Information Technology

Fast growth of computer Networks require efficient network management and monitoring tools for better utilization of resources. Traditional centralized Network Management Systems (NMS) are not adapted to wide spectrum of heterogeneous network installations and configurations. Major problem areas are heterogeneity, complex topologies, scalability, limited bandwidth constraints etc. Present research activity is centered around providing distributed network management oriented intelligence to different network components. Mobile Agent (MA) paradigm seems to offer a good solution. Most network devices employ Simple Network Management Protocol (SNMP) agents for network management based on traditional centralized client/server architecture. In this paper, we propose a Mobile Agent Framework (MAF) integrated with SNMP. It provides a mobile network manager with Mobile Agent Generator (MAG) functionality. Generated MA can aggregate SNMP data, semantically compress and intelligently filter it. We present a novel approach to write string based network health functions and SNMP table filtering expression. A new concept of customized reliable traps based on health functions is introduced. Implemented MAF is also strengthened to handle and transfer large SNMP tables.

Global System for Mobile Communications (GSM) is the most widely used cellular technology in the world. Main objective in mobile communication systems is security of data exchanged. GSM uses several cryptographic algorithms for security like A5/1, A5/2 and A5/3. But it has been found that these algorithms are cracked by various practical attacks so these algorithms do not provide sufficient levels of security for protecting the confidentiality of GSM therefore it is desirable to secure data by additional encryption. In this paper we have done additional encryption by implementing DES, TripleDES, and AES algorithms on GSM Network. This paper also analyzes the effectiveness of these algorithms against brute force attack implemented in MATLAB environment.

Stemming is an operation that conflates morphologically similar terms into a single term without doing complete morphological analysis. Stemming is used in information retrieval systems to improve performance. We describe a method to get the stem from the given word. Stemming is a technique which is required in information retrieval system and it is used to increase the performance of the retrieval result. All natural language processing systems must require a stemmer for it. The common goal of stemming is to standardize words by reducing a word to its base. Porter's stemmers have been used as a standard for English language. In this paper we have used Brute force technique with suffix stripping approach. Here we are using two approaches to get the maximum accuracy from the stemmer. For a language like Punjabi it is not easy to create a stemmer for it. Well known techniques for stemming are suffix removal, brute force technique, rule based technique and hybrid approaches. The need for good stemming algorithms for these languages has increased in the wake of search and retrieval system. In this we have created a huge database and a list of suffixes. With the help of big database we are getting higher accuracy than the other stemmers. We also reduce the over-stemming and understemming errors by finding number of words which causes these errors. We have already added these words in our database so to avoid the errors.

HMM has found its application in almost every field. Applying Hmm to biological sequences has its own advantages. HMM's being more systematic and specific, yield a result better than consensus techniques. Profile HMMs use position specific scoring for the matching & substitution of a residue and for the opening or extension of a gap. HMMs apply a statistical method to estimate the true frequency of a residue at a given position in the alignment from its observed frequency while standard profiles use the observed frequency itself to assign the score for that residue. This means that a profile HMM derived from only 10 to 20 aligned sequences can be of equivalent quality to a standard profile created from 40 to 50 aligned sequences.

HMM has found its application in almost every field. Applying HMM to biological sequences has its own advantages. HMM's being more systematic and specific, yield a result better than consensus techniques. In this research work we have applied HMM to profile analysis. With our technique it was found that HMM outperformed Dot plots, local alignment and global alignment techniques considerably.

this paper presents the comparison between Gaussian classifiers and Nearest Neighbor Classifiers for filtering spam emails. The results are in the form of traces of probability of error and time taken for classification, both with respect to the number of emails. Since spam emails are increasingly becoming difficult to filter, so these automated techniques will help in saving lot of time and resources required to handle the same.

Mobility models are the foundation of the simulation study of various protocols in Mobile Adhoc Networks. The comparative Performance of Dynamic Source Routing and Adhoc On-Demand Distance Vector routing protocols using the two Chain Mobility Models against Performance parameters like Throughput, Packet Delivery Fraction, End to End Delay is presented in this paper. The study is oriented towards deployment of sparse ad-hoc networks by suitably selecting the number of nodes. With the help of NS-2 simulator performance of Reactive Routing Protocols in Chain mobility models is analysed and hence comparative study is put farward.

Hidden markov models is a statistical tool largely used to study protein alignments and profile analysis of a set of proteins. Finite state machines like HMM move through a series of states and produce output either when the machine has reached a particular state or when it is moving from state to another. It generates a protein sequence by emitting amino acids as it progresses through a series of states. Multiple sequence alignment is a powerful technique that is used by modern bioinformatics systems almost in all their applications. The biomedical methods and algorithms used in MSA have vast importance in solving a series of related biological problems. The well-known and widely used statistical method of characterizing the spectral properties of the residues of a genomic or proteomic pattern is the HMM approach. Profile HMMs have proved to offer a robust solution for MSA.

Prostate tumors are among the most heterogeneous of cancers, both histologically and clinically. Microarray expression analysis was used to determine whether global biological differences underlie common pathological features of prostate cancer and to identify genes that might anticipate the clinical behavior of this disease. While no expression correlates of age, serum prostate specific antigen (PSA), and measures of local invasion were found, a set of genes was identified that strongly correlated with the state of tumor differentiation as measured by Gleason score. Moreover, a model using gene expression data alone accurately predicted patient outcome following prostatectomy. These results support the notion that the clinical behavior of prostate cancer is linked to underlying gene expression differences that are detectable at the time of diagnosis.