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检索条件"机构=Computer Science with Artificial Intelligence and Machine Learning"

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Improving the Quality of Diabetic Data with Large Language Model-driven Cleaning Techniques

Improving the Quality of Diabetic Data with Large Language M...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Biradar, Divya Dattangire, Rahul Vaidya, Ruchika Inti, NagaSuryaShivani University of Texas at Arlington Computer Science ArlingtonTX76013 United States Data Engineering HoustonTX77002 United States Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Wardha442001 India University of Texas at Arlington Computer and Information Science ArlingtonTX76013 United States

ISBN: (纸本)9798331539948

The data-cleaning approach applies the capabilities of large language models to reduce the noise in the extracted and received data from healthcare sources. The aim will be to clean the collected and extracted data by looking up and dealing with the inaccuracies encountered during the project like missing or duplicated records, outliers, and data that will not add any value to the project. The 'Diabetes' data set from Kaggle is divided into implementing the combined approach Data-cleaning process such as removing duplicate entries, and emptying fields by a Large Language Model, to improve the accuracy is assessed. Three components will increase the reliability of the collected data: 1. Gateway, 2. Storage 3. Cleaning. The outcomes demonstrated the LLMbased approach's effective impact in correcting and consistent data within the healthcare dataset. © 2024 IEEE.

关键词： Electronic health record

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A Review on the Developments in the Field of AI-Based Gait Analysis 2

A Review on the Developments in the Field of AI-Based Gait A...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Wankhede, Vidhi Verma, Prateek Gahane, Shailesh Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Sciences and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

The examination of human gait is essential for comprehending motor function, the evolution of diseases, and the effectiveness of rehabilitation in different clinical and research environments. artificial intelligence (AI) advancements have recently improved gait analysis methodologies, resulting in increased accuracy, efficiency, and therapeutic effectiveness. This review article compiles the existing literature on AI-based gait analysis. The review emphasizes the role of AI in gait analysis, namely via the use of vision-based approaches, wearable sensor technologies, markerless motion capture systems, and inertial sensor-based methodologies. AI has shown potential in identifying musculoskeletal problems including osteopenia and sarcopenia, forecasting the start of Parkinson's disease, assessing the effects of post-stroke rehabilitation, and diagnosing neurological conditions. In addition, the integration of AI with new technologies such as augmented reality (AR), smart sensors, and wearable devices presents intriguing opportunities to improve gait rehabilitation, enable individualized therapies, and facilitate remote patient monitoring. Although there have been significant breakthroughs, there are still obstacles in creating AI models that can be easily understood for clinical decision-making, establishing standardized procedures for collecting data, and verifying AI algorithms across various populations. This study underlines the transformational potential of AI-based gait analysis in customized healthcare, rehabilitation techniques, and clinical results. In order to fully harness the potential of AI in enhancing human health and well-being, it is imperative to do more research and foster multidisciplinary cooperation. © 2024 IEEE.

关键词： Clinical research

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Enhancing the Spatial Resolution of Aerial Images using a Deep Generative Approach with Multi-Stage Residual Refinement

Enhancing the Spatial Resolution of Aerial Images using a De...

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

作者： Shree Hari, B. Joy, Emmanuel Aroma, Jenice Division Of Data Science And Cyber Security Karunya Institute Of Technology And Sciences Coimbatore India Division Of Artificial Intelligence And Machine Learning Karunya Institute Of Technology And Sciences Coimbatore India Mgk Logistics Madurai India

ISBN: (纸本)9798350371567

Aerial images are photos taken from above, usually from planes or drones, displaying a landscape as seen from above. High-quality aerial images are crucial in various tasks, like precision agriculture, post-disaster monitoring, and land use classification. However, acquiring these images can be challenging due to the constraints of underlying image sensors and several other factors. This paper introduces a deep generative approach to enhance the spatial resolution of aerial imagery. The proposed method uses an Enhanced Deep Super-Resolution (EDSR) model architecture with a multi-stage residual refinement strategy. This particular model is comprised of residual blocks with two convolutional layers and residual connections. Through a succession of these residual blocks, the model processes low-resolution, 3-channel aerial images and finally upscales the images with a convolutional layer and a depth-to-space transformation. By increasing the spatial resolution of aerial imagery, this deep generative method seeks to overcome the problems that low-quality images present in real-world applications. Findings from the experiment on the VEDAI dataset demonstrate the effectiveness of the proposed method in producing high-quality aerial images. The improvements of PSNR and SSIM metrics by 1.05% and 0.34% respectively and the reduction in MAE and NRMSE by 0.0045 and 1.23% highlight its potential impact on various fields that depend on accurate and comprehensive aerial information. The outcomes of this work will contribute to the future development of picture super-resolution techniques for use in remote sensing and geospatial analysis. © 2024 IEEE.

关键词： Antennas

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A Hybrid Solution for Summarizing Diverse Medical Texts in the Health Domain

A Hybrid Solution for Summarizing Diverse Medical Texts in t...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Samuel, Prithi Berna, Eugene Kumar, Arun Reshmy, A.K. Sriraj, Swaroop Kadaba Saketh, Yanda Srm Institute Of Science And Technology Department Of Computational Intelligence Kattankulathur India Bannari Amman Institute Of Technology Department Of Artificial Intelligence And Machine Learning Erode India University Of South Wales Department Of Digital Forensics And Cyber Security Treforest United Kingdom

ISBN: (纸本)9798350366846

Since medical text summarization has become essential in facilitating rapid access to vital information for healthcare professionals, this research paper introduces a novel hybrid medical text summarizer that combines both extractive and abstractive summarization techniques while incorporating domain knowledge for the extractive process. Our approach first employs a domain-specific knowledge graph to guide the identification of salient and clinically relevant content from medical literature. This is followed by the application of advanced natural language processing techniques for abstractive summarization, ensuring the generated summaries maintain coherence and readability. We detail the use of the domain-specific knowledge base containing embedder, which captures medical concepts and relationships, enabling the system to discern important information from the input text. The cosine similarity-based ranking algorithm is adapted to prioritize sentences based on their relevance to the domain and their connectivity. The hybrid medical text summarizer is evaluated on a diverse set of medical articles at the same time to value summarizing multiple documents at the same time and compare its performance with existing approaches. Results demonstrate significant improvements in summary quality, as measured by ROUGE scores and human evaluations. Furthermore, we observe that incorporating domain knowledge in the extractive process enhances the overall effectiveness of the summarization system. We achieve a range-topping ROUGE score of a bit more than 0.6 for most of the texts summarized. © 2024 IEEE.

关键词： Domain Knowledge

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Home Automation Using Wi-Fi: ESP32-Based System for Remote Control and Environmental Monitoring 3

Home Automation Using Wi-Fi: ESP32-Based System for Remote C...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Abdul Lateef Haroon, P.S. Shafiulla, Mohammed Naveed, Shaik Mohammed Ahmed, Suhail Nawaz, Shaik Mohammed Kumar, Utkarsh Ballari Institute Of Technology & Management Department Of Electronics And Communication Engineering Ballari India Ballari Institute Of Technology And Management Department Of Computer Science And Engineering Ballari India Ballari Institute Of Technology & Management Department Of Computer Science And Engineering Ballari India Ballari Institute Of Technology & Management Department Of Artificial Intelligence And Machine Learning Ballari India

ISBN: (纸本)9798350318609

This project introduces a comprehensive home automation system leveraging Wi-Fi connectivity and ESP32 micro controllers, aimed at enhancing modern living through seamless automation and user-centric design. The background emphasizes the fusion of IoT technology with home automation, highlighting Wi-Fi's significance in enabling remote monitoring and control of household devices. Methodologically, the project integrates relay modules and DHT11 sensors, orchestrated through a user-friendly smartphone application. Results demonstrate successful implementation and testing, showcasing functionality, reliability, and user-friendliness. Real-time environmental monitoring and remote appliance control fulfill the objectives of enhancing home comfort, convenience, and energy efficiency. The conclusion underscores the project's contribution to advancing smart home technologies, offering users a scalable and customizable solution. Future research opportunities include enhancing system capabilities and addressing security and privacy concerns. © 2024 IEEE.

关键词： Microcontrollers

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Next-Gen Dermatology: Revolutionizing Dermatological Diagnostics with AI and Emergency Care Solutions

Next-Gen Dermatology: Revolutionizing Dermatological Diagnos...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Yashas D M Shivani Kashyap Charan PR Hemanth Kumar GP Abhishek BK Deekshith C Dept of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru Dept of Information Science and Engineering New Horizon College of Engineering Bengaluru Dept of Artificial Intelligence and Machine Learning BNM Institute of Technology Bengaluru Dept of Artificial Intelligence and Data Science Siddaganga Institute of Technology Tumkur Dept of Computer Science and Engineering Vidyavardhaka College of Engineering Mysore

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Skin health conditions should be diagnosed and treated on time so as to prevent complications and promote better health results. This paper presents an AI-based system that makes use of Convolutional Neural Networks with the InceptionV3 Architecture for predicting skin diseases at higher levels. The model, which is based on the large DermaNet dataset, classifies skin disorders fast and effectively. The system is coupled with a simple Telegram based application for the users to get a complete solution. A user can upload a photo of the affected skin area and get a diagnosis within seconds, an explanation of the disease and how severe it is in percentage. The bot also has a location and contact sharing feature that assists users in finding nearby doctors and clinics that can provide further assistance. It further provides on the spot appropriate cures for the particular condition diagnosed. Moreover, this makes it possible for the customer to fill the healthcare void in particular the neglected ones by making it easy for them to access real time high quality services. By equipping users with relevant information and appropriate actions required at the right time, this project has showcased how AI has the ability to transform the dermatological field.

关键词： Deep learning Dermatology Medical services Transforms Chatbots Skin Real-time systems Convolutional neural networks artificial intelligence Diseases

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An autonomous robot for removing Underwater pollution : OCTACLEANER

An autonomous robot for removing Underwater pollution : OCTA...

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Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE International Conference on

作者： A. Nymisha Nandini Reddy Dhanni Pavani B. Shiva Rakshitha Neha Saw Computer science with artificial intelligence and machine learning Hyderabad Institute of Technology and Management Hyderabad India

In the contemporary world, in which all life forms, counting with aquatic life, are stricken by global warming, there are various answers for numerous kinds of problems, however there’s very moderate interest paid to the waste that is amassing underwater. The waste that we dump into bodies of water does not simply float, but settles under water after a period of time. To resolve this, we proposed a solution in the form of an octopus-like robot named OCTACLEANER. This robot would help in the removal of trash accumulated beneath, with the added benefit of removing muck.

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Hybrid Fuzzy Assisted RNN Algorithm for Diabetic Retinopathy Identification

Hybrid Fuzzy Assisted RNN Algorithm for Diabetic Retinopathy...

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2023 International Conference on Energy, Materials and Communication Engineering, ICEMCE 2023

作者： Sivaprakash, P. Gifta Jerith, G. Packiaraj, H. Manikandan, R.A. Thanish Kumar, T. Ashok, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu India Malla Reddy University School of Engineering Department of Artificial Intelligence & Machine Learning Hyderabad India

ISBN: (纸本)9798350393378

The most common cause of blindness in diabetics is diabetes retinopathy. The importance of early detection in preventing the DR cannot be overstated. However, because eye checkups are so expensive, many people let DR worsen and eventually lead to blindness. To identify persons with DR, the current study employs a color fundus image and a Fuzzy RNN algorithm. Although the purpose of this study is to save money, it could be a game changer for people with DR who cannot afford a medical diagnosis. Prior processing methods include green band extraction, histogram equalization, filtration, optic disc removal with organizational components on architecturally procedures, and illumination changes. The data acquired by GLCM contains contrast, correlation, energy, and homogeneity, and it is utilised to extract characteristics from the preprocessing results. This paper describes a threshold segmentation-based DR detection system. As a result of this study, a fuzzy-RNN methodology for identifying diabetic retinopathy has been developed. Threshold-based categorization is used to determine the diabetic zone. GLCM (Grey Level Co-occurrence Matrix) is the feature extraction technique applied in this case. These characteristics are utilised for categorization. For classification, an RNN classifier is utilised. To simulate the image processing using Phython in diabetic retinopathy is used to validate the final outcome. © 2023 IEEE.

关键词： Recurrent neural networks

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Secure Healthcare Monitoring: Algorithms and Techniques for Heart Disease Detection and Data Privacy

Secure Healthcare Monitoring: Algorithms and Techniques for ...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Poonam Sujata V. Mallapur Department of Computer Science and Engineering Sharnbasva University Kalaburagi India Department of Artificial Intelligence & Machine Learning Sharnbasva University Kalaburagi India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

This survey offers the review of Healthcare Monitoring Systems (HMS) and Privacy Preservation (PP) approaches. The main objective is based on the detection of heart disease and maintain the security for patient data. The machine learning models such as SVM, decision trees, and neural networks for heart disease detection and encryption approaches such as AES, ECC, and homomorphic encryption for securing the data are the major topics that reviewed in this survey. Moreover, the survey highlights the performance evaluation for heart disease prediction and the datasets used for heart disease. It also ensures the privacy to tackle the challenges in real time monitoring. A higher accuracy of 99.8 was reached by FHE-based encryption method. Moreover, it discusses the research gaps like scalability, efficiency, and regulatory compliance which motivates the need of novel privacy-preserving techniques for healthcare systems, which is highlighted in the future scope.

关键词： Heart Surveys Privacy Accuracy Reviews Scalability Medical services Real-time systems Monitoring Diseases

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Water Management for IoT-Based Smart Agriculture Using machine learning Algorithms

Water Management for IoT-Based Smart Agriculture Using Machi...

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International Conference on Robotics, Control, Automation and artificial intelligence, RCAAI 2023

作者： Geetha Rani, E. Tukkoji, Chetana Anusha, D. Dhanalakshmi, M. Bharath, B. Department of Computer Science Engineering Alliance University Karnataka Bengaluru India Department of Computer Science Engineering Gitam University Karnataka Bengaluru India Department of CSE- Artificial Intelligence and Machine Learning SRKIT Andhra Pradesh Vijayawada India Department of Computer Science Engineering NHCE Karnataka Bengaluru India Department of Canteen Stores Department MVJ College of Engineering Karnataka Bengaluru India

ISBN: (纸本)9789819746491

Agriculture relies substantially on water. The IoT-based farm water management system explains how IoT technology can control irrigation intelligently. This system may be used to collect data by connecting several sensors such as soil moisture, temperature, and PIR motion sensors, among others. The collected data will be transferred to the user's mobile phone and sent to the users, ensuring crops receive the appropriate quantity of water and optimize irrigation schedules. By monitoring water levels, farmers may detect possible water loss locations and take action to prevent them. The primary goal of our article is to increase productivity, efficiency, and water use through live monitoring, water management, crop growth development, and the potential use of virtual water. Furthermore, breakthroughs in machine learning, such as crop prediction and recommendation, fertilizer detection, and leaf disease detection, will allow for more complex analysis of agricultural data, resulting in even great gains in water management and crop yield. However, it is important to ensure that these technologies are accessible to all farmers, regardless of the location or resources. If giving detailed information on soil properties and crop genetics, machine learning algorithms can forecast agricultural yields and find the best crops for a giving region. Now, farmers can maximize their crops by making data-driven decisions. This one system’s capability to exactly predict crops yields and resources requirements be one of its key advantages. To promote more sustainable and productive farming, farmers should also control the quantity of water and other resources used by them. By assuring a sufficient supply and reducing the likelihood of crops failure, crops forecast, leaf disease diagnosis, and guidance systems can assist address concern related to global food securities in addition to enhancing productivity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 20

关键词： Soil moisture

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