Object detection has become faster and more precise due to improved computer vision systems. Many successful object detections have dramatically improved owing to the introduction of machine learning methods. This study incorporated cutting- edge methods for object detection to obtain high-quality results in a competitive timeframe comparable to human perception. Object-detecting systems often face poor performance issues. Therefore, this study proposed a comprehensive method to resolve the problem faced by the object detection method using six distinct machine learning approaches: stochastic gradient descent, logistic regression, random forest, decision trees, k-nearest neighbor, and naive Bayes. The system was trained using Common Objects in Context (COCO), the most challenging publicly available dataset. Notably, a yearly object detection challenge is held using COCO. The resulting technology is quick and precise, making it ideal for applications requiring an object detection accuracy of 97%.
Facial emotion recognition finds many real applications in the daily life like human robot interaction, eLearning, healthcare, customer services etc. The task of facial emotion recognition is not easy due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the facial expression accurately. Graph mining techniques are exploited in this paper to solve facial emotion recognition problem. After determining positions of facial landmarks in face region, twelve different graphs are constructed using four facial components to serve as a source for sub-graphs mining stage using gSpan algorithm. In each group, the discriminative set of sub-graphs are selected and fed to Deep Belief Network (DBN) for classification purpose. The results obtained from the different groups are then fused using Naïve Bayes classifier to make the final decision regards the emotion class. Different tests were performed using Surrey Audio-Visual Expressed Emotion (SAVEE) database and the achieved results showed that the system gives the desired accuracy (100%) when fusion decisions of the facial groups. The achieved result outperforms state-of-the-art results on the same database.
Health Information Technology (HIT) provides many opportunities for transforming and improving health care systems. HIT enhances the quality of health care delivery, reduces medical errors, increases patient safety, facilitates care coordination, monitors the updated data over time, improves clinical outcomes, and strengthens the interaction between patients and health care providers. Living in modern large cities has a significant negative impact on people's health, for instance, the increased risk of chronic diseases such as diabetes. According to the rising morbidity in the last decade, the number of patients with diabetes worldwide will exceed 642 million in 2040, meaning that one in every ten adults will be affected. All the previous research on diabetes mellitus indicates that early diagnoses can reduce death rates and overcome many problems. In this regard, machine learning (ML) techniques show promising results in using medical data to predict diabetes at an early stage to save people's lives. In this paper, we propose an intelligent health care system based on ML methods as a real-time monitoring system to detect diabetes mellitus and examine other health issues such as food and drug allergies of patients. The proposed system uses five machine learning methods: K-Nearest Neighbors, Naïve Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM). The system selects the best classification method with high accuracy to optimize the diagnosis of patients with diabetes. The experimental results show that in the proposed system, the SVM classifier has the highest accuracy of 83%.
COVID-19 emerged in 2019 in china, the worldwide spread rapidly, and caused many injuries and deaths among humans. Accurate and early detection of COVID-19 can ensure the long-term survival of patients and help prohibit the spread of the epidemic. COVID-19 case classification techniques help health organizations quickly identify and treat severe cases. Algorithms of classification are one the essential matters for forecasting and making decisions to assist the diagnosis, early identification of COVID-19, and specify cases that require to intensive care unit to deliver the treatment at appropriate timing. This paper is intended to compare algorithms of classification of machine learning to diagnose COVID-19 cases and measure their performance with many metrics, and measure mislabeling (false-positive and false-negative) to specify the best algorithms for speed and accuracy diagnosis. In this paper, we focus onto classify the cases of COVID-19 using the algorithms of machine learning. we load the dataset and perform dataset preparation, pre-processing, analysis of data, selection of features, split of data, and use of classification algorithm. In the first using four classification algorithms, (Stochastic Gradient Descent, Logistic Regression, Random Forest, Naive Bayes), the outcome of algorithms accuracy respectively was 99.61%, 94.82% ,98.37%,96.57%, and the result of execution time for algorithms respectively were 0.01s, 0.7s, 0.20s, 0.04. The Stochastic Gradient Descent of mislabeling was better. Second, using four classification algorithms, (eXtreme-Gradient Boosting, Decision Tree, Support Vector Machines, K_Nearest Neighbors), the outcome of algorithms accuracy was 98.37%, 99%, 97%, 88.4%, and the result of execution time for algorithms respectively were 0.18s, 0.02s, 0.3s, 0.01s. The Decision Tree of mislabeling was better. Using machine learning helps improve allocate medical resources to maximize their utilization. Classification algorithm of clinical data for confirmed COVID-19 cases can help predict a patient's need to advance to the ICU or not need by using a global dataset of COVID-19 cases due to its accuracy and quality.