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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Narwadkar, Yogita P. Bagade, Anant M. SCTR's Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331539948

Text summarization is critical for managing the overwhelming volume of digital information because it condenses significant texts into concise summaries. This paper presents a comprehensive review of optimization methods used in text summarization, drawing insights from various literature surveys in the field. This paper examines different approaches, including statistical analysis, graph-based algorithms, machine learning techniques, and deep learning methods, to enhance the quality and coherence of generated summaries. The review categorizes and analyzes these optimization methods' strengths, weaknesses, and performance characteristics. Additionally, the review identifies challenges and open research questions in text summarization optimization and discusses potential avenues for future research. © 2024 IEEE.

关键词： Adversarial machine learning

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Workload Pattern Learning-based Cloud Resource Management Models: Concepts and Meta-analysis

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 418-438页

作者： Saxena, Deepika Singh, Ashutosh Kumar The University of Aizu Department of Computer Science and Engineering Japan Director with Indian Institute of Information Technology Bhopal India

Workload pattern learning-based resource management is crucial for cloud computing environments for achieving higher performance, sustainability, fault-tolerance, and quality of service. The existing literature lacks a comprehensive discussion and metaanalysis of workload pattern learning centered cloud resource management. In this context, this paper presents a first comprehensive study about five pattern learning and analysis-driven techniques applied for achieving higher efficiency and performance during multi-constrained cloud resource management. The paper manifests utility and significance of workload pattern learning-based resource management as compared with traditional resource management. The five principle techniques are thoroughly discussed with coherent depiction of intended concept alongwith numerical illustration. The most prominent state-of-the-art models belonging to each technique are further distinguished based on distinct objectives conferring an extensive survey and comparison. Besides, conceptual and theoretical analysis, the leading models underlying the major resource management techniques are implemented on a common platform and thoroughly examined using real-world Google Cluster workload traces. Based on the all-inclusive study and performance evaluation, trade-off discussion among these techniques are capsuled to put forward imperative concluding remarks with concrete open issues and insightful future research directions. © 2024 IEEE.

关键词： Quality of service

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Heart Disease Risk Analysis and Assessment: A Data Driven Approach

Heart Disease Risk Analysis and Assessment: A Data Driven Ap...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Kumar, B.H. Manjunatha Kumar, Abhinav Iyer, Aditya Soumika, M.L. Mayuri, S. SJC Institute of Technology Computer Science & Engineering Chikkaballapur India SJC Institute of Technology Department of Computer Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

The heart is indispensable for body. Diagnosis of heart disease must be complete and accurate. Heart disease is a dangerous disease. This disease is caused by many problems such as excessive stress, diabetes, high blood pressure, cholesterol and other factors in the human body. This article reviews and predicts heart disease using Python and machine learning. We can anticipate the occurrence of this disease by analyzing various features within the dataset to analyze the patient's performance, we have compiled a dataset containing 13 scores and 38 individual values. The principal focus of this article is to enhance the exactness of cardiac disorder assesment through employing machine learning algorithms. © 2024 IEEE.

关键词： Support vector machines

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Hand Gesture-Based Security and Authentication System

Hand Gesture-Based Security and Authentication System

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Porwal, Kalpesh Pulate, Krushna Waghmare, Akanksha Mamdapure, Pratik Kulkarni, Rutuja A. Pune Institute of Computer Technology Computer Engineering Department Dhankawadi Pune411043 India

ISBN: (纸本)9798331539948

In an era marked by growing concerns about data breaches and the limitations of standard authentication systems, this paper introduces an innovative solution: the 'Hand Gesture-Based Security and Authentication' system. Recognizing the limitations of passwords and biometric identifiers such as facial recognition, especially in situations when recovery or alteration is impossible, our solution takes advantage of the diversity and durability of hand gestures. Using advanced computer vision and deep learning techniques, we present a system capable of securely authenticating users by deciphering complex hand movements. This unique solution not only reduces the hazards associated with password-based systems, but also tackles the drawbacks of facial recognition, particularly in areas requiring face and hand coverage, such as military, industrial, and hospital settings. This study illustrates the system's effectiveness in preventing illegal access while maintaining user privacy and adaptability through thorough deployment and evaluation. Our research not only provides a comprehensive answer to today's authentication difficulties, but it also paves the way for safe access in a variety of operational contexts. The accuracy of 9 5. 9 6 is achieved with the ResNet50 model. © 2024 IEEE.

关键词： Differential privacy

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Detecting and Preventing of Attacks in Cloud Computing Using Hybrid Algorithm

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Intelligent Automation & Soft Computing 2023年第1期35卷 79-95页

作者： R.S.Aashmi T.Jaya Department of Computer Science and Engineering CSI Institute of TechnologyThovalaiIndia Department of Electronic Communications and Engineering CSI Institute of TechnologyThovalaiIndia

Cloud computing is the technology that is currently used to provide users with infrastructure,platform,and software services *** this system,Platform as a Service(PaaS)offers a medium headed for a web development platform that uniformly distributes the requests and *** using Denial of service(DoS)and Distributed Denial of Service(DDoS)attacks abruptly interrupt these *** though several existing methods like signature-based,statistical anomaly-based,and stateful protocol analysis are available,they are not sufficient enough to get rid of Denial of service(DoS)and Distributed Denial of Service(DDoS)attacks and hence there is a great need for a definite *** this issue,we propose an improved hybrid algorithm which is a combination of Multivariate correlation analysis,Spearman coefficient,and mitigation *** can easily differentiate common traffic and attack *** only that,it greatly helps the network to distribute the resources only for authenticated *** effects of comparing with the normalized information have shown an extra encouraging detection accuracy of 99%for the numerous DoS attack as well as DDoS attacks.

关键词： Hybrid algorithm(HA) distributed denial of service(DDoS) denial of service(DoS) platform as a service(PaaS) infrastructure as a service(IaaS) software as a service(SaaS)

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Unmasking Disinformation: Detection of Fake News Online Using Learning Techniques 6

Unmasking Disinformation: Detection of Fake News Online Usin...

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6th IEEE International Conference on Emerging Smart Computing and informatics, ESCI 2024

作者： Shaikh, Numair Petkar, Pallavi Sonawane, Sheetal SCTR's Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350306613

In an era where fake news proliferates effortlessly across social media platforms, the task of discerning reliable news sources has become increasingly challenging. Detecting fake news in the realm of social media presents unique characteristics and complexities that render traditional detection algorithms ineffective. Recent research endeavors have sought to address these challenges by leveraging a diverse array of methods, including machine learning, deep learning, feature engineering and graph mining. In our research, we investigate the detection of fake news by employing a combination of traditional machine learning algorithms and deep learning models for fake news classification. Furthermore, we extend our research by conducting real-Time analysis for fake news detection using Twitter data and data from fact-checking sites. By combining the strengths of traditional machine learning and deep learning, our research offers a holistic approach to fake news detection, enhancing the accuracy and robustness of our results. © 2024 IEEE.

关键词： Fake News Detection Natural Language Processing Text Classification

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Security to text (S2T): multi-layered based security approaches for secret text content

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Multimedia Tools and Applications 2025年第15期84卷 15151-15171页

作者： Kashid, Shamal Awasthi, Lalit K. Berwal, Krishan Department of Computer Science and Engineering National Institute of Technology Uttarakhand IN Srinagar India Faculty of Communication Engineering Military College of Telecommunication Engineering IN Mhow India

In the digital world, text data is produced in an unstructured manner across various communication channels. Extracting valuable information from such data with security is crucial and requires the development of techniques in text mining, information retrieval, and natural language processing (NLP). To solve this issue, we introduce two novel approaches: keyword extraction (KE) and a multi-layered secret sharing scheme (MLSS) to provide security to extracted keywords rather than overall text documents. The KE approach encompasses a sequence of text pre-processing procedures, including tokenization, stopword removal, stemming, and bag of words representation, followed by indexing. This methodology aims to efficiently extract keywords from text datasets. For this research work, we have proposed three datasets, including text messages, whatsapp messages, and electronic mail. MLSS enhances the security of extracted textual data by leveraging text pre-processing steps. This scheme ensures better confidentiality and the non-revealment of sensitive information. Additionally, we evaluate our KE model on our dataset as well as on standard datasets. Experimental results demonstrate the effectiveness of our proposed security to text (S2T) model, which outperform existing state-of-the-art approaches. The model obtains a 100% correlation between the reconstructed text and the original text. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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A Fully Homomorphic Encryption based approach for Privacy Preserved Pre-processing of MedicalTranscripts 15

A Fully Homomorphic Encryption based approach for Privacy Pr...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Gaikwad, Vijayendra S. Patil, Aditya Panditrao, Ruturaj Pareek, Tanisha Agrawal, Muskan Department of Computer Engineering SCTR's Pune Institute of Computer Technology Pune India

ISBN: (纸本)9798331300579

Natural Language Processing holds immense potential for extracting insights from healthcare data, but it demands stringent privacy ***-preserving NLP techniques, notably Fully Homomorphic Encryption (FHE), offer a path to advanced analytics while preserving patient data *** paper marks the culmination of our initial phase, focusing on data preparation and ***'ve employed FHE with the CKKS scheme to ensure data remains *** evaluation adapts multiclass classification metrics, addressing the distinct nature of healthcare *** we conclude this phase, we emphasize that this is only the *** steps will focus on developing NLP models trained on encrypted *** work highlights the intersection of data privacy and advanced analytics in healthcare, ultimately benefiting healthcare providers and patients. © Grenze Scientific Society, 2024.

关键词： Differential privacy

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Review and analysis for state-of-the-art NLP models

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International Journal of Systems, Control and Communications 2023年第1期15卷 48-78页

作者： Dikshit, Subrit Dixit, Rahul Shukla, Abhiram Department of Computer Science and Engineering Indian Institute of Information Technology Pune India Department of Civil Engineering Indian Institute of Technology Kanpur India

Natural language processing (NLP) is an area of research and study that makes it possible for computers to comprehend human language by utilising software engineering concepts from computer science and artificial intelligence. This work presents seven classifications and 21 state-of-the-art models with a survey of the operating principles. We also provide a comparative study of all models based on metrics such as accuracy, F1 score, exact match, squad scores, glue and superglue dataset scores. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Artificial intelligence

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Multimodal Integration, Fine Tuning of Large Language Model for Autism Support 5

Multimodal Integration, Fine Tuning of Large Language Model ...

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5th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2024

作者： Pai, Krishna Jagwani, Vidhita Pandita, Shivalik Kalbande, Dhananjay Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India Sardar Patel Institute of Technology Department of Computer Science and Engineering Mumbai India

ISBN: (纸本)9798350395235

This research project centres on creating an inclusive environment for the specially-abled. Created an app that integrates real-time object detection and speech-to-text capabilities which provides a multi-modal user experience with the pre-trained Model. Incorporated parameter-efficient fine-tuning using Low-Rank Adaptation (LoRA) to emulate the behaviour of an autism *** second module of the research employs YOLOv5 and machine learning to analyze therapy videos to get interaction timestamps and statistical insights. It automates the identification of interactions and serves as a valuable aid to guardians and *** paper details the methodology for real-time object detection using YOLO, SSD Mobilenet and enhances understanding in the context of adaptation of Language Models for a particular use case. Furthermore, dives deeper into the intricacies of interaction monitoring using bounding boxes in YOLO. By pushing the boundaries of Large Language Model capabilities, this research not only refines language models but also lays the foundation for their application in dynamic contexts of the real world, exemplifying their revolutionary potential. © 2024 IEEE.

关键词： Image recognition

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