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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Pokkuluri, Kiran Sree Gurujukota, Ramesh Babu Chintalapati, Phaneendra Varma Rao, P. B. V. Raja Raju, P. J. R. Shalem Prasad, Maddula Devi, N. SSSN Usha Murthy, Poppoppula T. Satyanarayana Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering University College of Engineering JNTU Kakinada India

ISBN: (纸本)9783031770807

Generative Adversarial Networks (GANs) are used in drug discovery by offering a computational framework to generate novel molecular structures with preferred properties. This paper highlights GANs’ role in accelerating de novo drug design, enhancing virtual screening, and addressing data scarcity challenges. Evaluating chemical validity, diversity, and bioactivity prediction, GANs provide a versatile platform for generating molecules that align with pharmaceutical requirements. This paper emphasizes the ethical considerations, stability, and interpretability of GAN-generated structures, positioning GANs as a transformative force in reshaping and expediting drug discovery processes. The use of GANs in drug discovery holds promise for accelerating the identification of lead compounds, optimizing existing molecules, and exploring new avenues in pharmaceutical research. We have effectively tested our proposed model with various parameters to obtain the usability and efficiency. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Drug discovery

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Tracing Information Flow in Clustered Complex Networks: Unraveling Centrality Patterns with m-PageRank

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SN computer science 2025年第3期6卷 1-11页

作者： Cheriyan, Jo Department of Computer Science and Engineering Saintgits College of Engineering Kerala Kottayam 686532 India

Social network analysis (SNA) examines the social structures and relational patterns among entities, which are represented as nodes and edges within a network. It finds extensive application in various fields to derive insight into how these entities interact and exert influence on one another. Clustering refers to the tendency of nodes to form cohesive groups, whereby nodes within the same group exhibit higher levels of interconnection compared to those outside it. Understanding the flow of information through these clusters requires advanced methodologies to account for the intricacies and dynamics inherent in clusters, as their complexity often poses significant challenges. This research addresses the complexities associated with the exchange of information within clusters of real-world networks by analyzing the interactions among individual nodes and the pathways along which information is disseminated within these clusters. The article introduces the novel Awareness-Regulated-Spreading (ARS) model, an epidemic-based framework that elucidates how information propagates across networks by considering varying levels of transmissibility, ranging from localized sources to widespread dissemination within complex networks through activation probability. The experimental analysis reveals that the diffusion rate through a single edge is superior to that through triangular edges, and the activation probability surpasses that of state-of-the-art models such as the LACS and GACS models. As the probability of activation of clusters increases, the diffusion of information within these clusters also intensifies, although the clustering coefficient negatively impacts the diffusion process. In addition, the article explores the dynamics of information diffusion in the context of feedback loops and various edge characteristics. These advanced techniques offer deep insights into the flow of information, thereby facilitating more informed decision making in a highly connected worl

关键词： Activation probability Centrality Clusters Complex networks Information diffusion

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Unguided Machine Learning-Based Computation Offloading for Near-Memory Processing 38

Unguided Machine Learning-Based Computation Offloading for N...

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38th International Conference on VLSI Design, VLSID 2025

作者： Maity, Satanu Ghose, Manojit Kumar, Avinash Chakraborty, Anol Chakraborty, Ankit Indian Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India Jorhat Engineering College Department of Computer Science and Engineering Assam India

ISBN: (纸本)9798331522445

Recent data-intensive applications encounter memory wall bottlenecks in the traditional processor-centric computing architecture due to the need for frequent and extensive off-chip data movement. The emerging 3D-enabled near-memory processing (NMP) is a potential solution where computation can be offloaded and performed near memory, eliminating the need for expansive off-chip data transfer. Recently, some crucial works in this context have been proposed that use a supervised machine learning approach that requires a well-labeled dataset, which necessitates creating a time-consuming simulation process. Furthermore, their major focus was on profiling and offloading the entire application to the NMP side, causing a sub-optimal performance because of the low processing power on the NMP cores. This paper presents an unguided machine-learning-based approach (UCO) for computation offloading in NMP architecture that offloads only the memory-intensive regions of an application. The approach extracts several application performance characteristics and creates two clusters using them - one for the CPU side and another for the NMP side. The proposed approach's effectiveness is demonstrated through a series of comprehensive experiments with standard simulators and dataintensive benchmark applications. The results indicate that UCO improves performance (1.31x and 1.10x), saves energy (16% and 12%), and reduces off-chip data transfer (78% and 73%) compared to conventional and state-of-the-art approaches. © 2025 IEEE.

关键词： Unsupervised learning

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ColorVein: Colorful Cancelable Vein Biometrics

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4943-4955页

作者： Wang, Yifan Gui, Jie Shi, Xinli Gui, Linqing Tang, Yuan Yan Kwok, James Tin-Yau Southeast University School of Cyber Science and Engineering Nanjing210000 China Ministry of Education Purple Mountain Laboratories Engineering Research Center of Blockchain Application Nanjing210000 China Nanjing University of Posts and Telecommunications College of Computer Nanjing210003 China University of Macao Department of Computer and Information Science 999078 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering 999077 Hong Kong

Vein recognition technologies have become one of the primary solutions for high-security identification systems. However, the issue of biometric information leakage can still pose a serious threat to user privacy and anonymity. Currently, there is no cancelable biometric template generation scheme specifically designed for vein biometrics. Therefore, this paper proposes an innovative cancelable vein biometric generation scheme: ColorVein. Unlike previous cancelable template generation schemes, ColorVein does not destroy the original biometric features and introduces additional color information to grayscale vein images. This method significantly enhances the information density of vein images by transforming static grayscale information into dynamically controllable color representations through interactive colorization. ColorVein allows users/administrators to define a controllable pseudo-random color space for grayscale vein images by editing the position, number, and color of hint points, thereby generating protected cancelable templates. Additionally, we propose a new secure center loss to optimize the training process of the protected feature extraction model, effectively increasing the feature distance between enrolled users and any potential impostors. Finally, we evaluate ColorVein’s performance on all types of vein biometrics, including recognition performance, unlinkability, irreversibility, and revocability, and conduct security and privacy analyses. ColorVein achieves competitive performance compared with state-of-the-art methods. © 2005-2012 IEEE.

关键词： Anonymity

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Deep Learning-Driven Land Cover Classification: A Comparative Study of CNN Architectures Using Transfer Learning

Deep Learning-Driven Land Cover Classification: A Comparativ...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Purohit, Shreedhar Devdatt Joshi, Maharshi Bhargav Ramoliya, Dipak Computer Science & Engineering Department Changa Anand India

ISBN: (纸本)9798331509828

Remote sensing or satellite imagery classification is essential in many applications including urban development and planning environmental monitoring and land management. Among others this through research investigates the use of three popular deep learning models DenseNet169, Xception and VGG19 for land use scene classification. A comprehensive experimental setting has been built to carry out such comparison through data pre-processing training the model and performance evaluation. The UC Merced Dataset was enhanced fourfold in high-resolution aerial images across different scenes of land use, thereby enriching model training. Extensive performance evaluation based on diverse evaluation metrics such as accuracy, precision, recall, f1-score and inference time and the size of models followed the fine-tuning of each model. Among the models discussed the feature capture structure for DenseNet169 was the greatest and attained an accuracy of 92.86% Xception dealt with scale variation and rotation with ease with a passable accuracy of 90.19% and in any case VGG19 finds a good balance between simplicity and accuracy hitting 90.00%. © 2025 IEEE.

关键词： Deep learning

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Threats to medical diagnosis systems: analyzing targeted adversarial attacks in deep learning-based COVID-19 diagnosis

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Soft Computing 2025年第3期29卷 1879-1896页

作者： Haque, Sheikh Burhan Ul Zafar, Aasim Haq, Sheikh Riyaz Ul Haque, Sheikh Moeen Ul Ahmad, Mohassin Roshan, Khushnaseeb School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Department of Computer Science Aligarh Muslim University Uttar Pradesh Aligarh202002 India Department of Computer Science Engineering Guru Nanak Institution Ibrahimpatnam Telangana Hyderabad501506 India Department of Electronics and Communication Guru Nanak Institution Ibrahimpatnam Telangana Hyderabad501506 India

Deep and machine learning models have become pivotal in medical image analysis, especially for diagnosing COVID-19 using X-rays and CT scans. While these models, including transfer learning-based approaches, have achieved high accuracy, they remain highly vulnerable to adversarial attacks, which can manipulate input data and cause misclassification, posing critical risks in clinical applications. This study introduces a novel approach to addressing this issue by systematically evaluating the impact of adversarial attacks on COVID-19 diagnosis models built with two leading architectures, VGG-16 and DenseNet-121, using the Fast Gradient Sign Method (FGSM). The FGSM attack causes a dramatic drop in accuracy, reducing VGG-16’s accuracy from 95.12 to 9.97% and DenseNet-121’s from 96.51 to 10.13%. To counter these vulnerabilities, we propose a novel defense mechanism that combines adversarial training with Gaussian noise data augmentation, a dynamic approach that generates perturbations across various epsilon values during the training phase. This innovative method significantly enhances model robustness, restoring accuracy to over 92% on adversarial examples. These findings emphasize the need for strong defense mechanisms in deep learning models for COVID-19 diagnosis, ensuring reliability and security against adversarial threats in clinical environments. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Adversarial machine learning

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SKPD Emergency@NLU of Devanagari Script Languages 2025: Devanagari Script Classification using CBOW Embeddings with Attention-Enhanced BiLSTM 1

SKPD Emergency@NLU of Devanagari Script Languages 2025: Deva...

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1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Shakya, Shubham Sainju, Saral Shrestha, Subham Krishna Dawadi, Prekshya Khatiwada, Shreya Department of Computer Science and Engineering Kathmandu University Nepal

ISBN: (纸本)9798891762015

Devanagari script, encompassing languages such as Nepali, Marathi, Sanskrit, Bhojpuri and Hindi, involves challenges for identification due to its overlapping character sets and lexical characteristics. To address this, we propose a method that utilizes Continuous Bag of Words (CBOW) embeddings integrated with attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) network. Our methodology involves meticulous data preprocessing and generation of word embeddings to better the model’s ability. The proposed method achieves an overall accuracy of 99%, significantly outperforming character level identification approaches. The results reveal high precision across most language pairs, though minor classification confusions persist between closely related languages. Our findings demonstrate the robustness of the CBOW-BiLSTM model for Devanagari script classification and highlights the importance of accurate language identification in preserving linguistic diversity in multilingual environments. © 2025 International Committee on Computational Linguistics.

关键词： Network embeddings

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A feature selection-aided deep learning based deepfake video detection method

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Multimedia Tools and Applications 2025年 1-24页

作者： Mohiuddin, Sk Roy, Ayush Pani, Saptarshi Malakar, Samir Sarkar, Ram Department of Computer Science Asutosh College Kolkata700026 India Department of Electrical Engineering Jadavpur University Kolkata700032 India Department of Computer Science UiT The Arctic University of Norway Tromsø9019 Norway Department of Computer Science and Engineering Jadavpur University Kolkata700032 India

The rapid advancements in deepfake technology are imposing significant challenges in detecting manipulated media contents. In this work, we have introduced a deepfake detection method that utilizes three pre-trained convolutional neural network models, namely DenseNet-121, EfficientNet-B0, and Xception to extract deep learning features from preprocessed extracted video frames. Next, these features are stacked to generate diverse feature representations, and thus end up with high-dimensional features. After that, a feature selection strategy is employed on the combined features to eliminate redundant and less effective features. The selected features are then fed into the K-nearest neighbors classifier to decide whether a questioned video is a fake video or not. Two widely used deepfake video datasets, namely FaceForensics++ and Celeb-DF (V2) are used to evaluate the performance of the proposed technique following two experimental setups: intra- and inter-datasets. The results show that the model achieves area under curve scores of 98.50% (85.70%) and 97.70% (79.00%) in the intra-dataset (inter-dataset) setups while trained on the FaceForensics++ and Celeb-DF (V2) datasets, respectively. The performance of the proposed model is comparable to that of state-of-the-art methods. Source code of the proposed method can be found at: here. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Multimodal Deep Learning Framework for Non-Intrusive Autism Spectrum Disorder Detection 3

Multimodal Deep Learning Framework for Non-Intrusive Autism ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, G. Rajganesh, N. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Sriperumbudur Chennai India

ISBN: (纸本)9798331509675

The non-intrusive detection of Autism Spectrum Disorder (ASD) marks a significant advancement in early diagnosis and intervention. This approach allows users to upload videos to a web interface, where visual and auditory data are processed separately. Visual data is analyzed using a customized VGG19 Convolutional Neural Network (CNN) to identify facial expressions and movements indicative of autism, while auditory data is evaluated by a Recurrent Neural Network (RNN) to detect auditory cues like tone and rhythm. A voting mechanism across 10 frames determines the visual classification, labelling the subject autistic if most frames indicate autistic traits. Audio analysis provides an independent prediction. The subject is classified as autistic if either analysis indicates autistic features. By leveraging deep neural networks to process multimodal data, this method offers an accessible, early ASD detection tool for caregivers and professionals, promoting timely interventions and improved outcomes. © 2025 IEEE.

关键词： Convolutional neural networks

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Non-invasive respiratory infection monitoring using AI-driven thermal imaging and signal classification

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Signal, Image and Video Processing 2025年第7期19卷 1-14页

作者： D, Abisha Department of Computer Science and Engineering National Engineering College Tamil nadu Kovilpatti India

The COVID-19 pandemic has highlighted the need for efficient and non-contact health screening methods. Signal-based infrared imaging is an emerging field in biomedical engineering that enables remote monitoring of vital signs. While fever is a common symptom, respiratory abnormalities often appear earlier, necessitating advanced screening systems that monitor both body temperature and respiratory patterns. This research presents an artificial intelligence-based screening device for health that identifies human respiratory patterns based on a deep learning model. The device is built with a Convolutional Neural Network (CNN) to extract features and a Long Short-Term Memory (LSTM) network to classify time-series patterns. The Softmax classifier accurately classifies respiratory patterns. It is learned on a specialized dataset of six breathing signal patterns, making it an effective model for real-time public health surveillance. The experimental result demonstrates that the proposed CNN-LSTM model achieves 91% accuracy, 90% precision, 93% recall, and an F1-score of 91%. It can be scaled up even further for medical real-time applications, paves the way to even greater future advancements in automated health surveillance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Diagnosis

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