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.2018.8692142
Priyanka Natrajan, Smruthi Rajmohan, S. Sundaram, S. Natarajan, R. Hebbar
Hyperspectral images (HSIs) are satellite images that provide spectral and spatial detail of a given region. This makes them uniquely suitable to classify objects in the scene. Classification of Hyperspectral images can be efficiently performed using the Convolutional Neural Network (CNN) in Machine Learning. In this research, a framework is proposed that leverages Transfer Learning and CNN to classify crop distributions of Horticulture Plantations. The Hyperspectral dataset consists of images and known labels, also known as groundtruth. However, some of the HSIs are unlabelled due to the lack of groundtruth available for the same. Hence, the proposed method adopts the Transfer Learning technique to overcome this. The model was trained on a publicly available and labelled hyperspectral dataset. This was then tested on the field samples of Chikkaballapur district of Karnataka, India which was provided by the Indian Space Research Organisation (ISRO). The CNN built leverages both the spectral and spatial correlations of the HSIs. Due to the amount of detail in HSIs, they are fed in as patches into the convolutional layers of the network. The diverse information provided by these images is exploited by deploying a three-dimensional kernel. This joint representation of both spectral and spatial information provides higher discriminating power, thus allowing a more accurate classification of the crop distributions in the field. The experimental results of this method prove that feeding images as patches trains the CNN better and applying Transfer Learning has a more generic and wider scope.
{"title":"A Transfer Learning based CNN approach for Classification of Horticulture plantations using Hyperspectral Images","authors":"Priyanka Natrajan, Smruthi Rajmohan, S. Sundaram, S. Natarajan, R. Hebbar","doi":"10.1109/IADCC.2018.8692142","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692142","url":null,"abstract":"Hyperspectral images (HSIs) are satellite images that provide spectral and spatial detail of a given region. This makes them uniquely suitable to classify objects in the scene. Classification of Hyperspectral images can be efficiently performed using the Convolutional Neural Network (CNN) in Machine Learning. In this research, a framework is proposed that leverages Transfer Learning and CNN to classify crop distributions of Horticulture Plantations. The Hyperspectral dataset consists of images and known labels, also known as groundtruth. However, some of the HSIs are unlabelled due to the lack of groundtruth available for the same. Hence, the proposed method adopts the Transfer Learning technique to overcome this. The model was trained on a publicly available and labelled hyperspectral dataset. This was then tested on the field samples of Chikkaballapur district of Karnataka, India which was provided by the Indian Space Research Organisation (ISRO). The CNN built leverages both the spectral and spatial correlations of the HSIs. Due to the amount of detail in HSIs, they are fed in as patches into the convolutional layers of the network. The diverse information provided by these images is exploited by deploying a three-dimensional kernel. This joint representation of both spectral and spatial information provides higher discriminating power, thus allowing a more accurate classification of the crop distributions in the field. The experimental results of this method prove that feeding images as patches trains the CNN better and applying Transfer Learning has a more generic and wider scope.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","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":"130543907","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.2018.8692099
Sai Sreekar Siddula, P. Jain, M. D. Upadhayay
Dams provide us with a wide range of social, economic, environmental benefits by helping us in controlling the flow of water, generating hydroelectric power, flood control, waste management, navigational purposes and act as habitats for aquatic life. India has progressed a lot in the construction of dams and water reservoirs after Independence and now we are among the best dam builders in the world. We have around 4300 dams in India and many more are already under the process of construction. But even today most of these dams use the conventional methods of dam management for controlling the dam gates and dam maintenance. In the current fast paced modern world where we are trying to automate all the processes around us, it’s high time that we revamp the management of our dams using Internet of Things. In this paper we have proposed and implemented a novel idea of automating the process of dam management from collecting the data of water level to control the dam gates. This idea will help us to streamline the control of dams throughout the country and reduce the manpower for dam maintenance.
{"title":"Real Time Monitoring and Controlling of Water Level in Dams using IoT","authors":"Sai Sreekar Siddula, P. Jain, M. D. Upadhayay","doi":"10.1109/IADCC.2018.8692099","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692099","url":null,"abstract":"Dams provide us with a wide range of social, economic, environmental benefits by helping us in controlling the flow of water, generating hydroelectric power, flood control, waste management, navigational purposes and act as habitats for aquatic life. India has progressed a lot in the construction of dams and water reservoirs after Independence and now we are among the best dam builders in the world. We have around 4300 dams in India and many more are already under the process of construction. But even today most of these dams use the conventional methods of dam management for controlling the dam gates and dam maintenance. In the current fast paced modern world where we are trying to automate all the processes around us, it’s high time that we revamp the management of our dams using Internet of Things. In this paper we have proposed and implemented a novel idea of automating the process of dam management from collecting the data of water level to control the dam gates. This idea will help us to streamline the control of dams throughout the country and reduce the manpower for dam maintenance.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"31 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":"128222042","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.2018.8692087
P. Das, C. Giri
Elliptic curve cryptography (ECC) is an emerging and efficient cryptography technique which can be applied in various fields of application such as sensor network, network security, authentication, signature verification and in the different applications of the internet of things (IOT). ECC is lightweight, efficient and more secure compare to any other public key cryptography. Different methods have been proposed in the literature to convert input message to elliptic curve point but all of them lack in security, scalability and computationally inefficient for large input size. So, a scalable and computationally efficient algorithm is highly required. In this paper, we propose two different algorithms for input message to elliptic curve point conversion which will reduce communication cost and computational cost of encryption and decryption. The experimental result also shows that the proposed algorithms give better performance and best suitable for large size input text compared to any other existing algorithms.
{"title":"An Efficient Method for text Encryption using Elliptic Curve Cryptography","authors":"P. Das, C. Giri","doi":"10.1109/IADCC.2018.8692087","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692087","url":null,"abstract":"Elliptic curve cryptography (ECC) is an emerging and efficient cryptography technique which can be applied in various fields of application such as sensor network, network security, authentication, signature verification and in the different applications of the internet of things (IOT). ECC is lightweight, efficient and more secure compare to any other public key cryptography. Different methods have been proposed in the literature to convert input message to elliptic curve point but all of them lack in security, scalability and computationally inefficient for large input size. So, a scalable and computationally efficient algorithm is highly required. In this paper, we propose two different algorithms for input message to elliptic curve point conversion which will reduce communication cost and computational cost of encryption and decryption. The experimental result also shows that the proposed algorithms give better performance and best suitable for large size input text compared to any other existing algorithms.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"32 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":"128114015","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.2018.8692109
Riti Kushwaha, N. Nain, Gaurav Singal
Person authentication using footprint is still an abandoned field even though it has physiological and behavioral both types of available features due to unavailibilty of dataset. To examine the credibility of footprint we have collected the footprint dataset. This dataset collection is done in 2 phases. 1) We have collected the 2 footprint samples of each foot from 110 persons and 2) We have collected the 5 footprint sample of each foot from 80 people. The paper scanner is used for the data collection and whole footprint is captured. The collected samples are taken at different orientations and position, sometimes scanner is not aligned and creates noise.To overcome these problem a footprint image requires extensive preprocessing. To make any image invariant to translation and rotation, we use Hu’s 7 moment invariant features. It can efficiently check that an input image belongs to a particular person or not even after translation, scaling and rotation. The probability of translation and scaling is very less in footprint, but slight rotation in foot image is noticeable, which could result in different geometry features for same person. This technique is not suitable for the authentication but it can surely reduce the sample space by rejecting the samples. If the difference of 3rd order moment invariant value of two samples is more then the decided threshold, then samples surely does not belong to the same person. This reduced sample size could be used further in authentication. It reduces the time complexity and computation cost. We tested it on 1320 images with the FMR of 4.52% and FNMR of 5.18%. It leads us to the conclusion that 3rd order of moment is enough to make any image rotation invariant.
{"title":"MIRA : Moment Invariability Analysis of Footprint Features","authors":"Riti Kushwaha, N. Nain, Gaurav Singal","doi":"10.1109/IADCC.2018.8692109","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692109","url":null,"abstract":"Person authentication using footprint is still an abandoned field even though it has physiological and behavioral both types of available features due to unavailibilty of dataset. To examine the credibility of footprint we have collected the footprint dataset. This dataset collection is done in 2 phases. 1) We have collected the 2 footprint samples of each foot from 110 persons and 2) We have collected the 5 footprint sample of each foot from 80 people. The paper scanner is used for the data collection and whole footprint is captured. The collected samples are taken at different orientations and position, sometimes scanner is not aligned and creates noise.To overcome these problem a footprint image requires extensive preprocessing. To make any image invariant to translation and rotation, we use Hu’s 7 moment invariant features. It can efficiently check that an input image belongs to a particular person or not even after translation, scaling and rotation. The probability of translation and scaling is very less in footprint, but slight rotation in foot image is noticeable, which could result in different geometry features for same person. This technique is not suitable for the authentication but it can surely reduce the sample space by rejecting the samples. If the difference of 3rd order moment invariant value of two samples is more then the decided threshold, then samples surely does not belong to the same person. This reduced sample size could be used further in authentication. It reduces the time complexity and computation cost. We tested it on 1320 images with the FMR of 4.52% and FNMR of 5.18%. It leads us to the conclusion that 3rd order of moment is enough to make any image rotation invariant.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"76 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":"127460143","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.2018.8692114
Shefali Arora, M. Bhatia
Biometric systems are playing an important role in identifying a person, thus contributing to global security. There are many possible biometrics, for example height, DNA, handwriting etc., but computer vision based biometrics have found an important place in the domain of human identification. Computer vision based biometrics include identification of face, fingerprints, iris etc. and using their abilities to create efficient authentication systems. In this paper, we work on a dataset [1] of iris images and make use of deep learning to identify and verify the iris of a person. Hyperparameter tuning for deep networks and optimization techniques have been taken into account in this system. The proposed system is trained using a combination of Convolutional Neural Networks and Softmax classifier to extract features from localized regions of the input iris images. This is followed by classification into one out of 224 classes of the dataset. From the results, we conclude that the choice of hyperparameters and optimizers affects the efficiency of our proposed system. Our proposed approach outperforms existing approaches by attaining a high accuracy of 98 percent.
{"title":"A Computer Vision System for Iris Recognition Based on Deep Learning","authors":"Shefali Arora, M. Bhatia","doi":"10.1109/IADCC.2018.8692114","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692114","url":null,"abstract":"Biometric systems are playing an important role in identifying a person, thus contributing to global security. There are many possible biometrics, for example height, DNA, handwriting etc., but computer vision based biometrics have found an important place in the domain of human identification. Computer vision based biometrics include identification of face, fingerprints, iris etc. and using their abilities to create efficient authentication systems. In this paper, we work on a dataset [1] of iris images and make use of deep learning to identify and verify the iris of a person. Hyperparameter tuning for deep networks and optimization techniques have been taken into account in this system. The proposed system is trained using a combination of Convolutional Neural Networks and Softmax classifier to extract features from localized regions of the input iris images. This is followed by classification into one out of 224 classes of the dataset. From the results, we conclude that the choice of hyperparameters and optimizers affects the efficiency of our proposed system. Our proposed approach outperforms existing approaches by attaining a high accuracy of 98 percent.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","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":"131157236","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 : 2018-10-01DOI: 10.1109/CIMCA.2018.8739721
S. Shankar, R. Suresh, Viswanath Talasila, Vinay Sridhar
Rapid advancement in the development of Internet of Things (IoT) based smart wearable devices has motivated us to develop a device which can monitor the performance and analyze the shooting form of basketball players remotely. In this paper, we present the design of a system that can measure and analyze in real time, the free throw shooting action of a professional basketball player. A new heuristic tool has also been developed to analyse every phase of the shooting action to segment out an ideal shooting action of individual players. The developed tool is proven to be more efficient than the conventional k-map clustering approach.
{"title":"Performance measurement and analysis of shooting form of basketball players using a wearable IoT system","authors":"S. Shankar, R. Suresh, Viswanath Talasila, Vinay Sridhar","doi":"10.1109/CIMCA.2018.8739721","DOIUrl":"https://doi.org/10.1109/CIMCA.2018.8739721","url":null,"abstract":"Rapid advancement in the development of Internet of Things (IoT) based smart wearable devices has motivated us to develop a device which can monitor the performance and analyze the shooting form of basketball players remotely. In this paper, we present the design of a system that can measure and analyze in real time, the free throw shooting action of a professional basketball player. A new heuristic tool has also been developed to analyse every phase of the shooting action to segment out an ideal shooting action of individual players. The developed tool is proven to be more efficient than the conventional k-map clustering approach.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116665115","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：