作者: propose 時(shí)間: 2025-3-21 21:20
Salp Chain-Based Optimization of?Support Vector Machines and Feature Weighting for Medical Diagnostin support systems have a profound impact on healthcare informatics. Integrating machine learning classifier systems into computer-aided diagnosis systems promotes the early detection of diseases, which results in more effective treatments and prolonged survival. In this chapter, we address popular d作者: hemoglobin 時(shí)間: 2025-3-22 01:37 作者: homeostasis 時(shí)間: 2025-3-22 06:40
Efficient Moth-Flame-Based Neuroevolution Models-flame optimizer (MFO) is one of the effective swarm-based metaheuristic methods inspired by the natural direction-finding behaviours of moth insects and their well-known entrapment phenomena when they circulate the non-natural lights and flames. MFO is capable of demonstrating a very promising perf作者: macular-edema 時(shí)間: 2025-3-22 12:38 作者: 我要沮喪 時(shí)間: 2025-3-22 14:43
Link Prediction Using Evolutionary Neural Network Modelsmodel can help in understanding the evolution of interactions and relationships between network members. Many applications use link prediction such as recommendation systems. Most of the existing link prediction algorithms are based on similarity measures, such as common neighbors and the Adamic/Ada作者: 我要沮喪 時(shí)間: 2025-3-22 20:38 作者: NUL 時(shí)間: 2025-3-22 23:50 作者: Granular 時(shí)間: 2025-3-23 03:07
Multi-objective Particle Swarm Optimization: Theory, Literature Review, and Application in Feature Stment. Incorporating intelligent classification models and data analysis methods has intrinsic impact on converting such trivial, row data into worthy useful knowledge. Due to the explosion in computational and medical technologies, we observe an explosion in the volume of health- and medical-relate作者: 頑固 時(shí)間: 2025-3-23 06:14
Multi-objective Particle Swarm Optimization for Botnet Detection in?Internet of Things care, industry, and transportation. As we are entering Internet of things (IoT) digital era, IoT devices not only hack our world, but also start to hack our personal life. The widespread IoT has created a rich platform for potential IoT cyberattacks. Data mining and machine learning techniques have作者: micturition 時(shí)間: 2025-3-23 11:30 作者: Ige326 時(shí)間: 2025-3-23 17:05
Binary Harris Hawks Optimizer for High-Dimensional, Low Sample Size Feature Selectionchniques. The negative influence is due to the possibility of having many irrelevant and/or redundant features. In this chapter, a binary variant of recent Harris hawks optimizer (HHO) is proposed to boost the efficacy of wrapper-based feature selection techniques. HHO is a new fast and efficient sw作者: 消音器 時(shí)間: 2025-3-23 18:05
A Review of Grey Wolf Optimizer-Based Feature Selection Methods for Classifications. The area of feature selection deals reducing the dimensionality of data and selecting only the most relevant features to increase the classification performance and reduce the computational cost. This problem has exponential growth, which makes it challenging specially for datasets with a large n作者: 種類 時(shí)間: 2025-3-23 23:15 作者: 圓柱 時(shí)間: 2025-3-24 05:27
Evolutionary Machine Learning Techniques978-981-32-9990-0Series ISSN 2524-7565 Series E-ISSN 2524-7573 作者: unstable-angina 時(shí)間: 2025-3-24 08:54 作者: 農(nóng)學(xué) 時(shí)間: 2025-3-24 13:02 作者: 流行 時(shí)間: 2025-3-24 16:57 作者: Aggressive 時(shí)間: 2025-3-24 20:42 作者: 最低點(diǎn) 時(shí)間: 2025-3-25 00:44
https://doi.org/10.1057/9780230348448sk. On the other hand, training of gradient descent algorithms suffers from being trapped in local optima and slow convergence speed in the last iterations. The moth-flame optimization (MFO) is a novel evolutionary method based on navigation paths of moths in nature. This algorithm showed its effect作者: Instinctive 時(shí)間: 2025-3-25 05:06
,De Beauvoir’s ,: A Critical Appraisal,model can help in understanding the evolution of interactions and relationships between network members. Many applications use link prediction such as recommendation systems. Most of the existing link prediction algorithms are based on similarity measures, such as common neighbors and the Adamic/Ada作者: CT-angiography 時(shí)間: 2025-3-25 09:53 作者: 桶去微染 時(shí)間: 2025-3-25 14:11
https://doi.org/10.1007/978-1-349-00080-7tomate this process. In this chapter, an EvoloPy-FS framework is proposed, which is a Python open-source optimization framework that includes several well-regarded swarm intelligence (SI) algorithms. It is geared toward feature selection optimization problems. It is an easy to use, reusable, and ada作者: 薄膜 時(shí)間: 2025-3-25 17:09 作者: Fester 時(shí)間: 2025-3-25 21:48
https://doi.org/10.1007/978-3-642-99339-8 care, industry, and transportation. As we are entering Internet of things (IoT) digital era, IoT devices not only hack our world, but also start to hack our personal life. The widespread IoT has created a rich platform for potential IoT cyberattacks. Data mining and machine learning techniques have作者: 濃縮 時(shí)間: 2025-3-26 02:24 作者: Myosin 時(shí)間: 2025-3-26 04:46 作者: 奇怪 時(shí)間: 2025-3-26 09:45 作者: 浮雕寶石 時(shí)間: 2025-3-26 16:05
Seyedali Mirjalili,Hossam Faris,Ibrahim AljarahProvides an in-depth analysis of the current evolutionary machine learning techniques.Includes training algorithms for machine learning techniques.Covers the application of improved artificial neural 作者: OPINE 時(shí)間: 2025-3-26 18:19
Algorithms for Intelligent Systemshttp://image.papertrans.cn/e/image/317971.jpg作者: RLS898 時(shí)間: 2025-3-26 22:46
https://doi.org/10.1007/978-981-32-9990-0Artificial Neural Network; Probabilistic Neural Network; Self-Optimizing Neural Network; Feedforward Ne作者: 從容 時(shí)間: 2025-3-27 03:50
https://doi.org/10.1007/978-3-031-24315-8 are discussed to show where AI optimization algorithms and machine learning techniques fit in. Different types of learning are briefly covered as well including supervised, unsupervised, and reinforcement techniques. The last part of this chapter includes discussions on evolutionary machine learning, which is the focus of this book.作者: pineal-gland 時(shí)間: 2025-3-27 05:56
Introduction to Evolutionary Machine Learning Techniques, are discussed to show where AI optimization algorithms and machine learning techniques fit in. Different types of learning are briefly covered as well including supervised, unsupervised, and reinforcement techniques. The last part of this chapter includes discussions on evolutionary machine learning, which is the focus of this book.作者: Generalize 時(shí)間: 2025-3-27 09:34
2524-7565 niques.Covers the application of improved artificial neural .This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support作者: 闖入 時(shí)間: 2025-3-27 16:41 作者: miracle 時(shí)間: 2025-3-27 19:36 作者: Statins 時(shí)間: 2025-3-28 00:42
Religiosity in the Films of Ingmar Bergman and optimizing the wear loss in carbon steel and was evaluated and tested using different performance criteria to ensure its reliability. The generated model can be utilized to monitor wear in mechanical components without requiring any human efforts to enhance the monitoring efficiency and reduce human errors.作者: Costume 時(shí)間: 2025-3-28 03:18
Salp Chain-Based Optimization of?Support Vector Machines and Feature Weighting for Medical Diagnostis (SVMs) simultaneously. A new and powerful metaheuristic called salp swarm algorithm is combined with SVM for this task. The designed SSA-SVM approach shows several merits compared to other SVM-based frameworks with well-regarded algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO).作者: GORGE 時(shí)間: 2025-3-28 08:28
Efficient Moth-Flame-Based Neuroevolution Modelshe results are compared to well-known methods such as particle swarm optimizer (PSO), population-based incremental learning (PBIL), differential evolution (DE), and genetic algorithm (GA). The obtained results indicate the efficacy of the MFO-embedded neuroevolution model as a potential method in dealing with classification cases.作者: cartilage 時(shí)間: 2025-3-28 12:06
Evolving Genetic Programming Models for Predicting Quantities of Adhesive Wear in Low and Medium Car and optimizing the wear loss in carbon steel and was evaluated and tested using different performance criteria to ensure its reliability. The generated model can be utilized to monitor wear in mechanical components without requiring any human efforts to enhance the monitoring efficiency and reduce human errors.作者: hypnogram 時(shí)間: 2025-3-28 18:02
Book 2020sification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimi作者: Adenoma 時(shí)間: 2025-3-28 22:08
https://doi.org/10.1007/978-3-642-99339-8-dominating sorting genetic algorithm (NSGA-II), common traditional machine learning algorithms, and some conventional filter-based feature selection methods. As per the obtained results, MOPSO is competitive and outperforms NSGA-II, traditional machine learning methods, and filter-based methods in most of the studied datasets.作者: CLOWN 時(shí)間: 2025-3-29 02:31 作者: Keratectomy 時(shí)間: 2025-3-29 03:20 作者: obscurity 時(shí)間: 2025-3-29 10:43 作者: 策略 時(shí)間: 2025-3-29 13:39
Support Vector Machine: Applications and Improvements Using Evolutionary Algorithmsr. The method has been applied to a set of experimental data for diabetes mellitus diagnosis. Results show that the method leads to a classifier which distinguished healthy and patient cases with 87.5% of accuracy.
