Kamaljit Kaur

A CORRELATIVE STUDY ON ANTIMUTAGENIC AND CHEMOPREVENTIVE ACTIVITY OF ACACIA AURICULIFORMIS A. CUNN. AND ACACIA NILOTICA (L.) WILLD. EX DEL

The present study provides a correlation of the antimutagenic and chemopreventive activity of the barks of two commonly observed plants viz. Acacia auriculiformis and Acacia nilotica. We used the Ames antimutagenicity assay and the mouse mammary gland organ culture (MMOC) model. The plants were extracted with organic solvents to obtain chloroform fractions and acetone extracts. The antimutagenic activity was determined in two different strains using both direct-acting [4-nitro-o-phenylenediamine (NPD) or sodium azide] and indirect-acting [2-aminofluorene (2AF)] mutagens. The anticarcinogenic activity was evaluated based on the development of preneoplastic lesions in response to the chemical carcinogen 7,12-dimethylbenz[a]anthracene (DMBA). The results showed that the activity resulting from the 2AF mutagen was selectively greater than the activity from the direct-acting mutagens. Moreover, in general, acetone extracts were more potent in suppressing mutagenesis than the chloroform extracts. The antimutagenicity results obtained with extracts using the 2AF – TA100 system were comparable to the chemopreventive results with DMBA-induced mammary lesions. The order of activity in both tests was A. nilotica > A. auriculiformis. These results exhibited a good correlation between the antimutagenesis assay and the MMOC model, suggesting that these plants may contain active chemopreventive agents.

Secure Cloud Communication for Effective Cost Management System Through MSBE

In Cloud Computing Architecture, Brokers are responsible to provide services to the end users. An Effective Cost Management System (ECMS) which works over Secure Cloud Communication Paradigm (SCCP) helps in finding a communication link with overall minimum cost of links. We propose an improved Broker Cloud Communication Paradigm (BCCP) with integration of security issues. Two algorithms are included, first is Secure Optimized Route Cost Finder (S-ORCF) to find optimum route between broker and cloud on the behalf of cost factor and second is Secure Optimized Route Management (S-ORM) to maintain optimum route. These algorithms proposed with cryptographic integrity of the secure route discovery process in efficient routing approaches between broker and cloud. There is lack in Dynamic Source Routing Approach to verify whether any intermediate node has been deleted, inserted or modified with no valid authentication. We use symmetric cryptographic primitives, which is made possible due to multisource broadcast encryption scheme. This paper outlines the use of secure route discovery protocol (SRDP)that employs such a security paradigm in cloud computing.

ACO based graph partitioning algorithm for optimistic deployment of software in MCC

Mobile Cloud Computing has become very popular because it integrates mobile computing and cloud computing. This paper has presented a critical review on existing MCC techniques especially for optimistic deployment of software. The optimistic deployment mainly focuses on partitioning an application in such a way so that energy consumption will be minimized. The objective of this paper is to study and explore several graph partitioning algorithms and propose a new ACO based technique to deploy software in mobile clouds in optimistic approach. The recommended algorithm has the ability to overcome the limitations which are present in earlier techniques.

Comparative analysis of Black Hole attack over Cloud Network using AODV and DSDV

Cloud Network is very broad and unreliable due to its property of elasticity in number of VM and physical nodes in its Virtual Cloud Network (VCN). Nodes can join and withdraw the network at any time. Elasticity is the advantage of cloud computing but it comes with lots of security issues in routing and data forwarding in between packets. VCN working is very much similar to Mobile Ad-Hoc Networks (MANETs) which rely on the cooperation of all participating nodes to provide the fundamental operations. Due to non centralized security in VCN and MANET, lots of security attacks and threats are all in the way to exploit security of data routing. Malicious node may intervene and can exploit the data during wireless communication. Different protocols have different impact of such attacks of malicious nodes. In this paper Comparative analysis of Black Hole attack for both Proactive and Reactive protocol is taken into account. The impact of Black Hole attack on the performance of VCN as well as MANET is evaluated and finding out which protocol is more vulnerable to the attack and how much is the impact of the attack on both protocols.

A Novel Context and Load-Aware Family Genetic Algorithm Based Task Scheduling in Cloud Computing

With the advent of web technologies and efficient networking capabilities, desktop applications are increasingly getting amalgamated with the touch of cloud computing. Most of the recent developments are dominated by consumer centric market, ensuring best quality of service and hence, greater customer base, leading to the rise of peaks in the profit charts. However, certain challenges in the field of cloud need to be dealt with, before peak performance is achieved and resource scheduling is one of these. This paper aims to present a context and load aware methodology for efficient task scheduling using modified genetic algorithm known as family genetic algorithm. Based on analysis of user characteristics, user requests are fulfilled by the right type of resource. Such a classification helps attain efficient scheduling and improved load balancing and will prove advantageous for the future of the cloud. Results show that the proposed technique is efficient under various circumstances.

Web usage mining-current trends and future challenges

Web collaborate the huge amount of information logged about user accesses. Mining this information to gain deep insights into the Web site, its usage, and user visiting pattern is known as web data mining. In this paper the concept of data mining is summarized specifying about data type generalization and comparing various mining algorithms based on application and type of data. It discuss about Web Mining and its various categories like web content, web structure and web usage based upon the area to be mined on the internet. It also exposes comparison between pattern discovery techniques based upon various parameters and finally focusing on scope of web usage mining which will be helpful in providing web services to user.

Extending IAMCTD using data fusion and lossless data compression for UWSN

The paper has a proposal of new compressive sensing and fusion based protocol for Underwater Wireless Sensor Networks(UWSNs). The offered protocol has the ability to reduce the amount of data which is going to be transmitted. The reduction in packet size would be done by removing the redundant data using the data fusion at intra and inter level i.e. on sensors as well as on acoustic nodes. However further reduction would also be done using the LZW based compression. The reduction in packet size would improve the QoS parameters of UWSNs as it will not cause congestion at any stage.

Dermatoglyphic patterns in children with dental caries: An In vivo Study

Background: Dental caries is the predominant cause of tooth loss in children and young adults. Mutans streptococci are the principal etiological agents of dental caries, of which Streptococcus mutans (SM) and Streptococcus sobrinus are most important in terms of human caries. Genetic factors also contribute to dental caries of which dermatoglyphics is one. Aim: The aim of the study was to explore the unique relationship between genetic component (dermatoglyphics) and dental components (dental caries, salivary pH, SM level) and to identify children at particular risk of dental decay. Settings and Design: One hundred children, 6–12 years of age, were selected and divided into two groups: Group 1 (children with dental caries, subject group) and Group 2 (children without dental caries, control group). Methods: Dermatoglyphic patterns were recorded using a digital scanner (CanoScan LiDE), and dental caries status was recorded with “decayed-extracted-filled teeth” index for primary teeth and “decayed-missing-filled teeth” index for permanent teeth. Salivary pH was determined using pH meter strips. SM level was estimated by microbial culture of collected saliva samples. Statistical Analysis: Mann–Whitney test, Wilcoxon test, and Z-test were applied. Results and Conclusion: (1) Subject group had a decreased frequency of loops, whereas control group had increased frequency of loop pattern on palmer digits (P < 0.001). (2) Subject group had a low salivary pH toward normal and control group had high salivary pH values toward normal (P < 0.001). (3) Subject group had high SM level as compared to control group (P < 0.001). Therefore, there exists a relationship between genetic component (dermatoglyphics) and dental component (dental caries, salivary pH, and SM level).

A Survey on Computation Offloading Techniques in Mobile Cloud Computing and Their Parametric Comparison

Mobile Cloud Computing (MCC) is a distributed computing model which outspreads the idea of utility computing of the Cloud Computing to the Smart Mobile Devices (SMDs). Outsourcing intensive applications of the SMDs to the remote servers is the key idea of Mobile Cloud Computing. Many techniques have been developed for offloading computation intensive application code on the cloud servers for execution for saving scarce resources of the mobile devices such as battery life, network bandwidth, device’s storage memory, processing unit’s performance etc. This paper presents review on techniques for computational offloading. Computation offloading is relocating some computation concentrated part of an application code to a cloud server for execution to fulfil the source requirements. A comparative study on the techniques for computational offloading has been shown on the basis of parameters such as bandwidth, network latency, cost, energy consumption, execution time etc.

Comparative Study of Techniques and Issues in Data Clustering

Data mining refers to the extraction of obscured prognostic details of data from large databases. The extracted information is visualized in the form of charts, graph, tables and other graphical forms. Clustering is an unsupervised approach under data mining which groups together data points on the basis of similarity and separate them from dissimilar objects. Many clustering algorithms such as algorithm for mining clusters with arbitrary shapes (CLASP), Density peaks (DP) and k-means are proposed by different researchers in different areas to enhance clustering technique. The limitation addressed by one clustering technique may get resolved by another technique. In this review paper our main objective is to do comparative study of clustering algorithms and issues arising during clustering process are also identified.