International Journal of Information and Communication Technology最新文献_第2页

Investigating Job Mismatch in Software Industry through News Big Data 基于新闻大数据的软件行业岗位错配研究

International Journal of Information and Communication Technology Pub Date : 2023-01-19 DOI: 10.32890/jict2023.22.1.2

Juho Song, Ho Lee, O-young Kwon

{"title":"Investigating Job Mismatch in Software Industry through News Big Data","authors":"Juho Song, Ho Lee, O-young Kwon","doi":"10.32890/jict2023.22.1.2","DOIUrl":"https://doi.org/10.32890/jict2023.22.1.2","url":null,"abstract":"The purpose of this study is to identify issues related to software manpower, which became more important in the era of the FourthIndustrial Revolution in Korea. The results of this study can provide guidelines for those who establish software manpower training policies for solving the software industry’s human resource paradox. As for the research method, the quantitative text network and qualitative analyses from industry experts were used to interpret the results. A total of 14,752 news data mentioning software manpower were extracted, and data pre-processing for the synonyms and negative words were performed. The network was non-directional and consisted of 14,074 words (nodes) and 1,542,383 word combinations (edges). In addition, the network was clustered based on Modularity, and the degree of connection and eigenvector centrality were used to determine the importance of nodes. The analysis of the results showed that the government’s efforts through the Korean Ministry of Science and ICT were vital in creating jobs that fueled software innovation growth, and that software education was actively promoted to develop software talent. This study had the following implications. It was confirmed that software is making a high contribution to the expansion of business opportunities and job creation in the fields of new technology and software convergence technology. To resolve the software manpower supply-demand mismatch, it is necessary to cultivate high-quality software talent and provide mid- to long-term activities to attract competent human resources. In addition, it is necessary to develop and expand programs that link education and recruitment in terms of public-private cooperation along with government-led investment to strengthen national software competitiveness.","PeriodicalId":39396,"journal":{"name":"International Journal of Information and Communication Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83873866","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

A Novel Method for Fashion Clothing Image Classification Based on Deep Learning 基于深度学习的时尚服装图像分类新方法

International Journal of Information and Communication Technology Pub Date : 2023-01-19 DOI: 10.32890/jict2023.22.1.6

Seong-Yoon Shin, Gwanghyun Jo, Guangxing Wang

{"title":"A Novel Method for Fashion Clothing Image Classification Based on Deep Learning","authors":"Seong-Yoon Shin, Gwanghyun Jo, Guangxing Wang","doi":"10.32890/jict2023.22.1.6","DOIUrl":"https://doi.org/10.32890/jict2023.22.1.6","url":null,"abstract":"Image recognition and classification is a significant research topic in computational vision and widely used computer technology. Themethods often used in image classification and recognition tasks are based on deep learning, like Convolutional Neural Networks(CNNs), LeNet, and Long Short-Term Memory networks (LSTM). Unfortunately, the classification accuracy of these methods isunsatisfactory. In recent years, using large-scale deep learning networks to achieve image recognition and classification canimprove classification accuracy, such as VGG16 and Residual Network (ResNet). However, due to the deep network hierarchyand complex parameter settings, these models take more time in the training phase, especially when the sample number is small, which can easily lead to overfitting. This paper suggested a deep learning-based image classification technique based on a CNN model and improved convolutional and pooling layers. Furthermore, the study adopted the approximate dynamic learning rate update algorithm in the model training to realize the learning rate’s self-adaptation, ensure the model’s rapid convergence, and shorten the training time. Using the proposed model, an experiment was conducted on the Fashion-MNIST dataset, taking 6,000 images as the training dataset and 1,000 images as the testing dataset. In actual experiments, the classification accuracy of the suggested method was 93 percent, 4.6 percent higher than that of the basic CNN model. Simultaneously, the study compared the influence of the batch size of model training on classification accuracy. Experimental outcomes showed this model is very generalized in fashion clothing image classification tasks. ","PeriodicalId":39396,"journal":{"name":"International Journal of Information and Communication Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90498560","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

It’s Cool to be Healthy! The Effect of Perceived Coolness on the Adoption of Fitness Bands and Health Behaviour 保持健康很酷!感知凉爽对健身手环采用和健康行为的影响

International Journal of Information and Communication Technology Pub Date : 2023-01-19 DOI: 10.32890/jict2023.22.1.5

Nur Hanis Mohamad Noor, Izzal Asnira Zolkepli, Bahiyah Omar

{"title":"It’s Cool to be Healthy! The Effect of Perceived Coolness on the Adoption of Fitness Bands and Health Behaviour","authors":"Nur Hanis Mohamad Noor, Izzal Asnira Zolkepli, Bahiyah Omar","doi":"10.32890/jict2023.22.1.5","DOIUrl":"https://doi.org/10.32890/jict2023.22.1.5","url":null,"abstract":"Contemporary technology success is frequently associated with the competitive advantage of being cool. A fitness band is one of thesmart wearable devices promoting health behaviours, which is one of the cool lifestyle trends in modern societies. Although past research established the profound effects of coolness on user technology acceptance, the influencing role in fostering health behaviour remained obscure. To bridge the existing literature gap, the current study aims to examine the perception of coolness as a higher-order construct with multiple dimensions, namely originality, attractiveness, and sub-cultural appeals, by investigating the direct effect on fitness band adoption and indirect influence on users’ health behaviour. An online survey was conducted on 280 fitness band users, and the data was subsequently analysed via the Partial Least Squares-Structural Equation Modeling (PLS-SEM). The study results demonstrated that the perceived coolness of fitness bands significantly affects users’ device adoption levels, which subsequently influence personal health behaviour. This study thus contributes to health communication research by testing the coolness concept and developing the diffusioninnovation framework from current human-computer interaction literature. The findings would guide future developers of fitness bands to emphasise the coolness functions for higher degrees of adoption and positive impact on society.","PeriodicalId":39396,"journal":{"name":"International Journal of Information and Communication Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85707405","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

Enhanced Robust Univariate Classification Methods for Solving Outliers and Overfitting Problems 解决异常值和过拟合问题的增强鲁棒单变量分类方法

International Journal of Information and Communication Technology Pub Date : 2023-01-19 DOI: 10.32890/jict2023.22.1.1

F. Okwonu, N. Ahad, Hashibah Hamid, N. Muda, Olimjon Shukurovich Sharipov

{"title":"Enhanced Robust Univariate Classification Methods for Solving Outliers and Overfitting Problems","authors":"F. Okwonu, N. Ahad, Hashibah Hamid, N. Muda, Olimjon Shukurovich Sharipov","doi":"10.32890/jict2023.22.1.1","DOIUrl":"https://doi.org/10.32890/jict2023.22.1.1","url":null,"abstract":"The robustness of some classical univariate classifiers is hampered if the data are contaminated. Overfitting is another hiccup when the data sets are uncontaminated with a considerable sample size. The performance of the classification models can be easily biased by the outliers’ problems, of which the constructed model tends to be overfitted. Previous studies often used the Bayes Classifier (BC) and the Predictive Classifier (PC) to address two groups of univariate classification problems. Unfortunately for substantial large sample sizes and uncontaminated data, the BC method overfits when the Optimal Probability of Exact Classification (OPEC) is used as an evaluation benchmark. Meanwhile, for small sample sizes, the BC and PC methods are extremely susceptible to outliers. To overcome these two problems, we proposed two methods: the Smart Univariate Classifier (SUC) and the hybrid classifier. The latter is a combination of the SUC and the BC methods, known as the Smart Univariate Bayes Classifier (SUBC). The performance of the new classification methods was evaluated and compared with the conventional BC and PC methods using the OPEC as a benchmark value. To validate the performance of these classification methods, the Probability of Exact Classification (PEC) was compared with the OPEC value. The results showed that the proposed methods outperformed the conventional BC and PC methods based on the real data sets applied. Numerical results also revealed that the SUC method could solve the overfitting problem. The results further indicated that the two proposed methods were robust against outliers. Therefore, these new methods are helpful when practitioners are confronted with overfitting and data contamination problems.","PeriodicalId":39396,"journal":{"name":"International Journal of Information and Communication Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86602694","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

Time-Distributed Attention-Layered Convolution Neural Network with Ensemble Learning using Random Forest Classifier for Speech Emotion Recognition 基于随机森林分类器集成学习的时间分布注意力分层卷积神经网络用于语音情感识别

International Journal of Information and Communication Technology Pub Date : 2023-01-18 DOI: 10.32890/jict2023.22.1.3

Y. Bhanusree, Samayamantula Srinivas Kumar, Anne Koteswara Rao

{"title":"Time-Distributed Attention-Layered Convolution Neural Network with Ensemble Learning using Random Forest Classifier for Speech Emotion Recognition","authors":"Y. Bhanusree, Samayamantula Srinivas Kumar, Anne Koteswara Rao","doi":"10.32890/jict2023.22.1.3","DOIUrl":"https://doi.org/10.32890/jict2023.22.1.3","url":null,"abstract":"Speech Emotion Detection (SER) is a field of identifying human emotions from human speech utterances. Human speech utterancesare a combination of linguistic and non-linguistic information. Nonlinguistic SER provides a generalized solution in human–computerinteraction applications as it overcomes the language barrier. Machine learning and deep learning techniques were previously proposed for classifying emotions using handpicked features. To achieve effective and generalized SER, feature extraction can be performed using deep neural networks and ensemble learning for classification. The proposed model employed a time-distributed attention-layered convolution neural network (TDACNN) for extracting spatiotemporal features at the first stage and a random forest (RF) classifier, which is an ensemble classifier for efficient and generalized classification of emotions, at the second stage. The proposed model was implemented on the RAVDESS and IEMOCAP data corpora and compared with the CNN-SVM and CNN-RF models for SER. The TDACNN-RF model exhibited test classification accuracies of 92.19 percent and 90.27 percent on the RAVDESS and IEMOCAP data corpora, respectively. The experimental results proved that the proposed model is efficient in extracting spatiotemporal features from time-series speech signals and can classify emotions with good accuracy. The class confusion among the emotions was reduced for both data corpora, proving that the model achieved generalization.","PeriodicalId":39396,"journal":{"name":"International Journal of Information and Communication Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78756334","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

Concentration Separation Prediction Model to Enhance Prediction Accuracy of Particulate Matter 提高颗粒物预测精度的浓度分离预测模型

International Journal of Information and Communication Technology Pub Date : 2023-01-18 DOI: 10.32890/jict2023.22.1.4

Yonghan Jung, Chang-heon Oh

{"title":"Concentration Separation Prediction Model to Enhance Prediction Accuracy of Particulate Matter","authors":"Yonghan Jung, Chang-heon Oh","doi":"10.32890/jict2023.22.1.4","DOIUrl":"https://doi.org/10.32890/jict2023.22.1.4","url":null,"abstract":"Demand for more accurate particulate matter forecasts is accumulating owing to the increased interest and issues regarding particulate matter. Incredibly low concentration particulate matter, which accounts for most of the overall particulate matter, is often underestimated when a particulate matter prediction model based on machine learning is used. This study proposed a concentration-specific separation prediction model to overcome this shortcoming. Three prediction models based on Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), commonly used for performance evaluation of the proposed prediction model, were used as comparative models. Root mean squared error (RMSE), mean absolute percentage error (MAPE), and accuracy were utilized for performance evaluation. The results showed that the prediction accuracy for all Air Quality Index (AQI) segments was more than 80 percent in the entire concentration spectrum. In addition, the study confirmed that the over-prediction phenomenon of single neural network models concentrated in the ‘normal’ AQI region was alleviated.","PeriodicalId":39396,"journal":{"name":"International Journal of Information and Communication Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80178628","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

Real-time recommendation method of online education resources based on improved decision tree algorithm 基于改进决策树算法的在线教育资源实时推荐方法

International Journal of Information and Communication Technology Pub Date : 2023-01-01 DOI: 10.1504/ijict.2023.134840

Shufang Xiao, Jue Liu, Ping Xu

引用次数: 0

A feature extraction method of English learning behaviour data based on improved maximum expectation clustering 基于改进最大期望聚类的英语学习行为数据特征提取方法

International Journal of Information and Communication Technology Pub Date : 2023-01-01 DOI: 10.1504/ijict.2023.134254

Rui Yang

引用次数: 0

International Journal of Information and Communication Technology Pub Date : 2023-01-01 DOI: 10.1504/ijict.2023.134247

Yanhua Guo

引用次数: 0

Study on fast collection method of massive marketing data based on crawler technology 基于爬虫技术的海量营销数据快速采集方法研究

International Journal of Information and Communication Technology Pub Date : 2023-01-01 DOI: 10.1504/ijict.2023.134251

Shuiying Hu

引用次数: 0