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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Divya, S.B. Mary, S. Prince Department of Computer Science and Engineering Sathyabhama Institute of Science & Technology Chennai India Mangalam College of Engineering Department of Computer Applications India

ISBN: (纸本)9798350372816

The broad utilization of interconnectivity and interoperability of processing frameworks have turned into an irreplaceable need to improve our everyday activities. All the while, it opens a way to exploitable weaknesses that work out positively past human control capacity. The weaknesses consider network protection instruments fundamental to accepting correspondence trade. Secure correspondence requires safety efforts to battle the dangers and necessities headways to safety efforts that counter advancing security dangers. So, emerging technologies like Deep Learning/ Machine Learning (DL/ML) come in handy for integration and better prediction. So, in this paper, we depict various deep learning/ML strategies used in detecting the imbalance in network traffic. The document also includes research that has been proposed on the topic of intrusion detection in network traffic over the previous four years (2020-2024) by a variety of research specialists. The main achievements of the security measures are recognized in this review together with their quantitative and qualitative success indicators. Additionally, as a guide for upcoming discoveries, this research study examines the major discoveries and the rationale for the lessons discovered. © 2024 IEEE.

关键词： Network intrusion

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Smart Monitoring and Irrigation Regulation via IoT and Cloud 8

Smart Monitoring and Irrigation Regulation via IoT and Cloud

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sumalatha, Konatham Harshdeep, Kumar Vellore Institute of Technology School of Computer Science & Engineering Department of Database Systems Vellore India Vellore Institute of Technology Computer Science and Engineering Vellore India

ISBN: (纸本)9798350376425

The increasing demand for agricultural produce and the strain on global water resources highlight the need for innovative solutions to improve water efficiency in farming. This work introduces an IoT-powered Smart Irrigation System designed to optimize water usage, ensuring sustainable crop cultivation. Traditional irrigation methods often lead to inefficiencies, wasting water and increasing costs, as they fail to consider varying soil moisture and environmental conditions across fields. This can result in suboptimal crop growth and resource depletion. To overcome these challenges, the proposed system integrates IoT technology with smart irrigation practices. Sensors deployed in the field collect real-time data on soil moisture, temperature, humidity, and light intensity, which a central controller then processes. Using cloud-based analytics, the controller adjusts irrigation schedules and water distribution to match current conditions precisely. The system's primary goal is to create an eco-friendly, cost-effective solution that reduces water waste, lowers operational costs, and minimizes environmental impact. Scalable and adaptable to various crops and environments, this smart irrigation system promises to improve crop yields, promote sustainable agriculture, and address water scarcity challenges globally. © 2024 IEEE.

关键词： Cloud Computing Real-Time Data Analytics Smart Irrigation Soil Moisture Sensors

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Enhancing IoT Security and Efficiency: Advanced Public Key Cryptographic Solutions for the Modern Era 4

Enhancing IoT Security and Efficiency: Advanced Public Key C...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Sekaran, Ramesh Ramasamy, Dhanalakshmi Basha, John Basha Mohamed Maruthapillai, Kumaresan Annamalai, S. Parasuraman, Manikandan Department of Computer Science and Engineering Bangalore India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering-AI Madanapalle India

ISBN: (纸本)9798350387490

The landscape of the Internet of Things is evolving rapidly. The security and efficiency of data handling and device management are multifaceted and complex problems, which are only exacerbated by the huge volumes of data and the sprawling growth of IoT deployments. The goal of this paper is to outline innovative solutions to the most pressing issues in the field through the use of advanced cryptographic protocols and scalable management systems. The integrity and maintainability of IoT ecosystems face threats not only from quantum-capable attackers but also from vendor lock-in, which necessitates special attention. First, the Secure Data Retrieval Encryption system will be presented, which is based on the Learning with Errors assumptions, Self-Managed Public Key Infrastructure, Next-generation Hashed Elliptic Curve Protocol, and Dynamic Unclonable Physical Properties. In a single round of queries, SDRE enables forward security and efficient searches defined by authorized clients. This work presents a distinctive innovation. Initially, the search process was accelerated greatly, and the data being transmitted and decrypted was implemented based on secure computation. In addition, the Self-Managed Public Key Infrastructure protocol is a simple and standard-acquiescent answer for changing control between IoT service providers. SMS-PKI seeks to diminish this necessity of manual effort, especially in light of the massive upsurge in IoT deployments. SM-PKI also mechanizes the IoT PKI credentials updating and trusted domains process, allowing multiple domains to be run on one client apparatus. Tamarin is used to prove security and efficiency, which demonstrates the necessity of using SM-PKI to maintain desired security properties with the minimal cost and device overhead. Finally, a universal authentication scheme grounded on edge computing and involving Next-generation Hashed Elliptic Curve Protocol and Dynamic Unclonable Physical Properties will be shown. This approach

关键词： Information management

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Noise Robust ASV Spoof Detection Using Integrated Features and Time Delay Neural Network

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SN computer Science 2023年第2期4卷 1-12页

作者： Chakravarty, Nidhi Dua, Mohit Department of Computer Engineering National Institute of Technology Kurukshetra India

Recent advancements in research for development of countermeasure systems for Spoofed Audio detection has helped in building more robust Automatic Speaker Verification (ASV) System. However, available countermeasure systems are not able to generalize well against unknown attacks. The lack of context-dependent information extracted from the given speech at fine grained level is the dominating reason for poor performance of these systems against unknown attacks. To build a noise robust anti-spoof system, in this paper, we propose a Time Delay Neural Network (TDNN)-based countermeasure system that captures context-dependent information well. We devise a three-stage design where at first audio is pre-processed to extract useful information using three different types of features, that are, Mel Frequency Cepstral Coefficients (MFCC), noise robust Gammatone Cepstral Coefficients (GTCC) features and integration of MFCC-GTCC features. These features are then input to proposed Deep Neural Network (DNN) model that uses Long Short-Term Memory (LSTM) network for recurrent aggregation of layer wise generated shallow features in TDNN. Finally, the output is passed through context-dependent pooling layer to generate fixed-length representation that is further used at third stage to classify speech as genuine or spoofed. The proposed system is tested on Logical Access (LA) track of ASV Spoof 2019 dataset, and achieves performance improvement of about 59.7% and 65.9% relative to earlier proposed Linear-Frequency Cepstral Coefficients-Gaussian Mixture Model (LFCC-GMM) and Constant Q Cepstral Coefficients-Gaussian Mixture Model (CQCC-GMM) baseline models, respectively. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： ASV Deep neural network GTCC LSTM MFCC X-vector

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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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Medical Plant Detection using Transfer learning Coupled with K-Nearest Neighbour Algorithm 3

Medical Plant Detection using Transfer learning Coupled with...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Srambickal, Sai Subramanian Srivastava, Aman Somasundaram, S.K. School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science and Engineering Department of Information Security Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

Medical Plants are being used for more than a thousand year, with evidences dating before the Mauryain Era (around 322 BCE), they are widely used in the Ayurvedic school of medicine for therapeutic as well as medicinal purposes, it requires an expert to identify the species as well as the medical property associated with a given plant. This paper aims to use deep learning in order to identify a given plant using the leaves of the plant by using a pretrained Convolutional Neural Network (CNN) model namely ResNet50 and MobileNet to extract the latent vector which will then be passed to K-Nearest Neighbour (KNN) algorithm in order to identify the plant class, the best accuracy of the proposed models comes out to be 97.65%. © 2024 IEEE.

关键词： Neural network models

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KGFakeNet: A Knowledge Graph-Enhanced Model for Fake News Detection 31

KGFakeNet: A Knowledge Graph-Enhanced Model for Fake News De...

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2025 Workshop on Generative AI and Knowledge Graphs, GenAIK 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Kumar, Anuj Kumar, Pardeep Yadav, Abhishek Ahlawat, Satyadev Prasad, Yamuna Department of Computer Science & Engineering Indian Institute of Technology Jammu India Department of Electrical Engineering Indian Institute of Technology Jammu India

ISBN: (纸本)9798891762121

The proliferation of fake news on social media has intensified the spread of misinformation, promoting societal biases, hate, and violence. While recent advancements in Generative AI (GenAI), particularly large language models (LLMs), have shown promise, these models often need more structured representation for accurate verification, as they rely on pre-trained data patterns without access to real-time or validated information. This study presents a framework that utilizes Open Information Extractor 6 (OpenIE6) to extract triplet relationships (subject-predicate-object) from statements and justifications to compute the cosine similarity between the Knowledge Graphs (KGs) of the statements and their supporting justification to precisely measure the relevance and alignment between them. This similarity feature is integrated with an attention mechanism over GenAI-generated embeddings to enhance the model’s ability to capture semantic features accurately. In addition, a Multi-Layer Perceptron (MLP) classifier is employed to integrate all features, resulting in a 4% improvement in accuracy and a 5% increase in F1-score over state-of-the-art LLM-based approaches. ©2025 International Committee on Computational Linguistics (ICCL)

关键词： Knowledge graph

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A Systematic Literature Review of XAI-based Approaches on Brain Disease Detection using Brain MRI Images 4

A Systematic Literature Review of XAI-based Approaches on Br...

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4th International Conference on Advanced Research in Computing, ICARC 2024

作者： Madapatha, Savin Fernando, Pumudu University of Westminster Department of Computer Science and Engineering London United Kingdom School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (纸本)9798350384864

Brain Magnetic Resonance Imaging (MRI) analysis is a widely used medical procedure for the early diagnosis of various brain diseases. Accurate pathology identification during the brain MRI analysis procedure is crucial as misdiagnoses or missed findings can greatly affect a patient's treatment and long-term prediction. With the recent advancement of Artificial Intelligence (AI) in the medical field, researchers have approached various techniques to detect brain diseases using AI. Although AI models exhibit high accuracy, they suffer from a lack of transparency and interpretability, paving the way for the development of eXplainable Artificial Intelligence (XAI) methods in brain disease diagnosis. Image segmentation, machine learning, deep learning and XAI are important for assisting the diagnostic procedure. In this paper, a comprehensive overview of various existing techniques in brain disease detection using MRI is presented, starting with image segmentation techniques, followed by classification techniques, and finally, XAI techniques. In conclusion, the paper identifies a critical need for further research on XAI integration to advance brain disease detection. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Prediction of Insurance Claims for Health Sector using Machine Learning Techniques 3

Prediction of Insurance Claims for Health Sector using Machi...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pragatheeswari, E. Kavitha, M.N. Saranya, S.S. Dhanushree, D. Jasmitha, S. Abiradhie, P.V. Department of Computer Technology Kongu Engineering College Perundurai Erode India Department of Computer Science and Design Kongu Engineering College Perundurai Erode India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Nowadays, health issues play a tremendous role in day-to-day life and the medical expenditure to get treatment becomes more difficult for the ordinary people. Health insurance has become a vital aspect of people's lives. In this massive community, to access healthcare services such as insurance policies, LIC, ICICI, HDFC ERGO, Star Healthcare are benefits for claiming an amount for their medical expenses. The dataset encompasses three categories, such as generalized data, hospitalized data and claim data. Each record in the dataset represents an individual’s health insurance charges along with corresponding demographic and health-related characteristics. In this paper, Machine Learning techniques are used to group the claim amount that has been adopted by the individuals based on the hospitalized expenditure and insurance charges paid by the individuals. In the health sector, machine learning techniques are used to forecast insurance claims by building predictive models from historical data on claims, patient demographics, treatment types, and results. By predicting future claims based on trends seen in the data, these models help insurance firms better manage risk and allocate resources. In case of a fresher to this concept, it feels challenging to understand whereas generating as a pattern can be more flexible to acquire knowledge about how much amount has been claimed. © 2025 IEEE.

关键词： Electronic health record

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Early Diabetics Prediction Using Multi Model Approaches in Machine Learning 9

Early Diabetics Prediction Using Multi Model Approaches in M...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Renugadevi, G. Sasi Kala Rani, K. Oswalt Manoj, S. Saranya, N. Goudhaman, M. Sri Krishna College of Engineering And Technology Department of Computer Science And Engineering Chennai India Dr Ngp Institute of Technology Department of Computer Science And Engineering Chennai India Jeppiaar Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365092

Diabetics is the maximum common non-transmissible disease and the deadliest disease worldwide, affecting five hundred and thirty-seven mountain individuals. Diabetics container be carried on by a variety of sources, with an unhealthy diet, high BP, abnormal cholesterol, a personal antiquity of the disease, and inactivity. Early diabetes prediction using IoT can be done by monitoring various health parameters and identifying patterns that may indicate the development of diabetes. In Proposed work, Mendeley Data Repository is a real-time data have been taken for early diabetes prediction. The Proposed hybrid Backward elimination method with Maximum Relevance, Minimum Redundancy (BE-MRMR) algorithm When integrating backward elimination and the MRMR algorithm, you can use the strengths of both strategies to identify a small but informative set of features for early diabetic prediction, potentially enhancing model interpretability and generalisation. Finally, after eliminating features, left with a subdivision of structures that subsidize most to the replica's performance. This feature given to various machine learning classifiers RF, ANN, SVM to predict early diabetics. These models produce performance matrices is calculated in terms of accuracy, specificity, precision and sensitivity. © 2024 IEEE.

关键词： Forecasting

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