Pub Date : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862131
B. Vijaya, P. Gharpure
The growth of semantic web has given rise to proliferation of data sources wherein the task of recognizing real world entities and identifying multiple references of the same real world entity becomes an essential task in order to facilitate sharing and integration of data. Due to the heterogeneous nature of data on the semantic web, entities belonging to different sources are compared by assessing the similarity of features that are common in order to identify matches. With the increasing size of data sets Candidate generation methods are generally employed to avoid quadratic time complexity that would otherwise be incurred if pairwise similarity of all entities are computed. Here we propose a novel index based approach for candidate generation and reduction. The evaluation shows that the proposed method scales well and improves recall significantly.
{"title":"Candidate Generation for Instance Matching on Semantic Web","authors":"B. Vijaya, P. Gharpure","doi":"10.1109/ICCIDS.2019.8862131","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862131","url":null,"abstract":"The growth of semantic web has given rise to proliferation of data sources wherein the task of recognizing real world entities and identifying multiple references of the same real world entity becomes an essential task in order to facilitate sharing and integration of data. Due to the heterogeneous nature of data on the semantic web, entities belonging to different sources are compared by assessing the similarity of features that are common in order to identify matches. With the increasing size of data sets Candidate generation methods are generally employed to avoid quadratic time complexity that would otherwise be incurred if pairwise similarity of all entities are computed. Here we propose a novel index based approach for candidate generation and reduction. The evaluation shows that the proposed method scales well and improves recall significantly.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125913834","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862007
S. V. Jansi Rani, R. S. Milton, L. Yamini, K. Shivaani
Transmission Control Protocol(TCP) plays an important role in everyday life, right from accessing ones mails to browsing the internet. With revolutionary mechanisms to ensure safe and consistent delivery of data and reducing the loss in the data transferred, TCP has indeed paved way for a paradigm shift in the way data is delivered over a network. TCP is proven to work in traditional environments involving conventional wired transmission, with well formulated packet loss restricting mechanisms implemented in the form of congestion control techniques. It is, however, found wanting in environments which involve a degree of heterogeneity (composed of wired and wireless nodes) or in purely wireless networks, involving multimedia data transmission. The performance improvement is achieved by developing a system that can classify losses as occurring due to congestion or due to the wireless nature and consequently controlling the congestion window size. This work seeks to create such a system based on reinforcement learning, where it first learns to differentiate and then predict wireless and congestion loss and consequently, predict the ideal size of congestion window thereby increasing the throughput of the system.
{"title":"Reinforcement Learning Approach to Improve Transmission Control Protocol","authors":"S. V. Jansi Rani, R. S. Milton, L. Yamini, K. Shivaani","doi":"10.1109/ICCIDS.2019.8862007","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862007","url":null,"abstract":"Transmission Control Protocol(TCP) plays an important role in everyday life, right from accessing ones mails to browsing the internet. With revolutionary mechanisms to ensure safe and consistent delivery of data and reducing the loss in the data transferred, TCP has indeed paved way for a paradigm shift in the way data is delivered over a network. TCP is proven to work in traditional environments involving conventional wired transmission, with well formulated packet loss restricting mechanisms implemented in the form of congestion control techniques. It is, however, found wanting in environments which involve a degree of heterogeneity (composed of wired and wireless nodes) or in purely wireless networks, involving multimedia data transmission. The performance improvement is achieved by developing a system that can classify losses as occurring due to congestion or due to the wireless nature and consequently controlling the congestion window size. This work seeks to create such a system based on reinforcement learning, where it first learns to differentiate and then predict wireless and congestion loss and consequently, predict the ideal size of congestion window thereby increasing the throughput of the system.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125574758","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862138
J. Akshya, P. Priyadarsini
Numerous parts of southern India have recently encountered severe damage to lives and properties due to floods. Floods are one among the most destructive natural hazard and recovering to normal life takes ample time. During hazards, various technologies are in use for speeding up relief operations and to minimize the amount of damage, one such being the use of drones. Many algorithms are in need for automatic analysis of remote sensing and aerial images. Nowadays, drones are being used for taking images from varied heights similar to aerial images, as they have cameras with exceptional features and effective sensors. This paper proposes a hybrid approach to classify whether a region in an aerial image is flood affected or not. A combination of Support Vector Machine(SVM) and k-means clustering proved capable of detecting flooded areas with good accuracy, classifying about 92% of flooded images correctly. Performance analysis is done by changing various kernel functions in SVM. The results show that there is a decrease in the prediction and training time when quadratic SVM is used.
{"title":"A Hybrid Machine Learning Approach for Classifying Aerial Images of Flood-Hit Areas","authors":"J. Akshya, P. Priyadarsini","doi":"10.1109/ICCIDS.2019.8862138","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862138","url":null,"abstract":"Numerous parts of southern India have recently encountered severe damage to lives and properties due to floods. Floods are one among the most destructive natural hazard and recovering to normal life takes ample time. During hazards, various technologies are in use for speeding up relief operations and to minimize the amount of damage, one such being the use of drones. Many algorithms are in need for automatic analysis of remote sensing and aerial images. Nowadays, drones are being used for taking images from varied heights similar to aerial images, as they have cameras with exceptional features and effective sensors. This paper proposes a hybrid approach to classify whether a region in an aerial image is flood affected or not. A combination of Support Vector Machine(SVM) and k-means clustering proved capable of detecting flooded areas with good accuracy, classifying about 92% of flooded images correctly. Performance analysis is done by changing various kernel functions in SVM. The results show that there is a decrease in the prediction and training time when quadratic SVM is used.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116544433","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862155
M. Abirami, S. Jaganathan
The recognition of handwritten mathematical expressions has received an increasing amount of attention in pattern recognition research. It is the process of taking in raw data and making actions based on the category of the data. In this paper, we present a tool for recognizing handwritten mathematical expressions. The proposed architecture aims at handling the handwritten expressions by performing segmentation of the input based on each pen ups and pen downs followed by symbol classification. As a classifier, an Extreme Learning Machine and Support Vector machines are used, the classifier which produces a best accuracy is selected and then the symbols are trained among various handwritten mathematical expression and a promising result are achieved at symbol classification stage. Once the symbols are classified, the corresponding output is converted to LaTex format.
{"title":"Handwritten Mathematical Recognition Tool","authors":"M. Abirami, S. Jaganathan","doi":"10.1109/ICCIDS.2019.8862155","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862155","url":null,"abstract":"The recognition of handwritten mathematical expressions has received an increasing amount of attention in pattern recognition research. It is the process of taking in raw data and making actions based on the category of the data. In this paper, we present a tool for recognizing handwritten mathematical expressions. The proposed architecture aims at handling the handwritten expressions by performing segmentation of the input based on each pen ups and pen downs followed by symbol classification. As a classifier, an Extreme Learning Machine and Support Vector machines are used, the classifier which produces a best accuracy is selected and then the symbols are trained among various handwritten mathematical expression and a promising result are achieved at symbol classification stage. Once the symbols are classified, the corresponding output is converted to LaTex format.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122505437","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862065
D. Thenmozhi, P. Mirunalini, S. M. Jaisakthi, Srivatsan Vasudevan, V. Veeramani Kannan, S. Sagubar Sadiq
Cricket is one of the most popular sports in the whole world, and also one of the most popular sports in India. Cricketing events such as the Indian Premier League (IPL) are thoroughly enjoyed by fans all across the country. Fans of the game love predicting the ongoing match results, and this is something that has ended up being a hobby for several people who follow the game. This is a sport with abundant amount of data and using this data, we can make an evaluation on whether a team can win an ongoing IPL match or not. This prediction is implemented by using machine learning algorithms such as Gaussian Naive Bayes, Support Vector Machine, K-Nearest Neighbor and Random Forest. The required dataset is obtained by collecting using a website and consolidated. As a result, the output is obtained which lists whether the home team has won the match or not. The accuracies obtained are 75%, 80%, 55%, 75%, 80%, 80%, 75% and 84% for the teams CSK, RR, DD, RCB, MI, SRH, KXIP and KKR respectively.
{"title":"MoneyBall - Data Mining on Cricket Dataset","authors":"D. Thenmozhi, P. Mirunalini, S. M. Jaisakthi, Srivatsan Vasudevan, V. Veeramani Kannan, S. Sagubar Sadiq","doi":"10.1109/ICCIDS.2019.8862065","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862065","url":null,"abstract":"Cricket is one of the most popular sports in the whole world, and also one of the most popular sports in India. Cricketing events such as the Indian Premier League (IPL) are thoroughly enjoyed by fans all across the country. Fans of the game love predicting the ongoing match results, and this is something that has ended up being a hobby for several people who follow the game. This is a sport with abundant amount of data and using this data, we can make an evaluation on whether a team can win an ongoing IPL match or not. This prediction is implemented by using machine learning algorithms such as Gaussian Naive Bayes, Support Vector Machine, K-Nearest Neighbor and Random Forest. The required dataset is obtained by collecting using a website and consolidated. As a result, the output is obtained which lists whether the home team has won the match or not. The accuracies obtained are 75%, 80%, 55%, 75%, 80%, 80%, 75% and 84% for the teams CSK, RR, DD, RCB, MI, SRH, KXIP and KKR respectively.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132083304","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862073
M. Kuhail, Nikhil Sai Santosh Gurram
Many individuals like working professionals, students, and house makers often find lack of time and time management as problems forsuccessful task accomplishment. One of the key reasons for failure in task accomplishment is inefficient planning of the tasks. There are many task management and to-do-list applications, but most of them do not advise on optimal task management and guidance for optimal performance. This problem has driven us to contribute a task recommender system which suggests a specific type of tasks to users based on their history of tasks and various factors at that specific time. This system not only suggests a specific type of task for the user but also collects feedback from the user to make the recommender system learn on how to provide useful recommendations thus making the users time much productive.
{"title":"TaskDo: A Daily Task Recommender System","authors":"M. Kuhail, Nikhil Sai Santosh Gurram","doi":"10.1109/ICCIDS.2019.8862073","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862073","url":null,"abstract":"Many individuals like working professionals, students, and house makers often find lack of time and time management as problems forsuccessful task accomplishment. One of the key reasons for failure in task accomplishment is inefficient planning of the tasks. There are many task management and to-do-list applications, but most of them do not advise on optimal task management and guidance for optimal performance. This problem has driven us to contribute a task recommender system which suggests a specific type of tasks to users based on their history of tasks and various factors at that specific time. This system not only suggests a specific type of task for the user but also collects feedback from the user to make the recommender system learn on how to provide useful recommendations thus making the users time much productive.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131463472","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862121
S. Swarnalatha, I. Kesavarthini, S. Poornima, N. Sripriya
A recommender system is proposed and developed to help users to find the best hospital for a particular treatment. Finding a best hospital that can cure one’s ailment is of paramount importance. A good hospital is one in which there are always enough staff on duty with the right skills, knowledge and experience. Customer experience is how customers perceive their interactions with a company or an organization. A customer’s experience is reflected in the comments that he makes about the organization through online public forums. Med–recommender system aims to provide accurate analysis of hospitals by taking into account the reviews by thousands of patients, which were written by the patients themselves in various online forums. Our recommendation system performs sentiment analysis on the reviews of various patients using Natural Language Processing techniques to classify them as positive and negative reviews. It weighs the ranking of hospitals on three different parameters namely polarity, subjectivity and intensity. The hospital with the best ranking for curing a particular disease is then given as result to the user asking for a recommendation. The system is evaluated using 300 online reviews about hospitals and specialties and found to yield 90% of accuracy. The proposed system also helps the users to understand the quality of a certain hospital by providing star ratings for the hospital when the user needs.
{"title":"Med-Recommender System for Predictive Analysis of Hospitals and Doctors","authors":"S. Swarnalatha, I. Kesavarthini, S. Poornima, N. Sripriya","doi":"10.1109/ICCIDS.2019.8862121","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862121","url":null,"abstract":"A recommender system is proposed and developed to help users to find the best hospital for a particular treatment. Finding a best hospital that can cure one’s ailment is of paramount importance. A good hospital is one in which there are always enough staff on duty with the right skills, knowledge and experience. Customer experience is how customers perceive their interactions with a company or an organization. A customer’s experience is reflected in the comments that he makes about the organization through online public forums. Med–recommender system aims to provide accurate analysis of hospitals by taking into account the reviews by thousands of patients, which were written by the patients themselves in various online forums. Our recommendation system performs sentiment analysis on the reviews of various patients using Natural Language Processing techniques to classify them as positive and negative reviews. It weighs the ranking of hospitals on three different parameters namely polarity, subjectivity and intensity. The hospital with the best ranking for curing a particular disease is then given as result to the user asking for a recommendation. The system is evaluated using 300 online reviews about hospitals and specialties and found to yield 90% of accuracy. The proposed system also helps the users to understand the quality of a certain hospital by providing star ratings for the hospital when the user needs.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115032049","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862041
S. Kanimozhi, G. Gayathri, T. Mala
Real time object detection is one of the challenging task as it need faster computation power in identifying the object at that time. However the data generated by any real time system are unlabelled data which often need large set of labeled data for effective training purpose. This paper proposed a faster detection method for real time object detection based on convolution neural network model called as Single Shot Multi-Box Detection(SSD).This work eliminates the feature resampling stage and combined all calculated results as a single component. Still there is a need of a light weight network model for the places which lacks in computational power like mobile devices( eg: laptop, mobile phones, etc). Thus a light weight network model which use depth-wise separable convolution called MobileNet is used in this proposed work. Experimental result reveal that use of MobileNet along with SSD model increase the accuracy level in identifying the real time household objects.
{"title":"Multiple Real-time object identification using Single shot Multi-Box detection","authors":"S. Kanimozhi, G. Gayathri, T. Mala","doi":"10.1109/ICCIDS.2019.8862041","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862041","url":null,"abstract":"Real time object detection is one of the challenging task as it need faster computation power in identifying the object at that time. However the data generated by any real time system are unlabelled data which often need large set of labeled data for effective training purpose. This paper proposed a faster detection method for real time object detection based on convolution neural network model called as Single Shot Multi-Box Detection(SSD).This work eliminates the feature resampling stage and combined all calculated results as a single component. Still there is a need of a light weight network model for the places which lacks in computational power like mobile devices( eg: laptop, mobile phones, etc). Thus a light weight network model which use depth-wise separable convolution called MobileNet is used in this proposed work. Experimental result reveal that use of MobileNet along with SSD model increase the accuracy level in identifying the real time household objects.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117009714","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862071
K. M. Yatheendra Pravan, Udhayakumar Shanmugam, P. Rajaraman
The core of technological advancements in the current trend is based on the manipulation of the inestimable amount of data that is generated every second around us. Gaining interesting insights from the data is of utmost importance and the need of the hour. The proposed system makes use of advancements in the domain of deep learning by implementing various algorithms and methodologies to automate the process of data analytics. The intended insights platform is developed using various deep learning frameworks such as Tensorflow, Keras and delivered to the end user as a web platform using Django Framework. The underlying algorithm of insights which makes the automation of analytics possible relies on the efficacy of feature name substitution network implemented using LSTM and the enhanced correlation analysis. These are then used to determine a measure called Insight Relevance Index (IRI) which then updates the global rule set records in the centralized data store accordingly. Employing the proposed system will definitely aid the profit and future growth of an institution or an organization.
在当前的趋势中，技术进步的核心是基于对我们周围每秒产生的不可估量的数据的操纵。从数据中获得有趣的见解是至关重要的，也是当前的需要。提出的系统通过实现各种算法和方法来自动化数据分析过程，利用深度学习领域的进步。预期的洞察平台是使用各种深度学习框架(如Tensorflow, Keras)开发的，并使用Django框架作为web平台交付给最终用户。使分析自动化成为可能的底层洞察算法依赖于使用LSTM实现的特征名称替换网络的有效性和增强的相关性分析。然后使用这些数据来确定称为Insight Relevance Index (IRI)的度量，IRI随后相应地更新集中式数据存储中的全局规则集记录。采用拟议的系统肯定会有助于一个机构或组织的利润和未来的增长。
{"title":"INSIGHTS! - a modern deep learning approach to data analysis using Feature Name Substitution Network","authors":"K. M. Yatheendra Pravan, Udhayakumar Shanmugam, P. Rajaraman","doi":"10.1109/ICCIDS.2019.8862071","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862071","url":null,"abstract":"The core of technological advancements in the current trend is based on the manipulation of the inestimable amount of data that is generated every second around us. Gaining interesting insights from the data is of utmost importance and the need of the hour. The proposed system makes use of advancements in the domain of deep learning by implementing various algorithms and methodologies to automate the process of data analytics. The intended insights platform is developed using various deep learning frameworks such as Tensorflow, Keras and delivered to the end user as a web platform using Django Framework. The underlying algorithm of insights which makes the automation of analytics possible relies on the efficacy of feature name substitution network implemented using LSTM and the enhanced correlation analysis. These are then used to determine a measure called Insight Relevance Index (IRI) which then updates the global rule set records in the centralized data store accordingly. Employing the proposed system will definitely aid the profit and future growth of an institution or an organization.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116851797","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862054
P. Lahane, Jay Jagtap, Aditya Inamdar, Nihal Karne, Ritwik Dev
In recent times, the advancements in Brain-Computer Interface has not only been instrumental in achieving its fundamental purpose of aiding disabled people, but also in creating novel applications like playing games without physical controls or operating home appliances merely by the power of your brain. The electrical activity generated in the brain is measured by an EEG device after which the collected raw data undergoes through various steps, namely: Signal acquisition, Data Preprocessing, Feature Extraction, and Classification. This paper helps the reader in understanding the different algorithms and methods used in each of these processes. A detailed survey of various applications of BCI using different feature extraction and classification techniques is done. Finally, we have compiled all the current issues which hinder the efficiency of BCI systems.
{"title":"A review of recent trends in EEG based Brain-Computer Interface","authors":"P. Lahane, Jay Jagtap, Aditya Inamdar, Nihal Karne, Ritwik Dev","doi":"10.1109/ICCIDS.2019.8862054","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862054","url":null,"abstract":"In recent times, the advancements in Brain-Computer Interface has not only been instrumental in achieving its fundamental purpose of aiding disabled people, but also in creating novel applications like playing games without physical controls or operating home appliances merely by the power of your brain. The electrical activity generated in the brain is measured by an EEG device after which the collected raw data undergoes through various steps, namely: Signal acquisition, Data Preprocessing, Feature Extraction, and Classification. This paper helps the reader in understanding the different algorithms and methods used in each of these processes. A detailed survey of various applications of BCI using different feature extraction and classification techniques is done. Finally, we have compiled all the current issues which hinder the efficiency of BCI systems.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123843259","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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