2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)最新文献_第10页

Internet of Things (IoT) based Object Recognition Technologies 基于物联网(IoT)的对象识别技术

2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2019-12-01 DOI: 10.1109/I-SMAC47947.2019.9032689

K. Srinivasan, V. Azhaguramyaa

引用次数: 2

DeepStreak: Automating Car Racing Games for Self Driving using Artificial Intelligence DeepStreak:使用人工智能自动驾驶赛车游戏

2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2019-12-01 DOI: 10.1109/I-SMAC47947.2019.9032527

Aryan Khanna, B. Manoj, Yashashwini T, G. M. Parihar, Richa Maurya, R. B

引用次数: 0

AI-Powered Image-Based Tomato Leaf Disease Detection 基于人工智能图像的番茄叶片病害检测

2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2019-12-01 DOI: 10.1109/I-SMAC47947.2019.9032621

L. S. P. Annabel, V. Muthulakshmi

{"title":"AI-Powered Image-Based Tomato Leaf Disease Detection","authors":"L. S. P. Annabel, V. Muthulakshmi","doi":"10.1109/I-SMAC47947.2019.9032621","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032621","url":null,"abstract":"The development of Artificial Intelligence over few decades had been incredible, where it converts every segment of the global economy, including agriculture. The traditional approach of the agricultural industry is experiencing a vital revolution. With requiremesnts of better crop yield, AI has been developed as a powerful tool to permit farmers in monitoring and detecting the crop diseases. In addition, farmers can easily identify the crop diseases in early stage by using AI. As traditional plant disease identification includes expertise and high processing time, AI is integrated with image processing with an objective of providing accurate, fast, efficient and inexpensive solution for disease detection. In this paper, novel tomato leaf disease detection is proposed which comprises of four different phases that includes image preprocessing, segmentation, feature extraction and image classification. RGB to grayscale conversion, thresholding, GLCM and random forest classifier are the various algorithms that are used for implementation of the proposed method. The results indicate that the proposed method classifies the diseases with an accuracy of 94.1%.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124790119","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: 10

Hybrid Bat and Genetic Algorthim Approach for Cost Effective SaaS Placement in Cloud Environment 混合蝙蝠和遗传算法在云环境下成本有效的软件即服务放置方法

2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2019-12-01 DOI: 10.1109/I-SMAC47947.2019.9032665

Jemal Nuradis, Frezewud Lemma

{"title":"Hybrid Bat and Genetic Algorthim Approach for Cost Effective SaaS Placement in Cloud Environment","authors":"Jemal Nuradis, Frezewud Lemma","doi":"10.1109/I-SMAC47947.2019.9032665","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032665","url":null,"abstract":"The increasing demand of software service in cloud environment needs strategic placement in the cloud infrastructure. Thus, in which the users use the service based on the service model of the provider and pays based on their use of the resources. These resources are storage, memory processing element and bandwidth. Efficient optimal placement is the main issue in order to provide a cost effective service to the user. This research has proposed hybrid approaches to addresses the initial software task placement problem by exploring the advantage of both Bat algorithm (BA) and Genetic algorithm (GA), to make the initial ST placement processes optimum and cost effective. In order to evaluate the performance of the proposed hybrid algorithms, an experimental environment had configured using CloudSim simulation tool. The proposed solution performance has evaluated by compared with those existing placement algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization algorithm (PSO). According to the result the proposed algorithm has reduced the placement cost up to 2 −13% on a cloud environment.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125131385","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

Intrusion Detection in Internet of Things (IoTs) Based Applications using Blockchain Technolgy 基于区块链技术的物联网应用中的入侵检测

2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2019-12-01 DOI: 10.1109/I-SMAC47947.2019.9032557

Shashvi Mishra, A. Tyagi

{"title":"Intrusion Detection in Internet of Things (IoTs) Based Applications using Blockchain Technolgy","authors":"Shashvi Mishra, A. Tyagi","doi":"10.1109/I-SMAC47947.2019.9032557","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032557","url":null,"abstract":"Data security plays an important role in the healthcare monitoring systems, where critical patient data is transacted over the internet especially through wireless devices, wireless routes such as optical radio channels, or optical fiber-related transport networks. In one way or the other every device is connected to internet and we address such things as internet connected things. As the network is moving towards wireless applications, the threat to attack is also becoming a crucial issue. These attacks can be identified through various intrusion detection techniques and some of which were discussed in the previous decades. The intrusion detection technique is used to identify the privacy breach in the network. Its main purpose is to detect the unauthorized access. Some of the systems or networks that need protection are part of wireless networks (Things connected to internet). Wireless network applications comprises of WLANs (Wireless Local Area Networks), WPANs (Wireless Personal Area Networks), ad hoc networks etc. Since digitization is taking place in each and every sector and so the threat to data also exist. In this article, we protect IoT based E-healthcare systems by using a novel concept called “Blockchain Technology”.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127174686","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: 9

Shadow Removal from Digital Images using Multi-channel Binarization and Shadow Matting 使用多通道二值化和阴影抠图从数字图像中去除阴影

2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2019-12-01 DOI: 10.1109/I-SMAC47947.2019.9032447

Saiqa Khan, Zainab Pirani, Taniya Fansupkar, Umama Maghrabi

{"title":"Shadow Removal from Digital Images using Multi-channel Binarization and Shadow Matting","authors":"Saiqa Khan, Zainab Pirani, Taniya Fansupkar, Umama Maghrabi","doi":"10.1109/I-SMAC47947.2019.9032447","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032447","url":null,"abstract":"Shadow removal has developed gradually as a preprocessing step for image classification, object detection, information extraction, etc. Existing techniques used by researchers typically make use of segmentation, deep learning to obtain accurate results, which leads to the high cost of processing the image. This paper presents a comprehensive survey study of shadow detection and removal from images. We present our methodology for improving the process of shadow removal which will use machine learning to train the system with standard dataset available (for eg. SBU->Stony Brook University[12]) and then a test data will be entered by the user via an easy to use user application. Training the images will include converting the image into grayscale, YCbCr and CIE L*a*b* colorspace then performing multi-channel binarization which will convert the image to binary on the basis of a threshold value for shadow detection. This shadow detected image will then be filtered to remove noisy false positive regions. This filtered image will be passed to the Canny edge detection algorithm for detecting edges of shadow and then repainting it by shadow matting technique to remove shadows.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117002742","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

A Study of Intrusion Detection System using Machine Learning Classification Algorithm based on different feature selection approach 基于不同特征选择方法的入侵检测系统机器学习分类算法研究

2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2019-12-01 DOI: 10.1109/I-SMAC47947.2019.9032499

P. Illavarason, B. Kamachi Sundaram

{"title":"A Study of Intrusion Detection System using Machine Learning Classification Algorithm based on different feature selection approach","authors":"P. Illavarason, B. Kamachi Sundaram","doi":"10.1109/I-SMAC47947.2019.9032499","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032499","url":null,"abstract":"Network security is the most challenging task of the modern digital era. Due to the development in internet, the number of network attacks has also increased, this is prevented by access control, key manager, and intrusion detection system. Among these the most challenging task is intrusion detection system that ensures the network security. The current approach focuses on the important issues in intrusion detection system, which will identify the unwanted attacks and unauthorized access in the network. The comprehensive overview of the detailed survey is analyzed with the existing data set for identifying the unusual attacks that can understand the current issues in intrusion detection problems. The detailed investigation is reported for observing several issues on the intrusive performance by using the machine learning classification. Here machine learning classification algorithm is used for detecting the several category of attacks. Furthermore, this study evaluates the performance criteria based on the feature extraction and machine learning classification techniques algorithm. Finally, based on the results observed we recommend some important features by using machine learning classification in order to find out the efficient method for detecting the particular attack.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127095521","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: 13

A Cloud computing-based Advanced Encryption Standard 基于云计算的高级加密标准

2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2019-12-01 DOI: 10.1109/I-SMAC47947.2019.9032581

Quazi Warisha Ahmed, S. Garg

引用次数: 3

Q-Learning Elicited Disaster Management System Using Intelligent Mapping Approach 基于智能地图方法的Q-Learning灾害管理系统

2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2019-12-01 DOI: 10.1109/I-SMAC47947.2019.9032630

Nilatpal Chakrabarty, Ankita Debnath, Deepanwita Mallick, S. Banerjee

引用次数: 1

Prediction of Hepatitis Disease Using Machine Learning Technique 利用机器学习技术预测肝炎疾病

2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2019-12-01 DOI: 10.1109/I-SMAC47947.2019.9032585

Vedha Krishna Yarasuri, Gowtham Kishore Indukuri, Aswathy K. Nair

引用次数: 19