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Deep learning framework for automatic detection and classification of sleep apnea severity from polysomnography signals

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Neural Computing and Applications 2024年第28期36卷 17483-17493页

作者： Raja Brundha, A. Lakshmi Sangeetha, A. Balajiganesh, A. Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Chennai India Department of Computer Science Engineering Velammal Engineering College Chennai India

Sleep apnea (SA) is a sleep-related breathing disorder characterized by breathing pauses during sleep. A person’s sleep schedule is significantly influenced by that person’s hectic lifestyle, which may include unhealthy eating habits and their line of work. Polysomnographic (PSG) sleep studies examine sleep-related disorders by recording various biosignals from the human body. However, SA classification methods could be more robust in terms of performance because they rely on feature-engineering strategies or employ a particular signal from PSG recording for diagnosis. This study aims to classify the severity of SA according to the apnea–hypopnea index (AHI) into normal, mild, and moderate-to-severe groups using oxygen saturation (SpO2), electroencephalogram (EEG), and electrocardiogram (ECG) signals. The proposed deep neural network (DNN)-bidirectional long short-term memory (DNN-BiLSTM) framework addresses the issue of low detection accuracy in analysis. The DNN-BiLSTM approach employs features extracted from a multiscale dilation attention 1D convolutional neural network (MSDA-1DCNN) as input for detection and classification purposes. The MSDA-1DCNN network extracts deep features from processed SpO2, EEG, and ECG signals. The developed firefly combined electric fish optimization (FCEFO) algorithm improves performance by optimizing the hidden neuron count of the DNN and the learning rate of the BiLSTM framework. The performance measures proved the effectiveness of the model over conventional machine and deep learning approaches. With the integration of deep features, the proposed DNN-BiLSTM model provides enhanced performance in terms of accuracy and precision. Thus, the proposed approach is progressive and can be used for medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electrocardiograms

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Secure Cloud based Process-Oriented Teaching-Quality Approach in Higher Education with General AI

Secure Cloud based Process-Oriented Teaching-Quality Approac...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Tingare, Bhagyashree Ashok Kale, Pranav Nikam, Sakshi Vikas D Y Patil College of Engineering Akurdi Department of Artificial Intelligence and Data Science Pune India D. Y. Patil College of Engineering Akurdi Artificial Intelligence and Data Science Pune India D Y Patil College of Engineering Akrudi Artificial Intelligence and Data Science India

ISBN: (纸本)9798350390049

The integration of General AI with secure cloud-based technologies offers transformative potential for enhancing teaching quality in higher education. This paper explores a process-oriented approach that combines advanced AI capabilities with robust cloud infrastructure to address contemporary challenges in educational institutions. By focusing on systematic instructional design, data-driven decision-making, and stringent security measures, this framework aims to improve pedagogical practices, streamline administrative processes, and safeguard sensitive information. The study evaluates the impact of various AI technologies on teaching quality and student satisfaction, highlighting significant improvements in personalized learning, automated grading, and predictive analytics. The findings suggest that AI-driven solutions not only enhance educational outcomes but also contribute to greater operational efficiency and data security. This innovative paradigm provides a comprehensive model for modernizing higher education through secure, scalable, and process-oriented approaches. © 2024 IEEE.

关键词： Contrastive Learning

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Secure AI based Chatbot Web Application with IBM Watson 3

Secure AI based Chatbot Web Application with IBM Watson

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Selvi, A. Abishak, D. Harish Sriraj, N. Navaneeth, S. Prasanna, R. Department of Artificial Intelligence and Data Science M Kumarasamy College of Engineering Karur India

ISBN: (纸本)9798331527549

This project focuses on developing a secure, AI-powered chatbot for retail chains, integrated within a web application to efficiently handle Frequently Asked Questions (FAQs). Utilizing IBM Watson’s advanced Natural Language Processing (NLP), the chatbot delivers precise, context-aware responses, enhancing the customer experience across retail environments. The platform offers a user-friendly interface for seamless interactions, a robust backend powered by IBM Watson’s AI model, and a database optimized for managing customer queries. A dedicated security module ensures data protection through encryption, authentication, and access control. This chatbot manages high volumes of inquiries with real-time responses, boosting customer engagement and operational efficiency while generating valuable insights for retail decision-making. Designed to be scalable and secure, the system upholds high standards of accessibility and data integrity, meeting the specific demands of the retail sector. © 2025 IEEE.

关键词： Sales

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Comparing the Effectiveness of GAN-based Methods for Enhancing EuroSat Satellite Imagery Classification 5

Comparing the Effectiveness of GAN-based Methods for Enhanci...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Gurav, Aryan Sharad Karanjikar, Yathharth Munesh Bhikle, Omkar Arun Deshpande, Himani Thadomal Shahani Engineering College Department of Artificial Intelligence and Data Science Mumbai India Vidyavardhini's College of Engineering and Technology Department of Artificial Intelligence and Data Science Mumbai Vasai India

ISBN: (纸本)9798350379945

Addressing the challenge of limited data in remote sensing, this research investigates the application of Generative Adversarial Networks (GANs) to augment the EuroSat dataset. Four GAN variants (Info-GAN, C-GAN, CDC-GAN, and AC- GAN) were employed to expand a reduced EuroSat dataset by 50%. The augmented datasets were subsequently used to train Convolutional Neural Networks (CNNs) for image classification. Comprehensive evaluation metrics (accuracy, recall, precision, Fl-score, AUC, IS, and FID) were employed to assess the performance of the generated images and the trained models. Results indicate that CDC-GAN exhibited the highest classification accuracy and MCC, while AC-GAN excelled in ROC-AUC, precision, and recall. This study underscores the potential of GAN s as a valuable tool for enhancing remote sensing image datasets and improving classification performance. © 2024 IEEE.

关键词： Gallium nitride

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Smart Barcode Scanning using X-ray Technology with Computer Vision and Machine Learning 4

Smart Barcode Scanning using X-ray Technology with Computer ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vijayaraj, J. Hari Yogith, S. Rithikravivarma, V. Siddharth, S. Easwari Engineering College Dept. of Artificial Intelligence and Data Science Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

Barcode scanning errors in retail environments contribute to inventory discrepancies, financial losses, and inefficiencies in stock management. Traditional optical barcode scanning methods are prone to failures due to occlusion, poor lighting conditions, and barcode damage. While recent advancements such as computer vision, deep learning-based error detection, and RFID integration have improved scanning accuracy, these approaches still face challenges related to high computational requirements, environmental dependencies, and implementation costs. This paper proposes a novel method utilizing X-ray imaging technology and advanced machine learning algorithms to detect improperly scanned or missing barcodes in real time. The system captures detailed X-ray images of items as they enter retail stores or warehouses and applies deep learning models to analyze barcode visibility. Real-time alerts are generated when scanning anomalies are detected, enabling prompt corrective action. Experimental evaluations demonstrate that the proposed approach significantly improves barcode detection accuracy, enhances inventory management, and reduces operational costs. The findings highlight the potential for widespread deployment in retail and logistics environments, offering a robust solution to barcode scanning challenges. © 2025 IEEE.

关键词： Sales

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Dynamic Algorithmic Configuration for Enhanced Malware Detection 3

Dynamic Algorithmic Configuration for Enhanced Malware Detec...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ramya, S. Periyasamy, M. Ramprasanth, P.S. Srinivasa Aravindh, S. Mohan kumar, B. Department of Artificial Intelligence And Data Science M Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

As edge-based decisions continue to grow in demand, organizations are increasingly seeking user data, raising privacy concerns. Federated Learning (FL) addresses this problem but is typically dependent on computationally intensive Neural Network (NN) models. This work introduces a novel lightweight approach by integrating Support Vector Machines (SVMs) into FL frameworks. Optimized methodology SVM with the compatibility of SGD is used where the meta-trained controller dynamically adapts the learning rate to the improvement of models. The proposed method shows equal performance to those based on the NN-based FL methods but drastically reduces the cost of computation for being applicable to resource-constrained environments. © 2025 IEEE.

关键词： Federated learning

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Retinal Disease Classification: Binary and Multiclass Perspectives 4

Retinal Disease Classification: Binary and Multiclass Perspe...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Lanjewar, Rutuja Nitin Pacharaney, Utkarsha Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India

ISBN: (纸本)9798331523923

Retinal diseases, among which diabetic retinopathy, age-related macular degeneration, and glaucoma account for the greatest number of blindness cases worldwide, affect more than 338 million people. It is essential that early detection leads to timely intervention to preserve vision, but it is limited due to the ever-increasing demand for ophthalmic care and a shortage of specialists. Machine learning-driven systems, especially Convolutional Neural Networks (CNNs), have shown great promise in overcoming these challenges, achieving high accuracy in medical imaging tasks. This paper explores a unified framework for both multiclass and binary classification of retinal diseases, addressing issues like class imbalance and inter-class similarity while leveraging CNNs for robust feature extraction. Techniques like Grad-CAM also increase model interpretability, allowing clinicians to understand and trust AI-driven diagnostic processes. © 2025 IEEE.

关键词： Convolutional neural networks

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A Long Short-Term Memory Network-Based Approach for Predicting the Trends in the S&P 500 Index

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 19-26页

作者： Siddesh, G.M. Sekhar, S. R. Mani Srinivasa, K.G. Department of Artificial Intelligence & Data Science M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Data Science and Artificial Intelligence Programme International Institute of Information Technology Naya Raipur India

The price level of a considered security in the stock market is largely determined by perceived consumer demand. Variations in price level in the stock market are essentially a manifestation of public psychology aiming to either cut losses or maximize profits. The assumption that market movements are controlled by a guiding trend is proved by establishing that the time-series variations of daily S&P 500 indices are a non-stationary process using the Dickey–Fuller test. This paper presents a long short-term memory (LSTM) network adaptation of an autoregressive predictive model for S&P 500 indices. Later, an LSTM network-based model is devised to learn the underlying patterns that are responsible for driving the trends in market dynamics. The proposed model accepts as input at each time-step a vector of prior S&P indices over the past two weeks and estimates the current market index. The network weights assume the correct settings by training the model using daily S&P indices over the past ten years. The proposed model decisively captures subtleties in market trends, which are in turn verified using the Granger causality approach. The result demonstrates that the proposed model achieved an accuracy of 82.6%, whereas random forest results in 79.12%. The results obtained are truly the latest in this line of predictive models and reaffirm that LSTM networks are powerful learning models capable of deciphering intricate underlying patterns. © 2023, The Institution of Engineers (India).

关键词： Long short-term memory

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Extended Reality E-Commerce: The Evolution of E-Commerce through XR Based Technologies

Extended Reality E-Commerce: The Evolution of E-Commerce thr...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Jawale, Supriya Bondade, Aparna Sontakke, Manish Sohagpure, Aditya Muneshwar, Akash Wankhede, Nisarga Priyadarshini College of Engineering Artificial Intelligence and Data Science Department Nagpur India

ISBN: (纸本)9798350373783

As the usage of apps and e-commerce platforms continues to rise, especially with the integration of virtual tryon features, the importance of being able to compare products and assess their compatibility in 3D becomes increasingly evident. This capability not only allows stores to keep their products available round the clock but also enhances the overall shopping experience for consumers. Moreover, it sets the stage for the integration of artificial intelligence into online retail environments. The very definition of products in this digital landscape is deeply influenced by the advancements in mobile networks and the adoption of Extended Reality (XR) applications. XR, serving as an umbrella term that encompasses a range of immersive technologies such as Augmented Reality (AR), Mixed Reality (XR), and Virtual Reality (VR), is revolutionizing how consumers interact with products and brands online. This article seeks to delve into the intricate factors that shape customer experience and satisfaction when engaging with XR and AI-enabled applications for online shopping. By incorporating dimensions like relative trust, social value, and novelty - parameters commonly explored in similar XR studies - we aim to measure various aspects including intention, perceived utilitarian value, immersion, and hedonic value. Through our research, we have discovered that each of these dimensions significantly influences consumers' willingness to utilize basic XR and AI applications during their shopping journey. When evaluating the collective impact, encompassing both direct and indirect effects, we have identified a sequence of influence, with adequate trust, innovativeness, immersion, social value, perceived health value, and utilitarian value playing crucial roles. Our preliminary findings underscore the significance of social value, assurance of satisfaction, usefulness, and immersion in shaping consumer behavior within the realm of XR and AI applications for online shopping. By u

关键词： Virtual environments

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Employee Attrition Analysis Using Machine Learning 2

Employee Attrition Analysis Using Machine Learning

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Solomon, Clorin Haffrin Mohankumar, Dharshini Sivanandam, Chakaravarthi Panimalar Engineering College Dept of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350328202

The production, morale and financial health of an organization can all be significantly impacted by staff attrition or the voluntary withdrawal of employees. An employee attrition study is a crucial step that enables organizations to identify the cause of the attrition and resolve the issue in order for them to grasp and successfully manage this challenge. In order to perform the research, extensive data on employee exits, job satisfaction, and other factors are initially acquired. data preprocessing and cleaning ensure that the data are accurate and suitable for analysis. It is possible to identify patterns and trends in attrition using statistical analysis and data visualization tools, and discover how turnover is connected to factors like income, work-life balance, performance, and a variety of other factors. In this project, we experiment with numerous algorithms in order to determine which one would best match the model. We are able to comprehend the top most important factors after selecting the ideal algorithm. We will examine employee attrition across a number of businesses in this article to determine its causes, which will assist the organization understand and manage a challenging position and resolve the issue. © 2024 IEEE.

关键词： Job satisfaction

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