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Author

Uma Kumari

Bio: Uma Kumari is an academic researcher from Mody University of Science & Technology. The author has contributed to research in topics: Cloud computing & Load balancing (computing). The author has an hindex of 4, co-authored 8 publications receiving 29 citations.

Papers
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Proceedings ArticleDOI
01 Dec 2016
TL;DR: This paper presents cloud computing, cloud computing architecture, virtualization, load balancing, challenges and various currently available load balancing algorithms for cloud computing.
Abstract: Cloud computing is latest emerging technology for large scale distributed computing and parallel computing. Cloud computing gives large pool of shared resources, software packages, information, storage and many different applications as per user demands at any instance of time. Cloud computing is emerging quickly; a large number of users are attracted towards cloud services for more satisfaction. Balancing the load has become more interesting research area in this field. Better load balancing algorithm in cloud system increases the performance and resources utilization by dynamically distributing work load among various nodes in the system. This paper presents cloud computing, cloud computing architecture, virtualization, load balancing, challenges and various currently available load balancing algorithms.

20 citations

Proceedings ArticleDOI
01 Oct 2017
TL;DR: Techniques of research problem and achievements in the field of security of big data using anomaly based IDS, a software application for detecting and monitoring the network activities and protect from unknown and suspicious access of device.
Abstract: Security from intruders in data mining and machine learning have been important area of research during the last few years. Today malicious attack is serious security threat. These malicious executables are created at the rate of thousands every year and create serious security problems. Intrusion Detection System (IDS) is used to Access unauthorized and malicious attacks over the network. Data mining techniques that can be applied to IDS to detect normal and abnormal behavior patterns. Intrusion detection systems analyzes network activities and identify suspicious activity in the network to improve accuracy and security and detect anomalies attacks. Data mining provide a way to analyze, classify, clean and eliminate the large amount of network data through intrusion detection system. Several privacy and security techniques and algorithms have been proposed recently. In this paper we provide techniques of research problem and achievements in the field of security of big data using anomaly based IDS. Intrusion detection is a software application for detecting and monitoring the network activities and protect from unknown and suspicious access of device.

9 citations

Proceedings ArticleDOI
01 Oct 2017
TL;DR: This paper focuses on overview, importance and applications of soft computing techniques, which are used to develop intelligent reasonable machines to provide solutions to real world problems which are difficult to model using traditional methods.
Abstract: Soft computing is a new approach to computing. It has ability to reason and learn in an environment of uncertainty, approximation and imprecision. Soft computing combines many technologies like fuzzy logic, probabilistic reasoning, artificial neural network, genetic algorithm, evolutionary computing and machine learning. The main aim of this combination is to solve real-world problems, which are not solved by hard computing. Hard computing is traditional computing which require precisely stated analytical model and very large computation time. Soft computing techniques play an important role in solving approximate, imprecise and vague results. Soft computing is used to develop intelligent reasonable machines to provide solutions to real world problems which are difficult to model using traditional methods. This paper focuses on overview, importance and applications of soft computing techniques. Soft computing applications list is wide and covers almost all diverse areas of human interaction. Success of these applications spurred widespread acceptance of these novel and powerful nonlinear modeling techniques.

6 citations

Proceedings ArticleDOI
08 Oct 2015
TL;DR: Some of major geographic routing protocols for WSNs are presented and it is shown that geographical routing uses location information to send packets to the target region.
Abstract: Wireless Sensor Networks (WSN) consists of many small compact devices equipped with sensors. It is a very popular area for exploration. The nodes are movable and set themselves into a network. However in larger networks, geographical routing protocol is required. Geographical routing uses location information to send the packets to the target region. In this paper, some of major geographic routing protocols for WSNs are presented.

4 citations


Cited by
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Journal ArticleDOI
TL;DR: The research paper identifies the need for FT efficiency metric in LB algorithms which is one of the main concerns in cloud environments and proposes a novel algorithm that employs FT metrics in LB.
Abstract: The past few years have witnessed the emergence of a novel paradigm called cloud computing. CC aims to provide computation and resources over the internet via dynamic provisioning of services. There are several challenges and issues associated with implementation of CC. This research paper deliberates on one of CC main problems i.e. load balancing (LB). The goal of LB is equilibrating the computation on the cloud servers such that no host is under/ overloaded. Several LB algorithms have been implemented in literature to provide effective administration and satisfying customer requests for appropriate cloud nodes, to improve the overall efficiency of cloud services, and to provide the end user with more satisfaction. An efficient LB algorithm improves efficiency and asset's usage through effectively spreading the workload across the system's different nodes. This review research paper objective is to present critical study of existing techniques of LB, to discuss various LB parameters i.e. throughput, performance, migration time, response time, overhead, resource usage, scalability, fault tolerance, power savings, etc. The research paper also discusses the problems of LB in the CC environment and identifies the need for a novel LB algorithm that employs FT metrics. It has been found that traditional LB algorithms are not good enough and they do not consider FT efficiency metrics for their operation. Hence, the research paper identifies the need for FT efficiency metric in LB algorithms which is one of the main concerns in cloud environments. A novel algorithm that employs FT in LB is therefore proposed.

36 citations

Proceedings ArticleDOI
23 Apr 2019
TL;DR: This paper includes what is docker and container and shows how the services is been access by the node in a cluster with the help out docker swarm and kubernetes and also it provide the difference between them.
Abstract: Distributed computing is a technology which contributes new stack of computing placed on virtualization of assets. With the most recent pattern of building up the applications on cloud, which empower the customer to get to it in an expanding way, by this the heap climbing quickly on the servers. Due to this sense of growing the stack on the servers, the resources are not efficiently exploited. So this is the logic why it has been brought in this era. The main ambition of us is to balance the work on each & every nodes. This will help to distribute the work on different nodes by remembering that no hubs ought to be overloaded. Thus this paper include what is docker and container by this clear cut ideas will helps us out to understand the docker swarm and kubernetes technology. Finally the paper shows how the services is been access by the node in a cluster with the help out docker swarm and kubernetes and also it provide the difference between them.

32 citations

Journal ArticleDOI
TL;DR: A hybrid classification algorithm based on Multi-stage Weights Adjustment in the MLP (MWAMLP) neural network in two parts in two part to improve the breast cancer diagnosis is proposed.
Abstract: Breast cancer is the most common kind of cancer, which is the cause of death among the women worldwide. There is evidence that shows that the early detection and treatment can increase the survival rate of patients who suffered this disease. Therefore, this paper proposes an automatic breast cancer diagnosis technique using a genetic algorithm for simultaneous feature selection and parameter optimization of an Multi Layer Perceptron (MLP) neural network. The aim of this paper is to propose a hybrid classification algorithm based on Multi-stage Weights Adjustment in the MLP (MWAMLP) neural network in two parts to improve the breast cancer diagnosis. In the first part, the three classifiers are trained simultaneously on the learning dataset. The output of the first part classifier together with the learning dataset is placed in a new dataset. This dataset uses a hybrid classifier method to model the mapping between the outputs of each ordinary classifier of the first part with real output labels. The proposed algorithm is implemented with three different variations of the backpropagation (BP) technique, namely the Levenberg–Marquardt, resilient BP and gradient descent with momentum for fine tuning of the weight of MLP neural network and their performances are compared. Interestingly, one of the proposed algorithms titled MWAMLP-RP produces the best and on average, 99.35% and 98.74% correct classification, respectively, on the Wisconsin Breast Cancer Database dataset, which is comparable with the obtained results from the methods titled GP-DLNN, GAANN and CAFS and other works found in the literature.

17 citations