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SADAK: Simple, and Automatic Detection of Accidents on roads using Kolmogorov-Arnold Networks

International Journal of information technology (Singapore) 2025年 1-8页

作者： Dutta, Anurag Kumar, Pijush Kanti Lakshmanan, K. Ghosh, Soubhik Department of Computer Science Government College of Engineering & Textile Technology Calcutta Serampore India Department of Information Technology Government College of Engineering & Textile Technology Calcutta Serampore India Department of Mathematics Kuwait American School of Education Hawalli Salmiya Kuwait Department of Computer Science Bengal College of Engineering and Technology Calcutta Durgapur India

Road accidents are one of the major contributors to mortality in India with nearly 1.68 lakh deaths in 2023 accounting for nearly 19 deaths per hour. While road accidents aren’t always “fatal”, delayed detection leads to the loss of many lives. Deep Learning deliberately showed terrific results for a lot of tasks, but for a long time, the Deep Learning models utilized the standard Multi-Layered Perceptron (MLP) architecture as its’ skeleton. Recently, a state-of-the-art architecture—Kolmogorov Arnold Network (KAN) showed up, with variable activation functions in each edge of the graphical representation of the network. Several works have inherited the KAN architecture and have shown better results in many instances. This research proposes a class of accident detection models, SADAK (Simple, and Automatic Detection of Accidents on roads using Kolmogorov–Arnold Networks) for detection of road accidents inheriting KAN architecture. To benchmark its efficiency, several well-known MLP based architectures have been considered, alongside some state-of-the-art models, which result in accuracies as high as 89% using the MLP (Vision Transformer), and 97% using the KAN architecture (KAN-Convolutional-MLP). © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Deep learning Kolmogorov Arnold network Multi-layered perceptron Road accidents

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A method of embedding a high-resolution image into a large field-of-view image

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Multimedia Tools and Applications 2025年第14期84卷 13037-13056页

作者： Dong, Yanmei Pei, Mingtao Jia, Yunde Key Laboratory of Intelligent Information Processing and Graph Processing Tus College of Digit Guangxi University of Science and Technology Liuzhou545006 China Beijing Laboratory of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing100081 China

Images captured by wide-angle cameras or fisheye cameras are with large Field-of-View (FOV) but low resolution, while images captured by conventional cameras are with high-resolution but limited FOV. To handle this contradiction, in this paper, we propose to improve the resolution of a large FOV image by embedding a high-resolution image into it. We present an image embedding method based on non-rigid transformations and mesh optimization. Without any radial distortion correction on large FOV images, we utilize non-rigid transformations to model the mathematical transformation relationship between the high-resolution image and the large FOV image. Meanwhile, we exploit mesh optimization to generate evenly distributed control points for maintaining the shape and content of the large FOV image. To verify the effectiveness of the proposed method, we capture some image pairs from a conventional camera and a large FOV camera, and exploit several classic image alignment methods to implement four baseline methods. The experimental results show that our method preserves the shape and content of the original FOV image from 58% to 82%, which significantly outperforms all baseline methods. The results indicate that the proposed method can effectively embed a high-resolution image into a large FOV image as well as preserve the shape and content of the large FOV image. In other words, the resolution of a large FOV image can be improved via high-resolution image embedding. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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Private, Accurate and Communication Efficient Clustering Over Vertically Distributed Dataset

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IEEE Transactions on information Forensics and Security 2025年 20卷 5094-5108页

作者： Yuan, Shaoxian Xue, Kaiping Zhao, Jingcheng Wei, David S.L University of Science and Technology of China School of Cyber Science and Technology Anhui Hefei230027 China Fordham University Department of Computer and Information Science BronxNY10458 United States

Clustering is a crucial unsupervised machine learning algorithm extensively used in various practical applications, such as patient refinement and fraud detection, which often involve vertically distributed data across multiple data centers. However, sharing datasets directly is typically prohibited under GDPR due to potential privacy breaches. Therefore, privacy-preserving joint clustering for vertically distributed datasets is highly desired. In this paper, we propose Privacy-Preserving Vertically Federated Clustering (PPVFC), a solution that not only achieves this goal but also significantly reduces computational and communication overhead for each data owner (DO). Unlike most previous works that achieve the goal with a single privacy-enhancing technology, PPVFC jointly leverages multiparty homomorphic encryption (MHE) and multiparty computation (MPC) to efficiently interleave communication-lightweight homomorphic computations on the local dataset with operations over collectively secret-shared intermediate data. Specifically, we design a coefficient-wise encoding for MHE to pack large datasets and minimize communication costs. Additionally, we develop a round-efficient bit extraction protocol for determining the minimum distance. Through extensive experiments and security analysis, we demonstrate the practical performance and robust security guarantees of PPVFC. © 2005-2012 IEEE.

关键词： Learning algorithms

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A novel dominating set and centrality based graph convolutional network for node classification

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Multimedia Tools and Applications 2025年第14期84卷 12979-13004页

作者： Garg, Neeraj Garg, Sneha Jadeja, Mahipal Meena, Yogesh Kumar Gopalani, Dinesh Chauhan, Ganpat Singh Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Information Technology School of IT Manipal University Jaipur Rajasthan Jaipur302034 India

To learn and analyze graph-structured data, Graph Neural Networks (GNNs) have emerged as a powerful framework over traditional neural networks, which work well on grid-like or sequential structure data. GNNs are particularly designed to record dependencies and relations in large and complicated networks such as social networks, molecular structures, citation networks and recommendation systems. The main idea behind GNN is the message-passing mechanism that allows sharing of information between nodes and edges in a graph network, enabling the model to learn about graph structures and perform well even with limited training data. GNNs have three different architectures: Graph Convolutional Network (GCN), GraphSAGE, Graph Attention Model (GAT) and other hybrid models. In this paper, we have proposed novel GCN models namely dominating Set with Degree Centrality based GCN (DS_DC_GCN) and dominating Set with Betweenness Centrality based GCN (DS_BWC_GCN) model, for node classification task. In the traditional GNN models, training nodes are selected randomly. In our proposed models, we first identify a subset of the nodes with the help of a dominating set (D) of the underlying network. Then we consider final training nodes (say D′) from set D based on the centrality scores (degree or betweenness). We give preference to nodes having high centrality scores for training. To evaluate the performance of the proposed models, we have used three standard citation network datasets - Cora, Citeseer and Pubmed. Our models obtain improved node classification performance as compared to other state-of-the-art-methods. The consideration of high centrality nodes of dominating set in training helps to improve the feature availability in the underlying computational graphs, which in turn improves learning and hence, model performance. These models also have the potential to offer better results for other network tasks, including link prediction and community detection. © The Author(s), under

关键词： Graph neural networks

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Artificial intelligence-inspired comprehensive framework for desertification prediction

International Journal of Cognitive Computing in Engineering

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International Journal of Cognitive Computing in Engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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IoT-Aware Real-Time Healthcare Diagnostic Framework For Diabetes Using Wearable Sensors through Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年第11期12卷 18183-18194页

作者： Farman, Haleem Shahzad, Yasir Jan, Bilal Nasralla, Moustafa M. Sallam, Karam M. Munasinghe, Kumudu Jamalipour, Abbas Prince Sultan University Smart Systems Engineering Lab Department of Communications and Networks Engineering College of Engineering Riyadh Saudi Arabia University of Peshawar Department of Computer Science Pakistan FATA University Department of Computer Science Kohat Pakistan University of Sharjah Computer Science Department Sharjah United Arab Emirates University of Canberra School of Information Technology and Systems ACT2617 Australia The University of Sydney School of Electrical and Information Engineering NSW2006 Australia

Machine Learning (ML) with 5G technology has revolutionized smart healthcare. It has helped improve the quality of care, such as real-time analysis, decision-making, patient monitoring, and personalized treatments. In this paper, a 5G aware real-time diabetes prediction framework is proposed using optimized Bidirectional Long Short-Term Memory (Bi-LSTM) with Deep Reinforcement Learning (DRL). Bi-LSTM can analyze time-series data in forward and backward passes on patient health metrics, such as Blood Glucose (BG) levels of a diabetic patient, to identify patterns and trends and make predictions about future health outcomes. A local dataset using a wearable sensor is collected of 10 Type-2 diabetic patients, encompassing daily BG levels at different times, alongside additional parameters such as blood pressure and body weight. The proposed framework leverages a dataset of 1,830 data points to forecast glucose levels for the following day. It also harnesses DRL to improve and optimize the model's future predictive performance. The obtained results are evaluated with other ML algorithms to validate the effectiveness of the proposed framework, which shows an improvement from 93.1% to 98.6% accuracy. The patient's diabetic condition is categorized using the Surveillance Error Grid (SEG) to increase the clinical impact of glucose prediction and make informed decisions. The results show that Bi-LSTM-DRL is an effective approach to predicting glucose levels in real-time and can adapt to health-related changes and optimize its predictions accordingly. © 2014 IEEE.

关键词： Deep reinforcement learning

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Optimizing Secure Multi-User ISAC Systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

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IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

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Detection of Acute Lymphoblastic Leukemia Using a Novel Bone Marrow Image Segmentation

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Tsinghua science and technology 2025年第2期30卷 610-623页

作者： M.Anline Rejula Ben M Jebin Ravi Selvakumar S.Amutha Eberlein George Department of Computer Applications-PG Scott Christian College(Autonomous)Nagercoil 629003India Department of Computer Science Malankara Catholic CollegeMariagiri 629153India School of Computer Science and Engineering Vellore Institute of TechnologyChennai 600127India Department of Pediatrics Government Medical College and HospitalGovernment of Tamil NaduNamakkal 637001India

In our study,we present a novel method for automating the segmentation and classification of bone marrow images to distinguish between normal and Acute Lymphoblastic Leukaemia(ALL).Built upon existing segmentation techniques,our approach enhances the dual threshold segmentation process,optimizing the isolation of nucleus and cytoplasm *** is achieved by adapting threshold values based on image characteristics,resulting in superior segmentation outcomes compared to previous *** address challenges,such as noise and incomplete white blood cells,we employ mathematical morphology and median filtering *** methods effectively denoise the images and remove incomplete cells,leading to cleaner and more precise ***,we propose a unique feature extraction method using a hybrid discrete wavelet transform,capturing both spatial and frequency *** allows for the extraction of highly discriminative features from segmented images,enhancing the reliability of *** classification purposes,we utilize an improved Adaptive Neuro-Fuzzy Inference System(ANFIS)that leverages the extracted *** enhanced classification algorithm surpasses traditional methods,ensuring accurate identification of acute lymphoblastic *** innovation lies in the comprehensive integration of segmentation techniques,advanced denoising methods,novel feature extraction,and improved *** extensive evaluation on bone marrow samples from the Acute Lymphoblastic Leukemia Image DataBase(ALL-IDB)for Image Processing database using MATLAB 10.0,our method demonstrates outstanding classification *** segmentation accuracy for various cell types,including Band cells(96%),Metamyelocyte(99%),Myeloblast(96%),***(97%),***(97%),and Neutrophil cells(98%),further underscores the potential of our approach as a high-quality tool for ALL diagnosis.

关键词： Adaptive Neuro-Fuzzy Inference System(ANFIS) blood cells segmentation leukemia classification

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ClipArtGAN: An Application of Pix2Pix Generative Adversarial Network for Clip Art Generation

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Multimedia Tools and Applications 2025年 1-25页

作者： Elnabawy, Reham H. Abdennadher, Slim Hellwich, Olaf Eldawlatly, Seif Technische Universität Berlin Berlin Germany Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer Science Department Faculty of Informatics and Computer Science German International University New Administrative Capital Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University 1 El-sarayat st. Abbassia Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt School of Information Technology New Giza University Giza Egypt

An image can convey a thousand words. This statement emphasizes the importance of illustrating ideas visually rather than writing them down. Although detailed image representation is typically instructive, there are several contexts where simplification of image representations may be more informative. One way to provide an abstract representation of an object is via clip art, which is a simple graphic illustration of an object that can be included in documents or presentations. Clip art representation can be utilized in graphic design for concepts expression and daily life objects illustration. Furthermore, clip art had shown remarkable advantages when compared to real images in terms of editing and manipulation since clip art geometry can be easily edited in addition to the feasibility of storing it in a resolution-independent manner. However, it can be difficult to automatically create clip arts that resemble any object. Here, we propose a novel deep learning model, named ClipArtGAN, that utilizes Generative Adversarial Networks (GANs) to convert a photo of an object to its corresponding clip art. To our knowledge, this is the first GAN model based on Pix2Pix architecture that is capable of automatically generating clip art for any arbitrary object. Three training datasets were collected to train and compare two GAN models based on Pix2Pix and CycleGAN architectures. Our results demonstrate that the mean accuracy obtained using ClipArtGAN utilizing Pix2Pix outperforms that of CycleGAN by 58% and that of Google Images-based search by 54%. These results indicate the efficacy of ClipArtGAN in generating representative clip art images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Joint Power Control and Multi-Path Routing for Internet of Underwater Things in Varying Environments

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IEEE Internet of Things Journal 2025年第11期12卷 15197-15210页

作者： Xu, Cangzhu Song, Shanshan Liu, Jun Pan, Miao Xu, Gaochao Cui, Jun-Hong Jilin University Department of Computer Science and Technology Changchun130012 China Beihang University School of Electronic and Information Engineering Beijing100191 China Smart Ocean Technology Co. Ltd Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Chengdu China Jilin University College of Computer Science and Technology Changchun130012 China

Internet of Underwater Thing (IoUT) stands as promising technology facilitating diverse underwater applications. Nevertheless, IoUT across vast marine regions is challenged by highly diverse and fluctuating channel environments, which results in unreliable point-to-point (PTP) transmissions. Moreover, its multi-hop nature exacerbates severe unreliable end-to-end (ETE) transmissions. Existing methods utilize routing protocols to address the above challenges by independently power control for PTP reliability or multi-path transmission for ETE reliability. However, these methods ignore the interdependencies between power control and multi-path transmission, which fail to guarantee high energy-efficient reliability in resource-constrained and harsh underwater environments. To this end, we propose a joint power Control And Multi-Path routing (CAMP) protocol for IoUTs in varying environments. Specifically, we develop PTP and ETE reliability models by analyzing the interrelation between power control and multi-path routing, incorporating historical, current, and predictive information. A hybrid routing strategy is designed based on the reliability models to accommodate changing environmental conditions, residual energy, and link quality. This strategy initiates multi-path routing at the source and single-path forwarding at relay nodes, combined with power control. Extensive simulations demonstrate that CAMP achieves superior reliability (packet delivery rate) and energy efficiency, while simultaneously improving network performance in terms of latency and throughput. © 2014 IEEE.

关键词： Routing Reliability Power control Routing protocols Energy efficiency Relays Internet of Things Sensors Reliability engineering computer network reliability

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