Big Data最新文献

{"title":"A Deep Learning Model for Predicting Under-Five Mortality in Zimbabwe","authors":"John Batani","doi":"10.1109/icABCD59051.2023.10220453","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220453","url":null,"abstract":"The death of children before they reach five years old (under-five mortality or U5M) is a global scourge that has attracted the attention of many governments, including the World Health Organisation and the United Nations. Children under-five in Sub-Saharan Africa are disproportionately susceptible to death, with a fifteen-fold likelihood of death compared to their counterparts in developed countries. Regardless of the numerous efforts by the Zimbabwean Government to improve child health, such as free access to care, provision of nutritional supplements, immunisation programmes and prevention of mother-to-child transmission, the country still has high under-five mortality rates (U5MRs). Zimbabwe's failure to reduce U5MRs to acceptable levels suggests that the current methods must be complemented. Identifying contextual risk factors and children at risk of death could help paediatricians to make timely and targeted interventions and policymakers to review existing and craft new policies to save children's lives. Therefore, this study applied deep learning to Zimbabwe's 2019 Multiple Indicator Cluster Survey data to predict under-five mortality and identify its associated risk factors. The study used a deep neural network with four hidden layers, k-fold cross-validation and the stochastic gradient descent (SGD) optimiser. All layers used the Rectified Linear Unit activation function except the output layer, which used the sigmoid activation for binary classification. The model produced a 90.04% accuracy, 92.39% precision, 87.30% recall and 95.04% area under the curve. Though the model predicts under-five mortality, it does not prescribe the appropriate interventions to save lives, a gap that future studies could fill.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"195 1","pages":"1-6"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77115425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Copyright Page","authors":"","doi":"10.1109/icabcd59051.2023.10220548","DOIUrl":"https://doi.org/10.1109/icabcd59051.2023.10220548","url":null,"abstract":"","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"91 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90703918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perception and Expectations of Vote Counting and Validation Systems: A Survey of Electoral Stakeholders","authors":"Patrick Mwansa, Boniface Kabaso","doi":"10.1109/icABCD59051.2023.10220571","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220571","url":null,"abstract":"Inconsistencies, unclear processes, and procedures in most democratic countries' election management, particularly vote-counting, cause mistrust and dispute in election results. This study explores the perceptions and expectations of electoral stakeholders on vote counting and validation processes in different African countries. Employing thematic analysis and using the Activity Theory as a lens, the research identifies key themes, such as technical aspects, accuracy, speed, efficiency, transparency, security, challenges, improvements, and the roles of observers and Election Management Bodies (EMBs). The findings highlight various challenges, such as poor network coverage, insufficient staff training, and corruption, informing the formation of a requirement specification for vote counting and validation systems. Despite potential drawbacks and challenges associated with technology solutions, the study proposes a set of ideal requirements specifications for an accurate, efficient, transparent, and secure election vote counting and validation process. The study contributes to ongoing discussions on transparency, accessibility, and the use of electronic voting systems to enhance electoral accuracy and integrity. It suggests avenues for future research, including evaluating legal and regulatory frameworks, voter education, technical challenges, and alternative technological approaches to vote counting and validation.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"2 1","pages":"1-10"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89214510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Construction of a Web-Based Communication Interface for Home Automation","authors":"Lesetja S. Mabunda, T. S. Hlalele","doi":"10.1109/icABCD59051.2023.10220488","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220488","url":null,"abstract":"A web-based communication interface for home automation enables users to remotely control and monitor their home appliances and devices using a web browser. This technology has the potential to improve energy efficiency and convenience of homes, but also raises security and privacy concerns. This paper focus on the development of a web-based home automation communication interface employing the ThingSpeak platform as the cloud interfacing service, an Arduino board as the microcontroller, and the ESP8266 as the internet module. The subsystem components were tested using hardware testing circuits and a software code written using Arduino IDE to determine the standard operating procedure and results show that they all conform to the operating standards. When the PIR sensor was tested a voltage of 0 V was measured on the digital pin 2 of the Arduino when no motion is detected and a voltage of 3.25 V when motion is detected. The MQ7 sensor gave a reading of 1.73 V on the analog data pin when not exposed to CO (carbon monoxide) and a reading of 3.25 V when exposed to CO.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"16 1","pages":"1-7"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88755506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Constraints of The Adoption of Gamification for Education and Training in Higher Education Institutions: A Systematic Literature Review","authors":"S. Oguta, Akindele Akinyinka, S. Ojo, B. Maake","doi":"10.1109/icABCD59051.2023.10220568","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220568","url":null,"abstract":"Gamification, a concept that describes the use of game design elements in non-game scenarios, inan attempt to induce fun and motivation in non-game scenarios, has found application in a variety of contexts. In education, popular gamification elements that have been introduced to make learning fun includes the use of points, levels, missions, leaderboards, badges, and avatars. New gamification such as the use of social robots is now in vogue. Despite the enormous potential of these gaming concepts, researchers have unearthed some challenges that face the adoption of gamification in educational systems of Higher Education Institutions. In this study's systematic literature review, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework was used to explore the difficulties encountered when using gamification in training and education at colleges and universities and tosuggest potential solutions. Several researches that employed gamification were elicited from databases such as google scholar, Scopus and research gate. The shortcomings encountered during the deployment of gamification in these studies were summarized. The study's findings reveal that he difficulties encountered when adopting gamification in education and training in higher education institutions can be divided into three categories: design concerns, issues with short-term engagement, and problems with user adaptability. Likewise, potential fixes for the issues were mapped out for future designers of educational gamified systems to follow.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"16 1","pages":"1-6"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89522631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Improved Dual-Channel Deep Q-Network Model for Tourism Recommendation.","authors":"Shengbin Liang,&nbsp;Jiangyong Jin,&nbsp;Jia Ren,&nbsp;Wencai Du,&nbsp;Shenming Qu","doi":"10.1089/big.2021.0353","DOIUrl":"10.1089/big.2021.0353","url":null,"abstract":"<p><p>Tourism recommendation results are affected by many factors. Traditional recommendation methods have problems such as low recommendation accuracy and lack of personalization due to sparse data. This article uses implicit features such as contextual information, time-series travel trajectories, and comment data to address these issues. First, the Long Short-Term Memory (LSTM) network is introduced as the model basis, and deals with the input data of the model such as contextual information, scenic spot information, and tourist comments and so on for feature extraction. Then, the online behavior and long-term interest preference of users are analyzed, using positive feedback and negative feedback mechanism, the Deep Q-Network (DQN) value function of dual-channel mechanism is constructed. Finally, we propose a recommendation strategy, in which, a value evaluation network and a target network are proposed for each agent to learn the optimal strategy. The model is trained on the Yelp, DP, and Tourism datasets covering multiple scenarios to provide users with tourism recommendation services. Compared with baseline models such as Ultra Simplification of Graph Convolutional Networks, DQN, Actor-Critic, and Latent Factor Model, this model has an average increase of 76.61% in accuracy compared with the comparison model, and an average increase of 43.48% in the normalized discounted cumulative gain compared with the baseline model.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"11 4","pages":"268-281"},"PeriodicalIF":4.6,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9880099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>Correction to:</i> An Improved Multiexposure Image Fusion Technique by Nazish, et al. Big Data 2023;11(3):215-224; doi: 10.1089/big.2021.0223.","authors":"","doi":"10.1089/big.2021.0223.correx","DOIUrl":"https://doi.org/10.1089/big.2021.0223.correx","url":null,"abstract":"","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"11 4","pages":"320"},"PeriodicalIF":4.6,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9881171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Influential Nodes in Social Networks: Exploiting Self-Voting Mechanism.","authors":"Panfeng Liu,&nbsp;Longjie Li,&nbsp;Yanhong Wen,&nbsp;Shiyu Fang","doi":"10.1089/big.2022.0165","DOIUrl":"10.1089/big.2022.0165","url":null,"abstract":"<p><p>The influence maximization (IM) problem is defined as identifying a group of influential nodes in a network such that these nodes can affect as many nodes as possible. Due to its great significance in viral marketing, disease control, social recommendation, and so on, considerable efforts have been devoted to the development of methods to solve the IM problem. In the literature, VoteRank and its improved algorithms have been proposed to select influential nodes based on voting approaches. However, in the voting process of these algorithms, a node cannot vote for itself. We argue that this voting schema runs counter to many real scenarios. To address this issue, we designed the VoteRank* algorithm, in which we first introduce the self-voting mechanism into the voting process. In addition, we also take into consideration the diversities of nodes. More explicitly, we measure the voting ability of nodes and the amount of a node voting for its neighbors based on the H-index of nodes. The effectiveness of the proposed algorithm is experimentally verified on 12 benchmark networks. The results demonstrate that VoteRank* is superior to the baseline methods in most cases.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"11 4","pages":"296-306"},"PeriodicalIF":4.6,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9883008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Churn in Online Games by Quantifying Diversity of Engagement.","authors":"Idan Weiss,&nbsp;Dan Vilenchik","doi":"10.1089/big.2022.0109","DOIUrl":"10.1089/big.2022.0109","url":null,"abstract":"<p><p>Understanding engagement patterns of users in online platforms, may it be games, online social networks, or academic websites, is a widely studied topic with many real-world applications and economic consequences. A holy grail in this area of research is to develop an automatic prediction algorithm for when a user is going to leave the platform and devise proper intervention. In this work, we study online recreational games and propose to model the engagement patterns of players through an unsupervised learning framework. We think of engagement as a continuous temporal process, measured along specific axes derived from gaming users' data using principal component analysis. We track the overall trend of the projection of the data along the significant principal components. We find that the geometric variability of the trajectory is a good predictor of the users' engagement level. Users characterized by a time series with large variability are users with higher engagement; namely, they will continue playing the game for prolonged periods of time. We evaluated our methodology on two data sets of very different game types and compared the performance of our method with state-of-the-art black-box machine learning algorithms. Our results were fairly competitive with these methods, and we conclude that churn can be predicted using an explainable, intuitive, and white-box decision-rule algorithm.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"11 4","pages":"282-295"},"PeriodicalIF":4.6,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9916270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Raw Electroencephalogram-Based Cognitive Workload Classification Using Directed and Nondirected Functional Connectivity Analysis and Deep Learning.","authors":"Anmol Gupta,&nbsp;Ronnie Daniel,&nbsp;Akash Rao,&nbsp;Partha Pratim Roy,&nbsp;Sushil Chandra,&nbsp;Byung-Gyu Kim","doi":"10.1089/big.2021.0204","DOIUrl":"10.1089/big.2021.0204","url":null,"abstract":"<p><p>With the phenomenal rise in internet-of-things devices, the use of electroencephalogram (EEG) based brain-computer interfaces (BCIs) can empower individuals to control equipment with thoughts. These allow BCI to be used and pave the way for pro-active health management and the development of internet-of-medical-things architecture. However, EEG-based BCIs have low fidelity, high variance, and EEG signals are very noisy. These challenges compel researchers to design algorithms that can process big data in real-time while being robust to temporal variations and other variations in the data. Another issue in designing a passive BCI is the regular change in user's cognitive state (measured through cognitive workload). Though considerable amount of research has been conducted on this front, methods that could withstand high variability in EEG data and still reflect the neuronal dynamics of cognitive state variations are lacking and much needed in literature. In this research, we evaluate the efficacy of a combination of functional connectivity algorithms and state-of-the-art deep learning algorithms for the classification of three different levels of cognitive workload. We acquire 64-channel EEG data from 23 participants executing the n-back task at three different levels; 1-back (low-workload condition), 2-back (medium-workload condition), and 3-back (high-workload condition). We compared two different functional connectivity algorithms, namely phase transfer entropy (PTE) and mutual information (MI). PTE is a directed functional connectivity algorithm, whereas MI is non-directed. Both methods are suitable for extracting functional connectivity matrices in real-time, which could eventually be used for rapid, robust, and efficient classification. For classification, we use the recently proposed BrainNetCNN deep learning model, designed specifically to classify functional connectivity matrices. Results reveal a classification accuracy of 92.81% with MI and BrainNetCNN and a staggering 99.50% with PTE and BrainNetCNN on test data. PTE can yield a higher classification accuracy due to its robustness to linear mixing of the data and its ability to detect functional connectivity across a range of analysis lags.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"11 4","pages":"307-319"},"PeriodicalIF":4.6,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10238452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}