Int. J. Next Gener. Comput.最新文献_第3页

A CNN Based Approach for Crowd Anomaly Detection 基于CNN的人群异常检测方法

Int. J. Next Gener. Comput. Pub Date : 2021-04-03 DOI: 10.47164/IJNGC.V12I1.624

K. Joshi, N. Patel

引用次数: 5

Unified model towards Scalability in Software Defined Networks 面向软件定义网络可扩展性的统一模型

Int. J. Next Gener. Comput. Pub Date : 2021-04-03 DOI: 10.47164/IJNGC.V12I1.690

Amit Nayyer, A. Sharma, L. Awasthi

{"title":"Unified model towards Scalability in Software Defined Networks","authors":"Amit Nayyer, A. Sharma, L. Awasthi","doi":"10.47164/IJNGC.V12I1.690","DOIUrl":"https://doi.org/10.47164/IJNGC.V12I1.690","url":null,"abstract":"Software Defined Network is a paradigm that enables the network administrators to manage and control the network from a centralized location using software programs. The limitations and complexities of the traditional network are handled by separating the control plane from the data plane in this setup. The main idea is to have centralized control over network devices. Scalability is one of the main concerns in such a paradigm. Various independent solutions to improve scalability are available in the literature. In this paper, two approaches for the solutions of scalability are studied and implemented: Topology based solutions and Routing based solutions. Different evaluation parameters are selected for evaluating a framework combined with a specific routing protocol. Frameworks from different categories are implemented along with different routing protocols. Putting the routing protocols one by one in a single framework, nine such models are implemented for evaluation. Results are provided for consideration before network setup for the network administrators. Furthermore, the discussions based on the results are presented regarding the combination of a particular framework with routing solution to get better results in specific conditions.","PeriodicalId":351421,"journal":{"name":"Int. J. Next Gener. Comput.","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125406955","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}

引用次数: 0

Machine Learning Classifier Model for Prediction of COVID-19 新型冠状病毒预测的机器学习分类器模型

Int. J. Next Gener. Comput. Pub Date : 2021-04-03 DOI: 10.47164/IJNGC.V12I1.186

Jhimli Adhikari

引用次数: 0

Control and Coordination of Self-Adaptive Traffic Signal Using Deep Reinforcement Learning 基于深度强化学习的自适应交通信号控制与协调

Int. J. Next Gener. Comput. Pub Date : 2021-03-01 DOI: 10.17762/ITII.V9I1.141

Pallavi A. Mandhare, Jyoti Y. Yadav, Vilas Kharat, C. Patil

{"title":"Control and Coordination of Self-Adaptive Traffic Signal Using Deep Reinforcement Learning","authors":"Pallavi A. Mandhare, Jyoti Y. Yadav, Vilas Kharat, C. Patil","doi":"10.17762/ITII.V9I1.141","DOIUrl":"https://doi.org/10.17762/ITII.V9I1.141","url":null,"abstract":"The most observable obstacle to sustainable mobility is traffic congestions. These congestions cannot effectively be fixed by traditional control of traffic signals. Safe and smooth movement of traffic is ensured by a self-controlled traffic signal. As such, to coordinate the traffic flow it is necessary to implement dynamic traffic signal subsequences. Primarily, Traffic Signal Controllers (TSC) provides sophisticated control and coordination of vehicles. The control and coordination of traffic signal control systems can be effectively achieved by implementing the Deep Reinforcement Learning (DRL) approaches. \u0000The decision-making capabilities at intersections are improved by having variations of traffic signal timing using an adaptive TSC. Alternatively, the actual traffic demand is nothing but managing the traffic systems. It analyses the incoming number and type of vehicles and gives a real-time response at intersection geometrics and controls the traffic signals accordingly. \u0000The proposed DRL algorithm observes traffic data and operates optimum management plans for the regulation of the traffic flow. Furthermore, an existing traffic simulator is used to help provide a realistic environment to support the proposed algorithm. \u0000 ","PeriodicalId":351421,"journal":{"name":"Int. J. Next Gener. Comput.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130258708","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}

引用次数: 0

Multi-Level Fuzzy Cluster Based Trust Estimation for Hierarchical Wireless Sensor Networks 基于多级模糊聚类的分层无线传感器网络信任估计

Int. J. Next Gener. Comput. Pub Date : 2020-12-22 DOI: 10.47164/IJNGC.V11I3.661

Rahul Das, M. Dwivedi

引用次数: 1

Fuzzy Logic Based Detection of SLA Violation in Cloud Computing - A Predictive Approach 云计算中基于模糊逻辑的SLA违规检测——一种预测方法

Int. J. Next Gener. Comput. Pub Date : 2020-12-22 DOI: 10.47164/IJNGC.V11I3.560

P. K. Upadhyay, A. Pandita, Nisheeth Joshi

引用次数: 0

Hexagonal Picture Languages Generated By Assembling Hexagonal Tiles 六边形拼贴生成的六边形图片语言

Int. J. Next Gener. Comput. Pub Date : 2020-12-22 DOI: 10.47164/IJNGC.V11I3.608

P. Anitha

引用次数: 1

An Empirical Analysis of Threshold Techniques for identifying Faulty Classes 故障类识别的阈值技术实证分析

Int. J. Next Gener. Comput. Pub Date : 2020-12-22 DOI: 10.47164/IJNGC.V11I3.642

N. Kaur, Hardeep Singh

{"title":"An Empirical Analysis of Threshold Techniques for identifying Faulty Classes","authors":"N. Kaur, Hardeep Singh","doi":"10.47164/IJNGC.V11I3.642","DOIUrl":"https://doi.org/10.47164/IJNGC.V11I3.642","url":null,"abstract":"The experimental validation of the proficiency of the proposed techniques is a mandatory task in the research regime. The existing literature has proved the presence of extensive work attained the statistical validation of software metrics by utilizing them in the development of fault prediction models, where, both statistical and machine learning techniques were engaged into the construction of the models being capable of identifying faulty and non-faulty classes. On the contrary, the research area involving the investigation of threshold concept has not gained sufficient maturity. An effective threshold technique can assist in the identification of optimal cut-off value in software metric which can discriminate the faulty from non-faulty classes with minimal misclassification rate. The idea of threshold calculation can make the applicability of the existing metrics in software industries, a much easier task. As the developers only need to know the cut-off values which can help them to concentrate on the specific classes that exceeds the computed thresholds. Also, the presence of peculiarity in the software metric index can alert the testers and in turn helps them to disburse the resources systematically. The current study empirically validated and compared the discriminating strength of two threshold techniques, i.e., ROC curve and Alves Rankings, on the public dataset. This study selected twenty Object Oriented (OO) measures for the process of threshold calculation. Besides, the widely addressed metric suite proposed by Chidamber and Kemerer, this study also considered other fourteen OO measures for the experiment. Furthermore, Wilcoxon signed ranks test was used to enquire the classification difference between the aforementioned threshold techniques. The outcome from the statistical analysis revealed the better predictive capability of ROC curve than the Alves Rankings.","PeriodicalId":351421,"journal":{"name":"Int. J. Next Gener. Comput.","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123627387","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}

引用次数: 0

A Secure Source Routing Protocol for Mobile Ad Hoc Networks 移动自组织网络的安全源路由协议

Int. J. Next Gener. Comput. Pub Date : 2020-12-22 DOI: 10.47164/IJNGC.V11I3.647

Baban A. Mahmood, D. Manivannan

引用次数: 0

Analytics Dashboard on Talent search Examination Data using Structure of Intellect Model 基于智力模型结构的人才招聘考试数据分析仪表板

Int. J. Next Gener. Comput. Pub Date : 2020-11-26 DOI: 10.47164/IJNGC.V12I2.766

V. Munde, Binod Kumar, Anagha Vaidya, S. Shirwaikar

{"title":"Analytics Dashboard on Talent search Examination Data using Structure of Intellect Model","authors":"V. Munde, Binod Kumar, Anagha Vaidya, S. Shirwaikar","doi":"10.47164/IJNGC.V12I2.766","DOIUrl":"https://doi.org/10.47164/IJNGC.V12I2.766","url":null,"abstract":"The potential of Analytics and Data mining methodologies, that extract useful and actionable information from \u0000large data-sets, has transformed one field of scientific inquiry after another. Analytics has been widely applied \u0000in Business Organizations as Business Analytics and when applied to education, these methodologies are referred \u0000to as Learning Analytics and Educational Data mining. Learning Analytics proposes to collect, measure and \u0000analyze data in learning environments to improve teaching and learning process. Educational Data mining (EDM) \u0000thrives on existing data collected by learning management systems. The applicability of Learning Analytics and \u0000Educational Data mining can be extended to traditional learning processes by suitably combining data collected \u0000from technology enabled processes such as Admission and Assessment with data generated from analysis of learning \u0000interactions. The intellectual performance of the students can be analyzed using some well known Learning \u0000Frameworks. This paper demonstrates the Complete Analytics process from data collection, measurement to \u0000Analysis using Guilford’s structure of intellect model. An analytic dashboard provides the necessary information \u0000in concise and visual form and in an interactive mode. The analytic process presented on talent examination data \u0000can be generalized to similar examinations in traditional educational setup.","PeriodicalId":351421,"journal":{"name":"Int. J. Next Gener. Comput.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131502890","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}

引用次数: 0