Short Message Service (SMS) is one of the well-known and reliable communication services in which a message sends electronically. In the current era, the declining in the cost per SMS day by day by overall all the telecom organizations in India has encouraged the extended utilization of SMS. This ascent pulled in assailants, which have brought about SMS Spam problem. Spam messages include advertisements, free services, promotions and marketing, awards, etc. Individuals are utilizing the ubiquity of cell phone gadgets is growing day by day as telecom giants give a vast variety of new and existing services by reducing the cost of all services. Short Message Service (SMS) is one of the broadly utilized communication services. Due to the high demand for SMS service, it has prompted a growth in mobile phones attacks like SMS Spam. In our proposed approach, we have presented a general model that can distinguish and filter the spam messages utilizing some existing machine learning classification algorithms. Our approach builds a generalized SMS spam-filtering model, which can filter messages from various backgrounds (Singapore, American, Indian English etc.). In our approach, preliminary results are mentioned below based on Singapore and Indian English based publicly available datasets. Our approach showed promise to accomplish a high precision utilizing Indian English SMS large datasets and others background’s datasets also.
{"title":"SMS Spam Filtering on Multiple Background Datasets Using Machine Learning Techniques: A Novel Approach","authors":"Rohit Kumar Kaliyar, Pratik Narang, Anurag Goswami","doi":"10.1109/IADCC.2018.8692097","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692097","url":null,"abstract":"Short Message Service (SMS) is one of the well-known and reliable communication services in which a message sends electronically. In the current era, the declining in the cost per SMS day by day by overall all the telecom organizations in India has encouraged the extended utilization of SMS. This ascent pulled in assailants, which have brought about SMS Spam problem. Spam messages include advertisements, free services, promotions and marketing, awards, etc. Individuals are utilizing the ubiquity of cell phone gadgets is growing day by day as telecom giants give a vast variety of new and existing services by reducing the cost of all services. Short Message Service (SMS) is one of the broadly utilized communication services. Due to the high demand for SMS service, it has prompted a growth in mobile phones attacks like SMS Spam. In our proposed approach, we have presented a general model that can distinguish and filter the spam messages utilizing some existing machine learning classification algorithms. Our approach builds a generalized SMS spam-filtering model, which can filter messages from various backgrounds (Singapore, American, Indian English etc.). In our approach, preliminary results are mentioned below based on Singapore and Indian English based publicly available datasets. Our approach showed promise to accomplish a high precision utilizing Indian English SMS large datasets and others background’s datasets also.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123994790","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/IADCC.2018.8692105
R. Murugesh, Aravind Hanumanthaiah, Ullas Ramanadhan, Nirmala Vasudevan
Wireless sensor networks (WSNs) are often deployed remotely; hence, typical disposable chemical batteries with limited lifetimes may not be suitable for powering the network. In such cases, photovoltaic (PV) systems that generate electricity from sunlight can serve as a better alternative energy source. The intensity of sunlight varies over time, and thus the rates at which the batteries in the PV system get charged also vary. Monitoring the charging and discharging currents and voltages of the batteries enables us to modify the operation of the system in order to improve its overall efficiency. Moreover, it enables us to detect any fault in the solar panel, battery, or network node. We have designed an independent, low cost, ultra-low power microcontroller-based wireless solar power monitor that can be plugged easily into a PV system. The monitor measures the currents and voltages across the panels, batteries, and the load, and periodically transmits these values through an independent wireless interface to a control center for observation and analysis. We have performed a power analysis of the monitor and learnt about the power consumption in its various states. The use of this power monitor should extend the overall life of the PV system and also minimize power failures in the WSN nodes powered by the PV system. This paper reports about the design of the power monitor as well as the results of our analyses.
{"title":"Designing a Wireless Solar Power Monitor for Wireless Sensor Network Applications","authors":"R. Murugesh, Aravind Hanumanthaiah, Ullas Ramanadhan, Nirmala Vasudevan","doi":"10.1109/IADCC.2018.8692105","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692105","url":null,"abstract":"Wireless sensor networks (WSNs) are often deployed remotely; hence, typical disposable chemical batteries with limited lifetimes may not be suitable for powering the network. In such cases, photovoltaic (PV) systems that generate electricity from sunlight can serve as a better alternative energy source. The intensity of sunlight varies over time, and thus the rates at which the batteries in the PV system get charged also vary. Monitoring the charging and discharging currents and voltages of the batteries enables us to modify the operation of the system in order to improve its overall efficiency. Moreover, it enables us to detect any fault in the solar panel, battery, or network node. We have designed an independent, low cost, ultra-low power microcontroller-based wireless solar power monitor that can be plugged easily into a PV system. The monitor measures the currents and voltages across the panels, batteries, and the load, and periodically transmits these values through an independent wireless interface to a control center for observation and analysis. We have performed a power analysis of the monitor and learnt about the power consumption in its various states. The use of this power monitor should extend the overall life of the PV system and also minimize power failures in the WSN nodes powered by the PV system. This paper reports about the design of the power monitor as well as the results of our analyses.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"366 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125624892","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/IADCC.2018.8692092
Sai Satyanarayana Reddy Seelam, Shrawan Kumar, Chand M Gopi, Reddy T. Raghunadha
The Internet is growing rapidly with huge amount of data mainly through social media. Most of the text in the World Wide Web is anonymous. In recent days, knowing the details of the anonymous text is the hot research area to the research community. Author Profiling is one such area attracted by the several researchers to know the information about the anonymous text. Author Profiling is a technique of predicting the demographic characteristics like gender, age and location of the authors by analyzing their written texts. The field of Stylometry is one area used by the researchers to discriminate the authors style of writing. In Author Profiling approaches the researchers proposed various types of stylistic features to distinguish the authors style of writing. The accuracies of demographic characteristics of the authors are not satisfactory when stylometric features were used. Later the researchers experimented with different types of term weight measures to improve the accuracies. In this work, we concentrated on two demographic characteristics such as gender and age. The experimentation is performed on 2014 PAN competition reviews corpus in English language. In this work, a new Profile specific Supervised Term Weight measure is proposed to predict the accuracy of gender and age of the author’s anonymous text. The experimental results of proposed measure is compared with different weight measures and identified that the proposed weight measure obtained best results for predicting gender and age.
{"title":"A New Term Weight Measure for Gender and Age Prediction of the Authors by analyzing their Written Texts","authors":"Sai Satyanarayana Reddy Seelam, Shrawan Kumar, Chand M Gopi, Reddy T. Raghunadha","doi":"10.1109/IADCC.2018.8692092","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692092","url":null,"abstract":"The Internet is growing rapidly with huge amount of data mainly through social media. Most of the text in the World Wide Web is anonymous. In recent days, knowing the details of the anonymous text is the hot research area to the research community. Author Profiling is one such area attracted by the several researchers to know the information about the anonymous text. Author Profiling is a technique of predicting the demographic characteristics like gender, age and location of the authors by analyzing their written texts. The field of Stylometry is one area used by the researchers to discriminate the authors style of writing. In Author Profiling approaches the researchers proposed various types of stylistic features to distinguish the authors style of writing. The accuracies of demographic characteristics of the authors are not satisfactory when stylometric features were used. Later the researchers experimented with different types of term weight measures to improve the accuracies. In this work, we concentrated on two demographic characteristics such as gender and age. The experimentation is performed on 2014 PAN competition reviews corpus in English language. In this work, a new Profile specific Supervised Term Weight measure is proposed to predict the accuracy of gender and age of the author’s anonymous text. The experimental results of proposed measure is compared with different weight measures and identified that the proposed weight measure obtained best results for predicting gender and age.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131441340","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/IADCC.2018.8692095
Lokesh Jain, R. Katarya
In today human life, a social network plays a significant role in the user’s decision-making. In the social network, an opinion leader is a critical person who influences the behavior of the person with their own knowledge and skills. The major contribution of this paper is to recommend an advance approach to discover the opinion leader in the social network using fuzzy logic and trust generation model. In the first step, we evaluate the fuzzy trust rules based on the user’s trust. In the next step, these fuzzy trust rules apply to the online social network and then the de-fuzzification process applied to find out the trust value for each user and at last, identify the top-N user according to their prominence value that directly used to obtain their trust value for each user. We demonstrate our approach on the synthesized dataset and show the result that is better than the standard Social network analysis measures with respect to accuracy, precision, F1-score, and recall.
{"title":"Identification of opinion leader in online social network using fuzzy trust system","authors":"Lokesh Jain, R. Katarya","doi":"10.1109/IADCC.2018.8692095","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692095","url":null,"abstract":"In today human life, a social network plays a significant role in the user’s decision-making. In the social network, an opinion leader is a critical person who influences the behavior of the person with their own knowledge and skills. The major contribution of this paper is to recommend an advance approach to discover the opinion leader in the social network using fuzzy logic and trust generation model. In the first step, we evaluate the fuzzy trust rules based on the user’s trust. In the next step, these fuzzy trust rules apply to the online social network and then the de-fuzzification process applied to find out the trust value for each user and at last, identify the top-N user according to their prominence value that directly used to obtain their trust value for each user. We demonstrate our approach on the synthesized dataset and show the result that is better than the standard Social network analysis measures with respect to accuracy, precision, F1-score, and recall.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"270 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131499054","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/IADCC.2018.8692119
T. Sharma, Nitya Kritin Valivati, Arvind Puthige, Unnikrishnan Hari
This paper aims to develop a method to extract 3D information from surrounding space in real time and to develop a control system to track a target object continuously. We used two cameras and utilized the concepts of ray optics, epipolar geometry and image processing to identify the target and find its world coordinates with reference to the cameras.
{"title":"Object Position Estimation Using Stereo Vision","authors":"T. Sharma, Nitya Kritin Valivati, Arvind Puthige, Unnikrishnan Hari","doi":"10.1109/IADCC.2018.8692119","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692119","url":null,"abstract":"This paper aims to develop a method to extract 3D information from surrounding space in real time and to develop a control system to track a target object continuously. We used two cameras and utilized the concepts of ray optics, epipolar geometry and image processing to identify the target and find its world coordinates with reference to the cameras.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131182828","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/IADCC.2018.8692137
S. Yadav, Aman Jain, Deepti Singh
Bill Gates was once quoted as saying, "You take away our top 20 employees and we [Microsoft] become a mediocre company". This statement by Bill Gates took our attention to one of the major problems of employee attrition at workplaces. Employee attrition (turnover) causes a significant cost to any organization which may later on effect its overall efficiency. As per CompData Surveys, over the past five years, total turnover has increased from 15.1 percent to 18.5 percent. For any organization, finding a well trained and experienced employee is a complex task, but it’s even more complex to replace such employees. This not only increases the significant Human Resource (HR) cost, but also impacts the market value of an organization. Despite these facts and ground reality, there is little attention to the literature, which has been seeded to many misconceptions between HR and Employees. Therefore, the aim of this paper is to provide a framework for predicting the employee churn by analyzing the employee’s precise behaviors and attributes using classification techniques.
{"title":"Early Prediction of Employee Attrition using Data Mining Techniques","authors":"S. Yadav, Aman Jain, Deepti Singh","doi":"10.1109/IADCC.2018.8692137","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692137","url":null,"abstract":"Bill Gates was once quoted as saying, \"You take away our top 20 employees and we [Microsoft] become a mediocre company\". This statement by Bill Gates took our attention to one of the major problems of employee attrition at workplaces. Employee attrition (turnover) causes a significant cost to any organization which may later on effect its overall efficiency. As per CompData Surveys, over the past five years, total turnover has increased from 15.1 percent to 18.5 percent. For any organization, finding a well trained and experienced employee is a complex task, but it’s even more complex to replace such employees. This not only increases the significant Human Resource (HR) cost, but also impacts the market value of an organization. Despite these facts and ground reality, there is little attention to the literature, which has been seeded to many misconceptions between HR and Employees. Therefore, the aim of this paper is to provide a framework for predicting the employee churn by analyzing the employee’s precise behaviors and attributes using classification techniques.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121770173","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/IADCC.2018.8692131
Anish Cheriyan, R. Gondkar, T. Gopal, Suresh Babu S
This paper provides the details about the Quality Assurance practices and techniques to be followed by the QA professional (also called SQA-Software Quality Assurance) in continuous delivery mode of software development. QA professionals are responsible for the process definition, audit, training and other assurance activites in the project. The paper provides a QA model named 'ACID-QA' model which comprises of key practices which can be used by the QA professional in continuous delivery mode of software development. The objective of the 'ACID-QA' model is to provide a working model for the SQA which can be used during the planning, requirement, design, coding, testing, continuous integration, audit and release activities of the project. The paper provides an overview of each of the practice areas of the model in the further sections. This model is implemented in Big Data Hadoop File system and Map Reduce and it is found that the product quality issues found by SQA Professionals are improved by 100%. The audit findings are further detailed down in the paper.
{"title":"Quality Assurance Practices in Continuous Delivery - an implementation in Big Data Domain","authors":"Anish Cheriyan, R. Gondkar, T. Gopal, Suresh Babu S","doi":"10.1109/IADCC.2018.8692131","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692131","url":null,"abstract":"This paper provides the details about the Quality Assurance practices and techniques to be followed by the QA professional (also called SQA-Software Quality Assurance) in continuous delivery mode of software development. QA professionals are responsible for the process definition, audit, training and other assurance activites in the project. The paper provides a QA model named 'ACID-QA' model which comprises of key practices which can be used by the QA professional in continuous delivery mode of software development. The objective of the 'ACID-QA' model is to provide a working model for the SQA which can be used during the planning, requirement, design, coding, testing, continuous integration, audit and release activities of the project. The paper provides an overview of each of the practice areas of the model in the further sections. This model is implemented in Big Data Hadoop File system and Map Reduce and it is found that the product quality issues found by SQA Professionals are improved by 100%. The audit findings are further detailed down in the paper.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126970439","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/IADCC.2018.8692091
S. Jain, Md. Umar Farooque, Vinayak Sharma
A large part of the video surveillance systems involves dealing with face detection techniques on unlabeled faces. We define several classes of faces to detect them from a surveillance footage defined using different clustering algorithms. In this paper, authors have proposed a facial clustering technique for low-resolution facial dataset obtained from video surveillance footage with the help of HAAR cascade classifier. Different models like ResNet 50 and Inception ResNet V2 were used for feature extraction with weights pre-trained on ImageNet Dataset. Further, several combinations of Scaling and calculated Dimensionality Reduction techniques were applied before being fed into clustering algorithms and finally accuracy was calculated on obtained clusters.
{"title":"Comparative Analysis of Clustering Algorithm for Facial Recognition System","authors":"S. Jain, Md. Umar Farooque, Vinayak Sharma","doi":"10.1109/IADCC.2018.8692091","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692091","url":null,"abstract":"A large part of the video surveillance systems involves dealing with face detection techniques on unlabeled faces. We define several classes of faces to detect them from a surveillance footage defined using different clustering algorithms. In this paper, authors have proposed a facial clustering technique for low-resolution facial dataset obtained from video surveillance footage with the help of HAAR cascade classifier. Different models like ResNet 50 and Inception ResNet V2 were used for feature extraction with weights pre-trained on ImageNet Dataset. Further, several combinations of Scaling and calculated Dimensionality Reduction techniques were applied before being fed into clustering algorithms and finally accuracy was calculated on obtained clusters.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125926193","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/IADCC.2018.8692088
D. S. Reddy, D. Rajesh Reddy, R. Usha, Ankit Chaudhary, SS Solanki
Imaging from space involves certain complications which are quite different from airborne platforms such as MAVs, UAVs and drones. All these platforms require mathematical models to represent the geometry of image acquisition and further georeferencing the acquired image. Conventionally, a Rigorous Sensor Model (RSM) involving mission critical parameters and a sequence of rotations serves the purpose, alternately Rational Functional Models (RFM) are developed which empirically mimics RSM to certain degree of acceptable accuracy. In this paper, a machine learning approach is proposed for georeferencing of satellite images and compares the results with RFM and RSM.
{"title":"A Machine Learning Approach to Georeferencing","authors":"D. S. Reddy, D. Rajesh Reddy, R. Usha, Ankit Chaudhary, SS Solanki","doi":"10.1109/IADCC.2018.8692088","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692088","url":null,"abstract":"Imaging from space involves certain complications which are quite different from airborne platforms such as MAVs, UAVs and drones. All these platforms require mathematical models to represent the geometry of image acquisition and further georeferencing the acquired image. Conventionally, a Rigorous Sensor Model (RSM) involving mission critical parameters and a sequence of rotations serves the purpose, alternately Rational Functional Models (RFM) are developed which empirically mimics RSM to certain degree of acceptable accuracy. In this paper, a machine learning approach is proposed for georeferencing of satellite images and compares the results with RFM and RSM.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120840341","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/IADCC.2018.8692126
H. Palo, Sangeet Sagar
The work attempts to characterize and classify speech emotions using the spectrogram. Initially, it extracts the individual Red, Green, and Blue parameters from the raw speech spectrogram image of every individual emotional utterance. Further, it computes the statistical parameters of individual RGB components to characterize the chosen emotional states. The utterances of anger, happiness, neutral, and sad emotional states from the standard Berlin (EMO-DB) database has been used for this purpose. The individual statistical R, G, and B spectrogram parameters are found to be different within an emotion as well as across emotional states. Thus, these values have been used as different feature sets to classify the designated emotional states using the popular Multilayer Perceptron Neural Network (MLPNN).
{"title":"Characterization and Classification of Speech Emotion with Spectrograms","authors":"H. Palo, Sangeet Sagar","doi":"10.1109/IADCC.2018.8692126","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692126","url":null,"abstract":"The work attempts to characterize and classify speech emotions using the spectrogram. Initially, it extracts the individual Red, Green, and Blue parameters from the raw speech spectrogram image of every individual emotional utterance. Further, it computes the statistical parameters of individual RGB components to characterize the chosen emotional states. The utterances of anger, happiness, neutral, and sad emotional states from the standard Berlin (EMO-DB) database has been used for this purpose. The individual statistical R, G, and B spectrogram parameters are found to be different within an emotion as well as across emotional states. Thus, these values have been used as different feature sets to classify the designated emotional states using the popular Multilayer Perceptron Neural Network (MLPNN).","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121589067","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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