2020 2nd International Conference on Advancements in Computing (ICAC)最新文献_第3页

Plant Leaf Recognition: Comparing Contour-Based and Region-Based Feature Extraction 植物叶片识别:基于轮廓和基于区域的特征提取的比较

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357152

S. Donesh, U. Piumi Ishanka

{"title":"Plant Leaf Recognition: Comparing Contour-Based and Region-Based Feature Extraction","authors":"S. Donesh, U. Piumi Ishanka","doi":"10.1109/ICAC51239.2020.9357152","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357152","url":null,"abstract":"Plants play a vital role in the environment. Identifying them and classifying them is an important task for botanists. This study briefly points out- how to recognize plant species using image processing techniques that can help botanists and scientists, the appropriate features for plant species recognition in feature extraction, how can a classification help to increase the accuracy of the plant leaf classification. There are four major phases used in here for the recognition, and they are image input, image pre-processing, feature extraction, and SVM classification. This automatic recognition system is developed using python with Jupyter Notebook environment gives higher accuracy for the plant recognition for the botanists and comparing the feature extractions such as Contour-based and Region-based to get down more accurate results than previous researches is the main purpose of the proposed study. Contour-based and Region-based features were calculated through equations. SVM classification is used for both feature extraction methods. For individual feature extraction the Contour-based feature extraction is more efficient with 72.25% accuracy than Region-based feature extraction with 70.41% accuracy, and for combining both feature extraction SVM classification gives 68.58% accuracy. Contour-based feature is the most appropriate feature for a plant species recognition.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131795614","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

Utalk: Sri Lankan Sign Language Converter Mobile App using Image Processing and Machine Learning Utalk:斯里兰卡手语转换手机应用程序，使用图像处理和机器学习

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357300

I. Dissanayake, P.J Wickramanayake, M.A.S Mudunkotuwa, P. Fernando

{"title":"Utalk: Sri Lankan Sign Language Converter Mobile App using Image Processing and Machine Learning","authors":"I. Dissanayake, P.J Wickramanayake, M.A.S Mudunkotuwa, P. Fernando","doi":"10.1109/ICAC51239.2020.9357300","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357300","url":null,"abstract":"Deaf and mute people face various difficulties in daily activities due to the communication barrier caused by the lack of Sign Language knowledge in the society. Many researches have attempted to mitigate this barrier using Computer Vision based techniques to interpret signs and express them in natural language, empowering deaf and mute people to communicate with hearing people easily. However, most of such researches focus only on interpreting static signs and understanding dynamic signs is not well explored. Understanding dynamic visual content (videos) and translating them into natural language is a challenging problem. Further, because of the differences in sign languages, a system developed for one sign language cannot be directly used to understand another sign language, e.g., a system developed for American Sign Language cannot be used to interpret Sri Lankan Sign Language. In this study, we develop a system called Utalk to interpret static as well as dynamic signs expressed in Sri Lankan Sign Language. The proposed system utilizes Computer Vision and Machine Learning techniques to interpret sings performed by deaf and mute people. Utalk is a mobile application, hence it is non-intrusive and cost-effective. We demonstrate the effectiveness of the our system using a newly collected dataset.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116238749","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

Experimental Determination of CNN Hyper-Parameters for Tomato Disease Detection using Leaf Images 基于叶片图像的番茄病害检测CNN超参数的实验确定

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357284

M. Gunarathna, R. Rathnayaka

{"title":"Experimental Determination of CNN Hyper-Parameters for Tomato Disease Detection using Leaf Images","authors":"M. Gunarathna, R. Rathnayaka","doi":"10.1109/ICAC51239.2020.9357284","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357284","url":null,"abstract":"Today, deep learning has become an emerging topic widely used in pattern recognition and classification problems. The design choice of the deep learning models entirely depends on who it‘s going to create. Still, it requires prior experience because identifying the best combination of parameters is a challenging task. So, the main objective of this study is to develop an accurate model for tomato disease classification while exploring the possible range of parameters that highly affects the performance of the Convolutional Neural Network (CNN). A simple CNN model was first built and train from scratch by using 22930 tomato leaf images collected from the Plant Village dataset in Kaggle. The model was tested for many cases by changing the values of a set of parameters while keeping other parameters constant. Finally, performance metrics were evaluated for every chosen parameter comparing with the base model. The highest prediction accuracy, training accuracy, and validation accuracy achieved from the study are 92%, 94%, and 92%, respectively. Rather than offering a guess, this study can, at most, give a definite answer that will assist new researchers in understanding how the accuracy and loss vary for every parameter within the area of tomato plant disease classification.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"294 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123128329","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

Automated Non-verbal Child Intelligent Assessment Tool 自动非语言儿童智能评估工具

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357276

K. P. D. Madushanka, U. G. N. Hasaranga, M. D. Gunasinghe, S. Seneviratne, P. Samarasingha, D. Dahanayaka, Samanthi Siriwardhana

引用次数: 0

The 2nd International Conference On Advancements in Computing 第二届计算机发展国际会议

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/icac51239.2020.9357259

引用次数: 0

VirtualPT: Virtual Reality based Home Care Physiotherapy Rehabilitation for Elderly VirtualPT:基于虚拟现实的老年人家庭护理物理治疗康复

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357281

Thamindu Anusara Heiyanthuduwa, K. W. Nikini Umasha Amarapala, K. D. Vinura Budara Gunathilaka, K. Ravindu, J. Wickramarathne, D. Kasthurirathna

{"title":"VirtualPT: Virtual Reality based Home Care Physiotherapy Rehabilitation for Elderly","authors":"Thamindu Anusara Heiyanthuduwa, K. W. Nikini Umasha Amarapala, K. D. Vinura Budara Gunathilaka, K. Ravindu, J. Wickramarathne, D. Kasthurirathna","doi":"10.1109/ICAC51239.2020.9357281","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357281","url":null,"abstract":"This paper describes the development of Personal computer based Virtual Reality home-care Physiotherapy system aimed for rehabilitating full body function in elders. VirtualPT is a true virtual reality platform where the environment is completely replaced by a virtual reality platform based on the mental condition of the person at the time. While doing the home-based prescribed physiotherapy exercises, the key health metrics are continuously monitored and tracked by combining the immersive Virtual Reality with the wearable VirtualPT Sensor kit. Virtual Reality combined with 3D motion capture lets real time movements to be accurately translated onto the virtual reality avatar that can be viewed in a virtual environment to assist physiotherapist to add exercises to the system easily. This ultimate virtual reality Physiotherapy assistant avatar is used to provide guidance to elders at home, to demonstrate and assist elders in adhering to the prescribed exercises. As a significant aspect of social interactions, mirroring of movements has been added to focus on whether the elder is able to accurately follow the movements of avatar. Furthermore, the insightful dashboard offers the elders and physiotherapists an interactive platform through virtual reality capabilities. VirtualPT physiotherapy system is cost effective and makes recovery and more convenient to elders at home while the participatory and immersive nature of Virtual Reality offers a unique realistic quality that is not generally existing in clinical-based physiotherapy. When looking at the broader concept of VirtualPT; continuity of care, integration of services, quality of life and access are equally important criteria which add more value.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121275878","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

A Geophone Based Surveillance System Using Neural Networks and IoT 基于神经网络和物联网的检波器监控系统

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/icac51239.2020.9357257

Supun Hettigoda, Chamath Jayaminda, Udayanga Amarathunga, Shiraz Thaha, M. Wijesundara, J. Wijekoon

引用次数: 1

Enhanced Symmetric Cryptography for IoT using Novel Random Secret Key Approach 基于新型随机密钥方法的物联网增强对称加密

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357316

Gopinath Sittampalam, N. Ratnarajah

{"title":"Enhanced Symmetric Cryptography for IoT using Novel Random Secret Key Approach","authors":"Gopinath Sittampalam, N. Ratnarajah","doi":"10.1109/ICAC51239.2020.9357316","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357316","url":null,"abstract":"The deployment of IoT devices in different domains enables technical innovations and value-added services to users but also creates multiple requirements in terms of effective communication and security. IoT devices are constrained by less computing resources and limited battery power. Generally, the TLS/SSL protocol is used to provide communication security on IoT and the protocol utilizes important encryption algorithms like RSA, Elliptic Curve Cryptography, and AES. However, these conventional encryption algorithms are computationally and economically expensive to implement in IoT devices. Lightweight Cryptography (LWC) algorithms were introduced recently for IoT and the aim of the algorithms is to provide the same level security with a minimal amount of computing resources and power. This paper proposes a novel Random Secret Key (RSK) technique to provide an additional security layer for symmetric LWC algorithms for IoT applications. In RSK, IoT devices do not transmit keys over the network; they share a random matrix, calculate their own RSK, encrypt, and transmit the cipher text. When a random matrix lifetime expires new matrix published and RSK resets. Regular change in the RSK makes the IoT networks resistant to brute-force/dictionary attacks. The RSK added one more simple and effective secure layer to strengthen the security of the original secret key and is successfully implemented in a smart greenhouse environment. The outcomes of the experiments prove that the RSK provides enhanced and efficient protection for symmetric LWC algorithms in any IoT systems, consume a minimum amount of resources and more resistant to key-based attacks.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121397065","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

WANHEDA: A Machine Learning Based DDoS Detection System WANHEDA:一个基于机器学习的DDoS检测系统

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357130

A.U Sudugala, W.H Chanuka, A.M.N Eshan, U. Bandara, K. Abeywardena

{"title":"WANHEDA: A Machine Learning Based DDoS Detection System","authors":"A.U Sudugala, W.H Chanuka, A.M.N Eshan, U. Bandara, K. Abeywardena","doi":"10.1109/ICAC51239.2020.9357130","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357130","url":null,"abstract":"In today's world computer communication is used almost everywhere and majority of them are connected to the world's largest network, the Internet. There is danger in using internet due to numerous cyber-attacks which are designed to attack Confidentiality, Integrity and Availability of systems connected to the internet. One of the most prominent threats to computer networking is Distributed Denial of Service (DDoS) Attack. They are designed to attack availability of the systems. Many users and ISPs are targeted and affected regularly by these attacks. Even though new protection technologies are continuously proposed, this immense threat continues to grow rapidly. Most of the DDoS attacks are undetectable because they act as legitimate traffic. This situation can be partially overcome by using Intrusion Detection Systems (IDSs). There are advanced attacks where there is no proper documented way to detect. In this paper authors present a Machine Learning (ML) based DDoS detection mechanism with improved accuracy and low false positive rates. The proposed approach gives inductions based on signatures previously extracted from samples of network traffic. Authors perform the experiments using four distinct benchmark datasets, four machine learning algorithms to address four of the most harmful DDoS attack vectors. Authors achieved maximum accuracy and compared the results with other applicable machine learning algorithms.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130451396","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: 7

Behavior Segmentation based Micro-Segmentation Approach for Health Insurance Industry 基于行为分割的健康保险行业微观分割方法

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357282

E. Nandapala, K. Jayasena, R. Rathnayaka

{"title":"Behavior Segmentation based Micro-Segmentation Approach for Health Insurance Industry","authors":"E. Nandapala, K. Jayasena, R. Rathnayaka","doi":"10.1109/ICAC51239.2020.9357282","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357282","url":null,"abstract":"To manage the company's future growth, the relationship between companies and customers is important. This can be referred to as Customer Relationship Management (CRM). By applying the micro-segmentation process companies can succeed in this CRM process. Micro-segmentation is a breakdown into micro-segments of the entire data collection. The user can easily be deeply defined with this segmentation process. Demographic segmentation is a breakdown of the dataset based on the consumers' age, gender, etc. Behavior segmentation is diving the whole dataset based on customers' behaviors. RFM analysis is a behavioral segmentation process based on consumer's behaviors. There is no exact way to precisely conduct micro-segmentation. Thus, this study proposed a new micro-segmentation process. That is applying demographic segmentation with the support of the RFM analysis. This method can easily determine the customers' behaviors accurately and deeply. Insurance companies offer different types of insurance and health insurance is the most critical insurance type for humans. By applying the proposed method in this research, health insurance companies can determine the policyholder's behaviors, claiming patterns, claiming chargers, and other information precisely. Furthermore, health insurance providers can effectively manage their claims using this knowledge.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"9 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116911825","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