Titlebook: Artificial Intelligence for Internet of Things (IoT) and Health Systems Operability; AI for IoT and Healt Alireza Souri,Salaheddine Bendak

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書目名稱Artificial Intelligence for Internet of Things (IoT) and Health Systems Operability影響因子(影響力)




書目名稱Artificial Intelligence for Internet of Things (IoT) and Health Systems Operability影響因子(影響力)學(xué)科排名




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書目名稱Artificial Intelligence for Internet of Things (IoT) and Health Systems Operability網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書目名稱Artificial Intelligence for Internet of Things (IoT) and Health Systems Operability被引頻次




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書目名稱Artificial Intelligence for Internet of Things (IoT) and Health Systems Operability讀者反饋




書目名稱Artificial Intelligence for Internet of Things (IoT) and Health Systems Operability讀者反饋學(xué)科排名

armistice

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慷慨不好

An IoT-Based Telemedicine System for the Rural People of Bangladesh,care services within the reach of the rural people of Bangladesh. Proposed system enables transmission of patient’s body parameters in real-time to a remote doctor. The proposed system also has real-time patient monitoring capability which is based on ECG signal classification. A feed-forward neural

廚師

Comparison of Predicting Regional Mortalities Using Machine Learning Models,East Asia and Pacific, Europe and Central Asia, Europe alone, South Asia regions have been studied and predictions are made using fourteen machine learning techniques. These are linear, polynomial, ridge, Bayesian ridge, lasso, elastic net, k-nearest neighbors, support vector (with linear, polynomia

Cirrhosis

聲音刺耳

An Integrated Deep Learning Approach for Computer-Aided Diagnosis of Diverse Diabetic Retinopathy Gr-assisted diagnosis has emerged as a potent technique to examine medical images of patients’ eyes and detect damage to blood vessels. Nevertheless, the effectiveness of deep learning models has been impeded by factors such as imbalanced datasets, annotation inaccuracies, limited available images, a

Indent

辭職

A Stochastic Gradient Support Vector Optimization Algorithm for Predicting Chronic Kidney Diseases,wn that machine-learning techniques are trustworthy enough for medical care. With the benefit of important results achieved from machine learning classifier algorithms, clinicians and medical staff can detect the disease on time. Besides, by employing unbalanced and small datasets of Chronic Kidney