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    IoMT–Fog–Cloud-based AI frameworks for chronic disease diagnosis: updated comparative analysis with recent AI-IoMT models (2020–2025)
    (2026-01-01)
    Locharoenrat, Kitsakorn
    Chronic diseases such as diabetes and cardiovascular disease require frequent monitoring and timely clinical feedback to prevent complications. Internet of Medical Things (IoMT) systems increasingly combine near-patient sensing with Fog and Cloud computing so that time-critical preprocessing and inference can run close to the patient while compute-intensive training and population-level analytics remain in the Cloud. This review synthesizes primary studies published between 2020 and 2025 that implement AI-enabled IoMT, with an emphasis on systems that report both diagnostic performance and network quality-of-service (QoS). Following PRISMA 2020, we screened database records and included 14 primary studies; we focus the joint performance–QoS synthesis on six IoMT–Fog–Cloud frameworks for diabetes and cardiovascular disease and compare them with two recent multi-disease AI-IoMT models (DACL and TasLA). Diabetes-oriented implementations commonly report accuracy around 95%–96% using explainable or ensemble deep learning, whereas some cardiovascular frameworks report >99% accuracy in controlled settings; we therefore discuss plausible sources of optimistic performance, including small datasets, class imbalance, curated benchmarks, and potential leakage/overfitting in simulation-based evaluations. Across IoMT–Fog–Cloud studies, placing preprocessing and/or inference at the Fog layer repeatedly reduces end-to-end latency for streaming biosignals, but multi-Fog provisioning can increase energy and power demands. To support more reproducible comparisons, we organize 14 extracted metrics into (i) diagnostic performance (accuracy, precision, recall, F1-score, sensitivity, specificity) and (ii) system/network QoS (latency, jitter, throughput, bandwidth utilization, processing/execution time, network usage, energy consumption, power consumption), and we translate the evidence into study-linked design recommendations for future deployments.
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    The supply chain information sharing path based on the internet of things
    (2024-07-01)
    Zeng, Xiao
    ;
    Yi, Jing
    In the context of globalization and digitization, supply chain information sharing and circulation have become important links in supply chain management. The current difficulty in sharing supply chain information is significant, and there are issues of information asymmetry and incompleteness among co participants. In order to improve the integrity and efficiency of supply chain information sharing and enhance the competitiveness of the high supply chain, this article conducts in-depth research on the path of supply chain information sharing using Internet of Things technology. This article first analyzes the level of information sharing, influencing factors, and existing problems, then explores the Internet of Things technology, and finally establishes specific methods and means for the supply chain information sharing path through the Internet of Things technology. To verify the effectiveness of the supply chain information sharing path based on the Internet of Things, in this article, a comparison was made between the development effects of enterprise supply chains before and after applying the supply chain information sharing path based on the Internet of Things. The results show that after applying the IoT based supply chain information sharing path, the market price, production capacity, and market forecasting ability of enterprises have all increased by more than 20%. The conclusion indicates that the Internet of Things can help optimize supply chain information sharing and provide a new perspective for enterprise supply chain management and development.
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    Real Time Display on IoT Mosquitoes Count Detection
    (2021-04-16)
    Parnklang, Jirawath
    ;
    Aroonrua, Peerapat
    The designing of mosquitoes counting system instrument is presented in this work. The mosquitoes that were counted died in order not to measure duplicate counting data. As soon as the input source counting machine can detect the mosquito, the single trigger signal is transmitted to the IOT system to interrupt the server immediately. The number of real mosquito is not transmitting to the IOT but only a signal to interrupt the server.The server records the number of the interrupt signal with real-time clock. Then the interrupt information will be further handled. The front end counting machine consist of the high voltage generate with the suitable voltage value and electrode distance for the required mosquitoes size.The low trigger pulse signals of the mosquitoes killed by high voltage are sending to the controller unit. Immediately, interrupt counting signal of the number of mosquitoes is sent to the big stream data collection on IOT system by the time stamp technique. Form the measurement results, 10 live sample mosquitoes in a limited space box to fly though the counting machine show that the count results are 100 %correct count.
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    Design and improvement of wireless crayfish breeding system by controlling water temperature and monitoring pH via cloud system services
    (2018-06-08)
    Photsathian, Thongchai
    ;
    Suttikul, Thitiporn
    ;
    Tangsrirat, Worapong
    This work proposes the design and improvement of the wireless cloud system services for controlling water temperature and monitoring the pH-value for the crayfish breeding system. The cooling system employs a set of peltier, microcontroller board, and temperature and pH sensors. The circulate water system is used in order to maintain well-distributed water temperature in the water tank. The record data is displayed via the cloud computing processing system, which can be access the measured data at any time and everywhere by various PDA devices via internet network.