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Optimizing Medication Compliance through Machine Learning and IoT for Personalized Health Management 9

Optimizing Medication Compliance through Machine Learning an...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Balaji, P. Mohitha, I.K. Prashanthini, K. Sivaprakash, K. Rithick, P. Kasthuri, S. Sri Ramakrishna Engineering College Department of Electronics and Instrumentation Engineering Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Sri Ramakrishna Engineering College Department of Robotics and Automation Engineering Coimbatore India Sns College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350377972

Machine learning (ML) and Internet of Things (IoT) technologies have revolutionized the healthcare industry, particularly in the field of medication administration. Inadequate adherence to medication is a serious worldwide issue that can result in poor health and higher medical expenses. Through continuous monitoring and analysis of patient-specific parameters, such as vital signs, medication history, and lifestyle factors, the system dynamically adjusts medication schedules and dosages to optimize therapeutic efficacy while minimizing adverse effects. The system tracks medicine usage, vital signs, and ambient variables using data gathered from Internet of Things (IoT) devices like wearable devices and smart pill bottles. An ML model, trained to anticipate potential adherence problems, is then provided with this preprocessed data. Personalized interventions, like medicine reminders, educational content, and progress reports, are delivered by the Intelligent Medication Compliance System (IMAC) based on these predictions. The integration of IoT-enabled smart sensors and connectivity allows for smooth communication between patients, healthcare professionals, and medicine dispensers, permitting prompt interventions, prescription reminders, and remote monitoring. Additionally, the system makes it easier for patients and medical professionals to communicate, allowing for prompt modifications to treatment programs. Serving as an anticipatory system, the IMAC provides personalized interventions. Predicting possible adherence problems, such as missing doses or inadvertent drug interactions, is done by the model through data analysis. The proposed IMAS seeks to empower patients, personalized medication management, and eventually improve medication adherence by fusing real-time IoT data with ML-driven insights. © 2024 IEEE.

关键词： Personalized medicine

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Cloud-Based Oxygen Management System with Seamless HTTP Protocol Integration 4

Cloud-Based Oxygen Management System with Seamless HTTP Prot...

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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Hemalatha, S. Ramasamy, Jayabharathi Selvi, K. Anandan, P. Meenakshi, R. Maniraj, S.P. Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India S. A. Engineering College Faculty of Management Studies Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350307757

In this study, an efficient system to improve the oxygen supply in hospitals and other healthcare institutions is proposed. It uses Internet of things (IoT) devices and cloud based data transmission and processing system. The controller unit collects the oxygen sensor data for the analysis and uses HTTP protocols to send and receive data with cloud computing in HTTP website server. The cloud is data storage, processes and real-time monitoring, analysis and control system. It has a user interface with viewed administrators and medical professionals. The interface provides real-time data regarding oxygen supply, consumption patterns, and possible concerns. Authorized workers can access and manage the system via the display, allowing for timely adjustments to oxygen supply settings, optimizations to refill schedules, and responses to emergency circumstances. It's adaptable, flexible, and resilient, which are benefits. There will be less need to worry about oxygen shortages and interruptions, and healthcare institutions of all sizes can benefit from this. The cloud's remote access and control capabilities provide continuous functioning and rapid reaction times. It's an effective solution for hospitals and other healthcare facilities that need to control their oxygen supplies. With its cloud-based design, the solution increases productivity, improves patient safety, and simplifies oxygen management. © 2024 IEEE.

关键词： HTTP

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Intelligent diagnosis of Alzheimer’s disease based on BP neural network 3

Intelligent diagnosis of Alzheimer’s disease based on BP ne...

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3rd Guangdong-Hong Kong-Macao Greater Bay Area artificial intelligence and Big data Forum, AIBDF 2023

作者： Zhao, Jia Huang, Jilin Cheng, Jiejia Cai, Shiyuan Faculty of Information Engineering Guangxi Polytechnic of Construction Nanning530007 China School of Arts and Sciences China University of Petroleum-Beijing At Karamay Karamay834000 China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300401 China

ISBN: (纸本)9798400716362

Alzheimer’s disease (AD) is a progressive neurodegenerative disease, and its onset is unknown. It usually occurs in the elderly and dies due to complications 10 to 20 years after onset. Therefore, early and accurate diagnosis of AD and mild cognitive impairment is of great significance. Firstly, preprocess the data, manually remove the data with no specific information, and then use Python to remove duplicate feature variables with the same name and data with more than 10% missing features. Fill in the remaining data with missing values and fill in the average values, leaving 16 sample labels;Then, the processed data is divided into training and testing sets to train a random forest model, and the correlation scores between data features and Alzheimer’s disease diagnosis are calculated separately. Some results are as follows: LDELTOTAL_ BL-18.81%, PACCDigit 14.30%, and combined with the existing dataset with nonlinear data features for comprehensive analysis. Based on the advantages of BP neural network nonlinear mapping function and existing feature labels, a model is established to intelligently diagnose Alzheimer’s disease. Through testing and analysis, when the number of hidden neurons is 9 and the model training method is Bayesian regularization, the solution effect is better. Some of the optimal intelligent diagnosis schemes are: FAQ: 0.0-30.0;ADAS13:0.0-54.68;AGE: 50.4-91.4;MMSE: 16.0-30.0. © 2023 Copyright held by the owner/author(s).

关键词： Diagnosis

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Exploring Diabetic Retinopathy Diagnosis: Insights from a Comprehensive Literature Review and Initial Fundus Image Analysis

Exploring Diabetic Retinopathy Diagnosis: Insights from a Co...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar Reddy, K Uday Malleswari, M. Kumar Reddy, P Phanindra Karimullah, Shaik Shaik, Fahimuddin Hariobulesu, Peddapullaiahgari Annamacharya University Artificial Intelligence and Data Science Department Rajampet India Sri Annamacharya Institute of Technology and Sciences Computer Science and Engineering Department India Annamacharya University AI&DS Department Rajampet India Annamacharya University ECE Department Rajampet India

ISBN: (纸本)9798350378092

This article looks at recent advances in diabetic retinopathy (DR) detection with retinal fundus and optical coherence tomography angiography (OCTA) pictures. Key studies have investigated a variety of methods, including combining Multiscale Retinex with Chromaticity Preservation (MSRCP) and Bi-LSTM networks for high accuracy, integrating the Internet of Things (IoT) with deep learning for increased sensitivity, and using Multi-Stream Deep Neural Networks to classify DR severity. Innovations like the PMNPDR system for microaneurysm detection and deep learning ensembles for OCT images have also performed well. Despite these gains, issues like as data transfer latency, model complexity, and reliance on tiny datasets remain. Overall, while current models show great potential, they require further modification and validation to assure their efficacy and applicability in clinical situations. © 2024 IEEE.

关键词： Optical coherence tomography

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Plant Digital Twins Based on Model Phenotyping and Functional-Structural Plant Modeling 4

Plant Digital Twins Based on Model Phenotyping and Functiona...

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4th IEEE International Conference on Digital Twins and Parallel intelligence, DTPI 2024

作者： Liu, Zhiqing Wang, Xiujuan Hua, Jing Wang, Haoyu Huo, Qirun Kang, Mengzhen Capital Normal University Information Engineering College Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Macau University of Science and Technology Faculty of Innovation Engineering China

ISBN: (纸本)9798350349252

In this digital age, digital twin technology has been widely applied in various fields. However, its use in agriculture is still in the early stages. Existing literature on plant digital twins indicates that they have a limited capacity to interact with real plants or to predict the complete plant growth process. This paper aims to propose a new approach to plant digital twin by combining them with actual plant phenotyping technology. By obtaining plant phenotypic data and integrating them with the plant model, we aim at predicting the entire plant growth process, thereby achieving a dynamic plant digital twin. © 2024 IEEE.

关键词： Plant diseases

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FoSoHA-Net: Deep Learning for Accurate Breast Cancer Classification Leveraging Hybrid Attention Mechanisms 2

FoSoHA-Net: Deep Learning for Accurate Breast Cancer Classif...

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2nd International Conference on Intelligent Perception and Computer Vision, CIPCV 2024

作者： Addo, Daniel Al-Antari, Mugahed A. Zhou, Shijie Ashalley, Eric Muoka, Gladys Wavinya Nartey, Obed T. University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Sejong University AISSLab Department of Artificial Intelligence and Data Science Seoul Korea Republic of University of Electronic Science and Technology of China Chengdu610054 China Sino-British Collaborations ChengduUniversity of Technology of China Erxianqiao Campus Chengdu610059 China

ISBN: (纸本)9798350360097

Breast cancer diagnosis is crucial for timely treatment and management. This study introduces FoSoHA-Net, a novel methodology for precise breast cancer classification using histopathological images. FoSoHA-Net integrates a hybrid attention module (HAM) and depthwise separable convolution to enhance feature extraction and improve classification accuracy. The hybrid attention module combines first-order pooling attention (FoPA) and second-order pooling attention (SoPA) to leverage global and local image information, augmenting the model's discriminative power. Through comprehensive evaluation of the BreaKHis dataset, FoSoHA-Net demonstrates superior performance in accurately classifying histopathological images across various magnification levels. Comparative analysis against established pre-trained models further validates the effectiveness of FoSoHA-Net in breast cancer classification tasks. Overall, FoSoHA-Net shows promise in advancing breast cancer diagnosis through innovative AI-driven approaches. © 2024 IEEE.

关键词： Convolutional neural networks

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Product Quality Enhancement in Social Media using Emotion-Enhanced Bert (EE-BERT) Model

Product Quality Enhancement in Social Media using Emotion-En...

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Self Sustainable artificial intelligence Systems (ICSSAS), International Conference on

作者： V S Raj Kumar T Kumaresan V Mythily N Raghavendran Department of Artificial Intelligence and Data science Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Artificial Intelligence and Data science RMK college of Engineering and Technology Chennai India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Twitter plays an important role in understanding the consumer sentiment about the products. The advanced analytics and Natural Language Processing (NLP) are used to extract actionable insights from this data and useful for a business to improve features and address issues on time for better customer satisfaction and brand loyalty. Most of the current models suffer from a lack of effective ways to handle subtle sentiment analysis because of a lack of contextual understanding and proper feature extraction. Most traditional models cannot combine and optimize features that lead to obtaining sentiments. This leads to overfitting and problems caused by local minima. The proposed Emotion-Enhanced BERT (EE-BERT) model is an extension of the standard BERT to learn contextual embeddings in user sentiment. In this work, data preprocessing includes text cleaning, tokenization, dealing with emojis and emoticons, and lemmatization with the best quality data. The proposed work is basically a dual-stream architecture extended with butterfly optimizer. First, the advanced tokenization and semantic understanding techniques is used for extracting textual features. Second, the emotion-enhanced features extracted through emotion-hash vectorization to enhance the model’s contextual awareness in social media data. This provides a unified framework that combines the feature extraction methods to enhance the accuracy in sentiment analysis. It demonstrated with an impressive accuracy of ${9 8. 2 \%}$ on sentiment in posts and comments with 0.15 losses. The proposed work is free from overfitting and local minima in product quality enhancement.

关键词： Accuracy Social networking (online) Computational modeling Blogs Feature extraction Product design Encoding data models Quality assessment Context modeling

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Optimizing Supply Chain Operations through Machine Learning: Opportunities and Challenges

Optimizing Supply Chain Operations through Machine Learning:...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Santhakumar, D. Soundararajan, Gopalakrishnan Chintala, Suman Bharath, E. Murthy Inumula, Krishna Chouhan, Kuldeep Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Thandalam Tamil Nadu Chennai602105 India Muscat College Ruwi Oman Business Intelligence Architect United States C K College of Engineering & Technology Anna University Department of Artificial Intelligence & Data Science Tamil Nadu Cuddalore India Maharashtra Pune411057 India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida201310 India

ISBN: (纸本)9798331508685

In order to create enduring partnerships that are essential to attaining long-term economic success, supply chain management, or SCM, is essential. Strict standards and decision-making procedures are necessary for efficient SCM, and they have a big influence on the final results. In order to efficiently manage inventory levels and logistical procedures, this study offers a complex ML system that combines demand forecasts and optimisation methods. For predicting demand, the approach uses a Generative Adversarial Networks (GAN) model, taking advantage of its capacity to process time-series data and identify intricate trends. The algorithm achieves an impressive 96.4% forecasting of demand accuracy by examining extensive historical sales data and a variety of supply chain variables. In order to optimise inventory rules, the investigation also uses optimisation techniques, particularly Genetic Algorithms (GA) in conjunction with Simulated Annealing (SA). The model demonstrated significant improvements when implemented to an actual commercial logistics system. Notably, it reduced storage expenses by 19% and increased order fulfillment rates by 24%, proving its capacity for combining service standards with financial efficiency. The findings highlight the significant impact of ML on improving supply chain efficiency and show how it can result in significant cost savings and improve service quality. © 2025 IEEE.

关键词： Simulated annealing

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Optimizing Resource Management and data Security in Cloud Computing Environments 3

Optimizing Resource Management and Data Security in Cloud Co...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Shri, S.Jothi Karthiyayini, G. Vignesh, L.S. Upender, Talluri Shobana, D. Patra, Ladi Sandip Kumar Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Ramco Institute of Technology Department of CSE Tamil Nadu Rajapalayam India Nadar Saraswathi College of Engineering and Technology Department of Artificial Intelligence and Data Science Theni India CMR College of Engineering and Technology Medchal Department of CSE Telangana Hyderabad India Rajalakshmi Engineering College Department of Mechatronics Engineering Chennai India MLR Institute of Technology Department of ECE Hyderabad India

ISBN: (纸本)9798350354171

The quick uptake of cloud computing has completely changed how companies run since it offers flexible and scalable resources that promote productivity and creativity. However, the dynamic and complex nature of cloud environments presents significant challenges in optimizing resource management and ensuring robust data security. This paper investigates advanced strategies and methodologies for enhancing resource allocation, virtualization, and scheduling to maximize performance and cost-efficiency in cloud infrastructures. Key techniques explored include dynamic scaling, load balancing, VM migration, containerization, and cost-effective resource usage through spot and reserved instances. The important facets of cloud computing data security, such as encryption, identity and access management (IAM), data loss prevention (DLP), and intrusion detection and prevention systems (IDPS), are also covered in tandem by this research. The study offers novel approaches for reducing security threats and guaranteeing data integrity by fusing cutting-edge technology like blockchain and machine intelligence. The comprehensive analysis aims to provide actionable insights and practical recommendations for IT professionals and organizations striving to optimize their cloud computing operations. By balancing resource management efficiency with stringent security measures, this research contributes to the development of a more secure and efficient cloud ecosystem. The results emphasise the significance of a comprehensive strategy for managing cloud systems, where data security and performance optimisation work together to provide durable and sustainable cloud computing environments. © 2024 IEEE.

关键词： Cloud security

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Avian Identification: Utilizing Xception Algorithm for Bird Breed Discrimination 2

Avian Identification: Utilizing Xception Algorithm for Bird ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Raja, S.R. Suganthi, P. Vivekanandan, Munian Vanitha, N. Vivekanandan, G. Venu, Nookala Centre for Open and Digital Education Chennai India Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Chennai India Rajalakshmi Institute of Technology Artificial Intelligence and Data Science Chennai India Sri Sairam Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai 44 India Sri Sairam Institute of Technology Department Computer Science and Engineering Chennai India Madhya Pradesh Gwalior474 005 India

ISBN: (纸本)9798350384277

In this ground breaking project, we delve into the fascinating realm of ornithology through an extensive dataset comprising 507 distinct bird breeds and a staggering 61,250 high-resolution images. Employing cutting-edge machine learning techniques, our aim is to develop a robust classification model capable of accurately identifying bird species based on visual attributes. The dataset, meticulously curated for diversity, spans a myriad of habitats, climates, and avian characteristics. Through this expansive collection, our model not only encapsulates the beauty of various feathered species but also ensures a broad spectrum of challenges in classification, pushing the boundaries of current bird recognition systems. Our approach transcends conventional methods by leveraging state-of-the-art neural networks, harnessing the power of deep learning to discern subtle nuances in plumage, size, and distinctive markings. The model's ability to adapt and learn from this vast dataset promises breakthroughs in bird breed classification, offering insights into the intricate world of avian biodiversity. This project stands as a testament to the synergy between ornithology and artificial intelligence, fostering a deeper understanding of avian ecosystems. By avoiding plagiarism and embracing creativity, our abstract articulates the uniqueness of our endeavour, propelling the field of bird breed classification into uncharted territories. © 2024 IEEE.

关键词： Birds

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