Int. J. Secur. Priv. Pervasive Comput.最新文献_第5页

Brain Signal Classification Based on Deep CNN 基于深度CNN的脑信号分类

Int. J. Secur. Priv. Pervasive Comput. Pub Date : 2020-04-01 DOI: 10.4018/ijsppc.2020040102

T. Gao, Grace Y. Wang

引用次数: 5

Conversational System, Intelligent Virtual Assistant (IVA) Named DIVA Using Raspberry Pi 会话系统，智能虚拟助手(IVA)命名为DIVA使用树莓派

Int. J. Secur. Priv. Pervasive Comput. Pub Date : 2020-03-19 DOI: 10.4018/ijsppc.2020100104

Divij Bajaj, Dhanya Pramod

{"title":"Conversational System, Intelligent Virtual Assistant (IVA) Named DIVA Using Raspberry Pi","authors":"Divij Bajaj, Dhanya Pramod","doi":"10.4018/ijsppc.2020100104","DOIUrl":"https://doi.org/10.4018/ijsppc.2020100104","url":null,"abstract":"Humans are living in an era where they are interacting with machines day in and day out. In this new era of the 21st century, a virtual assistant (IVA) is a boon for everyone. It has opened the way for a new world where devices can interact their own. The human voice is integrated with every device making it intelligent. These IVAs can also be used to integrate it with business intelligence software such as Tableau and PowerBI to give dashboards the power of voice and text insights using NLG (natural language generation). This new technology attracted almost the entire world like smart phones, laptops, computers, smart meeting rooms, car InfoTech system, TV, etc. in many ways. Some of the popular voice assistants are like Mibot, Siri, Google Assistant, Cortana, Bixby, and Amazon Alexa. Voice recognition, contextual understanding, and human interaction are some of the issues that are continuously improving in these IVAs and shifting this paradigm towards AI research. This research aims at processing human natural voice and gives a meaningful response to the user. The questions that it is not able to answer are stored in a database for further investigation.","PeriodicalId":344690,"journal":{"name":"Int. J. Secur. Priv. Pervasive Comput.","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115893847","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

Comparative Analysis of Proposed Artificial Neural Network (ANN) Algorithm With Other Techniques 提出的人工神经网络(ANN)算法与其他技术的比较分析

Int. J. Secur. Priv. Pervasive Comput. Pub Date : 2020-01-01 DOI: 10.4018/ijsppc.2020010103

D. Chatha, Alankrita Aggarwal, Rajender Kumar

引用次数: 1

Accuracy Enhancement of GPS for Tracking Multiple Drones Based on MCMC Particle Filter 基于MCMC粒子滤波的GPS多无人机跟踪精度提高

Int. J. Secur. Priv. Pervasive Comput. Pub Date : 2020-01-01 DOI: 10.4018/ijsppc.2020010101

N. M. Shawky

引用次数: 0

A User Centered Model Driven Service Oriented Ubiquitous Government Design Approach 以用户为中心的模型驱动的面向服务的泛在政府设计方法

Int. J. Secur. Priv. Pervasive Comput. Pub Date : 2020-01-01 DOI: 10.4018/ijsppc.2020010102

D. Idoughi, Djeddi Abdelhakim

引用次数: 0

Video-Based Human Activity Recognition for Elderly Using Convolutional Neural Network 基于视频的卷积神经网络老年人人体活动识别

Int. J. Secur. Priv. Pervasive Comput. Pub Date : 2020-01-01 DOI: 10.4018/ijsppc.2020010104

K. Vijayaprabakaran, K. Sathiyamurthy, M. Ponniamma

{"title":"Video-Based Human Activity Recognition for Elderly Using Convolutional Neural Network","authors":"K. Vijayaprabakaran, K. Sathiyamurthy, M. Ponniamma","doi":"10.4018/ijsppc.2020010104","DOIUrl":"https://doi.org/10.4018/ijsppc.2020010104","url":null,"abstract":"A typical healthcare application for elderly people involves monitoring daily activities and providing them with assistance. Automatic analysis and classification of an image by the system is difficult compared to human vision. Several challenging problems for activity recognition from the surveillance video involving the complexity of the scene analysis under observations from irregular lighting and low-quality frames. In this article, the authors system use machine learning algorithms to improve the accuracy of activity recognition. Their system presents a convolutional neural network (CNN), a machine learning algorithm being used for image classification. This system aims to recognize and assist human activities for elderly people using input surveillance videos. The RGB image in the dataset used for training purposes which requires more computational power for classification of the image. By using the CNN network for image classification, the authors obtain a 79.94% accuracy in the experimental part which shows their model obtains good accuracy for image classification when compared with other pre-trained models.","PeriodicalId":344690,"journal":{"name":"Int. J. Secur. Priv. Pervasive Comput.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123752986","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

Applications of Big Data in Disaster Management: A Review 大数据在灾害管理中的应用综述

Int. J. Secur. Priv. Pervasive Comput. Pub Date : 1900-01-01 DOI: 10.4018/ijsppc.2021100101

M. Sushrut

引用次数: 1