2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)最新文献

Design Of A Monitoring System For Waste Management Using IoT 基于物联网的废物管理监控系统设计

2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT) Pub Date : 2019-12-05 DOI: 10.1109/ICIICT1.2019.8741499

Aravindaraman B A, P. Ranjana

引用次数: 9

Support Vector Machine Based Method for Automatic Detection of Diabetic Eye Disease using Thermal Images 基于支持向量机的糖尿病眼病热图像自动检测方法

2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT) Pub Date : 2019-04-25 DOI: 10.1109/ICIICT1.2019.8741450

D. Selvathi, K. Suganya

{"title":"Support Vector Machine Based Method for Automatic Detection of Diabetic Eye Disease using Thermal Images","authors":"D. Selvathi, K. Suganya","doi":"10.1109/ICIICT1.2019.8741450","DOIUrl":"https://doi.org/10.1109/ICIICT1.2019.8741450","url":null,"abstract":"Diabetic eye disease is one of the major problems worldwide. That can cause major impairment to the eyes, including a permanent loss of vision. Early detection of eye diseases increase the survival rate by successful treatment. The proposed methodology is to explore machine learning technique to detect diabetic diseased using thermography images of an eye and to introduce the effect of thermal variation of abnormality in the eye structure as a diagnosis imaging modality which are useful for ophthalmologists to do the clinical diagnosis. Thermal images are pre-processed, and then Gray Level Co-occurrence Matrix (GLCM) based texture features from gray images, statistical features from RGB and HSI images are extracted and classified using classifier with various combination of features. To detect diabetic diseased eye, here Support Vector Machine classifier is used for classification and their performance are compared. A 5-fold cross validation scheme is used to enhance the generalization capability of the proposed method. Experimental results obtained for various feature combinations gives maximum accuracy of 86. 22%, sensitivity of 94. 07% and specificity of 79. 17% using SVM classifier with five-fold validation.","PeriodicalId":118897,"journal":{"name":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131203644","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: 14

Prediction of Heart Disease Using Machine Learning Algorithms. 使用机器学习算法预测心脏病。

2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT) Pub Date : 2019-04-25 DOI: 10.1109/ICIICT1.2019.8741465

Santhana Krishnan. J, G. S

引用次数: 18

Wavefront Compensation Technique for Terrestrial Line of Sight Free Space Optical Communication 地面无瞄准线空间光通信的波前补偿技术

2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT) Pub Date : 2019-04-25 DOI: 10.1109/ICIICT1.2019.8741495

R. Rajeshwari, T. Pasupathi, J. A. Vijaya Selvi

{"title":"Wavefront Compensation Technique for Terrestrial Line of Sight Free Space Optical Communication","authors":"R. Rajeshwari, T. Pasupathi, J. A. Vijaya Selvi","doi":"10.1109/ICIICT1.2019.8741495","DOIUrl":"https://doi.org/10.1109/ICIICT1.2019.8741495","url":null,"abstract":"Free Space Optical Communication (FSOC) refers to an optical communication where unguided visible, infrared or ultraviolet light is used to carry the signal. In Wireless Optical Communication systems, optical signal is modulated and transmitted over the free space atmospheric channel. When the laser beam is propagating through the turbulent atmospheric channel it is heavily affected by various parameters. Generally, the intensity of the laser beam is greatly degraded by the phenomenon such as absorption and scattering effect due to natural atmospheric components namely gases, dust, smoke, precipitation, fog, rain etc. In other hand, the performance of FSOC is heavily affected by the fluctuation in the atmosphere. This fluctuation results in atmospheric turbulence effect such as beam wandering beam scintillation and wavefront aberration. Therefore, the performance of the FSOC is degraded by the atmospheric turbulence tremendously. Hence it is necessary to develop a suitable optoelectronic arrangements and algorithms to compensate the atmospheric turbulences. This paper shows the viability to improve the performance of FSOC by compensating the atmospheric turbulence effect. In this paper, a wavefront aberration compensation technique to mitigate the wavefront aberrations due to the channel is developed using the necessary opto electronic assembly. This paper mainly elaborates experimental implementation for calculation of wavefront aberration and also demonstrates the correction achieved experimentally.","PeriodicalId":118897,"journal":{"name":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125854282","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: 1

Survey on Private Blockchain Consensus Algorithms 私有区块链共识算法研究综述

2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT) Pub Date : 2019-04-25 DOI: 10.1109/ICIICT1.2019.8741353

Sunny Pahlajani, Avinash A. Kshirsagar, V. Pachghare

{"title":"Survey on Private Blockchain Consensus Algorithms","authors":"Sunny Pahlajani, Avinash A. Kshirsagar, V. Pachghare","doi":"10.1109/ICIICT1.2019.8741353","DOIUrl":"https://doi.org/10.1109/ICIICT1.2019.8741353","url":null,"abstract":"A blockchain is a distributed ledger of records called as blocks. These blocks are linked using cryptographic hash. Each block contains a hash of the previous block, a timestamp, and transaction data. Consensus layer is the main layer in Blockchain Architecture, in which consensus protocol is configured to decide how new block is added in blockchain. Consensus algorithm solves the problem of trust in blockchain. Consensus algorithms can be classified into two classes. The first class is voting-based consensus, which requires nodes in the blockchain network to broadcast their results of mining a new block or transaction, before appending the block to blockchain. The second class is proof-based consensus, which requires the nodes joining the blockchain network to solve and mathematical puzzle to show that they are more eligible than the others to do the appending or mining work. Performance of blockchain can be increased with the use of suitable consensus algorithm. However, theory and data support for the selecting suitable consensus in private blockchain is very limited. This paper contributes theory and data used for selecting suitable consensus algorithm and would help researchers for further exploring of consensus in private blockchain environment.","PeriodicalId":118897,"journal":{"name":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116272829","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: 59

Preprocessing Techniques for High Quality Text Extraction from Text Images 文本图像中高质量文本提取的预处理技术

2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT) Pub Date : 2019-04-25 DOI: 10.1109/ICIICT1.2019.8741488

Alan Koshy, N. Mj, Prof. Shyna A, Prof. Ansamma John

引用次数: 1

Object Recognition and Classification Based on Improved Bag of Features using SURF AND MSER Local Feature Extraction 基于SURF和MSER局部特征提取改进特征袋的目标识别与分类

2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT) Pub Date : 2019-04-25 DOI: 10.1109/ICIICT1.2019.8741434

R. P, A. James

{"title":"Object Recognition and Classification Based on Improved Bag of Features using SURF AND MSER Local Feature Extraction","authors":"R. P, A. James","doi":"10.1109/ICIICT1.2019.8741434","DOIUrl":"https://doi.org/10.1109/ICIICT1.2019.8741434","url":null,"abstract":"Object recognition and classification is a challenging task in computer vision because of the large variation in shape, size and other attributes within the same object class. Also we need to consider other challenges such as the presence of noise and haze, occlusion, low illumination conditions, blur and the cluttered backgrounds. Due to these facts, object recognition and classification gained attention in recent years. Many researchers have proposed different methods to address the problem of recognition. This paper proposes a method for object recognition and classification based improved bag of features using SURF(Speeded Up Robust Features) and MSER(Maximally Stable External Regions) local feature extraction. Combination of SURF and MSER feature extraction algorithm can improve the recognition efficiency and the classification accuracy can be improved by spatial pyramid matching. SURF and MSER extracts the local features of an image and generate a image histogram codebook. Spatial pyramid matching is applied to this histogram, which improves the accuracy of classification. The experiment is conducted on Caltech 101 and Caltech 256 dataset.","PeriodicalId":118897,"journal":{"name":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121524400","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: 4

Arduino Based Smart Fingerprint Authentication System 基于Arduino的智能指纹认证系统

2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT) Pub Date : 2019-04-25 DOI: 10.1109/ICIICT1.2019.8741459

N. Meenakshi, M. Monish, K. Dikshit, S. Bharath

{"title":"Arduino Based Smart Fingerprint Authentication System","authors":"N. Meenakshi, M. Monish, K. Dikshit, S. Bharath","doi":"10.1109/ICIICT1.2019.8741459","DOIUrl":"https://doi.org/10.1109/ICIICT1.2019.8741459","url":null,"abstract":"Security is the serious issue looked by everybody when we are far from our family unit. In the present situation acceptable answer for the above issue isn’t yet found. Introduced here is an electronic securing framework which Arduino assumes the job of the preparing unit. Arduino which is a microcontroller board has a place with at uber family. It is an open source straight forward instrument. It can detect, screen, store and control application. Access control for the entryway is accomplished utilizing Arduino Mega 2560 board. This task displays a keyless framework for locking and opening purposes utilizing a predefined PICTURE secret key and OTP. Unauthorized person access is ensured by sending OTP and PICTURE password to ADMIN to get OTP and PICTURE password where the person needs to contact the ADMIN to get OTP and PICTURE password. It is entered through the 2.8″ TFT touch display, which display all the UI messages and takes inputs from user. In case of authorized user, the system allows fingerprint sensor to validate the person followed by sending either PICTURE password or OTP via SIM using GSM module to the user registered mobile number saved in database (local SD card) in order to access the door. If the entered password matches, door will be opened automatically otherwise a message showing incorrect password will be displayed on TFT display and a notification will be sent to the owner that the security was tried to be breached. This hardware project achieves 3 levels of security with commonly available component and also consumes less power. This system also has an option to unlock the door through SMS in case of emergency by the ADMIN.","PeriodicalId":118897,"journal":{"name":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128427685","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

Machine Learning Algorithm in Two wheelers fuel Prediction 两轮车燃料预测中的机器学习算法

2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT) Pub Date : 2019-04-25 DOI: 10.1109/ICIICT1.2019.8741426

P. Ranjana, S. Sridevi, T. Sudalai Muthu, V. V. Gnanaraj

引用次数: 2

Survey of Sentiment Analysis Using Deep Learning Techniques 使用深度学习技术的情感分析综述

2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT) Pub Date : 2019-04-25 DOI: 10.1109/ICIICT1.2019.8741438

Indhraom Prabha M, G. Umarani Srikanth

{"title":"Survey of Sentiment Analysis Using Deep Learning Techniques","authors":"Indhraom Prabha M, G. Umarani Srikanth","doi":"10.1109/ICIICT1.2019.8741438","DOIUrl":"https://doi.org/10.1109/ICIICT1.2019.8741438","url":null,"abstract":"This paper presents a detailed review of deep learning techniques used in Sentiment Analysis. Sentiment analysis is one of the most researched areas in natural language processing. Natural language processing has a wide range of applications like voice recognition, machine translation, product review, aspect oriented product analysis, sentiment analysis and text classification like email categorization and spam filtering. The conventional methods used for sentiment analysis is lexicon based processing. However, with the advancements in the field of artificial intelligence, the machine learning algorithms started to play a major role in sentiment analysis applications. Currently deep learning technique is the latest hotspot being used for predicting the sentiments. Several research works have been carried out in the Natural Language Processing (NLP) using the deep learning methods. The most popular deep learning methods employed includes Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) particularly the Long Short Term Memory (LSTM). These techniques are used in combination or as stand-alone based on the domain area of application. The focus of this survey is on the various flavors of the deep learning methods used in different applications of sentiment analysis at sentence level and aspect/target level. Furthermore, the advantages and drawbacks of the methods are discussed along with their performance parameters.","PeriodicalId":118897,"journal":{"name":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","volume":"198200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132605392","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: 40