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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Dixit, Unnati Vittal, Shwetha Antony Franklin, A. Indian Institute of Technology Hyderabad Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

The widespread adoption of 5G networks has revolutionized communication, providing great connectivity and services across various verticals. Specifically, on the 5G Core (5GC) this has been made possible by leveraging network slicing and Network Function Virtualization (NFV) where multiple Network Functions (NFs) are involved to serve different user requests on the control plane and a variety of service traffic on the data plane. However, as 5G networks embrace zero trust and multi-tenant architectures, this rapid technological advancement has also exposed the 5GC to various security risks such as Distributed Denial of Service (DDoS), Man in The Middle (MITM), cross-slice disruptions, and side-channel attacks. In this regard, this paper presents a comprehensive approach to enhance the security of 5GC through a systematic study of dependency graphs, Betweenness Centrality (BC), and attack graphs. Potential attack scenarios are carefully analyzed by modeling the 3GPP-defined 5GC, performing vulnerability assessments, building attack graphs, analyzing them, and presenting the key inferences from the study. © 2024 IEEE.

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FMeAR: FACS Driven Ensemble Model for Micro-Expression Action Unit Recognition

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SN computer Science 2024年第5期5卷 598页

作者： Chauhan, Anjaly Jain, Shikha Department of Computer Science & amp Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Micro-expressions (MEs) are fleeting involuntary facial movements, which occur frequently when people attempt to conceal their emotions. Since human eyesight cannot detect fleeting and slight changes in facial expression, it is difficult to determine when spontaneous and low-intensity micro-expression occurs. The paper presents FMeAR which is a Facial action coding scheme (FACS) driven ensemble model for the micro-expression action unit recognition. Considering the simultaneous triggering of multiple facial AUs (action units), the problem is designed as a multiple-binary-class framework. First, each frame of the video is extracted and pre-processed thoroughly to eliminate any noise in the input. Then, features are extracted using an LBP descriptor from the pre-processed frames. To reduce the processing time, the size of the feature vector is reduced using PCA. Finally, reduced features are processed using a multiple ensemble model (comprising of Support Vector Machine, Linear Discriminant Analysis, and Decision Tree classifiers) to identify the micro-expressions action units. The presented model, FMeAR, outperforms several baseline frameworks and shows an accuracy of 95.3% for the CASME dataset, 95.9% for CASME-II, and 92.6% for MEVIEW. Intensive experiments demonstrated the effectiveness and generalization of FMeAR emphasizing its potential for various applications such as deception detection, depression detection, medical diagnosis, and many more. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Action units Ensemble-model FACS FMeAR Micro-expression

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A Comparative Analysis of Deep Learning Approaches for Knee Osteoarthritis Detection Using Indian and Multi-Centric Datasets

A Comparative Analysis of Deep Learning Approaches for Knee ...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Malik, Sanjeev Singhal, Nikita Army Institute of Technology Department of Computer Science and Engineering Maharashtra Pune India

ISBN: (纸本)9798331530259

Knee osteoarthritis affects 10-15% of the global population over the age of 60, mostly women. Knee osteoarthritis is growing more widespread in India due to changing lifestyle choices, rising urbanisation, and demographics. Furthermore, many serious cases in India go untreated since many believe it is only a concern for the elderly. Additionally, the diagnosis of knee osteoarthritis has been difficult in rural India due to a lack of skilled medical professionals. Additionally, people are ignorant of its severity, as evidenced by KL's grading system, which ranges from normal to severe (Grades 0-4). This study compares various deep learning architectures and their performance metrics in order to analyze the effectiveness of deep learning-based prediction models for knee osteoarthritis detection. This study utilized an Indian dataset because there is limited research conducted on Indian subjects. The OAI and MOST datasets, which comprise data from Western people, were used in the majority of the investigations. This is the first study, in which we compare the effects of knee osteoarthritis using three distinct datasets. In the study, various pre-trained CNN-based models for 5-class and 2-class classification was used. On an Indian dataset, the EfficientNetB1 model achieved testing accuracy of 84% and EfficientNetB7 on a 2-class classification achieved accuracy of 90.2%. Subsequently, 61% and 77.7% of the western dataset's accuracy comes from 5-class and 2-class categorization using VGG-19 model, respectively. Furthermore, the accuracy of the multi-centric dataset using EfficientNetB1, B7 for 5-class classification is 61%, whereas for 2-class classification it is 80.82% respectively. © 2025 IEEE.

关键词： Deep learning

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Evaluation of Security Approaches Towards Vulnerable Attacks in Real-Time Monitoring and Customised Support for IoT-Healthcare System 3

Evaluation of Security Approaches Towards Vulnerable Attacks...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Pal, Purba Das, Smita Department of Computer Science and Engineering National Institute of Technology Agartala799046 India

IoT in healthcare has revolutionized patient care, simplifying tasks for patients and service providers. However, it also introduces significant cyber security risks, such as various cyber attacks that threaten system safety and usability. Current security measures are inadequate, as evidenced by persistent issues like poor user authentication and authorization. Our evaluation gives an organised literature review of IoT healthcare systems and assesses various methods used to prevent potential cyber threats such as DDoS, MitM, Sinkhole, and cyber-physical attacks. In this paper, various threat prevention approaches have been evaluated concerning several parameters to show the present-day drawbacks and issues in securing IoT healthcare systems. The results highlight the need for focused studies to identify existing IoT healthcare vulnerabilities and outline steps to enhance system functionality and safety. © 2025 The Author(s).

关键词： Patient treatment

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Detection of DDoS Attacks in Fog Computing using Interpretable Quantum-based 2Q Learning Approach 3

Detection of DDoS Attacks in Fog Computing using Interpretab...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Sohan Jayram Reddy, K. Bhargavi, K. Siddaganga Institute of Technology Department of Computer Science and Engineering Karnataka Tumakuru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

It is seen that there is a rapid increase in Internet of Things (IoT) devices, and fog computing has emerged as an efficient and viable solution to reduce latency by performing computations closer to the edge. However, this nature of fog computing makes it vulnerable to Distributed Denial of Service (DDoS) attacks, which can disrupt services. DDoS causes major impact over the fog performance which include resource saturation, increased latency, reduced resource availability, decrease in processing capacity of the fog nodes, and many more. There have been various works proposed to detect DDoS attacks in fog computing, but they lack scalability and have high response times. In this paper, a novel Quantum-based-2QLearning (Q2QL) framework is designed to efficiently detect and mitigate DDoS attacks in fog computing. This framework using quantum-enhanced reinforcement learning reduces overestimation bias and improve speed of convergence. Q2QL framework also achieves high detection accuracy and reduces response time. The results show that the proposed framework performs well across critical metrics, including latency, attack detection time, and overall system performance. © 2025 IEEE.

关键词： Fog computing

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Efficient Parallel Algorithms For Exact SimRank Computations 31

Efficient Parallel Algorithms For Exact SimRank Computations

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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Mundhara, Aditya Nijhara, Prajjwal Banerjee, Dip Sankar Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798331509118

SimRank is a popular measure for evaluating node similarities in graphs, but its high computational cost limits scalability for large graphs. The ExactSim [1] algorithm achieves precise single-source SimRank similarities but suffers from significant computation time. In this work, we parallelize key components of ExactSim - Personalized PageRank (PPR) computations and SimRank calculations on a CPU-based shared memory multi-core system. By optimizing these modules, we significantly reduce runtime, making ExactSim more practical for large-scale applications. Our results (on graph sizes ranging from 0.1 million edges to 100 million edges) demonstrate the effectiveness of parallelization in improving both efficiency and scalability. © 2024 IEEE

关键词： Graph algorithms

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Smart Lock Security System 2

Smart Lock Security System

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Ravikumar, Pushpa Arpitha, C.N. Hemanth, N.G. Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (纸本)9798350383867

With the percentage of single-parent households rising quickly, criminal vulnerability is becoming more of a social issue The main issue is often worry over stranger visiting, especially in the case of single female families. This project focuses on the design strategy for an embedded real-time surveillance system based on a Raspberry Pi for the purpose of detecting intruders. Surveillance technology is reinforced to provide vital security for our daily lives, along with related control and alert functions. Our creative integration of cameras and motion detectors into a web application is the foundation of the suggested security system. Remote sensing cameras and motion detectors are managed and operated by Raspberry Pi. The goal of this research is to create a system that can quickly identify outsiders and respond by taking pictures and sending them to a wireless module that is based on the owner. If owner wish to add the other members to home, then he need to save the person photo in database. © 2024 IEEE.

关键词： Remote sensing

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Hyperspectral Image Processing with Machine Learning and Mathematical Models 7th

Hyperspectral Image Processing with Machine Learning and Ma...

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7th International Conference on Soft Computing and Signal Processing, ICSCSP 2024

作者： Augustin, Anju Kiliroor, Cinu C. Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam India

ISBN: (纸本)9789819799251

Hyperspectral imaging is a fast-growing imaging technique in many fields, like remote sensing, fruit analysis, clinical images, etc. The spectral image consists of two parts: spectral data and spatial data. The processing of spectral images includes the processing of the spatial part as well as the spectral part. This paper analyzes various methods and algorithms that are used for spectral image processing in existing systems and proposes a diagram for hyperspectral image processing. There are five important steps for processing the spectral image and analyzing the region selection and dimensionality reduction methods in detail. Comparing the performance of region selection and dimensionality reduction techniques, the Rembg algorithm and Principal Component Analysis (PCA), respectively, show very good results. A well processed image has better performance in the classification and regression steps, and it reduces the computational time and cost. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Principal component analysis

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On the Role of Adversarial Perturbations in Unsupervised Deep Learning for Enhancing Image Quality of Degraded Medical-Images 22

On the Role of Adversarial Perturbations in Unsupervised Dee...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Sharma, Vatsala Goyal, Ashwin Awate, Suyash P. Indian Institute of Technology Bombay Computer Science and Engineering Department Mumbai India

ISBN: (纸本)9798331520526

Unsupervised learning enables deep-neural-network (DNN) training without high-quality data. Typical DNN methods for unsupervised image quality enhancement either (i) focus only on spatially uncorrelated noise, (ii) demand a training set comprising multiple noisy acquisitions per scene, or (iii) apply only to specific data;these aspects limit their applicability to medical images in clinical applications. Moreover, most such existing methods lack robustness to out-of-distribution (OOD) images observed during deployment. We propose a novel unsupervised DNN learning formulation to enhance a variety of medical images. Our unsupervised DNN training optimizes sets of multistage adversarial perturbations to (i) auto-generate multiple degraded inputs and latent-space encodings for each training image, (ii) adapt to the textural statistics of real-world degradations across medical imaging domains, and (iii) gain robustness to OOD images. This formulation also includes uncertainty modeling and estimation. Results on three publicly available datasets (CT, PET, MRI) show the benefits of our method in image-quality enhancement on in-distribution and OOD images. © 2025 IEEE.

关键词： Neuroimaging

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Evaluating Transformer Models for Named Entity Recognition in Indian Legal Texts 3

Evaluating Transformer Models for Named Entity Recognition i...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Bhandari, Ayush Giriyan, Priyanshu Gawade, Prathamesh Sahitya, Aarti K J Somaiya Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798331519582

Named Entity Recognition (NER) in legal texts is vital for extracting information from vast, unstructured legal documents. The complexity of legal language - characterized by formal style, domain-specific terminology, and unique entity types such as case numbers, legal citations, court names, judges, and involved parties - poses significant challenges. Traditional machine learning models fall short due to their reliance on manual feature engineering and limited adaptability to the nuanced legal language. This paper evaluates transformer-based models, DistilBERT, DistilRoBERTa, and InLegalBERT, for NER in Indian court judgments using a comprehensive dataset prepared by OpenNyAI, which contains 46,545 annotated legal entities across 14 distinct types (e.g., petitioner, respondent, statute). To address imbalanced entity distribution, we propose a cleaning process and optimization of dataset splits using Jensen-Shannon Divergence, ensuring a more balanced representation of entities across training, validation, and test sets. We fine-tuned each model on three versions of the dataset: (1) the original dataset, (2) a cleaned dataset with sentences lacking tagged entities removed, and (3) a version with optimized entity distribution. Performance evaluation based on precision, recall, and F1-score revealed that DistilRoBERTa achieved the highest F1-score of 0.8672, significantly outperforming DistilBERT and demonstrating improvements over InLegalBERT, which is pre-trained on legal texts. Our results underscore that balanced dataset splits and robust preprocessing are crucial for enhancing model generalizability and performance in legal NER tasks, as evidenced by substantial gains in F1-scores following these preprocessing steps. © 2025 IEEE.

关键词： Purging

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