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

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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...

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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...

引用

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...

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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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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IoT Empowered AI: Transforming Object Recognition and NLP Summarization with Generative AI 2

IoT Empowered AI: Transforming Object Recognition and NLP Su...

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2nd IEEE International Conference on computer Vision and machine intelligence, CVMI 2023

作者： Joseph, Samuel Priya, Bhagavathi S. Poorvaja, R. Santhosh Kumaran, M. Shivaraj, S. Jeyanth, V. Shivesh, R.P. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Amrita School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350305142

In anticipation of the widespread adoption of augmented reality in the future, this paper introduces an advanced mobile application that seamlessly integrates AR and IoT technologies. The application aims to make these cutting-edge technologies more affordable and accessible to users while highlighting their immense benefits in assisting with household appliance control, as well as providing interactive and educational experiences. The app employs advanced algorithms such as object detection, Natural Language Processing (NLP), and Optical Character Recognition (OCR) to scan the smartphone's camera feed. Upon identification, AR controls for appliances, their power consumption, and electric bill tracking are displayed. Additionally, the application makes use of APIs to access the internet, retrieving relevant 3D generative models, 360-degree videos, 2D images, and textual information based on user interactions with detected objects. Users can effortlessly explore and interact with the 3D generative models using intuitive hand gestures, providing an immersive experience without the need for additional hardware or dedicated VR headsets. Beyond home automation, the app offers valuable educational benefits, serving as a unique learning tool for students to gain hands-on experience. Medical practitioners can quickly reference organ anatomy and utilize its feature-rich functionalities. Its cost-effectiveness, requiring only installation, ensures accessibility to a wide audience. The app's functionality is both intuitive and efficient, detecting objects in the camera feed and prompting user interactions. Users can select objects through simple hand gestures, choosing desired content like 3D generative models, 2D images, textual information, 360-degree videos, or shopping-related details. The app then retrieves and overlays the requested information onto the real-world view in AR. In conclusion, this groundbreaking AR and IoT -powered app revolutionizes home automation and le

关键词： Optical character recognition

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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...

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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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Develop a Smart Android Application to Provide Laundry Service 15

Develop a Smart Android Application to Provide Laundry Servi...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Jeyapradesh, Samuel Jebaseeli, T. Jemima Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Department of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798331300579

In the era of the Internet and smart applications, companies and institutions providing various services face significant challenges. The digital landscape, characterized by limitless connectivity and accessibility, poses a new paradigm where services transcend traditional boundaries of time and space. To remain at the forefront in their respective domains, businesses must embrace the electronic realm. Notably, web programming languages like PHP and ASP, alongside mobile application development languages such as Android have emerged as pivotal tools in this technological evolution. This design focuses on the development of an Android application integrated with a web control panel tailored for the service sector, with a specific emphasis on public laundry facilities. The study encompasses the system's architecture technological frameworks employed, and subsequently delves into the target audience, market requirements, and additional services to be offered. Throughout this exploration, the design aims to harness the potential inherent in these programming languages, drawing parallels to human language for enhanced user engagement and accessibility. © Grenze Scientific Society, 2024.

关键词： Laundries

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Smart Security Management using IoT and HC-05 Bluetooth Module 8

Smart Security Management using IoT and HC-05 Bluetooth Modu...

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

作者： Sam Michael, S. Ancy Jenifer, J. Benjamin, Ruth Moly Jacob, Cyril Shanthosh, S. Karunya Institute of Technology and Sciences Department of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350386578

Safety and security are essential for every individual worldwide, particularly in the context of home security. Modern advancements in science and technology have introduced various innovative solutions, including smart security locking mechanisms. This research study presents a novel smart lock system that leverages mobile phone applications integrated with built-in fingerprint authentication. Unlike other smart locks that rely on external biometric devices, this system utilizes the smartphone's internal fingerprint sensor, thereby reducing hardware costs and complexity. Additionally, the application supports multiple authentication methods, such as fingerprint and facial recognition, enhancing security and convenience. This solution eliminates the risk of key loss and ensures that only authenticated users can access the home. The lock mechanism operates automatically, allowing the door to open and close without human intervention, providing a seamless and secure user experience. © 2024 IEEE.

关键词： Keys (for locks)

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Understanding Red Wine Quality with Feature Importance in machine learning and Explainable AI 15

Understanding Red Wine Quality with Feature Importance in Ma...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： John, Ayain Damodar, Navya Mohith, N. Vijayan, Bharathy Dayananda Sagar University Dept. Artificial Intelligence and Machine Learning Bangalore India Dayananda Sagar University Dept. Computer Science Engineering Bangalore India

ISBN: (纸本)9798350370249

The research work investigates the factors that influence the quality of red wine and how they contribute to positive outcomes. It uses machine learning techniques like Support Vector machines (SVM) and K-nearest neighbors (KNN) to classify and assess red wine quality data. Traditionally, red wine quality has been evaluated subjectively, which presents challenges. machine learning algorithms offer an objective alternative. This study compares SVM and KNN algorithms in assessing red wine quality and aims to determine the crucial attributes that affect its quality. To evaluate the classification performance of different machine learning algorithms, metrics like specificity, sensitivity, selectivity, and F1 measure are used. Additionally, grid search algorithms are employed for parameter optimization. In the red wine datasets, the Support Vector machine (SVM) model obtains an accuracy rate of 89%, surpassing the K-Nearest Neighbors (KNN) model's accuracy rate of 87%, especially regarding predictive specificity. Furthermore, using grid search algorithms improves the overall performance of the classifiers. This study highlights the effectiveness of machine learning in assessing red wine quality, providing valuable information for producers to optimize wine production processes and ensure high-quality products for consumers. © 2024 IEEE.

关键词： Adversarial machine learning

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Aspect Sentiment Analysis with Triplet Extraction for E-Commerce 7

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

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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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