检索结果-内蒙古大学图书馆

Modeling and optimization of non-edible papaya seed waste oil synthesis using data mining approaches

South African Journal of Chemical Engineering 2020年 33卷 151-159页

作者： Sultana, N. Hossain, S.M.Z. Taher, S. Khan, A. Razzak, S.A. Haq, B. Al Shehri, D. Department of Computer Science College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Saudi Arabia Department of Chemical Engineering College of Engineering University of Bahrain Bahrain Department of Chemical Engineering King Fahd University of Petroleum & Minerals Saudi Arabia Department of Petroleum Engineering King Fahd University of Petroleum & Minerals Saudi Arabia

Fossil fuels are a major contributor of greenhouse gas emissions (CO2, NOx, etc.). These fuels are non-renewable energy sources that will eventually be exhausted. Currently, biodiesel has gained attention as a renewab... 详细信息

Fossil fuels are a major contributor of greenhouse gas emissions (CO₂, NO_x, etc.). These fuels are non-renewable energy sources that will eventually be exhausted. Currently, biodiesel has gained attention as a renewable green energy source and means of supporting the minimization of fossil fuel use. However, obtaining biodiesel from edible seeds has been criticized as unethical, due to the source material being needed for human consumption. In this regard, papaya seed waste could be utilized as a potential feedstock because it is non-edible and its high lipid content makes it excellent for producing biodiesel. Papaya fruit is not seasonal. It is available at all times in tropical countries, and almost 75% of the total papaya is generated in the glove. Thus, different soft computing or data mining approaches such response surface methodology (RSM), artificial neural networks (ANNs), and support vector regression (SVR) can be utilized to predict oil yields from waste papaya seeds via solvent extraction. In the present research, the data for oil yields were obtained by experiments based on a central composite design. These data were then employed to develop, compare, and assess the suggested models. The results indicate that the SVR model performed much better for predicting oil yields than did the ANN and RSM models, with respect to various performance-measuring parameters (i.e., relative error, correlation coefficient, mean absolute error, and root mean squared error). It was observed that oil yields increase with an increase in extraction time but decrease as particle size increases. In order to find the global optimal set, an SVR and crow search algorithm-based interface was implemented. A maximum oil yield of 28.55% was achieved at 6.5 h of extraction and a particle size of 0.85 mm. The predicted oil yield was validated experimentally with less than a 5% rate of error. The extracted oil was also characterized by gas chromatography–mass spectr

关键词： crow search algorithm Modeling Optimization Seed oil Soft computing

来源：评论

学校读者我要写书评

暂无评论

Comparative Study of Five Metaheuristic algorithms for Team Formation Problem

Comparative Study of Five Metaheuristic Algorithms for Team ...

引用

Human Engineering Symposium, HUMENS 2021

作者： Kader, Md. Abdul Zamli, Kamal Z. Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pekan Pahang26600 Malaysia

ISBN: (纸本)9789811641145

This paper presents a comparative study of five metaheuristic algorithms, namely, salp swarm algorithm (SSA), owl search algorithm (OSA), sooty tern optimization algorithm (STOA), squirrel search algorithm (SqSA), and crow search algorithm (CSA) adopted in the Covid19 team formation (CTF) problem. The performance comparison of these algorithms is conducted by executing each algorithm twenty times to ensure the statistical significance. The study considers the minimum number of experts and the minimum team formation cost in defining the objective function. The CSA was found to be the more effective metaheuristic algorithm for the Covid19 team formation problem from the optimal results in terms of overall solution quality and runtime efficiency. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Covid19 team formation crow search algorithm Metaheuristic algorithms

来源：评论

学校读者我要写书评

暂无评论

Integrated Transfer Learning and Nature-Inspired Optimization for Enhanced Feature Extraction in Diabetic Retinopathy Image Analysis

Integrated Transfer Learning and Nature-Inspired Optimizatio...

引用

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Tiwari, Raj Gaurang Kumar, Anurag Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350372229

This research aims to detect diabetic retinopathy using optimized features extracted from deep learning model. Initially, several deep learning architectures are trained using retinal image dataset and the best model is determined. Regarding transfer learning approaches for diabetic retinopathy patients, SqueezeNet seems to be the best model. The proposed model in this research relies on a two-stage optimization process to enhance the features extracted by SqueezeNet. Deep features obtained by SqueezeNet are optimized using Particle Swarm Optimization (PSO) and the crow search algorithm (CSA). Merging the results of the two optimization methods with a value-maximizing solution is essential for producing an accurate and resilient feature vector. The proposed hybrid model employs a variety of machine-learning algorithms to classify diabetic retinopathy and non-diabetic retinopathy cases. The experimental findings indicate that the suggested method is effective with correct classification accuracy of 96.8%. © 2024 IEEE.

关键词： crow search algorithm Deep Architectures Diabetic Retinopathy Feature Extraction Nature-Inspired Optimization Particle Swarm Optimization (PSO) SqueezeNet Transfer Learning

来源：评论

学校读者我要写书评

暂无评论

Sentiment-oriented query-focused text summarization addressed with a multi-objective optimization approach

引用

APPLIED SOFT COMPUTING 2021年第PartA期113卷 107915-107915页

作者： Sanchez-Gomez, Jesus M. Vega-Rodriguez, Miguel A. Perez, Carlos J. Univ Extremadura Dept Comp & Commun Technol Campus Univ S-N Caceres 10003 Spain Univ Extremadura Dept Math Campus Univ S-N Caceres 10003 Spain

Nowadays, the automatic text summarization is a highly relevant task in many contexts. In particular, query-focused summarization consists of generating a summary from one or multiple documents according to a query given by the user. Additionally, sentiment analysis and opinion mining analyze the polarity of the opinions contained in texts. These two issues are integrated in an approach to produce an opinionated summary according to the user's query. Thereby, the query-focused sentiment-oriented extractive multi-document text summarization problem entails the optimization of different criteria, specifically, query relevance, redundancy reduction, and sentiment relevance. An adaptation of the metaheuristic population-based crow search algorithm has been designed, implemented, and tested to solve this multi-objective problem. Experiments have been carried out by using datasets from the Text Analysis Conference (TAC) datasets. Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics and the Pearson correlation coefficient have been used for the performance assessment. The results have reported that the proposed approach outperforms the existing methods in the scientific literature, with a percentage improvement of 75.5% for ROUGE-1 score and 441.3% for ROUGE-2 score. It also has been obtained a Pearson correlation coefficient of +0.841, reporting a strong linear positive correlation between the sentiment scores of the generated summaries and the sentiment scores of the queries of the topics. (C) 2021 The Author(s). Published by Elsevier B.V.

关键词： Query-focused summarization Sentiment-oriented summarization Sentiment analysis Multi-objective optimization crow search algorithm

来源：评论

学校读者我要写书评

暂无评论

The forecasting of air transport passenger demands in Turkey by using novel meta-heuristic algorithms

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第16期33卷 e6263-e6263页

作者： Korkmaz, Ersin Akgungor, Ali Payidar Kirikkale Univ Engn Fac Dept Civil Engn TR-71451 Kirikkale Turkey

The imbalance between modes of transport in our country appears as the most important problem. Therefore, in air transportation, which has a significant increasing trend, estimating the passenger demand with directly related parameters and novel algorithms is important for Turkey. In this study, different prediction models were developed applying for the first time with five different meta-heuristic algorithms which are Flower Pollination algorithm (FPA), Artificial Bee Colony algorithm (ABC), crow search algorithm (CSA), Krill Herd algorithm (KH), and the Butterfly Optimization algorithm (BOA) to estimate Turkey's air transport demand. While developing the models, Fuel Price, Gross Domestic Product per Capita, Seat Capacity, and Annual Fuel Consumption were selected as the model parameters. Although each model developed using different approaches is applicable, quadratic and power models developed using CSA showed the highest performance. For this reason, future projections were based on these models. Air transport passenger demand was examined using two scenarios in a process until 2035. In the first scenario, according to model forms, Turkey's future air transport passenger demand will reach about 460 and 490 million passengers, respectively. In the second scenario, the number of passengers will reach approximately 375 and 660 million for quadratic and power models, respectively. The results of this study will contribute to the evaluation of the current investment plans and the development of strategic plans that will meet the demands. Additionally, they will help take necessary measures and introduce some necessary regulations to ensure the income and expense balance so that the efficiency of airline companies can be improved.

关键词： air transport passenger demand artificial bee colony algorithm butterfly optimization algorithm crow search algorithm flower pollination algorithm krill herd algorithm

来源：评论

学校读者我要写书评

暂无评论

A multiobjective approach for design of an off-grid PV/Diesel system considering reliability and cost

引用

ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY 2019年第4期38卷 13101-13101页

作者： Movahediyan, Zahra Askarzadeh, Alireza Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran

The aim of the present study is to solve multiobjective optimization (MO) of an off-grid hybrid power generation system including photovoltaic (PV) and diesel generator by multiobjective version of a recently developed metaheuristic approach named crow search algorithm (CSA). For this goal, the objective functions are regarded as net present cost (NPC) and system reliability defined by loss of power supply probability (LPSP) index. In the optimization problem, operating limitations of diesel generator and uncertainties of solar radiation and load demand are considered. To solve this problem, a multiobjective CSA (MO-CSA) is developed and the obtained results are compared with the results of nondominated sorting genetic algorithm II (NSGA-II). On the case study, simulation results reveals that when diesel generator ramp rate is 100%, at LPSP = 0, MO-CSA reaches to 54.8 kW and 172.8 m(2) for rated power of diesel generator and PV surface area (corresponding cost is 3.7219 x 10(5) $), while the values found by NSGA-II are 55 kW and 86.04 m(2) (corresponding cost is 3.7345 x 10(5) $). Based on the results, it can be drawn that (1) MO-CSA finds more promising results than NSGA-II, (2) Combination of PV and diesel generator leads to having a cost-effective and reliable power generation system, and (3) by considering the solar radiation and load uncertainties, the system cost increases. (c) 2018 American Institute of Chemical Engineers Environ Prog, 38:e13101, 2019

关键词： multiobjective optimization hybrid photovoltaic diesel system crow search algorithm

来源：评论

学校读者我要写书评

暂无评论

Balancing module in evolutionary optimization and Deep Reinforcement Learning for multi-path selection in Software Defined Networks

引用

PHYSICAL COMMUNICATION 2023年 56卷

作者： Praveena, Hirald Dwaraka Srilakshmi, V. Rajini, S. Kolluri, Raju Manohar, M. Sree Vidyanikethan Engn Coll Dept ECE Tirupati 517102 Andhra Pradesh India BV Raju Inst Technol Dept Comp Sci & Engn AI & ML Narsapur India Vidyavardhaka Coll Engn Dept Informat Sci & Engn Mysuru India Narasaraopeta Engn Coll Dept ECE Guntur Andhra Pradesh India CHRIST Dept Comp Sci & Engn Bangalore India

Software Defined Network (SDN) has been used in many organizations due to its efficiency in trans-mission. Machine learning techniques have been applied in SDN to improve its efficiency in resource scheduling. The existing models in SDN have limitations of overfitting, local optima trap and lower efficiency in path selection. This study applied Balancing Module (BM)-Spider Monkey Optimization (SMO)-crow search algorithm (CSA) for multi path selection in SDN to improve its efficiency. The balancing module applies Gaussian distribution to balance between exploration and exploitation in the multi-path selection process. The Balancing module helps to escape local optima trap and increases the convergence rate. Deep Reinforcement learning is applied for resource scheduling in SDN. The Deep reinforcement learning technique uses the reward function to improve the learning performance, and the BM-SMO-CSA technique has 30 J energy consumption, where the existing models: DRL has 40 J energy consumption, and Graph-ACO has 62 J energy consumption. (c) 2022 Published by Elsevier B.V.

关键词： Balancing module crow search algorithm Deep Reinforcement Learning Software Defined Network Spider Monkey Optimization

来源：评论

学校读者我要写书评

暂无评论

Driver Identification Using Optimized Deep Learning Model in Smart Transportation

引用

ACM TRANSACTIONS ON INTERNET TECHNOLOGY 2022年第4期22卷 84-84页

作者： Ravi, Chandrasekar Tigga, Anmol Reddy, G. Thippa Hakak, Saqib Alazab, Mamoun Natl Inst Technol Puducherry Dept Comp Sci & Engn Karaikal 609609 India Natl Inst Technol Puducherry Comp Sci & Engn Karaikal 609609 India Vellore Inst Technol Sch Informat Technol & Engn Vellore 632014 Tamil Nadu India Univ New Brunswick Fac Comp Sci Canadian Inst Cybersecur Fredericton NB E3B 5A3 Canada Charles Darwin Univ Coll Engn IT & Environm Darwin NT 0909 Australia

The Intelligent Transportation System (ITS) is said to revolutionize the travel experience by making it safe, secure, and comfortable for the people. Although vehicles have been automated up to a certain extent, it still has critical security issues that require thorough study and advanced solutions. The security vulnerabilities of ITS allows the attacker to steal the vehicle. Therefore, the identification of drivers is required in order to develop a safe and secure system so that the vehicles can be protected from theft. There are two ways in which a driver can be identified: 1) face recognition of the driver, and 2) based on driving behavior. Face recognition includes image processing of 2-D images and learning of the features, which require high computational power. Drivers are known to have unique driving styles, whose data can be captured by the sensors. Therefore, the second method identifies drivers based on the analysis of the sensor data and it requires comparatively lesser computational power. In this paper, an optimized deep learning model is trained on the sensor data to correctly identify the drivers. The Long Short-Term Memory (LSTM) deep learning model is optimized for better performance. The novelty of the approach in this work is the inclusion of hyperparameter tuning using a nature-inspired optimization algorithm, which is an important and essential step in discovering the optimal hyperparameters for training the model which in turn increases the accuracy. The CAN-BUS dataset is used for experimentation and evaluation of the training model. Evaluation parameters such as accuracy, precision score, F1 score, and ROC AUC curve are considered to evaluate the performance of the model.

关键词： Intelligent Transportation Systems (ITS) security Auto -theft systems LSTM hyperparameter tuning crow search algorithm

来源：评论

学校读者我要写书评

暂无评论

Trajectory Planning Application of Rehabilitation Robots Based on Improved CSA algorithm

引用

IEEE ACCESS 2023年 11卷 144617-144630页

作者： Xia, Lizhen Henan Tech Inst Dept Informat Engn Zhengzhou 450042 Peoples R China

With the aging global population, there are more patients with hemiplegia caused by stroke. The demand for rehabilitation robots to assist rehabilitation therapists is also increasing. To solve the above problems, a reasonable trajectory planning of the rehabilitation robot joints is constructed to achieve rehabilitation assistance, improve safety and ***, a Kinematics model is constructed, and B-spline is applied to realize the trajectory planning of the rehabilitation robot. By introducing crow search algorithm and multiple crow search algorithms, the energy impact multi-objective optimization for the upper limb movement of the rehabilitation robot is carried out. Relevant experiments are designed to compare and verify the optimization effects of the algorithms. From the results, by optimizing the crow search algorithm, only 25.36% and 34.20% of the impact and energy are used. The average impact size per unit of time decreases by 74.68%. The energy consumption per unit time decreases by 65.39%.After optimizing the crow search algorithm, the impact and energy per unit time are only 14.41% and 17.25%, respectively. The energy per unit time decreases by 74.53%, and the impact decreases by 82.97%. Compared with other algorithms, the space complexity of the improved method is very small, and the standard deviation is 0.03. The optimization algorithm performs well in trajectory planning optimization. It is of great significance in the trajectory planning and rehabilitation role of rehabilitation robots.

关键词： Trajectory planning crow search algorithm rehabilitation robots multi objective optimization multiple group methods

来源：评论

学校读者我要写书评

暂无评论

An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment

引用

APPLIED SOFT COMPUTING 2021年 100卷 106997-106997页

作者： SaiSindhuTheja, Reddy Shyam, Gopal K. REVA Univ Sch Comp & Informat Technol Bengaluru 560064 Karnataka India Sreenidhi Inst Sci & Technol Dept CSE Hyderabad 501301 Telangana India

Detection of Denial of Service (DoS) attack is one of the most critical issues in cloud computing. The attack detection framework is very complex due to the nonlinear thought of interruption activities, unusual conduct of systems traffic, and many attributes in the issue space. This paper proposes an efficient DoS attack detection system that uses the Oppositional crow search algorithm (OCSA), which integrates the crow search algorithm (CSA) and Opposition Based Learning (OBL) method to address such type of issues. The proposed system consists of two stages viz. selection of features using OCSA and classification using Recurrent Neural Network (RNN) classifier. The essential features are selected using the OCSA algorithm and then given to RNN classifier. In the subsequent testing process, incoming data is classified using the RNN classifier. It ensures the separation of standard data (saved in cloud) and the removal of compromised data Using the benchmark data set, the results of experimental evaluation demonstrate that the proposed technique outperforms the other conventional methods by 98.18%, 95.13%, 93.56%, and 94.12% in terms of Precision, Recall, F-Measure, and Accuracy respectively. Further, the proposed work outperforms existing works by 3% on an average for all the metrics used. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Cloud computing DoS attack crow search algorithm Opposition based learning Recurrent neural network

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：