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8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Raaj Eshwar, S. Krishnan, Sanjay Prabu, M. Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331531539

Coral reefs are some of Earth's most varied and critical ecosystems however, they face an increasing threat because of climate change rising sea surface temperatures (SST). Long-term exposure to high SST can result in coral bleaching, which is when corals lose their symbiotic algae, causing them to lose their color and functionality, which may result in losing many corals. Thus for purposes of reducing the impact of Coral Bleaching it is important to devise predictive models that would be able to provide early warning to coral reef thermal stress. The proposed work focuses on forecasting Degree Heating Weeks (DHW), a statistic used in quantifying cumulative thermal stress upon corals over a period of 12 weeks including analysing past data and also monitoring current SST Levels with an aim to knowing how many DHWs there will be in spatial time frames, so as one can be prepared beforehand if they should happen at all. This method incorporates other factors besides sea surface temperature such as intensity and duration that affect bleeding more efficiently than just relying on sea temperature. © 2024 IEEE.

关键词： Bleaching

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Various methods to classify the polarity of text based customer reviews using sentiment analysis

Various methods to classify the polarity of text based custo...

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International Conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Sharma, Rahul Kumar Dagur, Arvind School of Computing Science & Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032669663

Sentiment analysis is the computational analysis of end users’ attitudes, opinions, and feelings regarding a specific subject or item. Sentiment analysis groups the message based on its polarity whether it is neutral, negative, and positive. Sentiment analysis in NLP is one that is rapidly developing with new approaches and methods are being created to increase its efficacy and accuracy. The most recent technologies and research methodologies for sentiment analysis is briefly summarized in this review article. The recent updates in this field are thoroughly summarized in this survey article. The paper presents recent studies systematically in areas associated with sentiment analysis (business surveillance, polarity observation, social media monitoring). In addition, it also analyses the challenges and drawbacks of the present sentiment analysis methods and suggests possibilities for future research. Overall, this study is a helpful resource for academics and industry professionals who want to stay on top of the most recent developments in sentiment analysis. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Emotion Recognition

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Traffic Sign Detection Using YOLOv8 2

Traffic Sign Detection Using YOLOv8

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Kumar, Rahul Gupta, Aniket Rajeswari, D. School of Computing College of Engineering and Technology Department of Data Science and Business Systems India

ISBN: (纸本)9798350327533

Traffic signals and other signs like parking., stop signs., etc. have become very crucial in autonomous and s elf-driving cars as it helps the smart system to comply with the basic traffic rules along with that it helps navigate routes based on the signs thus enabling a more secure driving experience for the drivers. There have been a lot of new algorithms that have emerged in the past recent years regarding this. In this paper., this research has used the new YOLOv8 object detection system to help us detect traffic signs as it is much fas ter and more precis e than its previous iterations. To improve the algorithm., this paper has used a dataset comprising photos of traffic signs taken at different angles and different light intensities. This system can predict the traffic signs with 93% accuracy. © 2024 IEEE.

关键词： Autonomous vehicle Deep Learning Traffic sign detection yolov8

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Real-Time Network Monitoring: A Big Data Approach 14

Real-Time Network Monitoring: A Big Data Approach

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Jaswanth, Kunisetty Sruthi, S. Ramachandrula, Pranav Saravanan, S. Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India Amrita School of Computing Dept. of Computer Science Engineering Chennai India

ISBN: (纸本)9798350335095

Real-time network monitoring is a critical requirement for tracking user activities and ensuring optimal network performance. In this paper, we propose a big data approach to real-time network monitoring that leverages the capabilities of Apache Spark, MongoDB, and Apache Kafka with Python. Our approach utilizes distributed stream processing of network data, enabling real-time analytics for user activity monitoring and network performance optimization. Our approach demonstrates the effectiveness of our system through experiments with real network traffic data, showing how it can identify and track user activities in real-time. Our system can be used by network administrators and IT professionals to optimize network performance, improve user experience, and enhance security through real-time monitoring and analysis of user activity data. This approach can also be opted as a feature in SDNs. © 2023 IEEE.

关键词： Network performance

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Safeguarding Financial Transactions using Customer Profiles

Safeguarding Financial Transactions using Customer Profiles

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Kumar, Anand Sountharrajan, S. School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

Credit card fraud is thereby a big challenge which results to enormous losses to both the individuals and the financial institutions. It is often the case that conventional measures of approach to fraud detection often fail to offer what is needed because they rely on broad characteristics of behaviour and not characteristics of the specific customer. The present work proposes a sophisticated fraud detection system equipped with a transaction-processing engine defining the fraud detection technology coupled with machine learning algorithms used to assess the credibility of transactions. To enhance the detection accuracy, the system incorporates Support Vector Classifier (SVC) and the MCC-based merchant profiles. To conduct a more realistic experiment, the synthetic dataset of 50,000 credit and debit card transactions with a minimal portion of fraudulent transactions was used. To assess the effectiveness of the Support Vector Machine (SVC), Decision Tree, and Random Forest algorithms, the study was carried out. The findings indicated that the use of MCC based profiles the category of Support Vector Machines (SVC) achieved an accuracy of 91 % which is remarkably higher than basic methods. To avoid the high risk of fraud and significant losses, the proposed system is designed to continuously check, verify, and process transaction requests. Further advancements will focus on enhancing the algorithm to adapt to the emerging techniques in fraudulent practices and enhance the security of transactions to a new level. © 2024 IEEE.

关键词： Support vector machines

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GeneOntology's Disease Gene Identification Assessment by DisGeNET 2

GeneOntology's Disease Gene Identification Assessment by Dis...

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2nd International Conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2024

作者： Umar, Yasmeen Raushan Mohd. Mishra, Nilamadhab School of Computing Science & Engineering Vit Bhopal Universty Madhya Pradesh 466114 India

ISBN: (纸本)9798350370188

All functional mechanisms in organisms are controlled by the genome. Gene changes and mutations can cause abnormalities and diseases. Diseases range from those caused by a single gene to complex ones. In an era of rapidly expanding environmental change, many diseases have occurred and new breeds have emerged due to mutations. It is important to diagnose the disease and start treatment immediately. Here, a technique was developed to subdivide disease and non-disease genes from GeneOntology based on GO aspect groupings. In this study, some existing research has been discussed. GeneOntology and DisGeNET are the two main data sources used in the system. The output of the system includes disease names caused by each aspect of GO and the disease gene. © 2024 IEEE.

关键词： Genes

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Secure location-aware geocast routing in Internet of Vehicles

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Multimedia Tools and Applications 2025年第18期84卷 19669-19692页

作者： Kumar, Sunil Dohare, Upasana Kumar, Sushil Department of Computer Science and Information Technology Central University of Haryana Mahendragarh India School of Computing Science & Engineering Galgotias University Greater Noida India School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

Efficient message dissemination in Vehicular Ad Hoc Networks (VANETs) relies on robust connectivity between neighboring vehicular nodes, yet it is often compromised by malicious intruders. While recent literature proposes various approaches to enhance geocasting performance, security considerations for connectivity are often overlooked. To address this gap, we propose the Secure Location-Aware Cache Agent based Geocast Routing (SL-CAGR) protocol, emphasizing an Intrusion Prevention Technique (IPT) for message integrity preservation and a Connectivity Reliant Packet Forwarding (CRPF) algorithm for improved VANET connectivity. Our protocol introduces a network model for cache agent-based geocast routing on the Internet of Vehicle (IoV) environment, implements IPT using Diffie-Hellman digital signatures, and presents a secure geocast routing strategy focusing on re-caching and cache agent selection. Empirical simulations demonstrate SL-CAGR's superiority in packet delivery ratio, packet loss, duplication, hop count, end-to-end delay, and network load compared to these protocols. Simulation results demonstrate that SL-CAGR outperforms state-of-the-art techniques like CAG, CTRC, and D-Flooding. SL-CAGR stands out by merging intrusion prevention, secure geocast routing, and cache agent selection, ensuring robust security and efficiency in VANETs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vehicular ad hoc networks

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Enhanced Fruit Fly Optimization Algorithm for Wrapper Based Feature Selection in Cancer Prediction 1

Enhanced Fruit Fly Optimization Algorithm for Wrapper Based ...

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1st International Conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Saranya, P. Viji, D. Jangiti, Aditya School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798350361780

Nowadays, smart healthcare appliances generate vast amount of medical data. Such huge amount of data requires classification methodologies, through which disease diagnostics can be carried out. Chronic diseases like cancer can be predicted in advance to help the patients to take treatment in initial stages of the disease. This article proposes an improvised feature selection and classification -method for cancer prediction. Here we propose an enhanced fruit fly optimization algorithm in KNN classifier for searching strategy with wrapper methods for feature selection. Lung cancer data set, breast cancer data set and leukemia data set has been taken from the UCI repository for testing the performance of the proposed model. For Lung cancer data set, accuracy, specificity, sensitivity, and fscore value observed are 96.97 %, 96.28 %, 96.35 %, and 96.34% respectively. For breast cancer dataset, accuracy, specificity, sensitivity, and fscore value observed are 99.97 %, 98.2 %, 96.85 %, and 90.35% respectively. For Breast cancer dataset, accuracy, specificity, sensitivity, and fscore value observed are 96.67%, 98.28 %, 95.85 %, and 93.85% respectively. The proposed method works well with various dataset and shows prominent accuracy improvement over the existing systems. © 2024 IEEE.

关键词： Optimization

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Multimodal Deep Learning with XAI for Transparent and Accurate Doctor Prescription Recognition 2

Multimodal Deep Learning with XAI for Transparent and Accura...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Sunil Reddy, J. Norbu, Tenzin Sungrab Kumar, Pavan Ajjanakatt, Praveen Vitthal Harsha, Reddyam Ananthanagu, U. Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798331534950

This paper presents a deep learning framework to automate recognition of medical prescriptions by using handwritten content, feature extraction from CNNs like VGG16, Alex Net, and MobileNetV2, and sequential data utilizing LSTMs. A multi-modal approach captures even the spatial and temporal details in a prescription, which assists in an accurate interpretation of complex handwriting. Specifically, key pre- processing like grayscale conversion, normalization and edge detection improving the quality of the image, reducing noise and bringing forth the prominent features that aid in the improvement of recognition accuracy and also implementation of XAI concepts to further enhance the interpretation. This model reduces transcriptions, making it easier for the patients as well as the practitioners to understand the handwriting through the computer screen. The method therefore assists in safer patient-centered healthcare by automating prescription processing thus triggering clearer communication towards more informed healthcare decisions. © 2024 IEEE.

关键词： Deep learning

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Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1513-1528页

作者： Zhang, Tiehua Liu, Yuze Shen, Zhishu Ma, Xingjun Qi, Peng Ding, Zhijun Jin, Jiong Tongji University School of Computer Science and Technology Shanghai201804 China Ant Group Shanghai200122 China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Fudan University School of Computer Science Shanghai200438 China Tongji University Department of Control Science and Engineering Shanghai201804 China Swinburne University of Technology School of Science Computing and Engineering Technologies Melbourne3122 Australia

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this article, we propose a hypergraph learning framework named learning from heterogeneity (LFH) that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterward, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with twelve state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experimental results demonstrate a significant performance gain (an average of 12.9% in node classification and 12.8% in link prediction) compared with recent state-of-the-art methods. © 2020 IEEE.

关键词： Contrastive Learning

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