Satyananda Reddy

Andhra University

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V. Valli Kumari

Andhra University

G. Lavanya Devi

Andhra University

K. V. S. V. N. Raju

Maharaj Vijayaram Gajapathi Raj College of Engineering

Srinivas Talabattula

Indian Institute of Science Bangalore

Parasana Sankara Rao

Jawaharlal Nehru Technological University, Kakinada

Prasad Devarasetty

Andhra University

Robb Richard A.

Peter the Great St. Petersburg Polytechnic University

Cooperative Institutions

Andhra University

GITAM University

Maharaj Vijayaram Gajapathi Raj College of Engineering

Indian Institute of Management Visakhapatnam

Indian Institute of Science Bangalore

Electronics and Radar Development Establishment

International Institute of Information Technology Bangalore

Toyohashi University of Technology

Interaction Institute for Social Change

Sify Technologies (India)

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Multi-path selection based on fractional cuckoo search algorithm for QoS aware routing in MANET

Sensor Review (2018)

Robb Richard A.Satyananda Reddy V. Valli Kumari

Purpose Multimedia applications such as digital audio and video have stringent quality of service (QoS) requirement in mobile ad hoc network. To support wide range of QoS, complex routing protocols with multiple QoS constraints are necessary. In QoS routing, the basic problem is to find a path that satisfies multiple QoS constraints. Moreover, mobility, congestion and packet loss in dynamic topology of network also leads to QoS performance degradation of protocol. Design/methodology/approach In this paper, the authors proposed a multi-path selection scheme for QoS aware routing in mobile ad hoc network based on fractional cuckoo search algorithm (FCS-MQARP). Here, multiple QoS constraints energy, link life time, distance and delay are considered for path selection. Findings The experimentation of proposed FCS-MQARP is performed over existing QoS aware routing protocols AOMDV, MMQARP, CS-MQARP using measures such as normalized delay, energy and throughput. The extensive simulation study of the proposed FCS-based multipath selection shows that the proposed QoS aware routing protocol performs better than the existing routing protocol with maximal energy of 99.1501 and minimal delay of 0.0554. Originality/value This paper presents a hybrid optimization algorithm called the FCS algorithm for the multi-path selection. Also, a new fitness function is developed by considering the QoS constraints such as energy, link life time, distance and delay.

Multipath routing

Cuckoo search

Path vector protocol

10.1108/sr-08-2017-0170

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A neural network approach for web cost estimation

International Conference on Software Engineering (2007)

Satyananda Reddy K. V. S. V. N. Raju Srinivas Talabattula G. Lavanya Devi

Accurate cost estimates are an essential element to remain successful in the market, so cost estimation initiatives have been in the center of attention for many firms. Web development projects are certainly different from traditional software development projects and hence, require differently tailored measures for accurate estimation. The use of neural network in estimating software cost by Nasser Tadayon [1] produced accurate results, but it can't be applied to web applications, because they do not take all of the web objects into consideration. In this paper, author explores the use of expert judgment and machine learning techniques using neural network as well as referencing WebMo Estimation model to predict the cost of software. The proposed network improves the accuracy of the estimation as the number of dataset increases with input from expert judgment that affects the learning procedure.

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Genetic algorithm for quality of service based resource allocation in cloud computing

Evolutionary Intelligence (2019)

Prasad Devarasetty Satyananda Reddy

10.1007/s12065-019-00233-6

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A New Approach For Estimating Software Effort Using RBFN Network

Satyananda Reddy Parasana Sankara Rao V. Valli Kumari

Summary The prediction of software development effort has been focused mostly on the accuracy comparison of algorithmic models rather than on the suitability of the approach for building software effort prediction systems. Several estimation techniques have been developed to predict the Effort estimation. In this paper the main focus is on investigating the accuracy of the prediction of effort using RBFN network which can be used for functional approximation. The use of RBFN to estimate software development effort requires the determination of its architecture parameters according to the characteristics of COCOMO, especially the number of input neurons, no of hidden neurons, centers ci, width σ1 and weight wi. In the aspect of learning, the RBFN network is much faster than other network because the learning process in this network has two stages and both stages can be made efficient by appropriate learning algorithms. The proposed network is empirically validated using COCOMO’81 dataset which is used to train and test the designed RBFN network and found that the RBFN designed with the K-means clustering algorithm performs better, in terms cost estimation accuracy.

COCOMO

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Fault-prone module prediction of a web application using artificial neural networks

International Conference on Software Engineering (2007)

Satyananda Reddy K. V. S. V. N. Raju V. Valli Kumari G. Lavanya Devi

The problem addressed in this research is the prediction of fault-prone modules in a web application using Artificial Neural Networks. Past research in this area focused on applications related to procedural paradigm and object-oriented paradigm. In this paper, we turned our attention to applying Artificial Neural Networks to fault module prediction of a web application. In our research, we implemented Principal Component Analysis technique and Error Back propagation training algorithm. The modules are classified into two classes- fault-prone and not fault-prone using web application quality metric data. The proposed model is based on supervised learning using Multilayer Perceptron Neural Network.

Multilayer perceptron

Backpropagation

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