Javed Ferzund

Internet of Things based Expert System for Smart Agriculture

Agriculture sector is evolving with the advent of the information and communication technology. Efforts are being made to enhance the productivity and reduce losses by using the state of the art technology and equipment. As most of the farmers are unaware of the technology and latest practices, many expert systems have been developed in the world to facilitate the farmers. However, these expert systems rely on the stored knowledge base. We propose an expert system based on the Internet of Things (IoT) that will use the input data collected in real time. It will help to take proactive and preventive actions to minimize the losses due to diseases and insects/pests.

Modern Data Formats for Big Bioinformatics Data Analytics

Next Generation Sequencing (NGS) technology has resulted in massive amounts of proteomics and genomics data. This data is of no use if it is not properly analyzed. ETL (Extraction, Transformation, Loading) is an important step in designing data analytics applications. ETL requires proper understanding of features of data. Data format plays a key role in understanding of data, representation of data, space required to store data, data I/O during processing of data, intermediate results of processing, in-memory analysis of data and overall time required to process data. Different data mining and machine learning algorithms require input data in specific types and formats. This paper explores the data formats used by different tools and algorithms and also presents modern data formats that are used on Big Data Platform. It will help researchers and developers in choosing appropriate data format to be used for a particular tool or algorithm.

An Empirical Evaluation of Error Correction Methods and Tools for Next Generation Sequencing Data

Next Generation Sequencing (NGS) technologies produce massive amount of low cost data that is very much useful in genomic study and research. However, data produced by NGS is affected by different errors such as substitutions, deletions or insertion. It is essential to differentiate between true biological variants and alterations occurred due to errors for accurate downstream analysis. Many types of methods and tools have been developed for NGS error correction. Some of these methods only correct substitutions errors whereas others correct multi types of data errors. In this article, a comprehensive evaluation of three types of methods (k-spectrum based, Multi- sequencing alignment and Hybrid based) is presented which are implemented and adopted by different tools. Experiments have been conducted to compare the performance based on runtime and error correction rate. Two different computing platforms have been used for the experiments to evaluate effectiveness of runtime and error correction rate. The mission and aim of this comparative evaluation is to provide recommendations for selection of suitable tools to cope with the specific needs of users and practitioners. It has been noticed that k-mer spectrum based methodology generated superior results as compared to other methods. Amongst all the tools being utilized, Racer has shown eminent performance in terms of error correction rate and execution time for both small as well as large data sets. In multisequence alignment based tools, Karect depicts excellent error correction rate whereas Coral shows better execution time for all data sets. In hybrid based tools, Jabba shows better error correction rate and execution time as compared to brownie. Computing platforms mostly affect execution time but have no general effect on error correction rate.

Social Network Link Prediction using Semantics Deep Learning

Currently, social networks have brought about an enormous number of users connecting to such systems over a couple of years, whereas the link mining is a key research track in this area. It has pulled the consideration of several analysts as a powerful system to be utilized as a part of social networks study to understand the relations between nodes in social circles. Numerous data sets of today’s interest are most appropriately called as a collection of interrelated linked objects. The main challenge faced by analysts is to tackle the problem of structured data sets among the objects. For this purpose, we design a new comprehensive model that involves link mining techniques with semantics to perform link mining on structured data sets. The past work, to our knowledge, has investigated on these structured datasets using this technique. For this purpose, we extracted real-time data of posts using different tools from one of the famous SN platforms and check the society’s behavior against it. We have verified our model utilizing diverse classifiers and the derived outcomes inspiring.

Designing Novel Queries for Analysing NoSQL Data of Gene-Disease Associations

To precisely identify gene associated diseases has been an open area of research for biological scientists to ensure clinical and psychological symptoms and treatment for human diseases. Because whole Human Genome is defined now it is the next step to find all necessary possible factors from such a complex data set that cause gene mutations and hence lead inherited and/or non-inherited diseases. So our research implementation combines all important factors from different biomolecular data sources to make one integrated data set and defines new relationships among these factors for gene associated disease/s that were not present in existing platforms. This paper presents a novel query model for NoSQL data storage that can help researchers to visualise relationships among gene factors and two new factors termed as “causative factors” and “drugs/treatment” for associated diseases. Since no data source applies graphical querying for gene associated diseases, our proposed novel cypher query model can help researchers to deeply analyse data set and get results in an efficient manner. The proposed query model writes novel cypher queries for this research domain on a graphical data model implemented in neo4j, which is a NoSQL (Not Only Structured) database. Use of NoSQL database and NoSQL query language has overcome certain limitations of relational databases, the existing data platforms had to cope up with. This paper gives a new suitable data storage format and effective data search queries for large, complex, semi-structured and multi-dimensional gene associated diseases data set to efficiently define new relationships among factors format to open new horizons of research.

Recommender System for Journal Articles using Opinion Mining and Semantics

Till date, the dominant part of Recommender Systems (RS) work focusing on single domain, i.e. for films, books and shopping and so on. However, human inclinations may traverse over numerous areas. Thus, utilization practices on related things from various domains can be valuable for RS to make recommendations. Academic articles, such as research papers are the way to express ideas and thoughts for the research community. However, there have been a lot of journals available which recognize these technical writings. In addition, journal selection procedure should consider user experience about the journals in order to recommend users most relevant journal. In this work of journal recommendation system, the data about the user experience targeting various aspects of journals has been gathered which addresses user experience about any journal. In addition, data set of archive articles has been developed considering the user experience in this regard. Moreover, the user experience and gathered data of archives are analyzed using two different frameworks based on semantics in order to have better consolidated recommendations. Before submission, we offer services on behalf of the research community that exploit user reviews and relevant data to suggest suitable journal according to the needs of the author.

Need and Role of Scala Implementations in Bioinformatics

Next Generation Sequencing has resulted in the generation of large number of omics data at a faster speed that was not possible before. This data is only useful if it can be stored and analyzed at the same speed. Big Data platforms and tools like Apache Hadoop and Spark has solved this problem. However, most of the algorithms used in bioinformatics for Pairwise alignment, Multiple Alignment and Motif finding are not implemented for Hadoop or Spark. Scala is a powerful language supported by Spark. It provides, constructs like traits, closures, functions, pattern matching and extractors that make it suitable for Bioinformatics applications. This article explores the Bioinformatics areas where Scala can be used efficiently for data analysis. It also highlights the need for Scala implementation of algorithms used in Bioinformatics.

A Novel Big Data Storage Model for Protein-Protein Interaction and Gene-Protein Associations

NGS (Next Generation Sequencing) technology has resulted in huge amount of proteomics data that exists in the form of interactions (protein-protein, gene-protein, and gene-disease). ETL (Extraction, Transformation, and Loading) techniques are very useful for Databases. Existing Rational Databases are not unified and having SQL (Structured Query Language). Proteomics data requires improvement for Integration of different Data sources. With the usage of NoSQL (not only SQL), improve the efficiency and performance. For this, a novel based unified model has been designed for protein interactions data (P-P, G-G, and G-D) by using Apache HBase to evaluate given the model, different case studies have been used.

Using PCA and Factor Analysis for Dimensionality Reduction of Bio-informatics Data

Large volume of Genomics data is produced on daily basis due to the advancement in sequencing technology. This data is of no value if it is not properly analysed. Different kinds of analytics are required to extract useful information from this raw data. Classification, Prediction, Clustering and Pattern Extraction are useful techniques of data mining. These techniques require appropriate selection of attributes of data for getting accurate results. However, Bioinformatics data is high dimensional, usually having hundreds of attributes. Such large a number of attributes affect the performance of machine learning algorithms used for classification/prediction. So, dimensionality reduction techniques are required to reduce the number of attributes that can be further used for analysis. In this paper, Principal Component Analysis and Factor Analysis are used for dimensionality reduction of Bioinformatics data. These techniques were applied on Leukaemia data set and the number of attributes was reduced from to.

Large Scale Graph Matching(LSGM): Techniques, Tools, Applications and Challenges

Large Scale Graph Matching (LSGM) is one of the fundamental problems in Graph theory and it has applications in many areas such as Computer Vision, Machine Learning, Pattern Recognition and Big Data Analytics (Data Science). Matching belongs to the combinatorial class of problems which refers to finding correspondence between the nodes of a graph or among set of graphs (subgraphs) either precisely or approximately. Precise Matching is also known as Exact Matching such as (sub)Graph Isomorphism and Approximate Matching is called Inexact Matching in which matching activity concerns with conceptual/semantic matching rather than focusing on structural details of graphs. In this article, a review of matching problem is presented i.e. Semantic Matching (conceptual), Syntactic Match-ing (structural) and Schematic Matching (Schema based). The aim is to present the current state of the art in Large Scale Graph Matching (LSGM), a systematic review of algorithms, tools and techniques along with the existing challenges of LSGM. Moreover, the potential application domains and related research activities are provided.