Sarwar Kamal

A MapReduce approach to diminish imbalance parameters for big deoxyribonucleic acid dataset

BACKGROUND In the age of information superhighway, big data play a significant role in information processing, extractions, retrieving and management. In computational biology, the continuous challenge is to manage the biological data. Data mining techniques are sometimes imperfect for new space and time requirements. Thus, it is critical to process massive amounts of data to retrieve knowledge. The existing software and automated tools to handle big data sets are not sufficient. As a result, an expandable mining technique that enfolds the large storage and processing capability of distributed or parallel processing platforms is essential. METHOD In this analysis, a contemporary distributed clustering methodology for imbalance data reduction using k-nearest neighbor (K-NN) classification approach has been introduced. The pivotal objective of this work is to illustrate real training data sets with reduced amount of elements or instances. These reduced amounts of data sets will ensure faster data classification and standard storage management with less sensitivity. However, general data reduction methods cannot manage very big data sets. To minimize these difficulties, a MapReduce-oriented framework is designed using various clusters of automated contents, comprising multiple algorithmic approaches. RESULTS To test the proposed approach, a real DNA (deoxyribonucleic acid) dataset that consists of 90 million pairs has been used. The proposed model reduces the imbalance data sets from large-scale data sets without loss of its accuracy. CONCLUSIONS The obtained results depict that MapReduce based K-NN classifier provided accurate results for big data of DNA.

Theoretical Analysis of Different Classifiers under Reduction Rough Data Set: A Brief Proposal

Rough set plays vital role to overcome the complexities, vagueness, uncertainty, imprecision, and incomplete data during features analysis. Classification is tested on certain dataset that maintain an exact class and review process where key attributes decide the class positions. To assess efficient and automated learning, algorithms are used over training datasets. Generally, classification is supervised learning whereas clustering is unsupervised. Classifications under mathematical models deal with mining rules and machine learning. The Objective of this work is to establish a strong theoretical and manual analysis among three popular classifier namely K-nearest neighbor K-NN, Naive Bayes and Apriori algorithm. Hybridization with rough sets among these three classifiers enables enable to address larger datasets. Performances of three classifiers have tested in absence and presence of rough sets. This work is in the phase of implementation for DNA Deoxyribonucleic Acid datasets and it will design automated system to assess classifier under machine learning environment.

Evolutionary framework for coding area selection from cancer data

AbstractCancer data analysis is significant to detect the codes that are responsible for cancer diseases. It is significant to find out the coding regions from diseases infected biological data. The infected data will be helpful to design proper drugs and will be supportable in laboratory assessments. Codes bear specific meaning on various features as well as symptoms of diseases. Coding of biological data is a key area to get exact information on animals to discover the desired medicine. In the current work, four different machine learning approaches such as support vector machine (SVM), principal component analysis (PCA) technique, neural mapping skyline filtering (NMSF) and Fisher’s discriminant analysis (FDA) were applied for data reduction and coding area selection. The experimental analysis established that the SVM outperforms PCA and FDA. However, due to the mapping facility, NMSF outperforms SVM. Thus, the NMSF achieved the preeminent results among the four techniques. Matthews’s correlation coefficient was used to evaluate the accuracy, specificity, sensitivity, F-measures and error rate of the four methods that are used to determine the coding area. Detailed experimental analysis included comparison study among the four classifiers for the deoxyribonucleic acid dataset.

FbMapping: An Automated System for Monitoring Facebook

In recent modernized era, the number of the Facebook users is increasing dramatically. Moreover, the daily life information on social networking sites is changing energetically over web. Teenagers and university students are the major users for the different social networks all over the world. In order to maintain rapid user satisfactions, information flow and clustering are essential. However, these tasks are very challenging due to the excessive datasets. In this context, cleaning the original data is significant. Thus, in the current work the Fishers Discrimination Criterion (FDC) is applied to clean the raw datasets. The FDC separates the datasets for superior fit under least square sense. It arranges datasets by combining linearly with greater ratios of between – groups and within the groups. In the proposed approach, the separated data are handled by the Bigtable mapping that is constructed with Map specification, tabular representation and aggregation. The first phase organizes the cleaned datasets in row, column and timestamps. In the tabular representation, Sorted String Table (SSTable) ensures the exact mapping. Aggregation phase is employed to find out the similarity among the extracted datasets. Mapping, preprocessing and aggregation help to monitor information flow and communication over Facebook. For smooth and continuous monitoring, the Dynamic Source Monitoring (DSM) scheme is applied. Adequate experimental comparisons and synthesis are performed with mapping the Facebook datasets. The results prove the efficiency of the proposed machine learning approaches for the Facebook datasets monitoring.

Impact analysis of facebook in family bonding

Abstract Nowadays, Facebook is a very popular social communication media. People utilize Facebook to express their thoughts, ideas, poems, and sorrows on Facebook. In the age of information superhighway, majority of the teenagers are not sharing their difficulties, problems, inconsistency, inability and failure with their parents in Bangladesh. However, they share with their friends on Facebook. Subsequently, their friends are making comments, providing shelters and affections to them. Due to lack of education and experiences on technology, guardians in Bangladesh are not aware about the communications and addictions on social Medias. Therefore, there are generating gaps between the guardians and their children. In this paper, a survey-based and Apriori algorithm analyzes the behaviors of teenagers’ by collecting information from their Facebook pages. Parents and teachers opinions are also considered about the activities of students on home and institutes. Here, age limits of targeted teen agers are between 16 and 18. From this analysis, vulnerable relationship between parents and their teenage children have been noticed. The pivotal problem was that teens are spending more time on Facebook and parents want them to the table during study time and school time.

An integrated algorithm for local sequence alignment

Local sequence alignment (LSA) is an essential part of DNA sequencing. LSA helps to identify the facts in biological identity, criminal investigations, disease identification, drug design and research. Large volume of biological data makes difficulties to the performance of efficient analysis and proper management of data in small space has become a serious issue. We have subdivided the data sets into various segments to reduce the data sets as well as for efficient memory use. The integration of dynamic programming (DP) and Chapman–Kolmogorov equations (CKE) makes the analysis faster. The subdivision process is named data reducing process (DRP). DRP is imposed before DP and CKE. This approach needs less space compared with other methods and the time requirement is also improved.

MetaG: a graph-based metagenomic gene analysis for big DNA data

Microbial interactions and relationships are significant for animals, insects and plants. Metagenomic research enables properassessments and analysis for microbial organs and communities. The analysis helps to gain detailed insights on miscopies insects. Recent machine learning techniques focused on algorithms and data mining tools to check the depth of interactions and relationships on metagenomic dataset. Accurate analysis over large genes helps to solve real-world problems for public interest. In this regard, graph-centric big gene dataset representations are very important. De Bruijn graph is one the pivotal media to demonstrate the relationships and interactions of large genes dataset or metagenomic dataset. In this research, mapping-based metagenomic graphical (MetaG) genomes representation has been demonstrated. Data cleaning is done before applying graphical illustration. Random mapping is used to assess the variations in dataset. Euler path-based De Bruijn graph is used to sketch the gene annotation, translations, signaling and coding. This research helps in computational biology to map the genomic information in graphical ways with clear conceptions. Adequate experimental comparisons as well as analysis established the claims with tables and graphs.

Fuzzy Logic over Ontological Annotation and Classification for Spatial Feature Extraction

With the advent of new web technology, Image Annotation and Classification has paved the way for invoking an efficient and effective research area as it is of immense importance in searching images from different categories of relevant images using keywords. This may be an impressive tool in describing image content as object or textual information to classify images. To serve this purpose, many techniques have been lunched for automatic image annotation and classification based on content and exit metadata. Automatic image annotation however, is highly difficult and challengeable. So users have to follow the annotation manually. In this paper, we applied fuzzy logic implication and fuzzy set operation for Historical image classification. We have compared the outcome of how fuzzy classification is better ontological image classification. Fuzzy logic plays an important rule so that the margin of the classification becomes more accurate. Here we imposed fuzzy matrix optimization for Spatial Image classification of Historical image data. Fuzzy matrix determines the optimal values of spatial data which are near about correct with less uncertainty. Fuzzy membership function also works to estimate the values before using in fuzzy matrix. We also pro- posed a manual method for image annotation based on IPTC metadata with a view to retrieving images with its corresponding information for automatic semantic ontological and fuzzy classification using linked data. We strived to experiment on about 400 images of different historical heritages.

Teenagers Sentiment Analysis from Social Network Data

Now a day’s social networks generate a huge data from user view, emotions, thoughts, opinions, suggestions regarding different products, events, places, brands, politics etc. Those data plays an important role in different ways. Technically, in the interval of every 60 s in a social network like Facebook, lots of comments and statuses are updated which are associated with thousands of contexts. However, realization of different ways in which texts are seems to be appeared on Facebook can help us to improve our products. In general, different organizations such as text organization used sentimental analysis for successful classification. They transpired feelings, emotions in different form like positive, negative, friendly, unfriendly etc. To solve this problem we have concentrated on different techniques of deep learning. In this paper we highlight about few deep learning implementation techniques known as Convolutional Neural Network and Recursive Neural Network with classification of different texts.