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

Enabling Vehicle Search Through Robust Licence Plate Detection 通过稳健的车牌检测实现车辆搜索

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220508

Alden Boby, Dane Brown, James Connan, Marc Marais, Luxulo Lethukuthula Kuhlane

{"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":"99 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":"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}

引用次数: 0

Optimising the Cuckoo Search Algorithm for Improved Quality of Service in Cognitive Radio ad hoc Networks 优化布谷鸟搜索算法以提高认知无线电自组织网络的服务质量

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220569

Ramahlapane Lerato Moila, M. Velempini

{"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":"74 1","pages":"1-5"},"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}

引用次数: 0

An Underwater Network for Mini-Submarine Underwater Observatory 小型潜艇水下观测站的水下网络

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220457

A. Periola, M. Sumbwanyambe

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":"5 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":"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

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

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

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":"20 1","pages":"1-5"},"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

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":"28 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":"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

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