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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Batabyal, Suvadip National Institute of Technology Department of Computer Science & Engineering Durgapur India

ISBN: (纸本)9798331531195

Device-to-device (D2D) communication underlay cellular network improves spectral efficiency by reusing cellular resources over short and low-power links between the devices. For this, the D2D paradigm is considered to be an integral part of all kinds of future networks and is an active area of research at different levels. This requires a simple, configurable, scalable, and realistic evaluation of the protocols proposed for the D2D networking paradigm. Although there exists a few platform to evaluate a D2D underlay network, they have one or more of the following drawbacks: (i) long development time and complex environment, (ii) limited support for mobility, (iii) no support for application specific development and evaluation. In this work, we developed a framework for simulating an application-driven mobile D2D underlay network using the Gym API, where D2D connections are made spontaneously based on the application requirements. The proposed framework includes modules for generating application-specific traffic, buffer for transmission and caching of application data, and mobility management. This complements several earlier works on application (or utility) driven resource allocation in D2D underlay network. The framework is used to evaluate a D2D network using different mobility models and performance of cache based video streaming in a D2D underlay network. Finally, the framework is compared with ns-3 and OMNET++ simulators and is observed to have lower CPU utilization and run-time under the same network settings. © 2025 IEEE.

关键词： Resource allocation

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Self-supervised few-shot medical image segmentation with spatial transformations

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Neural Computing and Applications 2024年第30期36卷 18675-18691页

作者： Titoriya, Ankit Kumar Singh, Maheshwari Prasad Singh, Amit Kumar Department of Computer Science & amp Engineering National Institute of Technology Patna Ashok Rajpath Bihar Patna800005 India

Deep learning-based segmentation models often struggle to achieve optimal performance when encountering new, unseen semantic classes. Their effectiveness hinges on vast amounts of annotated data and high computational resources for training. However, a promising solution to mitigate these challenges is the adoption of few-shot segmentation (FSS) networks, which can train models with reduced annotated data. The inherent complexity of medical images limits the applicability of FSS in medical imaging, despite its potential. Recent advancements in self-supervised label-efficient FSS models have demonstrated remarkable efficacy in medical image segmentation tasks. This paper presents a novel FSS architecture that enhances segmentation accuracy by utilising fewer features than existing methodologies. Additionally, this paper proposes a novel self-supervised learning approach that utilises supervoxel and augmented superpixel images to further enhance segmentation accuracy. This paper assesses the efficacy of the proposed model on two different datasets: abdominal magnetic resonance imaging (MRI) and cardiac MRI. The proposed model achieves a mean dice score and mean intersection over union of 81.62% and 70.38% for abdominal images, and 79.38% and 65.23% for cardiac images. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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Team IndiDataMiner at IndoNLP 2025: Hindi Back Transliteration - Roman to Devanagari using LLaMa 1

Team IndiDataMiner at IndoNLP 2025: Hindi Back Transliterati...

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1st Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, IndoNLP 2025

作者： Kumar, Saurabh Kakadiya, Dhruvkumar Babubhai Singh, Sanasam Ranbir Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891762145

The increasing use of Romanized typing for Indo-Aryan languages on social media poses challenges due to its lack of standardization and loss of linguistic richness. To address this, we propose a sentence-level back-transliteration approach using the LLaMa 3.1 model for Hindi. Leveraging fine-tuning with the Dakshina dataset, our approach effectively resolves ambiguities in Romanized Hindi text, offering a robust solution for converting it into the native Devanagari script. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Traffic Density Prediction System using Deep Learning 2

Traffic Density Prediction System using Deep Learning

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Verma, Hritik Sarkar, Sayan Sadhukhan, Souranil Mishra, Aniket Mondal, Md Ashifuddin Narula Institute Of Technology Department Of Computer Science And Engineering India

ISBN: (纸本)9798350381665

In major cities throughout the world, traffic congestion is still a major problem that negatively affects the efficiency of transportation system. Traffic congestion creates several issues that require a creative and adaptable strategy to solve. This work tackles the problem by accurately forecasting vehicle density by merging state-of-the-art deep learning models, specifically YOLO - v8 for real-time vehicles identification and Stacked Long Short-Term Memory (LSTM) models for forecasting traffic density. These methods are combined to create a powerful vehicle density prediction system. The integration of YOLOv8 with LSTM provides a practical response to the growing issues brought on by traffic congestion in cities. The simulation results shows that accuracy of YOLOv8 is 95.36, while average RMSE value of Stacked LSTM models is 0.591. The proposed model is compared with RNN model for vehicle density forecasting and the proposed model performs better than RNN in terms of accuracy. © 2024 IEEE.

关键词： Traffic congestion

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EssayDetect at GenAI Detection Task 2: Guardians of Academic Integrity: Multilingual Detection of AI-Generated Essays 1

EssayDetect at GenAI Detection Task 2: Guardians of Academic...

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1st Workshop on GenAI Content Detection, GenAIDetect 2025

作者： Agrahari, Shifali Jayant, Subhashi Kumar, Saurabh Singh, Sanasam Ranbir Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891762053

Detecting AI-generated text in the field of academia is becoming very prominent. This paper presents a solution for Task 2: AI vs. Human - Academic Essay Authenticity Challenge in the COLING 2025 DAIGenC Workshop1. The rise of Large Language models (LLMs) like ChatGPT has posed significant challenges to academic integrity, particularly in detecting AI-generated essays. To address this, we propose a fusion model that combines pre-trained language model embeddings with stylometric and linguistic features. Our approach, tested on both English and Arabic, utilizes adaptive training and attention mechanisms to enhance F1 scores, address class imbalance, and capture linguistic nuances across languages. This work advances multilingual solutions for detecting AI-generated text in academia. © 2025 International Conference on Computational Linguistics.

关键词： Computational linguistics

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3GPP Edge–Fog federation: Transparent 3rd-party authentication and application mobility

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computer Communications 2024年 222卷 220-229页

作者： Ali, Asad Islam, Minhajul Mallick, Tushin Islam, Mohammad Sakibul Sakib, Sadman Shohrab Hossain, Md. Lin, Ying-Dar National Institute of Cyber Security Ministry of Digital Affairs Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh

3GPP edge and fog computing paradigms provide computational services to users at low latency. These paradigms alone are not enough to fulfill the users’ requirements completely. Therefore, a federation among these computing paradigms is necessary. To realize such federation, there is a need of an authentication mechanism where subscribers of a fog, can access 3GPP edge's services, or vice versa, without buying new subscription, and an application mobility mechanism for continuous service during handover from 3GPP edge to fog, or vice versa, without re-authentication. In this work, we propose: (1) a proxy-based state transfer and third-party authentication (PS3A), that uses a transparent proxy to transfer the authentication and application state information between 3GPP edge and fog, and (2) a token-based state transfer and proxy-based third-party authentication (TSP3A), that uses the proxy to transfer the authentication information and tokens to transfer application state information between 3GPP edge and fog. The proxy plays different roles, via virtual counterparts of entities involved in these protocols, to provide transparency. When the 3GPP edge, using EPS-AKA, receives an authentication request, the proxy relays and behaves as a virtual Home Subscriber Server (vHSS) for the 3GPP edge, and behaves as a virtual user for the fog, which is using OIDC. We applied PS3A and TSP3A to three federation scenarios among 3GPP edge and fog. Experimental results show that PS3A and TSP3A provide authentication within 0.345–2.858 s for a 0–100 Mbps proxy load. The results further show that TSP3A provides application mobility, while taking 40%–52% less time than PS3A, using state tokens. TSP3A and PS3A also reduce the service interruption latency by 82.4% and 84.6%, compared to the cloud-based service, via tokens and prefetching. © 2024 Elsevier B.V.

关键词： Authentication

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Machine Learning-Based Air Pollution Monitoring and Forecasting

Machine Learning-Based Air Pollution Monitoring and Forecast...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： M, Naga Ravindra Babu Durga Satish, M. Prasanthi, B.V. Jagadeesh, S.V.V.D. Janardhana Naidu, J.N.S.S. Pradeep, Immidi Kali Department of Computer Science & Engineering Vishnu Institute of Technology Bhimavaram India

ISBN: (纸本)9783031513374

Today, the governments of developing countries are focusing more on air pollution. Air pollution occurs because of factors like fuel use in vehicles such as private transport, industries, and, more importantly, the burning of waste like plastic and grass. These are a few of the elements that contribute to air pollution. All of these factors have an impact on air quality. It can be calculated or determined by using PM2.5 and other variables. If this level is high, then it may be a serious problem for people’s health. To control air pollution, continuous pollution monitoring is required. Here, different machine learning models like linear regression, decision tree classifiers, random forest classifiers, and KNN classifiers are used to detect whether the sample of air is polluted or not. On the basis of previous readings of PM2.5, NH3, CO, NO, NOx, and NO2, SO2 is used to predict the future values of the AQI. By collecting datasets of the daily atmospheric conditions of a specific city or town, this system is able to predict the PM2.5 level and also detect the air pollution status. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Air quality

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AI-Enhanced Learning Assistant Platform 7

AI-Enhanced Learning Assistant Platform

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sumanth, N. Sai Priya, S. Vishnu Sankari, M. Kamatchi, K.S. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India Sathyabama Institute of Science And Technology School of Computing Sist Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

The AI-Enhanced Learning Assistant Platform is a revolutionary system designed to enhance learning, with cutting-edge features like question and answer generation, answer evaluation, identification of weak areas, recursive testing, an integrated query forum, and expert chat support. This platform makes use of artificial intelligence (AI) technology to try to satisfy the many needs that students and teachers have. Using natural language processing and machine learning, the platform's question and answer generating feature generates relevant questions on its own from the provided content. This encourages participation and in-depth subject understanding. The answer evaluation section provides quick feedback for improvement by utilizing AI algorithms to assess the accuracy and caliber of student responses. One of this platform's key advantages is its capacity to identify students' areas of weakness. Through the analysis of performance patterns and root causes, the system can generate customized recommendations and learning materials to help overcome those constraints. Through repeated practice, the program gradually pushes students to increase their understanding of the material by creating adaptive exams. Through the integrated query forum, students can collaborate and ask for assistance from others by asking questions and receiving answers from teachers and their peers. Furthermore, by enabling real-time communication between users and subject matter experts, the expert chat support tool fosters an engaging and motivating learning environment. © 2024 IEEE.

关键词： Students

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Ultra-Wideband technology for Healthcare Monitoring: A Machine Learning Approach

Ultra-Wideband Technology for Healthcare Monitoring: A Machi...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Raj, Raunak Awasthi, Avinash Arora, Komal Singh, Shreshth Miranka, Hemant Rakhra, Manik School of Computer Science Engineering Lovely Professional Univeristy Punjab India Malviya National Institute of Technology Department of Computer Science and Engineering Rajasthan India Visvesaraya National Institute of Technology Department of Computer Science and Engineering Maharashtra India Lakshmi Niwas Mittal Institute of Information Technology Department of Computer Science and Engineering Rajasthan India

ISBN: (纸本)9798331542344

Ultra-Wideband (UWB) technology has emerged as an emerging solution for a variety of applications in clinical establishments., offering significant advantages such as precise localization capabilities., high data rates., and low power consumption. The paper systematically examines the utilization of UWB technology within clinical settings., focusing on its applications in health care monitoring., medical imaging., indoor positioning., and wireless communication. By leveraging a comprehensive dataset from the UCI Machine Learning Repository., the authors provide detailed empirical insights into the performance and effectiveness of VWB-based systems in enhancing healthcare outcomes. The authors also identify and discuss specific challenges., such as integration complexities and regulatory hurdles., as well as opportunities., including potential advancements in patient care and health care delivery through the adoption of UWB technology. Additionally., the authors have built a model using a Random Forest classifier and achieved an accuracy of 91.8%. © 2024 IEEE.

关键词： Electronic health record

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Bayesian Neural Networks for Satellite Fog Detection: Quantifying Epistemic and Aleatoric Uncertainties

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Remote Sensing in Earth Systems sciences 2024年第1期8卷 232-250页

作者： Deshpande, Prasad Tripathi, Shivam Bhattacharya, Arnab Department of Civil Engineering Indian Institute of Technology Kanpur Kanpur India Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India

Conventional threshold-based dual-channel methods, as well as recent deep learning-based methods, can deterministically detect fog using satellite observations. However, stochastic processes like fog are best represented by probabilistic models. This study proposes a method for probabilistic fog detection from satellite data using the Bayesian neural network (BNN)-based model. The novelty of the method lies in quantifying fog detection uncertainty and disentangling its epistemic and aleatoric components. The model generates a probability of fog for each input, which can be thresholded into fog/no-fog status, if needed. Using data from the INSAT-3D geostationary satellite over 18 North Indian cities, we demonstrate that the proposed method significantly outperforms the operational fog detection product of the Indian Space Research Organization, INSAT-FOG. The critical success indices (CSI) of the proposed model during testing and the INSAT-FOG are 0.48 and 0.12, respectively, whereas their probabilities of fog detection (PoD) are 0.68 and 0.13, respectively. The diurnal evaluation of the results shows that the model performs better even during dawn and dusk, which is a critical challenge in satellite fog detection. Moreover, the proposed model can detect very dense fog observations (visibility < 50 m) accurately (with CSI 0.93). On average, the detection uncertainty is lower for correct detections than for incorrect detections, suggesting that the model is more confident about correct detections. The uncertainty analysis reveals that the aleatoric uncertainty is usually higher than the epistemic uncertainty, suggesting potential avenues for future improvements in satellite fog detection. It is concluded that the proposed BNN is a promising method for real-time operational fog detection. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Bayesian networks Indo-Gangetic Plains INSAT-3D Reliable AI Smog Visibility

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