Fan Zhang

Bio: Fan Zhang is an academic researcher from University of Science and Technology Beijing. The author has contributed to research in topics: Magnetic resonance imaging & Functional magnetic resonance imaging. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems

Abstract: Road transport is the most prone to accidents, resulting in significant fatalities and injuries. It also faces a plethora of never-ending problems, such as the frequent loss of lives and valuables during an accident. Appropriate actions need to be taken to address these problems, such as the establishment of an automatic incident detection system using artificial intelligence and machine learning. This article explores the overview of artificial intelligence and machine learning in facilitating automatic incident detector systems to decrease road accidents. The study examines the critical problems and potential remedies for reducing road traffic accidents and the application of artificial intelligence and machine learning in road transportation systems. More, new, and emerging trends that reduce frequent accidents in the transportation sector are discussed extensively. Specifically, the study organized the following sub-topics: an incident detector with machine learning and artificial intelligence and road management with machine learning and artificial intelligence. Additionally, safety is the primary concern of road transport; the internet of vehicles and vehicle ad hoc networks, including the use of wireless communication technologies such as 5G wireless networks and the use of machine learning and artificial intelligence for road transportation systems planning, are elaborated. Key findings from the review indicate that route optimization, cargo volume forecasting, predictive fleet maintenance, real-time vehicle tracking, and traffic management are critical to safeguarding road transportation systems. Finally, the paper summarizes the challenges facing the application of artificial intelligence in road transport systems, highlights the research trends, identifies the unresolved questions, and highlights the essential research takeaways. The work can serve as reference material for road transport system planning and management.

An Efficient, Ensemble-Based Classification Framework for Big Medical Data.

Abstract: Fetching useful information from big medical datasets is a complicated task in the big data age. Various classification algorithms are used in the data mining process to analyze information from the big medical dataset. Nevertheless, these classification algorithms are insufficient to handle big medical data. This work proposes an efficient, ensemble-based classification framework for big medical data to deal with this problem. The proposed work involves initially applying the preprocessing technique to remove noise, missing values, and unwanted features from big medical data. The process selects a subset of classifiers from a pool of classifiers. The selected classifiers are combined to form a hybrid system for efficient classification. The methodology further involves incremental learning from data samples, explaining the predicted outputs, and achieving high classification performance. Java is used for simulation, and the Cleveland Heart Disease big dataset and Diabetes big dataset are used for classification. The experimental result shows that the proposed ensemble algorithm provides an efficient classification compared with existing algorithms based on accuracy, precision, F-measure, recall, and execution time.

An Efficient, Ensemble-Based Classification Framework for Big Medical Data

Abstract: Fetching useful information from big medical datasets is a complicated task in the big data age. Various classification algorithms are used in the data mining process to analyze information from the big medical dataset. Nevertheless, these classification algorithms are insufficient to handle big medical data. This work proposes an efficient, ensemble-based classification framework for big medical data to deal with this problem. The proposed work involves initially applying the preprocessing technique to remove noise, missing values, and unwanted features from big medical data. The process selects a subset of classifiers from a pool of classifiers. The selected classifiers are combined to form a hybrid system for efficient classification. The methodology further involves incremental learning from data samples, explaining the predicted outputs, and achieving high classification performance. Java is used for simulation, and the Cleveland Heart Disease big dataset and Diabetes big dataset are used for classification. The experimental result shows that the proposed ensemble algorithm provides an efficient classification compared with existing algorithms based on accuracy, precision, F-measure, recall, and execution time.