2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)最新文献_第3页

Novel deep learning approaches for crop leaf disease classification: A review 农作物叶片病害分类的深度学习新方法综述

2021 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2021-09-16 DOI: 10.1109/scse53661.2021.9568324

E. Ekanayake, Ruwan Dharshana Nawarathna

{"title":"Novel deep learning approaches for crop leaf disease classification: A review","authors":"E. Ekanayake, Ruwan Dharshana Nawarathna","doi":"10.1109/scse53661.2021.9568324","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568324","url":null,"abstract":"To encourage sustainable progress, it is suggested that in a world connected by virtual platforms, modern society should merge big data, artificial intelligence, machine learning, information and communication technology (ICT), as well as the “Internet of Things” (IoT). When real-life problems are considered, the above technology processes are essential in solving the issues. Food is an essential need of human beings. Food supply has become crucial, and it is very important to increase the adequate cultivation of plants for large populations due to huge population growth. At the same time, farmers are struggling with a variety of food plant diseases that significantly affect the harvesting and production in agricultural fields. Nevertheless, the agricultural productivity of rural areas is directly involved with the increase in the economic growth of developing countries such as Sri Lanka, India, Myanmar and Indonesia. Early identification of crop disease, using a well-established modern technique, is vital. It necessitates a number of processes observing large-scale agricultural fields as a disease can infect different parts of the plant such as leaf, roots, stem and fruit. Most diseases appear in plant leaves and have the potential to spread them all over the field within a very short time. This paper reviews several state-of-the-art methods that can be used for plant leaf disease recognition with a special reference to deep learning based methods.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125923998","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: 2

Application of AlexNet convolutional neural network architecture-based transfer learning for automated recognition of casting surface defects 将基于 AlexNet 卷积神经网络架构的迁移学习应用于铸件表面缺陷的自动识别

2021 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2021-09-16 DOI: 10.1109/scse53661.2021.9568315

S. Thalagala, C. Walgampaya

{"title":"Application of AlexNet convolutional neural network architecture-based transfer learning for automated recognition of casting surface defects","authors":"S. Thalagala, C. Walgampaya","doi":"10.1109/scse53661.2021.9568315","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568315","url":null,"abstract":"Automated inspection of surface defects is beneficial for casting product manufacturers in terms of inspection cost and time, which ultimately affect overall business performance. Intelligent systems that are capable of image classification are widely applied in visual inspection as a major component of modern smart manufacturing. Image classification tasks performed by Convolutional Neural Networks (CNNs) have recently shown significant performance over the conventional machine learning techniques. Particularly, AlexNet CNN architecture, which was proposed at the early stages of the development of CNN architectures, shows outstanding performance. In this paper, we investigate the application of AlexN et CNN architecture-based transfer learning for the classification of casting surface defects. We used a dataset containing casting surface defect images of a pump impeller for testing the performance. We examined four experimental schemes where the degree of the knowledge obtained from the pre-trained model is varied in each experiment. Furthermore, using a simple grid search method we explored the best overall setting for two crucial hyperparameters. Our results show that despite the simple architecture, AlexN et with transfer learning can be successfully applied for the recognition of casting surface defects of the pump impeller.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"109 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130071158","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: 8

A MILP model to optimize the proportion of production quantities considering the ANP composite performance index 考虑ANP综合性能指标的生产数量比例优化的MILP模型

2021 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2021-09-16 DOI: 10.1109/scse53661.2021.9568287

N. T. H. Thalagahage, A. Wijayanayake, D. Niwunhella

{"title":"A MILP model to optimize the proportion of production quantities considering the ANP composite performance index","authors":"N. T. H. Thalagahage, A. Wijayanayake, D. Niwunhella","doi":"10.1109/scse53661.2021.9568287","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568287","url":null,"abstract":"The apparel industry is considered as one of the most labor-intensive industries where Production Planning and Control (PPC) is considered as an important function, because of its involvement from scheduling each task in the process to the delivery of customer demand. Line planning is a sub-process within PPC, through which the production orders are allocated to production lines according to their setting and due dates of production completion. The decisions that address line planning functions still heavily rely on the expertise of the production planner. When production planners are required to select production lines for the production of a particular type of product, little emphasis has been placed on ways to apportion certain production orders to the most appropriate production system. In this research, a framework is developed using Analytical Network Process (ANP) which is a Multi-Criteria Decision Making (MCDM) method, enabling the incorporation of all the planning criteria in the selection of a production line. The weighted scores obtained by the best alternative production lines are used in a Linear Programming model to optimize the resource allocation in an apparel firm.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129284440","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 tree structure-based classification of diabetic retinopathy stages using convolutional neural network 基于卷积神经网络的糖尿病视网膜病变分期树状结构分类

2021 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2021-09-16 DOI: 10.1109/scse53661.2021.9568361

M. S. H. Peiris, S. Sotheeswaran

{"title":"A tree structure-based classification of diabetic retinopathy stages using convolutional neural network","authors":"M. S. H. Peiris, S. Sotheeswaran","doi":"10.1109/scse53661.2021.9568361","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568361","url":null,"abstract":"Detection, and classification of medical images have become a trending field of study during the last few decades. There is a considerable amount of vital challenges to be overcome. Ample work has been carried out to provide proper solutions for those key challenges. This study was carried out to extend one such medical image classification process to classify the stages of Diabetic Retinopathy (DR) images from colour fundus images. The study proposes a novel Convolutional Neural Network (CNN) architecture which is considered to be one of the most trending and efficient forms of classification of DR stages. Initially, the preprocessing techniques were employed to the DR fundus images with Green channel extraction and Contrast Limited Adaptive Histogram Equalization (CLAHE). The data augmentation strategy was utilised to increase training images from the DR images. Finally, Feature extraction and classification were carried out by using the proposed CNN architecture. It consists of a 14 layered CNN model, which continues three main classifications. In this proposed classification, the images were classified into a tree structure based binary classification as No_DR and DR at the beginning, and then the DR images were again classified into two classes, namely Pre_Intermediate and Post_Intermediate. Moreover, those two classes were again separately classified into Mild, Moderate, and Proliferate_DR, Severe, respectively. The Kaggle is one of the benchmark dataset repositories which was used in this study. The proposed model was able to achieve accuracies of 81 %, 96%, 84%, and 97% for the above-mentioned classifications, respectively.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128066682","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

[Copyright notice] (版权)

2021 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2021-09-16 DOI: 10.1109/scse53661.2021.9568322

引用次数: 0

Exploiting optimum acoustic features in COVID-19 individual's breathing sounds 利用COVID-19患者呼吸声音的最佳声学特征

2021 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2021-09-16 DOI: 10.1109/scse53661.2021.9568369

M. G. M. Milani, M. Ramashini, Krishani Murugiah, Lanka Geeganage Shamaan Chamal

{"title":"Exploiting optimum acoustic features in COVID-19 individual's breathing sounds","authors":"M. G. M. Milani, M. Ramashini, Krishani Murugiah, Lanka Geeganage Shamaan Chamal","doi":"10.1109/scse53661.2021.9568369","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568369","url":null,"abstract":"The world is facing an extreme crisis due to the COVID-19 pandemic. The COVID-19 virus interrupts the world's economy and social factors; thus, many countries fall into poverty. Also, they lack expertise in this field and could not make an effort to perform the necessary polymerase chain reaction (PCR) or other expensive laboratory tests. Therefore, it is important to find an alternative solution to the early prediction of COVID-19 infected persons with a low-cost method. The objective of this study is to detect COVID-19 infected individuals through their breathing sounds. To perform this task, twenty-two (22) acoustic features are extracted. The optimum features in each COVID-19 infected breathing sound is identified among these features through a feature engineering method. This proposed feature engineering method is a hybrid model that includes; statistical feature evaluation, PCA, and k-mean clustering techniques. The final results of this proposed Optimum Acoustic Feature Engineering (OAFE) model show that breathing sound signals' Kurtosis feature is more effective in distinguishing COVID-19 infected individuals from healthy individuals.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126563365","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 decentralized social network architecture 去中心化的社交网络架构

2021 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2021-09-16 DOI: 10.1109/scse53661.2021.9568334

T. Sarathchandra, Damith Jayawikrama

引用次数: 3

Estimation of the incubation period of COVID-19 using boosted random forest algorithm 基于增强随机森林算法的COVID-19潜伏期估计

2021 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2021-09-16 DOI: 10.1109/scse53661.2021.9568282

P. Rathnayake, J. M. D. Senanayake, D. Wickramaarachchi

{"title":"Estimation of the incubation period of COVID-19 using boosted random forest algorithm","authors":"P. Rathnayake, J. M. D. Senanayake, D. Wickramaarachchi","doi":"10.1109/scse53661.2021.9568282","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568282","url":null,"abstract":"Coronavirus disease was first discovered in December 2019. As of July 2021, within nineteen months since this infectious disease started, more than one hundred and eighty million cases have been reported. The incubation period of the virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can be defined as the period between exposure to the virus and symptom onset. Most of the affected cases are asymptomatic during this period, but they can transmit the virus to others. The incubation period is an important factor in deciding quarantine or isolation periods. According to current studies, the incubation period of SARS-CoV-2 ranges from2 to 14 days. Since there is a range, it is difficult to identify a specific incubation period for suspected cases. Therefore, all suspected cases should undergo an isolation period of 14 days, and it may lead to unnecessarily allocation of resources. The main objective of this research is to develop a classification model to classify the incubation period using machine learning techniques after identifying the factors affecting the incubation period. Patient records within the age group 5–80 years were used in this study. The dataset consists of 500 patient records from various countries such as China, Japan, South Korea and the USA. This study identified that the patients' age, immunocompetent state, gender, direct/indirect contact with the affected patients and the residing location affect the incubation period. Several supervised learning classification algorithms were compared in this study to find the best performing algorithm to classify the incubation classes. The weighted average of each incubation class was used to evaluate the overall model performance. The random forest algorithm outperformed other algorithms achieving 0.78 precision, 0.84 recall, and 0.80 F1-score in classifying the incubation classes. To fine-tune the model AdaBoost algorithm was used.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115369442","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

An exploratory evaluation of replacing ESB with microservices in service-oriented architecture 在面向服务的体系结构中用微服务替换ESB的探索性评估

2021 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2021-09-16 DOI: 10.1109/scse53661.2021.9568289

L. Weerasinghe, I. Perera

{"title":"An exploratory evaluation of replacing ESB with microservices in service-oriented architecture","authors":"L. Weerasinghe, I. Perera","doi":"10.1109/scse53661.2021.9568289","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568289","url":null,"abstract":"With the continuous progress in technology during the past few decades, cloud computing has become a fast-growing technology in the world, making computerized systems widespread. The emergence of Cloud Computing has evolved towards microservice concepts, which are highly demanded by corporates for enterprise application level. Most enterprise applications have moved away from traditional unified models of software programs like monolithic architecture and traditional SOA architecture to microservice architecture to ensure better scalability, lesser investment in hardware, and high performance. The monolithic architecture is designed in a manner that all the components and the modules are packed together and deployed on a single binary. However, in the microservice architecture, components are developed as small services so that horizontally and vertically scaling is made easier in comparison to monolith or SOA architecture. SOA and monolithic architecture are at a disadvantage compared to Microservice architecture, as they require colossal hardware specifications to scale the software. In general terms, the system performance of these architectures can be measured considering different aspects such as system capacity, throughput, and latency. This research focuses on how scalability and performance software quality attributes behave when converting the SOA system to microservice architecture. Experimental results have shown that microservice architecture can bring more scalability with a minimum cost generation. Nevertheless, specific gaps in performance are identified in the perspective of the final user experiences due to the interservice communication in the microservice architecture in a distributed environment.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127976531","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: 3

Modelling and validation of arc-fault currents under resistive and inductive loads 电阻和感性负载下电弧故障电流的建模和验证

2021 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2021-09-16 DOI: 10.1109/scse53661.2021.9568358

Yashodha Karunarathna, J. Wijayakulasooriya, J. Ekanayake, Pasindu Perera

引用次数: 0