2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)最新文献_第10页

Mobile App Recommendation System Using Machine learning Classification 使用机器学习分类的手机应用推荐系统

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-000174

R. Jisha, J. M. Amrita, Aswini R Vijay, G. Indhu

{"title":"Mobile App Recommendation System Using Machine learning Classification","authors":"R. Jisha, J. M. Amrita, Aswini R Vijay, G. Indhu","doi":"10.1109/ICCMC48092.2020.ICCMC-000174","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000174","url":null,"abstract":"The introduction of new ideas with mobile applications can bring great change to people around the world. Nowadays Thousands of apps are developed to satisfy different needs of people such as for doing jobs, transactions, entertainment etc. and distributed over the Internet. So most of the existing app stores available might face difficulties for recommending a particular app to a particular user. So there is a need for recommending apps for the users according to their personal preferences and various other limitations. We made a mobile application recommendation system with ratings, Size, and Permission as parameters and we will recommend suitable apps to the user by evaluating these parameters. Here we are using Apkpure.com which is one of the famous android application markets and also makes use of Web Crawler which helps in collecting information about the website and helps in validating hyperlinks. After that by using the Clustering Algorithm, applications are grouped or clustered based on Popularity, Permission and Security aspects. This paper aims to provide a simple recommendation system without compromising rating, size and Permission aspects.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"189 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":"129008287","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: 6

NLP based Machine Learning Approaches for Text Summarization 基于NLP的文本摘要机器学习方法

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-00099

Rahul, Surabhi Adhikar, Monika

引用次数: 37

Traffic Sign Recognition Using Distributed Ensemble Learning 基于分布式集成学习的交通标志识别

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-000101

Satya Goutham Putrevu, M. Panda

{"title":"Traffic Sign Recognition Using Distributed Ensemble Learning","authors":"Satya Goutham Putrevu, M. Panda","doi":"10.1109/ICCMC48092.2020.ICCMC-000101","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000101","url":null,"abstract":"Traffic sign recognition is one of the active areas of research in recent years. The automotive technology is moving towards automation in most of the aspects including traffic sign recognition. In an attempt to focus on driving and concentrate on road the driver often misses out the traffic signs, results of which may lead to catastrophic events. This can be avoided by automating the tasks of traffic sign detection and recognition. In this paper, we implement the traffic signs recognition through distributed ensemble technique (DEL), which is an efficient method to automate traffic sign detection. The primary goal of distributed ensemble learning is to decrease the complexity, reduce the training load on each model and improve the convergence. The impact of load distribution with respect to the number of workers has been studied and thereby understanding the trends of a distributed ensemble. Here we use an ensemble of CNN models to train with standard German data set. Keras is used for implementation of distributed ensemble in CNN. Detailed analysis on data distribution between workers and how it impacts the model accuracy is discussed.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"15 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":"124545827","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

45nm CMOS 4-Bit Flash Analog to Digital Converter 45纳米CMOS 4位闪存模数转换器

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-0005

Vivek Urankar, Chiranjit R Patel, B. A. Vivek, V. Bharadwaj

引用次数: 0

Performance Monitoring and Failure Prediction of Industrial Equipments using Artificial Intelligence and Machine Learning Methods: A Survey 基于人工智能和机器学习方法的工业设备性能监测与故障预测研究综述

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-0000111

M. K. Das, K. Rangarajan

{"title":"Performance Monitoring and Failure Prediction of Industrial Equipments using Artificial Intelligence and Machine Learning Methods: A Survey","authors":"M. K. Das, K. Rangarajan","doi":"10.1109/ICCMC48092.2020.ICCMC-0000111","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-0000111","url":null,"abstract":"Performance monitoring and failure prediction of industrial equipment plays a very important role not only in the quality of the manufactured material but also in the amount of time and money saved in the overall maintenance. This paper seeks to survey the general research development and advancement in the use of AI/ML techniques for equipment fault prediction in industries over time. The topics surveyed in this paper include various algorithms, use cases and concepts that pertain to the use of such technology in a wide range of industries including oil and gas, coal, automotive industry, etc. This survey addresses early research work done between the late 80s to the early 2000s, the recent research done between the early 2000s to 2017 and the latest research, the work done in the past two years. It can be concluded that this paper makes a thorough survey of different ML/AI methods used in the Industrial Manufacturing domain. Methods like LSTM, Bi-LSTM, ANNs and SVM classifiers were found to be some of the popular approaches used.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"95 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":"123076388","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

Score-Based Feature Selection of Gene expression Data for Cancer Classification 基于评分的基因表达数据特征选择用于癌症分类

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-00049

K. R. Kavitha, Avani Prakasan, P.J Dhrishya

{"title":"Score-Based Feature Selection of Gene expression Data for Cancer Classification","authors":"K. R. Kavitha, Avani Prakasan, P.J Dhrishya","doi":"10.1109/ICCMC48092.2020.ICCMC-00049","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00049","url":null,"abstract":"Feature selection in machine learning can also be specified as attribute selection. It is a process of selection desired feature from a large amount of data set. A typical microarray data set has basic properties such as high-dimensionality and limited sample, which makes it less accurate for classification and also time-consuming. In order to increase the accuracy of the classification, we have to decrease the dimensionality of the dataset. To achieve this, there are two feature elimination methods namely, feature selection and feature extraction. The proposed study focuses on the filter-based feature selection method. The main aim of the proposed work is to decrease the computation time and increase the accuracy of classification and prediction. To achieve this, he proposed work reduces the dimensionality of data set and also the redundancy between various features. Several feature selection methods exist but most of them have increased computational time, so here we are using score-based criteria fusion method for feature selection, which improves the prediction accuracy and decreases the computational time.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"18 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":"130809107","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

Clusters Analyzer Algorithm for Informative Acquaintances - Quantum Clustering Algorithm 信息熟人的聚类分析算法——量子聚类算法

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-0007

Rupam Bhagawati

引用次数: 1

Brain Storm Optimization based Association Rule Mining Model for Intelligent Phishing URLs Websites Detection 基于头脑风暴优化的关联规则挖掘模型用于网络钓鱼url网站智能检测

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-000119

M. Sathish Kumar, B. Indrani

引用次数: 3

Intelligent Vision with TensorFlow using Neural Network Algorithms 使用神经网络算法的TensorFlow智能视觉

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-000175

A. Visalatchi, T. Navasri, P. Ranjanipriya, R. Yogamathi

{"title":"Intelligent Vision with TensorFlow using Neural Network Algorithms","authors":"A. Visalatchi, T. Navasri, P. Ranjanipriya, R. Yogamathi","doi":"10.1109/ICCMC48092.2020.ICCMC-000175","DOIUrl":"https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000175","url":null,"abstract":"Computer vision and video analytics are the torrid research area in Machine learning and their establishment process traditionally starts with object detection and eventually tracking. In recent years, there is a tremendous growth in performing comprehensive study based on the field of object detection and Pattern Analysis. In our system we have improvised and experimented with detection method based on machine learning and deep learning approach in object recognition and pattern analysis. We assume object detection as a retrogression problem to spatially separated corresponding class probabilities and bounding boxes. Many prominent algorithms have been designed for object detection, Pattern Analysis and tracking, which also includes edge tracking, color segmentation and pattern matching. A single neural network is capable of predicting class probabilities and bounding boxes directly from the full image per cycle. Therefore we have used various neural network algorithms such as YOLOv3, Single Shot Multiple detection algorithm to carry out video analysis using object detection and drowsiness detection using pattern or behavior analysis with the help of Tensorflow. The framework will recognize object continuously, from the input perceived through camera where it can apparently capture a required frames to predict the object and also to match the pattern. It has been accomplished using real-time video processing and a single camera. The proposed system is versatile to operate in complex, real time, non-plain environment.","PeriodicalId":130581,"journal":{"name":"2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)","volume":"112 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":"130710497","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

Tourism Recommendation System based on Knowledge Graph Feature Learning 基于知识图谱特征学习的旅游推荐系统

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2020-03-01 DOI: 10.1109/ICCMC48092.2020.ICCMC-00022

Fengsheng Zeng, Yan’e Zheng

引用次数: 3