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4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Sathya, V. Bhadula, Shuchi Srinivasan, R. Bhargav, H.K. Panda, Bhavani Sankar Barve, Amit Department of Computer Science Vallam Tamil Nadu Thanjavur613403 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Veltech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Avadi Tiruvallur Tamil Nadu Chennai600054 India Shridevi Institute of Engineering and Technology Department of Computer Science and Engineering Karnataka Tumakuru572106 India Raghu Engineering College Department of Computer Science and Engineering Andhra Pradesh Visakhapatnam531162 India Parul Institute of Technology Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering P.O.Limda Gujarat 391760 India

ISBN: (纸本)9798331540364

Disasters can be mitigated by an early warning signal and proper communication within the hazardous environment using the MANET technology. However, the exact prediction of disaster situation is needed for the timely disaster management. Hence in addition to MANET technology, deep learning algorithms and Inter of Things (IoT) can also be integrated into disaster detection and communication systems. The presented research developed a novel parallely distributed slimmable neural network with orchard algorithm for the accurate flood disaster prediction. The model processed the dataset containing various measurements such as temperature, humidity, precipitation, air, soil moisture and rainfall level that are collected by the IoT sensor deployed in the various location of the hazardous environment. The gathered data are initially pre-processed using the correlation coefficient min-max normalization approach. Further, the relevant characteristics of the flood disaster from the data are extracted through spike driven transformer process. These refined and dimensionality reduced data are then entered into the proposed framework for the disaster prediction. Here the integrated orchard algorithm increased the disaster monitoring accuracy on the basis of better optimized parameters. The model resulted 95% accuracy, 93% precision, 94% recall and 96% f-score. Therefore, the presented method is an effective prediction mechanism for the disaster management in IoT MANET. © 2024 IEEE.

关键词： Soil moisture

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Deep Learning-Based Intelligent GUI Tool For Skin Disease Diagnosis System 5th

Deep Learning-Based Intelligent GUI Tool For Skin Disease Di...

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5th International Conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023

作者： Karmakar, Mithun Mondal, Subhash Nag, Amitava Computer Science and Engineering Central Institute of Technology Kokrajhar BTR Assam Kokrajhar783370 India

ISBN: (纸本)9783031488757

Skin diseases are generally normal around the globe, as people get skin diseases because of inheritance and natural elements. Dermatologists rely heavily on visual examinations to diagnose skin diseases. However, this method can be inaccurate and time-consuming. The development of the technique in deep learning (DL) and the availability of GPUs can expand and improve the quality of computer-aided disease diagnostics systems. This paper proposes a DL-based smartphone application to aid a dermatologist in diagnosing skin disease in real-time. In the proposed work, we first used fine-tuned DenseNet201 for feature extraction and SVM to accurately classify normal and abnormal skin. A Deep Ensemble CNN (DECNN) framework with DenseNet-201, Resnet50, and MobileNet is further used for skin disease categorization if abnormal skin is detected. The experimental outcome of this study demonstrates that the framework can achieve an 84% accuracy in skin disease classification and outperform existing state-of-the-art works. The simple implementation and acceptable accuracy of the proposed method can be helpful for dermatologists in the diagnosis of skin disease. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Impact of Cluster Attack on Wireless Sensor Network

Impact of Cluster Attack on Wireless Sensor Network

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Brindha Shree, A. Harshinee, N. Ramya, M. St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309140

Wireless Sensor Networks (WSNs) are widely used for various applications, including environmental monitoring, healthcare, and industrial automation, due to their scalability, cost-effectiveness, and ease of deployment. However, the security of these networks remains a critical concern, as they are vulnerable to a variety of attacks. Cluster attacks, in particular, pose a significant threat to the integrity and availability of WSNs. The impact of cluster attacks on WSNs focuses on their detection, prevention, and *** attacks target the hierarchical structure of WSNs, which typically consist of sensor nodes organized into clusters with a centralized cluster head. Such attacks can disrupt communication, compromise data confidentiality, and drain the energy resources of cluster heads, leading to network degradation or even failure. Understanding the mechanisms and implications of cluster attacks is essential for developing robust security *** reviews of existing cluster attack models, categorize their impact on WSNs, and survey the state-of-the-art detection and prevention techniques. It also describes the trade-offs between security and resource constraints in WSNs, as these networks often operate in resource-constrained environments. This concludes by highlighting the need for a holistic approach to secure WSNs against cluster attacks, combining intrusion detection, encryption, and energy-efficient protocols. By shedding light on the challenges and potential solutions related to cluster attacks in WSNs, this research aims to contribute to the development of more resilient and secure WSNs, ensuring their continued effectiveness in various applications. © 2024 IEEE.

关键词： Sensor nodes

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Cross Border Secure Messaging with Blockchain A Global Perspective

Cross Border Secure Messaging with Blockchain A Global Persp...

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2024 International Conference on Advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Lokesh Kumar, V. Madhubalan, M. Sheeba, Adlin Subasini, C.A. St. Joseph's Institute Of Technology Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

Blockchain technology is widely recognized for its ability to secure data integrity and confidentiality in a trustless environment, safeguarding against cyber threats. Organizations face challenges in distinguishing personal and business communications, particularly with insecure workplace applications, heightening security risks. Lack of awareness about employee applications further complicates safeguarding crucial business communications. Public communication platforms often lack essential regulatory features for organizational use, falling short of data protection compliance and exposing industries to systemic risks. This research addresses these challenges by developing a secure, enterprise-level chat application, utilizing innovative technologies like blockchain for enhanced data security, privacy, and advanced auditing features. © 2024 IEEE.

关键词： Smart contract

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Experimental Thermal Model-Based SOC Estimation Algorithm for Lithium-Ion Phosphate Batteries Considering Internal Temperature

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Transactions of the Korean institute of Electrical Engineers 2024年第12期73卷 2289-2296页

作者： Kim, Dong Hwan Ko, Jeong Soo Kim, Do Hyeon Lim, Jong-Hun Lim, Je Yeong Lee, Jaein Lee, Byoung Kuk Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Advanced Defense Science & Technology Research Institute Agency for Defense Development Korea Republic of

This paper proposes a method for estimating the State of Charge (SOC) of lithium iron phosphate (LFP) batteries based on an experimental thermal model that considers internal battery temperatures. The proposed algorithm involves selecting a thermal model and suggesting methods for extracting parameters from the chosen model. Using the proposed thermal model, the algorithm compensates for temperature differences in cold and hot environments, thereby correcting the internal temperature to estimate SOC accurately. The effectiveness of the proposed algorithm is verified by estimating the SOC of electric vehicle LFP batteries and comparing the accuracy before and after correction. Copyright © The Korean institute of Electrical Engineers.

关键词： State of charge

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A Framework for Quantum based Software Development Process

A Framework for Quantum based Software Development Process

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2024 International Conference on Advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Nagarajan, Sathishkumar Malarvel, Muthukumaran Thangakumar, J. Hindustan Institute Of Technology And Sciences Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

Quantum software is essential for fully harnessing the capabilities of quantum computing systems. Consequently, it has been garnering growing interest in recent times. This document provides a precise definition of the concept of 'quantum software engineering' and presents a comprehensive framework for the life cycle of quantum software. Given the interdisciplinary nature of quantum computing, it is essential to establish a shared understanding of the process for developing and implementing a quantum software application. At present, there isn't a complete methodology or lifecycle that covers every significant stage that could come up along the process of development and execution. That's why we provide the quantum software lifecycle in this document. With this paradigm, practitioners, developers, and scholars have a clear path forward for quantum computing. The provision of a methodical development roadmap for quantum- principles-based software encourages creativity in this quickly expanding subject. © 2024 IEEE.

关键词： Error correction

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A model for screening eye diseases using optical coherence tomography images

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International Journal of computers and Applications 2022年第4期44卷 396-400页

作者： Gupta, Sanchay Chandra, Siddharth Maheswari, N. Sivagami, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

Age-Related Macular Degeneration (AMD) is one of the most common type of eye ailments which generally affects elderly people of age above 60 years and which damages their central vision. Diabetic Macular Edema (DME) is an another form of irreversible vision loss that occurs in diabetic people because of the accumulation of fat in retinal nerves. Therefore it is necessary to screen AMD and DME at an early stage, as it would help in the treatment and prevention of complete blindness. This paper aims to create a system for screening AMD and DME on Optical Coherence Tomography images using Image processing techniques in order to improve the feature to be extracted from the images using Convolutional Neural Network. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Support vector machines

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Study of the effectiveness of Generative Adversarial Networks towards Music Generation 2

Study of the effectiveness of Generative Adversarial Network...

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2nd International Conference on Informatics, ICI 2023

作者： Singhal, Megh Saxena, Bhawna Singh, Abhishek Pratap Baranwal, Anurag GL Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India Jaypee Institute of Information Technology Department of Computer Science and Engineering and Information Technology Noida India

ISBN: (纸本)9798350343830

Machine learning-based music generation has the potential to significantly change the music business and creative processes. The field of artificial intelligence offers new opportunities for creativity, artistic inquiry, and individualized musical experiences. Future music composition and generation will continue to be shaped by the research and development carried out in this area. Generative Adversarial Networks and reinforcement learning can be aptly used to improve the caliber and variety of music being created. The presented work discusses about the difficulties faced when creating music, whilst preserving musical coherence and preventing copying. A workflow is proposed for generating music using the LSTMGAN model. Loss graphs were used as the determinant of music quality. The results obtained show that LSTMGAN model performs remarkably well when used for music generation. © 2023 IEEE.

关键词： Music

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IoT cryptography with privacy-preserving ElGamal public key cryptosystem using oppositional artificial flora optimization algorithm

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Multimedia Tools and Applications 2025年第11期84卷 9287-9308页

作者： Paramasivam, Devi Sivaji, Sathyalakshmi Dayanandan, Venkata Subramanian Alqahtani, Abdullah Saleh Department of Computer Science and Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai India Department of CSE Hindustan Institute of Technology and Science Kelambakam Tamil Nadu Chennai603103 India Department of CSE Velammal Institute of Technology Chennai India Department of Self-Development Skills - Common First Year Deanship King Saud University Riyadh12373 Saudi Arabia

Privacy and security have received significant interest among the research communities Due to the increased exploitation of the Internet of Things (IoT). Since IoT devices gather most of the public data like identity, location, contact numbers, and energy utilization, security, and privacy become crucial. The lack of well-defined IoT-enabled security and privacy schemes will prevent the applicability of the IoT technologies from the client side. Therefore, this paper presents an ElGamal public key cryptosystem (EGPKC) for generating optimal keys utilizing an Oppositional Artificial Flora Optimization (EGPKC-OAFA) technique intended for preserving privacy in IoT. Additionally, the EGPKC approach with an optimum generation of authentication keys is applied on the medium access control (MAC) header for secure authentication. Besides, the optimal key selecting process for EGPKC occurs by employing the OAFA approach which inherits the oppositional-based learning (OBL) concept and classical artificial flora approach. To evaluate the effective analysis of the EGPKC-OAFA algorithm, huge simulations such as Known plaintext attack (KPA), chosen-plaintext attack (CPA), key sensitivity, and correlation coefficient are employed and the result is determined by different feature intervals. Accordingly, KPA and CPA reached the minimum value of 0.0987 and -0.0499 respectively. As a result, the proposed EGPKC-OAFA algorithm obtained superior performance compared to other methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Internet of things

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Advancing Healthcare Accessibility: Development of an AI-Driven Multimodal Chatbot 4

Advancing Healthcare Accessibility: Development of an AI-Dri...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Garimella, Bala Subrahmanyam Garlapati, Hari Sharan Choul, Sriharini Cherukuri, Rajesh Lanke, Pallavi B v Raju Institute of Technology Dept.Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350349900

In today's rapidly evolving healthcare sector, the need for efficient and accessible medical assistance is more crucial than ever. With the advancements in Artificial intelligence (AI), healthcare chatbots have emerged as a promising solution to address this need. These chatbots are capable of providing personalized medical information, scheduling appointments, and offering support to patients, thereby enhancing healthcare accessibility and patient engagement. The purpose of this project is to develop an advanced AI-powered healthcare chatbot that builds upon existing technologies to bring significant improvements in patient interaction and healthcare delivery. By leveraging the capabilities of Natural Language Processing (NLP) algorithms and deep learning techniques, the project aims to enhance the chatbot's functionality by integrating multimodal interaction features, enabling users to interact via text, voice, etc and provide more accurate and relevant responses to user queries. Additionally, the project seeks to create a comprehensive patient portal where users can securely access their medical records, reports, appointments, and engage with the chatbot for personalized assistance. © 2024 IEEE.

关键词： Deep learning

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