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Fuzzy Systems in Medicine and Healthcare
Published in Ashish Mishra, G. Suseendran, Trung-Nghia Phung, Soft Computing Applications and Techniques in Healthcare, 2020
Deepak K. Sharma, Sakshi, Kartik Singhal
Healthcare and medicine are fields of ambiguity and a multitude of possibilities. Each diagnosis or prognosis made can be affected by a plethora of factors, many unknown, and even a minute change reported could lead to a distinct interpretation. Thus, contrary to the stiff nature of hard computing techniques, soft computing methods such as fuzzy logic are required to incorporate fuzziness in health. Fuzzy logic is an extension of the domain of two-value logic, as it deals with the states that lie in between the extreme notations of true or false. It was first established in 1965 by Lotfi Zadeh on account of his work in mathematics of fuzzy sets [1]. Since then, it has been widely accepted in areas where approximate reasoning is inherent. Instead of dealing in numbers or binary logic to develop computational intelligence-based systems, fuzzy logic provides the capability to communicate in linguistic terms with the computer.
Object Detection and Tracking in Video Using Deep Learning Techniques: A Review
Published in K. Gayathri Devi, Mamata Rath, Nguyen Thi Dieu Linh, Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches, 2020
Dhivya Praba Ramasamy, Kavitha Kanagaraj
Modern ML tools incorporate neural networks in the sequence of layers to learn from the training data set. Computational intelligence (CI) has been developed as a powerful method for making a machine learn. It works well in the field of neural networks, fuzzy systems, and evolutionary algorithms. Recent advances in DL have been playing an important role in dealing with huge amounts of unlabeled data. Due to the remarkable successes of DL techniques, we are now able to boost quality of service (QoS) significantly. More complex features can be easily extracted by deep neural network (DNN) (also called deep belief network and convolutional neural network (CNN)) and used to efficiently learn their representations. However, implementing deep learning faces many implementation challenges such as large data sets (needed to ensure desired results), high complexity of the network, high computational power, etc., which need to be addressed to effectively implement deep learning to solve real world image processing problems.
Learning Fuzzy Rules from Imbalanced Datasets using Multi-objective Evolutionary Algorithms
Published in Nadia Nedjah, Luiza De Macedo Mourelle, Heitor Silverio Lopes, Evolutionary Multi-Objective System Design, 2020
Edward Hinojosa Cárdenas, Heloisa A. Camargo, Yván Jesús Túpac Valdivia
Computational intelligence is a research area oriented to model different aspects of intelligence embracing methodologies such as neural networks, fuzzy systems, and evolutionary algorithms. The main characteristic of computational intelligence is to explore the combination of two or more methodologies that cooperate with each other to improve results and enhance effectiveness and robustness of the resulting system. One of the most successful hybrid approaches in computational intelligence is the use of evolutionary algorithms, specially the Genetic Algorithms (GAs), to generate and tune fuzzy systems. From this type of combination emerged the Genetic Fuzzy Systems (GFSs) [111] [112].
Assessing the Behavioral Intention of Individuals to Use an AI Doctor at the Primary, Secondary, and Tertiary Care Levels
Published in International Journal of Human–Computer Interaction, 2023
Pelin Uymaz, Ali Osman Uymaz, Yakup Akgül
AI, often known as “computational intelligence” or “the science and engineering of constructing intelligent machines” refers to the rapidly expanding discipline of emulating intelligence (Radanliev & De Roure, 2022b), human-like behavior in computers and other technologies (Amisha et al., 2019; Ebermann et al., 2023). In 1950, Alan Turing, one of the pioneers of contemporary computers and AI, developed the “Turing test” which was based on the idea that intelligent behavior in a machine is the capacity to do cognition-related activities at a level comparable to that of a human (Mintz & Brodie, 2019). Edward Shortliffe created the MYCIN AI system in the 1970s, which was used to diagnose blood-borne bacterial infectious diseases and recommend the use of antibiotics (Guo & Li, 2018). Since then, disease diagnosis and treatment have been a focus of AI technology (Davenport & Kalakota, 2019).
Review on performance analysis in diffusion absorption refrigeration system (DARS) using different working fluids
Published in International Journal of Ambient Energy, 2023
Sreenesh Valiyandi, Gireeshkumaran Thampi
Table 4 represents the several optimisation techniques employed in the different refrigeration models. More researchers tried to optimise the energy consumption rate of refrigeration systems along with the coefficient of performance. Compared to mathematical models, several computational intelligence techniques, such as Genetic Algorithms, Particle Swarm Optimization, Differential evolution, Simulated annealing, etc., are mostly employed in different works to solve optimisation issues. The mathematical approach requires more time to solve the problems and may promote inappropriate results due to its complexity. Besides, it is not suited for solving complex issues. However, many existing authors focused on optimising the refrigeration characteristics only for the vapour compression cycle rather than the DAR system. The existing works summarise the performance considered in the DAR system in Table 5. It seems that COP is almost considered in the different DAR models.
An interpretable predictive modelling framework for the turning process by the use of a compensated fuzzy logic system
Published in Production & Manufacturing Research, 2022
Abdallah Alalawin, Wafa’ H. AlAlaween, Mohammad A. Shbool, Omar Abdallah, Lina Al-Qatawneh
With the huge advances in recent computing power, the development of computational intelligence has positive effects on several areas including, but not limited to, healthcare and manufacturing. Utilizing computer systems has also changed how researchers think in industry and academia. The observed/collected data are, therefore, used to develop and construct data-driven modelling approaches that mimic the human way of thinking. Such modelling paradigms can complement or replace the so-called physical-based models, in particular, for those processes where such models do not exist or they can be too complex to derive. Therefore, a plethora of data-driven paradigms (e.g. regression models and ANNs) have hitherto been developed and implemented in various research areas, such as healthcare, manufacturing and marine technology (AlAlaween et al., 2017; Shahani et al., 2009). Despite their powerful algorithms, some of the presented models (e.g. regression paradigms) are incapable of representing complex highly nonlinear relationships. Likewise, some of these models, in particular ANNs, are referred to as black-box ones, this being due to the low interpretability of such models(AlAlaween et al., 2018) . Therefore, the fuzzy logic system (FLS) has hitherto been utilized in various applications to develop an interpretable model that can efficiently take into consideration uncertainties (i.e. measurement uncertainties and uncertainties that may result from any uncontrollable and difficult to consider variables; AlAlaween et al., 2018).