arXiv subjects

Mansaf Alam

Publications and source records attributed to Mansaf Alam.

At least 37 records · Page 2Linked to original sources

arXiv2017

BAMCloud: A Cloud Based Mobile Biometric Authentication Framework

With an exponential increase in number of users switching to mobile banking, various countries are adopting biometric solutions as security measures. The main reason for biometric technologies becoming more common in the everyday lives of consumers is because of the facility to easily capture biometric data in real time, using their mobile phones. Biometric technologies are providing the potential security framework to make banking more convenient and secure than it has ever been. At the same time, the exponential growth of enrollment in the biometric system produces massive amount of high dimensionality data that leads to degradation in the performance of the mobile banking systems. Therefore, in order to overcome the performance issues arising due to this data deluge, this paper aims to propose a distributed mobile biometric system based on a high performance cluster Cloud. High availability, better time efficiency and scalability are some of the added advantages of using the proposed system. In this paper a Cloud based mobile biometric authentication framework (BAMCloud) is proposed that uses dynamic signatures and performs authentication. It includes the steps involving data capture using any handheld mobile device, then storage, preprocessing and training the system in a distributed manner over Cloud. For this purpose we have implemented it using MapReduce on Hadoop platform and for training Levenberg-Marquardt backpropagation neural network has been used. Moreover, the methodology adopted is very novel as it achieves a speedup of 8.5x and a performance of 96.23%. Furthermore, the cost benefit analysis of the implemented system shows that the cost of implementation and execution of the system is lesser than the existing ones. The experiments demonstrate that the better performance is achieved by proposed framework as compared to the other methods used in the recent literature.

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arXiv2017

BAMHealthCloud: A Biometric Authentication and Data Management System for Healthcare Data in Cloud

Advancements in healthcare industry with new technology and population growth has given rise to security threat to our most personal data. The healthcare data management system consists of records in different formats such as text, numeric, pictures and videos leading to data which is big and unstructured. Also, hospitals have several branches at different locations throughout a country and overseas. In view of these requirements a cloud based healthcare management system can be an effective solution for efficient health care data management. One of the major concerns of a cloud based healthcare system is the security aspect. It includes theft to identity, tax fraudulence, insurance frauds, medical frauds and defamation of high profile patients. Hence, a secure data access and retrieval is needed in order to provide security of critical medical records in health care management system. Biometric authentication mechanism is suitable in this scenario since it overcomes the limitations of token theft and forgetting passwords in conventional token id-password mechanism used for providing security. It also has high accuracy rate for secure data access and retrieval. In this paper we propose BAMHealthCloud which is a cloud based system for management of healthcare data, it ensures security of data through biometric authentication. It has been developed after performing a detailed case study on healthcare sector in a developing country. Training of the signature samples for authentication purpose has been performed in parallel on hadoop MapReduce framework using Resilient Backpropagation neural network. From rigorous experiments it can be concluded that it achieves a speedup of 9x, Equal error rate (EER) of 0.12, sensitivity of 0.98 and specificity of 0.95 as compared to other approaches existing in literature.

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arXiv2017

Exploiting Data Reduction Principles in Cloud-Based Data Management for Cryo-Image Data

Cloud computing is a cost-effective way for start-up life sciences laboratories to store and manage their data. However, in many instances the data stored over the cloud could be redundant which makes cloud-based data management inefficient and costly because one has to pay for every byte of data stored over the cloud. Here, we tested efficient management of data generated by an electron cryo microscopy (cryoEM) lab on a cloud-based environment. The test data was obtained from cryoEM repository EMPIAR. All the images were subjected to an in-house parallelized version of principal component analysis. An efficient cloud-based MapReduce modality was used for parallelization. We showed that large data in order of terabytes could be efficiently reduced to its minimal essential self in a cost-effective scalable manner. Furthermore, on-spot instance on Amazon EC2 was shown to reduce costs by a margin of about 27 percent. This approach could be scaled to data of any large volume and type.

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arXiv2017

Outcome-Based Quality Assessment Framework for Higher Education

This research paper proposes a quality framework for higher education that evaluates the performance of institutions on the basis of performance of outgoing students. Literature was surveyed to evaluate existing quality frameworks and develop a framework that provides insights on an unexplored dimension of quality. In order to implement and test the framework, cloud-based big data technology, BigQuery, was used with R to perform analytics. It was found that how the students fair after passing out of a course is the outcome of educational process. This aspect can also be used as a quality metric for performance evaluation and management of educational organizations. However, it has not been taken into account in existing research. The lack of an integrated data collection system and rich datasets for educational intelligence applications, are some of the limitations that plague this area of research. Educational organizations are responsible for the performance of their students even after they complete their course. The inclusion of this dimension to quality assessment shall allow evaluation of educational institutions on these grounds. Assurance of this quality dimension shall boost enrolments in postgraduate and research degrees. Moreover, educational institutions will be motivated to groom students for placements or higher studies.

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arXiv2016

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 task of user to resources for execution. Due to the novelty of Cloud Computing, traditional scheduling algorithms cannot satisfy the needs of cloud , the researchers are trying to modify traditional algorithms that can fulfill 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, make span, resource utilization, quality of service, energy consumption, response time and cost.

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arXiv2016

Big Data Analytics in Cloud environment using Hadoop

The Big Data management is a problem right now. The Big Data growth is very high. It is very difficult to manage due to various characteristics. This manuscript focuses on Big Data analytics in cloud environment using Hadoop. We have classified the Big Data according to its characteristics like Volume, Value, Variety and Velocity. We have made various nodes to process the data based on their volume, velocity, value and variety. In this work we have classify the input data and routed to various processing node. At the last after processing from each node, we can combine the output of all nodes to get the final result. We have used Hadoop to partition the data as well as process it.

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arXiv2016

Cloud-Based Big Data Management and Analytics for Scholarly Resources: Current Trends, Challenges and Scope for Future Research

With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate and aid in better management of research. In addition to this, the evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing need for scholarly applications like collaborator discovery, expert finding and research recommendation systems. This research paper reviews the current trends and identifies the challenges existing in the architecture, services and applications of big scholarly data platform with a specific focus on directions for future research.

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arXiv2016

GENMR: Generalized Query Processing through Map Reduce In Cloud Database Management System

Big Data, Cloud computing, Cloud Database Management techniques, Data Science and many more are the fantasizing words which are the future of IT industry. For all the new techniques one common thing is that they deal with Data, not just Data but the Big Data. Users store their various kinds of data on cloud repositories. Cloud Database Management System deals with such large sets of data. For processing such gigantic amount of data, traditional approaches are not suitable because these approaches are not able to handle such size of data. To handle these, various solutions have been developed such as Hadoop, Map Reduce Programming codes, HIVE, PIG etc. Map Reduce codes provides both scalability and reliability. But till date, users are habitual of SQL, Oracle kind of codes for dealing with data and they are not aware of Map Reduce codes. In this paper, a generalized model GENMR has been implemented, which takes queries written in various RDBMS forms like SQL, ORACLE, DB2, MYSQL and convert into Map Reduce codes. A comparison has been done to evaluate the performance of GENMR with latest techniques like HIVE and PIG and it has been concluded that GENMR shows much better performance as compare to both the techniques. We also introduce an optimization technique for mapper placement problems to enhance the effect of parallelism which improves the performance of such Amalgam approach.

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arXiv2015

Recurrent Neural Network Based Hybrid Model of Gene Regulatory Network

Systems biology is an emerging interdisciplinary area of research that focuses on study of complex interactions in a biological system, such as gene regulatory networks. The discovery of gene regulatory networks leads to a wide range of applications, such as pathways related to a disease that can unveil in what way the disease acts and provide novel tentative drug targets. In addition, the development of biological models from discovered networks or pathways can help to predict the responses to disease and can be much useful for the novel drug development and treatments. The inference of regulatory networks from biological data is still in its infancy stage. This paper proposes a recurrent neural network (RNN) based gene regulatory network (GRN) model 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 the biological closeness and mathematical flexibility to model GRN. The RNN is able to capture complex, non-linear and dynamic relationship among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation even in noisy data. Hence, non-linear version of Kalman filter, i.e., generalized extended Kalman filter has been applied for weight update during network training. The developed model has been applied on DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We compared our results with other state-of-the-art techniques that show superiority of our model. Further, 5% Gaussian noise has been added in the dataset and result of the proposed model shows negligible effect of noise on the results.

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arXiv2015

Detection of Information leakage in cloud

Recent research shows that colluded malware in different VMs sharing a single physical host may use a resource as a channel to leak critical information. Covert channels employ time or storage characteristics to transmit confidential information to attackers leaving no trail.These channels were not meant for communication and hence control mechanisms do not exist. This means these remain undetected by traditional security measures employed in firewalls etc in a network. The comprehensive survey to address the issue highlights that accurate methods for fast detection in cloud are very expensive in terms of storage and processing. The proposed framework builds signature by extracting features which accurately classify the regular from covert traffic in cloud and estimates difference in distribution of data under analysis by means of scores. It then adds context to the signature and finally using machine learning (Support Vector Machines),a model is built and trained for deploying in cloud. The results show that the framework proposed is high in accuracy while being low cost and robust as it is tested after adding noise which is likely to exist in public cloud environments.

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arXiv2015

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.

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arXiv2015

Web Search Result Clustering based on Heuristic Search and k-means

Giving user a simple and well organized web search result has been a topic of active information Retrieval (IR) research. Irrespective of how small or ambiguous a query is, a user always wants the desired result on the first display of an IR system. Clustering of an IR system result can render a way, which fulfills the actual information need of a user. In this paper, an approach to cluster an IR system result is presented.The approach is a combination of heuristics and k-means technique using cosine similarity. Our heuristic approach detects the initial value of k for creating initial centroids. This eliminates the problem of external specification of the value k, which may lead to unwanted result if wrongly specified. The centroids created in this way are more specific and meaningful in the context of web search result. Another advantage of the proposed method is the removal of the objective means function of k-means which makes cluster sizes same. The end result of the proposed approach consists of different clusters of documents having different sizes.

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arXiv2015

Exploring Non-Homogeneity and Dynamicity of High Scale Cloud through Hive and Pig

Cloud computing deals with heterogeneity and dynamicity at all levels and therefore there is a need to manage resources in such an environment and properly allocate them. Resource planning and scheduling requires a proper understanding of arrival patterns and scheduling of resources. Study of workloads can aid in proper understanding of their associated environment. Google has released its latest version of cluster trace, trace version 2.1 in November 2014.The trace consists of cell information of about 29 days spanning across 700k jobs. This paper deals with statistical analysis of this cluster trace. Since the size of trace is very large, Hive which is a Hadoop distributed file system (HDFS) based platform for querying and analysis of Big data, has been used. Hive was accessed through its Beeswax interface. The data was imported into HDFS through HCatalog. Apart from Hive, Pig which is a scripting language and provides abstraction on top of Hadoop was used. To the best of our knowledge the analytical method adopted by us is novel and has helped in gaining several useful insights. Clustering of jobs and arrival time has been done in this paper using K-means++ clustering followed by analysis of distribution of arrival time of jobs which revealed weibull distribution while resource usage was close to zip-f like distribution and process runtimes revealed heavy tailed distribution.

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arXiv2015

Web Search Result Clustering based on Cuckoo Search and Consensus Clustering

Clustering of web search result document has emerged as a promising tool for improving retrieval performance of an Information Retrieval (IR) system. Search results often plagued by problems like synonymy, polysemy, high volume etc. Clustering other than resolving these problems also provides the user the easiness to locate his/her desired information. In this paper, a method, called WSRDC-CSCC, is introduced to cluster web search result using cuckoo search meta-heuristic method and Consensus clustering. Cuckoo search provides a solid foundation for consensus clustering. As a local clustering function, k-means technique is used. The final number of cluster is not depended on this k. Consensus clustering finds the natural grouping of the objects. The proposed algorithm is compared to another clustering method which is based on cuckoo search and Bayesian Information Criterion. The experimental results show that proposed algorithm finds the actual number of clusters with great value of precision, recall and F-measure as compared to the other method

cs.IR↗

arXiv2015

Dengue disease prediction using weka data mining tool

Dengue is a life threatening disease prevalent in several developed as well as developing countries like India.In this paper we discuss various algorithm approaches of data mining that have been utilized for dengue disease prediction. Data mining is a well known technique used by health organizations for classification of diseases such as dengue, diabetes and cancer in bioinformatics research. In the proposed approach we have used WEKA with 10 cross validation to evaluate data and compare results. Weka has an extensive collection of different machine learning and data mining algorithms. In this paper we have firstly classified the dengue data set and then compared the different data mining techniques in weka through Explorer, knowledge flow and Experimenter interfaces. Furthermore in order to validate our approach we have used a dengue dataset with 108 instances but weka used 99 rows and 18 attributes to determine the prediction of disease and their accuracy using classifications of different algorithms to find out the best performance. The main objective of this paper is to classify data and assist the users in extracting useful information from data and easily identify a suitable algorithm for accurate predictive model from it. From the findings of this paper it can be concluded that Naïve Bayes and J48 are the best performance algorithms for classified accuracy because they achieved maximum accuracy= 100% with 99 correctly classified instances, maximum ROC = 1, had least mean absolute error and it took minimum time for building this model through Explorer and Knowledge flow results

cs.CY↗

arXiv2015

An Effective Framework for Managing University Data using a Cloud based Environment

Management of data in education sector particularly management of data for big universities with several employees, departments and students is a very challenging task. There are also problems such as lack of proper funds and manpower for management of such data in universities. Education sector can easily and effectively take advantage of cloud computing skills for management of data. It can enhance the learning experience as a whole and can add entirely new dimensions to the way in which education is imbibed. Several benefits of Cloud computing such as monetary benefits, environmental benefits and remote data access for management of data such as university database can be used in education sector. Therefore, in this paper we have proposed an effective framework for managing university data using a cloud based environment. We have also proposed cloud data management simulator: a new simulation framework which demonstrates the applicability of cloud in the current education sector. The framework consists of a cloud developed for processing a universities database which consists of staff and students. It has the following features (i) support for modeling cloud computing infrastructure, which includes data centers containing university database; (ii) a user friendly interface; (iii) flexibility to switch between the different types of users; and (iv) virtualized access to cloud data.

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arXiv2015

Seeking Black Lining In Cloud

This work is focused on attacks on confidentiality that require time synchronization. This manuscript proposes a detection framework for covert channel perspective in cloud security. This problem is interpreted as a binary classification problem and the algorithm proposed is based on certain features that emerged after data analysis of Google cluster trace that forms base for analyzing attack free data. This approach can be generalized to study the flow of other systems and fault detection. The detection framework proposed does not make assumptions pertaining to data distribution as a whole making it suitable to meet cloud dynamism.

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arXiv2015

Analysis and Clustering of Workload in Google Cluster Trace based on Resource Usage

Cloud computing has gained interest amongst commercial organizations, research communities, developers and other individuals during the past few years.In order to move ahead with research in field of data management and processing of such data, we need benchmark datasets and freely available data which are publicly accessible. Google in May 2011 released a trace of a cluster of 11k machines referred as Google Cluster Trace.This trace contains cell information of about 29 days.This paper provides analysis of resource usage and requirements in this trace and is an attempt to give an insight into such kind of production trace similar to the ones in cloud environment.The major contributions of this paper include Statistical Profile of Jobs based on resource usage, clustering of Workload Patterns and Classification of jobs into different types based on k-means clustering.Though there have been earlier works for analysis of this trace, but our analysis provides several new findings such as jobs in a production trace are trimodal and there occurs symmetry in the tasks within a long job type

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