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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Rajendar, Sivaramakrishnan Thangavel, Udhayakumar Devendran, Sarvesh Selvi, Vaishnavi Muthumanickam, Shanmuga Sundaram Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350397284

As Autonomous Vehicles (AVs) become increasingly prevalent, there is a growing need for secure and efficient data transfer between vehicles and other stakeholders in the transportation ecosystem. Blockchain technology has emerged as a promising solution to this challenge, offering a decentralized and tamper-resistant platform for storing and sharing data. This research study proposes a blockchain-based solution for addressing the challenges related to data security, ownership, trust, transparency, and auditability in AVs. It covers the use of blockchain for vehicle identity verification, secure data sharing, and real-time communication between AVs and other entities such as infrastructure and insurance providers. The proposed solution incorporates smart contracts, Merkle trees, and cloud storage to provide a trustless environment for data transfer and ensure that data is tamper-proof and auditable. The paper also provides a detailed implementation of the proposed solution. The findings of this research study provides a comprehensive overview of the state-of-the-art AVs and blockchain technology, highlighting the potential of this emerging field to transform the transportation industry. © 2023 IEEE.

关键词： Blockchain

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Deep Fish: An Approach to Fish Species Identification Through Deep Learning Techniques 3rd

Deep Fish: An Approach to Fish Species Identification Throug...

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3rd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Reddy, Penumalli Jithya Malathi, M. Julaiha, AG. Noorul Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department Computer Science Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9789819739905

This analysis introduces a system for automated identification of fish species and classification based on deep learning. This system can provide valuable insights to marine biologists, with better understanding on fish species habitats. The developed system relies on deep learning neural networks (DNNs) to extract features from underwater images and classify fish into different species. Classifying underwater images of fish species is crucial for conducting fish surveys, maintaining ecological balance, monitoring populations, and conserving endangered species. Yet, it proves challenging due to optical obstacles like smattering and raptness in ocean water, leading to dark and low images. This research delves into the application of deep learning in diverse aquatic tasks, encompassing fish identification, classification, feeding decisions and behavior analysis, estimation of its size, and quality of water prediction. Using a sizable labeled dataset from Kaggle, the proposed system employs a hierarchical CNN classification approach, achieving notable recognition rates and accuracy. The study highlights the efficacy of deep learning in fish species classification, emphasizing its automatic feature extraction capability, and removing the necessity for labor-intensive manual work when engineering features. Furthermore, the advantage of deep learning models lies in their capacity to attain high accuracy with extensive datasets, exemplified by the successful training on the Kaggle dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Deep Fake Detection using Inception ResNetV2 15

Deep Fake Detection using Inception ResNetV2

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

作者： Chemparathy, Alex B. Kavitha, T. Division of Computer Science Engineering Karunya Institute of Technology and Science Karunya Nagar Tamil Nadu Coimbatore641114 India

ISBN: (纸本)9798331300579

The increasing prevalence of DeepFake technology poses significant threats to various industries and public trust, making the development of robust detection methods crucial. In this study, we propose a novel approach for DeepFake detection using the InceptionResNetV2 architecture, leveraging its advanced capabilities in extracting features from images. Our method utilizes a deep learning framework to train the model on a diverse dataset of authentic and DeepFake videos, enabling it to learn distinct patterns and discrepancies between the two types of content. Through extensive experimentation and evaluation, we demonstrate the effectiveness of the proposed approach in accurately identifying DeepFake videos with high precision and recall rates. Furthermore, our method exhibits robustness against various manipulation techniques, showcasing its potential for real-world applications in combating the spread of misinformation and fraudulent content. The implementation of InceptionResNetV2 for DeepFake detection presents a promising solution to the growing challenges posed by synthetic media, providing a reliable tool for safeguarding the integrity of visual information in digital environments. © Grenze Scientific Society, 2024.

关键词： Deep learning

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Homogenous Ensemble Learning for Air Quality Index Prediction 15

Homogenous Ensemble Learning for Air Quality Index Predictio...

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

作者： Medhi, Shrabani Boruah, Rituraj Baruah, Sagar Pratim Das, Hiranya Kumar Girijananda Chowdhury University Dept. of Computer Science and Engineering Guwahati India Girijananda Chowdhury Institute of Management and Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331300579

Air pollution is a global issue of concern. Accurate prediction of the air quality index (AQI) plays a crucial role in understanding pollution patterns and implementing effective mitigation strategies. This research paper presents a comprehensive study on the application of ensemble learning techniques for AQI prediction, aiming to enhance prediction accuracy and provide insights into the driving factors behind pollution levels. The primary objective is to conduct a comparative analysis of homogenous ensemble method, namely, bagging in conjunction with individual base models, such as multiple linear regression, support vector regression, decision trees, and artificial neural networks. In total we have created 4 non-ensembled models and 4 ensembled models. Additionally, the research investigates the importance of different features in AQI prediction models, aiming to identify the key factors influencing air pollution. Hyperparameter tuning is done to optimize the models. © Grenze Scientific Society, 2024.

关键词： Prediction models

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Dynamic Topology Management In Ad-Hoc Networks For Improved Performance 2

Dynamic Topology Management In Ad-Hoc Networks For Improved ...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Anakath, A.S. Kannadasan, R. Margarat, G. Simi Pandian, A. Pasumpon Antony Sibiya Varghese, V. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology VIT University Tamil Nadu Vellore India Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering CARE College of Engineering Trichy India Department of Artificial Intelligence and Data Science GRT Institute of Engineering and Technology Tamil Nadu Tiruttani India

ISBN: (纸本)9798350383867

Optimization in ad hoc networks is a highly specialized task because the structure of the network is loosely formed, and each node is independently responsible for its operation. In order to obtain a better result from this solely formed networks this report offer extensive Dynamic Topology Management (DTM) method. Dynamic routing protocols lager lines, dividend balancing, Life-saving administration, guarantee of reliability, cross-layer design, security aspects and the on-time monitoring and discovery of topology changes are the main objectives and steps to be accomplished. Real-time Location monitoring and forest structure discovery are made possible by employing adaptive routing protocols, namely Dynamic Source Routing(DSR) and Ad-Hoc On-Demand Distance Vector(AODV). Also, the algorithms are capable of making advance adjustments to routing routes and resource allocations drawn from the mobility patterns forecasting which is vital. Load balancing methods, which distribute traffic in a network evenly, enable the system to run efficiently and avoid problems caused by overloading. Algorithms that are energy-efficient and make the best use of energy resources having their topology's optimized are introduced to the streamlined energy distribution systems. These algorithms are focused on the node's energy level. Through fault tolerance techniques which recognize and handle failures in nodes and partitioned networks, connectedness activity is a continuous one. Through QoS perception, the network topology might automatically be tailored to conform to the given quality criteria, thus not restricting the choice for differing applications. The proposed could have defenses against adversary nodes and their potential DDoS attacks which will enhance network protection. Yet another advantage of an architectural style that promotes cross-layer collaboration is enhanced topology management which is the goal around which the functions of various levels of the protocol stack revolve

关键词： Decision making

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Optimized Deep Learning Mechanism for Intrusion Detection: Leveraging RFE-Based Feature Selection and PCA for Improved Accuracy 5

Optimized Deep Learning Mechanism for Intrusion Detection: L...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rajkumar, M. Vs, Lakshi Karthik, R. Pavithra, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Smart Computing Tamil Nadu India B. Tech-Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India

ISBN: (纸本)9798331530013

With the growing demand for wireless networks, network traffic has surged, particularly involving large-scale data transmission. This results in an increased number of network security threats and the emergence of various cyber-attacks. To overcome these threats, the development of an efficient intrusion detection system (IDS) has become crucial. By leveraging the recent advancements in Artificial intelligence (AI), this project introduces an optimized deep learning mechanism utilizing a prevalent deep learning model, the Deep Neural Network (DNN) algorithm. This proposed method uses feature engineering techniques like feature selection and feature reduction. Given the high dimensionality of the selected datasets, these techniques are employed to prevent over-fitting. Recursive feature elimination performs feature selection followed by Principal Component Analysis for the feature reduction process, thus enhancing the model's efficiency. The system is assessed using two benchmark datasets: KDD and UNSW-NB15. The experimental results indicate that the presented DNN model on the KDD dataset has achieved 92.45% accuracy and 91.35% accuracy on the UNSW-NB15 dataset for intrusion classification. © 2024 IEEE.

关键词： Network intrusion

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EGANet: Elevation-guided attention network for scene classification in panchromatic remote sensing images

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Neural Computing and Applications 2024年第29期36卷 18251-18264页

作者： Datla, Rajeshreddy Swetha, G. Gayathri, C. Department of Space Akbar Road Tarbund Manovikas Nagar Telangana Secunderabad500009 India Department of Computer Science and Engineering Indian Institute of Technology Hyderabad TelanganaKandi Hyderabad502284 India Department of Computer Science and Engineering Mahindra University TelanganaBahadurpally Hyderabad500043 India

Scene classification in panchromatic (PAN) remote sensing images is a challenging task due to arbitrary spatial arrangement of a variety of objects with complex background in the absence of RGB-channel information. In this paper, we propose an elevation-guided attention network (EGANet) for multimodal scene classification in panchromatic images by leveraging elevation information from digital elevation model (DEM). The proposed network helps to identify the potential regions containing prominent class-specific features in the panchromatic image scene with the attention of elevation features extracted from a convolution neural network (CNN). Then, elevation-guided features in panchromatic image scene are obtained by the correlation of these two modalities for effective scene classification. The efficacy of the proposed method is demonstrated on Cartosat-1 panchromatic remote sensing image datasets with a lot of variations in view-angle, occlusion, background, and illumination conditions. The experimental results show that our proposed EGANet achieves scene classification accuracy with an improvement of 5% in comparison with the state-of-the-art approaches. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image classification

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Twitter Spam Detection Using Different Machine Learning Techniques 1

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2nd International Conference on Signal and Data Processing, ICSDP 2022

作者： Venkatesh, G. Reddy, D. Santosh Kavitha, C. Jinila, Y. Bevish Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Information Technology Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9789819914104

ISBN: (纸本)9789819914098

Vehicle reconciliation strategies have been involved a few times in spam channels to incorporate approaching/active messages, for example, spam and spam bunches. This technique expresses that each bunch contains little miniature groups, and each miniature group is dispersed. Notwithstanding, this thought ought not be trifled with, and the miniature group might have a lopsided dispersion. To build the respectability of the main strategy for appropriating the Internet class, we suggest supplanting the Euclidean space with a succession of models that incorporate into the miniature bunch connected with the circulation. Here, the Naïve Bayes (INB) classification has been carried out to carry out miniature bunches across the line. While these INBs can decide the distance and limits of micro clusters, Euclidean space considers the overall worth of the group and misdirects the bigger micro cluster. In this report, DenStream is upheld by a committed framework called INB DenStream. To represent the presentation of INB-DenStream, current techniques, like DenStream, StreamKM + +, and CluStream, have been utilized in the Twitter chronicle, and their exhibition has been estimated as far as quality, honesty as a general rule, memory as a rule, F1 measurements., markers, and complex estimations. The relatively close outcomes show that our techniques outflank its rivals in the set figures. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Fake News Detection Using Machine Learning 1st

Fake News Detection Using Machine Learning

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1st International Conference on Artificial Intelligence of Things, ICAIoT 2023

作者： Samad, Abdul Dhanda, Namrata Verma, Rajat Department of Computer Science and Engineering Amity University Uttar Pradesh Lucknow Campus Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (纸本)9783031487804

The news which is specially created to misguide as well as mislead the readers is termed fake news. Fake news can cause potential harm to both the individual and society. The problem of fake news has increased far more quickly in recent years. Social networks have significantly changed the scope and effect of their overall influence. Daily, a huge amount of information is released in print and online media, but it can be difficult to determine if the information is accurate or not. A significant amount of study has been done in this area in recent years with positive outcomes. In this modern era, machine learning is the answer to every issue. It can provide answers that people cannot readily think of and is applied in practically all disciplines, domains, and research. Many different algorithms in machine learning and artificial intelligence can help us in identifying as well as eliminating fake or false news. In this article, supervised learning is used. To determine the false news, the authors have used four machine learning algorithms in this study: Decision Tree (DT) classification, Logistic Regression (LR), Random forest (RF) and Passive Aggressive classifier (PAC). After comparing the output of each method, the one with the best accuracy is selected. The PAC outperforms the other classifiers and produces the best result. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision trees

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Accurate Brain Tumor Detection using Deep Learning: A Comprehensive Review and Implementation Study 3

Accurate Brain Tumor Detection using Deep Learning: A Compre...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Shruthi, G. Saravanan, M. Samyuktha, U. Thirumalaipathy KPR Institute of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350343632

The objective of this study is to develop a detailed method for applying deep learning techniques to the early diagnosis of brain tumors. It is imperative that brain tumor is diagnosed as early as possible to ensure the best possible treatment. Due to its potential to enhance the precision and efficiency of cancer detection, deep learning techniques have become increasingly popular in areas related to medical image analysis. In the proposed system, Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) are used to analyze medical images, including CT and MRI scans. To raise the caliber of the input data, preprocessing methods like segmentation and picture enhancement are used. Brain scan abnormalities, lesions, and tumor regions can be automatically recognized by the trained deep-learning model. © 2023 IEEE.

关键词： Convolution Neural Network (CNN) Medical Imaging Model Development Performance Evaluation Real-Time Detection Testing Model/QA Tumor Detection

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