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Intelligent wearable vision systems for the visually impaired in Saudi Arabia

Neural Computing and Applications 2025年第14期37卷 8571-8591页

作者： Talaat, Fatma M. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Abd Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Navigating the world with visual impairments presents unique challenges, often limiting independence and safety. This research introduces SafeStride, a novel algorithm designed to empower visually impaired individuals through real-time obstacle detection and navigation assistance. SafeStride functions through five interconnected phases: sensor data acquisition, data preprocessing, obstacle detection, path planning, and feedback and guidance. A combination of ultrasonic sensors, cameras, and inertial measurement units (IMUs) feed comprehensive environmental data into the system, enabling accurate obstacle detection and safe path planning. Evaluating SafeStride's performance involved the MNIST dataset for image recognition and a dedicated indoor object detection dataset. Additionally, comparisons were made against three deep learning models—convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The results showcased SafeStride's high accuracy and effectiveness in both obstacle detection and classification. Beyond its impressive performance, SafeStride represents a significant leap forward in navigation aids for the visually impaired. By offering a comprehensive solution that enhances safety and independence in diverse environments, it holds immense potential to improve the lives of countless individuals. Future efforts will focus on further optimizing the algorithm and testing its real-world capabilities, paving the way for a future where safe and independent navigation becomes a reality for all. All three deep learning models (CNN, LSTM, GRU) excelled in the experiment, achieving high accuracy above 0.98, strong recall and F1-scores near 0.99, and exceptional AUC-ROC scores exceeding 0.9998. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

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GPSOM: group-based particle swarm optimization with multiple strategies for engineering applications

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Journal of Big Data 2025年第1期12卷 1-40页

作者： Yan, Jialing Hu, Gang Jia, Heming Hussien, Abdelazim G. Abualigah, Laith Department of Applied Mathematics Xi’an University of Technology Xi’an710054 China School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China School of Information Engineering Sanming University Sanming365004 China Department of Computer and Information Science Linköping University Linköping58183 Sweden Faculty of Science Fayoum University Faiyum63514 Egypt Computer Science Department Al al-Bayt University Mafraq25113 Jordan

Particle Swarm Optimization (PSO) is a classic optimization algorithm;however, it has issues when solving high-dimensional complex optimization problems, such as sensitivity to initial parameters, insufficient population diversity, and susceptibility to becoming stuck in local optima. To this end, we propose a particle swarm optimization algorithm with multiple swarm strategies (GPSOM) that significantly improves the algorithm’s optimization performance by designing grouping and diversification strategies for the population. GPSOM divides the population into three subgroups and adopts targeted strategies based on different phases: sub-swarm 1 focuses on exploration and improves population diversity and local search ability by dynamically adjusting operators;sub-swarm 2 focuses on exploitation, introducing sine cosine factors and adaptive mechanisms to traverse the solution space efficiently;sub-swarm 3 achieves global and local balance, generating high-quality solutions through adaptive factors and position adjustment operators. The experimental results show that GPSOM performs significantly better than traditional algorithms on the Combinatorial Evolutionary Computing (CEC) 2020 benchmark test set (hereinafter referred to as 20TS), and its reliability and efficiency have been demonstrated in practical applications, such as 15 engineering examples, vehicle cruise control, and robot path optimization. GPSOM is an efficient, stable, practical optimization algorithm that effectively solves high-dimensional complex optimization problems. © The Author(s) 2025.

关键词： Particle swarm optimization (PSO)

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

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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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Machine Learning Model for Wind Power Forecasting Using Enhanced Multilayer Perceptron

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computers, Materials & Continua 2025年第5期83卷 2287-2303页

作者： Ahmed A.Ewees Mohammed A.A.Al-Qaness Ali Alshahrani Mohamed Abd Elaziz Computer Science and Artificial Intelligence Department College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia Department of Computer Damietta UniversityDamietta34517Egypt College of Physics and Electronic Information Engineering Zhejiang Normal UniversityJinhua321004China Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt

Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy *** enhances the efficiency and reliability of renewable energy *** approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy *** primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data *** research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original *** developed FHONO is then employed to optimize the MLP for enhanced wind power *** effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in *** results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all *** findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.

关键词： Wind power forecasting multilayer perceptron fire hawk optimizer non-monopolize search

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Single-Frequency Self-Alignment RF Resonant Beam for information and Power Transfer

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IEEE Internet of Things Journal 2025年第11期12卷 16622-16636页

作者： Jiang, Qingwei Liu, Mingqing Xu, Mengyuan Fang, Wen Xiong, Mingliang Liu, Qingwen Zhou, Shengli Tongji University College of Computer Science and Technology Shanghai201804 China Tongji University College of Electronics and Information Engineering Shanghai201804 China University of Connecticut Department of Electrical and Computer Engineering CT06250 United States

Due to power attenuation, improving transmission efficiency in the radio-frequency (RF) band remains a significant challenge, which hinders advancements in various fields of the Internet of Things (IoT), such as wireless power transfer (WPT) and wireless communication. Array design and retro-directive beamforming (RD-BF) techniques offer simple and effective ways to enhance transmission efficiency. However, when the target is an array or in the near field, the RD-BF system (RD-BFS) cannot radiate more energy to the target due to phase irregularities in the target region, resulting in challenges in achieving higher efficiency. To address this issue, we propose the RF-based resonant beam system (RF-RBS), which adaptively optimizes phase and power distribution between transmitting and receiving arrays by leveraging the resonance mechanism to achieve higher transmission efficiency. We analyze the system structure and develop an analytical model to evaluate power flow and resonance establishment. Numerical analysis demonstrates that the proposed RF-RBS achieves self-alignment without beam control and provides higher transmission efficiency compared to RD-BFS, with improvements of up to 16%. This self-alignment capability allows the system to effectively transfer power and information across varying distances and offsets. The numerical results indicate the capability to transmit watt-level power and achieve 21 bps/Hz of downlink spectral efficiency in indoor settings, highlighting the advantages of RF-RBS in information and power transfer for mobile applications. © 2025 IEEE.

关键词： Inductive power transmission

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Beyond administrative reports: a deep learning framework for classifying and monitoring crime and accidents leveraging large-scale online news

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Neural Computing and Applications 2025年第10期37卷 7183-7205页

作者： Tuarob, Suppawong Tatiyamaneekul, Phonarnun Pongpaichet, Siripen Tawichsri, Tanisa Noraset, Thanapon Faculty of Information and Communication Technology Mahidol University Nakhon Pathom Thailand Department of Computer Science University College London London United Kingdom Puey Ungphakorn Institute for Economic Research Bank of Thailand Bangkok Thailand

The escalating prevalence of violent crimes and accidents underscores the urgent need for efficient and timely monitoring systems. Traditional methods reliant on administrative reports often suffer from significant delays. This paper proposes CRIMSON, a novel framework that leverages large-scale online news to provide real-time insights into crime and accident trends. CRIMSON utilizes a multi-label classification technique that leverages a fine-tuned, pre-trained, cross-lingual language model to accurately categorize news articles. Our experimental results, conducted on a substantial dataset of Thai news articles, demonstrate superior performance, achieving an average F1 score of 86%. Beyond classification, CRIMSON aggregates categorized news into real-time statistics, revealing strong correlations between news-reported incidents and official crime data. This study pioneers online news as a reliable and timely crime and accident monitoring source, offering valuable insights for law enforcement, policymakers, and researchers. © The Author(s) 2025.

关键词： Deep learning

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IoT based Line of Control Monitoring system 2

IoT based Line of Control Monitoring System

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Petkar, Taniya Faculty of Engineering and Technology Department of Computer Science And Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This paper presents a novel line-of-control (LoC) monitoring system that leverages the Internet of Things (IoT) to improve border security. The system creates a strong infrastructure for real-time monitoring throughout critical border regions, especially the Line of Control, by utilizing IoT devices, sensors, and communication technologies. The coverage, timeliness, and data quality of conventional border surveillance systems are frequently compromised. As opposed to this, the suggested system makes use of a wirelessly networked network of intelligent sensors, such as infrared, acoustic, and video cameras. By utilizing sophisticated image processing algorithms and machine learning to analyze the gathered data, the system is able to differentiate between regular activities and any security risks. To help security staff make decisions quickly, real-time updates are transmitted to a centralized control center. Integration with cloud computing also makes data analysis, retrieval, and storage simpler and guarantees data accessibility for forensic investigations and trend research. © 2025 IEEE.

关键词： Network security

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RehabPal: Automated Physical Therapy Assistance

RehabPal: Automated Physical Therapy Assistance

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International Conference on Computing and Machine Learning, CML 2024

作者： Arya, P. Sharma, Treya Jatain, Aman Bhaskar, Shalini Bajaj Manju Kumar, Sandeep Department of Computer Science & Technology Amity University Gurgaon India School of Engineering & Technology K R Mangalam University Gurgaon India Department of Computer Science & Engineering and Technology Jaypee Institute of Information Technology Noida India CHRIST University Bengaluru India

ISBN: (纸本)9789819778386

This innovative research project transforms automated physical therapy support by combining 2D and 3D pose assessment approaches with tailored feedback systems. Utilizing Mediapipe technology, the apparatus attentively observes patients’ movements in a two-dimensional space and gives realtime data regarding their gait, exercise compliance, and range of motion. Accurate data on joint angles and body segment alignment are provided by the system's sophisticated 3D posture estimation algorithms, which enhance tracking precision. The system incorporates customized feedback systems that include individual patients’ goals, progress, and conditions and delivery and reward schemes. By increasing user engagement and adherence, the integration of gamification elements has the potential to revolutionize automated physical therapy support. This comprehensive approach aims to enhance patients’ quality of life while simultaneously enhancing long-term rehabilitation outcomes by providing more conveniently accessible, affordably priced, and specially designed therapies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Patient rehabilitation

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A Multi-Agent Garage Service Search and Recommendation with Hybrid MLs and LLMs

A Multi-Agent Garage Service Search and Recommendation with ...

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2025 International Conference on computer, Electrical and Communication Engineering, ICCECE 2025

作者： Shuvo, Mahfuzur Rahman Rahman, Ashifur Akuthota, Vishwanath Paul, Tanay Islam, Mahfujul Ashraf, Md Sadi Roy, Prosenjit Reza, Md Tanzim International American University Department of Management Information System United States Chittagong University of Engineering and Technology Department of Computer Science and Engineering Bangladesh TechOptima Hyderabad India Brac University Department of Computer Science and Engineering Bangladesh Prairie View A & M University Department of Computer Science TX United States

ISBN: (纸本)9798331513894

The automobile service industry's explosive growth highlights the need for creative approaches to boost operational effectiveness and user experience. This study introduces a Hybrid Garage Assistance system, integrating Classical Machine Learning (ML) techniques with Generative AI to optimize garage service discovery and analysis. The system employs sophisticated data processing methods, including Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and regex-based service detection, to extract actionable insights from unstructured garage *** to the system are machine learning models Random Forest (RF) and XGBoost (XGB) which achieve high precision and recall in classifying garage services. A hybrid search mechanism, combining cosine similarity with ML-driven predictions, ensures the delivery of highly personalized search results. To further refine decision-making, the system incorporates Generative AI models such as Perplexity for web-based research, Gemini for location-specific analysis, Mistral for email sending and GPT-4 for detailed service recommendations and dall-e for creating user specific parts images. These advanced tools provide users with comprehensive information that enables them to make well-informed decisions about garage *** evaluation of the system is conducted using robust metrics, including precision, recall, F1-score, and system latency. Experimental results reveal a precision of 85%, recall of 70.8%, and an F1-score of 77.2%, demonstrating the efficacy of integrating classical ML with generative AI. The system's average latency of 5.9 seconds ensures a seamless and responsive user *** hybrid framework highlights the potential of blending classical ML and Large Language Models (LLMs) to enhance search and recommendation functionalities, offering a scalable and robust blueprint for future advancements in the automotive service sector. The system's Propose a Multi-Agent system With high accuracy,

关键词： Inverse problems

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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