2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)最新文献_第6页

Agent Technology for Data Analytics of Gene Expression Data: A Literature Review 基因表达数据分析的Agent技术:文献综述

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-000189

K. Santhosh, S. Ajitha

{"title":"Agent Technology for Data Analytics of Gene Expression Data: A Literature Review","authors":"K. Santhosh, S. Ajitha","doi":"10.1109/ICCMC48092.2020.ICCMC-000189","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000189","url":null,"abstract":"Analytics of gene expression data is the prolonged research area of present situation. Analysis of gene expression data requires enormous amount of work and huge set of algorithms. Using agent computing we deal with complex systems which are discovered many opportunities for developing data mining systems in a different ways. Hence to create predictive models, there is a huge need for intelligent and autonomous software agents which can procure useful information from the large datasets of raw information. Predictive analytics models can be created from these datasets which can be further used for various applications in security, future prediction etc. This research paper gives an overall function of multi agent systems in analytics of gene expression data, in terms of characteristics, adaptability, reliability and robotics of agents. Analytics on gene expression data is one of the emerging research fields. A large set of methodology and algorithms are existing in the field but in the application of agent technology in the field of gene expression data is at the infant stage. So the aim of this review paper is to integrate agent technology in the gene expression data analytics.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121102353","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

Simulation Study of Cascade H-bridge Multilevel Inverter 7-Level Inverter by SHE Technique 级联h桥多电平逆变器的SHE技术仿真研究

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-000124

P. Bute, S. K. Mittal

引用次数: 6

A Review of IoT Based Smart Industrial System for Controlling and Monitoring 基于物联网的智能工业控制与监控系统研究综述

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-00012

Prajakta Karemore, P. Jagtap

{"title":"A Review of IoT Based Smart Industrial System for Controlling and Monitoring","authors":"Prajakta Karemore, P. Jagtap","doi":"10.1109/ICCMC48092.2020.ICCMC-00012","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00012","url":null,"abstract":"In recent years, Industrial Control [IC] is emerging as a fundamental aspect to gather all the pertinent data, insights and information identified with different mechanical procedures, engines, machines, and gadgets utilized in industrial premises. This focuses on delivering controlled access, better efficiency and great aftereffects of the modern items being manufactured. In this new period of innovative research improvements in remote control and monitoring by means of correspondence methods, for example, ZigBee, RF, Infrared, systems have been generally utilized in industries. In any case, these remote correspondence strategies are commonly confined to basic applications on account of their moderate correspondence paces, separations, and information security. The recently proposed framework is evolving as the fundamental need of the industry for monitoring, control, security and wellbeing of various exercises. The monitoring framework incorporates sensors like fire sensor, smoke sensor, ultrasonic sensor, moisture and temperature sensor, current and voltage with Wi-Fi module for control operations. With the benefits of unusual exercises, reasonable activities will be activated. This framework can likewise be controlled by using remote server with application in PC/Laptop. This undertaking additionally incorporates facial recognition by utilizing an open CV to perceive the essence of the approved individual of the industry to sign in / log out and the subtleties will be put away in the database sheet or refreshed to cloud. On the off chance that, any invalid section or attempt to the break-in, immediately an alarm email will be sent to the particular approved authority/individual/group.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127261398","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

Detection of Rice Leaf Diseases Using Image Processing 利用图像处理技术检测水稻叶片病害

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-00080

Minu Eliz Pothen, Dr. Maya L Pai

引用次数: 47

Efficient Handling of Incomplete basic Partitions by Spectral Greedy K-Means Consensus Clustering 基于谱贪婪k均值一致聚类的不完全基本分区的高效处理

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-00056

M. Vasuki, S. Revathy

引用次数: 2

Survey of Iris Image Segmentation and Localization 虹膜图像分割与定位研究进展

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-000100

S. S. Rao, R. Shreyas, G. Maske, A. Choudhury

{"title":"Survey of Iris Image Segmentation and Localization","authors":"S. S. Rao, R. Shreyas, G. Maske, A. Choudhury","doi":"10.1109/ICCMC48092.2020.ICCMC-000100","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000100","url":null,"abstract":"Iris recognition is one of the best methods in the biometric identification field because the iris has features that are not unique but also stay throughout the person’s lifetime. Iris recognition has multiple phases namely Image Acquisition, Iris Segmentation, Iris Localization, Feature Extraction and Matching. Image Acquisition is simply the capturing of the iris image at an optimal distance. Iris Segmentation is the process of obtaining all the different segments of the eye. Iris Localization is the process of finding inner and outer boundaries of the iris differentiating it from the sclera and pupil and mainly focusing on the iris alone. Feature extraction is the process of extracting the biometric template from the Iris, giving the unique data required for the next step. Matching is the process of finding the best match in the database for the extracted biometric template. The future implementation of this paper focuses only on the processes of Image Acquisition, Iris Segmentation and Iris Localization. The paper aims to optimise these processes in terms of image capture distance, computation time and memory requirement, using the Dynamic Reconfigurable Processor (DRP) technology along with suitable algorithms for segmentation and localization processes as described in sections 2.2 and 2.3 respectively.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"9 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120806530","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

Evaluation of Dimensionality Reduction Techniques for Big data 大数据降维技术评价

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-00043

R. Ramachandran, Gopika Ravichandran, Aswathi Raveendran

引用次数: 6

Enhanced Computer Aided Bone Fracture Detection Employing X-Ray Images by Harris Corner Technique 基于哈里斯角技术的x射线图像增强计算机辅助骨折检测

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-000184

C. Z. Basha, M. Reddy, K. Nikhil, P. M. Venkatesh, A. Asish

引用次数: 25

Application of RLSA for Skew Detection and Correction in Kannada Text Images RLSA在卡纳达语文本图像倾斜检测与校正中的应用

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-000146

R. Salagar, Pushpa B. Patil

{"title":"Application of RLSA for Skew Detection and Correction in Kannada Text Images","authors":"R. Salagar, Pushpa B. Patil","doi":"10.1109/ICCMC48092.2020.ICCMC-000146","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000146","url":null,"abstract":"The presence of the skew in a captured document image through a photographic camera, mobile camera or scanner is inevitable. In a document image detection and correction of skew are challenging phases before further processing like segmentation and analysis. In this paper, Run Length Smoothing Algorithm (RLSA) is proposed for the detection and correction of skew for handwritten Kannada document images. The proposed work has mainly two parts, the first part is preprocessing of a document using methods like thresholding, the maximum gradient for extraction of text and text line area with no loss of any data. The second part is skew detection and correction. The algorithm RLSA is used row and column-wise of a document image. The RLSA is applied for skew detection to determine skew (slant) angle further the document is turned in the anti-clockwise direction with the preferred angle, which will remove the skew of a document that has occurred while taking the photocopy of the document. The performance proposed method is evaluated for handwritten Kannada documents; the experiment outcomes are significantly better.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116940410","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

Diagnosis of Crime Rate against Women using k-fold Cross Validation through Machine Learning 通过机器学习使用k-fold交叉验证诊断针对妇女的犯罪率

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-000193

P. Tamilarasi, R. Rani

{"title":"Diagnosis of Crime Rate against Women using k-fold Cross Validation through Machine Learning","authors":"P. Tamilarasi, R. Rani","doi":"10.1109/ICCMC48092.2020.ICCMC-000193","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000193","url":null,"abstract":"Crime against women has become a very big problem of our nation. Many countries are trying to control this offence continuously and its prevention is an essential task. In recent years crimes are significantly increasing against women. Currently the Indian government show interest to address this problem and give more importance to develop our society. Every year a huge amount of data collection is generated on the basis of the crime reporting. This data can be very useful for assessing and predicting crime, and can help us to some degree stop the crime. Data analysis is a process of examining, cleansing, transformation and modelling data with the goal of establish useful information, reporting conclusion and sustaining decision-making. Feature Scaling is one of the most important techniques to standardize the independent features to place the data in a fixed range. It is performed at the time of data pre-processing. K-fold cross-validation is a re-sampling method used for calculating machine learning models on a small sample of data. It is a common strategy since it is easy to understand and usually results in a model deftness calculation that is less biased or less negative than other approaches, such as a simple train or test divide. Machine learning plays a large part in data processing. This paper introduces six different types of Machine learning algorithms such as KNN and decision trees, Naïve Bayes, Linear Regression CART (Classification and Regression Tree) and SVM using similar characteristics on crime data. Those algorithms are tested for accuracy. The main objective of this research is to evaluate the efficacy and application of the machine learning algorithms in data analytics.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132785991","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: 23