2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)最新文献_第7页

Threat Intelligence Framework for Vulnerability Identification and Patch Management for Virtual Environment 面向虚拟环境的漏洞识别与补丁管理威胁情报框架

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9754169

Kanchan Patil, Anand Vardhan Malla

{"title":"Threat Intelligence Framework for Vulnerability Identification and Patch Management for Virtual Environment","authors":"Kanchan Patil, Anand Vardhan Malla","doi":"10.1109/iciptm54933.2022.9754169","DOIUrl":"https://doi.org/10.1109/iciptm54933.2022.9754169","url":null,"abstract":"Virtualization has become the way of working for the organization in the 21st century. Everything connected to the cloud and workforce moves to virtual machines, and network data is generated exponentially, so make the threats to networks. With attacks getting sophisticated day-to-day cyber security teams must implement new techniques to keep attackers from stealing confidential information from the organizations. Developing patches for these attacks for virtual machine environments is difficult as the attack can happen in any form, and prioritizing patch development is a difficult task with limited information. Threat intelligence is one technique that gives security teams an edge to identify an attack in its root stage by gathering information from different sources. But threat intelligence is usually obtained in an unstructured manner from public sources such as security blogs, mailings, and organization-specific services. Threat intelligence integrated with internally collected network data can identify stressed areas in the network, attacks that usually go unnoticed. Threat intelligence also provides information about attacks, vulnerabilities, and procedures to avoid getting exploited in different organizations. In this paper, we propose a theoretical model for identifying vulnerabilities using internal and external threat intelligence and prioritization of patches based on the impact and sensitivity of the exposed data.","PeriodicalId":6810,"journal":{"name":"2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"76 4 1","pages":"787-793"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83463178","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

Development of Simulator to Recognize the Mood using Facial Emotion Detection 基于面部情绪检测的情绪识别模拟器的开发

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9754012

Seville Anna Maria Silveira, V. P. Mishra

引用次数: 1

Analysis of Textural Variations in Cerebellum in Brain to Identify Alzheimers by using Haralicks in Comparison with Gray Level Co-occurrence Matrix (GLRLM) 利用Haralicks与灰度共生矩阵(GLRLM)对比分析大脑小脑结构变化识别阿尔茨海默病

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9753940

U. Venkatesh, Bhuvaneswari Balachander

{"title":"Analysis of Textural Variations in Cerebellum in Brain to Identify Alzheimers by using Haralicks in Comparison with Gray Level Co-occurrence Matrix (GLRLM)","authors":"U. Venkatesh, Bhuvaneswari Balachander","doi":"10.1109/iciptm54933.2022.9753940","DOIUrl":"https://doi.org/10.1109/iciptm54933.2022.9753940","url":null,"abstract":"The aim of this research is to identify the textural variation in cerebellum of the brain to find the presence of Alzheimer's disease using Haralicks texture features in comparison with GLRLM texture features. For this analysis, MRI image dataset were extracted from OASIS database which consists of normal and Alzheimer's disease MRI images with sample size 50. The image dataset was used for feature extraction of texture images. Novel texture features are produced by the Haralicks and further extracted features are classified by KNN, SVM, Random Forest, Logistic Regression classifiers. From the results, novel texture features obtained from Haralicks provide best feature extraction from texture images such as mean values of normal is (0.62) and (0.54) for Alzheimer's. Loss in textural information is observed in the cerebellum of the brain. Classification using KNN classifiers, SVM classifiers, Random Forest classifier, Logistic Regression classifier for Haralicks features with accuracy (96%), Area Under Curve (AUC) (96%), F1-score (96%), precision (95%), recall (95%). The significance value is p<0.05. The G power is taken as 0.8. In this process we found that novel texture features extracted using Haralicks have performed better than the Gray Level Co-occurrence Matrix (GLRLM) texture features to identify the presence of Alzheimer's in the cerebellum of the brain MRI image.","PeriodicalId":6810,"journal":{"name":"2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"72 1","pages":"549-556"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74809269","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

Survey Paper: Comparative Study of Machine Learning Techniques and its Recent Applications 调查报告:机器学习技术及其最新应用的比较研究

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9754206

Basu Dev Shivahare, Shashikant Suman, Sai Sri Nandan Challapalli, P. Kaushik, A. Gupta, Vimal Bibhu

{"title":"Survey Paper: Comparative Study of Machine Learning Techniques and its Recent Applications","authors":"Basu Dev Shivahare, Shashikant Suman, Sai Sri Nandan Challapalli, P. Kaushik, A. Gupta, Vimal Bibhu","doi":"10.1109/iciptm54933.2022.9754206","DOIUrl":"https://doi.org/10.1109/iciptm54933.2022.9754206","url":null,"abstract":"The main objective of human evolution has always been to look for ways to mold the nature to satisfy our needs. A key milestone in this regard is the invention of a machine - called the computer that can complete a task given to it in fraction of time taken by an average human. While that sounds great, the only drawback is that the decision must still be taken by a man who is bound by limitations of the human body. The run to reap the complete benefits has given rise to what is called the Artificial Intelligence. Machine learning is a part of AI, which deals with imparting knowledge to the computer through various related examples. Throughout the years, various machine learning algorithms have been developed each with their own merits and demerits. This paper is a consolidated effort to bring together different ML algorithms like linear regression, KNN (k- nearest neighbours) etc. This research paper discusses the most recent developments in these areas of study and tries to define the best applications for each of those based on previous researches.","PeriodicalId":6810,"journal":{"name":"2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"12 1","pages":"449-454"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75094985","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: 5

A Comparative Study of Pose Estimation 姿态估计的比较研究

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9754071

Raghvender Changotra, Shreyashi Singh, P. Soni, Asmi Zutshi, Ayushi Gupta, Tushar Srivastava, Dinesh Vij, Gurjot Singh Sodhi

引用次数: 1

Microstructural Investigation with Process Variants in SPPS method of YSZ Coatings on SS304 SS304表面YSZ涂层SPPS工艺变化的显微组织研究

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9753882

R. Sudarshan, S. Venkatesh, K. Balasubramanian

引用次数: 0

Comparative Study on Various Storage Optimisation Techniques in Machine Learning based Cloud Computing System 基于机器学习的云计算系统中各种存储优化技术的比较研究

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9754179

Jyoti L. Bangare, Dhiraj Kapila, Pallavi Nehete, S. Malwade, K. Sankar, Samrat Ray

{"title":"Comparative Study on Various Storage Optimisation Techniques in Machine Learning based Cloud Computing System","authors":"Jyoti L. Bangare, Dhiraj Kapila, Pallavi Nehete, S. Malwade, K. Sankar, Samrat Ray","doi":"10.1109/iciptm54933.2022.9754179","DOIUrl":"https://doi.org/10.1109/iciptm54933.2022.9754179","url":null,"abstract":"Cloud computing is gaining more popularity in various industries as it enables in creating better services to different individuals, companies and government. With the progressive aspect on the concept, it offers flexible solutions to the growing computing problems related to infrastructure, application and other aspects. Cloud computing enables in minimizing the investments on IT infrastructure as it offers better and extensive support in saving various data and information of the companies, government and individuals, enable in retrieving them as and when required. Through the application of cloud computing the users received better and reliable services from the service providers which enables in reducing the investments, optimizing the cost and enable in enhancing products of the company in the long run. The cloud computing is taking better aspects and is considered as the paramount aspect for the business and individuals. This study is more focused in making a comparative understanding in the optimization techniques using machine learning based cloud computing systems. This research is more focused in analysing the application of machine learning as a key technique which can be applied in order to estimate the request patterns of the clients related to cloud storage. The research further focuses on the machine learning techniques covering linear regression model, Artificial neural networks, Support vector machines etc. are being explored in order to understand the optimization techniques for services the users in a better manner. The research applies questionnaire method for collecting the information and extensive analysis using SPSS is applied for analysing the data.","PeriodicalId":6810,"journal":{"name":"2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"1 1","pages":"53-57"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73610743","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

Remote Solar Microgrid Output Current Transient Diagnosis 远程太阳能微电网输出电流暂态诊断

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9754205

B. Chilukuri, N. Hemalatha, Anurag Shrivastava, Bhasker Pant, Sanjiv Kumar Jain, H. S

引用次数: 1

[Title page] (标题页)

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9754006

引用次数: 0

Dealing Big Data using Improved Fuzzy C Means Based Improved Redundant Particle Swarm Optimization with Map Reduction 基于改进模糊C均值的地图约简冗余粒子群优化大数据处理

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9754185

Venkata Subbaiah Desanamukula, K. N. Rao

{"title":"Dealing Big Data using Improved Fuzzy C Means Based Improved Redundant Particle Swarm Optimization with Map Reduction","authors":"Venkata Subbaiah Desanamukula, K. N. Rao","doi":"10.1109/iciptm54933.2022.9754185","DOIUrl":"https://doi.org/10.1109/iciptm54933.2022.9754185","url":null,"abstract":"There is a lot of interest in big data analysis from both the academic and commercial worlds. A number of algorithms have been implemented to improve the analysis process. Improved Fuzzy C-means clustering has been used to improve the MapReduce model in this study. 'In this work, Redundant Particle Swarm Optimization with Multi-Objective Optimization (MOO-MR-RPSO) based Improved Fuzzy C Means (IFCM) clustering mechanism is used along with MapReduce model. Each data element is mapped together and forms the data heterogeneity attributes. Due to unbalanced data in the large datasets, it is necessity to extract the features of the data; it is performed by using the Principle Component Analysis (PCA) based optimal feature extraction and feature selection respectively. Finally, MOO-MR-RPSO based optimization mechanism is developed for selecting the both appropriate clusters with optimal centroid(s) in the IFCM by using PCA features based objective function and the method named as the MOO-IFCM-RPSO with map reduce. The simulation results shows that the proposed MapReduce approach gives Maximum clustering accuracy compared to the state of art approaches.","PeriodicalId":6810,"journal":{"name":"2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"57 1","pages":"675-683"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85334217","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