Pub Date : 2017-07-03DOI: 10.1504/IJAPR.2018.090520
R. Tripathi, S. C. Agrawal, A. S. Jalal
In this paper, we present a human fall detection method from visual surveillance. In first step, background subtraction is performed using Improved GMM to find the foreground objects. In second step, contour based human template matching is applied to categorize the human or non-human object. It helps to detect fall incident by providing sudden change in generated score after matching. Height-width ratio is computed in third step to decide whether the human shape is changed or not. In fourth step, distance between top and mid centre of rectangle covering human is computed, if it is less than a certain threshold, then human fall is confirmed. Finally, if inactive pose of human is continued till 100 consecutive frames, then an alarm is generated to alert the people at home to provide treatment on time. Experiments have been performed on 21 video sequences having different usual and unusual fall incidents. Experimental results show that proposed system works well efficiently and effectively in real-time for recognizing human fall.
{"title":"Human-fall detection from an indoor video surveillance","authors":"R. Tripathi, S. C. Agrawal, A. S. Jalal","doi":"10.1504/IJAPR.2018.090520","DOIUrl":"https://doi.org/10.1504/IJAPR.2018.090520","url":null,"abstract":"In this paper, we present a human fall detection method from visual surveillance. In first step, background subtraction is performed using Improved GMM to find the foreground objects. In second step, contour based human template matching is applied to categorize the human or non-human object. It helps to detect fall incident by providing sudden change in generated score after matching. Height-width ratio is computed in third step to decide whether the human shape is changed or not. In fourth step, distance between top and mid centre of rectangle covering human is computed, if it is less than a certain threshold, then human fall is confirmed. Finally, if inactive pose of human is continued till 100 consecutive frames, then an alarm is generated to alert the people at home to provide treatment on time. Experiments have been performed on 21 video sequences having different usual and unusual fall incidents. Experimental results show that proposed system works well efficiently and effectively in real-time for recognizing human fall.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"4 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87374221","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 : 2017-07-03DOI: 10.1109/ICCCNT.2017.8203916
Bhavin Kakani, Divyang Gandhi, S. Jani
Significant research and development of algorithms in intelligent transportation has grabbed more attention in recent years. An automated, fast, accurate and robust vehicle plate recognition system has become need for traffic control and law enforcement of traffic regulations; and the solution is ANPR. This paper is dedicated on an improved technique of OCR based license plate recognition using neural network trained dataset of object features. A blended algorithm for recognition of license plate is proposed and is compared with existing methods for improve accuracy. The whole system can be categorized under three major modules, namely License Plate Localization, Plate Character Segmentation, and Plate Character Recognition. The system is simulated on 300 national and international motor vehicle LP images and results obtained justifies the main requirement.
{"title":"Improved OCR based automatic vehicle number plate recognition using features trained neural network","authors":"Bhavin Kakani, Divyang Gandhi, S. Jani","doi":"10.1109/ICCCNT.2017.8203916","DOIUrl":"https://doi.org/10.1109/ICCCNT.2017.8203916","url":null,"abstract":"Significant research and development of algorithms in intelligent transportation has grabbed more attention in recent years. An automated, fast, accurate and robust vehicle plate recognition system has become need for traffic control and law enforcement of traffic regulations; and the solution is ANPR. This paper is dedicated on an improved technique of OCR based license plate recognition using neural network trained dataset of object features. A blended algorithm for recognition of license plate is proposed and is compared with existing methods for improve accuracy. The whole system can be categorized under three major modules, namely License Plate Localization, Plate Character Segmentation, and Plate Character Recognition. The system is simulated on 300 national and international motor vehicle LP images and results obtained justifies the main requirement.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"71 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90479389","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 : 2017-07-03DOI: 10.1109/ICCCNT.2017.8203904
N. Gill, Ruhi Garg, Er. Amit Doegar
These days digital image forgery has turned out to be unsophisticated because of capable PCs, propelled image editing softwares and high resolution capturing gadgets. Checking the respectability of pictures and identifying hints of altering without requiring additional pre-embedded information of the picture or pre-installed watermarks are essential examine field. An endeavor is prepared to review the current improvements in the research area of advanced picture fraud detection and comprehensive reference index has been exhibited on passive methods for forgery identification. Passive techniques donot require pre-embedded information in the image. Several image forgery detection techniques are arranged first and after that their summed up organization is produced. Author will review the various image forgery detection techniques along with their results and also compare the various different techniques based on their accuracy.
{"title":"A review paper on digital image forgery detection techniques","authors":"N. Gill, Ruhi Garg, Er. Amit Doegar","doi":"10.1109/ICCCNT.2017.8203904","DOIUrl":"https://doi.org/10.1109/ICCCNT.2017.8203904","url":null,"abstract":"These days digital image forgery has turned out to be unsophisticated because of capable PCs, propelled image editing softwares and high resolution capturing gadgets. Checking the respectability of pictures and identifying hints of altering without requiring additional pre-embedded information of the picture or pre-installed watermarks are essential examine field. An endeavor is prepared to review the current improvements in the research area of advanced picture fraud detection and comprehensive reference index has been exhibited on passive methods for forgery identification. Passive techniques donot require pre-embedded information in the image. Several image forgery detection techniques are arranged first and after that their summed up organization is produced. Author will review the various image forgery detection techniques along with their results and also compare the various different techniques based on their accuracy.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"35 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2017-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89235239","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 : 2017-07-01DOI: 10.1109/ICCCNT.2017.8204015
G. Swetha, Rajeshreddy Datla
Temporal social network exhibits evolution of a network with different scales of dynamics over a period of time. Detection of community along with its strength is a challenging task in these time varying networks. The impact of the incremental changes in the network over time on community strength must be analyzed to capture its behavior. In this paper, we extended the notion of community detection and its strength of the static network to the temporal social network. A methodology is proposed to study the affect of the incremental links on strength of the detected communities within the network. It also includes a method to identify a set of instances during which information flow is effective by observing the strength pattern of the detected communities. It also demonstrates the utilization of the precise temporal information associated with each interaction. Proposed approach is applied on an email network and identified the instances over which the impact of the incremental links is observed.
{"title":"Identification of influential instances in temporal networks","authors":"G. Swetha, Rajeshreddy Datla","doi":"10.1109/ICCCNT.2017.8204015","DOIUrl":"https://doi.org/10.1109/ICCCNT.2017.8204015","url":null,"abstract":"Temporal social network exhibits evolution of a network with different scales of dynamics over a period of time. Detection of community along with its strength is a challenging task in these time varying networks. The impact of the incremental changes in the network over time on community strength must be analyzed to capture its behavior. In this paper, we extended the notion of community detection and its strength of the static network to the temporal social network. A methodology is proposed to study the affect of the incremental links on strength of the detected communities within the network. It also includes a method to identify a set of instances during which information flow is effective by observing the strength pattern of the detected communities. It also demonstrates the utilization of the precise temporal information associated with each interaction. Proposed approach is applied on an email network and identified the instances over which the impact of the incremental links is observed.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"10 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75344962","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 : 2017-07-01DOI: 10.1109/ICCCNT.2017.8203913
M. S. Sravan, Swetha Moolam, Sruthi Sreepathi, Priyanka Kokil
In this paper, implementation of a remote motion controller with visual feedback on an android device with Wi-Fi communication protocol is proposed. Control algorithm incorporates the real-time data available from the camera, which is used as feedback, which in turn, greatly improves efficiency. This application can be implemented on any Android device (e.g. smart phone) with a built-in camera, making it extremely compact, economical and user-friendly when compared to existing similar controllers. It also gives the flexibility to control the mobile robot via the internet.
{"title":"Implementation of remote motion controller with visual feedback","authors":"M. S. Sravan, Swetha Moolam, Sruthi Sreepathi, Priyanka Kokil","doi":"10.1109/ICCCNT.2017.8203913","DOIUrl":"https://doi.org/10.1109/ICCCNT.2017.8203913","url":null,"abstract":"In this paper, implementation of a remote motion controller with visual feedback on an android device with Wi-Fi communication protocol is proposed. Control algorithm incorporates the real-time data available from the camera, which is used as feedback, which in turn, greatly improves efficiency. This application can be implemented on any Android device (e.g. smart phone) with a built-in camera, making it extremely compact, economical and user-friendly when compared to existing similar controllers. It also gives the flexibility to control the mobile robot via the internet.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"16 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78608755","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 : 2017-07-01DOI: 10.1109/ICCCNT.2017.8203996
P. Juneja, Uma Ojha
In today's world, Social Networking website like Twitter, Facebook, Instagram, etc. plays a very significant role. Twitter is a social networking website which provides loads of data that can be used for various purposes such as Sentiment Analysis for predictions, marketing, elections, etc. The objective of this research is to classify twitter data into sentiments (positive or negative) by using different supervised machine learning classifiers to predict the Delhi Corporation Elections results and to identify the most accurate machine learning classifier. The research highlights the performance of different classifiers on the twitter dataset related to political parties. Experiments show that Multinomial Naïve Bayes classifier is the most accurate sentiment predictors with 78%.
{"title":"Casting online votes: To predict offline results using sentiment analysis by machine learning classifiers","authors":"P. Juneja, Uma Ojha","doi":"10.1109/ICCCNT.2017.8203996","DOIUrl":"https://doi.org/10.1109/ICCCNT.2017.8203996","url":null,"abstract":"In today's world, Social Networking website like Twitter, Facebook, Instagram, etc. plays a very significant role. Twitter is a social networking website which provides loads of data that can be used for various purposes such as Sentiment Analysis for predictions, marketing, elections, etc. The objective of this research is to classify twitter data into sentiments (positive or negative) by using different supervised machine learning classifiers to predict the Delhi Corporation Elections results and to identify the most accurate machine learning classifier. The research highlights the performance of different classifiers on the twitter dataset related to political parties. Experiments show that Multinomial Naïve Bayes classifier is the most accurate sentiment predictors with 78%.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"43 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75016243","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 : 2017-07-01DOI: 10.1109/ICCCNT.2017.8204022
G. Bansal, Abhay Chaturvedi, Manish Kumar
A high gain and high linear CMOS down-conversion mixer with improved noise figure is presented for Ultra-wideband (UWB) technology. The proposed mixer is designed and simulated using 0.18μm CMOS technology for 3.432 GHz RF input frequency, 3.696 GHz LO frequency and 264 MHz output IF frequency using Key sight Advanced Design System (ADS) tool. The double balanced bulk driven Gilbert mixer architecture is used as a core in proposed mixer. LC Differential matching is used both at the LO switching stages and RF trans conductance stage with R-L-C load as output stage to enhance the conversion gain of the mixer. The bulk driven technique is used for the low power consumption. The proposed mixer provides IIP3 of 20dBm, maximum conversion gain of 10.060 dB, P1 dB of −20.98 dBm and double side-band noise figure (DSB NF) of 0.58 dB. The mixer operates at DC supply of 3.1V with power consumption of 2 mW.
{"title":"High linearity and high gain bulk driven down conversion mixer for UWB system","authors":"G. Bansal, Abhay Chaturvedi, Manish Kumar","doi":"10.1109/ICCCNT.2017.8204022","DOIUrl":"https://doi.org/10.1109/ICCCNT.2017.8204022","url":null,"abstract":"A high gain and high linear CMOS down-conversion mixer with improved noise figure is presented for Ultra-wideband (UWB) technology. The proposed mixer is designed and simulated using 0.18μm CMOS technology for 3.432 GHz RF input frequency, 3.696 GHz LO frequency and 264 MHz output IF frequency using Key sight Advanced Design System (ADS) tool. The double balanced bulk driven Gilbert mixer architecture is used as a core in proposed mixer. LC Differential matching is used both at the LO switching stages and RF trans conductance stage with R-L-C load as output stage to enhance the conversion gain of the mixer. The bulk driven technique is used for the low power consumption. The proposed mixer provides IIP3 of 20dBm, maximum conversion gain of 10.060 dB, P1 dB of −20.98 dBm and double side-band noise figure (DSB NF) of 0.58 dB. The mixer operates at DC supply of 3.1V with power consumption of 2 mW.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"18 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77856356","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 : 2017-07-01DOI: 10.1109/ICCCNT.2017.8204051
S. Kumari, Pratima Walde, Asif Iqbal, A. Tyagi
This work presents a technique for the optimal Phasor Measurement Units (PMUs) placement for complete power network observability with number of PMUs as minimum as possible. Due to the high installation cost of PMUs it is necessary to make the system fully observable with minimum PMUs. A binary particle swarm optimization method (BPSO) is implemented on IEEE standard system and Puducherry 17 bus system. The BPSO method of Optimum PMU Placement can therefore be applied to any power system to make the system fully observable with different aspects of the power system. The obtained results are compared with other techniques and it is found that Adaptive GA, GILP, SA, TS, BSA and the proposed BPSO method was found better.
{"title":"Optimal phasor measuring unit placement by binary particle swarm optimization","authors":"S. Kumari, Pratima Walde, Asif Iqbal, A. Tyagi","doi":"10.1109/ICCCNT.2017.8204051","DOIUrl":"https://doi.org/10.1109/ICCCNT.2017.8204051","url":null,"abstract":"This work presents a technique for the optimal Phasor Measurement Units (PMUs) placement for complete power network observability with number of PMUs as minimum as possible. Due to the high installation cost of PMUs it is necessary to make the system fully observable with minimum PMUs. A binary particle swarm optimization method (BPSO) is implemented on IEEE standard system and Puducherry 17 bus system. The BPSO method of Optimum PMU Placement can therefore be applied to any power system to make the system fully observable with different aspects of the power system. The obtained results are compared with other techniques and it is found that Adaptive GA, GILP, SA, TS, BSA and the proposed BPSO method was found better.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"34 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80055467","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 : 2017-07-01DOI: 10.1109/ICCCNT.2017.8203947
T. Singh, Varun Jain, G. Babu
Data Center architectures are continually evolving to address the growing need to handle thousands of servers, Exabyte of storage, Tbps of traffic and multi-tenant environment. It has to be more flexible & scalable to cater to the need of Cloud services to provide Scalable compute, storage & network, Efficient resource utilization, Cost effectiveness, Reliable service and Security. The resources are interconnected through routers and switches that cater the multi-tenant virtual networking need within and across Data Center for which the WAN connectivity should be transparent in the Layer 2. At present the DC Interconnect (DCI) are through IP/MPLS Virtual Private Network (VPN) over which the L2 transparency is achieved majorly by the technologies like VPLS & EoMPLS. This paper focuses majorly on the latest evolving technology to extend VXLAN across DCs with EVPN control plane and concludes with its benefits like faster convergence, efficient, flexible & scalable DCI realized by the implementations of MP-BGP which optimizes unicast flooding, MAC Address purging & apply fine grain policy.
{"title":"VXLAN and EVPN for data center network transformation","authors":"T. Singh, Varun Jain, G. Babu","doi":"10.1109/ICCCNT.2017.8203947","DOIUrl":"https://doi.org/10.1109/ICCCNT.2017.8203947","url":null,"abstract":"Data Center architectures are continually evolving to address the growing need to handle thousands of servers, Exabyte of storage, Tbps of traffic and multi-tenant environment. It has to be more flexible & scalable to cater to the need of Cloud services to provide Scalable compute, storage & network, Efficient resource utilization, Cost effectiveness, Reliable service and Security. The resources are interconnected through routers and switches that cater the multi-tenant virtual networking need within and across Data Center for which the WAN connectivity should be transparent in the Layer 2. At present the DC Interconnect (DCI) are through IP/MPLS Virtual Private Network (VPN) over which the L2 transparency is achieved majorly by the technologies like VPLS & EoMPLS. This paper focuses majorly on the latest evolving technology to extend VXLAN across DCs with EVPN control plane and concludes with its benefits like faster convergence, efficient, flexible & scalable DCI realized by the implementations of MP-BGP which optimizes unicast flooding, MAC Address purging & apply fine grain policy.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"73 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79327720","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 : 2017-07-01DOI: 10.1109/ICCCNT.2017.8204042
Balu M. Menon, P. Aswathi, S. Deepu, R. R. Bhavani
Construction rebars (steel concrete reinforcing bars) are used to provide structural strength and reinforcement for the concrete structure. This requires the bending and cutting of the rebar to proper size before they can be used for construction. A novel haptic based barbending simulator has been devised which enables the trainees to learn and improve the construction rebar bending skill in a safe and controlled way. With its limited assessment and reporting features, the computerised virtual training simulator proves to be effective in training. Adding the features like personalized skill tracking and predictive performance modeling holds even more potential in supporting the training program. Towards this goal, a user performance modeling needs to be done which includes an initial study on performance parameters, assessment criteria and data collection before remodeling the simulator. This paper presents a study on the performance parameters for the bar bending simulator and also evaluates its effectiveness in modeling expert and novice performances. During this study we also hypothesize the parameters that can distinguish an expert and novice performances which was validated with 2 classification techniques — Support vector machine and J48 Decision tree. While revealing the classification rules J48 algorithm provides 78.94% accuracy where as SVM provides 94.737% accuracy. The study also shows that the 2 performance parameters force applied over time and bend angle accuracy are effective to distinguish expert and novice level of expertize for the construction rebar bending skill.
{"title":"Identification and evaluation of performance parameters for RE-BAR bending training simulator","authors":"Balu M. Menon, P. Aswathi, S. Deepu, R. R. Bhavani","doi":"10.1109/ICCCNT.2017.8204042","DOIUrl":"https://doi.org/10.1109/ICCCNT.2017.8204042","url":null,"abstract":"Construction rebars (steel concrete reinforcing bars) are used to provide structural strength and reinforcement for the concrete structure. This requires the bending and cutting of the rebar to proper size before they can be used for construction. A novel haptic based barbending simulator has been devised which enables the trainees to learn and improve the construction rebar bending skill in a safe and controlled way. With its limited assessment and reporting features, the computerised virtual training simulator proves to be effective in training. Adding the features like personalized skill tracking and predictive performance modeling holds even more potential in supporting the training program. Towards this goal, a user performance modeling needs to be done which includes an initial study on performance parameters, assessment criteria and data collection before remodeling the simulator. This paper presents a study on the performance parameters for the bar bending simulator and also evaluates its effectiveness in modeling expert and novice performances. During this study we also hypothesize the parameters that can distinguish an expert and novice performances which was validated with 2 classification techniques — Support vector machine and J48 Decision tree. While revealing the classification rules J48 algorithm provides 78.94% accuracy where as SVM provides 94.737% accuracy. The study also shows that the 2 performance parameters force applied over time and bend angle accuracy are effective to distinguish expert and novice level of expertize for the construction rebar bending skill.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"46 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84974043","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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