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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Dev, R.B. Shanthi, S. Brindha, V. Arunkarthi, T. Ashwinth, K. Kuppuchamy, R. Kongu Engineering College Department of Computer Science and Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Dindigul India

ISBN: (纸本)9798350349221

The process of identifying and categorizing lung cancer in its early stages is difficult, yet doing so will improve patient survival rates. There is a wealth of research that segments and categorizes lung nodules using convolution neural networks (CNN). Numerous factors, including batch size, epochs, learning rate, optimizers, activation function, and weight initialization, affect the model's performance. To categorize CT scan images as benign, malignant, or normal, a deep CNN model is developed. Optimizers are crucial to the training process of the model because they adjust the weight and learning rate of the network in each epoch, which lowers loss and raises classifier accuracy. Using ReLU and selu as activation functions, this work offers a thorough analysis of the performance for a number of optimizers, including Nesterov Adaptive Momentum (Nadam), Adaptive Delta (Adadelta), Adaptive Max Pooling (Adamax), Stochastic Gradient Descent (SGD), Adaptive gradient (Adagrad), Adaptive Momentum (Adam), Adaptive Gradient Descent (Adagrad), and Root Mean Square Propagation (RMSProp)[1][1][1][1][1][1]. The best training accuracy for the ReLu activation function is 92.67%, with a validation accuracy of 97.92, and for the Selu activation function, it is 95.42%, with a validation accuracy of 96.88, according to the Adam optimizer. The study aims to assess various optimization techniques in deep CNNs for lung cancer classification using CT scan images. It analyzes different optimizers to understand their impact on model convergence, accuracy, and generalization. This insight is crucial for selecting the most effective optimization methods in lung cancer diagnosis. © 2024 IEEE.

关键词： Biological organs

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Enhancing Predictive Capabilities for Cyber Physical Systems Through Supervised Learning

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第16期49卷 77-86页

作者： Dhanalakshmi, B. Tamije Selvy, P. Department of Computer Science and Engineering Dr.N.G. P Institute of technology India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology India

The rapid advancement and proliferation of Cyber-Physical Systems (CPS) have led to an exponential increase in the volume of data generated continuously. Efficient classification of this streaming data is crucial for predicting system behaviors and enabling proactive decision-making. This research aims to extract actionable knowledge from the continuous data streams of CPS and predict their behavior using advanced supervised learning algorithms. The predictions facilitate timely interventions and necessary actions within the interconnected physical network. The background of this work lies in the intersection of CPS, machine learning, and data stream mining. Traditional batch processing methods are inadequate for real-time analysis of CPS data due to their inherent latency and computational inefficiency. This research employs state-of-the-art techniques for real-time data processing, including incremental learning, sliding window models, and ensemble methods tailored for streaming data. Our approach differs from existing works by focusing on a comprehensive framework that integrates real-time data ingestion, preprocessing, feature extraction, and model updating in a seamless pipeline. Unlike previous studies that often rely on static datasets and offline analysis, our method ensures continuous learning and adaptation to evolving data patterns. Comparative analysis with existing techniques demonstrates superior performance in terms of accuracy, latency, and scalability. Specifically, our models achieved an average classification accuracy of 92%, with a precision of 90%, recall of 89%, and an F1 score of 89.5%. These metrics indicate significant improvements over traditional batch processing methods, which typically lag in responsiveness and adaptability. This research provides a robust and efficient solution for the real-time classification of streaming data from CPS, enhancing the system's ability to predict behaviors and take necessary actions promptly. © 2025 Slov

关键词： Self-supervised learning

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Enhancing URL Security and Access Control Using Hash-Based Shortening Algorithm 2

Enhancing URL Security and Access Control Using Hash-Based S...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Haripriya, K. Preethi, C. Hameed, S. Shahul Pavalarajan, S. Shushmmitha, T. Haripriya, V. PSNA College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798331534950

Shortening URLs has become an integral part of modern digital communication, as it simplifies long and complex internet addresses, making them more user-friendly. However, conventional URL shortening methods often lack adequate security provisions, leaving shortened links vulnerable to various risks. This project aims to develop a secure URL-shortening service by incorporating a hash-based access control mechanism. By using a hash-based approach, we can significantly enhance the security of shortened URLs. In this method, a unique hash value is generated for each URL, which serves as a link to the shortened URL. The generated hash is highly resistant to decryption, making it difficult for unauthorized users to retrieve the original URL, thereby ensuring that only the intended recipient can access the link. The goal of this project is to develop a protected and secure URL-shortening service that utilizes a hash-based access control mechanism. By generating a unique hash for each shortened URL, the system will enhance both security and user privacy. Furthermore, the implementation of granular access control will be a key factor in maintaining the system's overall security. SHA-256 can be utilized to generate unique, unpredictable shortened URLs by creating secure hash values. Integrating OAuth2 and JWT provides robust access control, enabling seamless user authentication and authorization across multiple systems. Additionally, AES encryption ensures the protection of sensitive data by offering strong encryption, safeguarding both the original URL and user information. © 2024 IEEE.

关键词： Cryptography

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Alzheimer's Disease Detection using Machine Learning Models 3

Alzheimer's Disease Detection using Machine Learning Models

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Nayaki, A. Periya Arrchit Ramana, M.S. Prithivi Raj, S.D. Santhosh, L. Swathi, B. Abinesh, N. Thanabal, M.S. Velammal College of Engineering and Technology Department of Information Technology Madurai India Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350369083

Alzheimer's disease is the most common cause of dementia in older persons, with its incidence rising rapidly. This neurodegenerative disorder affects brain function, impacting memory and cognition, and presents significant social, financial, and economic challenges. Early prediction of Alzheimer's disease is essential for effective treatment and reducing harm. This study utilizes machine learning algorithms to predict Alzheimer's disease using the "MRI and Alzheimer's"dataset from the Open Access Series of Imaging Studies (OASIS). The models evaluated include Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, AdaBoost, LightGBM, XGBRF Classifier, and a Voting Classifier. Their respective accuracies are 84.21 percent for Logistic Regression, 84.21 percent for Support Vector Machine, 81.58 percent for Decision Tree, 94.74 percent for Random Forest, 94.74 percent for AdaBoost, 52.63 percent for LightGBM, 89.47 percent for XGBRF Classifier, and 98.17 percent for the Voting Classifier. The Voting Classifier yielded the highest accuracy, indicating its effectiveness in predicting Alzheimer's disease. © 2024 IEEE.

关键词： Random forests

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DeepGAN: Utilizing generative adversarial networks for improved deep learning

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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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Experimental investigation on machining of titanium composite using artificial neural network

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Interactions 2025年第1期246卷 1-10页

作者： Perumal, B. Kannan, R. Arunkumar, R. Kumaresan, T. Subbiah, Ram Department of Mechanical Engineering Sri Venkateshwara College of Engineering Andhra Pradesh Tirupati India Department of Mechanical Engineering PSNA College of Engineering and Technology Dindigul India Department of Mechanical Engineering MRK Institute of Technology Tamilnadu Kattumannarkoil India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode India Department of Mechanical Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

Manufacturers must be able to figure out the most suitable technique capable of generating rapid and accurate performance when developing a precise modelling approach for the development of an efficient machining process. This paper assesses the predictive capabilities of the Artificial Neural Network (ANN) and the response surface methodology in the titanium machining process with respect to tool tip temperature and tool wear. The adequacy of the ANN in modeling and forecasting reactions were rigorously examined and contrasted using data collected from properly prepared machining experiments. Both approaches performed admirably in terms of forecasting machining process responses. The coefficient of correlation (R2) obtained from the analysis verifies the ANN’s choice, with a maximum value of 99.9% and with the response surface methodology with a maximum value of 99.8% was found. The experiment also demonstrates that when the proper parameters are used, the ANN approach can produce the best results. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Machining centers

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Machine Learning Based Alzheimer’s Disease Detection: A Comprehensive Approach 7th

Machine Learning Based Alzheimer’s Disease Detection: A Com...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Periya Nayaki, A. Vidyabharathi, A.K. Krishnaveni, S. Thanabal, M.S. Department of Information Technology Velammal College of Engineering and Technology Madurai India Computer Science and Engineering PSNA College of Engineering and Technology Madurai India

ISBN: (纸本)9783031699818

Alzheimer’s disease typically leads in the death of neurons and the connections between them in memory-related parts of the brain, such as the entorhinal cortex and hippocampus. Following that, it affects the areas of the cerebral cortex responsible for language, cognition, and social interaction. Several other regions of the brain get harmed at some time. Patients with Alzheimer’s disease gradually lose their capacity to function normally and go about their daily lives. Currently, age is regarded as one of the most important risk factors;the majority of patients with dementia are 65 or older. Symptoms of the condition include increased disorientation, memory loss, including the inability to recall recent events or personal history, and learning difficulties. As of yet, there is no treatment for Alzheimer’s. A vast quantity of data will be acquired from the previously affected person, and the user input data will be compared to past data using machine learning techniques to determine whether they are still impacted. Finding the best categorization approach is difficult because it necessitates adapting to regional datasets. The methods LightGBM, AdaBoost, Random Forest Classifier, Support-Vector-Machine, Decision-Tree, and XGBoost provided accuracy rates of 78%, 86%, 81%, 73%, and 84%, respectively. In this work, the Voting Classifier algorithm, which may be more accurate than other methods, will be employed. © IFIP International Federation for Information Processing 2024.

关键词： Decision trees

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Monitoring COVID-19 environment: a real-time facial mask detection using YOLO models

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Research on Biomedical engineering 2025年第1期41卷 1-18页

作者： Pandiyan, P. Thangaraj, Rajasekaran V, Balasubramanian V. K, Manavalasundaram S, Balakrishnan Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Information Technology Velalar College of Engineering and Technology Erode India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore India

Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective measure for themselves and others. Coronavirus protection guidelines have been published by the World Health Organization (WHO). According to WHO standards, COVID-19 can be prevented by wearing a mask in public places and congested regions. In these places, it is very difficult to personally check to see if people are wearing face masks or not. Methods: The objective of this research work is to build a powerful, efficient, and real-time approach for detecting people not wearing masks. Three cutting-edge object identification models, namely YOLOv4, Tiny-YOLOv4, and YOLOv5, are employed in this study for the identification of masked faces. Result: The proposed YOLOv5 model is evaluated using real-time images captured using a smartphone or tablet. The test images include both single and multiple people with and without masks. The YOLOv5 model achieved recognition accuracy of 88.90% with an average detection speed of 0.0316 s per image, whereas the YOLOv4 and Tiny-YOLOv4 produced recognition accuracy of 82.24% and 74.80% with an average detection speed of 0.0530 s and 0.0541 s per image, respectively. Conclusion: The comparative performance suggests that the YOLOv5 model has a maximum recognition accuracy of 88.90% in face mask identification tasks compared to other models such as the YOLOv4 and Tiny-YOLOv4. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： COVID-19

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Sentiment Analysis on Covid-19 data Using BERT Model

Sentiment Analysis on Covid-19 data Using BERT Model

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： ArunKarthi, T. Shanthi, S. Nirmaladevi, K. Ashwinth, K. Dev, R.B. Rajkumar, N. Computer Science And Engineering Kongu Engineering College Erode India Psna College Of Engineering And Technology Department Of Computer Applications Tamilnadu Dindigul India

ISBN: (纸本)9798350371567

In a world where microblogging has become an integral part of modern life., the sudden outbreak of COVID-19 swiftly infiltrated public opinion on various social networking platforms. With individuals adhering to social distancing measures and self-isolation protocols to combat the coronavirus, expressing their fears and concerns on social media became their primary outlet. In order to obtain a thorough comprehension of the pandemic's effects on individuals' lives and the environments in which they live, we must recognize the critical function of social media as a repository of emotions. This paper delves into the realm of sentiment analysis, focusing on the opinions disseminated through Twitter. Our primary dataset comprises these tweets, which we meticulously processed and converted into tokens for analysis. We performed fine-tuning and added an additional classifier layer to BERT, a pre-trained model, in order to classify these feelings into three categories: positive, negative, and neutral. Subsequently, we scrutinized BERT's performance in carrying out this task. Impressively, the BERT model demonstrated an outstanding accuracy rate of 94 percent on our dataset, supplemented by commendable scores in various evaluation metrics. © 2024 IEEE.

关键词： Sentiment analysis

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Sentimental Analysis on Twitter data using Deep Learning 2

Sentimental Analysis on Twitter data using Deep Learning

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Brindha, V. Shanthi, S. Bhavishya, C. Durgashree, V. Devi, K. Nirmala Jaganathan, P. Kongu Engineering College Department of Computer Science & Engineering Perundurai India PSNA College of Engineering and Technology Department of MCA Dindigul India

ISBN: (纸本)9798350349221

In today's interconnected world, social media platforms have enabled individuals to easily communicate and share their emotions with people worldwide. In the current context, some individuals exhibit a higher degree of extroversion on social media compared to their offline interactions. However, when they encounter difficulties or situations requiring support, they may not find the help they need because they tend to express their feelings on social media rather than seeking assistance from those close to them. Consequently, if these individuals are forewarned and equipped with effective strategies, it could potentially reduce the stress and mental health challenges they experience. As a result of the escalating global prevalence of these issues, numerous researchers have been drawn to this topic, focusing on the matter and proposing practical solutions. The study employs state-of-the-art NLP techniques, including tokenization, word embedding, and deep learning. © 2024 IEEE.

关键词： Social networking (online)

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