Int. J. Comput. Intell. Appl.最新文献_第6页

Human Emotion Recognition using Polar-Based Lagged Poincare Plot Indices of Eye-Blinking Data 基于极性滞后庞加莱图指标的眨眼数据情感识别

Int. J. Comput. Intell. Appl. Pub Date : 2021-11-03 DOI: 10.1142/s1469026821500231

Atefeh Goshvarpour, A. Goshvarpour

{"title":"Human Emotion Recognition using Polar-Based Lagged Poincare Plot Indices of Eye-Blinking Data","authors":"Atefeh Goshvarpour, A. Goshvarpour","doi":"10.1142/s1469026821500231","DOIUrl":"https://doi.org/10.1142/s1469026821500231","url":null,"abstract":"Emotion recognition using bio-signals is currently a hot and challenging topic in human–computer interferences, robotics, and affective computing. A broad range of literature has been published by analyzing the internal/external behaviors of the subjects in confronting emotional events/stimuli. Eye movements, as an external behavior, are frequently used in the multi-modal emotion recognition system. On the other hand, classic statistical features of the signal have generally been assessed, and the evaluation of its dynamics has been neglected so far. For the first time, the dynamics of single-modal eye-blinking data are characterized. Novel polar-based indices of the lagged Poincare plots were introduced. The optimum lag was estimated using mutual information. After reconstruction of the plot, the polar measures of all points were characterized using statistical measures. The support vector machine (SVM), decision tree, and Naïve Bayes were implemented to complete the process of classification. The highest accuracy of 100% with an average accuracy of 84.17% was achieved for fear/sad discrimination using SVM. The suggested framework provided outstanding performances in terms of recognition rates, simplicity of the methodology, and less computational cost. Our results also showed that eye-blinking data possesses the potential for emotion recognition, especially in classifying fear emotion.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"os-17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127766812","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

FUSIONET: A Hybrid Model Towards Image Classification 一种用于图像分类的混合模型

Int. J. Comput. Intell. Appl. Pub Date : 2021-11-03 DOI: 10.1142/s1469026821500218

Molokwu C. Reginald, Molokwu C. Bonaventure, Molokwu C. Victor, Okeke C. Ogochukwu

{"title":"FUSIONET: A Hybrid Model Towards Image Classification","authors":"Molokwu C. Reginald, Molokwu C. Bonaventure, Molokwu C. Victor, Okeke C. Ogochukwu","doi":"10.1142/s1469026821500218","DOIUrl":"https://doi.org/10.1142/s1469026821500218","url":null,"abstract":"Image classification, a topic of pattern recognition in computer vision, is an approach of classification based on contextual information in images. Contextual here means this approach is focusing on the relationship of the nearby pixels also called neighborhood. An open topic of research in computer vision is to devise an effective means of transferring human’s informal knowledge into computers, such that computers can also perceive their environment. However, the occurrence of object with respect to image representation is usually associated with various features of variation causing noise in the image representation. Hence, it tends to be very difficult to actually disentangle these abstract factors of influence from the principal object. In this paper, we have proposed a hybrid model: FUSIONET, which has been modeled for studying and extracting meaning facts from images. Our proposition combines two distinct stack of convolution operation (3 × 3 and 1 × 1, respectively). Successively, these relatively low-feature maps from the above operation are fed as input to a downstream classifier for classification of the image in question.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121742560","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

Heuristic-Assisted BERT for Twitter Sentiment Analysis Twitter情感分析的启发式辅助BERT

Int. J. Comput. Intell. Appl. Pub Date : 2021-09-01 DOI: 10.1142/s1469026821500152

Gokul Yenduri, R. RajakumarBoothalingam, K. Praghash, D. Binu

{"title":"Heuristic-Assisted BERT for Twitter Sentiment Analysis","authors":"Gokul Yenduri, R. RajakumarBoothalingam, K. Praghash, D. Binu","doi":"10.1142/s1469026821500152","DOIUrl":"https://doi.org/10.1142/s1469026821500152","url":null,"abstract":"The identification of opinions and sentiments from tweets is termed as “Twitter Sentiment Analysis (TSA)”. The major process of TSA is to determine the sentiment or polarity of the tweet and then classifying them into a negative or positive tweet. There are several methods introduced for carrying out TSA, however, it remains to be challenging due to slang words, modern accents, grammatical and spelling mistakes, and other issues that could not be solved by existing techniques. This work develops a novel customized BERT-oriented sentiment classification that encompasses two main phases: pre-processing and tokenization, and a “Customized Bidirectional Encoder Representations from Transformers (BERT)”-based classification. At first, the gathered raw tweets are pre-processed under stop-word removal, stemming and blank space removal. After pre-processing, the semantic words are obtained, from which the meaningful words (tokens) are extracted in the tokenization phase. Consequently, these extracted tokens are classified via optimized BERT, where biases and weight are tuned optimally by Particle-Assisted Circle Updating Position (PA-CUP). Moreover, the maximal sequence length of the BERT encoder is updated using standard PA-CUP. Finally, the performance analysis is carried out to substantiate the enhancement of the proposed model.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129585046","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: 8

A Real-Time Approach for Automatic Food Quality Assessment Based on Shape Analysis 一种基于形状分析的食品质量实时自动评估方法

Int. J. Comput. Intell. Appl. Pub Date : 2021-08-13 DOI: 10.1142/s146902682150019x

Luca Donati, Eleonora Iotti, A. Prati

{"title":"A Real-Time Approach for Automatic Food Quality Assessment Based on Shape Analysis","authors":"Luca Donati, Eleonora Iotti, A. Prati","doi":"10.1142/s146902682150019x","DOIUrl":"https://doi.org/10.1142/s146902682150019x","url":null,"abstract":"Products sorting is a task of paramount importance for many countries’ agricultural industry. An accurate quality check assures that good products are not wasted, and rotten, broken and bent food are properly discarded, which is extremely important for food production chains. Such products sorting and quality controls are often performed with consolidated instruments, since simple systems are easier to maintain, validate, and they speed up the processing in terms of production line speed and products per second. Moreover, industries often lack advanced formation, required for more sophisticated solutions. As a result, the sorting task for many food products is mainly done by color information only. Sorting machines typically detect the color response of products to specific LEDs with various light wavelengths. Unfortunately, a color check is often not enough to detect some very common defects. The shape of a product, instead, reveals many important defects and is highly reliable in detecting external objects mixed with food. Also, shape can be used to take detailed measurements of a product, such as its area, length, width, anisotropy, etc. This paper proposes a complete treatment of the problem of sorting food by its shape. It treats real-world problems such as accuracy, execution time, latency and it provides an overview of a full system used on state-of-the-art measurement machines.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128640871","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 Method of Environmental Sound Classification Based on Residual Networks and Data Augmentation 基于残差网络和数据增强的环境声分类方法

Int. J. Comput. Intell. Appl. Pub Date : 2021-08-13 DOI: 10.1142/s1469026821500188

Jinfang Zeng, Y. Li, Yu Zhang, Da Chen

{"title":"A Method of Environmental Sound Classification Based on Residual Networks and Data Augmentation","authors":"Jinfang Zeng, Y. Li, Yu Zhang, Da Chen","doi":"10.1142/s1469026821500188","DOIUrl":"https://doi.org/10.1142/s1469026821500188","url":null,"abstract":"Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. To date, a variety of signal processing and machine learning techniques have been applied to ESC task, including matrix factorization, dictionary learning, wavelet filterbanks and deep neural networks. It is observed that features extracted from deeper networks tend to achieve higher performance than those extracted from shallow networks. However, in ESC task, only the deep convolutional neural networks (CNNs) which contain several layers are used and the residual networks are ignored, which lead to degradation in the performance. Meanwhile, a possible explanation for the limited exploration of CNNs and the difficulty to improve on simpler models is the relative scarcity of labeled data for ESC. In this paper, a residual network called EnvResNet for the ESC task is proposed. In addition, we propose to use audio data augmentation to overcome the problem of data scarcity. The experiments will be performed on the ESC-50 database. Combined with data augmentation, the proposed model outperforms baseline implementations relying on mel-frequency cepstral coefficients and achieves results comparable to other state-of-the-art approaches in terms of classification accuracy.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114329252","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

Discrete Social Spider Algorithm for Solving Traveling Salesman Problem 离散社会蜘蛛算法求解旅行商问题

Int. J. Comput. Intell. Appl. Pub Date : 2021-08-13 DOI: 10.1142/s1469026821500206

A. Khosravanian, M. Rahmanimanesh, P. Keshavarzi

{"title":"Discrete Social Spider Algorithm for Solving Traveling Salesman Problem","authors":"A. Khosravanian, M. Rahmanimanesh, P. Keshavarzi","doi":"10.1142/s1469026821500206","DOIUrl":"https://doi.org/10.1142/s1469026821500206","url":null,"abstract":"The Social Spider Algorithm (SSA) was introduced based on the information-sharing foraging strategy of spiders to solve the continuous optimization problems. SSA was shown to have better performance than the other state-of-the-art meta-heuristic algorithms in terms of best-achieved fitness values, scalability, reliability, and convergence speed. By preserving all strengths and outstanding performance of SSA, we propose a novel algorithm named Discrete Social Spider Algorithm (DSSA), for solving discrete optimization problems by making some modifications to the calculation of distance function, construction of follow position, the movement method, and the fitness function of the original SSA. DSSA is employed to solve the symmetric and asymmetric traveling salesman problems. To prove the effectiveness of DSSA, TSPLIB benchmarks are used, and the results have been compared to the results obtained by six different optimization methods: discrete bat algorithm (IBA), genetic algorithm (GA), an island-based distributed genetic algorithm (IDGA), evolutionary simulated annealing (ESA), discrete imperialist competitive algorithm (DICA) and a discrete firefly algorithm (DFA). The simulation results demonstrate that DSSA outperforms the other techniques. The experimental results show that our method is better than other evolutionary algorithms for solving the TSP problems. DSSA can also be used for any other discrete optimization problem, such as routing problems.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131855805","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

The Exploitation of Distance Distributions for Clustering 距离分布在聚类中的应用

Int. J. Comput. Intell. Appl. Pub Date : 2021-08-12 DOI: 10.1142/S1469026821500164

Michael C. Thrun

{"title":"The Exploitation of Distance Distributions for Clustering","authors":"Michael C. Thrun","doi":"10.1142/S1469026821500164","DOIUrl":"https://doi.org/10.1142/S1469026821500164","url":null,"abstract":"Although distance measures are used in many machine learning algorithms, the literature on the context-independent selection and evaluation of distance measures is limited in the sense that prior knowledge is used. In cluster analysis, current studies evaluate the choice of distance measure after applying unsupervised methods based on error probabilities, implicitly setting the goal of reproducing predefined partitions in data. Such studies use clusters of data that are often based on the context of the data as well as the custom goal of the specific study. Depending on the data context, different properties for distance distributions are judged to be relevant for appropriate distance selection. However, if cluster analysis is based on the task of finding similar partitions of data, then the intrapartition distances should be smaller than the interpartition distances. By systematically investigating this specification using distribution analysis through the mirrored-density (MD plot), it is shown that multimodal distance distributions are preferable in cluster analysis. As a consequence, it is advantageous to model distance distributions with Gaussian mixtures prior to the evaluation phase of unsupervised methods. Experiments are performed on several artificial datasets and natural datasets for the task of clustering.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124746833","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: 5

Regularized Semi-Supervised Metric Learning with Latent Structure Preserved 保留隐结构的正则化半监督度量学习

Int. J. Comput. Intell. Appl. Pub Date : 2021-06-09 DOI: 10.1142/S1469026821500139

Qianying Wang, Ming Lu, Meng Li, Fei Guan

引用次数: 1

A Multi-Swarm ABC Algorithm for Parameters Optimization of SOFM Neural Network in Dynamic Environment 动态环境下SOFM神经网络参数优化的多群ABC算法

Int. J. Comput. Intell. Appl. Pub Date : 2021-06-09 DOI: 10.1142/S1469026821500140

Dongli Jia, Fan Li, Jun Tu

引用次数: 1

GA2RM: A GA-Based Action Rule Mining Method GA2RM:一种基于ga的动作规则挖掘方法

Int. J. Comput. Intell. Appl. Pub Date : 2021-06-09 DOI: 10.1142/S1469026821500127

S. Hashemi, P. Shamsinejad

引用次数: 1