Pub Date : 2020-04-01DOI: 10.4018/ijsppc.2020040102
T. Gao, Grace Y. Wang
It is essential to increase the accuracy and robustness of classification of brain data, including EEG, in order to facilitate a direct communication between the human brain and computerized devices. Different machine learning approaches, such as support vector machine (SVM), neural network, and linear discrimination analysis (LDA), have been applied to set up automatic subjective-classifier, and the findings for their capacities in this regard have been inconclusive. The present study developed an effective classifier for human mental status using deep learning in a convolutional neural network. In contrast to most previous studies commonly using EEG waveform or numeric value of brain signals for classification, the authors utilised imaging features generated from EEG data at alpha frequency band. A new model proposed in this study provides a simple and computationally efficient approach to distinguish mental status during resting. With training, this model could predict new 2D EEG images with above 90% accuracy, while traditional machine learning techniques failed to achieve this accuracy.
{"title":"Brain Signal Classification Based on Deep CNN","authors":"T. Gao, Grace Y. Wang","doi":"10.4018/ijsppc.2020040102","DOIUrl":"https://doi.org/10.4018/ijsppc.2020040102","url":null,"abstract":"It is essential to increase the accuracy and robustness of classification of brain data, including EEG, in order to facilitate a direct communication between the human brain and computerized devices. Different machine learning approaches, such as support vector machine (SVM), neural network, and linear discrimination analysis (LDA), have been applied to set up automatic subjective-classifier, and the findings for their capacities in this regard have been inconclusive. The present study developed an effective classifier for human mental status using deep learning in a convolutional neural network. In contrast to most previous studies commonly using EEG waveform or numeric value of brain signals for classification, the authors utilised imaging features generated from EEG data at alpha frequency band. A new model proposed in this study provides a simple and computationally efficient approach to distinguish mental status during resting. With training, this model could predict new 2D EEG images with above 90% accuracy, while traditional machine learning techniques failed to achieve this accuracy.","PeriodicalId":344690,"journal":{"name":"Int. J. Secur. Priv. Pervasive Comput.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132452182","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}
Pub Date : 2020-03-19DOI: 10.4018/ijsppc.2020100104
Divij Bajaj, Dhanya Pramod
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.
{"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}
Pub Date : 2020-01-01DOI: 10.4018/ijsppc.2020010103
D. Chatha, Alankrita Aggarwal, Rajender Kumar
The mortality rate among women is increasing progressively due to cancer. Generally, women around 45 years old are vulnerable from this disease. Early detection is hope for patients to survive otherwise it may reach to unrecoverable stage. Currently, there are numerous techniques available for diagnosis of such a disease out of which mammography is the most trustworthy method for detecting early cancer stage. The analysis of these mammogram images are difficult to analyze due to low contrast and nonuniform background. The mammogram images are scanned and digitized for processing that further reduces the contrast between Region of Interest and background. Presence of noise, glands and muscles leads to background contrast variations. Boundaries of suspected tumor area are fuzzy & improper. Aim of paper is to develop robust edge detection technique which works optimally on mammogram images to segment tumor area. Output results of proposed technique on different mammogram images of MIAS database are presented and compared with existing techniques in terms of both Qualitative & Quantitative parameters.
{"title":"Comparative Analysis of Proposed Artificial Neural Network (ANN) Algorithm With Other Techniques","authors":"D. Chatha, Alankrita Aggarwal, Rajender Kumar","doi":"10.4018/ijsppc.2020010103","DOIUrl":"https://doi.org/10.4018/ijsppc.2020010103","url":null,"abstract":"The mortality rate among women is increasing progressively due to cancer. Generally, women around 45 years old are vulnerable from this disease. Early detection is hope for patients to survive otherwise it may reach to unrecoverable stage. Currently, there are numerous techniques available for diagnosis of such a disease out of which mammography is the most trustworthy method for detecting early cancer stage. The analysis of these mammogram images are difficult to analyze due to low contrast and nonuniform background. The mammogram images are scanned and digitized for processing that further reduces the contrast between Region of Interest and background. Presence of noise, glands and muscles leads to background contrast variations. Boundaries of suspected tumor area are fuzzy & improper. Aim of paper is to develop robust edge detection technique which works optimally on mammogram images to segment tumor area. Output results of proposed technique on different mammogram images of MIAS database are presented and compared with existing techniques in terms of both Qualitative & Quantitative parameters.","PeriodicalId":344690,"journal":{"name":"Int. J. Secur. Priv. Pervasive Comput.","volume":"75 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":"121727183","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}
Pub Date : 2020-01-01DOI: 10.4018/ijsppc.2020010101
N. M. Shawky
GPS information when received from multi-unmanned aerial vehicles (UAVs), also called drones, via a ground control station can be processed for detecting and tracking estimate target position. Tracking drones based on GPS has had some issues with missed received information or received information with an error that can lead to lost tracking. The proposed algorithm, Markov chain Monte Carlo based particle filter (MCMC-PF) can be used to overcome these issues of error in received information with keeping tracks and provides continuous tracking with a higher accuracy. This is suitable for real time applications that deal with GPS receiver devices with low efficiency during tracking. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithms of the Kalman filter (KF).
{"title":"Accuracy Enhancement of GPS for Tracking Multiple Drones Based on MCMC Particle Filter","authors":"N. M. Shawky","doi":"10.4018/ijsppc.2020010101","DOIUrl":"https://doi.org/10.4018/ijsppc.2020010101","url":null,"abstract":"GPS information when received from multi-unmanned aerial vehicles (UAVs), also called drones, via a ground control station can be processed for detecting and tracking estimate target position. Tracking drones based on GPS has had some issues with missed received information or received information with an error that can lead to lost tracking. The proposed algorithm, Markov chain Monte Carlo based particle filter (MCMC-PF) can be used to overcome these issues of error in received information with keeping tracks and provides continuous tracking with a higher accuracy. This is suitable for real time applications that deal with GPS receiver devices with low efficiency during tracking. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithms of the Kalman filter (KF).","PeriodicalId":344690,"journal":{"name":"Int. J. Secur. Priv. Pervasive Comput.","volume":"101 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":"133658744","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}
Pub Date : 2020-01-01DOI: 10.4018/ijsppc.2020010102
D. Idoughi, Djeddi Abdelhakim
Delivering government services using information and communication technologies has gained great success, and raised citizens' need to access the presented services in ubiquitous ways. Leading governments and institutes in this field have already started to invest in this field and dispute that it has been over twenty years since the presentation of the concept of ubiquity there are no adaptable and reusable frameworks for creating large ubiquitous systems, since the developed ones were small and destined to create specific systems. In this article, the authors present a development approach that combines XP fast development, MDA's automated development and ease of modifying and updating, and the domain-oriented development that allows for the creation of a virtual image of governments agencies with a focus on active involvement of future system's users.
{"title":"A User Centered Model Driven Service Oriented Ubiquitous Government Design Approach","authors":"D. Idoughi, Djeddi Abdelhakim","doi":"10.4018/ijsppc.2020010102","DOIUrl":"https://doi.org/10.4018/ijsppc.2020010102","url":null,"abstract":"Delivering government services using information and communication technologies has gained great success, and raised citizens' need to access the presented services in ubiquitous ways. Leading governments and institutes in this field have already started to invest in this field and dispute that it has been over twenty years since the presentation of the concept of ubiquity there are no adaptable and reusable frameworks for creating large ubiquitous systems, since the developed ones were small and destined to create specific systems. In this article, the authors present a development approach that combines XP fast development, MDA's automated development and ease of modifying and updating, and the domain-oriented development that allows for the creation of a virtual image of governments agencies with a focus on active involvement of future system's users.","PeriodicalId":344690,"journal":{"name":"Int. J. Secur. Priv. Pervasive Comput.","volume":"34 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":"116055651","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}
Pub Date : 2020-01-01DOI: 10.4018/ijsppc.2020010104
K. Vijayaprabakaran, K. Sathiyamurthy, M. Ponniamma
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.
{"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}
Pub Date : 1900-01-01DOI: 10.4018/ijsppc.2021100101
M. Sushrut
Disasters can be both manmade or natural, but the consequences have been atrocious and require swift action to manage the devastating impact. The digital footprint is being left behind at a huge rate in the modern world. The overwhelming digital interactions across the technology-oriented world has necessitated the need of an efficient way to organise and utilise them for a better tomorrow. With ease of access to the internet and high percentage of e-literacy rates, involvement of the citizens of the world in the digital arena is increasing at an impeccable exponential rate. This data is generated at the rate of a quintillion bytes per day and has a total probability to increase furthermore. This paper was synthesized in an effort to consolidate the existing technology in handling the crisis with innovation. In this context, the paper has talked about the utility of big data and the related technological concepts, which help to monitor or detect the hazard, mitigate the efforts in tackling it, and systemize the post-disaster recovery process statistically.
{"title":"Applications of Big Data in Disaster Management: A Review","authors":"M. Sushrut","doi":"10.4018/ijsppc.2021100101","DOIUrl":"https://doi.org/10.4018/ijsppc.2021100101","url":null,"abstract":"Disasters can be both manmade or natural, but the consequences have been atrocious and require swift action to manage the devastating impact. The digital footprint is being left behind at a huge rate in the modern world. The overwhelming digital interactions across the technology-oriented world has necessitated the need of an efficient way to organise and utilise them for a better tomorrow. With ease of access to the internet and high percentage of e-literacy rates, involvement of the citizens of the world in the digital arena is increasing at an impeccable exponential rate. This data is generated at the rate of a quintillion bytes per day and has a total probability to increase furthermore. This paper was synthesized in an effort to consolidate the existing technology in handling the crisis with innovation. In this context, the paper has talked about the utility of big data and the related technological concepts, which help to monitor or detect the hazard, mitigate the efforts in tackling it, and systemize the post-disaster recovery process statistically.","PeriodicalId":344690,"journal":{"name":"Int. J. Secur. Priv. Pervasive Comput.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129863279","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}
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