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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Nagarathna, P. Vinutha, C.B. Tirumala Vasu, G. Kubra, Afreen Fiza, Samreen Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru560064 India Department of Electronics and Communications Engineering Presidency University Karnataka Bangalore560064 India

ISBN: (纸本)9783031810855

Scene recovery is a crucial imaging task that holds significant relevance in various practical domains, such as video surveillance and autonomous vehicles, among others. To improve the visual quality of sand-dust images captured under various weather and imaging situations, we propose the use of Anisotropic Guided Filter (AnisGF). This filter utilizes a weighted averaging technique for an image to minimize diffusion and preserve the sharpness of edges. The weights provided in this study are adjusted using local neighborhood variances in order to obtain robust anisotropic filtering, while the computational efficiency of the original guided filter is preserved. The proposed technique deals with the problem of detail halos and the management of inconsistent structures seen in earlier guided filter variants. Furthermore, experiments with sand-dust, haze and under water images enhancement also show how the AnisGF improves image quality in terms of quality metrics namely Blind/ Referenceless Image Spatial Quality Evaluator, Naturalness Image Quality Evaluator, and Perception based Image Quality Evaluator. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Ensuring Privacy and Integrity in IoT Supply Chains through Blockchain and Homomorphic Encryption

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IEEE Internet of Things Journal 2025年

作者： Din, Ikram Ud Almogren, Ahmad Han, Zhu Guizani, Mohsen The University of Haripur Department of Information Technology Haripur22620 Pakistan King Saud University College of Computer and Information Sciences Department of Cyber Security Department of Computer Science Riyadh11633 Saudi Arabia University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Ensuring data security and privacy in Internet of Things (IoT) is increasingly critical due to the growing interconnectedness of devices and the sensitivity of the data they handle. This paper presents a novel approach to enhancing data security in IoT through the integration of homomorphic encryption and blockchain technology. We conduct simulations using the Kaggle Smart Home Dataset to evaluate the effectiveness of our proposed methodology on smart home devices and wearable technology. Our approach not only secures data transmission but also guarantees data integrity and privacy through decentralized verification and secure aggregation techniques. Specifically, our evaluation demonstrates an encrypted data transmission rate exceeding 99.5%, a complete absence of unauthorized access instances in the simulated environment, and a verified data integrity rate of over 99.8%. Additionally, our method supports real-time processing and scalability, making it suitable for various IoT applications, including smart contract applications in IoT for privacy and security in supply chain transactions. The study highlights the robustness of combining homomorphic encryption and blockchain to protect sensitive data throughout its lifecycle. © 2014 IEEE.

关键词： Data integrity

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Nystromformer based cross-modality transformer for visible-infrared person re-identification

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Scientific Reports 2025年第1期15卷 1-19页

作者： Mishra, Ranjit Kumar Mondal, Arijit Mathew, Jimson Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta Bihar Patna 801106 India

Person re-identification (Re-ID) aims to accurately match individuals across different camera views, a critical task for surveillance and security applications, often under varying conditions such as illumination, pose, and background. Traditional Re-ID systems operate solely in the visible spectrum, which limits their effectiveness under varying lighting conditions and at night. To overcome these limitations, leveraging the visible-infrared (VIS-IR) domain becomes essential, as infrared imaging provides reliable information in low-light and night-time environments. However, the integration of VIS (visible) and IR (infrared) modalities introduces significant cross-modality discrepancies, posing a major challenge for feature alignment and fusion. To address this, we propose NiCTRAM: a Nyströmformer-based Cross-Modality Transformer designed for robust VIS-IR person re-identification. Our framework begins by extracting hierarchical features from both RGB and IR images through a shared convolutional neural network (CNN) backbone, ensuring the preservation of modality-specific characteristics. These features are then processed by parallel Nyströmformer encoders, which efficiently capture long-range dependencies in linear time using lightweight self-attention mechanisms. To bridge the modality gap, a cross-attention fusion block is introduced, where RGB and IR features interact and integrate second-order covariance statistics to model higher-order correlations. The fused features are subsequently refined through projection layers and optimized for re-identification using a classification head. Extensive experiments on benchmark VIS-IR person Re-ID datasets demonstrate that NiCTRAM outperforms existing methods, achieving state-of-the-art accuracy and robustness by effectively addressing the cross-modality challenges inherent in VIS-IR Re-ID. The proposed NiCTRAM model achieves significant improvements over the current SOTA in VIS-IR ReID. On the SYSU-MM01 dataset, it surpa

关键词： Cross-Modality Fusion Higher Order Attention Nyströmformer Person Re-identification

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P3AKA: A PUF based privacy preserving authentication and key agreement framework for secure communication in vehicle to grid network

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Vehicular Communications 2025年 54卷

作者： Mehta, Prarthana J. Parne, Balu L. Patel, Sankita J. Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat 395007 India

A traditional power grid integrates a Smart Grid (SG) technology to reduce greenhouse gases and increase the efficiency of energy transition. The Vehicle to Grid (V2G) is raised and combined with the SG network to fulfill these objectives of the SG technology. The two-way power flow in the V2G technology allows an Electrical Vehicle (EV) to charge its battery and discharge surplus energy back to the power grid through the Charging Stations (CSs). During the energy transfer, an EV shares identity, location, and charging preferences with the CS through an insecure channel. It raises significant security and privacy vulnerabilities for the V2G network. In addition, the EV and CS are situated in an exposed location that may increase the risk of physical attack. Hence, there is a need to preserve the security and privacy of the EV and CS in the V2G network. Moreover, a lightweight security solution is necessary for the resource constrained CS and EV in the V2G network. Several authentication and key agreement protocols were suggested in the literature to overcome the security challenges in the V2G network. However, the existing approaches fail to maintain the session key secrecy and preserve from the physical attack. Thus, we propose a Physically Unclonable Function (PUF) based Privacy Preserving Authentication and Key Agreement (P3AKA) framework for the V2G network using lightweight cryptographic operations. PUF protects the CS and EV from the physical attack and other lightweight cryptographic functions safeguard the network from other security attacks. Security analysis of the proposed P3AKA framework represents that it protects the V2G network from potential security threats such as impersonation, replay, Man-in-The-Middle (MiTM), physical, and machine learning attacks. Further, it ensures user anonymity and non-traceability of the EV user. The formal security verification uses the ROR model and Scyther tool to verify the proposed P3AKA framework. It illustrates that

关键词： Charging Station (CS) Electrical Vehicle (EV) Privacy PUF Security Smart Grid (SG) Vehicle to Grid (V2G)

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Anytime Incremental ρPOMDP Planning in Continuous Spaces

arXiv

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arXiv 2025年

作者： Benchetrit, Ron Lev-Yehudi, Idan Zhitnikov, Andrey Indelman, Vadim Department of Computer Science Israel Israel Department of Aerospace Engineering Israel Department of Data and Decision Sciences Technion - Israel Institute of Technology Haifa32000 Israel

Partially Observable Markov Decision Processes (POMDPs) provide a robust framework for decision-making under uncertainty in applications such as autonomous driving and robotic exploration. Their extension, ρPOMDPs, introduces belief-dependent rewards, enabling explicit reasoning about uncertainty. Existing online ρPOMDP solvers for continuous spaces rely on fixed belief representations, limiting adaptability and refinement - critical for tasks such as information-gathering. We present ρPOMCPOW, an anytime solver that dynamically refines belief representations, with formal guarantees of improvement over time. To mitigate the high computational cost of updating belief-dependent rewards, we propose a novel incremental computation approach. We demonstrate its effectiveness for common entropy estimators, reducing computational cost by orders of magnitude. Experimental results show that ρPOMCPOW outperforms state-of-the-art solvers in both efficiency and solution quality. © 2025, CC BY.

关键词： Markov processes

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DiagPCNN: Enhancing CNN through Pretrained Model for Brain Tumor Diagnosis

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Procedia computer science 2025年 258卷 2334-2342页

作者： Umang Kumar Agrawal Nibedan Panda B V Ramana Debabrata Singh A K Dalai School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India-751024 Department of Information Technology AITAM Tekkali AP India-532201 Department of CA SOA Deemed to be University Bhubaneswar India-751030 School of Computer Science and Engineering VIT-AP University Amaravati AP India

Analysis of medical images plays a vital role in the early detection and prognosis of diseases. Among various medical imaging, Magnetic Resonance Imaging(MRI) is valuable in providing insights into the brain artefact. Early classification of brain tumors has become indispensable for doctors and medical persons. With the recent advancements in deep learning, Convolution Neural Networks have gained widespread in the domain of disease diagnosis and medical imaging, due to their notable efficacy and high performance. In this study, we have proposed the DiagPCNN model which incorporates a pre-trained model with CNN for an increment in optimal efficacy of the manifest model. We have also analysed the pros and cons of the DiagPCNN model over eighteen different architectures and three DiagPCNN models outperforms the rest of models with superior accuracy of 96.63, 97.28 and 97.79% respectively. Hence, simplifying their skilfulness in detecting brain tumors to induce the manifest model of a productive and virtuous approach for enhanced prognosis.

关键词： Deep Learning CNN DiagPCNN Brain MRI Medical Imaging

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State-Aware Perturbation Optimization for Robust Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Zhang, Zongyuan Duan, Tianyang Lin, Zheng Huang, Dong Fang, Zihan Sun, Zekai Xiong, Ling Liang, Hongbin Cui, Heming Cui, Yong Department of Computer Science The University of Hong Kong Hong Kong Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Computer Science City University of Hong Kong Hong Kong School of Computer and Software Engineering Xihua University Chengdu610039 China National United Engineering Laboratory of Integrated and Intelligent Transportation The National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Department of Computer Science and Technology Ts-inghua University Beijing100084 China

Recently, deep reinforcement learning (DRL) has emerged as a promising approach for robotic control. However, the deployment of DRL in real-world robots is hindered by its sensitivity to environmental perturbations. While existing white-box adversarial attacks rely on local gradient information and apply uniform perturbations across all states to evaluate DRL robustness, they fail to account for temporal dynamics and state-specific vulnerabilities. To combat the above challenge, we first conduct a theoretical analysis of white-box attacks in DRL by establishing the adversarial victim-dynamics Markov decision process (AVD-MDP), to derive the necessary and sufficient conditions for a successful attack. Based on this, we propose a selective state-aware reinforcement adversarial attack method, named STAR, to optimize perturbation stealthiness and state visitation dispersion. STAR first employs a soft mask-based state-targeting mechanism to minimize redundant perturbations, enhancing stealthiness and attack effectiveness. Then, it incorporates an information-theoretic optimization objective to maximize mutual information between perturbations, environmental states, and victim actions, ensuring a dispersed state-visitation distribution that steers the victim agent into vulnerable states for maximum return reduction. Extensive experiments demonstrate that STAR outperforms state-of-the-art benchmarks. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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Contrastive Conditional Adversarial Autoencoder with Class-specific Forces for Imbalanced Open-set Fault Detection

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IEEE Transactions on Automation science and engineering 2025年 22卷 15757-15767页

作者： Zuo, Zhonglin Meng, Tao Li, Zheng Zhang, Hao Liu, Tong Chen, Zhansheng Pang, Zhibo Zhejiang University College of Control Science and Engineering Hangzhou310027 China Zhejiang University School of Aeronautics and Astronautics Hangzhou310027 China Hainan Institute of Zhejiang University Sanya572025 China Chongqing University School of Automation Chongqing400044 China University of Sheffield Department of Computer Science SheffieldS1 4DP United Kingdom Shanghai Aerospace Space Technology Co. Ltd Shanghai201109 China ABB Corporate Research Sweden Department of Automation Technology Vasteras Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm Sweden

In real-world industrial scenarios, fault detection faces the widely recognized challenge of data imbalance, which not only refers to the scarcity of fault data but also includes the imbalance in healthy data. This article is concerned with imbalanced open-set fault detection (IOSFD), a practical yet challenging scenario in industrial applications where multiple healthy operating conditions and multiple fault types are imbalanced. In this article, we propose a new contrastive conditional adversarial autoencoder for IOSFD. It constructs an end-to-end unified model based on multi-class known healthy and faulty data to address the reliance of traditional methods on fault samples, while optimizing with class-specific weighted forces to ensure equal attention to imbalanced known classes. Input and feature reconstruction conditioned on operating modes are utilized to learn a compact decision plane and achieve both unknown fault detection and known data classification. Significantly, we formulate the optimization objective of conditional reconstruction based on contrastive learning and introduce adversarial training to further enhance the model’s performance. The effectiveness of the proposed method is validated through real-world pipeline leak detection and Tennessee-Eastman multi-fault detection. © 2004-2012 IEEE.

关键词： Contrastive Learning

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Smart Mood Detection: Real-Time Emotion Analysis for Human-computer Interaction Using Deep Learning

Smart Mood Detection: Real-Time Emotion Analysis for Human-C...

引用

Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Chandrappa S Tapabrata Banerjee Sohan Saha Mohd Zaid Aditya Pai H Guru Prasad M S Rajesh I S Dept. of Computer Science and Engineering JAIN (Deemed to-be University) Bengaluru India Dept. of Computer Science and Engineering(AI and DE) JAIN (Deemed to-be University) Bengaluru India Department of CSE MIT Art Design and Technology University Pune India Department of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Dept. of AI & ML BMS Institute of Technology and Management Bengaluru India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

A convolutional neural network (CNN)-based face emotion recognition system that analyzes human emotions in real-time, including happy, sorrow, rage, and surprise, is described. Existing approaches often struggle with accurately recognizing subtle or complex emotions due to limited feature extraction capabilities and the need for large, diverse training datasets. Additionally, they face challenges in adapting to real-world conditions, such as variations in lighting, angles, and occlusions. In order to improve the model's generalizability, the proposed research used a preprocessing pipeline that included gray scale conversion, scaling, normalization, and augmentation techniques including rotation and flipping on a labeled and balanced dataset of facial photos. Dropout and regularization techniques were used to reduce over fitting, and the CNN architecture was meticulously adjusted to maximize speed. High accuracy and resilience across emotion categories are demonstrated by preliminary results, indicating the model's potential for real-world uses in security surveillance, healthcare, and human-computer interaction.

关键词： Human computer interaction Training Emotion recognition Mood Face recognition Surveillance Real-time systems Convolutional neural networks Security Resilience

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Hybrid AI Models for QoS Estimation in α-η-μ Fading and Co-Channel Interference Environments within 6G Networks

Hybrid AI Models for QoS Estimation in α-η-μ Fading and C...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Preeti Narooka Keshav Kaushik Saurabh Kansal Deepak Kejriwal Ravi Kant Mishra Vineeta Singh Artificial Intelligence and Machine Learning School of Computer and Engineering Manipal University Jaipur Rajasthan India Sharda School of Computer Science & Engineering Sharda University Greater Noida India Independent Researcher United States Department of Computer Applications School of Engineering and Technology Chhatrapati Shahu Ji Maharaj University Kanpur India Department of Computer Science and Engineering School of Engineering and Technology Chhatrapati Shahu ji Maharaj University Kalyanpur Kanpur Uttar Pradesh India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

This paper proposes a hybrid AI model for QoS estimation in 6G networks by mitigating the challenges caused due to α-η-μ fading and co-channel interference. These incorporate a variety of machine learning strategies that deliver improved accuracy with respect to forecasting and real-time updating capabilities. We evaluate it using an extensive set of simulations, including heatmaps and time series analysis; we show substantial reductions on system throughput, latency and utilization. This model makes the design highly dynamic to accommodate a wide spectrum of network conditions and thereby optimizes resources utilization in the overall performance. The comparison proves the high performance of hybrid AI model compared to conventional and state-of-the-art techniques, making it a promising resolution for QoS estimation in advanced network environments.

关键词： Fading channels 6G mobile communication Adaptation models Computational modeling Estimation Quality of service Machine learning Throughput Real-time systems Interchannel interference

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