Journal of Information and Telecommunication最新文献_第7页

Supportness of the protein complex standards in PPI networks PPI网络中蛋白质复合物标准的支持性

IF 2.7

Journal of Information and Telecommunication Pub Date : 2021-10-27 DOI: 10.1080/24751839.2021.1989241

Milana Grbić, Vukasin Crnogorac, M. Predojević, Aleksandar Kartelj, Dragan Matic

引用次数: 0

An enhanced evolutionary approach for solving the community detection problem 一种用于解决社区检测问题的增强进化方法

IF 2.7

Journal of Information and Telecommunication Pub Date : 2021-10-17 DOI: 10.1080/24751839.2021.1987076

Salmi Cheikh, Bouchema Sara, Zaoui Sara

{"title":"An enhanced evolutionary approach for solving the community detection problem","authors":"Salmi Cheikh, Bouchema Sara, Zaoui Sara","doi":"10.1080/24751839.2021.1987076","DOIUrl":"https://doi.org/10.1080/24751839.2021.1987076","url":null,"abstract":"ABSTRACT Community detection concepts can be encountered in many disciplines such as sociology, biology, and computer science, etc. Nowadays, a huge amount of data is produced by digital social networks and needs to be processed. In fact, the analysis of this data makes it possible to extract new knowledge about groups of individuals, their communication modes, and orientations. This knowledge can be exploited in marketing, security, Web usage, and many other decisional purposes. Community detection problem (CDP) is NP-hard and many algorithms have been designed to solve it but not to a satisfactory level. In this paper, we propose a hybrid heuristic approach based on a combination of genetic algorithms and tabu search that does not need any prior knowledge about the number or the size of each community to tackle the CDP. The method is efficient because it uses an enhanced encoding, which excludes redundant chromosomes while performing genetic operations. This approach is evaluated on a wide range of real-world networks. The result of experiments shows that the proposed algorithm outperforms many other algorithms according to the modularity measure.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"6 1","pages":"83 - 100"},"PeriodicalIF":2.7,"publicationDate":"2021-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45289137","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

Gender and region detection from human voice using the three-layer feature extraction method with 1D CNN 基于1D CNN的三层特征提取方法在人声性别和区域检测中的应用

IF 2.7

Journal of Information and Telecommunication Pub Date : 2021-10-10 DOI: 10.1080/24751839.2021.1983318

Mohammad Amaz Uddin, Refat Khan Pathan, Md Sayem Hossain, Munmun Biswas

{"title":"Gender and region detection from human voice using the three-layer feature extraction method with 1D CNN","authors":"Mohammad Amaz Uddin, Refat Khan Pathan, Md Sayem Hossain, Munmun Biswas","doi":"10.1080/24751839.2021.1983318","DOIUrl":"https://doi.org/10.1080/24751839.2021.1983318","url":null,"abstract":"ABSTRACT Analysing the human voice has always been a challenge to the engineering society for various purposes such as product review, emotional state detection, developing AI, and much more. Two basic grounds of voice or speech analysis are to detect human gender and the geographical region based on accent. This study presents a three-layer feature extraction method from the raw human voice to detect the gender as male or female, as well as the region from where that voice belongs. Fundamental frequency, spectral entropy, spectral flatness, and mode frequency have been calculated in the first layer of feature extraction. On the other hand, Mel Frequency Cepstral Coefficient has been used to extract the features in the second layer and linear predictive coding in the third layer. Regular voice contains some noises which have been removed with multiple audio data filtering processes to get noise-free smooth data. Multi-Output-based 1D Convolutional Neural Network has been used to recognize gender and region from a combined dataset which consists of TIMIT, RAVDESS, and BGC datasets. The model has successfully predicted the gender with 93.01% and region with 97.07% accuracy. This method works better than usual state-of-the-art methods in separate datasets along with the combined dataset on both gender and region classification.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"6 1","pages":"27 - 42"},"PeriodicalIF":2.7,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49539022","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: 6

New convolutional neural network models for efficient object recognition with humanoid robots 基于卷积神经网络的人形机器人高效目标识别新模型

IF 2.7

Journal of Information and Telecommunication Pub Date : 2021-10-06 DOI: 10.1080/24751839.2021.1983331

Simge Nur Aslan, A. Uçar, C. Güzelı̇ş

{"title":"New convolutional neural network models for efficient object recognition with humanoid robots","authors":"Simge Nur Aslan, A. Uçar, C. Güzelı̇ş","doi":"10.1080/24751839.2021.1983331","DOIUrl":"https://doi.org/10.1080/24751839.2021.1983331","url":null,"abstract":"ABSTRACT Humanoid robots are expected to manipulate the objects they have not previously seen in real-life environments. Hence, it is important that the robots have the object recognition capability. However, object recognition is still a challenging problem at different locations and different object positions in real time. The current paper presents four novel models with small structure, based on Convolutional Neural Networks (CNNs) for object recognition with humanoid robots. In the proposed models, a few combinations of convolutions are used to recognize the class labels. The MNIST and CIFAR-10 benchmark datasets are first tested on our models. The performance of the proposed models is shown by comparisons to that of the best state-of-the-art models. The models are then applied on the Robotis-Op3 humanoid robot to recognize the objects of different shapes. The results of the models are compared to those of the models, such as VGG-16 and Residual Network-20 (ResNet-20), in terms of training and validation accuracy and loss, parameter number and training time. The experimental results show that the proposed model exhibits high accurate recognition by the lower parameter number and smaller training time than complex models. Consequently, the proposed models can be considered promising powerful models for object recognition with humanoid robots.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"6 1","pages":"63 - 82"},"PeriodicalIF":2.7,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48223208","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

Development of real size IT systems with language competence as a challenge for a Less-Resourced Language: a methodological proposal for Indo-Aryan languages 开发具有语言能力的真实规模的IT系统是对资源较少的语言的挑战:印度雅利安语言的方法论建议

IF 2.7

Journal of Information and Telecommunication Pub Date : 2021-10-02 DOI: 10.1080/24751839.2021.1966236

Z. Vetulani, Grazyna Vetulani, P. Mohanty

引用次数: 1

Prediction of user loyalty in mobile applications using deep contextualized word representations 使用深度语境化词表示预测移动应用程序中的用户忠诚度

IF 2.7

Journal of Information and Telecommunication Pub Date : 2021-10-01 DOI: 10.1080/24751839.2021.1981684

Z. H. Kilimci

{"title":"Prediction of user loyalty in mobile applications using deep contextualized word representations","authors":"Z. H. Kilimci","doi":"10.1080/24751839.2021.1981684","DOIUrl":"https://doi.org/10.1080/24751839.2021.1981684","url":null,"abstract":"ABSTRACT Customer loyalty is important for many industries, including banking, telecommunications, gaming, and shopping, in terms of sustainability. In mobile applications, it is observed that the demand rises with the usage of mobile devices such as smartphones. Therefore, it is important to predict when players tend to leave an application. Most of the studies so far focus on churn prediction or customer loyalty in mobile applications by analyzing demographic, economic, and behavioral data about customers. In this work, we introduce sentiment analysis-based customer loyalty prediction in mobile applications using word embeddings, deep learning algorithms, and deep contextualized word representations. To our knowledge, this is the first study to evaluate loyalty of customers analyzing sentiments of users from their comments using deep learning, word embedding, and deep contextualized word representation models. For this purpose, CNNs, RNNs, LSTMs, BERT, MBERT, DistilBERT, RoBERT are used for classification purpose. On the other hand, word embedding models such as Word2Vec, GloVe, and FastText are employed for text representation. To demonstrate the impact of proposed model, comprehensive experiments are performed on seven different datasets. The experiment results show sentiment analysis of users in mobile applications can be a powerful indicator in terms of predicting customer loyalty.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"6 1","pages":"43 - 62"},"PeriodicalIF":2.7,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60140771","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: 3

The application of machine learning and deep learning in sport: predicting NBA players’ performance and popularity 机器学习和深度学习在体育中的应用:预测NBA球员的表现和受欢迎程度

IF 2.7

Journal of Information and Telecommunication Pub Date : 2021-09-28 DOI: 10.1080/24751839.2021.1977066

Nguyen Hoang Nguyen, Duy Thien An Nguyen, Bingkun Ma, Jiang Hu

{"title":"The application of machine learning and deep learning in sport: predicting NBA players’ performance and popularity","authors":"Nguyen Hoang Nguyen, Duy Thien An Nguyen, Bingkun Ma, Jiang Hu","doi":"10.1080/24751839.2021.1977066","DOIUrl":"https://doi.org/10.1080/24751839.2021.1977066","url":null,"abstract":"ABSTRACT Basketball is known for the vast amount of data collected for each player, team, game, and season. As a result, basketball is an ideal domain to work on different data analysis techniques to gain useful insights. In this study, we continued our previous study published in 2020 Computational Collective Intelligence (12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 – December 3, 2020, Proceedings) reviewing some important factors to predict players’ future performance and being selected in an All-Star game, one of the most prestigious events, of National Basket Association league. Besides traditional Machine Learning, Deep Learning is also applied in this study for prediction purpose. However, compared to traditional Machine Learning, Deep Learning’s performance is not as good for our dataset. It is understandable when our data are relatively small and structured with a few predictor variables which limited Deep Learning’s ability to deal with a vast amount of Big Data. Our final results, through both Regression and Classification Analysis, indicated that scoring is the most important factor from the primary players for any team and also basketball fan’s favourable style.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"6 1","pages":"217 - 235"},"PeriodicalIF":2.7,"publicationDate":"2021-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48649784","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: 14

A survey of deep learning approaches for WiFi-based indoor positioning 基于wifi的室内定位深度学习方法综述

IF 2.7

Journal of Information and Telecommunication Pub Date : 2021-09-20 DOI: 10.1080/24751839.2021.1975425

Xu Feng, K. Nguyen, Zhiyuan Luo

引用次数: 37

A knowledge-based model for context-aware smart service systems 上下文感知智能服务系统的基于知识的模型

IF 2.7

Journal of Information and Telecommunication Pub Date : 2021-08-23 DOI: 10.1080/24751839.2021.1962105

T. Dinh, Thanh Thoa Pham Thi, C. Pham-Nguyen, Le Nguyen Hoai Nam

引用次数: 6

A road accident pattern miner (RAP miner) 道路事故模式挖掘器(RAP挖掘器)

IF 2.7

Journal of Information and Telecommunication Pub Date : 2021-08-17 DOI: 10.1080/24751839.2021.1955533

S. M. N. Arosha Senanayake, Sisir Joshi

{"title":"A road accident pattern miner (RAP miner)","authors":"S. M. N. Arosha Senanayake, Sisir Joshi","doi":"10.1080/24751839.2021.1955533","DOIUrl":"https://doi.org/10.1080/24751839.2021.1955533","url":null,"abstract":"ABSTRACT Domain-specific data service models can retrieve critical features from frequently occurring road accident patterns (RAPs). The aim of this research is to propose scan efficient association rules’ mining-based pattern analysis which provides more accurate RAP prediction in frequent accident locations with the fastest matching pattern search from a RAP database (RAP DB). Association rules’ mining technique derives a correlation between frequent RAP and association among various attributes of a road accident. While the clustering technique discriminates different RAPs, Naïve Bayes Classification classifies and then predicts the severity of accident using Fuzzy Inference Engine (FIE) interfaced with RAP Case Library (RAP CL) using hybrid intelligence. The results of the proposed road accident data service model prove a significant increase in the accuracy of accident prediction compared to the reported results. A novel hybrid learning algorithm, interfaced with Scan Efficient Apriori (SEA) algorithm implemented, leads the fast RAP search from the first scan through RAP CL and retain new RAP in the RAP CL using case-based reasoning (CBR) during subsequent scanning. Thus, the RAP miner built proves road accident prediction using SEA, FIE and CBR with the highest accuracy and fast RAP set processing.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"5 1","pages":"484 - 498"},"PeriodicalIF":2.7,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47089545","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