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Mode Choice Behavior Modeling: A Synergy by Hybrid Neural Network and Fuzzy Logic System
Mode choice modeling is one of the crucial measurements in transportation policies as it helps in formulating city transport policies. Travel to...
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Towards Accurate Rainfall Volume Prediction: An Initial Approach with Deep Learning, Advanced Feature Selection, Parameter Optimisation, and Ensemble Techniques for Time-Series Forecasting
Accurate rainfall forecasting is crucial in sectors such as agriculture, transportation, and disaster prevention. This study introduces an initial...
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Spatiotemporal Clustering of Groundwater Depth in Ardabil Plain
One of the effective applications of clustering is to obtain a suitable pattern and limits a large amount of data to facilitate the assessment...
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FRS-SIFS: fuzzy rough set session identification and feature selection in web robot detection
Nowadays, web robots are a big part of web and useful in many cases. But, there are malicious web robots that need to be detected. Web robots often...
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EECAS: Energy Efficient Clustering and Aggregator Node Selection for Wireless Sensor Networks
Wireless sensor network is a collection of numerous compatible sensor nodes which are deployed in a random manner to study the phenomena for variety...
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Autonomous data partitioning for type-2 fuzzy set based time series
Time series forecasting is widely used to predict future values in several applications, such as climate, industries demand, stock markets and...
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Binary imbalanced big data classification based on fuzzy data reduction and classifier fusion
The era of big data has arrived, making it impossible for traditional machine learning algorithms to perform training in a stand-alone computing...
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Assessment, Categorisation and Prediction of the Landslide-Affected Regions Using Soft Computing and Clustering Techniques
Landslides are known natural hazard that significantly impacts human lives by damaging assets and infrastructures. It is tough to collect field data...
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Application of Ensemble Methods in Medical Diagnosis
Medical diagnoses with machine learning faces challenges of a high false negative rate, which can pose a threat to patients’ lives. Moreover, ML...
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An Optimally Selective Ensemble Classifier Based on Multimodal Perturbation and Its Application
The selective ensemble aims to search the optimal subset balanced accuracy and diversity from the original base classifier set to construct an...
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Increasing the Quality Indicators of the Functioning of Fuzzy Solvers at the Defuzzification Stage
AbstractAn approach to the implementation of the defuzzification procedure in microprocessor systems of fuzzy information processing is proposed. A...
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Missing Data Imputation Using Ensemble Learning Technique: A Review
For the past two decades, several studies have been conducted on missing value imputation in bioinformatics and offered the best method or approach...
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Rethinking Collaborative Clustering: A Practical and Theoretical Study Within the Realm of Multi-view Clustering
WithMulti-view clustering distributed and multi-view dataMulti-view data being more and more ubiquitous, the last 20 years have seen a surge in the...
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A universal ensemble temperature-sensitive point combination model for spindle thermal error modeling
Temperature-sensitive points are the prerequisite for the thermal error modeling and compensation of the machine tool in order to improve the...
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Ensemble Deep Learning Architectures in Bone Cancer Detection Based on Medical Diagnosis in Explainable Artificial Intelligence
AI (Artificial Intelligence), IoT (Internet of Things) and CC (Cloud Computing) have all lately gained popularity in the healthcare industry,...
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Clustering Validity Function Fusion Method of FCM Clustering Algorithm Based on Dempster–Shafer Evidence Theory
With the deepening of the research on clustering algorithm, clustering validity has become an indispensable part of cluster analysis. However, due to...
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A Clustering Algorithm for Triangular Fuzzy Normal Random Variables
In view of the fact that most clustering algorithms cannot solve the clustering problem about samples with uncertain information, according to the...
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A comprehensive ensemble classification techniques detecting and managing concept drift in dynamic imbalanced data streams
Data stream mining is essential in various fields such as education, the Internet of Things (IoT), social media, entertainment, weather monitoring,...
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Quantum Finance and Fuzzy Reinforcement Learning-Based Multi-agent Trading System
In a volatile stock market, an investor’s long-term goal involves determining the most effective buying, selling strategies, and money management...
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Enhancing EV charging predictions: a comprehensive analysis using K-nearest neighbours and ensemble stack generalization
Ensemble Stacking Generalization has emerged as a viable method for forecasting Electric Vehicle (EV) charging behaviour. This method uses a variety...
