Pub Date : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703357
Kuldeep Singh, J. Malhotra
Epilepsy is one of the major chronic nervous disorders, which affects the lives of millions of patients per annum globally, because of occurrence of sudden death or major injuries occurred during walk, driving or working in hazardous work environment. Its prognosis through modern technologies is the need of the day, which is attaining worldwide attention in research community with the use of latest technologies like internet of things, machine learning and cloud computing. This paper presents a model of automatic epileptic seizure detection model using Stacked Autoencoders based deep learning approach, which is an advanced form of machine leaning, employed for effectively handling the problem of big data with reduced complexity and processing time and to make this process more real time compatible with least delays. This model processes the sensed EEG signals by breaking it into short duration segments. Then, these EEG segments are fed to Stacked Autoencoders for its classification into different epileptic seizure stages like normal, preictal and ictal. The performance of this model has been compared with other existing models consisting of higher order spectral analysis based feature extraction and classification using traditional machine learning algorithms like Bayes Net, Naïve Bayes, Multilayer Perceptron, Radial basis function neural networks and C4.5 decision tree classifier. The analysis of performance through simulation results reveal that Stacked Autoencoders based deep learning approach is an efficient model for real time automatic epileptic seizures detection at early stage with classification accuracy 88.8%, sensitivity 89.44%, specificity 93.77% values and least value of processing time, which is approximately 23 times lesser than that of models utilizing traditional higher order statistics feature extraction and machine learning based classification approaches.
{"title":"Stacked Autoencoders Based Deep Learning Approach for Automatic Epileptic Seizure Detection","authors":"Kuldeep Singh, J. Malhotra","doi":"10.1109/ICSCCC.2018.8703357","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703357","url":null,"abstract":"Epilepsy is one of the major chronic nervous disorders, which affects the lives of millions of patients per annum globally, because of occurrence of sudden death or major injuries occurred during walk, driving or working in hazardous work environment. Its prognosis through modern technologies is the need of the day, which is attaining worldwide attention in research community with the use of latest technologies like internet of things, machine learning and cloud computing. This paper presents a model of automatic epileptic seizure detection model using Stacked Autoencoders based deep learning approach, which is an advanced form of machine leaning, employed for effectively handling the problem of big data with reduced complexity and processing time and to make this process more real time compatible with least delays. This model processes the sensed EEG signals by breaking it into short duration segments. Then, these EEG segments are fed to Stacked Autoencoders for its classification into different epileptic seizure stages like normal, preictal and ictal. The performance of this model has been compared with other existing models consisting of higher order spectral analysis based feature extraction and classification using traditional machine learning algorithms like Bayes Net, Naïve Bayes, Multilayer Perceptron, Radial basis function neural networks and C4.5 decision tree classifier. The analysis of performance through simulation results reveal that Stacked Autoencoders based deep learning approach is an efficient model for real time automatic epileptic seizures detection at early stage with classification accuracy 88.8%, sensitivity 89.44%, specificity 93.77% values and least value of processing time, which is approximately 23 times lesser than that of models utilizing traditional higher order statistics feature extraction and machine learning based classification approaches.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"29 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":"133470613","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/ICSCCC.2018.8703291
Bhupesh B. Lonkar, R. Nakhate, M. R. Sayankar
Water is most important substance on the earth. Water is resource consumed by human, animal and plants for its survival. The most of the water has stored in river, pond and tank. The quality of stored water is major issue in day to day life. It affects the health of human being. The design model has worked on the wireless sensors system to find out the quality measures of water. It implements PH, Turbidity, ultrasonic and temperature sensors for providing good quality of water in tank. The system connects with the microcontroller to take the inputs from the sensors and controller to perform the operation on given inputs. Server will received the information to server and send to the client system for further taking action.
{"title":"Smart Automatic Control and Monitor Water Purification Using Wireless Sensor System","authors":"Bhupesh B. Lonkar, R. Nakhate, M. R. Sayankar","doi":"10.1109/ICSCCC.2018.8703291","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703291","url":null,"abstract":"Water is most important substance on the earth. Water is resource consumed by human, animal and plants for its survival. The most of the water has stored in river, pond and tank. The quality of stored water is major issue in day to day life. It affects the health of human being. The design model has worked on the wireless sensors system to find out the quality measures of water. It implements PH, Turbidity, ultrasonic and temperature sensors for providing good quality of water in tank. The system connects with the microcontroller to take the inputs from the sensors and controller to perform the operation on given inputs. Server will received the information to server and send to the client system for further taking action.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"16 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":"123681599","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/ICSCCC.2018.8703292
M. Ahuja, A. L. Sangal
Music Lyrics is an important and meaningful part of any song that are helpful in investigations and classification of opinion (sentiment) develop from it. Opinion mining is also referred as sentiment analysis is the field of data processing that is used to find out opinion of an author, user and subjectivity from text. In this work we are considering only the English lyrical part of a song. WorldNet knowledge is then incorporate to find out synonyms of words. The Goal of this research is doing a linguistic investigation of music lyrics whether these songs useful for listeners or not and classifying them with positive and negative fulfilled present in them. In Order to evaluate this words involve opinion(sentiment) have been investigate with using POS tagger and classifying them into mood categories using different machine learning algorithms(supervised) Random Forest, Gradient Boosting and Voting Classifier(including logistic regression, Decision Tree and SVM) and compare with different parameters.
{"title":"Opinion Mining and Classification of Music Lyrics Using Supervised Learning Algorithms","authors":"M. Ahuja, A. L. Sangal","doi":"10.1109/ICSCCC.2018.8703292","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703292","url":null,"abstract":"Music Lyrics is an important and meaningful part of any song that are helpful in investigations and classification of opinion (sentiment) develop from it. Opinion mining is also referred as sentiment analysis is the field of data processing that is used to find out opinion of an author, user and subjectivity from text. In this work we are considering only the English lyrical part of a song. WorldNet knowledge is then incorporate to find out synonyms of words. The Goal of this research is doing a linguistic investigation of music lyrics whether these songs useful for listeners or not and classifying them with positive and negative fulfilled present in them. In Order to evaluate this words involve opinion(sentiment) have been investigate with using POS tagger and classifying them into mood categories using different machine learning algorithms(supervised) Random Forest, Gradient Boosting and Voting Classifier(including logistic regression, Decision Tree and SVM) and compare with different parameters.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"1 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":"124323011","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/ICSCCC.2018.8703281
Aditi Gupta, Y. P. Verma, A. Chauhan
In this article, economic impact of reactive power services in pool based deregulated power market integrated with wind units is analyzed. Reactive power cost of different generators including fixed speed wind units and doubly fed induction generator based variable speed wind units is calculated using the concept of loss of real power spinning reserve cost by using loading capability curve. The proposed optimization model is validated and implemented on a modified 5-bus test system and is then extended to a larger IEEE 24-bus RTS system using GAMS 23.4 software in interfacing with MATLAB 7.0. Results reveal better financial and technical performance of DFIG based wind farms as compared to fixed speed wind farms in reactive power management.
{"title":"Economic Analysis of Reactive Power Services in Deregulated Power Market Integrated with Doubly Fed Induction Generator Wind Unit","authors":"Aditi Gupta, Y. P. Verma, A. Chauhan","doi":"10.1109/ICSCCC.2018.8703281","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703281","url":null,"abstract":"In this article, economic impact of reactive power services in pool based deregulated power market integrated with wind units is analyzed. Reactive power cost of different generators including fixed speed wind units and doubly fed induction generator based variable speed wind units is calculated using the concept of loss of real power spinning reserve cost by using loading capability curve. The proposed optimization model is validated and implemented on a modified 5-bus test system and is then extended to a larger IEEE 24-bus RTS system using GAMS 23.4 software in interfacing with MATLAB 7.0. Results reveal better financial and technical performance of DFIG based wind farms as compared to fixed speed wind farms in reactive power management.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"21 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":"122444541","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/ICSCCC.2018.8703317
P. Dorge, Samiksha L. Meshram
A Mobile Adhoc Network is a network which is made up numerous mobile nodes, that are wireless in nature and they self-organize themselves to form an environment with an arbitrary and ever-changing topology. These networks do not have any pre-established infrastructure and they do not require some central management. Each of the mobile station in MANET can work as source, receiver and router then they have no restrictions to move anywhere in the network. MANETs can be uses in various civilian and military applications such as classrooms, battlefields and tragedy management activities. In such scenarios, we find correlated movement among the nodes. The Reference Point Group Mobility (RPGM) model is based on correlated node mobility. This work demonstrates design and performance analysis of RPGM model, with the help of the reactive routing protocols (RPs) like AODV which is Ad hoc On-demand Distance Vector and AOMDV which is Ad hoc On demand Multipath Distance Vector. The network simulator NS2 has been used to perform the simulations.
移动自组织网络是由众多移动节点组成的网络，这些移动节点是无线的，它们自组织形成一个具有任意和不断变化的拓扑环境。这些网络没有任何预先建立的基础设施，也不需要某种中央管理。在MANET中，每个移动站可以同时作为源、接收器和路由器，在网络中移动不受任何限制。manet可用于各种民用和军事应用，如教室、战场和悲剧管理活动。在这种情况下，我们发现节点之间的相关移动。参考点群移动(RPGM)模型基于关联节点移动。本文在AODV (Ad hoc On demand Distance Vector)和AOMDV (Ad hoc On demand multi - path Distance Vector)等响应路由协议的帮助下，演示了RPGM模型的设计和性能分析。采用网络模拟器NS2进行仿真。
{"title":"Design and Performance Analysis of Reference Point Group Mobility Model for Mobile Ad hoc Network","authors":"P. Dorge, Samiksha L. Meshram","doi":"10.1109/ICSCCC.2018.8703317","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703317","url":null,"abstract":"A Mobile Adhoc Network is a network which is made up numerous mobile nodes, that are wireless in nature and they self-organize themselves to form an environment with an arbitrary and ever-changing topology. These networks do not have any pre-established infrastructure and they do not require some central management. Each of the mobile station in MANET can work as source, receiver and router then they have no restrictions to move anywhere in the network. MANETs can be uses in various civilian and military applications such as classrooms, battlefields and tragedy management activities. In such scenarios, we find correlated movement among the nodes. The Reference Point Group Mobility (RPGM) model is based on correlated node mobility. This work demonstrates design and performance analysis of RPGM model, with the help of the reactive routing protocols (RPs) like AODV which is Ad hoc On-demand Distance Vector and AOMDV which is Ad hoc On demand Multipath Distance Vector. The network simulator NS2 has been used to perform the simulations.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"121 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":"116246696","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/ICSCCC.2018.8703326
Anterpreet Kaur Bedi, R. K. Sunkaria, Simarjot Kaur Randhawa
Local Binary Pattern (LBP) is a non-parametric descriptor that is used to study various local structures of an image. It is considered as simple and efficient texture operator for image analysis in challenging real-time situations. It has been applied successfully for various applications of computer vision and image processing, like pattern recognition, texture analysis, face detection, image retrieval etc. This paper covers different LBP variants in spatial domain, which were created in order to improve its robustness and efficiency.
{"title":"Local Binary Pattem Variants: A Review","authors":"Anterpreet Kaur Bedi, R. K. Sunkaria, Simarjot Kaur Randhawa","doi":"10.1109/ICSCCC.2018.8703326","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703326","url":null,"abstract":"Local Binary Pattern (LBP) is a non-parametric descriptor that is used to study various local structures of an image. It is considered as simple and efficient texture operator for image analysis in challenging real-time situations. It has been applied successfully for various applications of computer vision and image processing, like pattern recognition, texture analysis, face detection, image retrieval etc. This paper covers different LBP variants in spatial domain, which were created in order to improve its robustness and efficiency.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"106 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":"115071446","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/ICSCCC.2018.8703309
Shalu, A. Kamboj
Skin cancer cases are continuously arising from the past few years. Broadly skin cancer is of three types: Basal Cell Carcinoma, Squamous Cell Carcinoma, and Melanoma. Among all its types, melanoma is the dangerous form of skin cancer whose treatment is possible only if it is detected in early stages. Early detection of melanoma is really challenging. Therefore, various systems were developed to automate the process of melanoma skin cancer diagnosis. Features used to characterize the disease play a very important role in the diagnosis. It is also very important to find the correct combination of features and the machine learning techniques for classification. Here, a system for the melanoma skin cancer detection is developed by using a MED-NODE dataset of digital images. Raw images from the dataset contain various artifacts so firstly preprocessing is applied to remove these artifacts. Then to extract the region of interest Active Contour segmentation method is used. Various color features were extracted from the segmented part and the system performance is checked by using three classifiers (Naïve Bayes, Decision Tree, and KNN). The system achieves an accuracy of 82.35% on Decision Tree which is greater than other classifiers.
{"title":"A Color-Based Approach for Melanoma Skin Cancer Detection","authors":"Shalu, A. Kamboj","doi":"10.1109/ICSCCC.2018.8703309","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703309","url":null,"abstract":"Skin cancer cases are continuously arising from the past few years. Broadly skin cancer is of three types: Basal Cell Carcinoma, Squamous Cell Carcinoma, and Melanoma. Among all its types, melanoma is the dangerous form of skin cancer whose treatment is possible only if it is detected in early stages. Early detection of melanoma is really challenging. Therefore, various systems were developed to automate the process of melanoma skin cancer diagnosis. Features used to characterize the disease play a very important role in the diagnosis. It is also very important to find the correct combination of features and the machine learning techniques for classification. Here, a system for the melanoma skin cancer detection is developed by using a MED-NODE dataset of digital images. Raw images from the dataset contain various artifacts so firstly preprocessing is applied to remove these artifacts. Then to extract the region of interest Active Contour segmentation method is used. Various color features were extracted from the segmented part and the system performance is checked by using three classifiers (Naïve Bayes, Decision Tree, and KNN). The system achieves an accuracy of 82.35% on Decision Tree which is greater than other classifiers.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","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":"121179181","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}
In the Multimedia era, removal of the Noises from an image becomes a key challenge in the field of Digital Image Processing (DIP) and Computer Vision. Noise may be mixed with an image during capturing time, transmission time or due to dust particle on the screen of capturing device. Therefore, removal of these unwanted signals from the image is urgently required for the better analysis of the image and the de-noised image is more meaningful for Object detection, Edge detection and many more. There are various types of image noise, however, Gaussian Noise and Impulse Noise are commonly found in the image. This work focuses on the outliers and Mean Filter to improve the performance for Gaussian noise reduction from the image. In experimental assessments, artificial noise has been mixed using MATLAB to MSRA (10k images) dataset, this dataset is used to evaluate our proposed technique. The experiment results show that the proposed approach improves the performance in noise reduction over other filter approaches.
{"title":"An efficient Gaussian Noise Reduction Technique For Noisy Images using optimized filter approach","authors":"Sandeep Chand Kumain, Maheep Singh, Navjot Singh, Krishan Kumar","doi":"10.1109/ICSCCC.2018.8703305","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703305","url":null,"abstract":"In the Multimedia era, removal of the Noises from an image becomes a key challenge in the field of Digital Image Processing (DIP) and Computer Vision. Noise may be mixed with an image during capturing time, transmission time or due to dust particle on the screen of capturing device. Therefore, removal of these unwanted signals from the image is urgently required for the better analysis of the image and the de-noised image is more meaningful for Object detection, Edge detection and many more. There are various types of image noise, however, Gaussian Noise and Impulse Noise are commonly found in the image. This work focuses on the outliers and Mean Filter to improve the performance for Gaussian noise reduction from the image. In experimental assessments, artificial noise has been mixed using MATLAB to MSRA (10k images) dataset, this dataset is used to evaluate our proposed technique. The experiment results show that the proposed approach improves the performance in noise reduction over other filter approaches.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"8 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":"117331224","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/ICSCCC.2018.8703366
K. Ghanshala, Rahul Chauhan, R. Joshi
Agriculture is one of the area which required urgent attention and advancement for high yield and efficient utilization of resources. In this paper an approach of smart crop monitoring is presented through Internet of things (IOT). A 4 level framework is proposed namely sensing devices, sensor data level, base station level, edge computing and cloud data level for smart crop monitoring. Method proposed here focuses on analysing the soil nutrients (eg. NPK), soil moisture, temperature and humidity through a sensor node designed using arduino. Sensor node also consists of a wireless Zigbee module, metos NPK sensor, motor and water sprinklers. LAN of sensor node is designed using Zigbee and LEACH routing protocol is used for hopping. Collected data at gateway is being uploaded to cloud using an ESP8266 Wi-Fi module. An experimental setup was made in the field. Various data collected, analyzed and necessary information was sent to farmers for appropriate action. The data collected at cloud is analysed using machine learning technique and available to the farmers through soil nutrient index to monitor their soil nutrient requirements and ensure better crop yield.
{"title":"A Novel Framework for Smart Crop Monitoring Using Internet of Things (IOT)","authors":"K. Ghanshala, Rahul Chauhan, R. Joshi","doi":"10.1109/ICSCCC.2018.8703366","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703366","url":null,"abstract":"Agriculture is one of the area which required urgent attention and advancement for high yield and efficient utilization of resources. In this paper an approach of smart crop monitoring is presented through Internet of things (IOT). A 4 level framework is proposed namely sensing devices, sensor data level, base station level, edge computing and cloud data level for smart crop monitoring. Method proposed here focuses on analysing the soil nutrients (eg. NPK), soil moisture, temperature and humidity through a sensor node designed using arduino. Sensor node also consists of a wireless Zigbee module, metos NPK sensor, motor and water sprinklers. LAN of sensor node is designed using Zigbee and LEACH routing protocol is used for hopping. Collected data at gateway is being uploaded to cloud using an ESP8266 Wi-Fi module. An experimental setup was made in the field. Various data collected, analyzed and necessary information was sent to farmers for appropriate action. The data collected at cloud is analysed using machine learning technique and available to the farmers through soil nutrient index to monitor their soil nutrient requirements and ensure better crop yield.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"2 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":"125272702","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/ICSCCC.2018.8703324
Sourabh Yadav, K. P. Sharma
In the present era of the world, Stock market has become the place of high risks, but even then it is attracting the mass because of its high return value. Stock market tells about the economy of any country. Today, Stock market has become one of the biggest investment place for general public. In this manuscript we put forward the various forecasting approaches for predicting the BSE SENSEX using various forecasting models like ARIMA, BoxCox, Exponential Smoothing, Mean Forecasting, Naive, Seasonal Naive, Neural Network, and then comparing their mean error for deducing the best suitable model. The analysis is done on the Bombay Stock Exchange(BSE) SENSEX. Results of this analysis shows that, the Exponential smoothing and Neural network gives the best results if we compare the mean error of the both models with the other models.
{"title":"Statistical Analysis and Forecasting Models for Stock Market","authors":"Sourabh Yadav, K. P. Sharma","doi":"10.1109/ICSCCC.2018.8703324","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703324","url":null,"abstract":"In the present era of the world, Stock market has become the place of high risks, but even then it is attracting the mass because of its high return value. Stock market tells about the economy of any country. Today, Stock market has become one of the biggest investment place for general public. In this manuscript we put forward the various forecasting approaches for predicting the BSE SENSEX using various forecasting models like ARIMA, BoxCox, Exponential Smoothing, Mean Forecasting, Naive, Seasonal Naive, Neural Network, and then comparing their mean error for deducing the best suitable model. The analysis is done on the Bombay Stock Exchange(BSE) SENSEX. Results of this analysis shows that, the Exponential smoothing and Neural network gives the best results if we compare the mean error of the both models with the other models.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"17 2 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":"125785621","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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