to measure additional parameters such as Respiratory Rate (RR) and Oxygen Saturation (SpO₂), which are essential during outbreaks of respiratory diseases like COVID-19 and influenza. This solution offers an effective and accessible tool for early detection, timely intervention, and reduced burden on healthcare infrastructure, especially during health crises. © 2025 The Authors

关键词： mHealth

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Modeling uncertainties associated with decision-making algorithms based on similarity measures of possibility belief interval-valued fuzzy hypersoft setting

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Neutrosophic Sets and Systems 2025年 77卷 282-309页

作者： Romdhini, Mamika Ujianita Al-Sharqi, Faisal Al-Obaidi, R.H. Rodzi, Zahari Md. Department of Mathematics Faculty of Mathematics and Natural Science University of Mataram Mataram83125 Indonesia Department of Mathematics Faculty of Education for Pure Sciences University of Anbar Ramadi55431 Iraq Fuel and Energy Techniques Engineering Department College of Engineering and Technologies Al-mustaqbal University Babylon51001 Iraq College of Computing Informatics and Mathematics UiTM Cawangan Negeri Sembilan Kampus Seremban Negeri Sembilan 72000 Malaysia

Hypersoft sets (HSSs) were initiated as an extension of soft sets (SSs) to address real-life scenarios involving multiple disjoint sets with different traits. One such extension is the interval-valued fuzzy hypersoft set (IVFHSS), which has proven effective in decision-making (DM). However, the IVFHSS model lacks a mechanism to incorporate the degree of acceptance of DM opinions, which is crucial for accurate decision-making. To overcome this limitation, our work aims to develop a novel hyperstructure called a possibility interval-valued fuzzy hypersoft set (PIVFHS-set). We begin by introducing essential operations and their properties, such as PIVFHS-subset, PIVFHS-null set, PIVFHS-absolute set, and complement of a PIVFHS-set. These concepts are illustrated through numerical examples to demonstrate their practical applications. Next, we delve into set-theoretic operations of PIVFHS sets, including union, intersection, AND, OR, and relevant laws. These operations are further elucidated through numerical examples, matrix representations, and graphical illustrations. Additionally, we present two algorithms based on AND and OR operations, providing step-by-step explanations and showcasing their effectiveness through illustrative examples. Furthermore, we introduce a similarity measure to facilitate pattern recognition in PIVFHS-sets, aiding users in recruitment processes. Alongside an analytical study of the advantages and disadvantages of this model, we provide suggestions for future research based on the identified limitations. © 2025, University of New Mexico. All rights reserved.

关键词： Fuzzy sets

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