Big Data最新文献_第9页

Factors Affecting the use of Smartphones for Learning: A Proposed Model 影响使用智能手机学习的因素:一个建议的模型

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220478

Sithembiso Dyubele, S. Soobramoney, D. Heukelman

{"title":"Factors Affecting the use of Smartphones for Learning: A Proposed Model","authors":"Sithembiso Dyubele, S. Soobramoney, D. Heukelman","doi":"10.1109/icABCD59051.2023.10220478","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220478","url":null,"abstract":"Increased functionalities of smartphones, such as providing easy access to the internet, have offered multiple learning opportunities, especially in a world surrounded by unprecedented periods like COVID'19. Despite the benefits of smartphones mentioned above, academics still have significant concerns about the effective utilisation of these technological devices by students for learning purposes. This paper aims to examine the factors affecting the use of smartphones for learning. The study utilised a quantitative method to pursue its aim and objectives. Data were gathered from 80 academic staff members from five Departments under the Faculty of Accounting & Informatics. A stratified sampling approach was applied to ensure a more realistic and accurate estimation of the population had been used. After applying the above approach, a simple random sampling method was used for this population according to the number of academic staff members in the above-mentioned departments. The data were analysed to ensure reliability and validity, and descriptive statistics were applied, and correlations identified to develop the proposed model. The outcomes indicate that academic staff members believe that Attitudes towards Smartphones, Facilitating Conditions, Perceived Ease of Use, Perceived Usefulness, and Performance Expectations significantly impact the use of smartphones for learning. This study was limited to academic staff from five departments of a single faculty at a South African University of Technology.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"1 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":"79004119","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}

引用次数: 0

The Next Evolution of Web Browser Execution Environment Performance Web浏览器执行环境性能的下一个演变

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220564

Zahir Toufie, Boniface Kabaso

{"title":"The Next Evolution of Web Browser Execution Environment Performance","authors":"Zahir Toufie, Boniface Kabaso","doi":"10.1109/icABCD59051.2023.10220564","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220564","url":null,"abstract":"Web browsers have for long been wanting to host and execute feature-rich, compute-intensive, and complex applications or simply Compute-Intensive Applications (CIAs), within their Execution Environment (EE), with native desktop performance. There was Adobe Shockwave, Macromedia Flash, Java Applets, JavaScript Programming Language (JS) and recently WebAssembly Programming Language (WASM), but also short-lived relationships, such as Microsoft ActiveX, Silverlight and Apple Quicktime. One hindrance to web browsers hosting and executing CIAs with native desktop performance is that currently there is no web browser technology with the software architecture and design that can support them. This paper aims to review the evolution of the Web as an application platform since the rise of WASM, over the last decade or so, within the context of application performance relative to that of native desktop application performance. As well as to propose where researchers should focus their efforts in order to advance the Web as an application platform that is capable of executing CIAs. In future work, we plan to extend our study to include theoretical contributions, such as providing insights into how to improve the performance of web applications based on various software architectures and designs for web browser EEs, methodological contributions, such as providing methods and approaches developed, adapted or enhanced which detail the software architecture and design for web browser EEs that have higher performance than currently available, and practical contributions that will lay the groundwork for a production-ready web browser EE based on the prototype web browser EE produced by our study.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"183 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":"76639144","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}

引用次数: 0

A Rest API to Classify Pneumonia Infection From Chest X-ray Images Using Multi-Layer Perceptron and LeNet 基于多层感知机和LeNet的胸片肺炎感染分类Rest API

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220479

Tinashe Crispen Gadzirai, W. T. Vambe

{"title":"A Rest API to Classify Pneumonia Infection From Chest X-ray Images Using Multi-Layer Perceptron and LeNet","authors":"Tinashe Crispen Gadzirai, W. T. Vambe","doi":"10.1109/icABCD59051.2023.10220479","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220479","url":null,"abstract":"Pneumonia remains the most common reason for inpatient stays and fatalities among adults and children in the world. It became worse during Covid 19 pandemic. Most African countries like South Africa were and are still seriously affected. The situation is worse in rural areas because of several reasons, among them; not having enough X-rays machines, having no or few radiologists to analyze and interpret the X-ray pictures to determine if the pictures are normal pictures or pneumonia. The ability to accurately classify these two types of pneumonia can guarantee effective treatment which will boost survival chances. Artificial Intelligence (AI) is a cost-effective approach and can play a pivotal role in easily analyzing and interpreting X-ray images. This research used CRoss Industry Standard Process for Data Mining methodology in developing a simple Rest API model that would classify the chest X-ray image if it were normal, the person has pneumonia caused by bacteria or virus. Multi-Layer Perceptron (MLP) model had a training accuracy of 73.89%, validation accuracy of 75.46%, and test accuracy of 75.46% whereas LeNet had 78.49%, 76.51%, and 76,51%, respectively. This study demonstrated to the public that AI models may be developed to aid health professionals in the early diagnosis, classification, analysis, and interpretation of X-ray images for pneumonia. In the future, the model created should convert the English interpretations into South African local languages like isiXhosa, Zulu, Venda, and many others. Thus, making it easier for the local communities to understand giving them a sense of belonging.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"1 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":"84198355","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}

引用次数: 0

Deploying a Stable 5G SA Testbed Using srsRAN and Open5GS: UE Integration and Troubleshooting Towards Network Slicing 使用srsRAN和Open5GS部署稳定的5G SA测试平台:面向网络切片的UE集成和故障排除

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220512

Lusani Mamushiane, A. Lysko, H. Kobo, Joyce B. Mwangama

{"title":"Deploying a Stable 5G SA Testbed Using srsRAN and Open5GS: UE Integration and Troubleshooting Towards Network Slicing","authors":"Lusani Mamushiane, A. Lysko, H. Kobo, Joyce B. Mwangama","doi":"10.1109/icABCD59051.2023.10220512","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220512","url":null,"abstract":"Field trials and experimentation are crucial for accelerating the adoption of standalone (SA) 5G in Africa. Traditionally, only network operators and vendors had the opportunity for practical experimentation due to proprietary systems and licensing restrictions. However, the emergence of open source cellular stacks and affordable software-defined radio (SDR) systems is changing this landscape. Although these technologies are not yet fully developed for complete 5G systems, their progress is rapid, and the research community is using them to test different use cases like network slicing. Building a 5G network is complex, especially in uncontrolled RF environments with fluctuating physical conditions such as noise and interference. This necessitates proper RF planning and performance optimization. The complexity is further compounded by the variety of 5G end-user devices, each with unique configurations and integration requirements. Some devices are network locked and require rooting to connect to a 5G testbed, while others need expert APN configurations or have specific compatibility specifications like sub-carrier spacing (SCS) and duplex mode. Unfortunately, vendors often provide limited information about RF compatibility, making trial-and-error techniques necessary to uncover compatibility details. This paper presents best practices for deploying and configuring a 5G SA testbed, focusing on the integration challenges of consumer-grade devices, specifically 5G mobile phones connected to a 5G testbed. Additionally, the paper offers solutions for troubleshooting integration errors and performance issues, as well as a brief discussion on the realization of basic network slicing in a 5G SA network.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"390 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":"80438244","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}

引用次数: 0

A Scalable Semantic Framework for an Integrated Multi-Hazard Early Warning System 多灾种综合预警系统的可扩展语义框架

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220560

Yolo Madani, Adeyinka K. Akanbi, Mpho Mbele, M. Masinde

{"title":"A Scalable Semantic Framework for an Integrated Multi-Hazard Early Warning System","authors":"Yolo Madani, Adeyinka K. Akanbi, Mpho Mbele, M. Masinde","doi":"10.1109/icABCD59051.2023.10220560","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220560","url":null,"abstract":"The application of modern technologies in the environmental monitoring domain through the deployment of interconnected Internet of Things (loT) sensors, legacy systems, and enterprise networks has become an invaluable component of realising an efficient environmental monitoring system. Monitoring systems' requirements are extremely different depending on the environment, leading to ad-hoc implementations and integration of heterogeneous systems and applications. The resulting distributed systems lack flexibility with inherent issues such as data incompatibility, lack of data integration, and systems interoperability. Semantic representation of data is necessary to combine data from heterogeneous sources for consolidation into meaningful and valuable information and unlock the reusability of data between the monitoring systems. This research explores how a scalable semantic framework can ensure data representation using machine-readable languages for seamless data integration and interoperability of other heterogeneous sub-systems in a Multi-Hazard Early Warning System (MHEWS) as a case study. The study hypothesises that the challenge of ensuring data representation, data integration, and system interoperability within an MHEWS can be overcome through the application of semantic middleware.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"54 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":"90753084","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}

引用次数: 0

Development of a Sign Language Recognition System Using Machine Learning 基于机器学习的手语识别系统的开发

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220456

H. Orovwode, Ibukun Deborah Oduntan, J. Abubakar

{"title":"Development of a Sign Language Recognition System Using Machine Learning","authors":"H. Orovwode, Ibukun Deborah Oduntan, J. Abubakar","doi":"10.1109/icABCD59051.2023.10220456","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220456","url":null,"abstract":"Deafness and voice impairment have been persistent disabilities throughout history, hindering individuals from engaging in verbal communication and leading to their isolation from the predominantly vocally communicating society. Sign language has emerged as the primary mode of communication for people with these disabilities. However, it presents a language barrier as it is not commonly understood by those who can hear. To address this issue, various methods for recognizing sign language have been proposed. This paperaims to develop a machine learning-based system that can recognize sign language in real-time. The paper involved the acquisition of a dataset consisting of 44,654 images representing the static American Sign Language (ASL) alphabet signs. The HandDetector module was utilized to detect and capture images of the signer's hand forming each sign through a PC webcam. The dataset was split into three sets: training data (20,772 cases), validation data (8,903 cases), and test data (14,979 cases). Image pre-processing techniques were implemented on the images and a convolutional neural network (CNN) model was trained and compiled. The CNN utilized in the paper comprised of three convolutional layers and a SoftMax output layer and it was compiled using the Adam optimizer and categorical cross-entropy loss function. The performance of the system was evaluated using the test dataset. Notably, the system achieved remarkable accuracy rates, having a training accuracy of 99.86%, a validation accuracy of 99.94%, and a test accuracy of 94.68%. The results obtained from this study demonstrated significant advancements in sign language recognition, surpassing previous findings in the literature.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"15 1","pages":"1-8"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82770967","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}

引用次数: 0

Ocular Cataract Identification Using Deep Convolutional Neural Networks 基于深度卷积神经网络的白内障识别

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220532

Feliciana M. E. Manuel, S. Saide, Felermino M. D. A. Ali, Sanae Lotfi

引用次数: 0

Spatiotemporal Convolutions and Video Vision Transformers for Signer-Independent Sign Language Recognition 基于时空卷积和视频视觉变换的独立手语识别

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220534

Mark Marais, Dane Brown, James Connan, Alden Boby

{"title":"Spatiotemporal Convolutions and Video Vision Transformers for Signer-Independent Sign Language Recognition","authors":"Mark Marais, Dane Brown, James Connan, Alden Boby","doi":"10.1109/icABCD59051.2023.10220534","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220534","url":null,"abstract":"Sign language is a vital tool of communication for individuals who are deaf or hard of hearing. Sign language recognition (SLR) technology can assist in bridging the communication gap between deaf and hearing individuals. However, existing SLR systems are typically signer-dependent, requiring training data from the specific signer for accurate recognition. This presents a significant challenge for practical use, as collecting data from every possible signer is not feasible. This research focuses on developing a signer-independent isolated SLR system to address this challenge. The system implements two model variants on the signer-independent datasets: an R(2+ I)D spatiotemporal convolutional block and a Video Vision transformer. These models learn to extract features from raw sign language videos from the LSA64 dataset and classify signs without needing handcrafted features, explicit segmentation or pose estimation. Overall, the R(2+1)D model architecture significantly outperformed the ViViT architecture for signer-independent SLR on the LSA64 dataset. The R(2+1)D model achieved a near-perfect accuracy of 99.53% on the unseen test set, with the ViViT model yielding an accuracy of 72.19 %. Proving that spatiotemporal convolutions are effective at signer-independent SLR.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"7 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":"80853503","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}

引用次数: 0

A Swimming Goggles Optical Design by Fresnel Lenses 菲涅耳透镜的泳镜光学设计

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.3390/engproc2023038090

Feng-Ming Yeh, Liang-Ying Huang, Chao-Kai Chang, Ya-Hui Hsieh, Hsuan-Fu Wang, Rong-Seng Chang, Der-Chin Chen

引用次数: 0

Design of a 45 nm Complementary Metal Oxide Semiconductor Low Noise Amplifier for a 30 GHz Millimeter-Wave Wireless Transceiver in Radar Sensor Applications 用于雷达传感器中30ghz毫米波无线收发器的45 nm互补金属氧化物半导体低噪声放大器的设计

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220474

Shingirirai M. Chakoma, K. Ogudo

{"title":"Design of a 45 nm Complementary Metal Oxide Semiconductor Low Noise Amplifier for a 30 GHz Millimeter-Wave Wireless Transceiver in Radar Sensor Applications","authors":"Shingirirai M. Chakoma, K. Ogudo","doi":"10.1109/icABCD59051.2023.10220474","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220474","url":null,"abstract":"The millimeter-wave (mmWave) frequency band is rapidly becoming utilized in wireless technologies due to its large bandwidth and high data throughput. Wireless technology is increasingly becoming the backbone of the Internet of Things (IoT). This has resulted in increased applications of the radio frequency (RF) spectrum and congestion of the microwave band. This can be solved by utilizing more bandwidth at higher frequency bands. One notable application of IoT pertains to radar sensing, which has experienced increased popularity across various domains such as autonomous vehicles, gesture recognition, drones, and health monitoring. Radar sensors have been employed in these applications to perform tasks including proximity sensing, direction detection, speed measurement, target localization, and capturing physiological indicators such as heartbeat and breathing. Several factors have an impact on the performance of radar sensors, encompassing the maximum range for target detection, measurement precision, capability to differentiate between multiple targets, and ability to operate effectively in environments with high levels of noise. This paper presents the design of a 45 nm complementary metal-oxide-semiconductor (CMOS) low noise amplifier (LNA) for a mmWave Ka-band wireless transceiver for radar sensors. The LNA was designed to operate at 0.6V and 700 μA for low power consumption. The LNA consists of an inductive degenerated common source (CS) and a common gate (CG) diode-connected load. The LNA achieves a power gain of 31.19 dB and a noise figure (NF) of 0.133 dB at 30 GHz consuming 0.42 mW of power.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"35 4 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":"79166773","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}

引用次数: 0