Big Data最新文献_第8页

Classification and Detection of Cyanosis Images on Lightly and Darkly Pigmented Individual Human Skins using a Fine-Tuned MobileNet Architecture 使用微调的MobileNet架构对浅色和深色个体皮肤上的紫绀图像进行分类和检测

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220464

Lukoki Mpova, T. Shongwe, Ali N. Hasan

引用次数: 0

Realizing the Potential of Stratosphere Utilization via Stratosphere Data Centers 通过平流层数据中心实现平流层利用的潜力

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220520

A. Periola, K. Ogudo, A. Alonge

引用次数: 0

Performance Analysis of a Light Weight Ground Robotic Vehicle by Implementing Adaptive Neuro-Fuzzy Inference System (ANFIS) 基于自适应神经模糊推理系统(ANFIS)的轻型地面机器人车辆性能分析

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220494

M. Okwu, I. Emovon, O. J. Oyejide, Kingsley C. Ezekiel, Olaye Messiah, Perpetua C. Jones-Iwuagwu

{"title":"Performance Analysis of a Light Weight Ground Robotic Vehicle by Implementing Adaptive Neuro-Fuzzy Inference System (ANFIS)","authors":"M. Okwu, I. Emovon, O. J. Oyejide, Kingsley C. Ezekiel, Olaye Messiah, Perpetua C. Jones-Iwuagwu","doi":"10.1109/icABCD59051.2023.10220494","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220494","url":null,"abstract":"Automated Guided Vehicles (AGVs) are widely used as delivery agents and for material transportation in factories, hospital environment, and other facilities. Conducting performance tests on AGVs has the potential to ratify and improve the efficiency, and reliability of the system. However, published studies on performance analysis focused on classical metrics for such evaluation. In this study, the emphasis is on the performance evaluation of a developed lightweight AGV using the Adaptive Neuro-fuzzy Inference System (ANFIS). The developed line following AGV is flexible, intelligent, and nifty, and can be accessed wirelessly, and controlled by an operator. It was programmed to avoid collision with the help of a proximity sensor attached. The performance test was conducted by drawing black lines on a plain surface for easy navigation of the AGV. A series of experiments was carried out by using realistic test variables like the navigation pattern of AGV, test accuracy, energy efficiency, obstacle avoidance, task accomplishment, and others. Sensitivity analysis was done using the ANFIS surface plot. The total system intelligence (TSI) obtained for the different trials are 76%; 79%; 80%; 81%; 79% and 81 %, for the first, second, third, fourth, fifth, and final trials respectively. The preeminent observable performance was the fourth and sixth trials, obtained at 81 %. The outcome of the investigation reveals that the ANFIS model is an efficient soft computing technique capable of performing TSI tests of AGVs with a high degree of accuracy. The model is also recommended in AGV platooning.","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":"85157357","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

Artificial Intelligence and State Power 人工智能与国家权力

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220459

V. Maphosa

{"title":"Artificial Intelligence and State Power","authors":"V. Maphosa","doi":"10.1109/icABCD59051.2023.10220459","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220459","url":null,"abstract":"Artificial Intelligence (AI) is increasingly ubiquitous, transforming our everyday lives. AI is expected to improve human life amid growing concerns that unregulated AI could lead to disastrous outcomes. AI algorithms have become complex and more challenging to follow. The disruptive nature of AI is seen in state power through surveillance, facial recognition, and deployment of lethal autonomous weapons systems by superpowers. This review paper analyses how AI is deployed for state power to enhance policing and military operations. AI deployment by the police and army increases operational excellence and efficiency and offers simulated training, and predictive capabilities, while unregulated use raises ethical and human rights violations. Given the foreseeable pervasiveness and rapid AI development, more research is required to restrict coercive state power. This review paper raises awareness of AI's affordances and contributes to emergent literature on constraints and ethical and legal issues. It raises interest among scholars, policymakers, and practitioners for collaborative research. AI will reinforce the technology divide as developing countries face infrastructural, financial and digital skills barriers. The review concludes with future research implications.","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":"78064363","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

Plant Disease Detection using Vision Transformers on Multispectral Natural Environment Images 基于多光谱自然环境图像的视觉变换植物病害检测

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220517

Malithi De Silva, Dane Brown

{"title":"Plant Disease Detection using Vision Transformers on Multispectral Natural Environment Images","authors":"Malithi De Silva, Dane Brown","doi":"10.1109/icABCD59051.2023.10220517","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220517","url":null,"abstract":"Enhancing agricultural practices has become essential in mitigating global hunger. Over the years, significant technological advancements have been introduced to improve the quality and quantity of harvests by effectively managing weeds, pests, and diseases. Many studies have focused on identifying plant diseases, as this information aids in making informed decisions about applying fungicides and fertilizers. Advanced systems often employ a combination of image processing and deep learning techniques to identify diseases based on visible symptoms. However, these systems typically rely on pre-existing datasets or images captured in controlled environments. This study showcases the efficacy of utilizing multispectral images captured in visible and Near Infrared (NIR) ranges for identifying plant diseases in real-world environmental conditions. The collected datasets were classified using popular Vision Transformer (ViT) models, including ViT- S16, ViT-BI6, ViT-LI6 and ViT-B32. The results showed impressive training and test accuracies for all the data collected using diverse Kolari vision lenses with 93.71 % and 90.02 %, respectively. This work highlights the potential of utilizing advanced imaging techniques for accurate and reliable plant disease identification in practical field conditions.","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":"75417788","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: 2

Identification and classification of Green Leafy Vegetables using CNN models 基于CNN模型的绿叶蔬菜识别与分类

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220482

Eneia Filipe Vilanculos, T. Shongwe, Ali N. Hasan

引用次数: 0

A Resources Allocation Scheme For Joint Optical Wireless Transport Networks 一种联合无线光传输网络的资源分配方案

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220537

Mlungisi Molefe, K. Sibiya, B. Nleya

{"title":"A Resources Allocation Scheme For Joint Optical Wireless Transport Networks","authors":"Mlungisi Molefe, K. Sibiya, B. Nleya","doi":"10.1109/icABCD59051.2023.10220537","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220537","url":null,"abstract":"As the future of networking dives into a new era of connecting every single physical device into the internet termed Internet of Things (loT), this significantly means a rapid increase in the number of online connected devices, which leads to more bandwidth hungry and data consuming devices. The fifth generation (5G) of mobile communication has been deployed already in multiple countries, therefore researchers have migrated their focus to the sixth generation (6G) of mobile communication to cater for extensive coverage and massive number of loT devices. A promising architecture and technology to cope with massive number of online devices and extensive coverage is a joint optical wireless transport network which offers comparably ultra-high systems capacity and extremely low latency while maintaining an improved quality of service. Furthermore, an optical wireless transport network can accommodate high speed mobility for frequently moving end user devices which is essential for 6G. In this paper our focus is to explore and propose an ultimate optical wireless transport network architecture scheme that will cater for loT as well as networks beyond 5G. We thus propose an innovative Optical-Backhaul and Wireless Access (OBWA) network architecture as a favorable solution for future networks. We further present a joint channel and route allocation (JCRA) scheme for achieving optimal quality of experience. Performance evaluation of the proposed JCRA scheme for OBW A network architecture show a significant improvement in the network throughput as well as the network end-to-end delay despite varying load traffic or varying flow channels.","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":"84572092","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

Real- Time Detecting and Tracking of Squids Using YOLOv5 基于YOLOv5的鱿鱼实时检测与跟踪

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220521

Luxolo Kuhlane, Dane Brown, Marc Marais

引用次数: 0

Disease Motivated Model for Future Dynamic Computing 未来动态计算的疾病驱动模型

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220487

Dephney Mathebula

引用次数: 0

Improving Network Management with Software Defined Networking using OpenFlow Protocol 使用OpenFlow协议改进软件定义网络的网络管理

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220519

Koketso Molemane Rodney Mokoena, Ramahlapane Lerato Moila, Prof Mthulisi Velempini

{"title":"Improving Network Management with Software Defined Networking using OpenFlow Protocol","authors":"Koketso Molemane Rodney Mokoena, Ramahlapane Lerato Moila, Prof Mthulisi Velempini","doi":"10.1109/icABCD59051.2023.10220519","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220519","url":null,"abstract":"With the advancement of network-based devices, resulting in communication networks also growing rapidly and becoming more complex, resulting in large and heterogeneous network architecture has brought a lot of challenges in network management. Therefore, managing the network has become an increasingly a challenge given the existing network architectures. In this study, we have investigated how network operators operate, maintain and secure telecommunications networks. The study has also investigated the effectiveness of Software Defined Networking (SDN) in improving network management. The study has also investigated how the architecture minimizes the challenges users face. To improve network management with SDN using the OpenFlow protocol, we created network topologies and configured devices using the graphical network simulator 3, Oracle VM VirtualBox Manager, and Mininet VM. Our approach implemented both Git and Ansible in a centralized network architecture to solve the problems facing existing network architectures with the rapid growth of network-based devices on the Internet. This research paper has shown how to use Ansible playbooks to manage your network and overcome the challenges you face. The simulation results shows that the proposed scheme performs better in terms of efficiency and flexibility than the traditional OpenFlow protocol. These improvements have been achieved through the separation of the control and data planes, allowing for more centralized network management and easier implementation of network policies.","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":"87963992","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