Pub Date : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058084
A. Kumari, Chandan Chhabra, Saurabh Singh
The ability of modern smartphones to provide us with real time location-based data is one of its most important features. Being able to predict a person’s future location based on the real time location data would be the next step in utilizing this functionality. Using this functionality, combined with machine learning one’s probable destination can be predicted with a reasonable accuracy. People don’t always use map-based navigation for the places they visit every day, like their work place or school and there may be significant traffic on the regular route taken, however, if our device knows where we’re headed, it can warn us beforehand and help us reroute. This functionality can also be used by cops to determine the future location of a criminal fleeing a crime scene.These features and functionalities can be implemented through various machine learning algorithms which are compared to determine the most accurate one. The proposed system can predict a user’s future location using the current location and time, learning from the user’s previously visited locations.
{"title":"Future Location Prediction of a Mobile User Using Historic Visiting Patterns","authors":"A. Kumari, Chandan Chhabra, Saurabh Singh","doi":"10.1109/Confluence47617.2020.9058084","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058084","url":null,"abstract":"The ability of modern smartphones to provide us with real time location-based data is one of its most important features. Being able to predict a person’s future location based on the real time location data would be the next step in utilizing this functionality. Using this functionality, combined with machine learning one’s probable destination can be predicted with a reasonable accuracy. People don’t always use map-based navigation for the places they visit every day, like their work place or school and there may be significant traffic on the regular route taken, however, if our device knows where we’re headed, it can warn us beforehand and help us reroute. This functionality can also be used by cops to determine the future location of a criminal fleeing a crime scene.These features and functionalities can be implemented through various machine learning algorithms which are compared to determine the most accurate one. The proposed system can predict a user’s future location using the current location and time, learning from the user’s previously visited locations.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115667009","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058278
Parita Jain, Anupam Sharma, P. Aggarwal
The innovative advancement of cell phones, the significance of the Internet in the present society and the blasting market of the mobile devices have upset the mobile software programming altogether known as the product quality of portable intuitive gadgets. The mobile software programming gets increasingly competent and complex, which enables designers to apply entrenched quality strategies and models, from the work area of software programming advancement to mobile software programming. But still, mobile software programming moreover still has its portable explicit qualities, comparing models and techniques that must be balanced for its use in the larger domain. In the following research, some of the key attributes that must be incorporated and taken care for developing a portable quality mobile applications are identified. The key attributes determined by investigating before developed quality models which allows enhancing knowledge that can be drifted in the near future.
{"title":"Key Attributes for a Quality Mobile Application","authors":"Parita Jain, Anupam Sharma, P. Aggarwal","doi":"10.1109/Confluence47617.2020.9058278","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058278","url":null,"abstract":"The innovative advancement of cell phones, the significance of the Internet in the present society and the blasting market of the mobile devices have upset the mobile software programming altogether known as the product quality of portable intuitive gadgets. The mobile software programming gets increasingly competent and complex, which enables designers to apply entrenched quality strategies and models, from the work area of software programming advancement to mobile software programming. But still, mobile software programming moreover still has its portable explicit qualities, comparing models and techniques that must be balanced for its use in the larger domain. In the following research, some of the key attributes that must be incorporated and taken care for developing a portable quality mobile applications are identified. The key attributes determined by investigating before developed quality models which allows enhancing knowledge that can be drifted in the near future.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115359020","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057889
Pushkar Sharma, P. Hans, Subhash Chand Gupta
Agriculture is one of the main factor that decides the growth of any country. In India itself around 65% of the population is based on agriculture. Due to various seasonal conditions the crops get infected by various kind of diseases. These diseases firstly affect the leaves of the plant and later infected the whole plant which in turn affect the quality and quantity of crop cultivated. As there are large number of plants in the farm, it becomes very difficult for the human eye to detect and classify the disease of each plant in the field. And it is very important to diagnose each plant because these diseases may spread. Hence in this paper we are introducing the artificial intelligence based automatic plant leaf disease detection and classification for quick and easy detection of disease and then classifying it and performing required remedies to cure that disease. This approach of ours goals towards increasing the productivity of crops in agriculture. In this approach we have follow several steps i.e. image collection, image preprocessing, segmentation and classification.
{"title":"Classification Of Plant Leaf Diseases Using Machine Learning And Image Preprocessing Techniques","authors":"Pushkar Sharma, P. Hans, Subhash Chand Gupta","doi":"10.1109/Confluence47617.2020.9057889","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057889","url":null,"abstract":"Agriculture is one of the main factor that decides the growth of any country. In India itself around 65% of the population is based on agriculture. Due to various seasonal conditions the crops get infected by various kind of diseases. These diseases firstly affect the leaves of the plant and later infected the whole plant which in turn affect the quality and quantity of crop cultivated. As there are large number of plants in the farm, it becomes very difficult for the human eye to detect and classify the disease of each plant in the field. And it is very important to diagnose each plant because these diseases may spread. Hence in this paper we are introducing the artificial intelligence based automatic plant leaf disease detection and classification for quick and easy detection of disease and then classifying it and performing required remedies to cure that disease. This approach of ours goals towards increasing the productivity of crops in agriculture. In this approach we have follow several steps i.e. image collection, image preprocessing, segmentation and classification.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123833385","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058306
Ananthnarayan Rajappa, A. Upadhyay, A. Sabitha, Abhay Bansal, B. White, L. Cottrell
PingER (Ping End-to-End Reporting) is a tool developed by SLAC National Accelerator Laboratory for the purpose of Internet End-to-end Performance Monitoring (IEPM). The aim of this research work is to develop a mobile application for Android mobile devices using Firebase for storing the data, obtained from pinging the beacons, and authenticating the users. The Measuring Agent (MA) pings the beacon list, the data obtained is formatted with the help of a Regular Expression library before being pushed to Firebase. In addition, the location of the MA, latitude and longitude, is also tracked with the help of Google’s Geolocation API. This data is also stored in the database.
{"title":"Implementation of PingER on Android Mobile Devices Using Firebase","authors":"Ananthnarayan Rajappa, A. Upadhyay, A. Sabitha, Abhay Bansal, B. White, L. Cottrell","doi":"10.1109/Confluence47617.2020.9058306","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058306","url":null,"abstract":"PingER (Ping End-to-End Reporting) is a tool developed by SLAC National Accelerator Laboratory for the purpose of Internet End-to-end Performance Monitoring (IEPM). The aim of this research work is to develop a mobile application for Android mobile devices using Firebase for storing the data, obtained from pinging the beacons, and authenticating the users. The Measuring Agent (MA) pings the beacon list, the data obtained is formatted with the help of a Regular Expression library before being pushed to Firebase. In addition, the location of the MA, latitude and longitude, is also tracked with the help of Google’s Geolocation API. This data is also stored in the database.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130905301","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}
K-means clustering is an algorithm, which has been used to cluster the given data into k sets that are mutual exclusive of each other. The K-means algorithm is designed to work with the Euclidean distance but there are many measures to identify the dissimilarity of the dataset. The aim of this paper is to discuss the performance of K-means clustering algorithm on city block, cosine, and correlation distance which are used to get the results and further their performance has been shown in terms of accuracy. For classification, authors have chosen the IRIS data set. K means have claimed 98% accuracy on city block and correlation distance.
k -means聚类是一种算法，它被用来将给定的数据聚类成k个相互排斥的集合。K-means算法是设计用来处理欧几里得距离的，但是有很多方法可以识别数据集的不相似性。本文的目的是讨论K-means聚类算法在城市街区、余弦和相关距离上的性能，并进一步在精度方面展示了它们的性能。对于分类，作者选择了IRIS数据集。K均值在城市街区和相关距离上的准确率达到98%。
{"title":"Comparative Study of K-Means Clustering Using Iris Data Set for Various Distances","authors":"Adrija Chakraborty, Neetu Faujdar, Akash Punhani, Shipra Saraswat","doi":"10.1109/Confluence47617.2020.9058328","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058328","url":null,"abstract":"K-means clustering is an algorithm, which has been used to cluster the given data into k sets that are mutual exclusive of each other. The K-means algorithm is designed to work with the Euclidean distance but there are many measures to identify the dissimilarity of the dataset. The aim of this paper is to discuss the performance of K-means clustering algorithm on city block, cosine, and correlation distance which are used to get the results and further their performance has been shown in terms of accuracy. For classification, authors have chosen the IRIS data set. K means have claimed 98% accuracy on city block and correlation distance.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125020366","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057955
Karman Singh, Renuka Nagpal, Rajni Sehgal
RMS Titanic was a British cruise ship said to be the largest cruise ever made in the history of world. It collided with an iceberg during its maiden journey across the pacific ocean from Southampton to New York City. With more than 2200 passengers on board, nearly half of them died after the unprecedented mishap. The infamous incident compels researchers to dig into the dataset. This research is aimed at achieving an exploratory data analysis and understand the effect or parameters key to the survival of a person had they been on the ship. The survival prediction has been done by applying various algorithms like Logistic Regression, K – nearest neighbours, Support vector machines, Decision Tree. Towards the end, accuracies of the algorithms based on features fed to them has been compared in a tabular form.
{"title":"Exploratory Data Analysis and Machine Learning on Titanic Disaster Dataset","authors":"Karman Singh, Renuka Nagpal, Rajni Sehgal","doi":"10.1109/Confluence47617.2020.9057955","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057955","url":null,"abstract":"RMS Titanic was a British cruise ship said to be the largest cruise ever made in the history of world. It collided with an iceberg during its maiden journey across the pacific ocean from Southampton to New York City. With more than 2200 passengers on board, nearly half of them died after the unprecedented mishap. The infamous incident compels researchers to dig into the dataset. This research is aimed at achieving an exploratory data analysis and understand the effect or parameters key to the survival of a person had they been on the ship. The survival prediction has been done by applying various algorithms like Logistic Regression, K – nearest neighbours, Support vector machines, Decision Tree. Towards the end, accuracies of the algorithms based on features fed to them has been compared in a tabular form.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125357982","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057938
Avneesh Vashistha, Pushpneel Verma
Resource management is one of the most challenging task in the cloud data center. These challenges have raised from the dynamic nature and high uncertainty in the cloud environment. Moreover, allocating resources over time may lead the sub-optimal execution environment due to significant up and drop in the workload that have some time dependent patterns. Therefore, it requires some time-sensitive techniques for optimising the resources utilization in cloud data center. In this paper, we discuss the workload prediction techniques that forecast the workload in the cloud environment and the value of predicted workload guides for optimising the resources. Furthermore, we present the workload taxonomy which is classified into (i) workload predictor and (ii) model fitting. In addition, we provide an extensive discussion on the workload predictors and further classified into temporal and non-temporal.
{"title":"A Literature Review and Taxonomy on Workload Prediction in Cloud Data Center","authors":"Avneesh Vashistha, Pushpneel Verma","doi":"10.1109/Confluence47617.2020.9057938","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057938","url":null,"abstract":"Resource management is one of the most challenging task in the cloud data center. These challenges have raised from the dynamic nature and high uncertainty in the cloud environment. Moreover, allocating resources over time may lead the sub-optimal execution environment due to significant up and drop in the workload that have some time dependent patterns. Therefore, it requires some time-sensitive techniques for optimising the resources utilization in cloud data center. In this paper, we discuss the workload prediction techniques that forecast the workload in the cloud environment and the value of predicted workload guides for optimising the resources. Furthermore, we present the workload taxonomy which is classified into (i) workload predictor and (ii) model fitting. In addition, we provide an extensive discussion on the workload predictors and further classified into temporal and non-temporal.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127694362","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058045
Sukanya, Gaurav Dubey
Road detection and segmentation is an important aspect in navigation system and is widely used to detect new roads and patterns in the region. These system has the main objective to help navigate the autonomous vehicle and robot on the ground. Road detection is very useful in finding valid road path where the vehicle can go for supportive vehicles preventing the collision with the obstacles, object detection on the road and other necessary information exchange. It has a variety of uses such as the disaster monitoring, traffic monitoring, crop monitoring, border surveillance, security and so on. There are several techniques used for detection and segmentation purpose of roads such as Artificial Neural Network, Support Vector Machine (SVM), Self-Organizing Map (SOM), Convolution Neural Network (CNN), and Deep learning techniques. In this paper, a new technique for road detection and segmentation is proposed which includes a combination algorithm of CNN and Random Field segmentation for road maps using aerial images. This road detection and segmentations give alternative solution for road classification and detection with a higher accuracy. In this system normally accuracy (ACC) have an average range of 97.7%.
{"title":"Segmentation and Detection of Road Region in Aerial Images using Hybrid CNN-Random Field Algorithm","authors":"Sukanya, Gaurav Dubey","doi":"10.1109/Confluence47617.2020.9058045","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058045","url":null,"abstract":"Road detection and segmentation is an important aspect in navigation system and is widely used to detect new roads and patterns in the region. These system has the main objective to help navigate the autonomous vehicle and robot on the ground. Road detection is very useful in finding valid road path where the vehicle can go for supportive vehicles preventing the collision with the obstacles, object detection on the road and other necessary information exchange. It has a variety of uses such as the disaster monitoring, traffic monitoring, crop monitoring, border surveillance, security and so on. There are several techniques used for detection and segmentation purpose of roads such as Artificial Neural Network, Support Vector Machine (SVM), Self-Organizing Map (SOM), Convolution Neural Network (CNN), and Deep learning techniques. In this paper, a new technique for road detection and segmentation is proposed which includes a combination algorithm of CNN and Random Field segmentation for road maps using aerial images. This road detection and segmentations give alternative solution for road classification and detection with a higher accuracy. In this system normally accuracy (ACC) have an average range of 97.7%.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114017974","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057829
Prabhat Singh, Abhay Bansal, Sunil Kumar
Roads are the main infrastructure of every city, state or country to grow but in accordance with the present scenario in road conditions, they are not up to the mark even to be said well. Similarly, major road causing incidents like vehicle accidents, traffic congestion etc are just because of the worse conditions of roads and their improper maintenance. So, it’s a great need of today time to bring a revolutionary change in the field of it. Further, this paper will help in putting forward a methodology in this noble cause. This paper focuses on regular monitoring of the roads and proper feedback system for monitoring from centers. Furthermore, various Infrastructures based and Infrastructure less approaches used for the detection of quality of Indian Roads. This is all being discussed in this paper along with the technologies used by us, their benefits and their way of working in this field.
{"title":"Performance Analysis of various Information Platforms for recognizing the quality of Indian Roads","authors":"Prabhat Singh, Abhay Bansal, Sunil Kumar","doi":"10.1109/Confluence47617.2020.9057829","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057829","url":null,"abstract":"Roads are the main infrastructure of every city, state or country to grow but in accordance with the present scenario in road conditions, they are not up to the mark even to be said well. Similarly, major road causing incidents like vehicle accidents, traffic congestion etc are just because of the worse conditions of roads and their improper maintenance. So, it’s a great need of today time to bring a revolutionary change in the field of it. Further, this paper will help in putting forward a methodology in this noble cause. This paper focuses on regular monitoring of the roads and proper feedback system for monitoring from centers. Furthermore, various Infrastructures based and Infrastructure less approaches used for the detection of quality of Indian Roads. This is all being discussed in this paper along with the technologies used by us, their benefits and their way of working in this field.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"282 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114456834","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058244
Amandeep Kaur
Regression testing is the backbone of the functional Software Testing. Unlike any other testing; regression validation evolves the whole suite of code which incorporates the existing code as well as new code or the change request. Validating all the possible scenarios is not effective as it increases the expenditure. This gains the outlook for the researchers to analyze a more efficient way for regression testing by electing a subset from the test suite to spot the defects. Ample research has crop up for this NP-Hard problem and folks are implementing the metaheuristic techniques and dominantly the nature-inspired ones. In this paper, to extract the optimal test cases we have utilized Harris Hawks Optimization (HHO) which is a nature-inspired technique and portrays chasing drive away style of Harris’ hawks termed as Surprise Pounce. In this tactic, assorted hawks combine together to pounce a prey through the offbeat directions to surprise the prey. This paper focuses on the Harris Hawks Optimization algorithm and its applications in the domain of software testing.
{"title":"An Approach To Extract Optimal Test Cases Using AI","authors":"Amandeep Kaur","doi":"10.1109/Confluence47617.2020.9058244","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058244","url":null,"abstract":"Regression testing is the backbone of the functional Software Testing. Unlike any other testing; regression validation evolves the whole suite of code which incorporates the existing code as well as new code or the change request. Validating all the possible scenarios is not effective as it increases the expenditure. This gains the outlook for the researchers to analyze a more efficient way for regression testing by electing a subset from the test suite to spot the defects. Ample research has crop up for this NP-Hard problem and folks are implementing the metaheuristic techniques and dominantly the nature-inspired ones. In this paper, to extract the optimal test cases we have utilized Harris Hawks Optimization (HHO) which is a nature-inspired technique and portrays chasing drive away style of Harris’ hawks termed as Surprise Pounce. In this tactic, assorted hawks combine together to pounce a prey through the offbeat directions to surprise the prey. This paper focuses on the Harris Hawks Optimization algorithm and its applications in the domain of software testing.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126279877","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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