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Smartphone-Based Wi-Fi Analysis for Bus Passenger Counting

computers, Materials & Continua 2024年第4期79卷 875-907页

作者： Mohammed alatiyyah Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

In the contemporary era of technological advancement,smartphones have become an indispensable part of individuals’daily lives,exerting a pervasive *** paper presents an innovative approach to passenger countingonbuses throughthe analysis ofWi-Fi signals emanating frompassengers’mobile *** study seeks to scrutinize the reliability of digital Wi-Fi environments in predicting bus occupancy levels,thereby addressing a crucial aspect of public *** proposed system comprises three crucial elements:Signal capture,data filtration,and the calculation and estimation of passenger *** pivotal findings reveal that the system demonstrates commendable accuracy in estimating passenger counts undermoderate-crowding conditions,with an average deviation of 20%from the ground truth and an accuracy rate ranging from 90%to 100%.This underscores its efficacy in scenarios characterized by moderate levels of ***,in densely crowded conditions,the system exhibits a tendency to overestimate passenger numbers,occasionally doubling the actual *** acknowledging the need for further research to enhance accuracy in crowded conditions,this study presents a pioneering avenue to address a significant concern in public *** implications of the findings are poised to contribute substantially to the enhancement of bus operations and service quality.

关键词： Public transportation digital environment passenger estimation signal capturing Wi-Fi

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GAPTCHA: A Game-Based Image-CAPTCHA 1st

GAPTCHA: A Game-Based Image-CAPTCHA

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Farooq, Umar Tariq, Noshina Mehmood, Danish almufareh, Maram Fahaad Humayun, Mamoona Khan, Farrukh Aslam Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Islamabad44000 Pakistan Department of Avionics Engineering Air University Islamabad44000 Pakistan Department of Information Systems College of Computer and Information Sciences Jouf University Al Jouf Sakakah72388 Saudi Arabia King Saud University Riyadh11653 Saudi Arabia

ISBN: (纸本)9783031776168

The threat of bot attacks has increased with the abundance of a diverse range of smart devices used globally. Users access the Internet for various needs, such as entertainment, education, e-commerce, and data exchange. It has tremendously increased the number of users and connected devices. The completely automated public Turing test to tell the computer and human apart (CAPTCHA) is a fully automated Turing test to differentiate a machine from a person and safeguard Internet services from spam and bot assaults. However, the distortion levels make human identification unpleasant and impossible. This paper proposes a novel CAPTCHA, named "GAPTCHA," based on a famous game called Snakes and Ladders game. It uses a simple keyboard, mouse, and touch inputs. The results showed that 3% of the people got it wrong, and about reusing the proposed GAPTCHA, 100% of the users said yes, which is a positive result for its successful user experience. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Bot (Internet)

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SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-Sampling From a Single Image

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年

作者： Hu, Bowen Yao, Weiheng Qiao, Sibo Pham, Hieu Wang, Shuqiang Ng, Michael Kwok-Po Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China Tiangong University School of Software Tianjin300387 China VinUniversity College of Engineering and Computer Science VinUni-Illinois Smart Health Center Hanoi100000 Viet Nam Hong Kong Baptist University Department of Mathematics Hong Kong

In minimally-invasive brain surgeries with indirect and narrow operating environments, 3D brain reconstruction is crucial. However, as requirements of accuracy for some new minimally-invasive surgeries (such as brain-computer interface surgery) are higher and higher, the outputs of conventional 3D reconstruction, such as point cloud (PC), are facing the challenges that sample points are too sparse and the precision is insufficient. On the other hand, there is a scarcity of high-density point cloud datasets, which makes it challenging to train models for direct reconstruction of high-density brain point clouds. In this work, a novel model named stereoscopic-aware graph generative adversarial network (SG-GAN) with two stages is proposed to generate fine high-density PC conditioned on a single image. The Stage-I GAN sketches the primitive shape and basic structure of the organ based on the given image, yielding Stage-I point clouds. The Stage-II GAN takes the results from Stage-I and generates high-density point clouds with detailed features. The Stage-II GAN is capable of correcting defects and restoring the detailed features of the region of interest (ROI) through the up-sampling process. Furthermore, a parameter-free-attention-based free-transforming module is developed to learn the efficient features of input, while upholding a promising performance. Comparing with the existing methods, the SG-GAN model shows superior performance in terms of visual quality, objective measurements, and performance in classification, as demonstrated by comprehensive results measured by several evaluation metrics including PC-to-PC error and Chamfer distance. © 2017 IEEE.

关键词： 3D reconstruction

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Comprehensive analysis of multiple machine learning techniques for rock slope failure prediction

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Journal of Rock Mechanics and Geotechnical engineering 2024年第11期16卷 4386-4398页

作者： Arsalan Mahmoodzadeh Abed alanazi Adil Hussein Mohammed Hawkar Hashim Ibrahim Abdullah alqahtani Shtwai alsubai Ahmed Babeker Elhag Department of Civil Engineering University of HalabjaHalabjaKurdistan Region46018Iraq Department of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Department of Communication and Computer Engineering Faculty of EngineeringCihan University-ErbilKurdistan RegionIraq Department of Civil Engineering College of EngineeringSalahaddin University-Erbil44002ErbilKurdistan RegionIraq Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Department of Civil Engineering College of EngineeringKing Khalid UniversityAbha61413Saudi Arabia

In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)*** models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio *** assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM *** results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models ***,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters *** is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering *** this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.

关键词： Rock slope stability Open-pit mines Machine learning(ML) Limit equilibrium method(LEM)

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Precision-Adaptive Task Offloading and Resource allocation for Efficient Positioning and Sensing in Near-Field IoV Systems

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IEEE Internet of Things Journal 2025年

作者： Dai, Cheng Bao, Song Chen, Songlin Garg, Sahil Kaddoum, Georges Sichuan University College of Computer Science Sichuan Chengdu China École de technologie supérieure Electrical Engineering Department MontréalQCH3C 1K3 Canada Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India École de technologie supérieure MontréalQCH3C1K3 Canada King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia

With the rapid advancement of sixth-generation (6G) network communication technology, improvements in data transmission rates, latency, and reliability have driven substantial growth in Internet of Vehicles (IoV) applications. Among these, the integration of 6G-enabled extremely large-scale antenna arrays (ELAAs) has extended the range of near-field (NF) communication, enabling their application in IoV to facilitate efficient and accurate environmental sensing. Through NF communication, vehicles can achieve high-accuracy localization and perception by analyzing the signal phase, channel state information, and beamforming calculations. However, positioning and sensing tasks place substantial computational and energy demands on edge devices, often exceeding traditional capacity limits. To address this challenge, task offloading has emerged as a solution, with mobile edge computing (MEC) offering a lower-latency alternative to centralized cloud computing by processing tasks at the network edge. Despite these advantages, MEC’s limited resources present challenges as the number of connected vehicles increases. Existing approaches to resource allocation often overlook the varied accuracy requirements of IoV tasks, where high-accuracy tasks like indoor navigation require stringent performance standards, while lower-accuracy tasks may tolerate reduced precision to save resources. Motivated by this, we propose an accuracy-based classification scheme for IoV positioning and sensing tasks, which dynamically adjusts accuracy requirements to reduce delay and energy consumption. Our approach maps total energy, accuracy loss, and delay to an overall quality of service (QoS) metric, and employs an optimization algorithm that leverages gradient descent and greedy strategies to balance resource allocation and accuracy selection. Extensive simulations demonstrate the effectiveness of the proposed scheme in reducing delay and energy consumption while maintaining high accuracy, signific

关键词： Resource allocation

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TrustChain-VANETs: Blockchain and IPFS Integration for Reliable and Secure Vehicular Communication in Intelligent Transportation Systems (ITS)

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IET Intelligent Transport Systems 2025年第1期19卷

作者： Zia Ullah Ghassan Husnain Abid Iqbal Ibrar ali Shah ali Saeed alzahrani Ramasamy Srinivasaga Naidu Yazeed Yasin Ghadi Mohammed S. al-Zahrani Department of Computer Software Engineering University of Engineering and Technology Mardan Mardan Pakistan Department of Computer Science CECOS University of IT and Emerging Sciences Peshawar Pakistan Department of Computer Engineering College of Computer Sciences and Information Technology King Faisal University AlAhsa Saudi Arabia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi UAE Department of Computer Networking College of Computer Sciences and Information Technology King Faisal University AlAhsa Saudi Arabia

Vehicular ad-hoc networks (VANETs) are pivotal in intelligent transportation systems (ITS), enabling enhanced traffic efficiency and safety. However, VANETs within ITS face critical challenges related to trust, privacy, and data reliability. To address these issues, this paper proposes a comprehensive solution that integrates blockchain and InterPlanetary file system (IPFS) technologies for ITS applications. We introduce a blockchain-based trust management system, TrustChain-VANETs, designed to ensure message credibility, privacy, and data reliability in ITS environments. Our model safeguards vehicle privacy while enabling credible messages to be shared through anonymous aggregate vehicular announcements, an essential feature for ITS. Reputation values, stored in the blockchain, allow roadside units (RSUs) to assess message reliability, achieving a 15% higher malicious vehicle detection rate compared to traditional methods at low probabilities of false reporting, crucial for trust in ITS. Additionally, conditional privacy is maintained by tracking malicious entities through public addresses, ensuring accountability in ITS. The system leverages IPFS on RSUs for secure, reliable data storage, with aggregated event data from vehicles stored in IPFS and vehicle reputation values maintained on the blockchain, addressing storage and cost challenges in ITS. This approach reduces transaction costs by 20% and decreases storage overhead by 30%, enhancing the efficiency of ITS data sharing. An incentive mechanism encourages honest data sharing among vehicles, with monetary rewards for aligning with verified event information, transparently recorded on the blockchain. Performance analysis demonstrates that TrustChain-VANETs reduces message verification time by an average of 25% compared to traditional proof-of-work blockchain models, making it suitable for the dynamic and demanding nature of ITS. This innovative framework addresses critical challenges in VANETs, delivering robu

关键词： data privacy security of data vehicular ad hoc networks

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ADAT: Time-Series-Aware Adaptive Transformer Architecture for Sign Language Translation

arXiv

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arXiv 2025年

作者： Shahin, Nada Ismail, Leila Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi Al Ain United Arab Emirates National Water and Energy United Arab Emirates University Abu Dhabi Al Ain United Arab Emirates Emirates Center for Mobility Research United Arab Emirates University Abu Dhabi Al Ain United Arab Emirates

Current sign language machine translation systems rely on recognizing hand movements, facial expressions and body postures, and natural language processing, to convert signs into text. Recent approaches use Transformer architectures to model long-range dependencies via positional encoding. However, they lack accuracy in recognizing fine-grained, short-range temporal dependencies between gestures captured at high framerates. Moreover, their high computational complexity leads to inefficient training. To mitigate these issues, we propose an Adaptive Transformer (ADAT), which incorporates components for enhanced feature extraction and adaptive feature weighting through a gating mechanism to emphasize contextually relevant features while reducing training overhead and maintaining translation accuracy. To evaluate ADAT, we introduce MedASL, the first public medical American Sign Language dataset. In sign-to-gloss-to-text experiments, ADAT outperforms the encoder-decoder transformer, improving BLEU-4 accuracy by 0.1% while reducing training time by 14.33% on PHOENIX14T and 3.24% on MedASL. In sign-to-text experiments, it improves accuracy by 8.7% and reduces training time by 2.8% on PHOENIX14T and achieves 4.7% higher accuracy and 7.17% faster training on MedASL. Compared to encoder-only and decoder-only baselines in sign-to-text, ADAT is at least 6.8% more accurate despite being up to 12.1% slower due to its dual-stream structure. © 2025, CC BY.

关键词： Neural machine translation

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Advanced feature fusion of radiomics and deep learning for accurate detection of wrist fractures on X-ray images

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BMC Musculoskeletal Disorders 2025年第1期26卷 1-22页

作者： Saadh, Mohamed J. Hussain, Qusay Mohammed albadr, Rafid Jihad Doshi, Hardik Rekha, M.M. Kundlas, Mayank Pal, Amrita Rizaev, Jasur Taher, Waam Mohammed alwan, Mariem Jawad, Mahmod Jasem al-Nuaimi, ali M. ali Farhood, Bagher Faculty of Pharmacy Middle East University Amman 11831 Jordan College of Pharmacy Alnoor University Mosul Iraq Ahl Al Bayt University Kerbala Iraq Department of Computer Engineering Faculty of Engineering & Technology Marwadi University Research Center Marwadi University Gujarat Rajkot 360003 India Department of Chemistry and Biochemistry School of Sciences JAIN (Deemed to Be University) Karnataka Bangalore India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Chemistry Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Public Health and Healthcare Management Rector Samarkand State Medical University 18 Amir Temur Street Samarkand Uzbekistan College of Nursing National University of Science and Technology Dhi Qar Iraq Pharmacy College Al-Farahidi University Baghdad Iraq Department of Pharmacy Al-Zahrawi University College Karbala Iraq Gilgamesh Ahliya University Baghdad Iraq Department of Medical Physics and Radiology Faculty of Paramedical Sciences Kashan University of Medical Sciences Kashan Iran

Objective: The aim of this study was to develop a hybrid diagnostic framework integrating radiomic and deep features for accurate and reproducible detection and classification of wrist fractures using X-ray images. Materials and Methods: A total of 3,537 X-ray images, including 1,871 fracture and 1,666 non-fracture cases, were collected from three healthcare centers. Radiomic features were extracted using the PyRadiomics library, and deep features were derived from the bottleneck layer of an autoencoder. Both feature modalities underwent reliability assessment via Intraclass Correlation Coefficient (ICC) and cosine similarity. Feature selection methods, including ANOVA, Mutual Information (MI), Principal Component Analysis (PCA), and Recursive Feature Elimination (RFE), were applied to optimize the feature set. Classifiers such as XGBoost, CatBoost, Random Forest, and a Voting Classifier were used to evaluate diagnostic performance. The dataset was divided into training (70%) and testing (30%) sets, and metrics such as accuracy, sensitivity, and AUC-ROC were used for evaluation. Results: The combined radiomic and deep feature approach consistently outperformed standalone methods. The Voting Classifier paired with MI achieved the highest performance, with a test accuracy of 95%, sensitivity of 94%, and AUC-ROC of 96%. The end-to-end model achieved competitive results with an accuracy of 93% and AUC-ROC of 94%. SHAP analysis and t-SNE visualizations confirmed the interpretability and robustness of the selected features. Conclusions: This hybrid framework demonstrates the potential for integrating radiomic and deep features to enhance diagnostic performance for wrist and forearm fractures, providing a reliable and interpretable solution suitable for clinical applications. © The Author(s) 2025.

关键词： Attention Mechanisms Classification Deep Learning Diagnostic Framework Feature Fusion Feature Selection Forearm Fractures Radiomics Wrist Fractures X-ray Imaging

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Comparative study of slow and fast diffusion processes via quadratic and cubic B-spline Galerkin method

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Ain Shams engineering Journal 2025年第8期16卷

作者： Arifeen, Shams Ul ali, Ihteram Haq, Sirajul Idris, Sahar Ahmed Islam, Saeed Department of Sciences and Humanities National University of Computer and Emerging Sciences KP Peshawar 25000 Pakistan Department of Mathematics & Statistics Women University Swabi KP Swabi 23430 Pakistan Faculty of Engineering Sciences GIK Institute of Engineering Sciences and Technology KP Topi 23640 Pakistan College of Science Department of Mathematics King Khalid University Abha Saudi Arabia Department of Mechanical Engineering Prince Mohammad Bin Fahd University P.O. Box 1664 Al Khobar 31952 Saudi Arabia

This study proposes a numerical scheme employing quadratic and cubic B-spline finite element methods (FEM) for solving nonlinear diffusion equations. For time discretization, a finite difference method is applied, while spatial approximation is performed using the B-spline-based Galerkin approach. The method's accuracy is demonstrated through three numerical examples, evaluated using the E₂ and E_∞ error norms. Stability is analyzed via the von Neumann method and confirmed using a matrix-based approach. The scheme is straightforward to implement and offers an efficient and reliable framework for the numerical solution of nonlinear diffusion problems. © 2025 The Authors

关键词： B-spline Finite difference Finite element method Slow and fast diffusion Stability analysis

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TANF–Trustworthy Adaptive Neural Framework for Reliable and Scalable 6G Internet of Things

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IEEE Internet of Things Journal 2025年

作者： Awan, Kamran Ahmad Din, Ikram Ud almogren, Ahmad Adnan, Muhammad altameem, Ayman Syed, Ikram Ahmad, Shabir University of Haripur Department of Information Technology Haripur22620 Pakistan King Saud University College of Computer and Information Sciences Chair of Cyber Security Department of Computer Science Riyadh11633 Saudi Arabia Higher Colleges of technology Al Ain United Arab Emirates Hankuk University of Foreign Studies Department of Information and Communication Engineering Yongin17035 Korea Republic of Center of Artificial Intelligence for Medical Instruments United States

The increasing complexity of 6G-IoT networks presents challenges in ensuring real-time trust assessment, computational efficiency, and security against adversarial threats. Existing frameworks struggle to dynamically adapt to evolving threats and high-volume data streams, leading to compromised decision reliability. This study proposes TANF (Trustworthy Adaptive Neural Framework), an advanced deep learning-driven trust evaluation system incorporating hierarchical processing, multi-domain trust layers, and Holo-Recursive Memory (HRM) for adaptive optimization. TANF prioritizes high-trust data streams using sensory stream balancing, dynamically allocates resources through task-specific synergy layers, and enhances memory recall by integrating past, present, and predictive state representations. The simulation, conducted in Edge-IIoTset, IoT-23 and CICIDS2017, evaluated trust assessment, computational latency, scalability, and adversarial detection. TANF achieves a precision of 92. 8%, a latency reduction of 34. 5% and a adversarial detection rate of 95. 6%, outperforming ERAI, ROBUST-6G, and IMCS. © 2014 IEEE.

关键词： Data streams

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