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检索条件"机构=Artificial Intelligence and Machine Learning School of Computer and Engineering"

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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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Bread Quality Assessment Using Deep learning with shape and Volume Metrics 10

Bread Quality Assessment Using Deep Learning with shape and ...

引用

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

作者： Rohan Joel, J. Benjamin, Ruth Moly 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

With the goal to guarantee customer satisfaction and optimize processes for manufacturing in the baking sector, bread quality evaluation is essential. Conventional techniques for evaluating bread quality are frequently time-consuming, subjective, and labor-intensive. In this study, we suggest a novel method for evaluating bread quality that combines shape and volume metrics with deep learning techniques, namely the YOLOv5 algorithm. We enable the YOLOv5 model to properly recognize and categorize bread quality attributes by training it on a dataset that consists of photos of various types of bread with corresponding quality labels. To provide a more thorough assessment of bread quality, we now include shape and volume metrics in addition to the conventional image-based quality assessment. Bakeries may increase productivity, save expenses, and guarantee a constant level of product quality by automating the quality assessment process. Furthermore, the suggested method may be modified for use in tasks other than evaluating the quality of bread, which would promote automation and quality control in the food sector. © 2024 IEEE.

关键词： Food industry

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Brain Tumor Detection using YOLO v7 and YOLO v8

Brain Tumor Detection using YOLO v7 and YOLO v8

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Vaghela, Rohan Sarda, Jigar Thakkar, Amit Vaishnani, Darsh Khokhariya, Avani Mehta, Pooja Charotar University of Science & Technology Department of Computer Science & Engineering Anand India Charotar University of Science & Technology Department of Artificial Intelligence & Machine Learning Anand India

ISBN: (纸本)9798331533311

The architecture of the brain is incredibly complex, with multiple areas managing various parts of the nervous system. An adult's health is greatly at risk from brain tumors because of their ability to severely impair organ function or possibly cause death due to their rapid growth of abnormal brain cells. Brain tumors can develop in any area of the skull or brain, including the protective layer around the brain and many other areas. Since these tumors can have a wide range of shapes, sizes, and locations, magnetic resonance imaging (MRI) is an essential tool for locating malignant tumors. MRIs are examined manually in order to diagnose brain tumors. However, manually identifying brain tumors is a challenging and time-consuming task that may result in errors. As a result, computer-aided brain tumor detection has been considered a crucial area of study in biomedical engineering. However, because tumor sizes and shapes vary enormously throughout subjects, developing a reliable brain tumor detector remains difficult. Thus, for the purpose of accurately identifying brain tumors in MRI scans, two popular deep learning detection algorithms - YOLO v7 and YOLO v8 - were tested and compared in this study. From the results, YOLO v8 model has achieved highest validation accuracy of 96.50% with average precision and recall value of 95.9% and 93.3% respectively, while YOLO v7 has validation accuracy of 87.6% with average precision and recall value of 81.9% and 85.4% respectively. © 2024 IEEE.

关键词： Deep learning

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Pneumonia Disease Detection Using Hyperparameter Tuned Convolutional Neural Network from Chest X-Ray Images 10

Pneumonia Disease Detection Using Hyperparameter Tuned Convo...

引用

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

作者： Jascar Benish, P. Manoranjitham, R. 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: (数字)9798350384369

ISBN: (纸本)9798350384369

Pneumonia is a common and possibly fatal respiratory illness, particularly affecting children, and is now one of the most destructive and fatal respiratory conditions. By obtaining a chest X-ray, the proposed model assists in determining whether or not an individual is suffering from pneumonia. This model seeks to deliver accurate and timely results. The proposed approach uses Convolutional Neural Network with random search algorithm to achieve higher accuracy, reaching a rate of 97%. Compared to current models, this proposed model demonstrates a significant improvement in prediction accuracy. This model has the potential for wider application in the medical domain and could be developed into a web application or program in the future. Its capacity to raise the accuracy of medical treatments, lessen the strain on healthcare systems, and aid in the battle against antibiotic resistance makes it an invaluable tool in the continuous quest for better respiratory care. © 2024 IEEE.

关键词： Convolutional neural networks

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TransUAAE-CapGen: Caption Generation from Histopathological Patches through Transformer and UNet-Based Adversarial Autoencoder

TransUAAE-CapGen: Caption Generation from Histopathological ...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Taslim Uddin Raju, S.M. Mohammad, Abdul Raqeeb Islam, Md. Milon Karray, Fakhri University of Waterloo Department of Electrical and Computer Engineering Ontario Canada University of Waterloo Centre for Pattern Analysis and Machine Intelligence Department of Electrical and Computer Engineering Ontario Canada Mohamed Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665410205

Captioning Whole Slide Images (WSIs) for pathological analysis is an essential but not extensively explored aspect of computer-aided pathological diagnosis. Challenges arise from insufficient datasets and the effectiveness of model training. Generating automatic caption reports for various gastric adenocarcinoma images is another challenge. In this paper, we introduce a hybrid method referred to as TransUAAE-CapGen to generate histopathological captions from WSI patches. The TransUAAE-CapGen architecture consists of a hybrid UNet-based Advereasrial Autoencoder (AAE) for feature extraction and a transformer for caption generation. The hybrid UNet-based AAE extracted complex tissue properties from histopathological patches, transforming them into low-dimensional embeddings. The embeddings are then fed into the transformer to generate concise captions. Our proposed method is validated using the PatchGastricADC22 dataset. The TransUAAE-CapGen model provides the best estimated accuracy of BLEU-4 = 86.8%, METEOR = 59.6%, a ROUGE = 89.3%, and CIDEr = 7.72%. Experimental analysis indicates that the TransUAAE-CapGen architecture outperforms the traditional LSTM-based model for the caption generation task. Our findings reveal that the proposed architecture can effectively generate accurate and precise reports for medical image analysis. © 2024 IEEE.

关键词： Medical imaging

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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...

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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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Multi-Criteria-based Graph Neural Networks for a Medical Emergency Response System 7

Multi-Criteria-based Graph Neural Networks for a Medical Eme...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Prakash, J. Princess, P. Joyce Beryl 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

The ability to get an immediate response from medical emergencies quickly and more effectively is essential for saving both lives and ensuring people's well-being. In the Traditional medical emergency response systems often struggle to correctly assess the seriousness and urgency of emergency response, leading to ineffective resource allocation and prolonged response times. This research work suggests a unique method for medical emergency response systems based on a multi-criteria-based graph neural network to resolve such problems in recent times. The process of choosing the best hospital based on a variety of conditions is optimized by the graph neural network (GNN), which makes use of the graph structure with hospital nodes and accident location nodes. The GNN will examine several conditions to choose the best hospital after identifying the nearby hospitals. It evaluates whether each nearby hospital has the medical resources required to meet the needs of the patient, including available beds, emergency gear, and doctor availability to get a quick medical treatment. To promote quick transportation, the GNN also confirms the availability of ambulances connected to each nearby hospital and alerts the hospital management to arrange needs for patients. The above approach improves the effectiveness and efficiency of medical emergency response systems, guaranteeing that injured persons receive immediate and appropriate medical treatment. © 2024 IEEE.

关键词： Hospitals

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Optimizing CNNs for Leaf Disease Detection: A Comparative Analysis of Algorithms, Activation functions, and Performance Metrics

Optimizing CNNs for Leaf Disease Detection: A Comparative An...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Nitisha, S. Mohith, N. Vinay, R. Agarwal, Monika Sinha, Aparajita Mokshitha, M. Dayananda Sagar University Dept. Artificial Intelligence and Machine Learning Bangalore India National Institute of Technology Dept. Computer Science Engineering Agartala India

ISBN: (纸本)9798331540821

Agriculture plays an important role in ensuring food security. However, the emergence of plant diseases causes significant economic losses. The routine method of detecting crop diseases is time consuming, requires technical skill and can be inaccurate, especially when new diseases are introduced. To solve these problems, we present a new and effective method for the early identification of leaf diseases using computer vision and machine learning. Extensive testing has been done on the widely used Plant Village dataset to ensure accurate findings. Our plans focus on disease diagnosis and classification. This study demonstrates the pros and drawbacks of this method. The testing process includes data collection, data training, feature extraction, model testing, and classification. The main processing techniques include image acquisition, image preprocessing, extraction and the use of neural networks. Combining computer vision and machine learning, our approach holds great promise for efficient and reliable detection of crop diseases. This has important implications for improving disease control, reducing economic losses and increasing food security. © 2024 IEEE.

关键词： Agriculture Classification CNN computer Vision Plant diseases

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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...

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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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Bird Species Classification and Prediction Using machine learning Algorithms 7

Bird Species Classification and Prediction Using Machine Lea...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Abhinav, Penchala Dhavale, Sunitha Vardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501286 India Defence Institute of Advanced Technology School of Computer Engg. & Mathematical Sciences Pune411025 India

ISBN: (纸本)9798331527822

The BirdClef-2024 dataset, which includes acoustic recordings of 182 Western Ghats species, was used to classify and predict bird species in this study. Three machine learning algorithms were analyzed for their accuracy, precision, recall, and F1 score - K Nearest Neighbors (KNN), Random Forest, and Support Vector machine (SVM). The Random Forest model outperformed the other models in all metrics. The paper contains sections on dataset composition, preprocessing steps, algorithm implementations as well comparisons of the model's performances. Such issues have been successfully addressed through advanced pre-processing techniques coupled with complex machine-learning algorithms despite the intricate nature of the sound signals. A Streamlit GUI that is user-friendly has been developed to facilitate real-time recognition of birds by breed;this can contribute to surveillance for environmental biodiversity conservation efforts. © 2024 IEEE.

关键词： Adversarial machine learning

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