Big Data最新文献

{"title":"Applications of Bayesian Neural Networks in Outlier Detection.","authors":"Chen Tao","doi":"10.1089/big.2021.0343","DOIUrl":"10.1089/big.2021.0343","url":null,"abstract":"<p><p>Anomaly detection is crucial in a variety of domains, such as fraud detection, disease diagnosis, and equipment defect detection. With the development of deep learning, anomaly detection with Bayesian neural networks (BNNs) becomes a novel research topic in recent years. This article aims to propose a widely applicable method of outlier detection (a category of anomaly detection) using BNNs based on uncertainty measurement. There are three kinds of uncertainties generated in the prediction of BNNs: epistemic uncertainty, aleatoric uncertainty, and (model) misspecification uncertainty. Although the approaches in previous studies are adopted to measure epistemic and aleatoric uncertainty, a new method of utilizing loss functions to quantify misspecification uncertainty is proposed in this article. Then, these three uncertainty sources are merged together by specific combination models to construct total prediction uncertainty. In this study, the key idea is that the observations with high total prediction uncertainty should correspond to outliers in the data. The method of this research is applied to the experiments on Modified National Institute of Standards and Technology (MNIST) dataset and Taxi dataset, respectively. From the results, if the network is appropriately constructed and well-trained and model parameters are carefully tuned, most anomalous images in MNIST dataset and all the abnormal traffic periods in Taxi dataset can be nicely detected. In addition, the performance of this method is compared with the BNN anomaly detection methods proposed before and the classical Local Outlier Factor and Density-Based Spatial Clustering of Applications with Noise methods. This study links the classification of uncertainties in essence with anomaly detection and takes the lead to consider combining different uncertainty sources to reform detection outcomes instead of using only single uncertainty each time.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10681813","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":"Big Data-Driven Futuristic Fabric System in Societal Digital Transformation.","authors":"Chinmay Chakraborty,&nbsp;Muhammad Khurram Khan","doi":"10.1089/big.2023.29062.editorial","DOIUrl":"10.1089/big.2023.29062.editorial","url":null,"abstract":"","PeriodicalId":51314,"journal":{"name":"Big Data","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41219740","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":"Social Listening for Product Design Requirement Analysis and Segmentation: A Graph Analysis Approach with User Comments Mining.","authors":"Xinjun Lai,&nbsp;Guitao Huang,&nbsp;Ziyue Zhao,&nbsp;Shenhe Lin,&nbsp;Sheng Zhang,&nbsp;Huiyu Zhang,&nbsp;Qingxin Chen,&nbsp;Ning Mao","doi":"10.1089/big.2022.0021","DOIUrl":"https://doi.org/10.1089/big.2022.0021","url":null,"abstract":"<p><p>This study investigates customers' product design requirements through online comments from social media, and quickly translates these needs into product design specifications. First, the exponential discriminative snowball sampling method was proposed to generate a product-related subnetwork. Second, natural language processing (NLP) was utilized to mine user-generated comments, and a Graph SAmple and aggreGatE method was employed to embed the user's node neighborhood information in the network to jointly define a user's persona. Clustering was used for market and product model segmentation. Finally, a deep learning bidirectional long short-term memory with conditional random fields framework was introduced for opinion mining. A comment frequency-invert group frequency indicator was proposed to quantify all user groups' positive and negative opinions for various specifications of different product functions. A case study of smartphone design analysis is presented with data from a large Chinese online community called Baidu Tieba. Eleven layers of social relationships were snowball sampled, with 14,018 users and 30,803 comments. The proposed method produced a more reasonable user group clustering result than the conventional method. With our approach, user groups' dominating likes and dislikes for specifications could be immediately identified, and the similar and different preferences of product features by different user groups were instantly revealed. Managerial and engineering insights were also discussed.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10508327","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 Expert Panel Discussion Embedding Ethics and Equity in Artificial Intelligence and Machine Learning Infrastructure.","authors":"Malaika Simmons, Rachele Hendricks-Sturrup, Gabriella Waters, Laurie Novak, Martin Were, Sajid Hussain","doi":"10.1089/big.2023.29061.rtd","DOIUrl":"10.1089/big.2023.29061.rtd","url":null,"abstract":"","PeriodicalId":51314,"journal":{"name":"Big Data","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10623060/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41166031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Analysis of Education Personalized Recommendation System under Vision of System Science Communication","authors":"Manying Shi, Fang Luo, Hanping Ke, Shiliang Zhang","doi":"10.3390/engproc2023038091","DOIUrl":"https://doi.org/10.3390/engproc2023038091","url":null,"abstract":"","PeriodicalId":51314,"journal":{"name":"Big Data","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90898197","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":"Optimising the Cuckoo Search Algorithm for Improved Quality of Service in Cognitive Radio ad hoc Networks","authors":"Ramahlapane Lerato Moila, M. Velempini","doi":"10.1109/icABCD59051.2023.10220569","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220569","url":null,"abstract":"This study proposes an optimised routing scheme, called OCS-AODV, for Cognitive Radio Ad Hoc Networks (CRAHNs) to enhance Quality of Service (QoS). The scheme applies the Cuckoo Search (CS) algorithm optimised with a fitness function to improve the performance of the Ad Hoc On-Demand Distance Vector (AODV). The objective of the study is to evaluate the proposed scheme's performance with respect to delay, packet loss, packet delivery ratio and throughput. The literature review shows that the existing routing protocols have limitations which impact performance in dynamic environments. The proposed OCS-AODV scheme aims to address these limitations by selecting reliable paths based on a fitness function that considers the lifetime of nodes, reliability, and available buffer capacity. The simulation results have shown that the OCS-AODV scheme outperforms the CS-DSDV and ACO-AODV schemes in terms of PDR, packet loss, delay, and throughput. The study concludes that the proposed scheme improves the QoS of routing in CRAHNs. However, the use of a single fitness function may not be optimal for all network scenarios. Multiple fitness functions may be considered in future and the schemes be evaluated in real-world CRAHNs","PeriodicalId":51314,"journal":{"name":"Big Data","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74075792","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 Underwater Network for Mini-Submarine Underwater Observatory","authors":"A. Periola, M. Sumbwanyambe","doi":"10.1109/icABCD59051.2023.10220457","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220457","url":null,"abstract":"Ice melting in the Arctic enables the conduct of underwater neutrino astronomy in new regions with maritime resources. The presented research proposes a novel underwater network that is integrated with terrestrial computing entities to obtain underwater astronomy-associated data. In addition, the proposed network architecture enhances the conduct of underwater neutrino astronomy. This is done by increasing the potential neutrino presence points. Analysis shows that the use of the arctic region in addition to the existing region of Lake Baikal in comparison to the existing case (where only Lake Baikal is utilized) increases the potential neutrino presence points by an average of (28.3 – 65.7) %.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75011344","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":"Malware detection using Explainable ML models based on Feature Extraction using API calls","authors":"Bhanu Prakash Reddy Banda, Bianca Govan, K. Roy, Kelvin S. Bryant","doi":"10.1109/icABCD59051.2023.10220515","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220515","url":null,"abstract":"Malware attacks have become a crucial problem in modern life. From 2015 to 2021 about 56.1billion malware attacks have taken place in the world. A malware attack typically costs a business over 2.5 million dollars to remediate. According to Cybersecurity Ventures, during the next five years, the cost of cybercrime would increase by 15% yearly, reaching 10.5 trillion USD annually by 2025 from 3 trillion USD in 2015. There is a global epidemic of malware. Studies imply that malware's effects are deteriorating. The main defense against malware tools is malware detectors. Therefore, it is crucial that we research malware detection methods to better comprehend their advantages and disadvantages. This research focuses on an Application Pro-gramming Interface (API) call-based malware detection strategy with Machine Learning to further improve malware detection. The Limitations that motivated to work on this project was the lack of datasets with newly attacked malware samples and also lack of detecting the malware with good accuracy. The main goal of this research is to understand the malware behavior on the Windows platform, use a dynamic analysis to identify various aspects or features that have dangerous code patterns from malware samples and employ various malware and benign samples to construct and validate machine learning-based malware detection models. The data was gathered from publicly accessible sites and sampled using a sandbox approach. Machine Learning models were built using the new dataset. The Supervised Learning models and deep Learning models were applied to the dataset and then the results were compared and cross-checked to get the best fit model. This investigation demonstrated the possibility of estab- lishing a high-precision capability for the detection of malware while combining API calls and Machine Learning models., The strategy yielded a high malware detection accuracy of 88.26% (XGBoost) model and 90.70% (MLP classifier) for Windows-based platforms. We have used Explainable Machine Learning, namely the SHapley Additive exPlanations (SHAP) value methods to demonstrate the important component or feature responsible for the prediction of the model.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85206168","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":"Enabling Vehicle Search Through Robust Licence Plate Detection","authors":"Alden Boby, Dane Brown, James Connan, Marc Marais, Luxulo Lethukuthula Kuhlane","doi":"10.1109/icABCD59051.2023.10220508","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220508","url":null,"abstract":"Licence plate recognition has many practical applications for security and surveillance. This paper presents a robust licence plate detection system that uses string-matching algorithms to identify a vehicle in data. Object detection models have had limited application in the character recognition domain. The system utilises the YOLO object detection model to perform character recognition to ensure more accurate character predictions. The model incorporates super-resolution techniques to enhance the quality of licence plate images to increase character recognition accuracy. The proposed system can accurately detect license plates in diverse conditions and can handle license plates with varying fonts and backgrounds. The system's effectiveness is demonstrated through experimentation on components of the system, showing promising license plate detection and character recognition accuracy. The overall system works with all the components to track vehicles by matching a target string with detected licence plates in a scene. The system has potential applications in law enforcement, traffic management, and parking systems and can significantly advance surveillance and security through automation.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78251777","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":"Early Detection of Lung Cancer via Breath Analysis Utilising Electronic Nose","authors":"Funmilayo S. Moninuola, E. Adetiba, Anthony A. Atayero, A. Awelewa, A. Adeyeye, Oluwadamilola Oshin, J. Ameh, A. Abayomi, Victor Ezekiel","doi":"10.1109/icABCD59051.2023.10220490","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220490","url":null,"abstract":"Lung Cancer (LC), have the highest mortality rate and the second-highest incidence rate of all cancers combined because of a pathophysiological imbalance in the fundamental mechanism of cell proliferation. For patients with LC, prompt diagnosis and treatment are of utmost importance. The orthodox methods employed for detecting LC are characterised by invasiveness, protracted duration, high cost and exhibit reduced efficacy in detecting malignant cells during the initial phases of the ailment. The increasing attention of researchers toward the potential of utilising Volatile Organic Compound (VOC) biomarkers for the non-invasive detection of LC can be attributed to the advancements in techniques and procedures. This study offers a state-of-the-art portable E-nose that has the potential to enhance clinical outcomes associated with the early diagnosis of LC. Three ML models - SVM, AdaBoost, and MLP were employed to discriminate LC from other respiratory breathprint dataset. The MLP model achieved the highest performance accuracy result of 89.05%, specificity 95.12%, and sensitivity of 80%.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82427584","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}