In this paper, an automatic algorithm for detecting the number of Mycobacterium tuberculosis is presented from the AFB smear image on I and V-shaped colonies, by applying fuzzy Intuitionistic based on the auto-thresho...
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The cooperative adaptive cruise control (CACC) functionality received significant interest in the state-of-the-art due to its advantages in optimizing traffic flow. The model-based predictive control (MPC) strategy wa...
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Environmental challenges, notably the emission of greenhouse gases as a consequence of global warming, pose significant concerns within the realm of energy production. This research investigates the integration of ene...
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Black tea production at Rwanda Mountain Tea Factory relies on human sensory analysis to determine optimum oxidation levels to produce customer-desired tea-grade quality. This process is subject to errors of omission r...
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In the healthcare systems, usage of advanced integrated technologies like Internet of things (IoT) and Machine learning (ML) techniques were limited. Different amalgams of IoT devices and ML mechanisms are available f...
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ISBN:
(纸本)9789380544441
In the healthcare systems, usage of advanced integrated technologies like Internet of things (IoT) and Machine learning (ML) techniques were limited. Different amalgams of IoT devices and ML mechanisms are available for medical sector but are limited to certain domains only. These models either provide patients current state data or specific domain analyzing and surveillance pre/post treatment data like heart or brain functions with corresponding medical aid. Also data available is used as clinical study for medical professionals and for better understandings of patients about their state. Specific domain gadgets' like wrist bands or smart bands uses some sensors about vital, temperature and pulse etc., checkups are available but they were not meant for diagnosing or for treatments. In this paper, we proposed an integrated model to use IoT and ML algorithms for a healthcare system. Tracking of patients' status can be done using some sensors such as lightweight, portable, and low-powered sensor nodes. These Sensors sense the patient's status and send the parametric data to the central controller, to take actions during the critical condition of the patients. The data sent to the controller always provided in secure and encryption form. At the same time, patient data is sent to doctors, so that they can provide the instructions to the caretakers of the patients with quick and proper solutions in real-time. For disease prediction, our model uses supervised machine learning algorithms, In order to get the efficient feature set and improve the better accuracy, and pre-processing techniques to eliminate features that are irrelevant, missing values and outliers from biomedical data which aids in better disease prediction. To further strengthen the proposed integrated model design is compared with various traditional classification algorithms to specify its improved accuracy and computational time for accurate prediction of the patient's disease and acts as a decision suppo
Reinforcement learning (RL)-based collaborative perception in vehicular networks chooses the sub-frame of radio channel resources for connected autonomous vehicles (CAVs) to exchange sensing data to enhance the percep...
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The paper provides a comprehensive overview of speech enhancement techniques and their applications. It discusses challenges in non-stationary noise, reverberation, and overlapping speech. Approaches like comb filteri...
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The use of robots in numerous industries has expanded in recent decades. Self-guiding robots have started to arise in human life, particularly in sectors pertaining to the lives of old people. Age-related population g...
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This work focuses on developing a sound-based anomaly detection model for predictive maintenance in vertical milling machines. The curated dataset includes a wide range of normal and anomalous sound patterns encounter...
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The promising optical code division multiple access (OCDMA) has a number of benefits over conventional multi-access techniques, including as ease, better resource utilization, asynchronous operation, and the capacity ...
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