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DeepGAN: Utilizing generative adversarial networks for improved deep learning

International Journal of Knowledge-Based and Intelligent engineering Systems 2024年第4期28卷 732-748页

作者： V, Edward Naveen A, Jenefa T.M, Thiyagu A, Lincy Taurshia, Antony Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences India Department of Computer Science and Engineering National Engineering College India

In the realm of deep learning, Generative Adversarial Networks (GANs) have emerged as a topic of significant interest for their potential to enhance model performance and enable effective data augmentation. This paper addresses the existing challenges in synthesizing high-quality data and harnessing the capabilities of GANs for improved deep learning outcomes. Unlike traditional approaches that heavily rely on manually engineered data augmentation techniques, our work introduces a novel framework that leverages DeepGANs to autonomously generate diverse and high-fidelity data. Our experiments encompass a diverse spectrum of datasets, including images, text, and time series data. In the context of image classification tasks, we conduct experiments on the widely recognized CIFAR-10 dataset, which consists of 50,000 image samples. Our results demonstrate the remarkable efficacy of DeepGANs in enhancing model performance across various data domains. Notably, in image classification using the CIFAR-10 dataset, our innovative approach achieves an impressive accuracy of 97.2%. This represents a substantial advancement beyond conventional CNN models, underscoring the profound impact of DeepGANs in the realm of deep learning. In summary, this research sheds light on DeepGANs as a fundamental component in the pursuit of enhanced deep learning performance. Our framework not only overcomes existing limitations but also heralds a new era of data augmentation, with generative adversarial networks leading the way. The attainment of an accuracy rate of 97.2% on CIFAR-10 serves as a compelling testament to the transformative potential of DeepGANs, solidifying their pivotal role in the future of deep learning. This promises the development of more robust, adaptive, and accurate models across a myriad of applications, marking a significant contribution to the field. © 2024 – IOS Press. All rights reserved.

关键词： Generative adversarial networks

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Ensemble of deep learning models with Walrus Optimization Algorithm for accurate botnet recognition and classification

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Iran Journal of computer science 2025年 1-18页

作者： Chacko, Ashwathy Anda Edwin, E. Bijolin Thanka, M. Roshni Ebenezer, V. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India

The rapid growth of Internet of Things (IoT) networks has introduced significant security challenges, with botnet attacks being one of the most prevalent threats. These attacks exploit vulnerabilities in IoT devices, leading to severe disruptions and damage to critical infrastructures. Detecting botnet attacks in IoT environments is challenging due to the large volume of data, the dynamic nature of traffic, and the diverse attack patterns. To address these issues, we propose a novel approach called Walrus Optimized Ensemble Deep Learning for Anomaly-Based Recognition Classifier (WOAEDL-ABRC), which leverages a combination of advanced machine learning techniques for effective botnet detection. The methodology of this research involves four key components: (1) data preprocessing through min–max normalization to scale the features appropriately, (2) feature selection using the social cooperation search algorithm (SCSA) to identify the most informative attributes, (3) an ensemble deep learning model combining convolutional autoencoder (CAE), bidirectional gated recurrent unit (BiGRU), and deep belief network (DBN) for robust anomaly detection, and (4) hyperparameter optimization using the Walrus Optimization Algorithm (WAOA), which fine-tunes the model parameters for optimal performance. This ensemble approach ensures that the model benefits from the strengths of each individual technique while mitigating the weaknesses of others. The dataset used for this research includes network traffic data from IoT environments, consisting of various botnet attack scenarios and normal traffic patterns. The data undergoes extensive preprocessing and feature selection to reduce dimensionality and enhance the model’s performance. The implementation is carried out in Python using TensorFlow for deep learning, with the WAOA applied to optimize hyperparameters. The results demonstrate the effectiveness of the WOAEDL-ABRC in detecting botnet attacks, achieving superior accuracy, precision

关键词： Botnet recognition Deep learning Feature selection Internet of things Walrus Optimization Algorithm

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Intelligent Traffic Management System using Multi-Agent Reinforcement Learning 3

Intelligent Traffic Management System using Multi-Agent Rein...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Shaheen, Dalaali Paulraj, Getzi Jeba Leelipushpam Jebadurai, Immanuel Johnraja Kirubakaran, Stewart Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Cse Coimbatore India

ISBN: (纸本)9798331509675

This paper presents a decentralized multi-agent system for intelligent traffic management in urban environments, where each agent represents a traffic light controller at an intersection. The proposed system leverages a combination of Distributed W-Learning and Deep Q-Networks (DQN) to optimize traffic flow. Distributed W-Learning enables agents to prioritize decisions based on multiple performance policies, while DQN enhances their ability to handle complex state-action mappings through neural network approximations. By utilizing locally available traffic data, each agent adapts dynamically to diverse traffic conditions. The integration of these machine learning techniques ensures a fully decentralized and self-organizing approach, minimizing congestion, improving traffic efficiency, and addressing multiple objectives simultaneously. Simulation experiments are conducted using SUMO (Simulation of Urban MObility) to evaluate the system's performance in realistic traffic scenarios. This study highlights the potential of combining Distributed W-Learning and DQN in multi-agent reinforcement learning to revolutionize traffic management systems and achieve scalable, adaptive, and efficient urban traffic control. © 2025 IEEE.

关键词： Traffic congestion

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Machine Learning-based Immune Cell Classification for Enhanced Immunological Analysis 3

Machine Learning-based Immune Cell Classification for Enhanc...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Naveen Pranouv, S.S. Isravel, Deva Priya Dhas, Julia Punitha Malar Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331509675

The automated classification of immune cells plays a vital role in advancing immunological research, diagnostics, and therapeutic monitoring. This paper leverages machine learning and image processing techniques to accurately classify various immune cell types from microscopic images by analyzing cellular features such as shape, size, and texture. The proposed Convolutional Neural Networks (CNN) based system integrates digital pathology workflows to provide a reliable and efficient tool for immune cell classification, reducing reliance on manual inspection. The model trained on a diverse dataset of labeled immune cell images to ensure high classification accuracy and adaptability across different cell types and imaging conditions. The proposed model provides a proactive solution to immunology research challenges, enhancing both the precision and efficiency of immune cell classification. © 2025 IEEE.

关键词： Convolutional neural networks

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A Comparative Analysis of Pancreatic Tumor Detection using VGG16, ResNet, and DenseNet 3

A Comparative Analysis of Pancreatic Tumor Detection using V...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Deepthi, Gannamani Anusha Bamini, A.M. Praveen, Yenumala Joseph Karunya Institute of Technology and Sciences Division of Computer Science and Engineering India

ISBN: (纸本)9798350375190

This study explores the transformative potential of image classification algorithms like VGG16, ResNet, and DenseNet, for the early detection of pancreatic tumors using medical imaging. One of the main causes of cancer-related deaths globally is pancreatic cancer. However, pancreatic cancer detected at early stages can be cured. This study navigates through the evolution of CNNs in medical image analysis and their specific applications in pancreatic cancer detection. Pancreatic cancer poses a significant global health challenge due to its high mortality rates, often attributed to late-stage detection. The primary objective is to enhance diagnostic accuracy and enable timely intervention. The obtained results demonstrated promising accuracy levels for each model. VGG16 achieved an accuracy of 96%, ResNet demonstrated an accuracy of 98.31%, and DenseNet showcased an accuracy of 99%. Precision values for VGG16, ResNet, and DenseNet were 95%, 96%, and 95.2%, respectively. Recall values were 98% for VGG16, 95% for ResNet, and 97% for DenseNet. The F1 score for 98% for VGG16, 97% for ResNet, and 98.1% for DenseNet. The study concludes that VGG16, ResNet, and DenseNet are valuable tools for pancreatic tumor detection, with each architecture exhibiting unique advantages. By aiding in the early detection of pancreatic cancer, the suggested models may enhance patient outcomes. The research provides insights into the comparative performance of these deep learning architectures, guiding future developments in medical image analysis. © 2024 IEEE.

关键词： Image classification

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A Confirmation Based Accident Detection System Using IoT for Smart Vehicles 3

A Confirmation Based Accident Detection System Using IoT for...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Ninan, Bettina Karunya Institute of Technology and Sciences Division of Computer Science and Engineering India

ISBN: (纸本)9798350384598

Accidents on the road continue to pose a significant threat to life and safety, necessitating innovative solutions to improve emergency response and minimize injuries. The proposed approach introduces an IoT-based Accident Detection System that integrates seamlessly with vehicle airbags, a confirmation button, GPS tracking, SOS calls, and a beeping system. The system harnesses a network of sensors to detect accidents accurately. When an accident occurs, it automatically deploys airbags to protect the passengers in the vehicle. A critical feature in the system is the manual confirmation button, which lets the occupants confirm the severity of the accident. If no confirmation is acquired within a set timeframe, the system proceeds to the next steps to save the occupant. The system uses GPS tracking with an accuracy of 96% to determine the location of the accident. Simultaneously, it initiates SOS calls to nearby hospitals and emergency services, ensuring seamless medical assistance. In addition, the system emits alerts to caution passersby and authorities about the *** system enhances emergency response, reducing the time between an accident occurring and medical attention. It not only minimizes injuries but also improves overall road safety. The proposed system aims to provide a brief overview of its multi-faceted capabilities, emphasizing its potential to save lives and reduce the severity of injuries in road accidents. © 2024 IEEE.

关键词： Automobile air bags

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Neuroimaging-based Machine Learning for Early Alzheimer's Disease Prediction 3

Neuroimaging-based Machine Learning for Early Alzheimer's Di...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Jerome, Jim Isravel, Deva Priya Dhas, Julia Punitha Malar Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331509675

Early detection of Alzheimer's disease (AD) is crucial for timely intervention and slowing its progression. This research leverages neuroimaging-based machine learning to classify cognitive impairment levels using MRI images. The proposed approach involves preprocessing MRI images, converting them into a numerical format, and training a BEiT (BERT for Image Transformation) model to classify individuals into one of four categories: No Impairment, Very Mild, Mild, or Moderate Impairment. A web interface, developed using Streamlit, enables user-friendly access, allowing real-time predictions by uploading MRI images. This system demonstrates the potential of integrating advanced machine learning techniques and neuroimaging for early Alzheimer's diagnosis, paving the way for personalized interventions and improved outcomes. © 2025 IEEE.

关键词： Adversarial machine learning

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A Comprehensive Food Identification and Waste Reduction Solution with Built-in Nutritional Tracking using Machine Learning 8

A Comprehensive Food Identification and Waste Reduction Solu...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Jonathan, J. Benjamin, Ruth Moly Prasad, Gunji Prem Karunya Institute of Technology and Sciences Computer Science Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350386578

Food Solutions and Nutrition Checker is an innovative and comprehensive project aimed at providing users with a reliable tool to identify various foods and dishes, while also offering a custom-built database for detecting fresh and wasted food items. With this system, users can easily determine the nutritional value of different foods, making informed decisions about their dietary choices. This study incorporates advanced technology that enables the recognition of a wide range of food items. By simply capturing an image or specifying the dish, users can obtain accurate information about the food, including its ingredients and origin. This feature is particularly helpful for individuals with specific dietary needs or restrictions, allowing them to easily identify suitable menu options. Additionally, this study includes a custom-built data system that helps to identify fresh and wasted food. By analyzing factors such as expiration dates, storage conditions, and quality indicators, the proposed system can determine the freshness status of various food items. This capability promotes food waste reduction by enabling users to make informed decisions regarding the consumption or disposal of perishable goods. Moreover, this study incorporates a nutritional tracker that provides users with comprehensive information regarding the nutritional composition of different foods. This feature includes details such as calorie count, macronutrient breakdown, vitamins, and minerals, empowering users to make healthier food choices aligned with their dietary goals and requirements. Overall, the proposed model offers a holistic approach for food identification, waste reduction, and nutritional tracking. By incorporating advanced technology and a robust database, this study aims to facilitate informed decision-making and promote healthier dietary practices. © 2024 IEEE.

关键词： Food ingredients

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Forecasting Bitcoin Price Trends: Integrating Sentiment Analysis and Machine Learning 3

Forecasting Bitcoin Price Trends: Integrating Sentiment Anal...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Aakash, Kasukurthy Reyana, A. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

With the increasing scrutiny of Bitcoin and other cryptocurrencies in recent years, there is a growing significance in studying methods to predict their values, including sentiment analysis on Twitter-collected data. The primary objective is to perform sentiment analysis on Bitcoin prices and examine the predictive accuracy of various machine learning algorithms. The approach cleans up Twitter data that is relevant to Bitcoin. Followed by quantifying people's opinions on Bitcoin using sentiment analysis. With this data and Bitcoin's price history, a binary target variable for price movement is built to specify the price variations. In order to predict the changes in Bitcoin price, several machine learning models are developed and evaluated. Among these models are Long Short-Term Memory networks, Decision Trees, Random Forests, and Linear Discriminant Analysis are mostly applied. The proposed approach is the enhanced Random Forest base classifier to improve the prediction accuracy. With an F1-score of 64% and 70% accuracy, the Enhanced Random Forest model performs better than the existing models. This model illustrates how sentiment analysis and machine learning may be combined to forecast cryptocurrency prices. © 2024 IEEE.

关键词： Machine learning

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Al-Proctored Exam Portal with Mobile Companion Application 3

Al-Proctored Exam Portal with Mobile Companion Application

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Daniel, Ronald C Caleb Andrew, H. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

Upholding academic integrity during exams has always been difficult in the world of online learning. We provide a novel approach to solve this problem: an AI-proctored exam Portal combined with a mobile companion app. This solution uses artificial intelligence for proctoring and enables instructors to prepare and conduct exams safely. The site includes elements like multiple-choice exams, written assessments with a question bank, and integrated course modules with quizzes and videos. With the use of AI algorithms, the exam process is tracked in real time, and each student receives a thorough report. Additionally, the mobile companion app improves the assessment process's integrity by acting as an additional monitoring tool and giving a side view of the student during the test. This article describes the architecture, functionality, and use of the AI-Proctored Exam Portal and its accompanying mobile application, emphasizing the platform's effectiveness in guaranteeing transparent and equitable testing in the online learning environment. © 2024 IEEE.

关键词： E-learning

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