Pub Date : 2018-12-01DOI: 10.1109/ICACAT.2018.8933702
Priyanka Sharma, Anjana Goen
Now a days, traffic congestion is the biggest problem facing city municipalities. There are larger portion of budget which are prepared to solve out this problem each and every year. There are many kind technologies which are introduced but still we are unable to finish such kind of problem. Smart traffic control approach can be a way to minimize this problem. In this system, we are going to use Arduino Uno board as comparator and weighting machines as input devices for comparison purpose. In this system we are going to check the traffic congestion of road by mean of weighting of the road. This system is going to be very useful for the remote areas where the traffic on any particular road is higher and on other one it is almost equal to zero. This would be easy in implementation and reliable in working.
{"title":"Analysis and Implementation of Smart Traffic Control System Using Weighted Data","authors":"Priyanka Sharma, Anjana Goen","doi":"10.1109/ICACAT.2018.8933702","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933702","url":null,"abstract":"Now a days, traffic congestion is the biggest problem facing city municipalities. There are larger portion of budget which are prepared to solve out this problem each and every year. There are many kind technologies which are introduced but still we are unable to finish such kind of problem. Smart traffic control approach can be a way to minimize this problem. In this system, we are going to use Arduino Uno board as comparator and weighting machines as input devices for comparison purpose. In this system we are going to check the traffic congestion of road by mean of weighting of the road. This system is going to be very useful for the remote areas where the traffic on any particular road is higher and on other one it is almost equal to zero. This would be easy in implementation and reliable in working.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"213 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76515861","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933672
Girraj Sharma, Ritu Sharma
In this paper Comparative analysis of hard and soft fusion techniques for Energy Efficient cooperative spectrum sensing (CSS) is presented. CSS is the efficient way of detecting spectrum holes in cognitive radio (CR) network by combining sensing information of various CR users also known as secondary users (SUs). In CSS, energy resources become precious when the SUs in CR network are battery operated. So it becomes important to use their energy efficiently. In this paper, Hard fusion techniques like AND, OR and Majority and soft fusion techniques like square law selection (SLS) and square law combine (SLC) fusion are explained and fusion rule threshold that maximizes the EE is calculated. Results show that for Energy Efficient CSS, Majority fusion outperforms other fusion techniques. For majority fusion EE is 4.4% more compared to SLS fusion and maximum EE is 3.8 Mbits/Hz/joule at sensing time equals to 2ms.
{"title":"Performance comparison of hard and soft fusion Techniques for Energy Efficient CSS in Cognitive Radio","authors":"Girraj Sharma, Ritu Sharma","doi":"10.1109/ICACAT.2018.8933672","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933672","url":null,"abstract":"In this paper Comparative analysis of hard and soft fusion techniques for Energy Efficient cooperative spectrum sensing (CSS) is presented. CSS is the efficient way of detecting spectrum holes in cognitive radio (CR) network by combining sensing information of various CR users also known as secondary users (SUs). In CSS, energy resources become precious when the SUs in CR network are battery operated. So it becomes important to use their energy efficiently. In this paper, Hard fusion techniques like AND, OR and Majority and soft fusion techniques like square law selection (SLS) and square law combine (SLC) fusion are explained and fusion rule threshold that maximizes the EE is calculated. Results show that for Energy Efficient CSS, Majority fusion outperforms other fusion techniques. For majority fusion EE is 4.4% more compared to SLS fusion and maximum EE is 3.8 Mbits/Hz/joule at sensing time equals to 2ms.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"120 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77451561","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933640
S. K. Dargar, V. Srivastava
Among the high field mobility materials, amorphous-InGaZnO (α-IGZO) is very prominent for their application as a channel material in Thin Film Transistors (TFTs). Electrical parameters for α-IGZO TFT are retrieved from the simulation results and the extraction of the switching parameters as threshold voltage, field effect mobility, subthreshold slope, and current ratio (ION/IOFF) have been reported in this paper. The results display large field effect mobility (μFE) 12.27 to 13.3 cm2/Vs ranging threshold voltage (Vth) 1.03 to 1.27 V, Subthreshold Swing (SS) 23.82 to 21.78 mV/decade and significant ON-OFF current ratio (ION/IOFF) 3.8 × 104 to 1.7×105. The reported characteristics from the simulation results demonstrated superior electrical parameters due to α-IGZO channel and shows that it can provide rapid switching, better resolution in flat-panel, and OLED displays.
{"title":"Performance Estimation of Amorphouss-IGZO Based Thin Film Transistor","authors":"S. K. Dargar, V. Srivastava","doi":"10.1109/ICACAT.2018.8933640","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933640","url":null,"abstract":"Among the high field mobility materials, amorphous-InGaZnO (α-IGZO) is very prominent for their application as a channel material in Thin Film Transistors (TFTs). Electrical parameters for α-IGZO TFT are retrieved from the simulation results and the extraction of the switching parameters as threshold voltage, field effect mobility, subthreshold slope, and current ratio (ION/IOFF) have been reported in this paper. The results display large field effect mobility (μFE) 12.27 to 13.3 cm2/Vs ranging threshold voltage (Vth) 1.03 to 1.27 V, Subthreshold Swing (SS) 23.82 to 21.78 mV/decade and significant ON-OFF current ratio (ION/IOFF) 3.8 × 104 to 1.7×105. The reported characteristics from the simulation results demonstrated superior electrical parameters due to α-IGZO channel and shows that it can provide rapid switching, better resolution in flat-panel, and OLED displays.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"16 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78333503","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933651
S. Sagar, A. Shrivastava, Chetan Gupta
The requisition or insistence of internet (web) connectivity i.e. wireless network like WSN, MANET, cellular network, broadband increases day by day. So it is obvious that increase demand of connectivity increase the problem also i.e. safety and security. In this paper discusses the security issue or problem on connectivity network generally define as the network intrusion (malicious activity) finding system. This system has to be used for secure or protect the information data from any unwanted activities. In this paper presents the feature reduction and selection based on an optimization mechanism which followed by supervised learning classifier. This paper introduce the hybrid intrusion detection system using supervised classifier i.e. SVM followed by the optimization mechanism i.e. PGO. Every IDS system needs reduce feature data set attributes to perform efficiently and smoothly that has to be major issue for any NIDS. The hybrid optimization mechanism provide the optimal solution, plant growth optimization mechanism inspired the natural tree growth process, here make this an artificial plant growth process and apply for data set attributes and set similar condition. That optimization method provide the best fitness value for branches and leaf for an artificial plant, these branches or leaf fit for artificial plant growth or not. According to these fitness values data set attributes further classified into intruder class. In this paper present mechanism or system use NSL-KDD data set (i.e. basically intruder class attribute data sets contain DOS, PROBE, R2L and U2R intruder class) for evaluation and comparing the mechanism performance in term of accuracy and Kappa. This hybrid mechanism based on optimization decreased the false alarm rate of the system and enhance the performance.
{"title":"Feature Reduction and Selection Based Optimization for Hybrid Intrusion Detection System Using PGO followed by SVM","authors":"S. Sagar, A. Shrivastava, Chetan Gupta","doi":"10.1109/ICACAT.2018.8933651","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933651","url":null,"abstract":"The requisition or insistence of internet (web) connectivity i.e. wireless network like WSN, MANET, cellular network, broadband increases day by day. So it is obvious that increase demand of connectivity increase the problem also i.e. safety and security. In this paper discusses the security issue or problem on connectivity network generally define as the network intrusion (malicious activity) finding system. This system has to be used for secure or protect the information data from any unwanted activities. In this paper presents the feature reduction and selection based on an optimization mechanism which followed by supervised learning classifier. This paper introduce the hybrid intrusion detection system using supervised classifier i.e. SVM followed by the optimization mechanism i.e. PGO. Every IDS system needs reduce feature data set attributes to perform efficiently and smoothly that has to be major issue for any NIDS. The hybrid optimization mechanism provide the optimal solution, plant growth optimization mechanism inspired the natural tree growth process, here make this an artificial plant growth process and apply for data set attributes and set similar condition. That optimization method provide the best fitness value for branches and leaf for an artificial plant, these branches or leaf fit for artificial plant growth or not. According to these fitness values data set attributes further classified into intruder class. In this paper present mechanism or system use NSL-KDD data set (i.e. basically intruder class attribute data sets contain DOS, PROBE, R2L and U2R intruder class) for evaluation and comparing the mechanism performance in term of accuracy and Kappa. This hybrid mechanism based on optimization decreased the false alarm rate of the system and enhance the performance.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"18 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78521854","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933675
S. Saxena, A. Shrivastava, A. Saxena, M. Manoria
Cloud computing is the modern technology, that facilitates to the user various resources, data and files to access from anywhere that a network is available, it is today’s modern computing where we distribute our resources and software from one place to another, but still the security of the data is a major problem in the cloud. However, there is need of processing a huge number of data and maintained the security over cloud. In this research we proposed a software framework (hadoop) with the cloud which helps us to process the huge number of data by using map-reduce model. To add security we use Kerberos authentication protocol which helps us to enhance security level over the cloud and also it is used to provide authentication to the users. We analyze and compare our technique with existing techniques, the results shows that our technique is much more secure and provide user authentication.
{"title":"Protecting Data Storage on Cloud to Enhance Security Level and Processing of the Data by using Hadoop","authors":"S. Saxena, A. Shrivastava, A. Saxena, M. Manoria","doi":"10.1109/ICACAT.2018.8933675","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933675","url":null,"abstract":"Cloud computing is the modern technology, that facilitates to the user various resources, data and files to access from anywhere that a network is available, it is today’s modern computing where we distribute our resources and software from one place to another, but still the security of the data is a major problem in the cloud. However, there is need of processing a huge number of data and maintained the security over cloud. In this research we proposed a software framework (hadoop) with the cloud which helps us to process the huge number of data by using map-reduce model. To add security we use Kerberos authentication protocol which helps us to enhance security level over the cloud and also it is used to provide authentication to the users. We analyze and compare our technique with existing techniques, the results shows that our technique is much more secure and provide user authentication.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79134629","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933681
Nagendra Singh, Ritesh Tirole
A new nature inspired algorithm, that simulates the mating behavior of the bumble bees, the Bumble Bees Mating Optimization (BBMO) algorithm, is proposed in this work for optimization of economic load dispatch. Economic dispatch is a method to evaluate the performance of the generating units to fulfill the load demand on minimum fuel cost. The proposed method bumble bees mating optimization (BBMO), work on different three modes namely the queen, the workers and the drones (males). For the evaluation of performance this study consider case study of forty generating unit data. The case study data is tested in various algorithms like Ant colony optimization, particle swarm optimization and genetic algorithm along with BBMO. The performance of all considered algorithm in this work is compared and it is found that minimum operating cost of the forty generating unit system is evaluated by BBMO. Convergence rate of BBMO is also very fast as compared to other considered methods.
{"title":"Bumble Bees Mating Optimization Algorithm for Economic Load Dispatch with Pollution","authors":"Nagendra Singh, Ritesh Tirole","doi":"10.1109/ICACAT.2018.8933681","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933681","url":null,"abstract":"A new nature inspired algorithm, that simulates the mating behavior of the bumble bees, the Bumble Bees Mating Optimization (BBMO) algorithm, is proposed in this work for optimization of economic load dispatch. Economic dispatch is a method to evaluate the performance of the generating units to fulfill the load demand on minimum fuel cost. The proposed method bumble bees mating optimization (BBMO), work on different three modes namely the queen, the workers and the drones (males). For the evaluation of performance this study consider case study of forty generating unit data. The case study data is tested in various algorithms like Ant colony optimization, particle swarm optimization and genetic algorithm along with BBMO. The performance of all considered algorithm in this work is compared and it is found that minimum operating cost of the forty generating unit system is evaluated by BBMO. Convergence rate of BBMO is also very fast as compared to other considered methods.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"45 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80946976","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933803
Shubhi Shrivastava, Iti Mathur, Nisheeth Joshi
Web is the very fastest resource to search any kind of information. With the development of semantic web, the result of search has become more informed. Ontologies are the integral part of semantic web. Ontology is a knowledge representation system because of its distribution and sharing of information features. With the Ontologies it can focus on the main concepts and its relation rather than information. Protégé is the most popular and widely used tool for developing Ontology. Here it is going to use this tool for constructing university ontology. In this paper it shall discuss the development, verification and validation of ontology in university domain.
{"title":"An Ontology Development For University","authors":"Shubhi Shrivastava, Iti Mathur, Nisheeth Joshi","doi":"10.1109/ICACAT.2018.8933803","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933803","url":null,"abstract":"Web is the very fastest resource to search any kind of information. With the development of semantic web, the result of search has become more informed. Ontologies are the integral part of semantic web. Ontology is a knowledge representation system because of its distribution and sharing of information features. With the Ontologies it can focus on the main concepts and its relation rather than information. Protégé is the most popular and widely used tool for developing Ontology. Here it is going to use this tool for constructing university ontology. In this paper it shall discuss the development, verification and validation of ontology in university domain.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"8 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85763294","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933532
Subhanarayan Sahoo
High-energy ball milling(HEBM) used for synthesize nano crystalline Calcium Titanate, CaTiO3 (CT) ceramic. X-ray diffraction(XRD), impedance and time domain analysis applied for characterization. Effect of grain conduction observed from complex impedance spectrum in the Nyquist plot by the appearance of one semi-circular arc. Various time response parameters have been calculated. Green pellets in disk shaped sintered at 1000°C, 1100°C and 1200°C to study the effect of sintering on time response. Time domain response obtained by MATLAB programming of equivalent circuit parameters got from cole-cole plot of respective samples using partial fraction and inverse Laplace transform method. Potential of nano CaTiO3 as faster sensitive material for electrical and electronic device applications proven by the study.
{"title":"Effect of Sintering on Time Domain Response of CaTiO3 Nano Ceramics","authors":"Subhanarayan Sahoo","doi":"10.1109/ICACAT.2018.8933532","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933532","url":null,"abstract":"High-energy ball milling(HEBM) used for synthesize nano crystalline Calcium Titanate, CaTiO3 (CT) ceramic. X-ray diffraction(XRD), impedance and time domain analysis applied for characterization. Effect of grain conduction observed from complex impedance spectrum in the Nyquist plot by the appearance of one semi-circular arc. Various time response parameters have been calculated. Green pellets in disk shaped sintered at 1000°C, 1100°C and 1200°C to study the effect of sintering on time response. Time domain response obtained by MATLAB programming of equivalent circuit parameters got from cole-cole plot of respective samples using partial fraction and inverse Laplace transform method. Potential of nano CaTiO3 as faster sensitive material for electrical and electronic device applications proven by the study.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"70 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85806845","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933727
Jayasri Santhappan, P. Chokkalingam
Business forecast is a biggest factor which generally affects the economical condition of any Financial Industry. If the forecast model is not a better one then it can cause liquidation and spoil the trust of customers in the market. Early predictions based on social media clients’ opinion plays a major role in order to reduce risk on business and keep the trust of customer. According to the survey done by Fintech’s world topic analysis is treated as one of the vital factor used for the determination of client’s trends and for forecast analysis. Here we have performed a comparative analysis upon the social media data provide by Twitter in order to get an idea about the perception and understanding of clients’ requirements across the world. For the experimentation purpose we have used Tweeter data for tweet analysis, for stock price we have yahoo finance data and for number of stocks we have used morning star data set. For the processing of Tweets given by the clients we have built an automated system using Deep Learning. Here the problem is divided in to 2 parts. In first part Text classification is done using Tensorflow and Keras, Latent Dirichlet allocation (LDA), Natural Language Toolkit (NLTK-NLP).In this part using topic analysis the past tweet history is analyzed. In second part we are predicting forecastto identify multiple key business factors using Long Short term Memory (LSTM) using python/Rto. The actual aim of the system is to discover the effect of 3 fundamental parameters like security breaches, innovation, and stock exchange which are present in tweet given by the customers. Here the analysis is done on the last ten years tweets given by the clients for prediction of upcoming seven-day as well as monthly Market Cap. The actual intention of the work done here is to uncover the major diversity among two banks and bridge up the 3 gaps data breach, innovation and stock exchange in the available models. The latest information obtained in the system offers advantages to both Bank and customers to forecast Market value for the unbeaten estimation. We have obtained a prediction accuracy of 70.74% and 54.55% for monthly prediction and for weekly prediction we have obtained accuracy of 83.44% and 76.06% for Bank A and Bank B.
{"title":"An Intelligent Market Capitalization Predictive System Using Deep Learning","authors":"Jayasri Santhappan, P. Chokkalingam","doi":"10.1109/ICACAT.2018.8933727","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933727","url":null,"abstract":"Business forecast is a biggest factor which generally affects the economical condition of any Financial Industry. If the forecast model is not a better one then it can cause liquidation and spoil the trust of customers in the market. Early predictions based on social media clients’ opinion plays a major role in order to reduce risk on business and keep the trust of customer. According to the survey done by Fintech’s world topic analysis is treated as one of the vital factor used for the determination of client’s trends and for forecast analysis. Here we have performed a comparative analysis upon the social media data provide by Twitter in order to get an idea about the perception and understanding of clients’ requirements across the world. For the experimentation purpose we have used Tweeter data for tweet analysis, for stock price we have yahoo finance data and for number of stocks we have used morning star data set. For the processing of Tweets given by the clients we have built an automated system using Deep Learning. Here the problem is divided in to 2 parts. In first part Text classification is done using Tensorflow and Keras, Latent Dirichlet allocation (LDA), Natural Language Toolkit (NLTK-NLP).In this part using topic analysis the past tweet history is analyzed. In second part we are predicting forecastto identify multiple key business factors using Long Short term Memory (LSTM) using python/Rto. The actual aim of the system is to discover the effect of 3 fundamental parameters like security breaches, innovation, and stock exchange which are present in tweet given by the customers. Here the analysis is done on the last ten years tweets given by the clients for prediction of upcoming seven-day as well as monthly Market Cap. The actual intention of the work done here is to uncover the major diversity among two banks and bridge up the 3 gaps data breach, innovation and stock exchange in the available models. The latest information obtained in the system offers advantages to both Bank and customers to forecast Market value for the unbeaten estimation. We have obtained a prediction accuracy of 70.74% and 54.55% for monthly prediction and for weekly prediction we have obtained accuracy of 83.44% and 76.06% for Bank A and Bank B.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"33 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90531407","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933599
K. Kaushal, Mahesh Pawar, Sachin Goyal, Ratish Agrawal
Human-computer interactions result in psychological effects on human behavior. The analysis of the human behavior can be done using physiological data of a user in intense emotional states. A user may have intense emotions, which could make the user more nervous, sad or aggressive. This paper shows how physiological data can be used to analyze a user’s emotional state and summarizes the findings of using different feature selection and classification techniques to learn the user’s emotional states. The general flow of this approach is to record physiological signals from a person, extract features and feed them to a machine learning algorithm. This algorithm will then predict the user’s emotional state. The outcome will be helpful to analyze and understand how to train the models with the given dataset. Results of this study can be utilized for future research and applications for mitigating the effects of the content on user’s emotions.
{"title":"Comparing Physiological Feature Selection Methods for Emotion Recognition","authors":"K. Kaushal, Mahesh Pawar, Sachin Goyal, Ratish Agrawal","doi":"10.1109/ICACAT.2018.8933599","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933599","url":null,"abstract":"Human-computer interactions result in psychological effects on human behavior. The analysis of the human behavior can be done using physiological data of a user in intense emotional states. A user may have intense emotions, which could make the user more nervous, sad or aggressive. This paper shows how physiological data can be used to analyze a user’s emotional state and summarizes the findings of using different feature selection and classification techniques to learn the user’s emotional states. The general flow of this approach is to record physiological signals from a person, extract features and feed them to a machine learning algorithm. This algorithm will then predict the user’s emotional state. The outcome will be helpful to analyze and understand how to train the models with the given dataset. Results of this study can be utilized for future research and applications for mitigating the effects of the content on user’s emotions.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"21 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80649583","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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