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Enhancing Security and Transparency Through the Integration of Blockchain and Machine Learning

Enhancing Security and Transparency Through the Integration ...

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2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Bommi, R.M. Sundarambal, B. Karthikeyini, Chokiyan Subramanian, Suresh Chennai Institute of Technology Department of Ece Chennai India Chennai Institute of Technology Dept of Cse Chennai India Excel Engineering College Dept of Ece Chennai India Sri Indu Inst. of Engg & Technology Dept of Ece Chennai India

ISBN: (纸本)9798350348910

This article examines a new strategy in improving security and trust by fusing blockchain technology with machine learning techniques. This synergy exploits the decentralisation and immutability properties of blockchains alongside analytical capabilities provided by ML models to achieve optimal value creation. Through strict scrutiny, the employed technique reveals efficacy as regards boosting security measures as well as fostering transparency in all sorts of operations. It shows how such an integrated approach can take up issues pertaining to the problem of securing contemporary data which will pave way for reliable, trustable, strong systems. For this reason, it becomes possible to widen the application of these safe and transparent technologies as a contribution to enlargement into broader fields of science. © 2023 IEEE.

关键词： Transparency

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Cau-Net: Enhancing Medical Image Segmentation With Contour-Guided Attention for Accurate Stroke Prediction

Cau-Net: Enhancing Medical Image Segmentation With Contour-G...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Sivakumar Annamalai T. Nandhini Priya J. Deepika Jeevitha R B. Priyanka Titus Richard Department of Safety and Fire Engineering Excel Engineering College Komarapalayam Namakkal India Department of Electrical and Electronics Engineering Excel Engineering College Komarapalayam Namakkal India Department of Information Technology Sona College of Technology Salem India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of English Vels Institute of Science Technology & Advance Studies Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Accurate segmentation of medical images is necessary for accurate diagnosis and treatment planning. The achievement of the precise boundaries in complex anatomical structures is difficult. In this paper, a novel hybrid segmentation model called Contour-Guided Attention U-Net (CAU-Net) is proposed by combining the traditional region-based active contour models with the advanced deep learning capabilities of Attention U-Net. CAU-Net employs an active contour model that generates an initial segmentation mask that provides the boundary awareness needed to steer the attention U-Net refinement. The use of attention mechanisms will be allowed with the selective features on the regions of interest identified by active contours in order to refine it to high accuracy of results in segmentation. It was tested on a Kaggle MRI dataset of medical images, including stroke prediction tasks, and performed better in boundary accuracy and overall segmentation quality than standalone Attention U-Net and active contour models. In stroke prediction, the CAU-Net achieved 98.3% segmentation accuracy and a Dice coefficient of 0.87 in detecting stroke lesions and delineating fine anatomical details. From the results, the hybrid approach significantly amplifies the contour adherence in addition to contextual sensitivity such that these are effective methods in such complex segmentation task especially those in medical applications.

关键词： Image segmentation Accuracy Attention mechanisms Sensitivity Magnetic resonance imaging Transfer learning Stroke (medical condition) Predictive models Reliability Active contours

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AI Based Content Management System

AI Based Content Management System

引用

2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Sundarambal, B. Karthikeyini, Chokiyan Bommi, R.M. Subramanian, Suresh Lokesh, M. Chennai Institute of Technology Dept of Cse Chennai India Excel Engineering College Dept of Ece Chennai India Chennai Institute of Technology Department of Ece Chennai India SIMATS Dept of ECE India Chennai Institute of Technology Dept of Csbs Chennai India

ISBN: (纸本)9798350348910

The primary goal of this application is to create a cohesive and user-centric ecosystem within a college department, seamlessly integrating essential academic functions and student engagement features. By providing centralized access to study materials, facilitating easy attendance tracking, and ensuring timely communication through a digital notice board, the platform aims to enhance the overall educational experience. Additionally, the application strives to empower students with resources such as department-specific information, career guidance, and internship opportunities. Through features like feedback mechanisms and staff details, the goal is to foster transparency and collaboration between students and faculty. The inclusion of placement data and assessment score notifications further aligns with the objective of preparing students for successful academic and professional trajectories. Ultimately, this web application aspires to be a comprehensive and adaptable solution responsive to the evolving needs of the academic community it serves. © 2023 IEEE.

关键词： Students

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Secure and Efficient Modified Dynamic Partition Routing Algorithm for Mobile Ad Hoc Networks

Secure and Efficient Modified Dynamic Partition Routing Algo...

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2022 International Conference on Advanced Computing Technologies and Applications, ICACTA 2022

作者： Ramya, V. Kousika, N. Rajasekaran, P. Vijeyakaveri, V. Jaganpradeep, J. Excel Engineering College CSE Kumarapalayam India Sri Krishna College of Engineering and Technology CSE Coimbatore India Dr. N.G.P. Institute of Technology Information Technology Coimbatore India S.S.M. College of Engineering ECE Kumarapalayam India

ISBN: (纸本)9781665495158

Mobile Ad-hoc Network (MANET) can be a self configured commercial road and rail network with less community of cordless contact. A several routing standard protocol is being advanced for MANET routing plus provide safety measures mechanism to stop security posts. Dynamic Partition Routing (DPR), one of the popular reactive routing practices for MANET, establishes way between origin to hot spot before files communication come about using Route request (RREQ) and Route reply (RREP) control announcements. The Modified Dynamic Partition Routing in (MDPR) intended for prevent option diversion because of a malicious computer in the market using group Diffie-Hellman (GDH) key, about any intermediate dependable node commence to misbehave subsequently there is no reduction mechanism. The employed Hash scheme can identify the misbehaving beginner's node which could provide inappropriate target. Our planned system testifies the reliability of the desired location and also protect against from acting up intermediary states. © 2022 IEEE.

关键词： Mobile ad hoc networks

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An Effective Emotion Recognition Method Using Facial and Speech Features 3

An Effective Emotion Recognition Method Using Facial and Spe...

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3rd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

作者： Muthumanickam, K. Mahesh, P.C.Senthil Vijayalakshmi, P. Department of Information Technology Tamilnadu Thottiyam India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam India Knowledge Institute of Technology Department of Computer Science and Engineering Tamilnadu Salem India

ISBN: (纸本)9781665456647

In face-To-face conversations, facial expressions help humans communicate nonverbally. Since the early 1990s, academics' focus on automatic facial expression detection, which is essential for human-machine interfaces, has grown. Traditional machine learning techniques can demand a difficult feature extraction procedure and yield subpar results. Using current developments in deep learning, we present efficient deep Convolutional Neural Networks (CNNs) that can automatically and accurately evaluate the semantic information contained in faces and speech without manually defining feature descriptors. Additionally, in order to develop an effective CNNs based emotion recognition system with a high degree of classification capability, we employ various loss functions and training techniques. The experimental results demonstrate that, the proposed method outperforms existing solutions in terms of performance and makes it ideal for real-Time systems. © 2022 IEEE.

关键词： Emotion Recognition

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Applying Deep Neural Networks and NLP Techniques for Sentiment Analysis in Social Media Data

Applying Deep Neural Networks and NLP Techniques for Sentime...

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Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： B M Brinda C. Rajan K. Geetha N. Nathiya P.J. Raguraman K. Srinivasan Computer Science and Engineering Paavai College of Engineering Namakkal India Information Technology KSRCT College of Technology Namakkal India Computer Science and Engineering Excel Engineering College Namakkal India Artificial Intelligence and Data Science Paavai College of Engineering Namakkal India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

This work offers a comprehensive investigation of sentiment analysis in social media communication through the integration of deep learning techniques with a natural language processing (NLP) methodology. The goal of the project is to create a matching model that can be used in real-world social processes. This model will allow for the precise identification of pertinent content that is dynamically changing and the realtime selection of key phrases based on available data. The paper builds a message sentiment analysis model and an image message multimodal sentiment analysis model, exploring unimodal and multimodal sentiment analysis algorithms in social networks. The study shows how well it works to extract complex emotions from social media writing by fusing advanced deep neural networks—like transformers or recurrent neural networks—with natural language processing techniques. Furthermore, the study presents noteworthy results, such as 96.1% order accuracy for brief texts in deep learning models that have been optimized, sentiment filtering for positive, neutral, and negative comments in social network data that has been successful, and an assessment of sophisticated semantic similarity models that offers a thorough comprehension of their performance in classification tasks. This work provides insightful information that may be used to improve sentiment analysis models and their usefulness in tasks involving semantic similarity and social network data interpretation.

关键词： Sentiment analysis Analytical models Social networking (online) Heuristic algorithms Semantics Artificial neural networks Linguistics

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Enhancing Multimodal Sentiment Analysis with Deep Learning Techniques to Foster Emotional Intelligence

Enhancing Multimodal Sentiment Analysis with Deep Learning T...

引用

International Conference on Communications and Signal Processing

作者： N Kousika N Nanda Kumar Elango Baskaran M S Abbenaya G Arthika M Ashwanth Department of CSE Sri Krishna College of Engineering and Technology Coimbatore India Department of CSE Excel Engineering College Kumarapalayam India Department of ECE Adhiparasakthi Engineering College Melmaruvathur India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Sentiment analysis (SA) is a critical process in understanding the emotions associated with data on social networking sites. Building upon this foundational concept, this paper introduces a sophisticated web application aimed at enabling users to submit diverse forms of content, including text, images, videos, and audio, for hate content assessment. This platform employs BERT CNN and VGG16 CNN algorithms for performing a thorough analysis of provided information in various formats. Emphasizing the identification of emotions in text and the detection of hate speech, the methodology extends to incorporate facial emotion detection for images. Through meticulous preprocessing, feature extraction, and ensemble learning, the proposed solution provides a robust framework for accurately identifying and mitigating hate content across various modalities. Experimental tests employing varied datasets validated the usefulness of the strategy, contributing to the advancement of hate content detection systems and fostering a safer online environment.

关键词： Performance evaluation Sentiment analysis Social networking (online) Hate speech Refining Signal processing algorithms Signal processing

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RG Technique to Reduce the Fuel Consumption and Co2 Emission in VANET

RG Technique to Reduce the Fuel Consumption and Co2 Emission...

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International Conference on Computing, Communication, Electrical and Biomedical Systems, ICCCEBS 2021

作者： Prakash, J. Sengottaiyan, N. Anbukaruppusamy Sudhakar, K. Mahaveerakannan, R. Department of Computer Science and Engineering Hindusthan College of Engineering and Technology Coimbatore India Department of Computer Science and engineering Shanmuga College of Engineering and Technology Sankagiri Salem India Department of Electronics and Communication Engineering Excel Engineering College Komarapalayam India Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Thandalam Chennai India

ISBN: (纸本)9783030861643

In modern days, vehicular ad hoc network (VANET) was one of the rapidly escalating research areas. There were previously numerous ideas expected in this area, but the driving force in VANET was not completed yet. The various routing protocols and clustering algorithms used in VANET were not effectively concentrated on contamination drop in the environment. The day-to-day life demands additional vehicles with protection, but the greater part of us had not been accepted the reality to utilize additional vehicles with less contamination. In this paper, the ReGrouping (RG) technique is proposed, which condensed the fuel utilization of assorted vehicles in VANET. The CO2 emissions and traffic level in the scrupulous direction were reduced. VANET representation was urbanized using finite automata (FA). With the assistance of the FA model, the VANET is constructed. The proportional result between with RG technique and without RG technique was shown. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vehicular ad hoc networks

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AES Algorithm for the Next Generation of 5G Network Encryption Standards 2

AES Algorithm for the Next Generation of 5G Network Encrypti...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Manojkumar, T. Tamilarasi, K. Mahalakshmi, S. Vinayagam, P. Mahanthesha, U. Mohanraj, S. Karpagam Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Excel Engineering College Department of Electronics and Communication Engineering Tamilnadu Namakkal India R.M.K. Engineering College Department of Computer Science and Design Tamilnadu Chennai India Saveetha Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Bnm Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350315905

The Advanced Encryption Standard (AES) symmetric key encryption algorithm is the industry standard for 5G communication encryption. technology advancements nowadays are mostly aimed at improving the security, speed, and efficiency of processors. The main expansion phase is made easier by the study's pipeline structure. This method simplifies the process of creating subkeys in AES encryption for use in 5G communication networks. There is a 37% increase in throughput between the simulated and implemented versions of the original design on FPGA Virtex4 XC4VLX200, and this difference may be attributed to the reduction in propagation latency while producing subkeys. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Blockchain Oriented Hybrid Architecture for Crowdsource Model

Blockchain Oriented Hybrid Architecture for Crowdsource Mode...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Pachhaiammal Alias Priya, M. Rajulu, N. Sundara Jayanthi, S. Kavitha, G. Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Excel Engineering College Department of Information Technology Tamil Nadu Coimbatore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Namakkal India

ISBN: (纸本)9798350333602

Blockchain has been preserved as one of the strongest innovations to facilitate crowdsourcing as it offers new functionality, such as decentralization and transparency. Alas, certain intrinsic drawbacks of Blockchain, which become the efficiency bottleneck of crowdsourcing schemes, have hardly been tackled by the most current initiatives when implementing Blockchain to clustering. The followings are the few challenges faced by Blockchain: Scalability, the blockchain sector still face difficulties in managing a large number of users at once, Hackers and shadow dealing, Complex to understanding and adopting. In this article, to achieve decentralization and privacy protection, a modern hybrid blockchain crowd sourcing facility is proposed. With its hybrid Blockchain, doubleheaders, and double consensus algorithms, this network integrates stable connectivity between the requestors and the employees. In order to ensure the automated execution of the activities and the security of users' privacy, smart contracts and zero awareness evidence are both used. Finally, tests are performed to validate the effectiveness of the platform's consensus algorithm by matching it with the new one. The proposed hybrid framework proved that it is highly confidential and gives an extraordinary performance in regard to privacy. With the development of big data and medical information control systems, there is a growing interest in the sharing of electronic medical records between businesses for better medical care and advancement. On the other hand, the proliferation of large amounts of data, privacy issues, inherent business confidence issues, and complex legal requirements impede the quick development of healthcare intelligence. © 2023 IEEE.

关键词： Blockchain

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