Int. J. Intell. Comput. Cybern.最新文献

{"title":"Evaluating and visualizing QoS of service providers in knowledge-intensive crowdsourcing: a combined MCDM approach","authors":"Shixin Xie, Xu Wang, Biyu Yang, Longxi Li, Jinfeng Yu","doi":"10.1108/ijicc-06-2021-0113","DOIUrl":"https://doi.org/10.1108/ijicc-06-2021-0113","url":null,"abstract":"PurposeAs the number of joined service providers (SPs) in knowledge-intensive crowdsourcing (KI-C) continues to rise, there is an information overload problem for KI-C platforms and consumers to identify qualified SPs to complete tasks. To this end, this paper aims to propose a quality of service (QoS) evaluation framework for SPs in KI-C to effectively and comprehensively characterize the QoS of SPs, which can aid the efficient selection of qualified SPs.Design/methodology/approachBy literature summary and discussion with the expert team, a QoS evaluation indicator system for SPs in KI-C based on the SERVQUAL model is constructed. In addition, the Decision Making Trial and Evaluation Laboratory (DEMATEL) method is used to obtain evaluation indicators' weights. The SPs are evaluated and graded by the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and rank–sum ratio (RSR), respectively.FindingsA QoS evaluation indicator system for SPs in KI-C incorporating 13 indicators based on SERVQUAL has been constructed, and a hybrid methodology combining DEMATEL, TOPSIS and RSR is applied to quantify and visualize the QoS of SPs.Originality/valueThe QoS evaluation framework for SPs in KI-C proposed in this paper can quantify and visualize the QoS of SPs, which can help the crowdsourcing platform to realize differentiated management for SPs and assist SPs to improve their shortcomings in a targeted manner. And this is the first paper to evaluate SPs in KI-C from the prospect of QoS.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126493059","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 ensemble learning model for driver drowsiness detection and accident prevention using the behavioral features analysis","authors":"Sharanabasappa, S. Nandyal","doi":"10.1108/ijicc-07-2021-0139","DOIUrl":"https://doi.org/10.1108/ijicc-07-2021-0139","url":null,"abstract":"PurposeIn order to prevent accidents during driving, driver drowsiness detection systems have become a hot topic for researchers. There are various types of features that can be used to detect drowsiness. Detection can be done by utilizing behavioral data, physiological measurements and vehicle-based data. The existing deep convolutional neural network (CNN) models-based ensemble approach analyzed the behavioral data comprises eye or face or head movement captured by using a camera images or videos. However, the developed model suffered from the limitation of high computational cost because of the application of approximately 140 million parameters.Design/methodology/approachThe proposed model uses significant feature parameters from the feature extraction process such as ReliefF, Infinite, Correlation, Term Variance are used for feature selection. The features that are selected are undergone for classification using ensemble classifier.FindingsThe output of these models is classified into non-drowsiness or drowsiness categories.Research limitations/implicationsIn this research work higher end camera are required to collect videos as it is cost-effective. Therefore, researches are encouraged to use the existing datasets.Practical implicationsThis paper overcomes the earlier approach. The developed model used complex deep learning models on small dataset which would also extract additional features, thereby provided a more satisfying result.Originality/valueDrowsiness can be detected at the earliest using ensemble model which restricts the number of accidents.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114923065","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":"High accuracy offering attention mechanisms based deep learning approach using CNN/bi-LSTM for sentiment analysis","authors":"V. R. Kota, Shyamala Devi Munisamy","doi":"10.1108/ijicc-06-2021-0109","DOIUrl":"https://doi.org/10.1108/ijicc-06-2021-0109","url":null,"abstract":"PurposeNeural network (NN)-based deep learning (DL) approach is considered for sentiment analysis (SA) by incorporating convolutional neural network (CNN), bi-directional long short-term memory (Bi-LSTM) and attention methods. Unlike the conventional supervised machine learning natural language processing algorithms, the authors have used unsupervised deep learning algorithms.Design/methodology/approachThe method presented for sentiment analysis is designed using CNN, Bi-LSTM and the attention mechanism. Word2vec word embedding is used for natural language processing (NLP). The discussed approach is designed for sentence-level SA which consists of one embedding layer, two convolutional layers with max-pooling, one LSTM layer and two fully connected (FC) layers. Overall the system training time is 30 min.FindingsThe method performance is analyzed using metrics like precision, recall, F1 score, and accuracy. CNN is helped to reduce the complexity and Bi-LSTM is helped to process the long sequence input text.Originality/valueThe attention mechanism is adopted to decide the significance of every hidden state and give a weighted sum of all the features fed as input.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121253589","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":"Emphasizing privacy and security of edge intelligence with machine learning for healthcare","authors":"Sukumar Rajendran, S. Mathivanan, P. Jayagopal, Kumar Purushothaman Janaki, Benjula Anbu Malar Manickam Bernard, Suganya Pandy, Manivannan Sorakaya Somanathan","doi":"10.1108/ijicc-05-2021-0099","DOIUrl":"https://doi.org/10.1108/ijicc-05-2021-0099","url":null,"abstract":"PurposeArtificial Intelligence (AI) has surpassed expectations in opening up different possibilities for machines from different walks of life. Cloud service providers are pushing. Edge computing reduces latency, improving availability and saving bandwidth.Design/methodology/approachThe exponential growth in tensor processing unit (TPU) and graphics processing unit (GPU) combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care. A significant role of pushing and pulling data from the cloud, big data comes into play as velocity, veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record (EHR).FindingsThe primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence (PoP). The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients. The impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.Originality/valueThe utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the ODL. The scalability is at 50% with respect to the sensitivity and preservation of the PII values in the local ODL.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116588178","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":"Knowledge diffusion trajectories in the Pythagorean fuzzy field based on main path analysis","authors":"Liu Meng, Chonghui Zhang, Chenhong Yu, Yujing Ye","doi":"10.1108/ijicc-06-2021-0128","DOIUrl":"https://doi.org/10.1108/ijicc-06-2021-0128","url":null,"abstract":"PurposeThe purpose of this article is to conduct a main path analysis of 627 articles on the theme of Pythagorean fuzzy sets (PFSs) in the Web of Science (WoS) from 2013 to 2020, to provide a conclusive and comprehensive analysis for researchers in this field, and to provide a study on preliminary understanding of PFSs.Design/methodology/approachThe research topic of Pythagorean fuzzy fields, through keyword extraction and describing the changes in characteristic themes over the past eight years, are firstly examined. Main path analysis, including local and global main paths and key route paths, is then used to reveal the most influential relationships between papers and to explore the trajectory and structure of knowledge transmission.FindingsThe application of Pythagorean fuzzy theory to the field of decision-making has been popular, and combinations of the traditional Pythagorean fuzzy decision-making method with other fuzzy sets have attracted widespread attention in recent years. In addition, over the past eight years, research interest has shifted to different types of PFSs, such as interval-valued PFSs.Research limitations/implicationsThis paper implicates to investigate the growth in certain trends in the literature and to explore the main paths of knowledge dissemination in the domain of PFSs in recent years.Originality/valueThis paper aims to identify the topics in which researchers are currently interested, to help scholars to keep abreast of the latest research on PFSs.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"51 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114128131","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":"Evaluation of green building suppliers based on IVPLTS-CBR decision-making method","authors":"Peng Li, Hui-Hsia Chen","doi":"10.1108/ijicc-06-2021-0118","DOIUrl":"https://doi.org/10.1108/ijicc-06-2021-0118","url":null,"abstract":"PurposeThe purpose of this paper is to propose a multi-criteria decision-making model based on the case-based reasoning (CBR) method for interval-valued probabilistic linguistic term set (IVPLTS), which can cluster different categories of building suppliers for targeted management.Design/methodology/approachFirst, a new score function and distance measure for IVPLTS are proposed. Second, a green building supplier evaluation criterion system is constructed from five aspects: operation management, green management, cooperation potential, service level and product information. Finally, the IVPLTS-CBR model is used to evaluate the green building suppliers and groups them into three preset categories.FindingsThe feasibility and validity of the proposed method are verified by comparing with the advanced TOPSIS method and the IVPLTS-based VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. The compared results show that the proposed method is more consistent with the actual situation and has strong theoretical significance and practical value.Research limitations/implicationsThis paper presents a new method for clustering construction suppliers. Decision makers can use this method to classify construction suppliers into different categories, so that they can be targeted management. In this way, suppliers can be better guided and motivated to accelerate the green transformation and contribute their share to achieve the strategic goal of carbon neutral and carbon peak as soon as possible.Originality/valueA new score function and distance measure for IVPLTS are proposed. Besides, a novel IVPLTS-CBR method is applied to rank and cluster building suppliers.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121968538","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":"Auto-attentional mechanism in multi-domain convolutional neural networks for improving object tracking","authors":"Jinchao Huang","doi":"10.1108/ijicc-04-2021-0067","DOIUrl":"https://doi.org/10.1108/ijicc-04-2021-0067","url":null,"abstract":"PurposeMulti-domain convolutional neural network (MDCNN) model has been widely used in object recognition and tracking in the field of computer vision. However, if the objects to be tracked move rapid or the appearances of moving objects vary dramatically, the conventional MDCNN model will suffer from the model drift problem. To solve such problem in tracking rapid objects under limiting environment for MDCNN model, this paper proposed an auto-attentional mechanism-based MDCNN (AA-MDCNN) model for the rapid moving and changing objects tracking under limiting environment.Design/methodology/approachFirst, to distinguish the foreground object between background and other similar objects, the auto-attentional mechanism is used to selectively aggregate the weighted summation of all feature maps to make the similar features related to each other. Then, the bidirectional gated recurrent unit (Bi-GRU) architecture is used to integrate all the feature maps to selectively emphasize the importance of the correlated feature maps. Finally, the final feature map is obtained by fusion the above two feature maps for object tracking. In addition, a composite loss function is constructed to solve the similar but different attribute sequences tracking using conventional MDCNN model.FindingsIn order to validate the effectiveness and feasibility of the proposed AA-MDCNN model, this paper used ImageNet-Vid dataset to train the object tracking model, and the OTB-50 dataset is used to validate the AA-MDCNN tracking model. Experimental results have shown that the augmentation of auto-attentional mechanism will improve the accuracy rate 2.75% and success rate 2.41%, respectively. In addition, the authors also selected six complex tracking scenarios in OTB-50 dataset; over eleven attributes have been validated that the proposed AA-MDCNN model outperformed than the comparative models over nine attributes. In addition, except for the scenario of multi-objects moving with each other, the proposed AA-MDCNN model solved the majority rapid moving objects tracking scenarios and outperformed than the comparative models on such complex scenarios.Originality/valueThis paper introduced the auto-attentional mechanism into MDCNN model and adopted Bi-GRU architecture to extract key features. By using the proposed AA-MDCNN model, rapid object tracking under complex background, motion blur and occlusion objects has better effect, and such model is expected to be further applied to the rapid object tracking in the real world.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128232305","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":"Deep restricted and additive homomorphic ElGamal privacy preservations over big healthcare data","authors":"K. Sujatha, V. Udayarani","doi":"10.1108/ijicc-05-2021-0094","DOIUrl":"https://doi.org/10.1108/ijicc-05-2021-0094","url":null,"abstract":"PurposeThe purpose of this paper is to improve the privacy in healthcare datasets that hold sensitive information. Putting a stop to privacy divulgence and bestowing relevant information to legitimate users are at the same time said to be of differing goals. Also, the swift evolution of big data has put forward considerable ease to all chores of life. As far as the big data era is concerned, propagation and information sharing are said to be the two main facets. Despite several research works performed on these aspects, with the incremental nature of data, the likelihood of privacy leakage is also substantially expanded through various benefits availed of big data. Hence, safeguarding data privacy in a complicated environment has become a major setback.Design/methodology/approachIn this study, a method called deep restricted additive homomorphic ElGamal privacy preservation (DR-AHEPP) to preserve the privacy of data even in case of incremental data is proposed. An entropy-based differential privacy quasi identification and DR-AHEPP algorithms are designed, respectively, for obtaining privacy-preserved minimum falsified quasi-identifier set and computationally efficient privacy-preserved data.FindingsAnalysis results using Diabetes 130-US hospitals illustrate that the proposed DR-AHEPP method is more significant in preserving privacy on incremental data than existing methods. A comparative analysis of state-of-the-art works with the objective to minimize information loss, false positive rate and execution time with higher accuracy is calibrated.Originality/valueThe paper provides better performance using Diabetes 130-US hospitals for achieving high accuracy, low information loss and false positive rate. The result illustrates that the proposed method increases the accuracy by 4% and reduces the false positive rate and information loss by 25 and 35%, respectively, as compared to state-of-the-art works.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123841180","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":"Web-based remote sensing image retrieval using multiscale and multidirectional analysis based on Contourlet and Haralick texture features","authors":"Rajakumar Krishnan, A. Thangavelu, P. Prabhavathy, D. Sudheer, D. Putrevu, A. Misra","doi":"10.1108/ijicc-05-2021-0095","DOIUrl":"https://doi.org/10.1108/ijicc-05-2021-0095","url":null,"abstract":"PurposeExtracting suitable features to represent an image based on its content is a very tedious task. Especially in remote sensing we have high-resolution images with a variety of objects on the Earth's surface. Mahalanobis distance metric is used to measure the similarity between query and database images. The low distance obtained image is indexed at the top as high relevant information to the query.Design/methodology/approachThis paper aims to develop an automatic feature extraction system for remote sensing image data. Haralick texture features based on Contourlet transform are fused with statistical features extracted from the QuadTree (QT) decomposition are developed as feature set to represent the input data. The extracted features will retrieve similar images from the large image datasets using an image-based query through the web-based user interface.FindingsThe developed retrieval system performance has been analyzed using precision and recall and F1 score. The proposed feature vector gives better performance with 0.69 precision for the top 50 relevant retrieved results over other existing multiscale-based feature extraction methods.Originality/valueThe main contribution of this paper is developing a texture feature vector in a multiscale domain by combining the Haralick texture properties in the Contourlet domain and Statistical features using QT decomposition. The features required to represent the image is 207 which is very less dimension compare to other texture methods. The performance shows superior than the other state of art methods.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132765566","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":"Forecasting the momentum using customised loss function for financial series","authors":"N. Prabakaran, Rajasekaran Palaniappan, R. Kannadasan, Satya Vinay Dudi, V. Sasidhar","doi":"10.1108/ijicc-05-2021-0098","DOIUrl":"https://doi.org/10.1108/ijicc-05-2021-0098","url":null,"abstract":"PurposeWe propose a Machine Learning (ML) approach that will be trained from the available financial data and is able to gain the trends over the data and then uses the acquired knowledge for a more accurate forecasting of financial series. This work will provide a more precise results when weighed up to aged financial series forecasting algorithms. The LSTM Classic will be used to forecast the momentum of the Financial Series Index and also applied to its commodities. The network will be trained and evaluated for accuracy with various sizes of data sets, i.e. weekly historical data of MCX, GOLD, COPPER and the results will be calculated.Design/methodology/approachDesirable LSTM model for script price forecasting from the perspective of minimizing MSE. The approach which we have followed is shown below. (1) Acquire the Dataset. (2) Define your training and testing columns in the dataset. (3) Transform the input value using scalar. (4) Define the custom loss function. (5) Build and Compile the model. (6) Visualise the improvements in results.FindingsFinancial series is one of the very aged techniques where a commerce person would commerce financial scripts, make business and earn some wealth from these companies that vend a part of their business on trading manifesto. Forecasting financial script prices is complex tasks that consider extensive human–computer interaction. Due to the correlated nature of financial series prices, conventional batch processing methods like an artificial neural network, convolutional neural network, cannot be utilised efficiently for financial market analysis. We propose an online learning algorithm that utilises an upgraded of recurrent neural networks called long short-term memory Classic (LSTM). The LSTM Classic is quite different from normal LSTM as it has customised loss function in it. This LSTM Classic avoids long-term dependence on its metrics issues because of its unique internal storage unit structure, and it helps forecast financial time series. Financial Series Index is the combination of various commodities (time series). This makes Financial Index more reliable than the financial time series as it does not show a drastic change in its value even some of its commodities are affected. This work will provide a more precise results when weighed up to aged financial series forecasting algorithms.Originality/valueWe had built the customised loss function model by using LSTM scheme and have experimented on MCX index and as well as on its commodities and improvements in results are calculated for every epoch that we run for the whole rows present in the dataset. For every epoch we can visualise the improvements in loss. One more improvement that can be done to our model that the relationship between price difference and directional loss is specific to other financial scripts. Deep evaluations can be done to identify the best combination of these for a particular stock to obtain better results.","PeriodicalId":352072,"journal":{"name":"Int. J. Intell. Comput. Cybern.","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132101676","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}