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According to World Health Organization, 5—18% births around the world are premature, and this rate is on its rise. Recent trend has been to develop computational tools which could support obstetricians in their daily practice. This work is aimed at extracting novel diagnostic features for term vs. preterm births classification based on the dynamics of contraction and non-contractions (dummy) intervals. To achieve this, 26 uterine records of 18 pregnant women, resulting in 13 term and 13 preterm deliveries, were analyzed in this study. Each record was of 30-min duration, and comprised three electrohysterographic (EHG) and one externally recorded tocographic (Toco) signal. Granger causal analysis of contraction and dummy intervals of recorded signals was performed to extract potential features. These include coupling strength and directionality indices, and were subsequently utilized by a quadratic discriminator to classify between term and preterm births. The extracted features achieved high discriminatory power of 94%, 95%, 91%, and 97% for sensitivity, specificity, classification accuracy, and area under the ROC curve, respectively. These findings show that Granger causal analysis might be adopted as a computational tool to quantify EHG-Toco dynamics for the risk-assessment of the preterm delivery.

Business owners and managers need strategic information to plan and execute their decisions regarding business operations. They work in a cyclic plan of execution and evaluation. In order to run this cycle smoothly, they need a mechanism that should access the entire business performance. The sole purpose of this study is to assist them through applied research framework-based analysis to obtain effective results. The backbone of the purposed framework is a hybrid mechanism that comprises business intelligence (BI) and machine learning (ML) to support 360-degree organization-wide analysis. BI modeling gives descriptive and diagnostic analysis via interactive reports with quick ad hoc analysis which can be performed by executives and managers. ML modeling predicts the performance and highlights the potential customers, products, and time intervals. The whole mechanism is resource-efficient and automated once it binds with the operational data pipeline and presented results in a highly efficient manner. Data analysis is far more efficient when it is applied to the right data at the right time and presents the insights to the right stakeholders in a friendly, usable environment. The results are beneficial to viewing the past, current, and future performance with self-explanatory graphical interpretation. In the proposed system, a clear performance view is possible by utilizing the sales transaction data. By exploring the hidden patterns of sales facts, the impact of the business dimensions is evaluated and presented on a dynamically filtered dashboard.

In the education system, the students may find counselors, but student-to-counselor ratio is higher, which forces us to implement an automated system for the guidance of the students. Career counseling can be useful for students to evaluate their careers and select the best direction for the future. This chapter aims to explore, develop, and implement the effective means of analyzing student career counseling, guidelines, and decision making. The authors have developed a realistic dataset from a different mindset of students. The research started once the student provides the machine input about the individual choices about taking admission for matriculation, intermediate, and or short course. The machine learning algorithms like logistic model tree, naïve Bayes, J48, and random forest are used to predict career options. In evaluated results, they found the best algorithm based on the accuracy of kappa statistics, mean absolute error, and correctly classified or incorrectly classified for career-related problems.

This article describes how the mobile app market is growing day by day. Mobile app stores have created the opportunity for the users to publicly provide feedback on mobile apps that they have installed or used. In this way, users are involved in the design and development of mobile apps, which was done by designers and developers before. Online user reviews are a useful source to know the user's perception about mobile apps and thus provide a way of co-value creation. This article is conducted to investigate the factors affecting the acceptability of mobile Apps. Main purpose of this article is to use online reviews for construction of a model instead of using existing acceptance theories. The model proposed in this research is based on the analysis of reviews and app information extracted from the Google Play Store. The ratings and number of installs are two key indicators of the popularity of an app. Other characteristics like price, category and size also influence the user's selection of an app. The findings showed the appropriateness of the proposed model and hypotheses for evaluating mobile apps acceptability and popularity. This article provides mobile app developers and marketers with an insight into the mobile app popularity and acceptability dynamics.

Depression is a common mental health disorder. Despite its high prevalence, the only way of diagnosing depression is through self-reporting. However, 70% of the patients would not consult doctors at an early stage of depression. Meanwhile people increasingly relying on social media for sharing emotions, and daily life activities thus helpful for detecting their mental health. Inspired by these a total of 179 depressive individuals selected from Twitter, who have reported depression and they are on medical treatment. A sample of their recent tweets collected ranges from (200 to 3200) tweets per person. From their tweets, we selected 100 most frequently used words using Term Frequency-Inverse Document Frequency (TF-IDF). Later, we used the 14 psychological attributes in Linguistic Inquiry and Word Count (LIWC) to classify these words into emotions. Moreover, weights were assigned to each word from happy to unhappy after classification by LIWC and trained machine learning classifiers to classify the users into three classes of depression High, Medium, and Low. According to our study, better features selections and their combination will help to improve performance and accuracy of classifiers.

With the advent of recent technology, smart systems are replacing traditional systems. Wireless sensors and RFID are important components of Smart systems and have many applications such as Smart Sewerage, Smart Transportation, Smart Grid, Smart Agriculture, Smart Health and Smart Homes. Busy airports in the world have to manage a large number of flights per hour. Even, within a minute many flights land and takeoff. So, Airport Apron Management System must be efficient and error free. In this paper, we have presented the Smart Airport Apron Management System for managing flights landing, parking and takeoff. The system supports operations like finding free parking space, total allocated or free slots, flights handling and information display using lights. We have modeled the heterogeneous sensors of the system and their interaction with the system using Internet of Things (IoT). All the operations statically and dynamically are modeled in Vienna Development Method Language (VDM-SL) to provide proof of correctness of the proposed system. VDM-SL is a specification language which models a system by mathematical notations using discrete mathematics concepts.

Predicting the drug targets in the base of cancer cell line is one of the hottest issues in cancer treatment. Drug sensitivity describes which drug is perfect for cell line in certain condition or disease. This condition exists due to change in human metabolism. Different techniques are used for cancer treatment such as radiotherapy, hormone therapy, chemotherapy, and surgery. Many statistical methods and machine learning algorithms such as support vector machine, principal component analysis (PCA), logistic regression, simple linear regression, naive Bayes classifier, generalized linear regression, and random forest have been used for drug target prediction. However, these predictors take more time for computation using different tools such as MATLAB and R tool. In this study, different machine learning techniques are applied using Apache Spark to predict drug targets. Apache Spark uses the resilient distributed dataset (RDD) technique for in-memory fast computation and fault tolerance. The obtained results indicate that Spark provides better accuracy in short time when compared with existing tools.

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.

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.

Gene expressions are controlled by a series of processes known as Gene Regulation, and their abstract mapping is represented by Gene Regulatory Network (GRN) which is a descriptive model of gene interactions. Reverse engineering GRNs can reveal the complexity of gene interactions whose comprehension can lead to several other details. RNA-seq data provides better measurement of gene expressions, however it is difficult to infer GRNs using it because of its discreteness. Multiple other methods have already been proposed to infer GRN using RNA-seq data, but these methodologies are difficult to grasp. In this paper, a simple model is presented to infer GRNs, using RNA-seq based coexpression map provided by GeneFriends database, and a graph-based database tool is used to create regulatory network. The obtained results show that it is convenient to use graph database tools to work with regulatory networks instead of developing a new model from scratch.

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., 12 pages, 11 figures, 2 tables

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.

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.

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.

Graph is an expressive way to represent dynamic and complex relationships in highly connected data. In today’s highly connected world, general purpose graph databases are providing opportunities to experience benefits of semantically significant networks without investing on the graph infrastructure. Examples of prominent graph databases are: Neo4j, Titan and OrientDB etc. In biological OMICS landscape, Interactomics is one of the new disciplines that focuses mainly on the data modeling, data storage and retrieval of biological interaction data. Biological experiments generate prodigious amount of data in various formats(semi-structured or unstructured). The large volume of such data posses challenges for data acquisition, data integration, multiple data modalities (either data model of storage model, storage, processing and visualization. This paper aims at designing a well suited graphical data storage model for biological information which is collected from major heterogeneous biological data repositories, by using graph database.

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.

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.

Machine learning techniques provide learning mechanism that can be used to induce knowledge from data. A few studies exist on the use of machine learning techniques for medical diagnosis, prediction and treatment. In this study we evaluate different machine learning techniques for birth classification (cesarean or normal). Data on cesarean section is collected and different medical factors are identified that result in cesarean births. A birth classification model is built using decision tree and artificial neural networks. It can classify the births into normal and cesarean with an average accuracy, precision and recall of 80%, 85% and 84% respectively. Association rule mining is used to extract disease patterns from the collected data. It highlights the important medical factors that are associated with cesarean births.

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.

In a gradually idealized, homogeneous and sanitized world, Security and confidentiality of organization’s policies, practices, rules and procedure is becoming a major issue for any enterprise or association and there are also such a variety of criminal cases. So to maintain the security of organization’s future projects and secret missions, to help to avoid criminal cases like child kidnapping, mobile snatching and etc. by monitoring geographical location and to monitor the children by their parents after tracking their daily activities along with its date and time, we proposed a mobile and web based monitoring system “A Mobile, Enterprise Solution (True Monitor)” which “ll run invisibly on target device to track all the activities including device information (Inc. Id, Name, Model, OS, OS Version, IMEI No., Carrier, Carrier No., IMSI No., Battery, Wi-Fi/GPRS/3G, Location Tracker Status, Microphone Detection, Status (Lock/Unlock), Last Login, IP Address, Primary Email ID), Contact Logs, Call logs (Incoming, Outgoing, Missed), Call Recordings, Call Duration, Text Message Logs (Inbox, Sent) with its source and destination mobile number, GPS Location, Instant Messages (Facebook, Whatsapp, Skype, Viber & etc.), Mailbox, Browser History, Multimedia files and Blocked Websites along with its date and time. This information would have sent to the server (monitored by administrator) with an alert notification to the administrator in the result of any activity which does from the user mobile with detailed information.

A lot of information can be obtained from configuration management systems and post-release bug databases like Bugzilla. In this paper we focus on the question whether there are language specific bug patterns in large programs. For this purpose we implemented a system for extracting the necessary information from the Mozilla project files. A comparison of the extracted information with respect to the programming language showed that there are bug patterns specific to programming languages. In particular we found that Java files of the Mozilla project are less error prone than C and C++ files. Moreover, we found out that the bug lifetime when using Java was almost double the lifetime of bugs in C or C++ file.

A bug triage system is used for validation and allocation of bug reports to the most appropriate developers. An automatic bug triage system may reduce the software maintenance time and improve its quality by correct and timely assignment of new bug reports to the appropriate developers. In this paper, we present the techniques behind an automatic bug triage system, which is based on the categorization of bug reports. In order to obtain an automatic bug triage system we used these techniques and performed comparative experiments. We downloaded 1,983 resolved bug reports along with the developer activity data from the Mozilla open source project. We extracted the relevant features like report title, report summary etc., from each bug report, and extracted developer’s name who resolved the bug reports from the developers activity data. We processed the extracted textual data, and obtained the term-to-document matrix using parsing, filtering and term weighting methods. For term weighting methods we used simple term frequency and TF×IDF (term frequency inverse document frequency) methods. Furthermore, we reduced the dimensionality of the obtained term-to-document matrix by applying feature selection and latent semantic indexing methods. Finally we used seven different machine learning methods for the classification of bug reports. The best obtained bug triage system is based on latent semantic indexing and support vector machine having 44.4% classification accuracy. The average precision and recall values are 30% and 28%, respectively.

Change management is a challenging task in software maintenance. Changes are made to the software during its whole life. Some of these changes introduce errors in the code which result in failures. Software changes are composed of small code units called hunks, dispersed in source code files. In this paper we present a technique for classifying software changes based on hunk metrics. We classify individual hunks as buggy or bug-free, thus we provide an approach for bug prediction at the smallest level of granularity. We introduce a set of hunk metrics and build classification models based on these metrics. Classification models are built using logistic regression and random forests. We evaluated the performance of our approach on 7 open source software projects. Our classification approach can classify hunks as buggy or bug free with 81 percent accuracy, 77 percent buggy hunk precision and 67 percent buggy hunk recall on average. Most of the hunk metrics are significant predictors of bugs but the set of significant metrics varies among different projects.

Reducing bugs in software is a key issue in software development. Many techniques and tools have been developed to automatically identify bugs. These techniques vary in their complexity, accuracy and cost. In this paper we empirically investigate the language constructs which frequently contribute to bugs. Revision histories of eight open source projects developed in multiple languages are processed to extract bug-inducing language constructs. Twenty six different language constructs and syntax elements are identified. We find that most frequent bug-inducing language constructs are function calls, assignments, conditions, pointers, use of NULL, variable declaration, function declaration and return statement. These language constructs account for more than 70 percent of bug-inducing hunks. Different projects are statistically correlated in terms of frequencies of bug-inducing language constructs. Developers within a project and between different projects also have similar frequencies of bug-inducing language constructs. Quality assurance developers can focus code reviews on these frequent bug-inducing language constructs before committing changes.