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共 9843 条记录，以下是91-100 订阅

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Detection of Sleep Apnea: A Comparative Analysis of Advanced Machine Learning and Deep Learning Approaches 11

Detection of Sleep Apnea: A Comparative Analysis of Advanced...

引用

11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Prajitha, M.V. Naveen Sundar, G. Narmadha, D. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350379136

Sleep apnea is a prevalent and serious sleep disorder caused by breathing interruptions during sleep, which is lead to critical health issues like cardiovascular diseases. Traditional diagnostic methods, such as polysomnography, are often very large process, costly, and not easily accessible, highlighting the need for automated and affordable detection solutions. This review paper examines the utilization of machine learning (ML) and deep learning (DL) methods for detecting and diagnosing sleep apnea. We offer a thorough overview of the current research, analyzing various studies that utilize different data types, including electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmography (PPG) signals. The review assesses the effectiveness of different algorithms, feature extraction techniques, and the integration of multiple signal types to improve detection accuracy. By summarizing key developments and identifying existing challenges, we aim to outline future research directions to enhance the robustness, interpretability, and real-time identification of sleep apnea using machine learning and deep learning techniques. © 2024 IEEE.

关键词： Sleep research

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Enhancing Nutrition Tracking: Webcam-based Fruit and Vegetable Recognition and Calorie Measurement using Image Processing and Convolutional Neural Networks 5

Enhancing Nutrition Tracking: Webcam-based Fruit and Vegetab...

引用

5th IEEE International Conference for Emerging technology, INCET 2024

作者： Leo, Sam S. Benjamin, Ruth Moly Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350361155

The "Enhancing Nutrition Tracking" wander epitomizes a groundbreaking agreeable vitality between fake bits of knowledge (AI) and food science. Through the utilization of Convenient Net convolutional neural frameworks, this creative system passes on real-time normal item affirmation and correct calorie estimations, in this way empowering clients to make taught dietary choices. Central to its value is the visual emphasis on carbohydrate substance, ensuring clients have a comprehensive understanding of their food choices and dietary intake. This project's trademark lies in its user-friendly interface, arranged to streamline the strategy of capturing characteristic item and vegetable pictures for incite dietary bits of information. By reliably joining AI advancement into conventional assignments, individuals can effortlessly get to basic wholesome information, empowering more advantageous eating affinities and way of life choices. Besides, continuous ask almost endeavors search for to refine the system's real-time characteristic item recognizable confirmation capabilities and examine the immersive potential of virtual reality for in-depth wholesome assessments. By initiating the integration of AI and virtual reality advancements, the "Enhancing Nutrition Tracking" amplify focuses to catalyze a worldview move in food watching. © 2024 IEEE.

关键词： Fruits

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Cancer Medical Image Analysis Across Modalities Using Attention Mechanism and Capsule Network 10

Cancer Medical Image Analysis Across Modalities Using Attent...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sharon Deborah, A. Princess, P. Joyce Beryl Paulraj, Getzi Jeba Leelipushpam Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350384369

Medical image analysis is a critical component of accurate disease diagnosis and treatment planning which relies on traditional methods such as MRI, CT scans, and X-rays. However, challenges arise from these traditional methods due to human errors in image interpretation, lack of standardization in medical image analysis, varying expertise levels among healthcare professionals, and the potential oversight of critical details which leads to variability in diagnosis and treatment outcomes across facilities. To address these issues, advanced computational methods are imperative for medical conditions that are hard to detect visually. This paper proposes an integrated approach using the Deep neural network with Attention mechanism and Capsule Network to deal with diverse imaging modalities, especially histology, microscopy, and MR scans. This innovative approach represents a superior alternative to traditional methods by emphasizing its potential to focus on crucial regions within medical images and to enhance disease prediction which holds a significant progress to contribute to a faster and more accurate diagnostic process, ultimately benefiting both the healthcare industry and patients. © 2024 IEEE.

关键词： computerized tomography

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AI-Driven Diagnostics: Comparative Analysis of Machine Learning and Deep Learning Approaches in Multiple Sclerosis Diagnostics 11

AI-Driven Diagnostics: Comparative Analysis of Machine Learn...

引用

11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Anitha, L. Narmadha, D. Naveen Sundar, G. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350379136

Multiple Sclerosis (MS) is a chronic autoimmune disorder that targets the central nervous system, resulting in considerable physical and cognitive disabilities. Timely and precise diagnosis is essential for the effective management and treatment of Multiple Sclerosis (MS). Predicting MS involves using various types of data to assess the risk and diagnose the disease. The data commonly used are clinical, imaging, speech and gene data. This work presents a review of machine learning and deep learning methods, encompassing a broad spectrum of algorithms such as Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Convolutional Neural Networks (CNNs), and Artificial Neural Networks (ANNs). Furthermore, the survey gives a comparative analysis of the dataset used, algorithms employed and the performance metrics. The survey also compares the implemented results with DL using the gene data. It also identifies current challenges in MS prediction, such as heterogeneity of MS, integration of multimodal data, validation and clinical implementation. Our goal is to offer a valuable resource for researchers and practitioners. © 2024 IEEE.

关键词： Convolutional neural networks

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Diagnosis of Plant Leaf Disease using Vision Transformer 10

Diagnosis of Plant Leaf Disease using Vision Transformer

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Sundaraj, Ashik Isravel, Deva Priya Dhas, Julia Punitha Malar Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353068

In precision agriculture, it is crucial to have a reliable system for identifying diseases and suggesting measures to maintain crop health and enhance yield performance. Addressing the persistent challenge of accurate and timely identification of plant illnesses, this study introduces a new understanding of the approach utilizing the Vision Transformer (ViT) architecture. Leveraging a diverse dataset encompassing both diseased and healthy plant images, the ViT model demonstrates efficacy by achieving an precision of 94% in recognizing and categorizing diseases based on plant leaves. The success of this transformer-based methodology underscores its potential to significantly enhance the field of agricultural diagnostics, offering a promising solution to improve crop management practices and mitigate potential losses in yield. © 2024 IEEE.

关键词： Convolutional neural networks

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Advancing Alzheimer's Diagnosis Through Transfer Learning with Deep MRI Analysis

Advancing Alzheimer's Diagnosis Through Transfer Learning wi...

引用

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

作者： Adityasai, B. Babu, Nirmal Varghese Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India

ISBN: (纸本)9798350371567

Alzheimer's disease (AD) poses a significant health-care challenge, necessitating early and accurate diagnosis for effective management. This study presents a novel approach aimed at enhancing AD diagnosis through the integration of transfer learning, deep learning techniques, and meticulous data preprocessing methodologies. Leveraging a rich dataset from the Open Access Series of Imaging Studies (OASIS), comprising MRI scans from both healthy individuals and those with AD, the proposed method seeks to identify subtle abnormalities indicative of the disease. The methodology encompasses several key steps. Initially, MRI data undergoes rigorous cleaning and standardization using histogram equalization to ensure compatibility and remove irrelevant information. Subsequently, a two-step feature extraction process involving an autoencoder neural network is employed to identify significant AD-related features. These features are then combined with other relevant data to provide a comprehensive understanding of the disease. To optimize model efficiency, three algorithms iteratively refine the feature set, focusing on the most informative features associated with AD. Finally, a pretrained deep learning model, VGG19, is fine-tuned using the AD-specific dataset. Results demonstrate a significant improvement in diagnostic accuracy post-hyperparameter tuning, with accuracy increasing from 76.5% to 89.5%. The enhanced model performance across various metrics underscores the potential of the proposed approach to revolutionize AD diagnosis, offering promise for earlier detection and more accurate prognostication. This research contributes to advancing the field of neuroimaging-based diagnostic techniques for neurodegenerative diseases like Alzheimer's. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Aspect Sentiment Analysis with Triplet Extraction for E-Commerce 7

Aspect Sentiment Analysis with Triplet Extraction for E-Comm...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Meenakshi Sundaram, A. Joyce Beryl Princess, P. Karunya Institute of Technology And Science Division of Computer Science And Engineering Coimbatore India Karunya Institute of Technology And Science Division of Artificial Intelligence And Machine Learning Coimbatore India

ISBN: (纸本)9798350359299

In the age of e-commerce, it is critical for companies to comprehend the thoughts and feelings of their customers in order to enhance their goods and services. A powerful method for deriving insights from consumer feedback is the combination of aspect sentiment analysis and triplet extraction. The objective of this method is to recognize the characteristics or facets of a good or service under discussion, gather feedback on each component, and create connections between the components, feedback, and viewpoints. Businesses can gain important insights into customer feelings towards certain features like design, usability, price, and customer service by examining customer reviews, comments, and ratings. Opinion sources (experts, customers, etc.), sentiment expressions (positive, negative, or neutral), and opinion targets (aspects) are all identified from the text as part of the triplet extraction process. This gives companies a comprehensive grasp of the thoughts and preferences of their customers, which helps them design products, set prices, and provide customer service with more focus and eventually increase customer happiness. © 2024 IEEE.

关键词： Sentiment analysis

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Ensemble of Vision Transformers and CNNs for Accurate Diabetic Foot Ulcer Classification 1

Ensemble of Vision Transformers and CNNs for Accurate Diabet...

引用

1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Pagadala, Arnold Anand Silas, Salaja Joy, Emmanuel Karunya Institute of Technology and Sciences Division of Computer Science Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350372748

Diabetic Foot Ulcers (DFU) are a significant diabetes complication that can lead to lower limb amputation. Currently, 537 million suffer from diabetes worldwide and it is anticipated to rise to 783 million by 2045. Given the speed at which DFU is developing, prompt action is necessary to avoid the serious consequences of amputation and associated various medical conditions. With the evolution of image-based ML algorithms, automated methods for identifying and assessing DFUs are becoming more common. Existing research works on visual computing techniques concerns tissue classification and detects the DFU's visual appearance. This research study combines ResNet50 with Vision Transformers (ViT) and MobileNet with Vision Transformers (ViT) to develop an ensemble model to classify the existence or absence of a Diabetic Foot Ulcer (DFU). Experimental results of the proposed model when tested on challenging datasets achieved a validation accuracy of 98.6%. © 2024 IEEE.

关键词： Neural networks

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VGG-16 based Deep Learning Approach for Plant Species Detection

VGG-16 based Deep Learning Approach for Plant Species Detect...

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

作者： Praveen, Bandi Isravel, Deva Priya Malar Dhas, Julia Punitha Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350371567

Automated plant species detection has gained interest due to the potential benefits in agriculture to significantly increase crop yields, decrease the use of chemicals, and promote sustainable agriculture practices. A deep learning model, for plant species identification is a promising approach. This paper presents a condensed version of the VGG-16 deep learning model for identifying plant species. The original VGG-16 model's complexity and computational overhead are minimized by lowering the number of convolutional layers, which lowers the amount of parameters and calculations needed while keeping a high level of accuracy in the identification of various plant species. The streamlined VGG-16 model obtains an improved accuracy rate in categorizing distinct plant species after being trained on a big dataset of plant species that includes a variety of images of different plants. The condensed VGG-16 model offers a viable method for precise and effective plant species identification. This study's methodology and results advances the area of plant species detection by offering a deeper learning model that is more effective and useful and can be used on devices with limited resources. © 2024 IEEE.

关键词： Agriculture

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A Deepfake Image Classifier System for real and Doctored Image Differentiation 7

A Deepfake Image Classifier System for real and Doctored Ima...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Thomas, Juanith Mathew Ebenezer, V. Richard, Rohan Paul Karunya Institute of Technology And Sciences Division of Computer Science And Engineering Coimbatore India Karunya Institute of Technology And Sciences Division of Data Science And Cyber Security Coimbatore India

ISBN: (纸本)9798350359299

The sudden proliferation of deepfake technology has raised concerns about the authenticity and integrity of digital media. In response to these concerns, this paper presents a Deepfake image classifier system designed to differentiate between real and doctored images using "MesoNet"architecture which utilizes convolutional layers and builds a suitable neural network for classification. Making efficient use of Deep Learning strategies, this system aims to detect subtle manipulations indicative of deepfake alterations. The proposed model is trained on two distinct datasets: Dataset I comprises authentic images while Dataset II consists of images subjected to various levels of manipulation, making them deepfakes. Through extensive training using these images and their labels, we'll train the neural network to adequately predict the authenticity of an image. The model has been trained on exactly 7104 images and judging from the results, the predictions made by the model are fairly accurate. Results yield an accuracy of 88.81% and a precision of 87.93%. © 2024 IEEE.

关键词： Digital storage

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