2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)最新文献_第8页

Prediction of Covid-19 Cases in India Through Machine Learning Using Python 使用Python的机器学习预测印度Covid-19病例

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596116

S. Mathur, Krishnasheesh Datta

引用次数: 0

AI & ML Based Anamoly Detection and Response Using Ember Dataset 基于Ember数据集的异常检测和响应

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596451

Viraj Rathod, C. Parekh, Dharati Dholariya

{"title":"AI & ML Based Anamoly Detection and Response Using Ember Dataset","authors":"Viraj Rathod, C. Parekh, Dharati Dholariya","doi":"10.1109/icrito51393.2021.9596451","DOIUrl":"https://doi.org/10.1109/icrito51393.2021.9596451","url":null,"abstract":"In the era of rapid technological growth, malicious traffic has drawn increased attention. Most well-known offensive security assessment todays are heavily focused on pre-compromise. The amount of anomalous data in today's context is massive. Analyzing the data using primitive methods would be highly challenging. Solution to it is: If we can detect adversary behaviors in the early stage of compromise, one can prevent and safeguard themselves from various attacks including ransomwares and Zero-day attacks. Integration of new technologies Artificial Intelligence & Machine Learning with manual Anomaly Detection can provide automated machine-based detection which in return can provide the fast, error free, simplify & scalable Threat Detection & Response System. Endpoint Detection & Response (EDR) tools provide a unified view of complex intrusions using known adversarial behaviors to identify intrusion events. We have used the EMBER dataset, which is a labelled benchmark dataset. It is used to train machine learning models to detect malicious portable executable files. This dataset consists of features derived from 1.1 million binary files: 900,000 training samples among which 300,000 were malicious, 300,000 were benevolent, 300,000 un-labelled, and 200,000 evaluation samples among which 100K were malicious, 100K were benign. We have also included open-source code for extracting features from additional binaries, enabling the addition of additional sample features to the dataset.","PeriodicalId":259978,"journal":{"name":"2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122796170","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

Socialization between Quarantine Vehicle and Road Side Unit for Handling COVID-19: A Concept 新型冠状病毒隔离车与路边处理单位的社会化:一个概念

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596333

Zaheeruddin, Hina Gupta, D. Mehrotra

引用次数: 1

Fostering Analysis of Livelihood Pre and Post Covid-19 using ML Techniques 利用ML技术对Covid-19前后的生计进行培养分析

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596079

S. Mahendher, Shivam Singhal, Khazi Mohammed Owais, Maheshwaran S., B. Robin

引用次数: 0

Consumer Characteristics and Consumption Patterns of Soft Drinks 软饮料消费者特征与消费模式

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596207

S. Sinha, Deepti Sinha, Neetu Mittal

{"title":"Consumer Characteristics and Consumption Patterns of Soft Drinks","authors":"S. Sinha, Deepti Sinha, Neetu Mittal","doi":"10.1109/icrito51393.2021.9596207","DOIUrl":"https://doi.org/10.1109/icrito51393.2021.9596207","url":null,"abstract":"A soft drink is generally treated as very common product aimed at a very casual consumption. Normally, not much of attention is paid to this product, which has almost become ‘commoditized’. But, a deeper and more careful observation would reveal that soft drinks are strong demographic descriptors of their consumers. Key insights into the characteristics and consumption patterns of consumers can be obtained through an incisive study of the soft drinks market. This research paper makes a concerted effort at unearthing the demographic details and consumption contours of the soft drink users in Kanpur, Agra, Varanasi, Allahabad, and Lucknow - the five representative cities of Uttar Pradesh, the most populous state of India. It has been conclusively established through this research that the residents of these five cities - which are demographically similar in nature - exhibit varying consumption patterns when it comes to soft drinks. It was also found that demographic variables like age, gender, educational qualification, income, and marital status do not significantly impact the consumption of soft drinks, whereas employment status is a key influencer of the same.","PeriodicalId":259978,"journal":{"name":"2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116587637","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

Fake News Detection Using Intelligent Techniques 利用智能技术检测假新闻

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596438

A. Vora, N. Shekokar

引用次数: 3

Experimental Comparison of Machine Learning Techniques for Analysing the Facial Expression 面部表情分析机器学习技术的实验比较

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596122

Kumud Kohli, Upasana Sharma, Mayank Sharma, A. Rana

引用次数: 0

Workflow Automation of Routing Rules in the Accounting Process for Online Travel Agency 在线旅行社会计流程中路由规则的工作流自动化

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596274

Mamta Nanda, Ashok Kumar

引用次数: 0

Comparison between Code Clone Detection and Model Clone Detection 代码克隆检测与模型克隆检测的比较

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596454

G. Shobha, A. Rana, Vineet Kansal, Sarvesh Tanwar

{"title":"Comparison between Code Clone Detection and Model Clone Detection","authors":"G. Shobha, A. Rana, Vineet Kansal, Sarvesh Tanwar","doi":"10.1109/icrito51393.2021.9596454","DOIUrl":"https://doi.org/10.1109/icrito51393.2021.9596454","url":null,"abstract":"In software, clones are syntactically or semantically identical of two code fragments. Cloning by either deliberately or by coincidence. Cloning has pros and cons in software development. Bug propagation may be caused by cloning from original to copied segments. Cloning may lead to difficulties in software maintenance. Cloning is helpful in many functions like code reusability, inheritance, and libraries. Software developers before scripting the code design the model for it. Cloning can exist at any stage either at the design or coding phase, which influences on development, quality, and maintenance of the software. All software has constraints like budget and time are associated. In software development, life cycle budget and time constraints depend on irregularity and risk. These irregularities and risks can be minimized by identifying clones. There are different kinds of clones in Code and Model. In this paper, we present a concise summary of design and coding phase clone detection of these irregularities and risk as well as their for's and against of code clone detection and model clone detection.","PeriodicalId":259978,"journal":{"name":"2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128400842","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 Review of Local Binary Pattern Based texture feature extraction 基于局部二值模式的纹理特征提取方法综述

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2021-09-03 DOI: 10.1109/icrito51393.2021.9596485

N. Kaur, Nahida Nazir, Manik

{"title":"A Review of Local Binary Pattern Based texture feature extraction","authors":"N. Kaur, Nahida Nazir, Manik","doi":"10.1109/icrito51393.2021.9596485","DOIUrl":"https://doi.org/10.1109/icrito51393.2021.9596485","url":null,"abstract":"In the sphere of image processing, image data investigation is required related to a specific application in order to extract the suggestive information and reach defined and crisp culminations. One of the most significant phase in image processing is feature extraction which is the third step following image acquisition and segmentation. The procedure of reconstructing the input image into a group of features is named as feature extraction. These features construe the textural characteristics of the image. Texture feature extraction is one such significant part of feature extraction that on majority influences the results of classification. A texture is principally based on recognizing the object or region of interest in an image. The Local Binary Pattern feature descriptor will be the pith of discussion of this paper. LBP is a texture operator that operates on an image by labeling its pixels by thresholding neighborhood of each pixel. Various quality journals have been referred in order to provide an insight into the trends in pattern recognition using LBP.","PeriodicalId":259978,"journal":{"name":"2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128780759","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