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IEEE International Conference on Healthcare informatics (ICHI)

作者： Aseem Baranwal Alexander Semenov Patricia De Oliveira Salgado Karen B. Priola Yingwei Yao Gail M. Keenan Tamara G. R. Macieira Department of Computer & Information Science & Engineering University of Florida Gainesville USA Department of Industrial and Systems Engineering University of Florida Gainesville USA Department of Medicine and Nursing Federal University of Vicosa Vicosa Brazil Department of Family Community and Health System Science University of Florida College of Nursing Gainesville USA Department of Biobehavioral Nursing Science University of Florida College of Nursing Gainesville USA

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

Care plans documented by nurses in electronic health records (EHR) are a rich source of data to generate knowledge and measure the impact of nursing care. However, these structured data are often documented using non- standardized local terms, which hinders interoperability and wide- scale analysis of these data. In this paper, we compared the performance of OpenAl's Generative Pre-Trained Transformer models (GPT -3.5 and GPT -4), accessed through the ChatGPT web application and Application Programming Interface (API) platforms, in mapping local care plan terms to standardized nursing terms. Our results demonstrate that the GPT -4 API model outperformed others by minimizing hallucinations, offering enhanced accuracy, and yielding a higher percentage of matched terms. GPT models are valuable tools in advancing the standardization and interoperability of nursing data.

关键词： Accuracy Medical services Standardization Transformers Chatbots Data models Task analysis

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Deep reinforcement learning based solution for sustainable energy management in photovoltaic systems

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Optik 2023年 295卷

作者： Alamro, Hayam Alqahtani, Hamed Alotaibi, Faiz Abdullah Othman, Kamal M. Assiri, Mohammed Alneil, Amani A. Narasimha Prasad, L.V. Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P. O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Science Center of Artificial Intelligence Unit of Cybersecurity King Khalid University Abha Saudi Arabia Department of Information science College of Humanities and Social Sciences King Saud University P. O Box 28095 Riyadh11437 Saudi Arabia Department of Electrical Engineering Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science College of Sciences and Humanities Aflaj Prince Sattam bin Abdulaziz University Aflaj16273 Saudi Arabia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of CSE Institute of Aeronautical Engineering Hyderabad India

Photovoltaic solar cells (PSCs) have emerged as a practical solution to meet the growing energy demands, offering advantages such as high efficiency, flexibility, and adaptability to diverse materials and manufacturing methods. However, fully realizing the potential of solar energy requires the development of affordable and widely applicable solar cells. This article underscores the crucial role of PSCs in reliably addressing future energy needs. Despite their inherent benefits, there remains a significant challenge in improving the affordability and applicability of these solar cells. To tackle these challenges, the article introduces a novel approach called Sustainable Energy Management Architecture in Photovoltaic systems (SEMA), which integrates the robust capabilities of Deep Reinforcement Learning (DRL) and the Deep Q-Network (DQN) technique with Proximal Policy Optimization (PPO) methods. By employing this comprehensive approach, the goal is to enhance cost-effectiveness and promote broader adoption of PSCs. The novelty of the research lies in the integration of DQN with PPO. This underscores the importance of exploring innovative manufacturing techniques and materials to overcome current limitations and fully unlock the potential of solar energy. Sustained efforts in this direction are essential for advancing and implementing PSCs as a reliable and sustainable energy solution for the future. Continuous research and development will drive improvements in solar cells' affordability, applicability, and efficiency. By addressing these challenges and harnessing advanced techniques like DRL-DQN and PPO, the objective is to pave the way for widespread and cost-effective adoption of PSCs, ultimately contributing to a greener and more sustainable energy future. © 2023 Elsevier GmbH

关键词： Reinforcement learning

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MINIATURIZED CAPSULE SYSTEM FOR HYDROGEN SULFIDE DETECTION IN THE GASTROINTESTINAL TRACT

MINIATURIZED CAPSULE SYSTEM FOR HYDROGEN SULFIDE DETECTION I...

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2022 Solid-State Sensors, Actuators and Microsystems Workshop, Hilton Head 2022

作者： Stine, Justin M. Botasini, Santiago Beardslee, Luke A. Levy, Joshua A. Ghodssi, Reza Department of Electrical and Computer Engineering University of Maryland College ParkMD United States Institute for Systems Research University of Maryland College ParkMD United States Department of Materials Science and Engineering University of Maryland College ParkMD United States Robert E. Fischell Institute for Biomedical Devices University of Maryland College ParkMD United States

ISBN: (纸本)9781940470047

This work presents an ingestible capsule platform for real-time, wireless detection of hydrogen sulfide (H2S) as a biomarker of inflammation in the gastrointestinal (GI) tract. The design integrates an analog front-end (AFE) potentiostat and Bluetooth Low Energy (BLE) microcontroller on a flex-rigid printed circuit board (PCB) with a miniaturized electrochemical H2S sensor to perform amperometric measurements with the desired capsule form factor. Packaged within a 3D-printed shell, the capsule successfully monitors physiological H2S concentrations within a linear range of 3.7–12 ppm with sensitivity of 0.17 μA/ppm, providing a framework for non-invasive, on-site monitoring of gaseous biomarkers in the GI tract. © 2022 TRF.

关键词： Hydrogen sulfide

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Predictive Design for Quality Assessment Employing Cloud Computing And Machine Learning

Predictive Design for Quality Assessment Employing Cloud Com...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Gaurav Jindal Vidhika Tiwari Riyaz Mahomad Anita Gehlot Mimoh Jindal Dibyhash Bordoloi Department of Master of Computer Applications G L Bajaj College of Technology and Management Gr. Noida Mechanical Engineering Department DPGITM Gurugram School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University India National Informatics Centre Delhi India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

In this work, we investigate a unique effective framework for projected design inspections in industrial manufacturing combining machine learning methodologies and edges cloud computing technologies. We recommend a thorough approach that involves targeted data gathering and broadcast, forecasting and supplying appropriate, as well as innovation with the current IT plant infrastructure, in contrary to state-of-the-art contributions. To highlight the steps and advantages of the suggested solution, a genuine business use case in the manufacture of SMT is described. The outcomes demonstrate that the proposed strategy can dramatically reduce inspection volumes, leading to economic gains. A key achievement basis for the drawn-out presentation of assembling ventures is the creation of imperfection-free, top-notch items. Indeed, even with the intricacy and assortment of items and the requirement for savvy production, an exhaustive and dependable quality investigation is habitually required. Along these lines, high examination volumes cause producing bottlenecks in the review processes. Thus, the purpose of this paper is to develop a predictive design for quality assessment regarding employing cloud computing and machine learning. Concerning this particular research, a descriptive research design has been used by depending on secondary data due to its reliability and validity. Moreover, it has been found that both cloud computing and machine learning contribute effectively for employing quality assessment because of their innovative features and infrastructure.

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Prediction of Fruit Texture Features Using Deep Learning Techniques

Prediction of Fruit Texture Features Using Deep Learning Tec...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： M. Sangeetha S.Rajes Kannan Sampath Boopathi J Ramya Mohammad Ishrat G. Sabarinathan Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer science and Engineering Chennai institute of Technology Chennai Tamil Nadu India Department of Mechanical Engineering Muthayammal Engineering College Rasipuram Tamil Nadu India Department of Computer Science and Engineering St. Joseph's College of Engineering Chennai Tamil Nadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India Department of Mathematics PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India

In this research, a prototype has been created to automatically collect the photos of fruits using a smartphone camera and a thermal camera, generating two distinct datasets: thermal and RGB. Fruit classification and bruise detection have both been accomplished using image processing, machine learning, and deep learning. To assess quality and shelf-life, a second set of RGB and thermal picture datasets was created. The suggested study enhances fruit categorization and flaw detection systems by utilizing multiple Grey level co-occurrence matrix (GLCM)-based texture characteristics. Using color-texture characteristics, pictures are classified using RF and KNN algorithms. RGB features were classified with 97.24 percent accuracy by K-Nearest Neighbor (KNN) and Random Forest (RF) classifiers, while GLCM-based features were classified with 96.40 percent accuracy by the RF classifier. Using thermal pictures, the KNN and RF algorithms' prediction accuracy was 81.70 percent and 84.12 percent, respectively. KNN and RF algorithms have kappa (percentage) values of 79.97 and 81.86, respectively. The random forest (RF) technique is hence the most appropriate for classifying fruit using a thermal picture dataset.

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Machine Learning Model for Glass type Prediction

Machine Learning Model for Glass type Prediction

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Advancement in Technology (ICONAT), International Conference for

作者： K. Logeswaran S. Savitha P. Suresh M. Vimaladevi P. Saliniyan A. Sanjay A. Yeswanth Department of Artificial Intelligence Kongu Engineering College Erode Tamilnadu India Departmen of CSE K.S.R College of Engineering Tiruchengode Tamilnadu India Department of Database Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Successful operations in industries such as recycling, manufacturing and quality control depend on identifying and classifying different types of glass. Traditional methods, including manual analysis and traditional image processing methods, often suffer from errors and inconsistencies due to human factors and variations. This paper introduces a new automatic glass type identification system that uses advanced learning techniques, specifically K-Nearest Neighbors (KNN), CNN,RNN. By applying these algorithms, it is aimed to increase the accuracy and performance of the system’s glass type classification. Next, we delve into the design and implementation of machine learning models by discussing data collection techniques, feature extraction technology, and training methods. A comparative analysis of the performance of KNN, RNN, Naive Bayes, Logistic Regression, Random Forest, Feed Forward Network, RNN, ANN, CNN. is performed and their accuracy, performance, and robustness are evaluated in different situations. It shows the most accurate and reliable glass type classification among traditional methods. This study also explores the benefits of using such systems in the manufacturing industry and shows how work efficiency can be increased and the number of errors reduced.

关键词： Training Analytical models Recurrent neural networks Accuracy Glass Predictive models Nearest neighbor methods Robustness Recycling Random forests

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Prominent Rule Control-based Internet of Things: Poultry Farm Management System 3

Prominent Rule Control-based Internet of Things: Poultry Far...

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3rd International Conference on Power, Energy, Control and Transmission systems, ICPECTS 2022

作者： Boopathil, Sampath Arigela, Sri Harsha Raman, Ramakrishnan Indhumathi, C. Kavitha, V. Bhatt, Bhuvan Chandra Muthayammal Engineering College Department of Mechanical Engineering Tamil Nadu Namakkal637408 India Koneru Lakshmaiah Education Foundation Department of Mechanical Engineering Andhra Pradesh Guntur522302 India University Maharashtra Pune412115 India R. M. K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai601206 India S. A. Engineering College Department of Computer Science Engineering Tamil Nadu Thiruverkadu600077 India Uttaranchal University Uttaranchal Institute of Technology Department of Civil Engineering Uttarakhand Dehradun248007 India

ISBN: (纸本)9781665462754

In this article, the various poultry farm parameters such as antitheft, fire protection, temperature, humidity, oxygen, carbon-di-oxide, ammonia gases, and light of the poultry have been monitored by the Prominent Rule Control (PRC) algorithm in Internet-of-Things devices. Food and water supply, fire and theft safety, air purity, and air conditioning in the poultry farm have all been conducted using sensor signals. In this system, power consumption, labour costs, time, and effective utilization of power and resources have been monitored to enhance the poultry production rate. The PRC algorithm has been working to simplify the process and save time for overall poultry monitoring and activating systems with low cost. It is also employed to condition the sensors' data, convert it, compare it with threshold values, write the Arduino program, and securely store it in the cloud system for the poultry system. This research is also focused on improving the poultry monitoring system and minimizing the fixed and operating costs of the IoT system. The microcontroller, GSM module/WIFI, temperature and humidity sensors, proximity sensors, fire sensors, different gas sensors, RFID, and light sensors have been working to monitor the poultry form. The egg collectors, fogger systems to decrease the temperature, water and food supply systems have been established and integrated with microcontrollers to perform the automation in the poultry farm. © 2022 IEEE.

关键词： Microcontrollers

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An IoT model for securing examinations from malpractices

An IoT model for securing examinations from malpractices

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2020 International Virtual Conference on Sustainable Materials, IVCSM 2k20

作者： Haque, Shameemul Zeba, Sana Alimul Haque, Md. Kumar, Kailash Ali Basha, Mudassir Peeran Al-Hafeez College VeerKunwar Singh University Ara India Department of Computer Engineering JamiaMiliaIslamia New Delhi India Department of Computer Science VeerKunwar Singh University Ara India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Information System University of Bisha Saudi Arabia

The primary objective of university education is to provide students with the required information and skills to enable them to make a successful contribution to the national development effort. In order to evaluate the level of expertise and skill of students, this preparation includes frequent examination and appraisal in the form of tests. Manual exams are carried out by most universities;Post Graduate and Under Graduate in India. Due to multiple uses made of the outcomes of the students, these examinations have high stakes. Consequently, the experiments became subject to different types of malpractice. Test malpractice affects the success of students in exams and leaves the scores/grades earned inaccurate, hence a challenge to examination bodies and the nation's education system (India). These are all disturbing signs that pose a danger to the nation. Examination malpractice has eaten deep into the Indian culture of education. Therefore, the object of this paper is to explore the problem of examination malpractice in India. The paper covered relevant measures, procedure setting/implementation and suggestions for this action. And finally, a model is proposed to stop malpractices in the examination. This IoT based model is used for detecting any metallic object or object in a range more than normal IR proximity sensor range. The block diagram of proposed model and circuit diagram for that model discussed in detail. This proposed model may be used for any investigation in the universities examinations. © 2021 Elsevier Ltd. All rights reserved.

关键词： Students

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Comparison of Speech Recognition and Natural Language Understanding Frameworks for Detection of Dangers with Smart Wearables 21st

Comparison of Speech Recognition and Natural Language Unders...

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21st International Conference on Computational science, ICCS 2021

作者： Mrozek, Dariusz Kwaśnicki, Szymon Sunderam, Vaidy Malysiak-Mrozek, Bożena Tokarz, Krzysztof Kozielski, Stanislaw Department of Applied Informatics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Department of Graphics Computer Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Department of Computer Science Emory University AtlantaGA30322 United States

ISBN: (纸本)9783030779696

Wearable IoT devices that can register and transmit human voice can be invaluable in personal situations, such as summoning assistance in emergency healthcare situations. Such applications would benefit greatly from automated voice analysis to detect and classify voice signals. In this paper, we compare selected Speech Recognition (SR) and Natural Language Understanding (NLU) frameworks for Cloud-based detection of voice-based assistance calls. We experimentally test several services for speech-to-text transcription and intention recognition available on selected large Cloud platforms. Finally, we evaluate the influence of the manner of speaking and ambient noise on the quality of recognition of emergency calls. Our results show that many services can correctly translate voice to text and provide a correct interpretation of caller intent. Still, speech artifacts (tone, accent, diction), which can differ even for each individual in various situations, significantly influences the performance of speech recognition. © 2021, Springer Nature Switzerland AG.

关键词： Speech recognition

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Blockchain based Public Auditing Outsourcing for Cloud Storage 27

Blockchain based Public Auditing Outsourcing for Cloud Stora...

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27th IEEE International Conference on Parallel and Distributed systems, ICPADS 2021

作者： Lin, Yangfei Li, Jie Kimura, Shigetomo Zhang, Yongbing Ji, Yusheng Yang, Yang Graduate School of Systems and Information Engineering University of Tsukuba Ibaraki Tsukuba Japan Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China University of Tsukuba Faculty of Engineering Information and Systems Ibaraki Tsukuba Japan National Institute of Informatics Information Systems Architecture Research Division Tokyo Japan School of Mathematics and Computer Science Fuzhou University Fuzhou China

ISBN: (纸本)9781665408783

Cloud storage services offer flexible, convenient solutions for business and personal users to store data. Traditionally, Third Party Auditors (TPAs) are introduced to ensure data integrity for public auditing. However, TPAs may also be untrusted for forging the auditing results or colluding with cloud storage servers to deceive users. In this paper, we propose a novel Blockchain-based Public Auditing Outsourcing system without TPAs (BPAO), in which the computationally expensive operations in public auditing are outsourced through blockchain to the cloud servers without risking users' privacy. Our security analysis indicates that BPAO achieves soundness and robustness. The experimental results show that BPAO is computationally efficient for cloud storage user. © 2021 IEEE.

关键词： Smart contract

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