2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)最新文献_第2页

How User Generated Content Impacts Consumer Engagement 用户生成内容如何影响用户粘性

2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2020-06-01 DOI: 10.1109/ICRITO48877.2020.9197985

Tomcy Thomas

{"title":"How User Generated Content Impacts Consumer Engagement","authors":"Tomcy Thomas","doi":"10.1109/ICRITO48877.2020.9197985","DOIUrl":"https://doi.org/10.1109/ICRITO48877.2020.9197985","url":null,"abstract":"Social media posts are full of potential for brands to get free advocacy. The purpose of this paper is to make brand managers and firms understand how important is User Generated Content. This paper examined the significance of User Generated Content (UGC) in Brand Management and how it could influence brand attitude and purchase intention. The benefits of UGC includes offering an effective channel of communication with users in an affordable and timely manner have been reviewed. To understand the User Generated Content better we created content, posted in Instagram and studied how consumers engaged with the post. A questionnaire survey was done with a sample size equal to the number of people who liked that post & correlation analysis was done using SPSS. From the analysis, it was evident that the post for informing, co-creating and co-communication influences strongly & moderately the purchase intention of the consumer respectively and pioneering influences weakly the brand attitude of the consumer. Therefore, the User Generated Content helps in informing, pioneering, co-communicating and co-creating a brand.","PeriodicalId":141265,"journal":{"name":"2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129474599","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

A Framework for Insight Finder by Object Detection Mechanism 基于目标检测机制的Insight Finder框架

2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2020-06-01 DOI: 10.1109/ICRITO48877.2020.9198009

Sachin Saxena, S. Neogi

{"title":"A Framework for Insight Finder by Object Detection Mechanism","authors":"Sachin Saxena, S. Neogi","doi":"10.1109/ICRITO48877.2020.9198009","DOIUrl":"https://doi.org/10.1109/ICRITO48877.2020.9198009","url":null,"abstract":"The basic design principle of insight finder includes Arduino as a microcontroller device, Pixy2 CMUCam5 for object detection and other different sensors used for finding paths. As a result, insight finder (IFBot) rover had been produced to find a suitable path between different types and colors of objects. This device can remember different color object signatures and objects at the same time, also have capabilities of handling 60 frames per second at super-fast processing and can process synchronous serial data for communication within short distance. Pixy2 has been used in IFBot Rover system as a color object sensor for object detection. The other hardware devices used to design IFBot are ultrasonic sensors, IR Sensors, Motor Drivers, gear motors. By adopting a simple wall following, line following and barcode reading algorithm, the device can detect object and path. Pixy Mon application software can produce high accuracy data with relatively high speed. The discussion in the result shows that the design of IFBot model can be further upgraded to perform better in terms of speed and accuracy.","PeriodicalId":141265,"journal":{"name":"2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129744173","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

Evaluate the Role of Big Data in Enhancing Strategic Decision Making for E-governance in E-Oman Portal 评估大数据在加强E-Oman门户网站电子政务战略决策中的作用

2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2020-06-01 DOI: 10.1109/ICRITO48877.2020.9197808

B. Al-Bahri, H. Noronha, Jitendra Pandey, A. Singh, A. Rana

{"title":"Evaluate the Role of Big Data in Enhancing Strategic Decision Making for E-governance in E-Oman Portal","authors":"B. Al-Bahri, H. Noronha, Jitendra Pandey, A. Singh, A. Rana","doi":"10.1109/ICRITO48877.2020.9197808","DOIUrl":"https://doi.org/10.1109/ICRITO48877.2020.9197808","url":null,"abstract":"This research study is to analyse the role of big data for enhancing the decision making for E-Oman. E-Oman is one of the It’s organisation in Oman which provides key to It solutions includes application, infrastructure in Oman. Big data has become a forthcoming part of all trades and business segment Oman in electronic portal is for the citizens which make easy use of a transactions. Enhancement of big data in e-Oman Company supports in providing perfect public services and also searching of big term in process. The range which is used in this topic is exemplified in a crucial panel conversation a recent big data conference. We present a cooperative big data analytics stage for big data as a service. It takes longer time to achieve wrinkle data, progress events and investigative services. The outdate technologies do not become an appropriate solution to process a big data platform has begun to appear. The quality of big data is of great significance is more significant because the quality of material is affected by size, speed and format in which the data is generated. The main benefit of the e-service is user-friendly. By implementing the e-services it makes easy communication between the government and the citizens. The quality of big data is great pertinent and it is more significant. Quality of information is affected by the size, speed, and the data in which the format is generated.","PeriodicalId":141265,"journal":{"name":"2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129799361","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

A Comparative Study Based on Different Energy Saving Mechanisms Based on Green Internet of Things (GIoT) 基于绿色物联网(GIoT)的不同节能机制比较研究

2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2020-06-01 DOI: 10.1109/ICRITO48877.2020.9197848

G. Verma, S. Prakash

{"title":"A Comparative Study Based on Different Energy Saving Mechanisms Based on Green Internet of Things (GIoT)","authors":"G. Verma, S. Prakash","doi":"10.1109/ICRITO48877.2020.9197848","DOIUrl":"https://doi.org/10.1109/ICRITO48877.2020.9197848","url":null,"abstract":"Internet of Things (IoT) is a rising idea which basically expects to associate various types of gadgets with one another. IoT gadgets sense, gather and then transmit significant data from the environment. This generates huge amount of data which among billions of gadgets which requires a huge amount of energy. Green IoT mainly targets the energy and efficiency related issues to minimize the energy consumption by the existing applications. We firstly give the review of Green IoT energy management and the difficulties that are looked because of over the top use of major IoT gadgets. We then talk about and assess the systems that can be utilized to limit the energy utilization in IoT like structuring and maintaining proficient data centers, vitality productive transmission of information from sensors and plans of energy effective arrangements and so forth. Since, the gadgets in IoT have restricted energy sources they frequently keep running on battery with a specific energy capacity. This paper tends to the energy proficiency issues crosswise over different IoT driven systems with different framework models for Green IoT and energy effective plan for the IoT gadgets to expand the future of the IoT domain. Besides, we fundamentally break down the green IoT techniques and propose some rules which can be used to accomplish green IoT. At last, we examine a comparative study on the basis of their techniques, types, limitations etc so that researchers can easily get information of this domain for enhancing the current practices for better energy management in the future.","PeriodicalId":141265,"journal":{"name":"2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126683766","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

Recommending Restaurants: A Collaborative Filtering Approach 推荐餐厅:协同过滤方法

2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2020-06-01 DOI: 10.1109/ICRITO48877.2020.9197946

Alpika Tripathi, A. Sharma

引用次数: 6

Feature Extraction to Heterogeneous Cross Project Defect Prediction 异构跨项目缺陷预测的特征提取

2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2020-06-01 DOI: 10.1109/ICRITO48877.2020.9197799

Rohit Vashisht, S. Rizvi

{"title":"Feature Extraction to Heterogeneous Cross Project Defect Prediction","authors":"Rohit Vashisht, S. Rizvi","doi":"10.1109/ICRITO48877.2020.9197799","DOIUrl":"https://doi.org/10.1109/ICRITO48877.2020.9197799","url":null,"abstract":"Cross Project Defect Prediction (CPDP) predicts faults in a target project (which has deficient faulty data) by defect prediction models learned from another project’s fault data. Nonetheless, these studies have a prevalent problem that needs uniform metrics, i.e. to describe themselves; distinct projects must have similar features. This article emphasizes heterogeneous CPDP (HCPDP) modeling that does not require the same set of metrics between two applications, and also builds model of defect prediction based on matched heterogeneous metrics that show comparable distribution in their values for a given pair of datasets. HCPDP modeling consists of three main phase feature ranking and feature selection, metric matching, and finally, the binary classification of unlabeled instances in the target application is performed as clean or buggy instances using an appropriate classification. This paper empirically and theoretically evaluates the effect of an additional modeling phase i.e. extraction of features on performance of the HCPDP model. Selection of features is to weed out obsolete or redundant features from your dataset. The key difference between selection and extraction of features is that selection of features preserves a subset of the original features while extraction of features also produces new ones. This paper compares the performance of the proposed HCPDP model on 13 benchmarked datasets of three source project groups AEEEM, ReLink& SOFTLAB with and without applying feature extraction phase using three machine learning classifiers. We have used Chi Square Test (CST) to pick features, and Principle Components Analysis (PCA) method to extract brand features of the dataset. Results show that for prediction pairs (JDT, ar1) & (Safe, ar3), prediction accuracies were significantly improved when we employed feature extraction phase in model. The comparative analysis among all three classifier demonstrates that GBM performs best among all for both prediction pairs.","PeriodicalId":141265,"journal":{"name":"2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121427632","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

Programme Schedule 项目时间表

2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2020-06-01 DOI: 10.1109/icrito48877.2020.9197839

引用次数: 0

A Proposed Framework for Controlling Cyber- Crime 控制网络犯罪的建议框架

2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2020-06-01 DOI: 10.1109/ICRITO48877.2020.9197975

Grace Odette Boussi, Himanshu Gupta

{"title":"A Proposed Framework for Controlling Cyber- Crime","authors":"Grace Odette Boussi, Himanshu Gupta","doi":"10.1109/ICRITO48877.2020.9197975","DOIUrl":"https://doi.org/10.1109/ICRITO48877.2020.9197975","url":null,"abstract":"The usage of the internet as part of our daily routine has brought many advantages and has also facilitated our lifestyle, the activities that required hours or days to be solved like sending a message to a far person has been reduced to a short time. The Internet has not only brought advantages to our community but also disadvantages, among the drawbacks of the internet we have cyber-crime activity. Cyber-crime activity is one of the biggest challenges that the world is facing. It is the concern of everyone as the main goal is to bring cybersecurity to reduce the number of losses that the crime generates across the globe. Cybercrime is happening every second anywhere, and it is causing much damage not only in terms of data or privacy breaches but financially also. Many countries are lacking an excellent policy to tackle cybercrime, and for that reason, the number of crimes is increasing day by day. As much as the number of crimes is increasing, likewise, the number of financial losses is also increasing. In this paper, we proposed a framework that will help us fight against cybercrime regardless of the place we are located by monitoring the activities done on our electronic devices.","PeriodicalId":141265,"journal":{"name":"2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124046532","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

Predictive Analysis of Machine Learning Algorithms for Breast Cancer Diagnosis 机器学习算法在乳腺癌诊断中的预测分析

2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2020-06-01 DOI: 10.1109/ICRITO48877.2020.9197945

Mudit Arora, S. Som, A. Rana

{"title":"Predictive Analysis of Machine Learning Algorithms for Breast Cancer Diagnosis","authors":"Mudit Arora, S. Som, A. Rana","doi":"10.1109/ICRITO48877.2020.9197945","DOIUrl":"https://doi.org/10.1109/ICRITO48877.2020.9197945","url":null,"abstract":"Cancer is one of the fastest growing disease around the world and subpart of it Breast Cancer that is growing rapidly and mostly affecting women. Early treatment of this disease is helpful and can act as an early prevention to the upcoming major cure. However this can only be possible only if women are able to know that they are suffering with such disease and this can be only diagnosed if they come up with it and openly sharing with family and Doctors about the disease. This can lead to be a bit challenging task as to detect this disease among women using mammography as patient communication can affect mammography performance. This disease has had many ideas and myths as to how we can diagnosed it but Machine Learning the subset of Artificial Intelligence that can help Doctors and Surgeons to learn from past experiments. To treat upcoming patients with similar anomalies has had the major help of saving many patients with its set of algorithms and set of applications it provides. This paper will be focusing on five of the popular supervised Machine Learning algorithms for Diagnosing Breast Cancer this will be K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB) and Decision Tree (DT) and the algorithm Random Forest gave the best results and the K Nearest Neighbor was the second best performing algorithm that produce desired results and the algorithm Naïve Bayes was the least performing algorithm","PeriodicalId":141265,"journal":{"name":"2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124099389","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 Multi Criteria Integrated Dynamic Futuristic Group Decision Making Model for Implementation of Intelligent Transportation System in India 印度智能交通系统多准则集成动态未来群体决策模型

2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Pub Date : 2020-06-01 DOI: 10.1109/ICRITO48877.2020.9197911

Aditi Rajput, Madhur Jain

引用次数: 0