Int. J. Comput. Intell. Appl.最新文献_第2页

Enhanced Differential Evolution and Particle Swarm Optimization Approaches for Discovering High Utility Itemsets 基于改进差分进化和粒子群优化的高效用项集发现方法

Int. J. Comput. Intell. Appl. Pub Date : 2023-03-31 DOI: 10.1142/s1469026823410055

N. Sukanya, P. R. J. Thangaiah

{"title":"Enhanced Differential Evolution and Particle Swarm Optimization Approaches for Discovering High Utility Itemsets","authors":"N. Sukanya, P. R. J. Thangaiah","doi":"10.1142/s1469026823410055","DOIUrl":"https://doi.org/10.1142/s1469026823410055","url":null,"abstract":"Mining patterns from High-utility itemsets (HUIs) have been exploited recently in place of frequent itemset mining (FIMs) or association-rule mining (ARMs) as they highlight profitability of products where quantity and profits are taken into account. Several techniques for HUIs have been proposed and they encounter exponential search spaces which have more distinct items or voluminous databases. Alternatively, Evolutionary Computations (ECs)-based meta-heuristics algorithms can be effective in solving issues in HUIs since a set of near-optimal solutions can be obtained within restricted periods. Current ECs-based techniques consume more time to identify HUIs in transactional databases, discover unacceptable combinations of HUIs, and finally fail to discover HUIs when neighborhood searches are not executed locally and globally. To overcome these challenges, a HUI mining algorithm based on Differential Evolution (DE) and Particle Swarm Optimization (PSO) using multiple strategies including elitism, population diversifications, exclusive preservations, and neighborhood exploration techniques has been proposed. Thus, this work defines mining patterns based on DE and PSO to identify HUIs in voluminous transactional databases. The HUIM-DE-PSO-DE algorithm proposed in this work discovers more number of HUIs which is revealed in experimental results obtained from a set of benchmark data instances. Results are compared with existing approaches using several performance metrics including convergence speeds, minimum utility threshold values, and execution time consumed.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130041569","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

Development of a Novel Artificial Intelligence Model for Better Balancing Exploration and Exploitation 一种新的人工智能模型的发展，以更好地平衡勘探和开发

Int. J. Comput. Intell. Appl. Pub Date : 2023-03-30 DOI: 10.1142/s1469026823500013

Pham Vu Hong Son, Nguyen Thi Nha Trang

{"title":"Development of a Novel Artificial Intelligence Model for Better Balancing Exploration and Exploitation","authors":"Pham Vu Hong Son, Nguyen Thi Nha Trang","doi":"10.1142/s1469026823500013","DOIUrl":"https://doi.org/10.1142/s1469026823500013","url":null,"abstract":"Grey Wolf optimizer (GWO) has been used in several fields of research. The main advantages of this algorithm are its simplicity, little controlling parameter, and adaptive exploratory behavior. However, similar to other metaheuristic algorithms, the GWO algorithm has several limitations. The main drawback of the GWO algorithm is its low capability to handle a multimodal search landscape. This drawback occurs because the alpha, beta, and gamma wolves tend to converge to the same solution. This paper presents HDGM – a novel hybrid optimization model of dragonfly algorithm and grey wolf optimizer, aiming to overcome the disadvantages of GWO algorithm. Dragonfly algorithm (DA) is combined with GWO in this study because DA has superior exploration ability, which allows it to search in promising areas in the search space. To verify the solution quality and performance of the HDGM algorithm, we used twenty-three test functions to compare the proposed model’s performance with that of the GWO, DA, particle swam optimization (PSO) and ant lion optimization (ALO). The results show that the hybrid algorithm provides more competitive results than the other variants in terms of solution quality, stability, and capacity to discover the global optimum.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126789928","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: 2

Rumor Detection on Social Media Using Deep Learning Algorithms with Fuzzy Inference System for Healthcare Analytics System Using COVID-19 Dataset 基于深度学习算法和模糊推理系统的社交媒体谣言检测:基于COVID-19数据集的医疗保健分析系统

Int. J. Comput. Intell. Appl. Pub Date : 2023-03-27 DOI: 10.1142/s1469026823410080

Akila Rathakrishnan, Revathi Sathiyanarayanan

{"title":"Rumor Detection on Social Media Using Deep Learning Algorithms with Fuzzy Inference System for Healthcare Analytics System Using COVID-19 Dataset","authors":"Akila Rathakrishnan, Revathi Sathiyanarayanan","doi":"10.1142/s1469026823410080","DOIUrl":"https://doi.org/10.1142/s1469026823410080","url":null,"abstract":"Spreading rumors on social media is a phenomenon that has destructive implication of societal interaction, diverts attention toward destructive behavior. The impact will be more influenced in healthcare management. This research aims to detect the rumors and identify the sources using deep learning algorithms. In our proposed system, after pre-processing, the tweet comments are extracted from topics and ranked as deny, support, query and comment. Then the comments are classified as positive, negative and neutral using Artificial Neural Network Neuro-fuzzy Inference System Spline-based pi-shaped Membership Function (ANISPIMF). Then the negative comments are classified into offensive, violence, misogyny and hate mongering by using Improved Deep Learning Neural Network (IDLNN) which is the combination of Deep Neural Network with Cuckoo Search–Flower Pollination Algorithm to optimize the weight values. The optimized ANISPIMF performs very well for the COVID-19 dataset in terms of Accuracy, Precision and Recall. The proposed system attains better performance and efficiency when weighted against prevailing methodologies — regarding the performance measures, there is an improvement of accuracy by 0.6%, recall by 0.7%, and precision by 1%, together with an [Formula: see text]1-score of 1.2% than the Multiloss Hierarchical Bi-LSTM with Attenuation Factor (MHA).","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"255 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131335225","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: 0

Guest Editorial - Introduction to the Special Issue on Smart Fuzzy Optimization for Decision-Making in Uncertain Environments 客座社论-不确定环境下决策的智能模糊优化特刊导论

Int. J. Comput. Intell. Appl. Pub Date : 2023-03-25 DOI: 10.1142/s1469026823020017

Er Meng Joo, D. Pelusi, Shixian Wen

引用次数: 0

An Efficiency Correlation between Various Image Fusion Techniques 各种图像融合技术之间的效率相关性

Int. J. Comput. Intell. Appl. Pub Date : 2023-03-21 DOI: 10.1142/s1469026823410109

S. BharaniNayagi, T. S. S. Angel

{"title":"An Efficiency Correlation between Various Image Fusion Techniques","authors":"S. BharaniNayagi, T. S. S. Angel","doi":"10.1142/s1469026823410109","DOIUrl":"https://doi.org/10.1142/s1469026823410109","url":null,"abstract":"Multi-focus images can be fused by the deep learning (DL) approach. Initially, multi-focus image fusion (MFIF) is used to perform the classification task. The classifier of the convolutional neural network (CNN) is implemented to determine whether the pixel is defocused or focused. The lack of available data to train the system is one of the demerits of the MFIF methodology. Instead of using MFIF, the unsupervised model of the DL approach is affordable and appropriate for image fusion. By establishing a framework of feature extraction, fusion, and reconstruction, we generate a Deep CNN of [Formula: see text] End-to-End Unsupervised Model. It is defined as a Siamese Multi-Scale feature extraction model. It can extract only three different source images of the same scene, which is the major disadvantage of the system. Due to the possibility of low intensity and blurred images, considering only three source images may lead to poor performance. The main objective of the work is to consider [Formula: see text] parameters to define [Formula: see text] source images. Many existing systems are compared to the proposed method for extracting features from images. Experimental results of various approaches show that Enhanced Siamese Multi-Scale feature extraction used along with Structure Similarity Measure (SSIM) produces an excellent fused image. It is determined by undergoing quantitative and qualitative studies. The analysis is done based on objective examination and visual traits. By increasing the parameters, the objective assessment increases in performance rate and complexity with time.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132742241","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

Intelligent Spectrum Sharing and Sensing in Cognitive Radio Network by Using AROA (Adaptive Rider Optimization Algorithm) 基于AROA (Adaptive Rider Optimization Algorithm)的认知无线网络智能频谱共享与感知

Int. J. Comput. Intell. Appl. Pub Date : 2023-03-21 DOI: 10.1142/s1469026823410079

R. Prasad, T. Jaya

{"title":"Intelligent Spectrum Sharing and Sensing in Cognitive Radio Network by Using AROA (Adaptive Rider Optimization Algorithm)","authors":"R. Prasad, T. Jaya","doi":"10.1142/s1469026823410079","DOIUrl":"https://doi.org/10.1142/s1469026823410079","url":null,"abstract":"Wireless spectrum has been allocated to licensees for large geographic areas on a long-term basis in recent years. Cognitive Radio Networks (CRN) will offer mobile users with a huge amount of available bandwidth. Due to spectrum management issues such as spectrum sensing and sharing, CRN networks pose some challenges. Hence in this paper, Adaptive Rider Optimization (AROA) is developed to improve the energy efficiency for different spectrum sensing conditions. The proposed algorithm is utilized to compute the sensing time, sequence length, and detection threshold. In order to detect the spectrum with optimal values of transmission power and sensing bandwidth, the AROA uses the adaptive threshold detection method. The spectrum sensing and sharing of the CRN network are achieved with the help of the AROA algorithm. The proposed method is implemented in MATLAB and the performances such as Normalized Energy consumption, delay, SNR, Jitter, blocking probability, convergence analysis, and Throughput are evaluated. The proposed method is contrasted with the existing methods such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), respectively.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126798453","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

Maneuver Conditioned Vehicle Trajectory Prediction Using Self-Attention 机动条件下车辆自注意轨迹预测

Int. J. Comput. Intell. Appl. Pub Date : 2023-03-20 DOI: 10.1142/s1469026823500050

Junan Huang, Zhiqiu Huang, Guohua Shen, Jinyong Wang, Xiaohua Yin

引用次数: 0

An Automatic Recognition System for Digital Collections of Indonesian Traditional Houses Using Convolutional Neural Networks for Cultural Heritage Preservation 基于卷积神经网络的印尼传统房屋数字馆藏自动识别系统

Int. J. Comput. Intell. Appl. Pub Date : 2023-02-21 DOI: 10.1142/s1469026823500037

Teny Handhayani, Ageng Hadi Pawening, J. Hendryli

{"title":"An Automatic Recognition System for Digital Collections of Indonesian Traditional Houses Using Convolutional Neural Networks for Cultural Heritage Preservation","authors":"Teny Handhayani, Ageng Hadi Pawening, J. Hendryli","doi":"10.1142/s1469026823500037","DOIUrl":"https://doi.org/10.1142/s1469026823500037","url":null,"abstract":"Indonesia is one of the archipelago countries located in Asia and it has diverse cultures. In modern society, Indonesian traditional houses have become rare and need to be preserved. This research is conducted to build a digital collection and to develop an image-based automatic recognition system for Indonesian traditional houses. In this paper, the traditional house images are collected in several ways: on-site image captures, receiving images from volunteers, and collecting public images from Google. The dataset is limited to the collection of building shape images, excluding the interior design. The authors implement Convolutional Neural Networks (ConvNets) to build a model for an automatic recognition system. The experiments run some deep network models: VGG, DenseNet, Inception, Xception, MobileNetV2, NasNetMobile, and EfficientNet. The experiments involve 1526 images of 16 classes. EfficientNet-Lite0 outperforms other models and produces the average F1-score and accuracy of 90.1% and 91.8%, respectively. ConvNets also outperform conventional classifiers.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114284907","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: 0

Detection and Classification of Skin Cancer Using Unmanned Transfer Learning Based Probabilistic Multi-Layer Dense Networks 基于概率多层密集网络的无人迁移学习皮肤癌检测与分类

Int. J. Comput. Intell. Appl. Pub Date : 2022-12-23 DOI: 10.1142/s1469026822500274

V. Nyemeesha, M. Kavitha, B. M. Ismail

{"title":"Detection and Classification of Skin Cancer Using Unmanned Transfer Learning Based Probabilistic Multi-Layer Dense Networks","authors":"V. Nyemeesha, M. Kavitha, B. M. Ismail","doi":"10.1142/s1469026822500274","DOIUrl":"https://doi.org/10.1142/s1469026822500274","url":null,"abstract":"Skin cancer is one of the most dangerous cancers that may occur for different age groups of people. As a result, early identification of skin cancer has the potential to save millions of lives. In Traditional machine learning approaches, there are various drawbacks in detection and classification of skin lesions. As a result, to achieve the robust performance, initially the joint trilateral and bilateral filter (JTBF) with convolutional auto encoder and decoder (CAED)-based preprocessing method is used to enhance the skin lesion and also removes hair from lesions. Then, transfer learning-based probabilistic multi-layer dense networks (PMDN) method-based unmanned Transfer learning segmentation method is adapted for accurately detecting the cancer region on skin lesions. Further, transfer learning convolution neural network (TL-CNN) is used to extract the features from the segmented region, which extracts the detailed inter-disease-dependent (IDD) and intra-disease specific (IDS) features. Finally, Alexa Net model is trained and tested with the IDD, IDS features and classifies the eight different skin cancer types. The complexity of the transfer learning networks is optimized by the using the Adam optimizer. Finally, the simulation results show that the proposed model resulted in superior segmentation, feature extraction, and classification performances as compared to conventional approaches. Further, the proposed method achieved 99.937% segmentation accuracy, 99.47% feature extraction accuracy, and 99.27% classification accuracy on ISIC-2019 public challenge dataset.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120938433","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

Building the Forecasting Model for Time Series Based on the Improved Fuzzy Relationship for Variation of Data 基于改进的数据变异模糊关系建立时间序列预测模型

Int. J. Comput. Intell. Appl. Pub Date : 2022-12-20 DOI: 10.1142/s1469026822500262

Ha Che-Ngoc, Luan Nguyenhuynh, Dan Nguyen-Thihong, Tai Vo-Van

引用次数: 3