Int. J. Comput. Intell. Appl.最新文献_第9页

Improved Flower Pollination Algorithm for Optimal Groundwater Management 地下水最优管理的改进花授粉算法

Int. J. Comput. Intell. Appl. Pub Date : 2020-08-05 DOI: 10.1142/s1469026820500224

S. Akram

{"title":"Improved Flower Pollination Algorithm for Optimal Groundwater Management","authors":"S. Akram","doi":"10.1142/s1469026820500224","DOIUrl":"https://doi.org/10.1142/s1469026820500224","url":null,"abstract":"Groundwater management problems are typically of a large-scale nature, involving complex nonlinear objective functions and constraints, which are commonly evaluated through the use of numerical simulation models. Given these complexities, metaheuristic optimization algorithms have recently become popular choice for solving such complex problems which are difficult to solve by traditional methods. However, the practical applications of metaheuristics are severely challenged by the requirement of large number of function evaluations to achieve convergence. To overcome this shortcoming, many new metaheuristics and different variants of existing ones have been proposed in recent years. In this study, a recently developed algorithm called flower pollination algorithm (FPA) is investigated for optimal groundwater management. The FPA is improved, combined with the widely used groundwater flow simulation model MODFLOW, and applied to solve two groundwater management problems. The proposed algorithm, denoted as IFPA, is first tested on a hypothetical aquifer system, to minimize the total pumping to contain contaminated groundwater within a capture zone. IFPA is then applied to maximize the total annual pumping from existing wells in Rhis-Nekor unconfined coastal aquifer on the northern of Morocco. The obtained results indicate that IFPA is a promising method for solving groundwater management problems as it outperforms the standard FPA and other algorithms applied to the case studies considered, both in terms of convergence rate and solution quality.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133680336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Spatial Relational Attention Using Fully Convolutional Networks for Image Caption Generation 使用全卷积网络生成图像标题的空间关系注意

Int. J. Comput. Intell. Appl. Pub Date : 2020-06-01 DOI: 10.1142/s146902682050011x

Teng Jiang, Liang Gong, Yupu Yang

引用次数: 1

Application of a Chaotic Quantum Bee Colony and Support Vector Regression to Multipeak Maximum Power Point Tracking Control Method Under Partial Shading Conditions 混沌量子蜂群和支持向量回归在部分遮阳条件下多峰最大功率点跟踪控制中的应用

Int. J. Comput. Intell. Appl. Pub Date : 2020-06-01 DOI: 10.1142/s1469026820500145

Xiang-ming Gao, Diankuan Ding, Shifeng Yang, Mingkun Huang

{"title":"Application of a Chaotic Quantum Bee Colony and Support Vector Regression to Multipeak Maximum Power Point Tracking Control Method Under Partial Shading Conditions","authors":"Xiang-ming Gao, Diankuan Ding, Shifeng Yang, Mingkun Huang","doi":"10.1142/s1469026820500145","DOIUrl":"https://doi.org/10.1142/s1469026820500145","url":null,"abstract":"In view of the multipeak characteristics of a photovoltaic (PV) array P–V curve under local shadow conditions and that the traditional maximum power point tracking (MPPT) algorithm cannot effectively track the maximum power point of the curve, a multipeak MPPT algorithm based on a chaotic quantum bee colony and support vector regression (SVR) is proposed. By constructing and analyzing the mathematical model of a photovoltaic array under a local shadow, the P–V characteristic equation of the photovoltaic array is obtained. The improved strategy of the artificial bee colony algorithm is studied, and the improved chaotic quantum bee colony algorithm (CQABC) is applied to the optimization of SVR parameters; this application improves the accuracy and generalization performance of the maximum power point prediction model based on SVR. The calculation process of the multipeak MPPT algorithm based on CQABC-SVR is given, and the effectiveness of the algorithm is verified by simulation and testing. The experimental results show that the algorithm can accurately track the global maximum power point under uniform illumination or local shadow conditions, effectively overcoming the problem of traditional MPPT algorithms easily falling into local extrema.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130323656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Novel Nature-Inspired Technique Based on Mushroom Reproduction for Constraint Solving and Optimization 一种基于蘑菇繁殖的自然启发约束求解与优化方法

Int. J. Comput. Intell. Appl. Pub Date : 2020-06-01 DOI: 10.1142/s1469026820500108

Mahdi Bidar, Malek Mouhoub, S. Sadaoui, H. Kanan

引用次数: 2

A Multilevel Image Thresholding Approach Based on Crow Search Algorithm and Otsu Method 基于Crow搜索算法和Otsu方法的多级图像阈值分割方法

Int. J. Comput. Intell. Appl. Pub Date : 2020-06-01 DOI: 10.1142/s1469026820500157

F. Shahabi, F. Poorahangaryan, Seyyed Ahmad Edalatpanah, H. Beheshti

{"title":"A Multilevel Image Thresholding Approach Based on Crow Search Algorithm and Otsu Method","authors":"F. Shahabi, F. Poorahangaryan, Seyyed Ahmad Edalatpanah, H. Beheshti","doi":"10.1142/s1469026820500157","DOIUrl":"https://doi.org/10.1142/s1469026820500157","url":null,"abstract":"Image segmentation is one of the fundamental problems in the image processing, which identifies the objects and other structures in the image. One of the widely used methods for image segmentation is image thresholding that can separate pixels based on the specified thresholds. Otsu method calculates the thresholds to divide two or multiple classes based on between-class variance maximization and within-class variance minimization. However, increasing the number of thresholds, surging the computational time of the segmentation. To combat this drawback, the combination of Otsu and the evolutionary algorithm is usually beneficial. Crow Search Algorithm (CSA) is a novel, and efficient swarm-based metaheuristic algorithm that inspired from the way crows storing and retrieving food. In this paper, we proposed a hybrid method based on employing CSA and Otsu for multilevel thresholding. The obtained results compared with the combination of the Otsu method with three other evolutionary algorithms consisting of improved Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and also the fuzzy version of FA. Our evaluation on the five benchmark images shows competitive/improved results both in time and uniformity.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130974493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

Deep Network based on Long Short-Term Memory for Time Series Prediction of Microclimate Data inside the Greenhouse 基于长短期记忆的温室小气候数据时间序列预测的深度网络

Int. J. Comput. Intell. Appl. Pub Date : 2020-06-01 DOI: 10.1142/s1469026820500133

S. Gharghory

{"title":"Deep Network based on Long Short-Term Memory for Time Series Prediction of Microclimate Data inside the Greenhouse","authors":"S. Gharghory","doi":"10.1142/s1469026820500133","DOIUrl":"https://doi.org/10.1142/s1469026820500133","url":null,"abstract":"An enhanced architecture of recurrent neural network based on Long Short-Term Memory (LSTM) is suggested in this paper for predicting the microclimate inside the greenhouse through its time series data. The microclimate inside the greenhouse largely affected by the external weather variations and it has a great impact on the greenhouse crops and its production. Therefore, it is a massive importance to predict the microclimate inside greenhouse as a preceding stage for accurate design of a control system that could fulfill the requirements of suitable environment for the plants and crop managing. The LSTM network is trained and tested by the temperatures and relative humidity data measured inside the greenhouse utilizing the mathematical greenhouse model with the outside weather data over 27 days. To evaluate the prediction accuracy of the suggested LSTM network, different measurements, such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), are calculated and compared to those of conventional networks in references. The simulation results of LSTM network for forecasting the temperature and relative humidity inside greenhouse outperform over those of the traditional methods. The prediction results of temperature and humidity inside greenhouse in terms of RMSE approximately are 0.16 and 0.62 and in terms of MAE are 0.11 and 0.4, respectively, for both of them.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114282004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Application of Deep Learning Technique in an Intrusion Detection System 深度学习技术在入侵检测系统中的应用

Int. J. Comput. Intell. Appl. Pub Date : 2020-06-01 DOI: 10.1142/s1469026820500169

Shideh Saraeian, Mahya Mohammadi Golchi

引用次数: 4

Enhanced Forecasting Accuracy of Fuzzy Time Series Model Based on Combined Fuzzy C-Mean Clustering with Particle Swam Optimization 基于模糊c均值聚类与粒子游优化相结合提高模糊时间序列模型预测精度

Int. J. Comput. Intell. Appl. Pub Date : 2020-06-01 DOI: 10.1142/s1469026820500170

Nghiem Van Tinh

{"title":"Enhanced Forecasting Accuracy of Fuzzy Time Series Model Based on Combined Fuzzy C-Mean Clustering with Particle Swam Optimization","authors":"Nghiem Van Tinh","doi":"10.1142/s1469026820500170","DOIUrl":"https://doi.org/10.1142/s1469026820500170","url":null,"abstract":"Over the past 25 years, numerous fuzzy time series forecasting models have been proposed to deal the complex and uncertain problems. The main factors that affect the forecasting results of these models are partition universe of discourse, creation of fuzzy relationship groups and defuzzification of forecasting output values. So, this study presents a hybrid fuzzy time series forecasting model combined particle swarm optimization (PSO) and fuzzy C-means clustering (FCM) for solving issues above. The FCM clustering is used to divide the historical data into initial intervals with unequal size. After generating interval, the historical data is fuzzified into fuzzy sets with the aim to serve for establishing fuzzy relationship groups according to chronological order. Then the information obtained from the fuzzy relationship groups can be used to calculate forecasted value based on a new defuzzification technique. In addition, in order to enhance forecasting accuracy, the PSO algorithm is used for finding optimum interval lengths in the universe of discourse. The proposed model is applied to forecast three well-known numerical datasets (enrolments data of the University of Alabama, the Taiwan futures exchange —TAIFEX data and yearly deaths in car road accidents in Belgium). These datasets are also examined by using some other forecasting models available in the literature. The forecasting results obtained from the proposed model are compared to those produced by the other models. It is observed that the proposed model achieves higher forecasting accuracy than its counterparts for both first—order and high—order fuzzy logical relationship.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131546021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

A Hybrid Machine Learning Approach for Flood Risk Assessment and Classification 洪水风险评估与分类的混合机器学习方法

Int. J. Comput. Intell. Appl. Pub Date : 2020-06-01 DOI: 10.1142/s1469026820500121

Udoinyang G. Inyang, E. E. Akpan, O. C. Akinyokun

{"title":"A Hybrid Machine Learning Approach for Flood Risk Assessment and Classification","authors":"Udoinyang G. Inyang, E. E. Akpan, O. C. Akinyokun","doi":"10.1142/s1469026820500121","DOIUrl":"https://doi.org/10.1142/s1469026820500121","url":null,"abstract":"Communities globally experience devastating effects, high monetary loss and loss of lives due to incidents of flood and other hazards. Inadequate information and awareness of flood hazard make the management of flood risks arduous and challenging. This paper proposes a hybridized analytic approach via unsupervised and supervised learning methodologies, for the discovery of pieces of knowledge, clustering and prediction of flood severity levels (FSL). A two-staged unsupervised learning based on [Formula: see text]-means and self-organizing maps (SOM) was performed on the unlabeled flood dataset. [Formula: see text]-means based on silhouette criterion discovered top three representatives of the optimal numbers of clusters inherent in the flood dataset. Experts’ judgment favored four clusters, while Squared Euclidean distance was the best performing distance measure. SOM provided cluster visuals of the input attributes within the four different groups and transformed the dataset into a labeled one. A 5-layered Adaptive Neuro Fuzzy Inference System (ANFIS) driven by hybrid learning algorithm was applied to classify and predict FSL. ANFIS optimized by Genetic Algorithm (GA) produced root mean squared error (RMSE) of 0.323 and Error Standard Deviation of 0.408 while Particle Swarm Optimized ANFIS model produced 0.288 as the RMSE, depicting 11% improvement when compared with GA optimized model. The result shows significant improvement in the classification and prediction of flood risks using single ML tool.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132740337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 31

Optimizing Design of Fuzzy Model for Software Cost Estimation Using Particle Swarm Optimization Algorithm 基于粒子群算法的软件成本估算模糊模型优化设计

Int. J. Comput. Intell. Appl. Pub Date : 2020-05-04 DOI: 10.1142/s1469026820500054

Sonia Chhabra, Harvir Singh

引用次数: 16