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Light field image coding using a residual channel attention network–based view synthesis Data Technol. Appl. (IF 1.6) Pub Date : 2024-02-21 Faguo Liu, Qian Zhang, Tao Yan, Bin Wang, Ying Gao, Jiaqi Hou, Feiniu Yuan
Purpose Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount
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False alarm detection in intensive care unit for monitoring arrhythmia condition using bio-signals Data Technol. Appl. (IF 1.6) Pub Date : 2024-02-13 Aleena Swetapadma, Tishya Manna, Maryam Samami
Purpose A novel method has been proposed to reduce the false alarm rate of arrhythmia patients regarding life-threatening conditions in the intensive care unit. In this purpose, the atrial blood pressure, photoplethysmogram (PLETH), electrocardiogram (ECG) and respiratory (RESP) signals are considered as input signals. Design/methodology/approach Three machine learning approaches feed-forward artificial
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Community relations discovery methods for users in Fancircle based on sentiment analysis in China Data Technol. Appl. (IF 1.6) Pub Date : 2024-01-29 Kai Wang
Purpose The identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among users, which provides necessary data support for the construction of knowledge graph. Design/methodology/approach A correlation identification method based on sentiment analysis (CRDM-SA) is put forward by extracting
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A Bayesian Inference-based approach for extracting driving data with implicit intention Data Technol. Appl. (IF 1.6) Pub Date : 2024-01-19 Ping Huang, Haitao Ding, Hong Chen, Jianwei Zhang, Zhenjia Sun
Purpose The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because
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A hybrid method for forecasting coal price based on ensemble learning and deep learning with data decomposition and data enhancement Data Technol. Appl. (IF 1.6) Pub Date : 2024-01-18 Jing Tang, Yida Guo, Yilin Han
Purpose Coal is a critical global energy source, and fluctuations in its price significantly impact related enterprises' profitability. This study aims to develop a robust model for predicting the coal price index to enhance coal purchase strategies for coal-consuming enterprises and provide crucial information for global carbon emission reduction. Design/methodology/approach The proposed coal price
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ID-SF-Fusion: a cooperative model of intent detection and slot filling for natural language understanding Data Technol. Appl. (IF 1.6) Pub Date : 2024-01-19 Meng Zhu, Xiaolong Xu
Purpose Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is to extract the information that is important to the intent from the input sentence. However, most of the existing methods use sentence-level intention recognition, which has the risk of error propagation, and the relationship
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Sleep arousal detection for monitoring of sleep disorders using one-dimensional convolutional neural network-based U-Net and bio-signals Data Technol. Appl. (IF 1.6) Pub Date : 2024-01-12 Priya Mishra, Aleena Swetapadma
Purpose Sleep arousal detection is an important factor to monitor the sleep disorder. Design/methodology/approach Thus, a unique nth layer one-dimensional (1D) convolutional neural network-based U-Net model for automatic sleep arousal identification has been proposed. Findings The proposed method has achieved area under the precision–recall curve performance score of 0.498 and area under the receiver
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On the differences between CNNs and vision transformers for COVID-19 diagnosis using CT and chest x-ray mono- and multimodality Data Technol. Appl. (IF 1.6) Pub Date : 2024-01-10 Sara El-Ateif, Ali Idri, José Luis Fernández-Alemán
Purpose COVID-19 continues to spread, and cause increasing deaths. Physicians diagnose COVID-19 using not only real-time polymerase chain reaction but also the computed tomography (CT) and chest x-ray (CXR) modalities, depending on the stage of infection. However, with so many patients and so few doctors, it has become difficult to keep abreast of the disease. Deep learning models have been developed
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Understanding the relationship between normative records of appeals and government hotline order dispatching: a data analysis method Data Technol. Appl. (IF 1.6) Pub Date : 2024-01-04 Zicheng Zhang
Purpose Advanced big data analysis and machine learning methods are concurrently used to unleash the value of the data generated by government hotline and help devise intelligent applications including automated process management, standard construction and more accurate dispatched orders to build high-quality government service platforms as more widely data-driven methods are in the process. Desi
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Optimized aspect and self-attention aware LSTM for target-based semantic analysis (OAS-LSTM-TSA) Data Technol. Appl. (IF 1.6) Pub Date : 2023-12-29 B. Vasavi, P. Dileep, Ulligaddala Srrinivasarao
Purpose Aspect-based sentiment analysis (ASA) is a task of sentiment analysis that requires predicting aspect sentiment polarity for a given sentence. Many traditional techniques use graph-based mechanisms, which reduce prediction accuracy and introduce large amounts of noise. The other problem with graph-based mechanisms is that for some context words, the feelings change depending on the aspect,
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Deep understanding of radiology reports: leveraging dynamic convolution in chest X-ray images Data Technol. Appl. (IF 1.6) Pub Date : 2023-11-29 Tarun Jaiswal, Manju Pandey, Priyanka Tripathi
Purpose The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image
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Analyzing audiovisual data for understanding user's emotion in human−computer interaction environment Data Technol. Appl. (IF 1.6) Pub Date : 2023-11-01 Juan Yang, Zhenkun Li, Xu Du
Purpose Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction
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AsCDPR: a novel framework for ratings and personalized preference hotel recommendation using cross-domain and aspect-based features Data Technol. Appl. (IF 1.6) Pub Date : 2023-09-20 Hei-Chia Wang, Army Justitia, Ching-Wen Wang
Purpose The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study
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CA-CD: context-aware clickbait detection using new Chinese clickbait dataset with transfer learning method Data Technol. Appl. (IF 1.6) Pub Date : 2023-08-29 Hei-Chia Wang, Martinus Maslim, Hung-Yu Liu
Purpose A clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there
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Analysis of automated guided vehicles performance based on process mining techniques Data Technol. Appl. (IF 1.6) Pub Date : 2023-08-24 Alejandro Ramos-Soto, Angel Dacal-Nieto, Gonzalo Martín Alcrudo, Gabriel Mosquera, Juan José Areal
Purpose Process mining has emerged in the last decade as one of the most promising tools to discover and understand the actual execution of processes. This paper addresses the application of process mining techniques to analyze the performance of automatic guided vehicles (AGVs) in one of the Body in White circuits of the factory that Stellantis has in Vigo, Spain. Design/methodology/approach Standard
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Blockchain solutions for scientific paper peer review: a systematic mapping of the literature Data Technol. Appl. (IF 1.6) Pub Date : 2023-08-17 Allan Farias Fávaro, Roderval Marcelino, Cristian Cechinel
Purpose This paper presents a review of the state of the art on the application of blockchain and smart contracts to the peer-review process of scientific papers. The paper seeks to analyse how the main characteristics of the existing blockchain solutions in this field to detect opportunities for the improvement of future applications. Design/methodology/approach A systematic review of the literature
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Measuring land lot shapes for property valuation Data Technol. Appl. (IF 1.6) Pub Date : 2023-08-08 Changro Lee
Purpose Unstructured data such as images have defied usage in property valuation for a long time. Instead, structured data in tabular format are commonly employed to estimate property prices. This study attempts to quantify the shape of land lots and uses the resultant output as an input variable for subsequent land valuation models. Design/methodology/approach Imagery data containing land lot shapes
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Savitar: an intelligent sign language translation approach for deafness and dysphonia in the COVID-19 era Data Technol. Appl. (IF 1.6) Pub Date : 2023-07-07 Wuyan Liang, Xiaolong Xu
Purpose In the COVID-19 era, sign language (SL) translation has gained attention in online learning, which evaluates the physical gestures of each student and bridges the communication gap between dysphonia and hearing people. The purpose of this paper is to devote the alignment between SL sequence and nature language sequence with high translation performance. Design/methodology/approach SL can be
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Giving life to dead: role of WayBack Machine in recovery of dead URLs Data Technol. Appl. (IF 1.6) Pub Date : 2023-07-05 Fayaz Ahmad Loan, Aasif Mohammad Khan, Syed Aasif Ahmad Andrabi, Sozia Rashid Sozia, Umer Yousuf Parray
Purpose The purpose of the present study is to identify the active and dead links of uniform resource locators (URLs) associated with web references and to compare the effectiveness of Chrome, Google and WayBack Machine in retrieving the dead URLs. Design/methodology/approach The web references of the Library Hi Tech from 2004 to 2008 were selected for analysis to fulfill the set objectives. The URLs
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Performance prediction of multivariable linear regression based on the optimal influencing factors for ranking aggregation in crowdsourcing task Data Technol. Appl. (IF 1.6) Pub Date : 2023-07-04 Yuping Xing, Yongzhao Zhan
Purpose For ranking aggregation in crowdsourcing task, the key issue is how to select the optimal working group with a given number of workers to optimize the performance of their aggregation. Performance prediction for ranking aggregation can solve this issue effectively. However, the performance prediction effect for ranking aggregation varies greatly due to the different influencing factors selected
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Impact of information consistency in online reviews on consumer behavior in the e-commerce industry: a text mining approach Data Technol. Appl. (IF 1.6) Pub Date : 2023-06-12 Qinglong Li, Jaeseung Park, Jaekyeong Kim
Purpose The current study investigates the impact on perceived review helpfulness of the simultaneous processing of information from multiple cues with various central and peripheral cue combinations based on the elaboration likelihood model (ELM). Thus, the current study develops and tests hypotheses by analyzing real-world review data with a text mining approach in e-commerce to investigate how information
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A hybrid learning method for distinguishing lung adenocarcinoma and squamous cell carcinoma Data Technol. Appl. (IF 1.6) Pub Date : 2023-05-19 Anil Kumar Swain, Aleena Swetapadma, Jitendra Kumar Rout, Bunil Kumar Balabantaray
Purpose The objective of the proposed work is to identify the most commonly occurring non–small cell carcinoma types, such as adenocarcinoma and squamous cell carcinoma, within the human population. Another objective of the work is to reduce the false positive rate during the classification. Design/methodology/approach In this work, a hybrid method using convolutional neural networks (CNNs), extreme
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A novel word-graph-based query rewriting method for question answering Data Technol. Appl. (IF 1.6) Pub Date : 2023-05-18 Rongen Yan, Depeng Dang, Hu Gao, Yan Wu, Wenhui Yu
Purpose Question answering (QA) answers the questions asked by people in the form of natural language. In the QA, due to the subjectivity of users, the questions they query have different expressions, which increases the difficulty of text retrieval. Therefore, the purpose of this paper is to explore new query rewriting method for QA that integrates multiple related questions (RQs) to form an optimal
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A novel twin-support vector machine for binary classification to imbalanced data Data Technol. Appl. (IF 1.6) Pub Date : 2023-05-17 Jingyi Li, Shiwei Chao
Purpose Binary classification on imbalanced data is a challenge; due to the imbalance of the classes, the minority class is easily masked by the majority class. However, most existing classifiers are better at identifying the majority class, thereby ignoring the minority class, which leads to classifier degradation. To address this, this paper proposes a twin-support vector machines for binary classification
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TikTok app usage behavior: the role of hedonic consumption experiences Data Technol. Appl. (IF 1.6) Pub Date : 2023-05-17 Amir Zaib Abbasi, Natasha Ayaz, Sana Kanwal, Mousa Albashrawi, Nadine Khair
Purpose TikTok social media app has become one of the most popular forms of leisure and entertainment activities, but how hedonic consumption experiences (comprising fantasy, escapism, enjoyment, role projection, sensory, arousal and emotional involvement) of the TikTok app determine users' intention to use the app and its resulting impact on the actual usage behavior remains limited in the information
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SanMove: next location recommendation via self-attention network Data Technol. Appl. (IF 1.6) Pub Date : 2023-05-17 Bin Wang, Huifeng Li, Le Tong, Qian Zhang, Sulei Zhu, Tao Yang
Purpose This paper aims to address the following issues: (1) most existing methods are based on recurrent network, which is time-consuming to train long sequences due to not allowing for full parallelism; (2) personalized preference generally are not considered reasonably; (3) existing methods rarely systematically studied how to efficiently utilize various auxiliary information (e.g. user ID and time
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A sensor data mining process for identifying root causes associated with low yield in semiconductor manufacturing Data Technol. Appl. (IF 1.6) Pub Date : 2023-05-17 Eunji Kim, Jinwon An, Hyun-Chang Cho, Sungzoon Cho, Byeongeon Lee
Purpose The purpose of this paper is to identify the root cause of low yield problems in the semiconductor manufacturing process using sensor data continuously collected from manufacturing equipment and describe the process environment in the equipment. Design/methodology/approach This paper proposes a sensor data mining process based on the sequential modeling of random forests for low yield diagnosis
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Multimodal Fast–Slow Neural Network for learning engagement evaluation Data Technol. Appl. (IF 1.6) Pub Date : 2023-05-17 Lizhao Zhang, Jui-Long Hung, Xu Du, Hao Li, Zhuang Hu
Purpose Student engagement is a key factor that connects with student achievement and retention. This paper aims to identify individuals' engagement automatically in the classroom with multimodal data for supporting educational research. Design/methodology/approach The video and electroencephalogram data of 36 undergraduates were collected to represent observable and internal information. Since different
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Educational data mining in the academic setting: employing the data produced by blended learning to ameliorate the learning process Data Technol. Appl. (IF 1.6) Pub Date : 2023-05-17 Konstantinos Chytas, Anastasios Tsolakidis, Evangelia Triperina, Christos Skourlas
Purpose The purpose of this paper is to introduce an interactive system that relies on the educational data generated from the online Universities services to assess, correct and ameliorate the learning process for both students and faculty. Design/methodology/approach In the presented research, data from the online services, provided by a Greek University, prior, during and after the COVID-19 outbreak
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Improving anti-money laundering in bitcoin using evolving graph convolutions and deep neural decision forest Data Technol. Appl. (IF 1.6) Pub Date : 2023-05-17 Anuraj Mohan, Karthika P.V., Parvathi Sankar, K. Maya Manohar, Amala Peter
Purpose Money laundering is the process of concealing unlawfully obtained funds by presenting them as coming from a legitimate source. Criminals use crypto money laundering to hide the illicit origin of funds using a variety of methods. The most simplified form of bitcoin money laundering leans hard on the fact that transactions made in cryptocurrencies are pseudonymous, but open data gives more power
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A model of image retrieval based on KD-Tree Random Forest Data Technol. Appl. (IF 1.6) Pub Date : 2023-05-05 Nguyen Thi Dinh, Nguyen Thi Uyen Nhi, Thanh Manh Le, Thanh The Van
Purpose The problem of image retrieval and image description exists in various fields. In this paper, a model of content-based image retrieval and image content extraction based on the KD-Tree structure was proposed. Design/methodology/approach A Random Forest structure was built to classify the objects on each image on the basis of the balanced multibranch KD-Tree structure. From that purpose, a KD-Tree
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Identifying business information through deep learning: analyzing the tender documents of an Internet-based logistics bidding platform Data Technol. Appl. (IF 1.6) Pub Date : 2023-05-05 Ying Yu, Jing Ma
Purpose The tender documents, an essential data source for internet-based logistics tendering platforms, incorporate massive fine-grained data, ranging from information on tenderee, shipping location and shipping items. Automated information extraction in this area is, however, under-researched, making the extraction process a time- and effort-consuming one. For Chinese logistics tender entities, in
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Risk assessment in machine learning enhanced failure mode and effects analysis Data Technol. Appl. (IF 1.6) Pub Date : 2023-05-04 Zeping Wang, Hengte Du, Liangyan Tao, Saad Ahmed Javed
Purpose The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA)
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Channel attention-based spatial-temporal graph neural networks for traffic prediction Data Technol. Appl. (IF 1.6) Pub Date : 2023-05-03 Bin Wang, Fanghong Gao, Le Tong, Qian Zhang, Sulei Zhu
Purpose Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is
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Machine learning approaches for prediction of ovarian cancer driver genes from mutational and network analysis Data Technol. Appl. (IF 1.6) Pub Date : 2023-05-03 Rucha Wadapurkar, Sanket Bapat, Rupali Mahajan, Renu Vyas
Purpose Ovarian cancer (OC) is the most common type of gynecologic cancer in the world with a high rate of mortality. Due to manifestation of generic symptoms and absence of specific biomarkers, OC is usually diagnosed at a late stage. Machine learning models can be employed to predict driver genes implicated in causative mutations. Design/methodology/approach In the present study, a comprehensive
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Property Assertion Constraints for ontologies and knowledge graphs Data Technol. Appl. (IF 1.6) Pub Date : 2023-04-21 Henrik Dibowski
Purpose The curation of ontologies and knowledge graphs (KGs) is an essential task for industrial knowledge-based applications, as they rely on the contained knowledge to be correct and error-free. Often, a significant amount of a KG is curated by humans. Established validation methods, such as Shapes Constraint Language, Shape Expressions or Web Ontology Language, can detect wrong statements only
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Research on the generalization of social bot detection from two dimensions: feature extraction and detection approaches Data Technol. Appl. (IF 1.6) Pub Date : 2023-04-21 Ziming Zeng, Tingting Li, Jingjing Sun, Shouqiang Sun, Yu Zhang
Purpose The proliferation of bots in social networks has profoundly affected the interactions of legitimate users. Detecting and rejecting these unwelcome bots has become part of the collective Internet agenda. Unfortunately, as bot creators use more sophisticated approaches to avoid being discovered, it has become increasingly difficult to distinguish social bots from legitimate users. Therefore,
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Impact on recommendation performance of online review helpfulness and consistency Data Technol. Appl. (IF 1.6) Pub Date : 2023-04-21 Jaeseung Park, Xinzhe Li, Qinglong Li, Jaekyeong Kim
Purpose The existing collaborative filtering algorithm may select an insufficiently representative customer as the neighbor of a target customer, which means that the performance in providing recommendations is not sufficiently accurate. This study aims to investigate the impact on recommendation performance of selecting influential and representative customers. Design/methodology/approach Some studies
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ABEE: automated bio entity extraction from biomedical text documents Data Technol. Appl. (IF 1.6) Pub Date : 2023-04-21 Ashutosh Kumar, Aakanksha Sharaff
Purpose The purpose of this study was to design a multitask learning model so that biomedical entities can be extracted without having any ambiguity from biomedical texts. Design/methodology/approach In the proposed automated bio entity extraction (ABEE) model, a multitask learning model has been introduced with the combination of single-task learning models. Our model used Bidirectional Encoder Representations
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A new approach for histological classification of breast cancer using deep hybrid heterogenous ensemble Data Technol. Appl. (IF 1.6) Pub Date : 2023-04-21 Hasnae Zerouaoui, Ali Idri, Omar El Alaoui
Purpose Hundreds of thousands of deaths each year in the world are caused by breast cancer (BC). An early-stage diagnosis of this disease can positively reduce the morbidity and mortality rate by helping to select the most appropriate treatment options, especially by using histological BC images for the diagnosis. Design/methodology/approach The present study proposes and evaluates a novel approach
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Social support on Reddit for antiretroviral therapy Data Technol. Appl. (IF 1.6) Pub Date : 2023-04-21 Yue Ming
Purpose Social media platforms such as Reddit can be used as a place for people with shared health problems to share knowledge and support. Previous studies have focused on the overall picture of how much social support people who live with HIV/AIDS (PLWHA) receive from online interactions. Yet, only few studies have examined the impact of social support from social media platforms on antiretroviral
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Do SEC filings indicate any trends? Evidence from the sentiment distribution of forms 10-K and 10-Q with FinBERT Data Technol. Appl. (IF 1.6) Pub Date : 2023-04-21 Hyogon Kim, Eunmi Lee, Donghee Yoo
Purpose This study quantified companies' views on the COVID-19 pandemic with sentiment analysis of US public companies' disclosures. The study aims to provide timely insights to shareholders, investors and consumers by exploring sentiment trends and changes in the industry and the relationship with stock price indices. Design/methodology/approach From more than 50,000 Form 10-K and Form 10-Q published
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News recommendations based on collaborative topic modeling and collaborative filtering with generative adversarial networks Data Technol. Appl. (IF 1.6) Pub Date : 2023-03-31 Duen-Ren Liu, Yang Huang, Jhen-Jie Jhao, Shin-Jye Lee
Purpose Online news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on collaborative filtering (CFGAN) can achieve effective recommendation quality. However, CFGAN ignores item contents, which contain more latent preference features than just user ratings. It is important to consider both ratings
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Data mining–based stock price prediction using hybridization of technical and fundamental analysis Data Technol. Appl. (IF 1.6) Pub Date : 2023-03-21 Jasleen Kaur, Khushdeep Dharni
Purpose The stock market generates massive databases of various financial companies that are highly volatile and complex. To forecast daily stock values of these companies, investors frequently use technical analysis or fundamental analysis. Data mining techniques coupled with fundamental and technical analysis types have the potential to give satisfactory results for stock market prediction. In the
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Music sentiment classification based on an optimized CNN-RF-QPSO model Data Technol. Appl. (IF 1.6) Pub Date : 2023-03-17 Rui Tian, Ruheng Yin, Feng Gan
Purpose Music sentiment analysis helps to promote the diversification of music information retrieval methods. Traditional music emotion classification tasks suffer from high manual workload and low classification accuracy caused by difficulty in feature extraction and inaccurate manual determination of hyperparameter. In this paper, the authors propose an optimized convolution neural network-random
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A new method based on ensemble time series for fast and accurate clustering Data Technol. Appl. (IF 1.6) Pub Date : 2023-03-16 Ali Ghorbanian, Hamideh Razavi
Purpose The common methods for clustering time series are the use of specific distance criteria or the use of standard clustering algorithms. Ensemble clustering is one of the common techniques used in data mining to increase the accuracy of clustering. In this study, based on segmentation, selecting the best segments, and using ensemble clustering for selected segments, a multistep approach has been
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Multi-relation global context learning for session-based recommendation Data Technol. Appl. (IF 1.6) Pub Date : 2023-03-16 Yishan Liu, Wenming Cao, Guitao Cao
Purpose Session-based recommendation aims to predict the user's next preference based on the user's recent activities. Although most existing studies consider the global characteristics of items, they only learn the global characteristics of items based on a single connection relationship, which cannot fully capture the complex transformation relationship between items. We believe that multiple relationships
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Machine learning methods for results merging in patent retrieval Data Technol. Appl. (IF 1.6) Pub Date : 2023-02-27 Vasileios Stamatis, Michail Salampasis, Konstantinos Diamantaras
Purpose In federated search, a query is sent simultaneously to multiple resources and each one of them returns a list of results. These lists are merged into a single list using the results merging process. In this work, the authors apply machine learning methods for results merging in federated patent search. Even though several methods for results merging have been developed, none of them were tested
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A meta-analysis of social commerce adoption and the moderating effect of culture Data Technol. Appl. (IF 1.6) Pub Date : 2023-02-28 Ning Wang, Yang Zhao, Ruoxin Zhou
Purpose As a derivative model of e-commerce, social commerce has received increasing attention in recent years. Empirical studies on social commerce have examined the key factors that influence users' attitudes or adoption intentions, but their conclusions are context-based and are not entirely consistent. This study aims to draw a general conclusion by systematically synthesizing the findings of previous
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Sentiment analysis of the Algerian social movement inception Data Technol. Appl. (IF 1.6) Pub Date : 2023-02-27 Meriem Laifa, Djamila Mohdeb
Purpose This study provides an overview of the application of sentiment analysis (SA) in exploring social movements (SMs). It also compares different models for a SA task of Algerian Arabic tweets related to early days of the Algerian SM, called Hirak. Design/methodology/approach Related tweets were retrieved using relevant hashtags followed by multiple data cleaning procedures. Foundational machine
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Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning Data Technol. Appl. (IF 1.6) Pub Date : 2023-02-27 Fatima-Zahrae Nakach, Hasnae Zerouaoui, Ali Idri
Purpose Histopathology biopsy imaging is currently the gold standard for the diagnosis of breast cancer in clinical practice. Pathologists examine the images at various magnifications to identify the type of tumor because if only one magnification is taken into account, the decision may not be accurate. This study explores the performance of transfer learning and late fusion to construct multi-scale
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Joint modeling method of question intent detection and slot filling for domain-oriented question answering system Data Technol. Appl. (IF 1.6) Pub Date : 2023-02-10 Huiyong Wang, Ding Yang, Liang Guo, Xiaoming Zhang
Purpose Intent detection and slot filling are two important tasks in question comprehension of a question answering system. This study aims to build a joint task model with some generalization ability and benchmark its performance over other neural network models mentioned in this paper. Design/methodology/approach This study used a deep-learning-based approach for the joint modeling of question intent
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CommunityGCN: community detection using node classification with graph convolution network Data Technol. Appl. (IF 1.6) Pub Date : 2023-02-07 Riju Bhattacharya, Naresh Kumar Nagwani, Sarsij Tripathi
Purpose A community demonstrates the unique qualities and relationships between its members that distinguish it from other communities within a network. Network analysis relies heavily on community detection. Despite the traditional spectral clustering and statistical inference methods, deep learning techniques for community detection have grown in popularity due to their ease of processing high-dimensional
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Chinese sentiment analysis model by integrating multi-granularity semantic features Data Technol. Appl. (IF 1.6) Pub Date : 2023-01-30 Zhongbao Liu, Wenjuan Zhao
Purpose In recent years, Chinese sentiment analysis has made great progress, but the characteristics of the language itself and downstream task requirements were not explored thoroughly. It is not practical to directly migrate achievements obtained in English sentiment analysis to the analysis of Chinese because of the huge difference between the two languages. Design/methodology/approach In view of
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Collaboration and interaction with smart mechanisms in flipped classrooms Data Technol. Appl. (IF 1.6) Pub Date : 2023-01-30 Wu-Yuin Hwang, Rio Nurtantyana, Uun Hariyanti
Purpose This study aimed to investigate learning behaviors deeply in flipped classrooms. In addition, it is worth considering how to help learners through recognition technology with natural language processing (NLP) when learners have question and answer (Q&A). In addition, the Internet of Things (IoT) can be utilized to make the physical learning environment more comfortable and smarter. Design/
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Analyzing students' attention by gaze tracking and object detection in classroom teaching Data Technol. Appl. (IF 1.6) Pub Date : 2023-01-25 Hui Xu, Junjie Zhang, Hui Sun, Miao Qi, Jun Kong
Purpose Attention is one of the most important factors to affect the academic performance of students. Effectively analyzing students' attention in class can promote teachers' precise teaching and students' personalized learning. To intelligently analyze the students' attention in classroom from the first-person perspective, this paper proposes a fusion model based on gaze tracking and object detection
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Topic optimization–incorporated collaborative recommendation for social tagging Data Technol. Appl. (IF 1.6) Pub Date : 2022-12-09 Xuwei Pan, Xuemei Zeng, Ling Ding
Purpose With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent
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Identifying surface points based on machine learning algorithms: a comprehensive analysis Data Technol. Appl. (IF 1.6) Pub Date : 2022-12-03 Vahide Bulut
Purpose Surface curvature is needed to analyze the range data of real objects and is widely applied in object recognition and segmentation, robotics, and computer vision. Therefore, it is not easy to estimate the curvature of the scanned data. In recent years, machine learning classification methods have gained importance in various fields such as finance, health, engineering, etc. The purpose of this
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Product competitiveness analysis from the perspective of customer perceived helpfulness: a novel method of information fusion research Data Technol. Appl. (IF 1.6) Pub Date : 2022-10-03 Zheng Wang, Ying Ji, Tao Zhang, Yuanming Li, Lun Wang, Shaojian Qu
Purpose With the continuous development of online shopping, analyzing the competitiveness of products in the fierce market competition is becoming increasingly crucial to position their own product development. However, the information overload brought by the network development makes it getting difficult to obtain the accurate competitiveness information. Therefore, competitiveness analysis research
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SeaRank: relevance prediction based on click models in a reinforcement learning framework Data Technol. Appl. (IF 1.6) Pub Date : 2022-09-08 Amir Hosein Keyhanipour, Farhad Oroumchian
Purpose User feedback inferred from the user's search-time behavior could improve the learning to rank (L2R) algorithms. Click models (CMs) present probabilistic frameworks for describing and predicting the user's clicks during search sessions. Most of these CMs are based on common assumptions such as Attractiveness, Examination and User Satisfaction. CMs usually consider the Attractiveness and Examination