A5039480864- Fuzzy Logic and Control Systems
- Neural Networks and Applications
- Anomaly Detection Techniques and Applications
- Fault Detection and Control Systems
- Time Series Analysis and Forecasting
- Data Stream Mining Techniques
- Advanced Control Systems Optimization
- Evolutionary Algorithms and Applications
- Advanced Image and Video Retrieval Techniques
- Fuzzy Systems and Optimization
- Network Security and Intrusion Detection
- Video Surveillance and Tracking Methods
- Adversarial Robustness in Machine Learning
- Advanced Clustering Algorithms Research
- Machine Learning and Data Classification
- Metaheuristic Optimization Algorithms Research
- Advanced Data Processing Techniques
- Spectroscopy and Chemometric Analyses
- COVID-19 diagnosis using AI
- Context-Aware Activity Recognition Systems
- Remote-Sensing Image Classification
- Spectroscopy Techniques in Biomedical and Chemical Research
- Robotics and Sensor-Based Localization
- Domain Adaptation and Few-Shot Learning
- Face and Expression Recognition
Lancaster University
2015-2024
Engineering Systems (United States)
2019-2024
University of Memphis
2022-2024
Institute of Electrical and Electronics Engineers
2013-2024
National Institute of Meteorology
2024
National Institute of Meteorology and Hydrology
2024
Antea Group (France)
2023
University of Sannio
2022-2023
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo"
2023
Università degli Studi eCampus
2023
An approach to the online learning of Takagi-Sugeno (TS) type models is proposed in paper. It based on a novel algorithm that recursively updates TS model structure and parameters by combining supervised unsupervised learning. The rule-base continually evolve adding new rules with more summarization power modifying existing parameters. In this way, inherited up-dated when data become available. By applying concept we arrive at adaptive called Evolving (ETS). nature these evolving combination...
Abstract This paper provides a brief analytical review of the current state‐of‐the‐art in relation to explainability artificial intelligence context recent advances machine learning and deep learning. The starts with historical introduction taxonomy, formulates main challenges terms building on recently formulated National Institute Standards four principles explainability. Recently published methods related topic are then critically reviewed analyzed. Finally, future directions for research...
A new approach to the online classification of streaming data is introduced in this paper. It based on a self-developing ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</i> volving) fuzzy-rule-based (FRB) classifier system xmlns:xlink="http://www.w3.org/1999/xlink">T</i> akagi- xmlns:xlink="http://www.w3.org/1999/xlink">S</i> ugeno xmlns:xlink="http://www.w3.org/1999/xlink">eTS</i> ) type. The proposed approach, called...
Most of the dynamics in real-world systems are compiled by shifts and drifts, which uneasy to be overcome omnipresent neuro-fuzzy systems. Nonetheless, learning nonstationary environment entails a system owning high degree flexibility capable assembling its rule base autonomously according nonlinearity contained system. In practice, growing pruning carried out merely benefiting from small snapshot complete training data truncate computational load memory demand low level. An exposure novel...
A bstract The COVID-19 disease has widely spread all over the world since beginning of 2020. On January 30, 2020 World Health Organization (WHO) declared a global health emergency. At time writing this paper number infected about 2 million people worldwide and took 125,000 lives, advanced public systems European countries as well USA were overwhelmed. In paper, we propose an eXplainable Deep Learning approach to detect from computer tomography (CT) - Scan images. rapid detection any case is...
As COVID-19 hounds the world, common cause of finding a swift solution to manage pandemic has brought together researchers, institutions, governments, and society at large. The Internet Things (IoT), artificial intelligence (AI)-including machine learning (ML) Big Data analytics-as well as Robotics Blockchain, are four decisive areas technological innovation that have been ingenuity harnessed fight this future ones. While these highly interrelated smart connected health technologies cannot...
This paper deals with a simplified version of the evolving Takagi-Sugeno (eTS) learning algorithm - computationally efficient procedure for on-line TS type fuzzy models. It combines concept scatter as measure data density and summarization ability rules, use Cauchy antecedent membership functions, an aging indicator characterizing stationarity recursive least square to dynamically learn structure parameters eTS model
An approach to real-time generation of fuzzy rule-base systems extended Takagi-Sugeno (xTS) type from data streams is proposed in the paper. The xTS system combines both zero and first order (TS) systems. (system structure) evolves starting 'from scratch' based on distribution joint input/output space. incremental clustering procedure that takes into account non-stationary nature pattern generates clusters are used form rule antecedent part on-line mode as a stage non-iterative learning...
Abstract Over the last quarter of a century, two types fuzzy rule-based (FRB) systems dominated, namely Mamdani and Takagi–Sugeno type. They use same type scalar sets defined per input variable in their antecedent part which are aggregated at inference stage by t-norms or co-norms representing logical AND/OR operations. In this paper, we propose significantly simplified alternative to define FRB data Clouds density distribution. This new goes further conceptual computational simplification...
Abstract Summary: IRootLab is a free and open-source MATLAB toolbox for vibrational biospectroscopy (VBS) data analysis. It offers an object-oriented programming class library, graphical user interfaces (GUIs) automatic code generation. The library contains large number of methods, concepts visualizations VBS analysis, some which are introduced in the toolbox. GUIs provide interface to including module merge several spectral files into dataset. Automatic allows developers quickly write...
Human action recognition is an imperative research area in the field of computer vision due to its numerous applications. Recently, with emergence and successful deployment deep learning techniques for image classification, object recognition, speech more directed from traditional handcrafted techniques. This paper presents a novel method human based on pre-trained CNN model feature extraction & representation followed by hybrid Support Vector Machine (SVM) K-Nearest Neighbor (KNN)...
Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, a new approach creating and recognizing automatically the behavior profile of user presented. case, represented as sequence commands she/he types during her/his work. This transformed into distribution relevant subsequences in order to find out that defines its behavior. Also, because not necessarily fixed but rather it evolves/changes, we propose an...