Int. J. Comput. Intell. Appl.最新文献

{"title":"Identification and Filtering of Web Spams Using a Machine Learning Method","authors":"Dawei Zhang, Yanyu Liu","doi":"10.1142/s1469026822500237","DOIUrl":"https://doi.org/10.1142/s1469026822500237","url":null,"abstract":"In order to enhance the filtering of spam on the Internet and improve the experience of Internet users, this paper proposed to convert the email text into vector features using the vector space model, constructed a two-dimensional matrix, and used a convolutional neural network (CNN) to identify spam on the Internet. The CNN was compared with other two classifiers, support vector machine (SVM), and backward-propagation neural network (BPNN), in simulation experiments. The final results showed that the spam recognition algorithm with CNN as the classifier had better recognition performance than the algorithms with SVM and BPNN classifiers and was also more advantageous in terms of recognition cost and time for spam; in addition, the CNN had the best recognition performance when the number of extracted features was 15.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129533986","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}

{"title":"An Optimized Flower Categorization Using Customized Deep Learning","authors":"Ritu Rani, Sandhya Pundhir, A. Dev, Arun Sharma","doi":"10.1142/s1469026822500298","DOIUrl":"https://doi.org/10.1142/s1469026822500298","url":null,"abstract":"Categorizing flowers is quite a challenging task as there is so much diversity in the species, and the images of the different flower species could be pretty similar. Flower categorization involves many issues like low resolution and noisy images, occluded images with the leaves and the stems of the plants and sometimes even with the insects. The traditional handcrafted features were used for extraction of the features and the machine learning algorithms were applied but with the advent of the deep neural networks. The focus of the researchers has inclined towards the use of the non-handcrafted features for the image categorization tasks because of their fast computation and efficiency. In this study, the images are pre-processed to enhance the key features and suppress the undesired information’s and the objects are localized in the image through the segmentation to extract the Region of Interest, detect the objects and perform feature extraction and the supervised classification of flowers into five categories: daisy, sunflower, dandelion, tulip and rose. First step involves the pre-processing of the images and the second step involves the feature extraction using the pre-trained models ResNet50, MobileNet, DenseNet169, InceptionV3 and VGG16 and finally the classification is done into five different categories of flowers. Ultimately, the results obtained from these proposed architectures are then analyzed and presented in the form of confusion matrices. In this study, the CNN model has been proposed to evaluate the performance of categorization of flower images, and then data augmentation is applied to the images to address the problem of overfitting. The pre-trained models ResNet50, MobileNet, DenseNet169, InceptionV3 and VGG16 are implemented on the flower dataset to perform categorization tasks. The pre-trained models are empirically implemented and assessed on the various flower datasets. Performance analysis has been done in terms of the training, validation accuracy, validation loss and training loss. The empirical assessment of these pre-trained models demonstrate that these models are quite effective for the categorization tasks. According to the performance analysis, the VGG16 outperforms all the other models and provides a training accuracy of 99.01%. Densenet169 and MobileNet also give comparable validation accuracy. ResNet50 gives the lowest training accuracy of 60.46% as compared with the rest of the pre-trained replica or models.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132027258","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}

{"title":"Investigation of the Optimal PID-Like Fuzzy Logic Controller for Ball and Beam System with Improved Quantum Particle Swarm Optimization","authors":"Okkes Tolga Altinöz, A. Yılmaz","doi":"10.1142/s1469026822500250","DOIUrl":"https://doi.org/10.1142/s1469026822500250","url":null,"abstract":"Fuzzy Logic Controllers (FLCs) are intelligent control methods, where membership functions and corresponding rules are defined to get a proper control signal. The parameters were defined for these controllers, and they are named as PID-like FLC since the input and output parameters are connected to the Fuzzy controller with integral and derivative action of the error signal to change the behavior/performance of FLC. In this research, three different rule sets for Fuzzy controllers; 3 × 3, 5 × 5, and 7 × 7 are used and parameters are optimized with; differential evolution, genetic algorithm, particle swarm optimization and quantum-behaved particle swarm optimization. In addition to these controllers, a novel algorithm named as improved quantum particle swarm optimization is proposed as a part of this research. The simulation and real-life implementation on the experimental set results of these controllers are discussed in this paper.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128394564","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}

{"title":"Aerial Image Denoising Using a Best-So-Far ABC-based Adaptive Filter Method","authors":"Anan Banharnsakun","doi":"10.1142/s1469026822500249","DOIUrl":"https://doi.org/10.1142/s1469026822500249","url":null,"abstract":"Nowadays, digital images play an increasingly important role in helping to explain phenomena and to attract people’s attention through various types of media rather than the use of text. However, the quality of digital images may be degraded due to noise that has occurred either during their recording or their transmission via a network. Therefore, removal of image noise, which is known as “image denoising”, is one of the primary required tasks in digital image processing. Various methods in earlier studies have been developed and proposed to remove the noise found in images. For example, the use of metric filters to eliminate noise has received much attention from researchers in recent literature. However, the convergence speed when searching for the optimal filter coefficient of these proposed algorithms is quite low. Previous research in the past few years has found that biologically inspired approaches are among the more promising metaheuristic methods used to find optimal solutions. In this work, an image denoising approach based on the best-so-far (BSF) ABC algorithm combined with an adaptive filter is proposed to enhance the performance of searching for the optimal filter coefficient in the denoising process. Experimental results indicate that the denoising of images employing the proposed BSF ABC technique yields good quality and the ability to remove noise while preventing the features of the image from being lost in the denoising process. The denoised image quality obtained by the proposed method achieves a 20% increase compared with other recently developed techniques in the field of biologically inspired approaches.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126686263","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}

{"title":"Multi-Stream Graph Convolutional Networks for Text Classification via Representative-Word Document Mining","authors":"Meng Li, Shenyu Chen, Weifeng Yang, Qianying Wang","doi":"10.1142/s1469026822500286","DOIUrl":"https://doi.org/10.1142/s1469026822500286","url":null,"abstract":"Recently, graph convolutional networks (GCNs) for text classification have received considerable attention in natural language processing. However, most current methods just use original documents and words in the corpus to construct the topology of graph which may lose some effective information. In this paper, we propose a Multi-Stream Graph Convolutional Network (MS-GCN) for text classification via Representative-Word Document (RWD) mining, which is implemented in PyTorch. In the proposed method, we first introduce temporary labels and mine the RWDs which are treated as additional documents in the corpus. Then, we build a heterogeneous graph based on relations among a Group of RWDs (GRWDs), words and original documents. Furthermore, we construct the MS-GCN based on multiple heterogeneous graphs according to different GRWDs. Finally, we optimize our MS-GCN model through updated mechanism of GRWDs. We evaluate the proposed approach on six text classification datasets, 20NG, R8, R52, Ohsumed, MR and Pheme. Extensive experiments on these datasets show that our proposed approach outperforms state-of-the-art methods for text classification.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"490 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115882727","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}

{"title":"Boil-Turbine System Identification Based on Robust Interval Type-2 Fuzzy C-Regression Model","authors":"J. Shi","doi":"10.1142/s1469026822500225","DOIUrl":"https://doi.org/10.1142/s1469026822500225","url":null,"abstract":"The boil-turbine system is a multivariable and strong coupling system with the characteristics of nonlinearity, time-varying parameters, and large delay. The accurate model can effectively improve the performance of turbine–boiler coordinated control system. In this paper, the boil-turbine model is established by interval type-2 (IT2) T-S fuzzy model. The premise parameters of IT2 T-S fuzzy model are identified by robust IT2 fuzzy c-regression model (RIT2-FCRM) clustering algorithm. The RIT2-FCRM is based on interval type-2 fuzzy sets (IT2FS) and applies a robust objective function, this clustering algorithm can reduce the impacts of outliers and noise points. The effectiveness and practicability of RIT2-FCRM are demonstrated by the identification results of the boiler–turbine system.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127429939","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}

{"title":"A Precise Computational Method for Hippocampus Segmentation from MRI of Brain to Assist Physicians in the Diagnosis of Alzheimer's Disease","authors":"T. Genish, S. Kavitha, S. Vijayalakshmi","doi":"10.1142/s1469026822500201","DOIUrl":"https://doi.org/10.1142/s1469026822500201","url":null,"abstract":"Hippocampus segmentation on magnetic resonance imaging is more significant for diagnosis, treatment and analyzing of neuropsychiatric disorders. Automatic segmentation is an active research field. Previous state-of-the-art hippocampus segmentation methods train their methods on healthy or Alzheimer’s disease patients from public datasets. It arises the question whether these methods are capable for recognizing the hippocampus in a different domain. Therefore, this study proposes a precise computational method for hippocampus segmentation from MRI of brain to assist physicians in the diagnosis of Alzheimer’s disease (HCS-MRI-DAD-LBP). Initially, the input images are pre-processed by Trimmed mean filter for image quality enhancement. Then the pre-processed images are given to ROI detection, ROI detection utilizes Weber’s law which determines the luminance factor of the image. In the region extraction process, Chan–Vese active contour model (ACM) and level sets are used (UACM). Finally, local binary pattern (LBP) is utilized to remove the erroneous pixel that maximizes the segmentation accuracy. The proposed model is implemented in MATLAB, and its performance is analyzed with performance metrics, like precision, recall, mean, variance, standard deviation and disc similarity coefficient. The proposed HCS-MRI-DAD-LBP method attains in OASIS dataset provides high disc similarity coefficient of 12.64%, 10.11% and 1.03% compared with the existing methods, like HCS-DAS-MLT, HCS-DAS-RNN and HCS-DAS-GMM and in ADNI dataset provides high precision of 20%, 9.09% and 1.05% compared with existing methods like HCS-MRI-DAD-CNN-ADNI, HCS-MRI-DAD-MCNN-ADNI and HCS-MRI-DAD-CNN-RNN-ADNI, respectively.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130648190","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}

{"title":"A Real-World Industrial Application of Particle Swarm Optimization: Baghouse Designing","authors":"Pouya Bolourchi, M. Gholami","doi":"10.1142/s1469026822500213","DOIUrl":"https://doi.org/10.1142/s1469026822500213","url":null,"abstract":"Due to the high ability and flexibility of meta-heuristic algorithms (MAs), they can widely be used in many applications to solve different problems. Recently, real-world engineering applications of these optimization algorithms have attracted researchers’ attention. This paper applies particle swarm optimization (PSO) as an effective population-based MAs to design the baghouse (BH). BH filters are among the most commonly used devices in air pollution control systems in mining and food manufacturers and power plants. Designing the BH depends on several parameters such as its capacity or airflow (Nm3/h), air-to-cloth ratio ([Formula: see text]), cam velocity, and installation limitations. Generally, industrial designers select the number and length of bags and their arrangement based on the experimental observations to meet the parameters mentioned above. The minimum cost or total weight of equipment is utilized for proposing a competitive price for suppliers. In this paper, a PSO algorithm is used to minimize the total cost by finding the best possible design (the number, length, and arrangement of bags). In addition, a real example of installed BH in a pelletizing plant is given and compared with PSO results to investigate the efficiency of the proposed algorithm. The results suggest that PSO can find a better design with minimum total cost than an installed BH filter, and therefore, PSO is applicable to industrial designers.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127486646","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}

{"title":"FNN Based-Virtual Screening Using 2D Pharmacophore Fingerprint for Activity Prediction in Drug Discovery","authors":"Seloua Hadiby, Y. M. B. Ali","doi":"10.1142/s1469026822500195","DOIUrl":"https://doi.org/10.1142/s1469026822500195","url":null,"abstract":"Drug discovery remains a hard field that faces from the beginning of its process to the end many difficulties and challenges in order to discover a new potential drug. The use of technology has helped a lot in achieving many goals at the lowest cost and in the shortest possible time. Machine learning methods have proven for many years their performance although their limitations in some cases. The use of deep learning for virtual screening in drug discovery allows to process efficiently the huge amount of data and gives more precise results. In this paper, we propose a procedure for virtual screening (VS) based on Feedforward Neural Network in order to predict the biological activity of a set of chemical compounds on a given receptor. we have proposed a distance interval and it divisions to describe the chemical compound by the 2D pharmacophore fingerprint. Our model was trained on a dataset of active and inactive chemical compounds on cyclin A kinase1 receptor (CDK1), a very important protein family which has a role in the regulation of the cell cycle and cancer development. The results have proven that the proposed model is efficient and comparable with some widely used machine learning methods in drug discovery.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"30 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131165318","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}

{"title":"Hybrid Grasshopper and Chameleon Swarm Optimization Algorithm for Text Feature Selection with Density Peaks Clustering","authors":"R. Purushothaman, S. Selvakumar, S. Rajagopalan","doi":"10.1142/s1469026822500183","DOIUrl":"https://doi.org/10.1142/s1469026822500183","url":null,"abstract":"Clustering consists of various applications on machine learning, image segmentation, data mining and pattern recognition. The proper selection of clustering is significant in feature selection. Therefore, in this paper, a Text Feature Selection (FS) and Clustering using Grasshopper–Chameleon Swarm Optimization with Density Peaks Clustering algorithm (TFSC-G-CSOA-DPCA) is proposed. Initially, the input features are pre-processed for converting text into numerical form. These preprocessed text features are given to Grasshopper–Chameleon Swarm Optimization Algorithm, which selects important text features. In Grasshopper–Chameleon Swarm Optimization Algorithm, the Grasshopper Optimization Algorithm selects local feature from text document and Chameleon Swarm Optimization Algorithm selects the best global feature from local feature. These important features are tested using density peaks clustering algorithm to maximize the reliability and minimize the computational time cost. The performance of Grasshopper–Chameleon Swarm Optimization Algorithm is analyzed with 20 News groups dataset. Moreover, the performance metrics, like accuracy, precision, sensitivity, specificity, execution time and memory usage are analyzed. The simulation process shows that the proposed TFSC-G-CSOA-DPCA method provides better accuracy of 97.36%, 95.14%, 94.67% and 91.91% and maximum sensitivity of 96.25%, 87.25%, 93.96% and 92.59% compared to the existing methods such as TFSC-BBA-MCL, TFSC-MVO-K-Means C, TFSC-GWO-GOA-FCM and TFSC-WM-K-Means C, respectively.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123311406","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}