Pests and insects pose a significant threat to global agriculture, causing crop damage and quality loss, with a massive annual costs. Early detection of these agricultural challenges is essential for sustainable farmi...
详细信息
The goal of the "Heart Disease Predictor" project is to create an AI-powered tool for cardiovascular disease (CVD) early detection and prevention. User requests are analysed through surveys and field trips, ...
详细信息
Intelligent waste management is becoming increasingly vital in addressing the challenges posed by escalating waste volumes and environmental concerns. The standard methods for handling waste are frequently economical ...
详细信息
With the technological advances in education where access to knowledge is more vital than ever, educational revision website applications have risen to the forefront as essential tools. For education, consolidating an...
详细信息
Voice Activity Detection (VAD) is a critical task in audio signal processing that is required in many different applications to separate speech parts from background noise. An in-depth evaluation of techniques for max...
详细信息
Accurate soil analysis is crucial for optimizing crop cultivation and management since soil quality and texture are so important in the agricultural industry. We have incorporated deep learning (DL) into agriculture f...
详细信息
Materials datasets usually contain many redundant(highly similar)materials due to the tinkering approach historically used in material *** redundancy skews the performance evaluation of machine learning(ML)models when...
详细信息
Materials datasets usually contain many redundant(highly similar)materials due to the tinkering approach historically used in material *** redundancy skews the performance evaluation of machine learning(ML)models when using random splitting,leading to overestimated predictive performance and poor performance on out-of-distribution *** issue is well-known in bioinformatics for protein function prediction,where tools like CD-HIT are used to reduce redundancy by ensuring sequence similarity among samples greater than a given *** this paper,we survey the overestimated ML performance in materials science for material property prediction and propose MD-HIT,a redundancy reduction algorithm for material *** MD-HIT to composition-and structure-based formation energy and band gap prediction problems,we demonstrate that with redundancy control,the prediction performances of the ML models on test sets tend to have relatively lower performance compared to the model with high redundancy,but better reflect models’true prediction capability.
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
With the explosive growth of mobile data traffic, roadside-unit (RSU) caching is considered an effective way to offload download traffic in vehicular ad hoc networks (VANETs). Many existing works investigate the conte...
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
暂无评论