Neeraj Rathore

Grid computing offers the network with large scale computing resources. Load balancing is effective for balancing the load of large scale heterogeneous grid resources that are typically owned by different organizations. Not all the techniques provide the same benefits for users in utilizing the resources in a quick response time. Similarly, the profit earned by resource providers also differs for different Load balancing technique. We surveyed the Load Balancing and Job Migration technique used in grid computing since its inception until 2013. The author discussed their advantages and disadvantages and analyze their suitability for usage in a dynamic grid environment. To the best of our knowledge, no such survey has been conducted in the literature up to now. A comparative study of some of them along with their pitfalls in case of huge distributed environment, like Grid, is discussed in this paper. The author also proposed efficient hierarchal Load Balancing algorithm to close all t...

Machine learning applications employ FFNN (Feed Forward Neural Network) in their discipline enormously. But, it has been observed that the FFNN requisite speed is not up the mark. The fundamental causes of this problem are: 1) for training neural networks, slow gradient descent methods are broadly used and 2) for such methods, there is a need for iteratively tuning hidden layer parameters including biases and weights. To resolve these problems, a new emanant machine learning algorithm, which is a substitution of the feed-forward neural network, entitled as Extreme Learning Machine (ELM) introduced in this paper. ELM also come up with a general learning scheme for the immense diversity of different networks (SLFNs and multilayer networks). According to ELM originators, the learning capacity of networks trained using backpropagation is a thousand times slower than the networks trained using ELM, along with this, ELM models exhibit good generalization performance. ELM is more efficient...

Deep-Convolution Neural Network (CNN) is the branch of computer science. Deep Learning CNN is a methodology that teaches computer systems to do what comes naturally to humans. It is a method that learns by example and experience. This is a heuristic-based method to solve computationally exhaustive problems that are not resolved in a polynomial computation time like NP-Hard problems. The purpose of this research is to develop a hybrid methodology for the detection and segmentation of flower images that utilize the extension of the deep CNN. The plant, leaf, and flower image detection are the most challenging issues due to a wide variety of classes, based on their amount of texture, color distinctiveness, shape distinctiveness, and different size. The proposed methodology is implemented in Matlab with deep learning Tool Box and the dataset of flower image is taken from Kaggle with five different classes like daisy, dandelion, rose, tulip, and sunflower. This methodology takes an input...

The existing approaches of block based recovery of progressive secret sharing paradigm have support for limited $${k}$$ value in (k, n). Sian et al.’s as well Quan et al.’s block based visual secret sharing scheme has limited bound; works for a limited $${k}$$ value of $$\left( {{k},{~n}} \right)$$ threshold scheme. A novel threshold based (any value of $${k~and~~n}$$ ) block recovery in progressive secret sharing has been introduced and analyzed in this paper. Scheme achieves higher value of threshold than the schemes proposed by Sian et al.’s as well Quan et al.’s. The scheme discusses the sharing ability in progressive block based secret sharing up to any general number of thresholds. The contrast of the recovered block wise secret is reasonably good and the scheme allows for perfect security using at least $${k}$$ numbers of participants before revealing any of the blocks of secret image. The work in this paper offering a reference point for developing solutions needed in the area of secure image sharing techniques.

ABSTRACT In this paper, a fault tolerance based QoS (Quality of Services) scheduling using HTF (Hash Table Functionality) in Social Grid Computing (SGC) is introduced, where HTF is used as the underlying mechanism in SGC to logically manage the locations of the devices. Fault tolerance based QoS scheduling consists of four sub-scheduling algorithms: Unauthorized-user filtering, Grid service delivery, QoS provisioning, and Replication and load-balancing. Under the proposed scheduling, a device is used as a resource for providing Grid services, faults caused due to reason like user mobility are tolerated and user requirements for QoS are considered. Simulations include the scheduling by with and without HTF. The simulation results show that our proposed scheduling algorithm enhances Grid service execution time, finish time, reliability and reduces the Grid service error rate.

ABSTRACT In this paper, a hierarchical load balancing technique has been presented, which is based on variable threshold value. Through this paper, an attempt has been made to solve the problem of load balancing while maintaining the resource utilization and response time with the help of sender initiative policy. The proposed technique is suitable for dynamic and decentralized Grid environments. The load is divided into different categories like lightly loaded, under-lightly loaded, overloaded, normally loaded, based thresholds values. A threshold value, which can be found out using load deviation, is responsible for transfer the task and flow of workload information. In order to increase the response time and decrease throughput of the Grid, a sender initiated policy has been introduced to reduce the communication overhead. The model has been rigorously examined over the GridSim simulator using various parameters like execution time, response time, makespan etc. Experimental results prove the superiority over existing techniques.

ABSTRACT Load balancing is an important aspect of Grid resource scheduling. This paper attempts to address the issue of load balancing in a Grid, while maintaining the resource utilization and response time for dynamic and decentralized Grid environment. Here, to its optimum value, a hierarchical load balancing technique has been analysed based on variable threshold value. The load is divided into different categories, such as lightly loaded, under-lightly loaded, overloaded, and normally loaded. A threshold value, which can be found out using load deviation, is responsible for transferring the task and flow of workload information. In order to improve response time and to increase throughput of the Grid, a random policy has been introduced to reduce the resource allocation capacity. The proposed model has been rigorously examined over the GridSim simulator using various parameters, such as response time, resource allocation efficiency, etc. Experimental results prove the superiority of the proposed technique over existing techniques, such as without load balancing, load balancing in enhanced GridSim.

Ad hoc Network is a temporary network and these networks nodes are free to move about and organize themselves into a network. These nodes change position frequently. A Reactive (on-demand) routing strategy is a popular routing category for wireless ad hoc routing. The design follows the idea that each node tries to reduce routing overhead by sending routing packets whenever a communication is requested. In this paper an attempt has been made to compare the performance of AODV & Multipath AODV on demand reactive routing protocols for MANETs. As per our simulation the differences in the protocol mechanism lead to significant performance for this protocol. The performance differentials are analyzed using varying Speed. These simulations are carried out using the ns-2.31 network simulator. The results presented in this work illustrate the importance in carefully evaluating and implementing routing protocols in an ad hoc environment. KeywordsMANET, AODV, Multipath AODV, Simulation, Perfo...

To improve the trustworthiness to assess the digital images by identifying authentic images and tampered images, this work is focused on Copy-Move based image Forgery Detection (CMFD) and classification using Improved Relevance Vector Machine (IRVM). In this paper, Biorthogonal Wavelet Transform with Singular Value Decomposition (BWT-SVD)-based feature extraction is applied to find the image forgery. The proposed method begins with dividing the test images into overlapping blocks, and then Biorthogonal Wavelet Transform (BWT) with Singular Value Decomposition (SVD) applies to extract the feature vector from the blocks. After that, the feature vectors are sorts and the duplicate vectors are identified by the similarity between two successive vectors. The occurrences of clone vectors are identified on the basis of Minkowski distance and the threshold value. Then, similarity criteria result in the existence of forgery in images. To classify images into the category of authentic images ...

... triggered me for my thesis work. I would also like to thank all the staff members, Thapar Grid Group and all my friends especially Vineet, Rohit, Kunal, Lokesh, Kabir, Satyarth ,Gurpreet, Anchal, Rajeev ,Ms ... (Neeraj Kumar Rathore) 80632016 Page 4. iii Abstract ...

Density, viscosity and ultrasonic velocity of the various compositions of liquid mixtures of aqueous solutions of Lithium chloride (LiCl) and Lithium hydroxide (LiOH) have been experimentally measured at 303,308,313 and 318K and at atmospheric pressure. From these experimental measurements the acoustic impedance (Z) and adiabatic compressibility (ad) have been calculated. The variations in these parameters have been correlated to derive the intermolecular interactions taking place between the mixtures of present study.

Ad hoc networks have been used in wide range of applications. These networks have become versatile as far implementation is concerned. Such networks have been used from static deployment to mobile network formations. Apart from Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), another ad hoc formation with mobile nodes is the aerial ad hoc networks also termed as Flying Ad Hoc Networks (FANETs). A coordinated network formation between the ground ad hoc network and aerial ad hoc network provides vast applications in both civilian and military activities. Various existing issues of mobile networks can easily be resolved using aerial network formations. However, these networks similar to the traditional ad hoc formations are having a major issue of broadcast storming and network partitioning. These issues hinder the performance of such networks. Broadcast storming refers to replication of similar data in the network that increases the overheads which ultimately le...

Machine learning applications employ FFNN (Feed Forward Neural Network) in their discipline enormously. But, it has been observed that the FFNN requisite speed is not up the mark. The fundamental causes of this problem are: 1) for training neural networks, slow gradient descent methods are broadly used and 2) for such methods, there is a need for iteratively tuning hidden layer parameters including biases and weights. To resolve these problems, a new emanant machine learning algorithm, which is a substitution of the feed-forward neural network, entitled as Extreme Learning Machine (ELM) introduced in this paper. ELM also come up with a general learning scheme for the immense diversity of different networks (SLFNs and multilayer networks). According to ELM originators, the learning capacity of networks trained using backpropagation is a thousand times slower than the networks trained using ELM, along with this, ELM models exhibit good generalization performance. ELM is more efficient...

In this scenario, dynamic and decentralized Load Balancing (LB) considers all the factors pertaining to the characteristics of the Grid computing environment. Dynamic load-balancing algorithms attempt to use the run-time state information to make more informative decisions in sharing the system load and in decentralization, algorithm is executed by all nodes in the system and the responsibility of LB is shared among all the nodes in the same pool. For this purpose, in this work, an extensive survey of the existing LB has been done. A detailed classification and gap analysis of the existing techniques is presented based on different parameters. The issue of LB in a Grid has been addressed while maintaining the resource utilization and response time for dynamic and decentralized Grid environment. Here, a hierarchical LB technique has been analyzed based on variable threshold value. The load is divided into different categories, like, lightly loaded, under-lightly loaded, overloaded, and normally loaded. A threshold value, which can be found out using load deviation, is responsible for transferring the task and flow of workload information. In order to improve response time and to increase throughput of the Grid, a random policy has been introduced to reduce the resource allocation capacity etc. Poisson process has been used for random job arrival and then load calculation has been done for assigning job to the appropriate Processing Entity for balancing the load in the pool. After balancing the load, it comes into the normally loaded pool, and then Job Migration process is executed. The performance of the proposed model, algorithms and techniques has been examined over the GridSim simulator using various parameters, such as response time, resource allocation efficiency, etc. Experimental results prove the superiority of the proposed techniques over the existing techniques.

For efficient resource management in Grid, the resources overloading must be prevented, which can be obtained by efficient Load Balancing and Job Migration (JM) mechanisms. JM addresses the problem of resource overloading through various techniques in a Grid environment. In this paper, JM model has been designed. The proposed JM model offers three policies based on the scheduling, checkpointing and replication for Grid environment decisions. The JM problem has been formulated as an optimization problem and resource scheduling algorithm minimizes the average finish time and makespan. For all the three proposed techniques, JM policies have been designed and the policy rules have been specified in Extensible Markup Language schema for decision making. Each of the above techniques has been executed according to its specific condition at run time. Finally, the performances of the proposed model, algorithms and techniques have been rigorously examined over the GridSim simulator using various parameters, such as makespan, hit-ratio etc. Experimental results establish the superiority of the proposed techniques over the existing techniques.

In this paper, a fault tolerance based QoS (Quality of Services) scheduling using HTF (Hash Table Functionality) in Social Grid Computing (SGC) is introduced, where HTF is used as the underlying mechanism in SGC to logically manage the locations of the devices. Fault tolerance based QoS scheduling consists of four sub-scheduling algorithms: Unauthorized-user filtering, Grid service delivery, QoS provisioning, and Replication and load-balancing. Under the proposed scheduling, a device is used as a resource for providing Grid services, faults caused due to reason like user mobility are tolerated and user requirements for QoS are considered. Simulations include the scheduling by with and without HTF. The simulation results show that our proposed scheduling algorithm enhances Grid service execution time, finish time, reliability and reduces the Grid service error rate.