Pub Date : 2019-03-01DOI: 10.1109/ICACCS.2019.8728314
B. Sathish, Ganesan P, A. Ranganayakulu, D. S, S. Jagan Mohan Rao
In this manuscript the projected method overcome the drawback created by this current GPS, the alternate system of finding a human position. An alternating technique be term as IPS technique. Present GPS structure provide us the essential direct of people’s spot, although its most important problem is more difficult while the individual depart inside or if he enter consign which have an extremely deprived signal connectivity.
{"title":"Advanced Determination of object location using IPS","authors":"B. Sathish, Ganesan P, A. Ranganayakulu, D. S, S. Jagan Mohan Rao","doi":"10.1109/ICACCS.2019.8728314","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728314","url":null,"abstract":"In this manuscript the projected method overcome the drawback created by this current GPS, the alternate system of finding a human position. An alternating technique be term as IPS technique. Present GPS structure provide us the essential direct of people’s spot, although its most important problem is more difficult while the individual depart inside or if he enter consign which have an extremely deprived signal connectivity.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122916833","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}
This paper provides an idea about the design of the modified circular patch, leading to the biconcave lens structure. Two elements of these patches have been taken to provide a novel array of patch for the 5G communication. The minimum distance between both the arc of the biconcave patch has been taken same as that of the wavelength that is determined by the design frequency. Rotman lens equations have been taken into account for the accurate design of the biconcave patch. FR4-epoxy dielectric material has been used for substrate. HFSS (High Frequency Structure simulator) has been used for the simulation of the proposed structure. The S-Parameter, VSWR, Gain, Directivity, etc. are determined from the simulation results.
{"title":"A Novel 2-Element Array of Perturbed Circular Patch for 5G Application","authors":"Rabindra Kumar Mishra, Ribhu Abhusan Panda, Udit Narayan Mohapatro, D. Mishra","doi":"10.1109/ICACCS.2019.8728464","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728464","url":null,"abstract":"This paper provides an idea about the design of the modified circular patch, leading to the biconcave lens structure. Two elements of these patches have been taken to provide a novel array of patch for the 5G communication. The minimum distance between both the arc of the biconcave patch has been taken same as that of the wavelength that is determined by the design frequency. Rotman lens equations have been taken into account for the accurate design of the biconcave patch. FR4-epoxy dielectric material has been used for substrate. HFSS (High Frequency Structure simulator) has been used for the simulation of the proposed structure. The S-Parameter, VSWR, Gain, Directivity, etc. are determined from the simulation results.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128347197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/ICACCS.2019.8728386
N. Sharma, Charvi Jain
Deep learning plays a significant role in the advancement of computer vision by improving the speed and accuracy to the assigned tasks. It is opening opportunities for improvement and enhancement of processes and to initiate the human-driven tasks in an automated manner. On the basis of this growth, deep-learning algorithms are finding applications in the field CNN and RNN. The key advantage of Deep Learning algorithm is that manually extraction of features from the image is not required. The network extracts the features while training. The only input required is to provide the image to the network. The CNN’s and RNN’s have given state-of-the art results on numerous classification tasks. The Deep learning algorithm are designed for feature detection / extraction, classification and recognition of the object. The key advantage of a CNN is to remove or reduce the reliance on physics-based models, other processing methods by enabling complete learning directly from the input images of the object. The CNN and RNN together has given effective results in the area of face recognition, object recognition, scene understanding and facial expression recognition.
{"title":"Characterization of Facial Expression using Deep Neural Networks","authors":"N. Sharma, Charvi Jain","doi":"10.1109/ICACCS.2019.8728386","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728386","url":null,"abstract":"Deep learning plays a significant role in the advancement of computer vision by improving the speed and accuracy to the assigned tasks. It is opening opportunities for improvement and enhancement of processes and to initiate the human-driven tasks in an automated manner. On the basis of this growth, deep-learning algorithms are finding applications in the field CNN and RNN. The key advantage of Deep Learning algorithm is that manually extraction of features from the image is not required. The network extracts the features while training. The only input required is to provide the image to the network. The CNN’s and RNN’s have given state-of-the art results on numerous classification tasks. The Deep learning algorithm are designed for feature detection / extraction, classification and recognition of the object. The key advantage of a CNN is to remove or reduce the reliance on physics-based models, other processing methods by enabling complete learning directly from the input images of the object. The CNN and RNN together has given effective results in the area of face recognition, object recognition, scene understanding and facial expression recognition.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124576065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/ICACCS.2019.8728547
Sayali Sunil Tandel, A. Jamadar, Siddharth Dudugu
As there is fast growth in digital data collection techniques it has made way for large amount of data. Greater than 85% of present day data is comprised of unsaturated and unstructured data. Determining the definite patterns and trends to examine a textual data is biggest issue in text mining The various domains associated together in data mining are text mining, web mining, graph mining, and sequencing mining. The selection of proper and correct technique of text mining enhances the hustle and by lowering the period and struggle done to mine important information. Here, we talk about text data mining, various techniques of text data mining and also application of text data mining. Text data mining is used for obtaining stimulating and fascinating designs from the unsaturated texts which are derived from various sources. It changes words, phrases and sentences of an unstructured information into mathematical value linking with the saturated information in the database and analyses it with traditional data mining techniques. Information extraction, information retrieval, summarization, categorization and clustering are the different techniques of text mining.
{"title":"A Survey on Text Mining Techniques","authors":"Sayali Sunil Tandel, A. Jamadar, Siddharth Dudugu","doi":"10.1109/ICACCS.2019.8728547","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728547","url":null,"abstract":"As there is fast growth in digital data collection techniques it has made way for large amount of data. Greater than 85% of present day data is comprised of unsaturated and unstructured data. Determining the definite patterns and trends to examine a textual data is biggest issue in text mining The various domains associated together in data mining are text mining, web mining, graph mining, and sequencing mining. The selection of proper and correct technique of text mining enhances the hustle and by lowering the period and struggle done to mine important information. Here, we talk about text data mining, various techniques of text data mining and also application of text data mining. Text data mining is used for obtaining stimulating and fascinating designs from the unsaturated texts which are derived from various sources. It changes words, phrases and sentences of an unstructured information into mathematical value linking with the saturated information in the database and analyses it with traditional data mining techniques. Information extraction, information retrieval, summarization, categorization and clustering are the different techniques of text mining.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124692793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/ICACCS.2019.8728484
T. Mujawar, L. B. Bhajantri
Cloud computing provides shared environment for different resources and services that are available for users at anytime and from anywhere. Cloud computing has gained considerable attention of users and businesses. However, security concern is one of the major hurdles for acceptance of cloud computing. In order to guarantee security of data, it is necessary to grant access of data, only to authorized users. The traditional system applies different access policies and permission while granting access to any user. The analysis of user behavior is also important aspect, which can be integrated into access control model. In this paper, the trust computation model is presented that takes user behavior into consideration while providing access to the cloud data. The recommendation for the user is also one of the important components to assess user behavior. The proposed model evaluates trustworthiness of user on basis of reputation and recommendation. With the advent in machine learning techniques, applying learning based techniques in security domain has gained lots of popularity. In the proposed method, the machine learning technique (k-means clustering Algorithm) is incorporated in the trust computation process and the users are classified according their trust values.
{"title":"Trust Computation Framework based on User Behavior and Recommendation in Cloud Computing","authors":"T. Mujawar, L. B. Bhajantri","doi":"10.1109/ICACCS.2019.8728484","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728484","url":null,"abstract":"Cloud computing provides shared environment for different resources and services that are available for users at anytime and from anywhere. Cloud computing has gained considerable attention of users and businesses. However, security concern is one of the major hurdles for acceptance of cloud computing. In order to guarantee security of data, it is necessary to grant access of data, only to authorized users. The traditional system applies different access policies and permission while granting access to any user. The analysis of user behavior is also important aspect, which can be integrated into access control model. In this paper, the trust computation model is presented that takes user behavior into consideration while providing access to the cloud data. The recommendation for the user is also one of the important components to assess user behavior. The proposed model evaluates trustworthiness of user on basis of reputation and recommendation. With the advent in machine learning techniques, applying learning based techniques in security domain has gained lots of popularity. In the proposed method, the machine learning technique (k-means clustering Algorithm) is incorporated in the trust computation process and the users are classified according their trust values.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127049400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/ICACCS.2019.8728441
S. Pattar
Iris segmentation has been an especially interesting research area from the last decade due to the increased security conditions for the sophisticated personal identification ideas based on biometrics. The rich distinctive and stable textural information of the iris models make iris a biometric modality for identifying each person correctly and reliably. Most recent iris segmentation techniques show the high segmentation accuracies in cooperative environments. However, the iris image segmentation remains a difficult topic. In this frame work, we proposed an innovative model as an improvement of Chan-Vese technique by incorporating B spline approach to perform iris segmentation. Proposed scheme has added enhanced segmentation for non-ideal iris images in visible light. The GLCM (Gray Level Co-occurrence Matrix) and LBP (Local Binary Pattern) are employed for feature extraction. This scheme is able to perform all the associated treating in 1-dimension as the B-spline task is divisible and is built as the result of n-1) , 1- D, B-splines. This presents superior control compared to other methods. Experimental results displays that the proposed iris segmentation technique considerably minimizes the required time to segment the iris without affecting the segmentation precision. The main benefits of this algorithm are: First, it can deal with the accurate recognition of smoothobjects. Second one is, it can powerfully handle the noisy images. Therefore, thereal boundaries are conserved and correctly distinguished. Additionally the comparison outcomes with related iris segmentation methods show the superiority of the proposed work in terms of segmentation accuracy and recognition performance. The NICE. I iris image database is used to compute the performance of the proposed technique.
{"title":"A Novel Approach towards Iris Segmentation and Authentication using Local Chan-Vese Method","authors":"S. Pattar","doi":"10.1109/ICACCS.2019.8728441","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728441","url":null,"abstract":"Iris segmentation has been an especially interesting research area from the last decade due to the increased security conditions for the sophisticated personal identification ideas based on biometrics. The rich distinctive and stable textural information of the iris models make iris a biometric modality for identifying each person correctly and reliably. Most recent iris segmentation techniques show the high segmentation accuracies in cooperative environments. However, the iris image segmentation remains a difficult topic. In this frame work, we proposed an innovative model as an improvement of Chan-Vese technique by incorporating B spline approach to perform iris segmentation. Proposed scheme has added enhanced segmentation for non-ideal iris images in visible light. The GLCM (Gray Level Co-occurrence Matrix) and LBP (Local Binary Pattern) are employed for feature extraction. This scheme is able to perform all the associated treating in 1-dimension as the B-spline task is divisible and is built as the result of n-1) , 1- D, B-splines. This presents superior control compared to other methods. Experimental results displays that the proposed iris segmentation technique considerably minimizes the required time to segment the iris without affecting the segmentation precision. The main benefits of this algorithm are: First, it can deal with the accurate recognition of smoothobjects. Second one is, it can powerfully handle the noisy images. Therefore, thereal boundaries are conserved and correctly distinguished. Additionally the comparison outcomes with related iris segmentation methods show the superiority of the proposed work in terms of segmentation accuracy and recognition performance. The NICE. I iris image database is used to compute the performance of the proposed technique.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129215060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/ICACCS.2019.8728416
R. Rajalaxmi, E. Vidhya
Support Vector Machine (SVM) is typically a supervised learning algorithm that carefully examines input and identifies distinct patterns. The function of SVM classifier relies on adjusting or controlling of kernel and penalty parameter values. Nature Inspired Algorithm helps to solve the natural problems and has been attracting considerable attention due to their better performance. Salp Swarm Algorithm (SSA) is a Nature Inspired Algorithm (NIA) which is used to control the finest SVM parameters value. To improve exploration capability of SSA, mutation method is developed to find the optimal value for kernel parameter and penalty parameter. The preliminary result indicates Mutated SSA with SVM increases classification accuracy than simple SSA with SVM.
{"title":"A Mutated Salp Swarm Algorithm for Optimization of Support Vector Machine Parameters","authors":"R. Rajalaxmi, E. Vidhya","doi":"10.1109/ICACCS.2019.8728416","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728416","url":null,"abstract":"Support Vector Machine (SVM) is typically a supervised learning algorithm that carefully examines input and identifies distinct patterns. The function of SVM classifier relies on adjusting or controlling of kernel and penalty parameter values. Nature Inspired Algorithm helps to solve the natural problems and has been attracting considerable attention due to their better performance. Salp Swarm Algorithm (SSA) is a Nature Inspired Algorithm (NIA) which is used to control the finest SVM parameters value. To improve exploration capability of SSA, mutation method is developed to find the optimal value for kernel parameter and penalty parameter. The preliminary result indicates Mutated SSA with SVM increases classification accuracy than simple SSA with SVM.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130585739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/ICACCS.2019.8728374
T. Tamilselvan, Rekha Marimuthu
Security and health care became more complicated now-a-days. IOT, AI and remote monitoring system stared to growing with huge impact in medical field. Let us consider "OUR SHADOW BE OUR DOCTOR ". A wireless system for monitoring, Storing and processing of patient health data. ZigBee Body controlling (ZBC) begins with getting data from various device either inserted in human body or gadgets externally connected to human parts. Next level involves the processing of data. The processing takes place continuously from the time when device and login begins. Then three modules will be taken place. First module is default module; it keeps on running throughout the life span. In case any of the devices connected stopped working it starts warning alert the patient, emergency contact and data canter. This module is capable of initiating the ZigBee to send a SOS message. Second module involve the alerting the patient in starting stage of any malfunction. For example, Temperature of his living area goes beyond the temperature level prescribed by consulting doctor, Patient will be getting a mobile alert shows that "Temperature increasing, Certain degrees high reduce now". This saves the patient from danger. Third module is emergency module; this module gets executed during critical situation. Let us consider, it’s a sudden heart attack, patient is unable to take remedy. At that time ZigBee start sending message to trust person of patient, nearby hospital Consulting doctor and ambulance near to patient. Location can be tracked with the help of RFID, VPS and GPS. Ambulance drivers once accepted the notification via message; he will automatically get an online Google map showing the patient location and the nearby hospital location. Once ambulance stared moving, condition of the patient get processed along with old data about the patient stored in cloud. The processed data will be forwarded to the nearby hospital. Nearby hospital doctor or hospital get a temporary access to patient cloud data, doctor will be able to add his treatment report and also able to view the data which are assigned in private visibility by patient personal doctor.
{"title":"A Study On Zigbee Body Controlling System","authors":"T. Tamilselvan, Rekha Marimuthu","doi":"10.1109/ICACCS.2019.8728374","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728374","url":null,"abstract":"Security and health care became more complicated now-a-days. IOT, AI and remote monitoring system stared to growing with huge impact in medical field. Let us consider \"OUR SHADOW BE OUR DOCTOR \". A wireless system for monitoring, Storing and processing of patient health data. ZigBee Body controlling (ZBC) begins with getting data from various device either inserted in human body or gadgets externally connected to human parts. Next level involves the processing of data. The processing takes place continuously from the time when device and login begins. Then three modules will be taken place. First module is default module; it keeps on running throughout the life span. In case any of the devices connected stopped working it starts warning alert the patient, emergency contact and data canter. This module is capable of initiating the ZigBee to send a SOS message. Second module involve the alerting the patient in starting stage of any malfunction. For example, Temperature of his living area goes beyond the temperature level prescribed by consulting doctor, Patient will be getting a mobile alert shows that \"Temperature increasing, Certain degrees high reduce now\". This saves the patient from danger. Third module is emergency module; this module gets executed during critical situation. Let us consider, it’s a sudden heart attack, patient is unable to take remedy. At that time ZigBee start sending message to trust person of patient, nearby hospital Consulting doctor and ambulance near to patient. Location can be tracked with the help of RFID, VPS and GPS. Ambulance drivers once accepted the notification via message; he will automatically get an online Google map showing the patient location and the nearby hospital location. Once ambulance stared moving, condition of the patient get processed along with old data about the patient stored in cloud. The processed data will be forwarded to the nearby hospital. Nearby hospital doctor or hospital get a temporary access to patient cloud data, doctor will be able to add his treatment report and also able to view the data which are assigned in private visibility by patient personal doctor.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114355594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/ICACCS.2019.8728342
Md Mojibur Rahaman, K. Sandhu
The Magnetic material is the fundamental player in the structural parts of the core of machines. Selection of suitable material for Electrical machine is one of the primary design considerations which has a significant impact on the power density and efficiency of the machine. This paper presents a different kind of soft magnetic materials, which might be used for manufacturing of magnetic cores of energy-efficient electrical machines. The characteristics of these materials are compared and the comparison is made through the performance of machines for the magnetic circuit. With the help of MATLAB coding, the performance of the materials is compared as Induction Machine as well as Transformer operation separately.
{"title":"Energy Efficient magnetic materials for Electrical Machines","authors":"Md Mojibur Rahaman, K. Sandhu","doi":"10.1109/ICACCS.2019.8728342","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728342","url":null,"abstract":"The Magnetic material is the fundamental player in the structural parts of the core of machines. Selection of suitable material for Electrical machine is one of the primary design considerations which has a significant impact on the power density and efficiency of the machine. This paper presents a different kind of soft magnetic materials, which might be used for manufacturing of magnetic cores of energy-efficient electrical machines. The characteristics of these materials are compared and the comparison is made through the performance of machines for the magnetic circuit. With the help of MATLAB coding, the performance of the materials is compared as Induction Machine as well as Transformer operation separately.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"103 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113990118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/ICACCS.2019.8728424
Prachi Rane, Sudhir Dhage
Bitcoin (BTC) is an internet-based world’s top-ranking cryptocurrency. Among widespread cryptocurrencies available in the market, Bitcoin is most experienced by the people due to anonymity and transparency in the system. Daily trends in the Bitcoin market has gained popularity among the spectators, investors, consumers and many more. Bitcoin price data exhibit desirable properties where some classical time series prediction methods exploit the behavior, producing poor predictions and also lack a probabilistic interpretation. This paper conducts an in-depth study on evolution of Bitcoin and also a systematic review is done on various machine learning algorithms used for predicting the prices. Comparative analysis envisions to select optimal technique to forecast prices more precisely.
{"title":"Systematic Erudition of Bitcoin Price Prediction using Machine Learning Techniques","authors":"Prachi Rane, Sudhir Dhage","doi":"10.1109/ICACCS.2019.8728424","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728424","url":null,"abstract":"Bitcoin (BTC) is an internet-based world’s top-ranking cryptocurrency. Among widespread cryptocurrencies available in the market, Bitcoin is most experienced by the people due to anonymity and transparency in the system. Daily trends in the Bitcoin market has gained popularity among the spectators, investors, consumers and many more. Bitcoin price data exhibit desirable properties where some classical time series prediction methods exploit the behavior, producing poor predictions and also lack a probabilistic interpretation. This paper conducts an in-depth study on evolution of Bitcoin and also a systematic review is done on various machine learning algorithms used for predicting the prices. Comparative analysis envisions to select optimal technique to forecast prices more precisely.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134501190","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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