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Image Segmentation Using Multilevel Thresholding: A Research Review

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2020年第1期44卷 1-29页

作者： Pare, S. Kumar, A. Singh, G. K. Bajaj, V. Indian Inst Informat Technol Design & Mfg Jabalpur 482005 MP India Indian Inst Technol Roorkee Dept Elect Engn Roorkee 247667 Uttrakhand India

Image segmentation is a basic problem in computer vision and various image processing applications. Over the years, commonly used image segmentation has become quite challenging because of its utilization in many applications. Image thresholding is one of the most exploited techniques to accomplish image segmentation. Multilevel thresholding is found to be most appropriate and well known among all the image segmentation techniques. The segmented image quality is based on the techniques incorporated to choose the threshold value. In this paper, an exhaustive survey has been carried out, considering both the general purpose and satellite images to cover the performance comparison of various image segmentation approaches based on meta-heuristics optimization algorithms, present in the literature for multilevel image thresholding. In addition, this paper also focuses on information theoretic approach-based objective criterion using different statistical properties such as between-class variance, entropy, moment and maximum likelihood for selecting multilevel thresholds. A list of 157 publications on the subject is also appended for quick reference.

关键词： Image segmentation Multilevel thresholding nature-inspired optimization algorithms Kapur's entropy Between-class variance Tsalli's entropy

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Determining the optimum installation of energy storage systems in railway electrical infrastructures by means of swarm and evolutionary optimization algorithms

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 124卷 106295-106295页

作者： Roch-Dupre, David Gonsalves, Tad Cucala, Asuncion P. Pecharroman, Ramon R. Lopez-Lopez, Alvaro J. Fernandez-Cardador, Antonio Comillas Pontifical Univ Madrid Spain Sophia Univ Tokyo Japan

The installation of wayside Energy Storage Systems (ESSs) in DC-electrified railway systems is one of the main measures to improve their energy efficiency. They store the excess of regenerated energy produced by the trains during the braking phases and give it back to the system when necessary. Nevertheless, the big cost of the associated installation can make railway operators hesitate about the convenience of the investment. Additionally, the decisions about the configuration of the installation (locations and sizes for the ESSs) are usually based on the previous experience of the railway operators or, at best, in assessments made with simulation tools with low accuracy. This paper proposes a model to optimize the profitability of the investment. nature-inspired optimization algorithms are applied in combination with a very realistic railway simulator. The flexibility of the natureinspired optimization algorithms, together with their ability to successfully deal with the computationally-intensive and highly non-linear and non-convex problem posed by the realistic railway simulator, makes them the perfect choice. Three different nature-inspired optimization algorithms have been selected and compared: the Genetic Algorithm (GA) as the main exponent of the evolutionary algorithms, the Particle Swarm optimization algorithm (PSO) as the main exponent of the swarm algorithms and the Fireworks Algorithm (FA) as another variant of the swarm algorithms. The algorithms have shown an excellent behavior, providing solutions that combine the increase of energy efficiency with a very good profitability of the installation required to obtain that increase.

关键词： nature-inspired optimization algorithms optimization of energy storage systems Railway power systems Railway simulation Energy efficiency

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A conceptual and practical comparison of PSO-style optimization algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 167卷 114430-114430页

作者： Tharwat, Alaa Schenck, Wolfram Univ Appl Sci FH Bielefeld Ctr Appl Data Sci Gutersloh CfADS Bielefeld Germany

optimization algorithms are widely employed for finding optimal solutions in many applications. Stochastic optimization algorithms including nature-inspired optimization algorithms are simple and easy to implement, and this is the reason why there is a growing interest in this research area. Recently, many nature-inspired optimization algorithms have been proposed for solving many optimization problems. Moreover, with the aim of improving the performance of optimization algorithms, some modifications were applied such as combining different algorithms and employing some sampling techniques for replacing critical parameters in the optimization algorithms. This paper compares five different widely used PSO-style optimization algorithms to investigate if there is a significant difference between them or not. Theoretically, we explain different PSO-style algorithms and discuss the similarities and differences between them. Practically, a number of experiments were conducted to compare these algorithms. Theoretical analysis and practical results prove that there is not any significant difference between the PSO-style algorithms regarding their performance.

关键词： Particle swarm optimization (PSO) nature-inspired optimization algorithms Swarm optimization algorithms Gray wolf optimization (GWO) Whale optimization algorithm (WOA) Sine-cosine algorithm (SCA) Bat algorithm

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optimization of β-Cyclodextrin-Assisted Extraction of Apigenin and Luteolin from Chrysanthemum indicum L. Using Response Surface Methodology Combined with Different optimization algorithms and Evaluation of Its Antioxidant Capacity

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CHEMISTRY & BIODIVERSITY 2023年第8期20卷 e202300873-e202300873页

作者： Thu, Hang Nguyen Thu, Hang Nguyen Nguyen-Van, Phuong Hanoi Univ Pharm Fac Pharmacognosy & Tradit Med Dept Pharmacognosy Hanoi 11000 Vietnam Hanoi Univ Pharm K72 Hanoi 11000 Vietnam

Cyclodextrins and their derivatives have shown successful applications in extracting active compounds from medicinal plants. However, the use of & beta;-cyclodextrin derivatives for extracting apigenin and luteolin from Chrysanthemum indicum L. remains unexplored. Additionally, the application of nature-inspired optimization algorithms in optimizing extraction conditions has been limited. Therefore, this study was performed with the aims of optimizing the extraction of apigenin and luteolin from C. indicum with the assistance of 2-hydroxypropyl-& beta;-cyclodextrin (HP-& beta;-CD) using response surface methodology combined with various optimization algorithms, including desirability function approach, genetic algorithm, particle swarm optimization, and firefly algorithm. The results showed that the optimal conditions obtained by the four algorithms were consistent, with an extraction time of 60 min, HP-& beta;-CD concentration of 30 mg/mL, and a solvent-to-solid ratio of 24 mg/mL. At these conditions, the apigenin and luteolin contents were 1.362 & PLUSMN;0.008 and 8.724 & PLUSMN;0.117 mg/g, respectively. The results also showed that HP-& beta;-CD-assisted extraction exhibited significantly higher apigenin and luteolin contents compared to conventional solvent. Comparable results were also yielded from the antioxidant assay. Our study suggested that the nature-inspired optimization algorithms might be potential options in enhancing the effectiveness of the traditional response surface methodology for the optimization of extraction of natural products.

关键词： antioxidants cyclodextrins flavonoid nature-inspired optimization algorithms response surface methodology

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Experiment-Based Comparison of nature-inspired algorithms for Optimal Tuning of PI-Fuzzy Controlled Nonlinear DC Servo Systems

Experiment-Based Comparison of Nature-Inspired Algorithms fo...

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International Symposium on Power Electronics, Electrical Drives, Automation, and Motion (SPEEDAM)

作者： Precup, Radu-Emil David, Radu-Codrut Radac, Mircea-Bogdan Voisan, Emil-Ioan Petriu, Emil M. Politehn Univ Timisoara Dept Automat and Appl Informat Timisoara Romania Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (纸本)9781509020676

This paper proposes the comparison of seven nature-inspired optimization algorithms (NIOAs) applied to the tuning of proportional-integral (PI)-fuzzy controllers for a class of nonlinear direct current (DC) servo systems. The servo systems are modeled by second order dynamics with a saturation and dead zone static nonlinearity specific to the actuator. Seven NIOAs are considered, namely Simulated Annealing, Particle Swarm optimization (PSO), Gravitational Search Algorithm (GSA), hybrid PSOGSA, Charged System Search (CSS), adaptive GSA and adaptive CSS, to solve optimization problem. The objective function is the weighted sum of time multiplied by squared control error plus squared output sensitivity function. The output sensitivity function is obtained from the state sensitivity models of the fuzzy control systems with respect to the modification of the process gain leading to a reduced process gain sensitivity. Three parameters of the PI-fuzzy controllers are tuned as variables of the objective function. Simulations and experimental results related to the angular position control of a laboratory nonlinear DC servo system are included.

关键词： nature-inspired optimization algorithms nonlinearity PI-fuzzy controllers servo systems

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Hybrid of Ensemble Machine Learning and nature-inspired algorithms for Divorce Prediction 2nd

Hybrid of Ensemble Machine Learning and Nature-Inspired Algo...

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2nd Pan-African Conference on Artificial Intelligence (PanAfriCon AI)

作者： Sahle, Kalkidan A. Yibre, Abdulkerim M. Bahir Dar Univ Dept Informat Technol Fac Comp Bahir Dar Inst Technol Bahir Dar Ethiopia Hawassa Univ Hawassa Inst Technol Dept Informat Technol Fac Informat Hawassa Ethiopia

ISBN: (纸本)9783031576386;9783031576393

Divorce is a global issue with profound emotional, psychological, and socio-economic consequences. In 2022, Addis Ababa witnessed 14,000 registered marriages but also recorded 1,623 divorces, while 2018 saw 1,923 divorces. Understanding the factors contributing to divorce is vital for prevention and support. Machine learning and AI play a critical role in predicting divorce, early marital distress detection, and personalized interventions. Their scalability aids in effective prevention strategies and targeted support. This research explores a Hybrid Approach AdaBoost, Gradient Boosting, Bagging, Stacking, XGBoost, and Random Forest with Jaya and Whale optimization. The Hybrid Approach is chosen to synergize the strengths of ensemble learning and nature-inspired optimization algorithms. The goal is to enhance divorce prediction accuracy by leveraging ensemble models' robustness and optimization inspired by natural processes. To assess model performance, train-test splits, and k-fold Cross-Validation techniques are used, with metrics like accuracy, precision, recall, F1 score, and AUC-ROC(Area Under the Receiver Operating Characteristic Curve). AdaBoost stands out, achieving 97%, and 96% accuracy in Jaya andWOA hyperparameter optimizations, respectively. This research aligns with Sustainable Development Goals (SDGs) by promoting gender equality (SDG 5), identifying inequalities and offering targeted support (SDG 10), and fostering stable families and social cohesion (SDG 16). By leveraging AI for divorce prediction, this work contributes to a more sustainable and inclusive world, advancing gender equality, reducing inequalities, and peaceful societies.

关键词： Divorce Ensemble Machine Learning nature-inspired optimization algorithms Sustainable Development Goals XGBoost

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Topics in Artificial Intelligence Applied to Industry 4.0 1st

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2024年

作者： Swetta Kukreja

ISBN: (数字)9781394216130;9781394216123

ISBN: (纸本)9781394216116

Topics in Artificial Intelligence Applied to Industry 4.0 Forward thinking resource discussing emerging AI and IoT technologies and how they are applied to Industry 4.0 Topics in Artificial Intelligence Applied to Industry 4.0 discusses the design principles, technologies, and applications of emerging AI and IoT solutions on Industry 4.0, explaining how to make improvements in infrastructure through emerging technologies. Providing a clear connection with different technologies such as IoT, Big Data, AR and VR, and Blockchain, this book presents security, privacy, trust, and other issues whilst delving into real-world problems and case studies. The text takes a highly practical approach, with a clear insight on how readers can increase productivity by drastically shortening the time period between the development of a new product and its delivery to customers in the market by 50%. This book also discusses how to save energy across systems to ensure competitiveness in a global market, and become more responsive in how they produce products and services for their consumers, such as by investing in flexible production lines. Written by highly qualified authors, Topics in Artificial Intelligence Applied to Industry 4.0 explores sample topics such as:

Quantum machine learning, neural network implementation, and cloud and data analytics for effective analysis of industrial data
Computer vision, emerging networking technologies, industrial data spaces, and an industry vision for 2030 in both developing and developed nations
Novel or improved nature-inspired optimization algorithms in enhancing Industry 5.0 and the connectivity of any components for smart environment
Future professions in agriculture, medicine, education, fitness, R&D, and transport and communication as a result of new technologies

Aimed at researchers and students in the interdisciplinary fields of Smart Manufacturing and Smart Applications, Topics in Artific

关键词： Quantum machine learning neural network implementation cloud analytics data analytics industrial data AI Blockchain Edge computing Green Computing Society 5.0 Computer vision nature-inspired optimization algorithms

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LGBM-based modeling scenarios to compressive strength of recycled aggregate concrete with SHAP analysis

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年第23期31卷 5999-6014页

作者： Xi, Bin Li, Enming Fissha, Yewuhalashet Zhou, Jian Segarra, Pablo Politecn Milan Dept Civil & Environm Engn Milan Italy Univ Politecn Madrid ETSI Minas & Energia Rios Rosas 21 Madrid 28003 Spain Akita Univ Grad Sch Int Resource Sci Dept Geosci Geotechnol & Mat Engn Resources Akita Japan Aksum Univ Dept Min Engn Aksum Tigray Ethiopia Cent South Univ Sch Resources & Safety Engn Changsha Peoples R China

Concrete production contributes significantly to global greenhouse gas emissions, and its manufacture requires substantial natural resources. These concerns can be partly mitigated by recycling construction demolition waste as aggregates to produce Recycled Aggregate Concrete (RAC). RAC has gained momentum due to its lower environmental impact, production costs, and increased sustainability. The aim of this study was to advance the reasonable use of recycled aggregate in concrete and achieve optimal mixture ratio design. Four advanced machine learning algorithms, Support Vector Machine (SVR), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Multi-Layer Perceptron (MLP), were employed, and the novel optimization algorithms, biogeography-based optimization (BBO), Multi-Verse Optimizer (MVO) and Gravitational Search Algorithm (GSA), were integrated to to predict the compressive strength of RAC. Six potential influential factors for RAC strength were considered in the models. The study employed four evaluation metrics, Taylor diagrams and Regression Error Characteristic plots to compare model performance. The result shows LGBM-based hybrid model outperformed other methods, demonstrating high accuracy in predicting compressive strength. The Shapley Additive Explanation (SHAP) results emphasize the importance of understanding the interactions between the various factors and their effects on the mechanical properties of the RAC. The findings can inform the development of more sustainable and environmentally friendly building materials.

关键词： Waste utilization Recycled aggregate concrete nature-inspired optimization algorithms Light Gradient Boosting Machine Shapley Additive Explanation

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Optimizing Finite-Difference Operator in Seismic Wave Numerical Modeling

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algorithms 2022年第4期15卷 132-132页

作者： Li, Hui Fang, Yuan Huang, Zhiguo Zhang, Mengyao Wei, Qing Beijing Univ Chem Technol Dept Comp Sci & Technol Beijing 100029 Peoples R China

The finite-difference method is widely used in seismic wave numerical simulation, imaging, and waveform inversion. In the finite-difference method, the finite difference operator is used to replace the differential operator approximately, which can be obtained by truncating the spatial convolution series. The properties of the truncated window function, such as the main and side lobes of the window function's amplitude response, determine the accuracy of finite-difference, which subsequently affects the seismic imaging and inversion results significantly. Although numerical dispersion is inevitable in this process, it can be suppressed more effectively by using higher precision finite-difference operators. In this paper, we use the krill herd algorithm, in contrast with the standard PSO and CDPSO (a variant of PSO), to optimize the finite-difference operator. Numerical simulation results verify that the krill herd algorithm has good performance in improving the precision of the differential operator.

关键词： seismic numerical modeling finite-difference scheme nature-inspired optimization algorithms particle swarm optimization center-decenter particle swarm optimization krill herd algorithm

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Experiment-Based Comparison of nature-inspired algorithms for Optimal Tuning of PI-Fuzzy Controlled Nonlinear DC Servo Systems

Experiment-Based Comparison of Nature-Inspired Algorithms fo...

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International Symposium on Power Electronics, Electrical Drives, Automation and Motion

作者： Radu-Emil Precup Radu-Codrut David Mircea-Bogdan Radac Emil-Ioan Voisan Emil M. Petriu Department of Automation and Applied Informatics Politehnica University of Timisoara School of Electrical Engineering and Computer Science University of Ottawa

ISBN: (纸本)9781509020683

关键词： nature-inspired optimization algorithms Nonlinearity PI-fuzzy controllers Servo systems

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