Journal of computing and natural science最新文献

Object Recognition to Content Based Image Retrieval: A Study of the Developments and Applications of Computer Vision 从物体识别到基于内容的图像检索：计算机视觉的发展与应用研究

Journal of computing and natural science Pub Date : 2024-01-05 DOI: 10.53759/181x/jcns202404005

Udula Mangalika

{"title":"Object Recognition to Content Based Image Retrieval: A Study of the Developments and Applications of Computer Vision","authors":"Udula Mangalika","doi":"10.53759/181x/jcns202404005","DOIUrl":"https://doi.org/10.53759/181x/jcns202404005","url":null,"abstract":"Natural Language Processing (NLP) and Computer Vision (CV) are interconnected fields within the domain of Artificial Intelligence (AI). CV is tasked with the process of engaging with computer systems to effectively interpret and recognize visual data, while NLP is responsible for comprehending and processing the human voice. The two fields have practical applicability in various tasks such as image description generation, object recognition, and question-based answering after a visual input. Deep learning algorithms such as word input are typically employed in enhancing the performance of Content-Based Image Processing (CBIR) techniques. Generally, NLP and CV play a vital role in enhancing computer comprehension and engagements with both visual and written information. This paper seeks to review various major elements of computer vision, such as CBIR, visual effects, image documentation, video documentation, visual learning, and inquiry to explore various databases, techniques, and methods employed in this field. The authors focus on the challenges and progress in each area and offer new strategies for improving the performance of CV systems.","PeriodicalId":484445,"journal":{"name":"Journal of computing and natural science","volume":"56 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139536157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image Signal Processing in the Context of Deep Learning Applications 深度学习应用背景下的图像信号处理

Journal of computing and natural science Pub Date : 2024-01-05 DOI: 10.53759/181x/jcns202404002

Ali Кhusein, Urquhart

{"title":"Image Signal Processing in the Context of Deep Learning Applications","authors":"Ali Кhusein, Urquhart","doi":"10.53759/181x/jcns202404002","DOIUrl":"https://doi.org/10.53759/181x/jcns202404002","url":null,"abstract":"Deep learning accelerators are a specialized sort of hardware architecture designed to enhance the computational efficiency of computers engaged in deep neural networks (DNNs) training. The implementation of DNNs in embedded vision applications might potentially be facilitated by the integration of energy-effective accelerators of deep learning into sensors. The lack of recognition for their significant impact on accuracy is a notable oversight. In previous iterations of deep learning accelerators integrated inside sensors, a common approach was bypassing the image signal processor (ISP). This deviation from the traditional vision pipelines had a detrimental impact on the performance of machine learning models trained on data that had undergone post-ISP processing. In this study, we establish a set of energy-efficient techniques that allow ISP to maximize their advantages while also limiting the covariate shift between the target dataset (RAW images) and the training dataset (ISP-analyzed images). This approach enables the practical use of in-sensor accelerators. To clarify, our results do not minimize the relevance of in-sensor accelerators. Instead, we highlight deficiencies in the methodology used in prior research and propose methodologies that empower in-sensor accelerators to fully exploit their capabilities.","PeriodicalId":484445,"journal":{"name":"Journal of computing and natural science","volume":"22 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139536024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing the Viability of Performance Evaluation Methods in Network Research 评估网络研究中绩效评估方法的可行性

Journal of computing and natural science Pub Date : 2024-01-05 DOI: 10.53759/181x/jcns202404003

Sim Sze Yin, Yoni Danieli

{"title":"Assessing the Viability of Performance Evaluation Methods in Network Research","authors":"Sim Sze Yin, Yoni Danieli","doi":"10.53759/181x/jcns202404003","DOIUrl":"https://doi.org/10.53759/181x/jcns202404003","url":null,"abstract":"Ad-hoc networks are to networks that are spontaneously and temporarily established without requiring any pre-existing infrastructure. These networks are often characterized by self-organization and may be spontaneously established to simplify communication across devices. Wireless Sensor Networks (WSNs) consist of diminutive, energy-efficient devices known as sensors, which are strategically placed in different settings to gather and send data without the need for physical connections. WSNs are often used for the purpose of monitoring and collecting data from the surrounding environment. This study focuses on the assessment of performance in network research, especially in the domains of Ad-Hoc and WSN. The analysis focuses on papers from renowned conferences in various domains and scrutinizes the validation methodologies used by authors. Simulations are the predominant approach used for performance assessment, with MATLAB being the favored simulator. Experimental verification is also carried out, but the articles lack comprehensive information, which poses a challenge for replicating the experiments. In general, a minuscule proportion of publications provide replicable results.","PeriodicalId":484445,"journal":{"name":"Journal of computing and natural science","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139450396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of Agricultural Information Systems Development Kit for Multimodal Agricultural systems 农业信息系统开发工具包在多模式农业系统中的应用

Journal of computing and natural science Pub Date : 2024-01-05 DOI: 10.53759/181x/jcns202404001

Agnar Alfons Ramel

{"title":"Application of Agricultural Information Systems Development Kit for Multimodal Agricultural systems","authors":"Agnar Alfons Ramel","doi":"10.53759/181x/jcns202404001","DOIUrl":"https://doi.org/10.53759/181x/jcns202404001","url":null,"abstract":"Multifunctional and diverse agriculture has the potential to effectively address conflicting demands and requirements by concurrently boosting biodiversity, production, and provision of environmental services. Digitalized technology can assist in the creation and control of agricultural models and systems that are resource-efficient and adaptable to their environments. In order to demonstrate the potential of digital technology in promoting decision-making processes that prioritize diverse and environmentally sustainable farming practices, we present an analysis of Agricultural Information Systems Development Kit (AISDK). The AISDK was developed through a comprehensive literature review to identify the limitations of current tools. Additionally, in collaboration with stakeholders, we established the criteria for a decision-support tool that is based on knowledge. The findings of the review emphasize the ongoing challenges associated with the integration of multiple spatial, temporal, and sustainability dimensions, as well as the promotion of effective collaboration and communications among farmers and key stakeholders in the agriculture sector.","PeriodicalId":484445,"journal":{"name":"Journal of computing and natural science","volume":"23 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139536017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review of Textual and Voice Processing Algorithms in the Field of Natural Language Processing 自然语言处理领域的文本和语音处理算法综述

Journal of computing and natural science Pub Date : 2023-10-05 DOI: 10.53759/181x/jcns202303018

Matt Bowden

{"title":"A Review of Textual and Voice Processing Algorithms in the Field of Natural Language Processing","authors":"Matt Bowden","doi":"10.53759/181x/jcns202303018","DOIUrl":"https://doi.org/10.53759/181x/jcns202303018","url":null,"abstract":"Currently, there is a significant focus on natural language processing (NLP) within academic circles. As one of the initial domains of inquiry in the domain of machine learning, it has been utilized in a variety of significant sub-disciplines, such as text processing, speech recognition, and machine translation. Natural language processing has contributed to notable progress in computing and artificial intelligence. The recurrent neural network serves as a fundamental component for numerous techniques in domain of NLP. The present article conducts a comprehensive evaluation of various algorithms for processing textual and voice data, accompanied by illustrative instances of their functionality. Various algorithmic outcomes exhibit the advancements achieved in this field during the preceding decade. Our endeavor involved the classification of algorithms based on their respective types and expounding on the scope for future research in this domain. Furthermore, the study elucidates the potential applications of these heterogeneous algorithms and also evaluates the disparities among them through an analysis of the findings. Despite the fact that natural language processing has not yet achieved its ultimate objective of flawlessness, it is plausible that with sufficient exertion, the field will eventually attain it. Currently, a wide variety of artificial intelligence systems use natural language processing algorithms to comprehend human-spoken directions.","PeriodicalId":484445,"journal":{"name":"Journal of computing and natural science","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135546645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Critical Review of Crack Detection Based on Image Processing 基于图像处理的裂纹检测技术综述

Journal of computing and natural science Pub Date : 2023-10-05 DOI: 10.53759/181x/jcns202303019

Zhu Jiping

{"title":"A Critical Review of Crack Detection Based on Image Processing","authors":"Zhu Jiping","doi":"10.53759/181x/jcns202303019","DOIUrl":"https://doi.org/10.53759/181x/jcns202303019","url":null,"abstract":"In order to extract meaningful observations from an image, it is essential to first convert it into a digital format and then apply a particular processing methodology. In the domain of image processing, it is a prevalent convention to consider all images as signals that are two-dimensional in nature, while utilizing conventional signal processing methodologies. The existence of surface fissures in concrete acts as an initial indication of probable structural deterioration. The utilization of image-based automated fracture identification is proposed as a viable alternative in situations where a human replacement is unavailable. This paper provides a critical review of crack detection using image processing. The scholarly literature encompasses a range of image processing techniques that can be employed for the automated identification of fractures and their respective depths. The present research involves a comprehensive examination with the objective of discerning the existing obstacles and past accomplishments within this area of investigation. A total of 24 publications related to the detection of Ato cracks have been selected for the purpose of conducting a comprehensive review. Following the review, a comprehensive analysis is performed on various image processing techniques, encompassing their respective objectives, degrees of accuracy and inaccuracy, as well as the datasets of images utilized. This study also presents future research efforts in identifying and resolving the problem of crack detection.","PeriodicalId":484445,"journal":{"name":"Journal of computing and natural science","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135546651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Definition, Challenges and Future Research for Internet of Things 物联网的定义、挑战与未来研究

Journal of computing and natural science Pub Date : 2023-10-05 DOI: 10.53759/181x/jcns202303020

Li Hua Fang, Dong Yonggui

引用次数: 0

An Analysis of Multi Agent Systems Agent Based Programming 多智能体系统基于智能体的规划分析

Journal of computing and natural science Pub Date : 2023-10-05 DOI: 10.53759/181x/jcns202303017

Ali Кhusein

引用次数: 0

A Review of Data Mining, Big Data Analytics and Machine Learning Approaches 数据挖掘、大数据分析和机器学习方法综述

Journal of computing and natural science Pub Date : 2023-10-05 DOI: 10.53759/181x/jcns202303016

Francisco Pedro

{"title":"A Review of Data Mining, Big Data Analytics and Machine Learning Approaches","authors":"Francisco Pedro","doi":"10.53759/181x/jcns202303016","DOIUrl":"https://doi.org/10.53759/181x/jcns202303016","url":null,"abstract":"The phenomenon of economic globalization has led to the swift advancement of industries across diverse domains. Consequently, big data technology has garnered increasing interest. The generation of network data is occurring at an unparalleled pace, necessitating the intelligent processing of vast amounts of data. To fully leverage the value inherent in this data, the implementation of machine learning techniques is imperative. The objective of machine learning in a vast data setting is to identify particular rules that are concealed within dynamic, variable, multi-origin heterogeneous data, with the ultimate aim of maximizing the value of the data. The integration of big data technology and machine learning algorithms is imperative in order to identify pertinent correlations within intricate and dynamic datasets. Subsequently, computer-based data mining can be utilized to extract valuable research insights. The present study undertakes an analysis of deep learning in comparison to conventional data mining and machine learning techniques. It conducts a comparative assessment of the strengths and limitations of the traditional methods. Additionally, the study introduces the requirements of enterprises, their systems and data, the IT challenges they face, and the role of Big Data in an extended service infrastructure. This study presents an analysis of the probability and issues associated with the utilization of deep learning, including machine learning and traditional data mining techniques, in the big data analytics context.","PeriodicalId":484445,"journal":{"name":"Journal of computing and natural science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135546648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0