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Diabetes Monitoring and Prediction Using computational intelligence techniques: A Systematic Review

SN Computer Science 2025年第3期6卷 1-25页

作者： Nayak, Padmalaya Jyothi, J. Siva Naga Harika, V. Swaraja, K. Hanuman, A. Sai Gokaraju Lailavathi Engineering College Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

Diabetes Mellitus (DM) is one of the most incurable and chronic diseases that poses a great hazard to human life. A person is considered diabetic when the blood glucose level of the human body rises from the normal level due to defective insulin secretion, impaired biological effects, or both. With the advancement of living standards, diabetes has affected the daily lives of humans. Therefore, rapid and accurate diagnosis and analysis of this disease is a matter of concern. The earlier the diagnosis can be conquered, the easier it is to control. Therefore, the entire research community must develop an autonomous diagnosis system using a computational intelligence approach. Furthermore, the integrated consequences of the Internet of Things (IoT) and Artificial intelligence (AI) have led to the identification of hidden and valid patterns in data, from which knowledge inference systems can be developed. Many approaches based on artificial neural networks and machine learning algorithms have been developed and tested against diabetes datasets, mostly from the Pima Indian dataset. Despite significant efforts and correctly predicted accuracies of up to 99% in diagnosing diabetes, none of these approaches have reached clinical verification yet. Diabetologists and clinical experts are assumed to be neither informed correctly nor trained using computational diagnostic tools. Therefore, this survey aims to present an overview of the wide range of opportunities, up-to-date developments, latent in IoT, and ML algorithms applied to diabetes as it is viewed as an alternative diabetes diagnosis tool for clinical assessment. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： computational intelligence techniques Diabetes mellitus (DM) Fuzzy logic Genetic algorithms IoT ML techniques

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Thermoeconomic optimization of cascade refrigeration system using computational intelligence techniques

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2022年第23期147卷 13805-13827页

作者： Nagraj, Makkitaya Swarna Maharana, Debasis Kotecha, Prakash Anandalakshmi, R. Indian Inst Technol Guwahati Dept Chem Engn Gauhati 781039 Assam India Indian Inst Technol Guwahati Sch Business Gauhati 781039 Assam India

This study presents the thermoeconomic optimization of LiBr-H2O, LiCl-H2O, (CaCl2-LiBr-LiNO3)-H2O with R290, R123, R1234yf, and R1234ze in the compression-absorption cascade refrigeration system. The detailed thermodynamics and economic analysis have been presented. Nonlinear objective function formulated based on the concept of energy, exergy, economic, and environmental performance is minimized by using five recent computational intelligence techniques. Optimum cascade, evaporator, absorber, generator, condenser, and overlap temperatures and the effectiveness of the solution heat exchanger have been reported for the cascade refrigeration system. The effect of decision variables on the coefficient of performance, total exergy destruction, total heat exchanger area, and the total annual cost of cascade refrigeration system has also been reported within their upper and lower bounds for all absorbent-refrigerant-refrigerant combinations. Among all the studied algorithms, the coyote optimization algorithm reports the minimum total annual cost for all considered absorbent-refrigerant-refrigerant combinations. The minimum and maximum total annual costs are reported as 13,164.76 $ year(-1) and 15,406.65 $ year(-1) for (CaCl2-LiBr-LiNO3)-H2O-R290 and LiBr-H2O-R1234yf, respectively.

关键词： Thermoeconomic optimization Cascade refrigeration system computational intelligence techniques Total exergy destruction Total annual cost

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Coronary Artery Heart Disease Prediction: A Comparative Study of computational intelligence techniques

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IETE JOURNAL OF RESEARCH 2022年第4期68卷 2488-2507页

作者： Ayon, Safial Islam Islam, Md. Milon Hossain, Md. Rahat Khulna Univ Engn & Technol Dept Comp Sci & Engn Khulna Bangladesh Cent Queensland Univ Sch Engn & Technol Rockhampton Qld Australia

Diseases is an unusual circumstance that affects single or more parts of a human's body. Because of lifestyle and patrimonial, different kinds of disease are increasing day by day. Among all those diseases, heart disease turns out to be the most common disease and the impact of this ailment is dangerous than all other diseases. In this paper, we compared a number of computational intelligence techniques for the prediction of coronary artery heart disease. Seven computational intelligence techniques named as Logistic Regression (LR), Support Vector Machine (SVM), Deep Neural Network (DNN), Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and K-Nearest Neighbor (K-NN) were applied and a comparative study was drawn. The performance of each technique was evaluated using Statlog and Cleveland heart disease dataset which are retrieved from the UCI machine learning repository database with several evaluation techniques. From the study, it can be carried out that the highest accuracy of 98.15% obtained by deep neural network with sensitivity and precision 98.67% and 98.01% respectively. The outcomes of the study were compared with the outcomes of the state of the art focusing on heart disease prediction that outperforms the previous study.

关键词： computational intelligence techniques Coronary artery heart disease Decision tree Deep neural network K-nearest neighbor Logistic regression Naive Bayes Random forest Support vector machine

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Performance optimization of generator in steam turbine power plants using computational intelligence techniques

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JOURNAL OF ENGINEERING MATHEMATICS 2024年第1期145卷 12-12页

作者： Kumar, Ashish Sinwar, Deepak Kumar, Naveen Saini, Monika Manipal Univ Jaipur Dept Math & Stat Jaipur 303007 India Manipal Univ Jaipur Dept IoT & Intelligent Syst Jaipur 303307 Rajasthan India

A generator is the crucial subsystem of steam turbine power plants. Its configuration is very complex, as it is assembled using seven different subsystems. The key objective of the present investigation is to develop an efficient stochastic model for generators under the concepts of cold standby redundancy and exponentially distributed failure and repair laws. The subsystem, namely the cooling and exhaust units, has the provision of cold standby redundancy. For this purpose, a novel stochastic model is proposed using the Markovian methodology, and Chapman-Kolmogorov differential-difference equations are derived. The switch devices are considered perfect, and units after repair work are considered new. To predict the optimal availability and profit of the proposed model, computational intelligence techniques, namely grey wolf optimization, whale optimization algorithm, moth-flame optimizer, dragonfly algorithm, grasshopper optimization algorithm, sine cosine algorithm, black hole algorithm, and ant lion algorithm are used. The impact of various numbers of iterations and population sizes is investigated on the availability, profit, and decision variables of the generator unit. It is revealed that the whale optimization algorithm predicts optimal availability of 0.9999905 after 10 iterations, while in a particular case, the optimal profit is 7199.924. The derived expressions of failure and repair rates, availability, and profit function are useful for system designers and maintenance engineers to design and plan maintenance strategies for generators and steam turbine power plants.

关键词： Availability computational intelligence techniques Generator Markov birth-death process Steam turbine power plant Stochastic model

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Exergetic optimization of solar water collectors using computational intelligence techniques

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CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2021年第6期23卷 1737-1768页

作者： Maharana, Debasis Bhattacharya, Tulika Kotecha, Prakash Anandalakshmi, R. Indian Inst Technol Guwahati Dept Chem Engn Gauhati 781039 Assam India Natl Inst Technol Durgapur Dept Chem Engn Durgapur 713209 W Bengal India

This article proposes a model to determine the optimal performance and design conditions for a flat plate solar water collector. The model uses the hourly solar irradiation data over a year for humid subtropical climatic conditions for estimating the thermal, optical, and exergy efficiency. The proposed model has been validated with the data in the literature. Six single-objective computational intelligence (CI) techniques are used to determine the maximum exergy efficiency by optimizing the plate area of the absorber, mass flow rate, and inlet temperature of the working fluid. The statistical analysis shows that the performance of water cycle algorithm is superior in every statistical parameter. Six multi-objective CI techniques are used to evaluate the trade-off solutions between the conflicting objectives of maximizing exergy efficiency and minimizing the area of the absorber plate. Three of these algorithms are able to determine the maximum exergy efficiency with the minimum absorber plate area. A MATLAB-based GUI has also been provided to help in determining the optimal values of the decision variables under various scenarios. Graphic abstract

关键词： Flat plate solar water collectors Humid subtropical climatic conditions computational intelligence techniques Exergy efficiency Graphical user interface

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A review of computational intelligence techniques in coral reef-related applications

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ECOLOGICAL INFORMATICS 2016年 32卷 107-123页

作者： Salcedo-Sanz, S. Cuadra, L. Vermeij, M. J. A. Univ Alcala de Henares Dept Signal Proc & Commun E-28871 Alcala De Henares Spain CARMABI Res Stn Willemstad Curacao Netherlands Univ Amsterdam Inst Biodivets & Ecosyst Dynam Dept Aquat Microbiol Sci Pk 700 NL-1098 XH Amsterdam Netherlands

Studies on coral reefs increasingly combine aspects of science and technology to understand the complex dynamics and processes that shape these benthic ecosystems. Recently, the use of advanced computational algorithms has entered coral reef science as new powerful tools that help solve complex coral reef related questions, which were unsolvable just a decade earlier. Some of these advanced algorithms consist of computational intelligence (CI) approaches, a branch of Artificial intelligence that uses intelligent systems to address complex real-world problems yielding more robust, tractable and simpler solutions than those obtained by conventional mathematical techniques. This paper describes the most commonly used CI techniques related to coral reefs and the main improvements obtained with these methods over classical algorithms in this field. Some recommendations are given for the application of CI techniques to complex coral reef related problems, and vice-versa, for the application of novel coral reef dynamics concepts to improve the Coral Reef Optimization (CRO) algorithm, an optimization method inspired by coral reef dynamics. (C) 2016 Elsevier B.V. All rights reserved.

关键词： computational intelligence techniques Coral reefs Applications Algorithms

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Big Data Security Analysis Approach Using computational intelligence techniques in R for Desktop Users

Big Data Security Analysis Approach Using Computational Inte...

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IEEE Symposium Series on computational intelligence (IEEE SSCI)

作者： Naik, Nitin Jenkins, Paul Savage, Nick Katos, Vasilios Minist Def Def Sch Commun & Informat Syst London England Univ Portsmouth Sch Comp Portsmouth Hants England Bournemouth Univ Dept Comp & Informat Poole Dorset England

ISBN: (纸本)9781509042401

Big Data security analysis is commonly used for the analysis of large volume security data from an organisational perspective, requiring powerful IT infrastructure and expensive data analysis tools. Therefore, it can be considered to be inaccessible to the vast majority of desktop users and is difficult to apply to their rapidly growing data sets for security analysis. A number of commercial companies offer a desktop-oriented big data security analysis solution;however, most of them are prohibitive to ordinary desktop users with respect to cost and IT processing power. This paper presents an intuitive and inexpensive big data security analysis approach using computational intelligence (CI) techniques for Windows desktop users, where the combination of Windows batch programming, EmEditor and R are used for the security analysis. The simulation is performed on a real dataset with more than 10 million observations, which are collected from Windows Firewall logs to demonstrate how a desktop user can gain insight into their abundant and untouched data and extract useful information to prevent their system from current and future security threats. This CI-based big data security analysis approach can also be extended to other types of security logs such as event logs, application logs and web logs.

关键词： Big Data Security Analysis computational intelligence techniques CI R Desktop User Windows Firewall Logs

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computational intelligence techniques WITH APPLICATION TO CRUDE OIL PRICE PROJECTION: A LITERATURE SURVEY FROM 2001-2012

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NEURAL NETWORK WORLD 2013年第6期23卷 523-551页

作者： Chiroma, Haruna Abdulkareem, Sameem Abubakarj, Adamu Usman, Mohammed Joda Univ Malaya Dept Artificial Intelligence Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Univ Malaya Dept Comp Sci Fac Informat & Commun Technol Kuala Lumpur Malaysia Liaoning Univ Technol Sch Elect & Informat Engn Jinzhou Peoples R China

This paper is an attempt to survey the applications of computational intelligence techniques for predicting crude oil prices over a period of ten years. The purpose of this research is to provide an exhaustive overview of the existing literature which may assist prospective researchers. The reviewed literature covers a spectrum of publications on the proposed model, source of experimental data, period of data collection, year of publication and contributors. The overall trend of the publications in this area of research issued within the last decade is also addressed. The existing body of research has been analyzed and new research directions have been outlined that have been previously ignored. It is expected that researchers across the globe may thus be encouraged to re direct their attention and resources in order to keep on searching for an optimum solution.

关键词： Crude oil price computational intelligence techniques west Texas intermediate factors affecting crude oil price fluctuation uncertainty events

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Systematic Analysis and computational intelligence Based Modeling of Photovoltaic Power Generation in Snow Conditions

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IEEE JOURNAL OF PHOTOVOLTAICS 2022年第1期12卷 406-420页

作者： Hashemi, Behzad Taheri, Shamsodin Cretu, Ana-Maria Univ Quebec Outaouais Dept Comp Sci & Engn Gatineau PQ J8X 3X7 Canada

Power prediction for photovoltaic (PV) installations in northern snow-prone areas remains a challenging problem. The behavior of a partially/fully snow-covered PV panel can be complex depending on the snow-related factors, disregarding, which causes large prediction errors in snow conditions. To address this issue, a systematic analysis of the effects of 16 meteorological parameters on the hourly power prediction for systems in a snow-prone area is conducted in this article. According to the best of our knowledge, such comprehensive analysis on various snow-related conditions datasets, i.e., full, snow-free condition, snow condition, and snow-cover condition datasets, for several PV systems is performed for the first time in the literature. The three latter datasets are extracted from the full dataset using a proposed three-step procedure. Moreover, different computational intelligence techniques are implemented to develop hourly prediction models for each dataset of each system. A detailed comparison is then performed between the performance of the proposed models, the Marion model, a modified version of the Marion model, the classic PV model, and a computational intelligence model combined with the Marion model's snow cover detection method. The hourly values of the electrical and meteorologicalparameters for 17 PV systems across Canada, with an aggregated time period of more than 55 years, have been extracted to perform the study. As the results show, categorizing data using the proposed three-step procedure and developing specific computational intelligence models for each condition can significantly improve the prediction accuracy especially when a full/partial snow cover is probable on the panels.

关键词： Snow computational modeling Predictive models Analytical models Correlation computational intelligence Photovoltaic systems computational intelligence techniques modeling photovoltaic (PV) power prediction snow condition snowfall

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A new methodology for the prediction of optimal conditions for dyes' electrochemical removal;Application of copula function, machine learning, deep learning, and multi-objective optimization

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2024年 182卷 298-313页

作者： Chianeh, Farideh Nabizadeh Anaraki, Mahdi Valikhan Mahmoudian, Fatemeh Farzin, Saeed Semnan Univ Fac Chem Dept Appl Chem Semnan *** Iran Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan *** Iran

In the present study, a new methodology has been introduced for predicting the simultaneous removal optimal conditions of Acid red 33 (AR33), Reactive orange 7 (RO7), Acid yellow 3 (AY3), and Malachite green (MG) dyes. Since industrial wastewater contains a variety of dyes, simultaneous prediction of their removal efficiency is a practical issue. The goal of this research was to address challenges associated with modeling this type of removal process. In the first part of this methodology, the c-vine copula function was used to generate synthetic data since the use of most modeling methods requires a large number of data, and studying different experimental conditions is time-and cost-consuming. Besides, Artificial neural network, Adaptive neuro-fuzzy inference system, Least-square support vector machine, and Long-short term memory algorithms were applied for modeling the dyes' removal efficiency using experimental and synthetic data. Utilizing synthetic data significantly improved the accuracy of modeling. For the purpose of finding the optimal amount of dyes' removal efficiency simultaneously, the removal efficiency of each dye is defined as an objective function. This leads to a problem requiring multi-objective optimization. Hence, the Multi-Objective Adaptive Guided Differential Evolution was employed for multi-objective optimization of dyes' removal efficiency. The optimization process was carried out based on the four objective functions including AR33, RO7, AY3, and MG dyes' removal efficiency. The results indicated a direct relationship between dyes' removal efficiency on the Pareto front as well as a highly oscillating and nonlinear relationship with decision variables (pH, NaCl concentration, current, and Time) under optimal conditions. Finally, the average results of Technique for Order of Preference by Similarity to Ideal Solution and VIekriterijumsko KOmpromisno Rangiranje were utilized to select the best optimal conditions from the Pareto front. With

关键词： Dyes mixture Dye removal modeling Copula Multi-objective optimization computational intelligence techniques

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