Mansaf Alam

A survey of application: Genomics and genetic programming, a new frontier

The aim of this paper is to provide an introduction to the rapidly developing field of genetic programming (GP). Particular emphasis is placed on the application of GP to genomics. First, the basic methodology of GP is introduced. This is followed by a review of applications in the areas of gene network inference, gene expression data analysis, SNP analysis, epistasis analysis and gene annotation. Finally this paper concluded by suggesting potential avenues of possible future research on genetic programming, opportunities to extend the technique, and areas for possible practical applications.

Recurrent neural network based hybrid model for reconstructing gene regulatory network

One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model.

Web Search Result Clustering using Heuristic Search and Latent Semantic Indexing

Giving user a simple and uncomplicated web search result representation is an active area of Information Retrieval research. Traditional search engines use the hyperlink structure of the web to retrieve documents or pages and give them in a ranked fashion to the user. In this paper, we propose a technique for grouping web search results into meaningful clusters. The proposed method performs heuristic search on the query result graph to prune undesired edges to form cluster and carries out Latent Semantic Indexing within these clusters to make them refined, meaningful, and relevant to the query.

A Review on Clustering of Web Search Result

The over abundance of information on the web, makes information retrieval a difficult process. Today’s search engines give too many results out of which only few are relevant. A user has to browse through the result pages to get the desired result. Web search result clustering is the clustering of results returned by the search engines into meaningful groups. This paper throws light and categorizes various clustering techniques that have been applied on the web search result.

A survey on scholarly data

Survey of big scholarly data with respect to the different phases of the big data lifecycle.Identifies the different big data tools and technologies that can be used for development of scholarly applications.Investigates research challenges and limitations specific to big scholarly data and its applications.Provides research directions and paves way towards the development of a generic and comprehensive big scholarly data platform. Recently, there has been a shifting focus of organizations and governments towards digitization of academic and technical documents, adding a new facet to the concept of digital libraries. The volume, variety and velocity of this generated data, satisfies the big data definition, as a result of which, this scholarly reserve is popularly referred to as big scholarly data. In order to facilitate data analytics for big scholarly data, architectures and services for the same need to be developed. The evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing demand for scholarly applications like collaborator discovery, expert finding and research recommendation systems, in addition to several others. This research paper investigates the current trends and identifies the existing challenges in development of a big scholarly data platform, with specific focus on directions for future research and maps them to the different phases of the big data lifecycle.

A relative study of task scheduling algorithms in cloud computing environment

Cloud Computing is a paradigm of both parallel processing and distributed computing. It offers computing facilities as a utility service in pay as par use manner. Virtualization, self-service provisioning, elasticity and pay per use are the key features of Cloud Computing. It provides different types of resources over the Internet to perform user submitted tasks. In cloud environment, huge number of tasks are executed simultaneously, an effective Task Scheduling is required to gain better performance of the cloud system. Various Cloud-based Task Scheduling algorithms are available that schedule the users task to resources for execution. Due to the novelty of Cloud Computing, traditional scheduling algorithms cannot satisfy the clouds needs, the researchers are trying to modify traditional algorithms that can fulfil the cloud requirements like rapid elasticity, resource pooling and on-demand self-service. In this paper the current state of Task Scheduling algorithms has been discussed and compared on the basis of various scheduling parameters like execution time, throughput, makespan, resource utilization, quality of service, energy consumption, response time and cost.

Cloud-Based Big Data Analytics—A Survey of Current Research and Future Directions

The advent of the digital age has led to a rise in different types of data with every passing day. In fact, it is expected that half of the total data will be on the cloud by 2016. This data is complex and needs to be stored, processed, and analyzed for information that can be used by organizations. Cloud computing provides an apt platform for big data analytics in view of the storage and computing requirements of the latter. This makes cloud-based analytics a viable research field. However, several issues need to be addressed and risks need to be mitigated before practical applications of this synergistic model can be popularly used. This paper explores the existing research, challenges, open issues, and future research direction for this field of study.

Cloud algebra for cloud database management system

The relational algebra is used in relational database management system. The relational algebra is helpful in query processing in SQL. The Object Algebra is used in object oriented database management system for query processing. The object algebra is useful to manage the object in object oriented database management system. Now the concept of Cloud computing is introduced in new the era of computer technology. The concept cloud is also introduced in the field of databases. The cloud database management system is newly introduced in the field of database technology to manage the cloud data. This paper introduces the concept of cloud algebra for the cloud database management system. This paper proposed the concept of cloud algebra for query processing in the cloud. The data are spread over the internet as cloud. The creation of new cloud and setting the relationship among various cloud of data are facilitated by cloud algebra. The updating, deleting and retrieval of data in cloud are done by cloud algebra. The cloud algebra provides powerful computation while using the query processing in CDBMS.

Generalized Query Processing Mechanism in Cloud Database Management System

This is an epoch of Big data, Cloud computing, Cloud Database Management techniques. Traditional database approaches are not suitable for such colossal amount of data. To overcome the limitations of RDBMS, Map Reduce codes can be considered as a probable solution for such huge amount of data processing. Map Reduce codes provide both scalability and reliability. Users till date can work snugly with traditional Database approaches such as SQL, MYSQL, ORACLE, DB2, etc., and they are not aware of Map Reduce codes. In this paper, we are proposing a model which can convert any RDBMS queries to Map Reduce codes. We also gear optimization technique which can improve the performance of such amalgam approach.

Fully Homomorphic Encryption Scheme with Probabilistic Encryption Based on Euler’s Theorem and Application in Cloud Computing

Homomorphic encryption is an encryption scheme that allows different operations on encrypted data and produces the same result as well that the operations performed on the plaintext. Homomorphic encryption can be used to enhance the security measure of un-trusted systems which manipulates and stores sensitive data. Therefore, homomorphic encryption can be used in cloud computing environment for ensuring the confidentiality of processed data. In this paper, we propose a fully Homomorphic Encryption Scheme with probabilistic encryption for better security in cloud computing.