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A Comparative Analysis of Filter and optimization Methods for Feature Selection

A Comparative Analysis of Filter and Optimization Methods fo...

2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Reddy, Keerthi Gabbi Basha, Mehfoozamunavar Mishra, Deepasikha VIT-AP University School of CSE Andhra Pradesh India

ISBN: (纸本)9798350319200

Selecting the most important features or attributes from a dataset is a crucial part of machine learning and data analysis. This paper explores the effectiveness of feature selection (FS) methods, comparing filter based techniques such as Pearson Correlation Coefficient (PCC), Spearman Rank Correlation Coefficient (SCC), and Mutual Information (MI) with optimization techniques including Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Cuckoo Search (CS), and Particle Swarm optimization (PSO). The evaluation involves diverse datasets with varying sizes and complexities. The results show that the choice of FS method significantly impacts accuracy. Filter methods perform better with smaller datasets, while optimization algorithms show adaptability across small and high dimensional datasets. This paper shows that the effectiveness of feature selection is closely tied to the specific dataset being analyzed. © 2023 IEEE.

关键词： Feature Selection Filter Methods Machine Learning optimization algorithms

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Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2023年第4期30卷 2431-2449页

作者： Daoud, Mohammad Sh. Shehab, Mohammad Al-Mimi, Hani M. Abualigah, Laith Zitar, Raed Abu Shambour, Mohd Khaled Yousef Al Ain Univ Abu Dhabi U Arab Emirates Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Al Zaytoonah Univ Dept Cybersecur Amman Jordan Al Al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq 25113 Jordan Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Pulau Pinang Malaysia Ho Chi Minh City Open Univ Ctr Engn Applicat & Technol Solut Ho Chi Minh Vietnam Sorbonne Univ Abu Dhabi Sorbonne Ctr Artificial Intelligence Abu Dhabi U Arab Emirates Umm Al Qura Univ Custodian Two Holy Mosques Inst Hajj & Umrah Res Mecca Saudi Arabia

This paper introduces a comprehensive survey of a new population-based algorithm so-called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as one of the most effective optimization algorithm where it was utilized in different problems and domains, successfully. This review introduces set of related works of GBO where distributed into;GBO variants, GBO applications, and evaluate the efficiency of GBO compared with other metaheuristic algorithms. Finally, the conclusions concentrate on the existing work on GBO, showing its disadvantages, and propose future works. The review paper will be helpful for the researchers and practitioners of GBO belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

关键词： Gradient-Based Optimizer optimization algorithms Engineering problems GBO's variants GBO's applications

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A General Framework to Distribute Iterative algorithms With Localized Information Over Networks

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第12期68卷 7358-7373页

作者： Timoudas, Thomas Ohlson Zhang, Silun Magnusson, Sindri Fischione, Carlo RISE Res Inst Sweden Div Digital Syst Comp Sci S-16440 Molndal Sweden MIT Lab Informat & Decis Syst Cambridge MA 02139 USA Stockholm Univ Dept Comp & Syst Sci S-11419 Stockholm Sweden KTH Royal Inst Technol Digital Futures S-10044 Stockholm Sweden

Emerging applications in the Internet of Things (IoT) and edge computing/learning have sparked massive renewed interest in developing distributed versions of existing (centralized) iterative algorithms often used for optimization or machine learning purposes. While existing work in the literature exhibits similarities, for the tasks of both algorithm design and theoretical analysis, there is still no unified method or framework for accomplishing these tasks. This article develops such a general framework for distributing the execution of (centralized) iterative algorithms over networks in which the required information or data is partitioned between the nodes in the network. This article furthermore shows that the distributed iterative algorithm, which results from the proposed framework, retains the convergence properties (rate) of the original (centralized) iterative algorithm. In addition, this article applies the proposed general framework to several interesting example applications, obtaining results comparable to the state of the art for each such example, while greatly simplifying and generalizing their convergence analysis. These example applications reveal new results for distributed proximal versions of gradient descent, the heavy ball method, and Newton's method. For example, these results show that the dependence on the condition number for the convergence rate of this distributed heavy ball method is at least as good as that of centralized gradient descent.

关键词： Agents and autonomous systems communication networks distributed algorithms optimization algorithms

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An optimal takeout supply system of intelligent self-pickup cabinets: evidence from dining service during COVID-19

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SOFT COMPUTING 2023年第21期27卷 15999-16018页

作者： Wan, Zhong Wang, Weitong Cent South Univ Sch Math & Stat Changsha Peoples R China

In order to reduce cross-infections during epidemics such as COVID-19, it is valuable to design a campus takeout system of intelligent self-pickup cabinets (ISPCs). This research aims to design such a system by optimizing the sites of cabinets, their capacity types, and the location-allocation scheme. In view of maximizing profits of the system, we formulate this design problem as a zero-one integer nonlinear programming model, where the demand of takeout is distance dependent and congestion dependent, rather than a fixed constant. On the basis of model property analysis, the original model is transformed into an integer linear programming problem such that it is solved by off-the-shelf solvers. By case study and sensitivity analyses, it is found that: The proposed method is valuable for providing an optimal strategy for a takeout system of ISPCs by optimizing the sites of the cabinets, the capacity types and the location-allocation strategy;With the optimal strategy derived from the proposed model and algorithm, the developed system is more applicable in the areas of intensively distributed users or the areas closer to canteens in view of creating significant effects of scale economy;For the groups of consumers with different distance-dependent or congestion-dependent sensitivities, it is suggested to implement distinct optimal strategies of building the takeout system even for those in the same service area;The takeout demand grows up with an increasing unit selling price in the developed system, rather than reduction as in an ordinary relation between the demand of products and the price. Thus, the designed self-pickup takeout system seems more applicable to be adopted for the high-quality takeout.

关键词： Modeling Numerical computing optimization algorithms Supply system of takeouts Intelligent express cabinets

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Effect of optimization Algorithm Choice on Photovoltaic Module Modeling Performance 8

Effect of Optimization Algorithm Choice on Photovoltaic Modu...

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8th Southern Power Electronics Conference and the 17th Brazilian Power Electronics Conference, SPEC / COBEP 2023

作者： Cavalcante, Valdemar M. Fernandes, Tiago Alves Freitas, Renato Andrade De Amâncio, Nayara A. M. S. Bradaschia, Fabricio Cavalcanti, Marcelo Cabral Federal University of Pernambuco Department of Electrical Engineering Recife Brazil

ISBN: (纸本)9798350321128

The article addresses the importance of accurate modeling and simulation of the behavior of photovoltaic, modules (PVs) to ensure optimal solar PV system performance. An evaluation of the effectiveness of new optimization tools in the parameter estimation process of the first global nonlinear model known to the scientific community is proposed. The research aims to verify whether different optimization tools can contribute to improve the accuracy and efficiency in parameter estimation, resulting in improved modeling and performance prediction of PV systems under different environmental conditions. © 2023 IEEE.

关键词： GNLM IV curve NMAEP optimization algorithms photovoltaic PV curve

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Multi-scale grid model-based optimization algorithm for tobacco logistics distribution routes

Multi-scale grid model-based optimization algorithm for toba...

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2024 International Conference on Smart Transportation and City Engineering, STCE 2024

作者： Zhou, Jingjing Longnan Company of Gansu Province Tobacco Corporation Gansu Longnan China

ISBN: (纸本)9781510690585

The current conventional tobacco logistics distribution route optimization algorithm mainly optimizes the route by optimizing the vehicle configuration and site selection nodes, which leads to poor optimization results due to the lack of consideration of distribution costs. In this regard, a multi-scale grid model-based tobacco logistics distribution route optimization algorithm is proposed. With the distribution cost as the optimization objective, a growth multi-scale network model is constructed with the distribution nodes, and a distribution route optimization model is built. In the experiments, the optimization performance of the proposed method is verified. The experimental results show that the distribution route optimization model constructed by the proposed method has a high convergence of the algorithm and has a more desirable optimization effect. © 2025 SPIE.

关键词： optimization algorithms

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Connection of Optimal Stopping Time to s-t Cut Problems on Trees

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IEEE CONTROL SYSTEMS LETTERS 2023年 7卷 3729-3734页

作者： Wang, Yijin Ornik, Melkior Dong, Roy Univ Illinois Elect & Comp Engn Dept Urbana IL 61801 USA Univ Illinois Aerosp Engn Dept Urbana IL 61801 USA Univ Illinois Ind Engn Dept Urbana IL 61801 USA

We present a method to transform any optimal stopping time problem with an underlying tree structure into an s-t min-cut problem on the same tree but with modified capacities, the details of which are lacking in existing optimal stopping time research. We also show that any s-t min/max-cut problem on a tree has an equivalent optimal stopping problem formulation. We provide a dynamic programming algorithm to solve this problem and also perform a sensitivity analysis on it. Our results imply that the s-t max-cut problem on a tree can be solved using an algorithm with runtime that is linear in the tree size.

关键词： optimization algorithms optimization stochastic optimal control

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A Review on Trust-Based Resource Allocation in Cloud Environment: Issues Toward Collaborative Cloud

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INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING 2023年第1期17卷 59-91页

作者： Pol, Pooja Shashank Pachghare, Vinod K. Coll Engn Pune Wellesley Rd Pune 411005 Maharashtra India

Due to the exponential rise in the usage of the internet and smart devices, there is a demand for enhanced network efficiency and user satisfaction in a cloud computing environment. Moreover, moving to the cloud systems, it mainly focuses on storage, computation and resources. Due to copious growth, there exist more challenges as well. Among those, resource allocation in cloud computing is the main study, which is essential to determine the QoS and improved performance concerning reliability, confidentiality, trust, security, user satisfaction, profits, etc. This paper plans to prepare a detailed review on trust-based resource allocation in the collaborative cloud. The cloud industry has been assessed in terms of trust-based and other important factors to produce a road plan for resource allocation. Many papers are reviewed here and give a substantial evaluation of cloud resources and their resource allocation models using machine learning and optimization models. First, this survey provides an elaborated study concerning the various cloud resources considering the performance and QoS. Eventually, it extends the research based on trust-based approaches, with the intention of motivating the researchers to focus on trust-based resource allocation on collaborative cloud computing (CCC) atmosphere.

关键词： Resource allocation trust computing collaborative cloud quality of service optimization algorithms cloud computing

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An Automated Lightweight Deep Neural Network for Diagnosis of COVID-19 from Chest X-ray Images

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第8期48卷 11085-11102页

作者： Nayak, Soumya Ranjan Nayak, Janmenjoy Sinha, Utkarsh Arora, Vaibhav Ghosh, Uttam Satapathy, Suresh Chandra Amity Univ Uttar Pradesh Amity Sch Engn & Technol Noida India Aditya Inst Technol & Management AITAM Dept Comp Sci & Engn K Kotturu 532201 AP India Vanderbilt Univ Dept Elect Engn & Comp Sci Nashville Nashville TN 37235 USA KIIT Univ Sch Comp Engn Bhubaneswar Odisha India

Coronavirus (COVID-19) is an epidemic that is rapidly spreading and causing a severe healthcare crisis resulting in more than 40 million confirmed cases across the globe. There are many intensive studies on AI-based technique, which is time consuming and troublesome by considering heavyweight models in terms of more training parameters and memory cost, which leads to higher time complexity. To improve diagnosis, this paper is aimed to design and establish a unique lightweight deep learning-based approach to perform multi-class classification (normal, COVID-19, and pneumonia) and binary class classification (normal and COVID-19) on X-ray radiographs of chest. This proposed CNN scheme includes the combination of three CBR blocks (convolutional batch normalization ReLu) with learnable parameters and one global average pooling (GP) layer and fully connected layer. The overall accuracy of the proposed model achieved 98.33% and finally compared with the pre-trained transfer learning model (DenseNet-121, ResNet-101, VGG-19, and XceptionNet) and recent state-of-the-art model. For validation of the proposed model, several parameters are considered such as learning rate, batch size, number of epochs, and different optimizers. Apart from this, several other performance measures like tenfold cross-validation, confusion matrix, evaluation metrics, sarea under the receiver operating characteristics, kappa score and Mathew's correlation, and Grad-CAM heat map have been used to assess the efficacy of the proposed model. The outcome of this proposed model is more robust, and it may be useful for radiologists for faster diagnostics of COVID-19.

关键词： COVID-19 Chest X-ray images Convolutional neural networks Transfer learning optimization algorithms

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Type-3 Fuzzy System-Based Intelligent Control Approaches and Applications

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ADVANCES IN FUZZY SYSTEMS 2025年第1期2025卷

作者： Abdalla, Turki Y. Abdalla, AbdulKareem Y. Chyaid, Adala M. Univ Basrah Coll Engn Dept Comp Engn Basrah Iraq Univ Basrah Coll Comp Sci & IT Dept Informat Syst Basrah Iraq Univ Basrah Coll Comp Sci & IT Dept Comp Sci Basrah Iraq

Type-3 fuzzy logic has been recently used in many control methods. The type-3 fuzzy controller enhances the handling of uncertainty and improves robustness by integrating fuzzy sets with fuzzy membership functions. The latest approaches using type-3 fuzzy logic in the field of control are studied and evaluated. An overview of developments in control methods based on type-3 fuzzy logic is also provided. It is shown that type-3 fuzzy system has many advantages compared to type-1 and type-2 fuzzy. The advantages and challenges of using type-3 fuzzy logic are identified and discussed. The studies are classified according to the type of control approach, as well as by the type of control applications. Finally, the main achievements, open challenges, and future directions and impacts are identified, to provide important guidance for interested researchers.

关键词： adaptive control fuzzy system machine learning optimization algorithms type-1 fuzzy system type-2 fuzzy system type-3 fuzzy controller type-3 fuzzy system

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