News recommendation is an important feature to help users discover news that matches their bias. In response to users’ growing privacy concerns, various recommendation models integrating joint learning have been deve...
详细信息
The advancement of AI-technologies has brought both innovation and considerable challenges particularly with the emergence of deep fakes-realistic but false images, videos or audios created using deep learning algorit...
详细信息
This study presents a highly efficient helmet detection system designed for real-time operation on low-cost edge devices, targeting helmet compliance monitoring in vehicular environments. A novel dataset was created b...
详细信息
Underwater Wireless Sensor Networks (UWSNs) are gaining importance for a wide range of applications, from environmental monitoring to underwater exploration. However, the challenging underwater environment poses signi...
详细信息
Numerous disorders that cannot be diagnosed medically have emerged throughout the world, including Autism Spectrum Disorder (ASD). It impacts on the numerous aspects of behavior, such as social and language abilities ...
详细信息
Flood forecasting methods based on deep learning rely on a large number of observational data, and are facing serious challenges in areas with scarce data. Aiming at the problems of flood inundated range prediction in...
详细信息
Elevators serve as vital components in modern buildings, yet optimizing passengers' waiting time remains a crucial challenge. This study proposes a machine learning-based approach to enhance the efficiency of elev...
详细信息
Financial Fraud Detection Model (FFDM) is used to develop an advanced detection framework utilizing Graph Neural Networks (GNNs) to accurately identify fraudulent transactions within the transactions. Traditional frau...
详细信息
The convergence of machine learning and medical data presents an exciting frontier in the realm of healthcare, with the potential to revolutionize the early detection of diseases. In this study, we introduce innovativ...
详细信息
ISBN:
(纸本)9789819739363
The convergence of machine learning and medical data presents an exciting frontier in the realm of healthcare, with the potential to revolutionize the early detection of diseases. In this study, we introduce innovative machine learning models designed for the early prediction of three critical ailments: diabetes, heart disease, and liver disorders. To enhance the performance of these models, we rigorously fine-tuned their hyperparameters, a critical aspect of the model development process. Our approach involved the utilization of various classification algorithms, such as logistic regression (LR), extra tree (ET), support vector machine (SVM), Naïve Bayes (NB), decision tree (DT), and random forest (RF). Furthermore, we employed ensemble learning techniques like bagging and boosting, using the aforementioned traditional algorithms as base estimators. All these algorithms underwent extensive hyperparameter tuning to optimize their predictive capabilities. To assess the performance of these models, we conducted a thorough tenfold cross-validation, enabling us to make a comprehensive comparative analysis and identify the most effective models for each dataset. Notably, our efforts bore fruit with exceptional results. For instance, we achieved an impressive accuracy rate of 99.22% in predicting diabetes using the traditional SVM classifier. In the case of the Statlog heart dataset, we reached an accuracy of 85.67% by utilizing the random forest classifier within a bagging ensemble. In predicting liver disorders, we achieved a 73.75% accuracy by employing both boosting random forest and boosting extra tree classifiers. Additionally, we elucidated the reasons behind the variation in results, providing valuable insights. These experimental findings underscore the superiority of our proposed models over existing methods in terms of predictive accuracy. Consequently, our research represents a significant step forward in the early diagnosis and prevention of diseases within t
Water is a precious commodity, and as the population grows, its importance increases. To solve this problem, a data-driven approach is used to predict the optimal water use for agriculture. The focus of this research ...
详细信息
暂无评论