检索结果-内蒙古大学图书馆

TSA-CNN-AOA: Twitter sentiment analysis using CNN optimized via arithmetic optimization algorithm

NEURAL COMPUTING & APPLICATIONS 2023年第14期35卷 10311-10328页

作者： Aslan, Serpil Kiziloluk, Soner Sert, Eser Malatya Turgut Ozal Univ Fac Engn & Nat Sci Dept Software Engn TR-44210 Malatya Turkiye Malatya Turgut Ozal Univ Fac Engn & Nat Sci Dept Comp Engn TR-44210 Malatya Turkiye

COVID-19, a novel virus from the coronavirus family, broke out in Wuhan city of China and spread all over the world, killing more than 5.5 million people. The speed of spreading is still critical as an infectious disease, and it causes more and more deaths each passing day. COVID-19 pandemic has resulted in many different psychological effects on people's mental states, such as anxiety, fear, and similar complex feelings. Millions of people worldwide have shared their opinions on COVID-19 on several social media websites, particularly on Twitter. Therefore, it is likely to minimize the negative psychological impact of the disease on society by obtaining individuals' views on COVID-19 from social media platforms, making deductions from their statements, and identifying negative statements about the disease. In this respect, Twitter sentiment analysis (TSA), a recently popular research topic, is used to perform data analysis on social media platforms such as Twitter and reach certain conclusions. The present study, too, proposes TSA using convolutional neural network optimized via arithmetic optimization algorithm (TSA-CNN-AOA) approach. Firstly, using a designed API, 173,638 tweets about COVID-19 were extracted from Twitter between July 25, 2020, and August 30, 2020 to create a database. Later, significant information was extracted from this database using FastText Skip-gram. The proposed approach benefits from a designed convolutional neural network (CNN) model as a feature extractor. Thanks to arithmetic optimization algorithm (AOA), a feature selection process was also applied to the features obtained from CNN. Later, K-nearest neighbors (KNN), support vector machine, and decision tree were used to classify tweets as positive, negative, and neutral. In order to measure the TSA performance of the proposed method, it was compared with different approaches. The results demonstrated that TSA-CNN-AOA (KNN) achieved the highest tweet classification performance with an a

关键词： COVID-19 Convolutional neural network arithmetic optimization algorithm Sentiment analysis Twitter

来源：评论

学校读者我要写书评

暂无评论

Adapted arithmetic optimization algorithm for multi-level thresholding image segmentation: a case study of chest x-ray images

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第14期83卷 41051-41081页

作者： Otair, Mohammad Abualigah, Laith Tawfiq, Saif Alshinwan, Mohammad Ezugwu, Absalom E. Zitar, Raed Abu Sumari, Putra Khawarizmi Univ Tech Coll Amman 11953 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 MEU Res Unit Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Sunway Univ Malaysia Sch Engn & Technol Petaling Jaya 27500 Malaysia Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Appl Sci Private Univ Fac Informat Technol Amman 11931 Jordan North West Univ Unit Data Sci & Comp 11 Hoffman St ZA-2520 Potchefstroom South Africa Sorbonne Univ Abu Dhabi Sorbonne Ctr Artificial Intelligence Abu Dhabi 38044 U Arab Emirates

Particularly in recent years, there has been increased interest in determining the ideal thresholding for picture segmentation. The best thresholding values are found using various techniques, including Otsu and Kapur-based techniques. These techniques work well for bi-level thresholding, but when used to find the appropriate thresholds for multi-level thresholding, there will be issues with long calculation times, high computational costs, and the need for accuracy improvements. This work investigates the capability of the arithmetic optimization algorithm to discover the best multilayer thresholding for picture segmentation to circumvent this issue. The leading mathematical arithmetic operators' distributional nature is used by the AOA method. The picture histogram was used to construct the candidate solutions in the modified algorithms, which were then updated according to the algorithm's features. The solutions are evaluated using Otsu's fitness function throughout the optimization process. The picture histogram is used to display the algorithm's potential solutions. The proposed approach is tested on five frequent photos from the Berkeley University database. The fitness function, root-mean-squared error, peak signal-to-noise ratio, and other widely used assessment metrics were utilized to assess the performance of the suggested segmentation approach. Many benchmark pictures were employed to verify the suggested technique's effectiveness and evaluate it against other well-known optimization methods described in the literature.

关键词： Image segmentation Multi-level thresholding Meta-heuristic algorithms arithmetic optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Chaotic arithmetic optimization algorithm

引用

APPLIED INTELLIGENCE 2022年第14期52卷 16718-16757页

作者： Li, Xu-Dong Wang, Jie-Sheng Hao, Wen-Kuo Zhang, Min Wang, Min Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

arithmetic optimization algorithm (AOA) is a meta-heuristic algorithm. Its main idea is to use the distribution behavior of the four main mathematical operators of addition(A), subtraction(S), multiplication(M) and division(D). Chaotic mapping strategy was introduced into the optimization process of AOA. Firstly, ten chaotic maps are separately embedded into two parameter arithmetic optimization Accelerator (MOA) and arithmetic optimization Probability (MOP) that affect the exploration and balance of AOA so as to enhance its ergodicity and non-repeatability, and improve its convergence speed and accuracy. Then a combination test was carried out by embedding ten chaotic maps into MOA and MOP at the same time, and their advantages and disadvantages were compared with the chaotic maps embedded separately. 26 benchmark functions in CEC-BC-2017 are used to examine the performance of the proposed chaotic arithmetic optimization algorithm (CAOA). Finally, four engineering design issues are optimized, involving three-bar truss design problem, welded beam design problem, pressure vessel design problem and spring design problem. The experimental results reveal that CAOA can obviously solve the function optimization and engineering optimization problems. AOA based on the chaotic interference factors has the merit of balancing the exploration and exploitation in the optimization process and enhances the convergence accuracy.

关键词： arithmetic optimization algorithm Chaotic map Function optimization Engineering optimization

来源：评论

学校读者我要写书评

暂无评论

Realization of Turkey's energy demand forecast with the improved arithmetic optimization algorithm

引用

ENERGY REPORTS 2022年 8卷 18-32页

作者： Aslan, Murat Beskirli, Mehmet Sirnak Univ Dept Comp Engn TR-73000 Sirnak Turkey

Due to the increasing energy consumption, energy has become a constant problem in the world. Rapidly increasing population, urbanization and economic activities increase the pressure of countries on energy. In a world where consumption is increasing, energy management has become a more important and challenging issue. For this reason, it is necessary to make proper estimations that will reduce the pressure of energy demand on this issue. In order to realize the estimation of energy demand, Turkey application is carried out in this study and arithmetic optimization algorithm (AOA) which is a stochastic metaheuristic algorithm has used to for solving energy demand problem. AOA is inspired from four substantial math functions such as subtraction, multiplication, addition and division for searching process of candidate solutions. The current position update rules of AOA are not powerful enough for solving the problem dealt with this study. Therefore, an improved version of AOA named as IAOA is proposed for solving energy demand problem. In the proposed algorithm, a new position update rule is incorporated to basic AOA in order to enhanced the exploration and exploitation capability of AOA. The linear regression model is used for the estimation of the energy demand and the population, domestic product, import and export data are used in estimation process. In the proposed model, Turkey's real data samples for the years 1979-2011 have been used, and Turkey's long-term energy demand has been estimated for the years 2012-2030. While performing the estimation process, Turkey's energy data of the years 1979-2011 have processed, and then Turkey's long-term energy demand estimations are realized for three different scenarios. Firstly, the experimental results of the proposed model are analyzed, then the results are compared with different studies proposed in the literature. As a result of the comparisons, it is seen that the IAOA method has achieved better or similar results th

关键词： arithmetic optimization algorithm Energy demand Estimation Linear regression model optimization

来源：评论

学校读者我要写书评

暂无评论

Tuning PID Controllers Based on Hybrid arithmetic optimization algorithm and Artificial Gorilla Troop optimization for Micro-Robotics Systems

引用

IEEE ACCESS 2023年 11卷 27138-27154页

作者： Ghith, Ehab Saif Tolba, Farid Abdel Aziz Ain shams Univ Fac Engn Mechatron Engn Dept Cairo 11566 Egypt

Micro particles have the potentials to be used for many medical purposes in-side the human body such as drug delivery and other operations. In the present paper, a novel hybrid algorithm based on arithmetic optimization algorithm (AOA) and Artificial Gorilla troop's optimization (GTO), (HAOAGTO) is compared with different four algorithms arithmetic optimization algorithm (AOA), Artificial Gorilla troop's optimization (GTO), Seagull optimization algorithm (SOA), Parasitism-predation algorithm (PPA). These approaches were used to calculate the PID controller optimal indicators with the application of different functions, including Integral Absolute Error (IAE), Integral of Time Multiplied by Square Error (ITSE), Integral Square Time multiplied square Error (ISTES), Integral Square Error (ISE), Integral of Square Time multiplied by square Error (ISTSE), and Integral of Time multiplied by Absolute Error (ITAE). Every method of controlling was presented in a MATLAB Simulink numerical model. It is observed that the PPA technique achieves the highest values of best fitness value for simulation results among other control approaches, while the HAOAGTO approach reduces the best fitness function compared to other optimization techniques used. We verified that the obtained results by application of the proposed hybrid algorithm-based AOA and GTO (HAOAGTO) is better than those obtained by arithmetic optimization algorithm (AOA), Artificial Gorilla troop's optimization (GTO), Seagull optimization algorithm (SOA), Parasitism-predation algorithm (PPA). it is implemented to obtain the optimal parameters of the PID for reduction the ISTES.

关键词： optimization Mathematical models Classification algorithms Metaheuristics Tuning Force PID controller arithmetic optimization algorithm artificial gorilla troop's optimization seagull optimization algorithm parasitism-predation algorithm hybrid between AOA and GTO (HAOAGTO) minimally invasive surgery

来源：评论

学校读者我要写书评

暂无评论

An intelligent approach using boosted support vector machine based arithmetic optimization algorithm for accurate detection of plant leaf disease

引用

PATTERN ANALYSIS AND APPLICATIONS 2023年第1期26卷 367-379页

作者： Prabu, M. Chelliah, Balika J. SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India

Leaf disease is considered a serious threat which affects agricultural productivity and ultimately reduces the GDP of the Indian economy. The precise detection and timely analysis of foliar diseases can mitigate the spread of the disease to other parties. However, certain complications such as low precision, high calculation cost, and low recognition speed are raised when detecting leaf diseases. Therefore, to overcome these limitations, we proposed a novel technique called Boosted support vector machine-based arithmetic optimization algorithm (BSVM-AOA) for accurate detection of plant leaf disease. In this case, image segmentation is done using the vector value active contour model, and feature extraction is done using the greyscale co-occurrence matrix. Furthermore, the performance of the proposed approach is determined by performance parameters such as accuracy, accuracy, recall, specificity, and f-rating. Finally, the comparative analysis is conducted between the different existing techniques and the proposed technique. The comparative results showed that the proposed BSVM-AOA approach is about 98.6% more accurate than other existing techniques.

关键词： Plant leaf disease Vector-valued active contour model Gray-level co-occurrence matrix Boosted support vector machine arithmetic optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Pattern Search Ameliorated arithmetic optimization algorithm for Engineering optimization and Infinite Impulse Response System Identification

ELECTRICA

引用

ELECTRICA 2024年第1期24卷 119-130页

作者： Ekinci, Serdar Izci, Davut Batman Univ Dept Comp Engn Batman Turkiye Middle East Univ MEU Res Unit Amman Jordan

In the field of science and engineering, significant attention can be observed recently for system identification as a complex optimization problem. As the infinite impulse response (IIR) models can achieve more accurate models of physical plants for real-world applications, they are mostly preferred over finite impulse response models. Despite the latter advantage of the IIR structures, it is not straightforward to minimize the related cost functions as they tend to generate multimodal error surfaces. Metaheuristic algorithms have already been shown for their excellent promise to deal with such difficulties as they operate independent of the nature of the problem. In this regard, this work aims to demonstrate the excellent promise of a novel developed metaheuristic algorithm named pattern search ameliorated arithmetic optimization algorithm. The proposed algorithm integrates the original form of the arithmetic optimization algorithm (for exploration) with the pattern search algorithm (for exploitation) such that a better-performing metaheuristic structure is achieved. The excellent ability of the proposed pattern search ameliorated arithmetic optimization algorithm is demonstrated against the original arithmetic optimization algorithm by using well-known classical benchmark functions and welded beam design problem. A significant improvement is achieved for benchmark functions, and an improvement of up to 25% is obtained for the optimal cost of the welded beam design. Then, different IIR model identification problems are considered, and a comparative assessment is performed using different metaheuristic optimization techniques: particle swarm optimization algorithm, artificial bee colony algorithm, electromagnetism-like optimization algorithm, cuckoo search algorithm, and flower pollination algorithm. The obtained statistical results from different systems confirm that the pattern search ameliorated arithmetic optimization algorithm can achieve better accuracy and r

关键词： arithmetic optimization algorithm digital IIR filters engineering optimization pattern search swarm-based optimization system identification

来源：评论

学校读者我要写书评

暂无评论

Novel Adaptive Sine Cosine arithmetic optimization algorithm For Optimal Automation Control of DG Units and STATCOM Devices

引用

SMART SCIENCE 2023年第1期11卷 16-37页

作者： Mandad, Belkacem Biskra Univ Dept Elect Engn Biskra Algeria

In this paper a practical power system planning and control strategy based on a new adaptive sine cosine arithmetic optimization algorithm (ASC_AOA) is proposed to enhance the technical performances of radial distribution (RD) systems. The main objective considered in this study is to optimize the location and the size of shunt compensators based STATCOM devices and multi distributed generation to reduce the total power losses, and to maximize the loading margin stability of practical RD systems. As well confirmed in many recent researches, the success of metaheuristic algorithms is related to the interactivity between intensification and diversification stages. In this study, an adaptive process based on sine and cosine functions is incorporated within the standard AOA to guide the search process toward the best solution. The particularity of the proposed variant (ASC_AOA) has been validated on many benchmark functions, and also applied for optimal automation control of multi DGs and shunt compensators based STATCOM Controllers to enhance the performances of various types of RD systems, such as the 33-bus, the 69-bus, and 85-bus. Obtained results are compared to many recent optimization methods. It is confirmed that the proposed variant achieves the best solution in all of the cases studies elaborated. The proposed variant seems to be a competitive technique and an alternative tool for solving various combinatorial planning and control problems of modern RD systems.

关键词： arithmetic optimization algorithm sine cosine algorithm radial distribution system distribution generation STATCOM power losses loading margin stability

来源：评论

学校读者我要写书评

暂无评论

Evaluation of several initialization methods on arithmetic optimization algorithm performance

引用

JOURNAL OF INTELLIGENT SYSTEMS 2022年第1期31卷 70-94页

作者： Agushaka, Jeffrey O. Ezugwu, Absalom E. Univ KwaZulu Natal Sch Math Stat & Comp Sci King Edward Rd ZA-3201 Pietermaritzburg Kwazulu Natal South Africa

arithmetic optimization algorithm (AOA) is one of the recently proposed population-based meta-heuristic algorithms. The algorithmic design concept of the AOA is based on the distributive behavior of arithmetic operators, namely, multiplication (M), division (D), subtraction (S), and addition (A). Being a new metaheuristic algorithm, the need for a performance evaluation of AOA is significant to the global optimization research community and specifically to nature-inspired metaheuristic enthusiasts. This article aims to evaluate the influence of the algorithm control parameters, namely, population size and the number of iterations, on the performance of the newly proposed AOA. In addition, we also investigated and validated the influence of different initialization schemes available in the literature on the performance of the AOA. Experiments were conducted using different initialization scenarios and the first is where the population size is large and the number of iterations is low. The second scenario is when the number of iterations is high, and the population size is small. Finally, when the population size and the number of iterations are similar. The numerical results from the conducted experiments showed that AOA is sensitive to the population size and requires a large population size for optimal performance. Afterward, we initialized AOA with six initialization schemes, and their performances were tested on the classical functions and the functions defined in the CEC 2020 suite. The results were presented, and their implications were discussed. Our results showed that the performance of AOA could be influenced when the solution is initialized with schemes other than default random numbers. The Beta distribution outperformed the random number distribution in all cases for both the classical and CEC 2020 functions. The performance of uniform distribution, Rayleigh distribution, Latin hypercube sampling, and Sobol low discrepancy sequence are relatively competi

关键词： arithmetic optimization algorithm AOA initialization methods

来源：评论

学校读者我要写书评

暂无评论

Spectral-spatial classification fusion for hyperspectral images in the probabilistic framework via arithmetic optimization algorithm

引用

INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION 2022年第3期13卷 262-277页

作者： Majdar, Reza Seifi Ghassemian, Hassan Islamic Azad Univ Dept Elect Engn Ardabil Branch Ardebil Iran Tarbiat Modares Univ Fac Comp & Elect Engn Image Proc & Informat Anal Lab Tehran Iran

Spectral data and spatial information such as shape and texture features can be fused to improve the classification of the hyperspectral images. In this paper, a novel approach of the spectral and spatial features (texture features and shape features) fusion in the probabilistic framework is proposed. The Gabor filters are applied to obtain the texture features and the morphological profiles (MPs) are used to obtain the shape features. These features are classified separately by the support vector machine (SVM);therefore, the per-pixel probabilities can be estimated. A novel meta-heuristic optimization method called arithmetic optimization algorithm (AOA) is used to weighted combinations of these probabilities. Three parameters, a, p and y determine the weight of each feature in the combination. The optimal value of these parameters is calculated by AOA. The proposed method is evaluated on three useful hyperspectral data sets: Indian Pines, Pavia University and Salinas. The experimental results demonstrate the effectiveness of the proposed combination in hyperspectral image classification, particularly with few labelled samples. As well as, this method is more accurate than a number of new spectral-spatial classification methods.

关键词： Hyperspectral image Spectral-Spatial classification spatial features Probabilistic Framework arithmetic optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：