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About this book

This book presents a key solution for current and future technological issues, adopting an integrated system approach with a combination of software engineering applications. Focusing on how software dominates and influences the performance, reliability, maintainability and availability of complex integrated systems, it proposes a comprehensive method of improving the entire process. The book provides numerous qualitative and quantitative analyses and examples of varied systems to help readers understand and interpret the derived results and outcomes. In addition, it examines and reviews foundational work associated with decision and control systems for information systems, to inspire researchers and industry professionals to develop new and integrated foundations, theories, principles, and tools for information systems. It also offers guidance and suggests best practices for the research community and practitioners alike.

The book’s twenty-two chapters examine and address current and future research topics in areas like vulnerability analysis, secured software requirements analysis, progressive models for planning and enhancing system efficiency, cloud computing, healthcare management, and integrating data-information-knowledge in decision-making.

As such it enables organizations to adopt integrated approaches to system and software engineering, helping them implement technological advances and drive performance. This in turn provides actionable insights on each and every technical and managerial level so that timely action-based decisions can be taken to maintain a competitive edge. Featuring conceptual work and best practices in integrated systems and software engineering applications, this book is also a valuable resource for all researchers, graduate and undergraduate students, and management professionals with an interest in the fields of e-commerce, cloud computing, software engineering, software & system security and analysis, data-information-knowledge systems and integrated systems.

Table of Contents

Frontmatter

Real-Time Distributed Denial-of-Service (DDoS) Attack Detection Using Decision Trees for Server Performance Maintenance

Abstract
With the incorporation of the Internet in our lives and its ever-increasing usage, it becomes all the more imperative to safeguard our systems and data against the malicious attacks. One of such malicious attacks is distributed denial-of-service (DDoS) attack, in which multiple nodes target a single target node to flood it. DDoS attacks in most of the cases directly target the server, thereby posing an important question on the security of the systems. DDoS attacks affect the entire network, thereby resulting in its downtime, unavailability of the services, and performance degradation. Due to this phenomenon, business losses are incurred, and hence, it becomes important to detect them before the damage is done. In this research work, a model is introduced which performs real-time detection of DDoS attacks. This model uses the decision tree classifier for detection of DDoS attacks. First, the features are extracted, and the information gain is calculated. Based on this, the decision tree is constructed which is then used to classify an instance as DDoS or not using a classifier algorithm. This model has demonstrated a success rate of 90.2% which is an improvement over the currently available algorithms. Furthermore, it also outperforms the existing algorithms in terms of sensitivity and specificity. The most significant feature of the model is that it operates in a live system in real-time conditions. This ensures protection against data losses and also helps in identifying the source of the attack. This system suggests a lightweight data mining approach to detect DDoS attacks using decision trees.
Mrunmayee Khare, Rajvardhan Oak

Cloud Computing: Vulnerability and Threat Indications

Abstract
An innovative approach of providing and delivering resources is the new emerging technology “cloud computing.” The time demands a reduction in expenditure as an end result of financial restrictions, where cloud computing has established productive position and is seeing immense large-scale investment. Still, despite the significance, users and clients are nervous at the prospects and security issues of cloud technology. Threat and vulnerability factors are majorly, one of the scrutinizing issues in cloud, if it is not properly secured due to which, a direct control loss over the system is creating nevertheless accountable threat. This paper explains, based on present scenarios, how the vulnerabilities analysis will evaluate current emerging threats of cloud technology. The paper focuses to provide indication of the recent threats and vulnerabilities in cloud. The study will help the providers and users to make knowledgeable decisions about threat mitigation and threat countermeasures within a cloud strategy.
Vaishali Singh, S. K. Pandey

Proposed Algorithm for Creation of Misuse Case Modeling Tree During Security Requirements Elicitation Phase to Quantify Security

Abstract
Gathering secure measurement is the first step toward the developed of comprehensive secured software. Security is an intangible measure also considered as a non-functional attribute which needs to be quantified in some manner using tools and techniques during the preliminary phases, i.e., the requirements engineering stage of software development process. Studies carried out so far have shown and suggested that among the available techniques of security measurement, misuse use case modeling which is a form of unified modeling approach is very easy to implement during the requirements engineering (RE) phase of SDLC. This research work proposes an algorithm for creation of misuse case modeling tree during the security requirements elicitation phase. This algorithm can be customized according to the specific software application and is supported and synchronized with industry accepted standards like Common Vulnerability Scoring System (CVSS) and Common Vulnerability Enumeration (CVE). The work proposed is an extension of Misuse Case Oriented Quality Requirements (MCOQR) Framework and metrics and includes software application-specific database. The proposed work also showcases the areas where future work can be carried out to further fortify the entire system during the software development process, thereby contributing to the enhancement of various measures of performance.
Ajeet Singh Poonia, C. Banerjee, Arpita Banerjee, S. K. Sharma

Big Data Analytics for Data Quality Improvement to Enhance Evidence-Based Health Care in Developing Countries

Abstract
Organizations need to overcome all data analytics barriers in order to realize value from data by transforming it into insight leading to action that can add value to their businesses. Some of these barriers are as a result of the existing applications and technologies that are too rigid and have not been revised to match the increasing users’ demands, whereas others arise from weaknesses in culture, stewardship, and governance that become salient when the need for quality data for decision-making increases. Attaining and keeping data quality is one of the most puzzling governance issues within organizations causing data quality errors that impede decision-making. Many health institutions in developing countries, like those in Africa, are faced with various challenges such as limited resources including manpower, unshared information, lack of privacy, poor drawing of insights from a variety of structured and unstructured data and insufficient budgets. The objective of this research work is to report on how big data analytics could be leveraged by these institutions to address these potential challenges. However, many health institutions still lack clarity of how big data analytics could be leveraged to improve data quality needed for evidence-based health care. This research work carried out a quantitative analysis of data collected from a health institution in South Africa. Results indicated that environment, tasks, data governance and structures, data quality management and technology are significant in improving data quality. This implies that to achieve and maintain data quality organizations need to pay attention to the elements such as people, process, technologies, and best practices that drive data governance. This study contributes to the ongoing debate on using big data analytics to enhance data quality.
Billy Mathias Kalema, Viola Vivian Busobozi

Securing the Data Deduplication to Improve the Performance of Systems in the Cloud Infrastructure

Abstract
Data duplication is a data quality problem which may exist in database system where the same record is stored multiple times in the same or different database systems. Data duplication issue may lead to issues like data redundancy, wasted cost, lost income, negative impact on response rate, ROI, and brand reputation, poor customer service, inefficiency and lack of productivity, decreased user adoption, inaccurate reporting, less informed decisions, and poor business process. The solution to the problem of data duplication may be countered with data deduplication which is often termed as intelligent compression or single instance storage. Data deduplication eradicates duplicate copies of information resulting in the reduction of storage overheads and in enhancement of various performance parameters. The recent study on data deduplication has shown that there exists modern data redundancy in primary storage in the cloud infrastructure. Data redundancy can be reduced in primary storage system of cloud architecture using data deduplication. The research work carried out highlights the identified and established methods of data deduplication based on capacity and performance parameters. In the research work, the authors have proposed a performance-oriented data (POD) deduplication scheme which improves performance and primary storage system in the cloud. In addition to this, security analysis using encryption technique has also been performed and demonstrated to protect the sensitive data after the completion of deduplication process.
Nishant N. Pachpor, Prakash S. Prasad

Implementation of Collaborative Filtering for Product Recommendation in E-Commerce to Enhance Scalability and Performance

Abstract
With the advancement of technology and the Internet facilities, e-commerce business is growing very fast. E-commerce is trending across geographical boundaries. A user can place order sitting at home, and the product is delivered to the given address within the specified time period. At the same time, user is given a variety of options to choose from, thus making e-commerce a much convenient way of shopping. E-commerce applications nowadays have millions of users worldwide and billions of products to offer. Thus, it becomes difficult for a user to find a product of their choice from millions of available choices. Recommendation plays an important role in e-commerce applications. A recent study on the concerned topic has advocated for a recommendation system which can be a potential solution to the mentioned problem. This system uses various parameters such as users’ purchase history, products in the cart, users’ ratings and review to recommend an item to the target user. A good recommendation system is one which helps the user to find the appropriate product and also helps the organization to grow vertically as well as horizontally. Further, collaborative filtering is an algorithm which may be applied to product recommendation which successfully satisfies customer’s needs and at the same time also helps in organization’s growth. One of the biggest challenges these days is to provide efficiency and scalability while handling a large number of users and products. The research work studies and discusses different approaches of implementation of collaborative filtering for product recommendation system. The study also shows how to overcome the drawbacks of user-based collaborative filtering by implementing item-based collaborative filtering. This research work also demonstrates how collaborative filtering can be implemented for big data using Hadoop, to overcome scalability problem and enhance performance.
Niti Vishwas, Tapajyoti Deb, Ashim Saha, Lalita Kumari

A Pre-emptive Goal Programming Model for Multi-site Production and Distribution Planning

Abstract
The aim of every manufacturing organization is to fulfil the demand of their customers at minimum total cost or at maximum profit. To fulfil the demand of geographically dispersed customers, manufacturing organizations need to produce products at multiple manufacturing sites located close to the customer. Although planning function in such a scenario becomes very complex, this multi-site environment has been found to improve efficiency and provide better services to customers. It involves interlinked decisions involving procurement, production and distribution at different plants and distribution sites. Production and distribution planning are very important aspects of planning in multi-site manufacturing. To compete in this globalized market, enterprises should focus on optimizing and integrating production and distribution functions simultaneously. The authors in the research work present an integrated production and distribution planning problem for a two-level supply chain consisting of multiple manufacturing sites serving multiple selling locations. The problem is formulated as a multi-objective mixed-integer programming model considering three important aspects of production and distribution planning, viz. set-up, backorder and transportation capacity. Total cost, delivery time and backlog level are the three conflicting objectives that need to be minimized. The proposed multi-objective mathematical problem is solved using pre-emptive goal programming method. The performance of the proposed model is illustrated through an example problem instance. Further analysis is conducted to visualize the effect of changing priority level on objective function and deviation variable values.
Gaurav Kumar Badhotiya, Gunjan Soni, M. L. Mittal

An Analysis of Comorbidities’ Role in Diabetes Mellitus and Its Data-Intensive Technology-Based Prediction to Reduce Risk and Diagnostic Costs

Abstract
Present disease management system focuses on identifying single disease and its curable methods. This approach is not suitable for patients with diabetes in present and in future. Diabetes must be concerned with healthcare providers (health care constituents) with new and evolving attributes of diabetes risk factors (like gut micro-biota, Hct, Plt, Hgb, and MPV) through data-intensive technology (Big Data), which has capability to ensure the accuracy delivery in the line of diabetes patients’ care prediction. Having an understanding of diabetes-related comorbid conditions is crucial when dealing with diabetes because comorbidities are known to significantly increase the risk of getting serious, and ultimately, the cost for the medication will increase. Comorbidity is the incidence of additional persistent conditions in the same patient with a prominent disease and occurs frequently among patients with diabetes. The main purpose of study is to identify the impact of data-driven comorbidity effects in diabetes patients by predicting their risk status through data-intensive technology (Big Data), as uncovered problem domain in computer application. Hence, this effort would open up more impacts to enhance the research potentials on the killer disease, diabetes mellitus.
M. Venkatesh Saravanakumar, M. Sabibullah

Interpreting the Objective Outcome of the Proposed Misuse Case Oriented Quality Requirements (MCOQR) Framework Metrics for Security Quantification

Abstract
A number of tools, techniques, methods, methodology, and standards are available to quantify the security aspect of software during its development and after it has been implemented. But the interpretation and analysis of the quantified security metrics thus obtained may be difficult for the software development team. Proper and comprehensive interpretation and analysis of quantified security metrics are essential to specify correct security requirements during the requirements engineering phase of SDLC which may result in more secured software. This research work shows how the proposed Misuse Case Oriented Quality Requirements (MCOQR) framework metrics may be used to provide identification, definition, interpretation, and analysis of security metrics during the requirements engineering phase of software development process. The authors also discuss the various primary outcomes that may be obtained using the proposed MCOQR framework metrics using the industry accepted standards like Common Vulnerability Scoring System (CVSS), Common Vulnerability Enumeration (CVE), and Common Weakness Enumeration (CWE). The work proposed is an extension of Misuse Case Oriented Quality Requirements (MCOQR) framework metrics and includes software application-specific database. The study also highlights the areas where future research work can be carried out to further strengthen the entire software system during the software development process.
Ajeet Singh Poonia, C. Banerjee, Arpita Banerjee, S. K. Sharma

A Comparative Performance Study of Machine Learning Algorithms for Sentiment Analysis of Movie Viewers Using Open Reviews

Abstract
The Internet facilitated the easy access to public opinions and reviews for any product or services. The collective opinions of people are significantly helpful for making decisions about any product or services. Movies are one of the most captivating pass times of the modern world on which people like to give their opinion/review. Movie reviews are personal opinions or comments shared via social media tool by a common viewer who has watched the movie. It provides an opportunity to know the outreach and response of the viewers to any film. Movie reviews influence the decision of prospective viewers as well as help producers, directors, and other stack holders for improving the quality aspect of the movie. Therefore, movie reviews play an important role in sentiment analysis of target viewers. In this research work, the sentiment analyses of viewers based on movie reviews using machine learning methods are discussed. The raw movie reviews are collected, and after performing preprocessing, features are extracted using bag of words, TF-IDF, bigram methods from text reviews. Various machine learning techniques including Naive Bayes classifier, Support Vector Machine, Decision trees, and ensemble learners are used with different feature extraction schemes to obtain a sentiment analysis model for positive or negative polarity in the movie review data sets. The performance of learners based sentiment analysis model is evaluated using accuracy, precision, recall, and f-measures. The objective of this research is to find the best classifier to test the reviews of movies given out by people so that we would know the overall general opinion of the audience. It is concluded that the set of classifiers can be used collaboratively to get effective results. Changes can be made from the very algorithmic level of the classifiers to gain better performance in the domain of study.
Dilip Singh Sisodia, Shivangi Bhandari, Nerella Keerthana Reddy, Abinash Pujahari

A Comparative Study on Different Approaches of Road Traffic Optimization Based on Big Data Analytics

Abstract
The emergence of big data has led to technological advancements in various fields including transportation systems. Traffic congestion is a noteworthy issue in numerous urban communities of India alongside other nations. Improper traffic signals usage, poor law enforcement, and poor traffic administration cause movement blockage. Elevated amounts of activity increment stress bring down the quality of life and make a city less engaging. Traffic engineers are accused of influencing transportation frameworks to keep running as effectively as could be expected under the circumstances; however, the assignment appears to be unmanageable. The interconnected technologies around the digital devices offer potential to optimize the road traffic flow. So, there is a need to develop systems for smarter living experience. In order to optimize traffic flow, the first step is to identify the vehicles and then count the traffic at particular intervals so that in case of a jam, the commuters should know the traffic situation and be able to take an alternate route in advance. In this research work, a comparative study on different approaches initiated for road traffic optimization is undertaken along with their advantages and drawbacks which will be beneficial in developing and improving real-time traffic system in near future. It is concluded that the big data analytics architecture for the acquisition and monitoring of real-time traffic information provides the ability to integrate various technologies with existing communications infrastructures, which can help reduce casualties, minimize congestion, and increase safety across street networks capacity and adequacy.
Tapajyoti Deb, Niti Vishwas, Ashim Saha

Comparative Study Between Cryptographic and Hybrid Techniques for Implementation of Security in Cloud Computing

Abstract
Cloud computing depicts the latest technology in the field of information communication technology. Cloud computing technology offers the user with an endless list of services which they can avail, and these services range from hardware and software and infrastructure to resource utilization. Cloud computing allows us to access online software application, data storage, and processing power of system from anywhere and at anytime. Cloud computing supports the organization to increase their capacity remotely without creating own infrastructure, platform, purchasing new licensed software that is required for automation of various processes. There are a number of dominant parameters like energy dissipation, resource allocation, virtualization, and security that a user needs to keep in mind while selecting cloud computing services. Of the dominant parameters, security as a parameter in cloud holds a special concern and is one of the biggest challenges in cloud computing as far as data transfer process and data storage process are a concern as it happens through the Internet. In this research work, the authors have presented a comparative study between cryptographic and hybrid techniques for security concerns of the cloud computing paradigm. Through the results obtained from the comparative study of these techniques, it is observed that the effectiveness of the hybrid techniques is better in terms of execution of the algorithm than the cryptographic techniques (common public- and private-key cryptography) of security implementation in cloud computing.
Sumit Chaudhary, Foram Suthar, N. K. Joshi

Functional Module Detection in Gene Regulatory Network Associated with Hepatocellular Carcinoma

Abstract
Hepatocellular carcinoma (HCC) is a common type of liver cancer and has a high mortality rate worldwide. Its prognosis remains poor due to tumor recurrence or tumor progression and diagnosed at advanced stage. Hence, there is a critical need to develop effective biomarker for understanding the HCC mechanism. Although the existing evidence demonstrates the important role of single-gene abnormality, often the genes modularity is ignored. In this research work, the authors aim to find modular structure with potential functional relevance. The authors aim to construct a gene regulatory network of DEGs and perform its topological analysis about its hidden structure. The authors have also detected the modules in constructed network, along with their enrichment, finding pathways they are involved in, and their biological functional analysis. There are three major steps adopted for carrying out this research work. Firstly, the authors filtered differentially expressed genes (DEGs) from gene expression data obtained from GEO database which included ten normal and ten HCC samples. Secondly, they constructed co-expressed gene regulatory network (GRN) of DEGs using Pearson’s correlation coefficient, then unraveled the characteristics of GRN, and finally, detected modules from GRN and their functional relevance along with the topological characters of network. The DEGs in normal and HCC were identified using MATLAB, while the network is constructed using Cytoscape. The modules in network have been derived from online software GraphWeb.
Sachin Bhatt, Kalpana Singh, Ravins Dohare

Comparative Analysis of Various Techniques of DDoS Attacks for Detection & Prevention and Their Impact in MANET

Abstract
Wireless nodes all connected together logically make a mobile ad hoc network (MANET). Fundamentally, MANET has no infrastructure which means all the connections are formed without the help of or use of one or more administration which is centralized. A mobile ad hoc network (MANET) can be termed as a network which is spontaneous and which can be set up without any fixed infrastructure. The main goal of denial-of-service (DoS) attack is to make the server and other resources too busy so that request for any information or resource is either not served at all or there is an unexpected delay in response time. When a DoS attack is converted into its severe form, then it is known as distributed denial-of-service (DDoS) attack. A DDoS attack uses several machines to launch the attack by making services block for genuine users. A DDoS attack which can be in form of a bandwidth depletion and resource depletion prevents the legitimate users by flooding the victim’s network with unwanted traffic in form of packets. Various techniques have been implemented to secure the channel, but still security is a major issue over the network. The aim of this research work is to present a comparative analysis of various proposed tools and techniques for detection and prevention of DDoS attacks.
Neha Singh, Ankur Dumka, Rakesh Sharma

A Comparative Study of Data Mining Tools and Techniques for Business Intelligence

Abstract
Business intelligence (BI) is a collection of different frameworks and tools that convert the required raw data into meaningful information which may aid in supporting the decision-making process of the management. The present-day BI gives a reporting functionality to the identification of data groups, i.e., clusters useful for data mining techniques and business performance maintenance with predictive analysis in real-time BI applications. In fact, the core function of BI is to support the effective decision-making process. The BI frameworks are often known to business clients as decision support systems (DSSs) or reality-based supporting systems that they utilize to analyze the data and extract information from data sources. Through this research work, the authors aim to discuss various tools, approaches, and techniques for data mining that has support for BI. The research work also aims to describe the study as processes and procedures to systematically identify, counter, store, analyze, and explore data accessibility for making effective operations in business decisions. Different algorithms, methods, and techniques of BI are also highlighted along with varied types of applications with preferable implementation. The later part of the study discusses BI applications, which include operations of decision supporting frameworks, data management frameworks, query and reporting with online analytical processing (OLAP), forecasting apart from statistical analysis used in distributed BI applications. This research work uses visualized charts to explain the usage frequency of BI techniques with their performance comparison.
G. S. Ramesh, T. V. Rajini Kanth, D. Vasumathi

Performance Analysis of E-Governance Citizen-Centric Services Through E-Mitra in Rajasthan

Abstract
Implementing a citizen-centric approach to delivering government services is the need of the hour which creates and maintains a level of dialogue and trust between citizen and government. As per the study conducted so far, it was seen that implementing such system aids in increasing the public satisfaction and reduces cost. In technical terms, this process is called e-Governance. E-Governance is the application of information and communication technology (ICT), which acts as a medium for the delivery of government services, information exchange, communication transactions, integration of different systems and services between government and citizen (G2C) and government and government (G2G). In Indian scenario, National e-Governance Plan (NeGP) is an initiative taken by Indian government to provide all types of government services to the citizen of India. Rajasthan, a state of Indian country which is considered to be lacking behind in terms of technological advancement, is possibly one of the very few states where e-Governance initiatives are going on successfully from a very long time. Presently, there are many e-Governance practices prevailing in the state like Bhamashah Yojana, e-PDS, Rajasthan Payment Platform, eSanchar, iFact, Sampark, eBazaar, Raj Wi-Fi, Raj eVault, etc., but the nationally renowned e-Mitra launched in 2005 has a wider scope in terms of both people reach and government services offered. The research work presents an analysis of e-Governance citizen-centric services through e-Mitra in Rajasthan. The research work attempts to provide an insight into the ongoing e-Governance practice of e-Mitra to reveal how digitization can be used to enhance the reach and the actual effect of such government services.
Praveen Kumar Sharma, Vijay Singh Rathore

A Critical Study on Disaster Management and Role of ICT in Minimizing Its Impact

Abstract
Disaster means “Bad Star” in Latin and is defined as an impact due to a natural or man-made hazard that results in the huge casualties and damage to resources. Recent events and studies have shown that disaster preparedness is no longer considered as a choice and has become a mandatory process irrespective of the geographical area and its distribution. The types of risk vary and increase depending on the geographical location of a country. If they are targeted proactively, the consequences of natural and man-made disasters and the vulnerabilities to which people are exposed can be mitigated. It has been proven from past experience and practice that the damage caused by any disaster can be minimized largely through careful planning, mitigation, and prompt action. In disaster prevention, mitigation, and management (disaster management), information and communications technology (ICT) can play a key role. Different available technologies, including telecommunication satellites, radar, telemetry, and meteorology, allow remote sensing for early warning. ICT includes both traditional media (radio, television) and new media (cellular broadcasting, Internet, satellite radio), which can play an important role in educating and raising public awareness of the risks of potential disaster. This research work highlights the issues of disaster management in relation to the Indian subcontinent. It explores the role of “National Disaster Management Authority” (NDMA) established in 2005 by the Government of India to study and minimize the effect of a disaster using various ICT tools and techniques.
Pratibha Choudhary, Rohit Vyas

Development of Arduino-Based Compact Heart Pulse and Body Temperature Monitoring Embedded System for Better Performance

Abstract
With lifestyle changes in modern time, ease of living due to technological advancement, and increase in urbanization and globalization, there is an increase in the cases of humans suffering from a vast variety of harmful diseases. According to the fundamental principle of protection from these harm diseases, two parameters of human body i.e., the current status of body temperature and running heartbeat measurement on regular basis, are vital and essential. With the advent of ICT tools and many advanced medical devices, these activities can be recorded with user-friendly display and interface in real time which can prove to be of more value and use when there is no nearby facility of hospital and medical care. This paper can create awareness about the one’s actual severity of sickness. The aim of this research work is to present medical devices which are portable and compact in size and can be easy operated without expertise for measuring and showing the body temperature and running heart pulse. In this research work, a processing assistive integrated heart rate with body temperature embedded supervising device is developed. The system provides the information of heart rate via serial communication on the PC or laptop and body temperature on liquid-crystal display (LCD). This system is very useful to monitor conditions at remote places. The proposed device includes the various applications such as Arduino Uno microcontroller system, various sensors, transmission system, and interfacing. The proposed system is economical, has compact design, and is a lightweight instrument. This system has been tested using data sets, and based on the outcomes of this device, it is concluded that it gives comparatively better performance than old hand measuring system.
Sandeep Gupta, Akash Talwariya, Pushpendra Singh

Performance Evaluation of Learners for Analyzing the Hotel Customer Sentiments Based on Text Reviews

Abstract
The world is in the midst of a digital revolution, and thus, it is natural that businesses are leveraging technology to position themselves well in the digital market. Travel planning and hotel bookings have become significant commercial applications. In recent years, there has been a rapid growth in online review sites and discussion forums where the critical characteristics of a customer review are drawn from their overall opinion/sentiments. Customer reviews play a significant role in a hotel’s persona which directly affects its valuation. This research work is intended to address the problem of analyzing the inundation of opinions and reviews of hotel services publicly available over the Web. Availability of large datasets containing such texts has allowed us to automate the task of sentiment profiling and opinion mining. In this study, over 800 hotel reviews are collected from travel information and review aggregator site like Trip Advisor, and after pre-processing of collected raw text reviews, various features are extracted using unigram, bigram, and trigram methods. The labeled feature vectors are used to train binary classifiers. The results are compared and contrasted among ensemble classifiers, support vector machines, and linear models using performance measures such as accuracy, F-measure, precision, and recall.
Dilip Singh Sisodia, Saragadam Nikhil, Gundu Sai Kiran, Hari Shrawgi

Proposed Data Structure for Storage of Metrics Values: Misuse Case Oriented Quality Requirements (MCOQR) Framework Perspective

Abstract
Security may be quantified to measure the level of software security implementation. These quantified measures are called security metrics. These metrics need to be stored in some central or local repository for further analysis and refinement of security of software proposed to be developed or modified. Through the research work carried out by the researchers, it is proposed to have a data structure for storage of metrics values which are identified and collected using the Misuse Case Oriented Quality Requirements (MCOQR) framework. The research work highlights and discusses the internal arrangement of various data sets in the data structure and their relationship with each other. The data sets thus proposed are properly synchronized with the industry accepted standards like Common Vulnerability Scoring System (CVSS), Common Vulnerability Enumeration (CVE), and Common Weakness Enumeration (CWE). The work proposed is an extension of Misuse Case Oriented Quality Requirements (MCOQR) framework and metrics and includes software application-specific database. The research work also highlights the areas where further research work can be carried out to further strengthen the entire system.
Sunita Choudhary, C. Banerjee, Ajeet Singh Poonia, Arpita Banerjee, S. K. Sharma

Comparative Analysis of Hindi Text Summarization for Multiple Documents by Padding of Ancillary Features

Abstract
There is an enormous amount of textual material, and it is only growing every single day. The data available on Internet comprised of Web pages, news articles, status updates, blogs which are unstructured. There is a great need to reduce much of these text data to shorter, focused summaries that capture the salient details so that the user can navigate it more effectively as well as check whether the larger documents contain the information that we are looking for. Text summary is generating a shorter version of the original text. The need of summarization arises because every time it is not possible to read the detailed document due to lack of time. Automatic text summarization methods are greatly needed to address the ever-growing amount of text data available online both to better help discover relevant information and to consume relevant information faster. To address the issue of time constraint, an extractive text summarization technique has been proposed in this research work which selects important sentences from a text document to get a gist of information contained in it. A fuzzy technique has been used to generate extractive summary from multiple documents by using eight and eleven feature sets. The eleven feature set combines the existing eight features (term frequency-inverse sentence, length of sentence in the document, location of sentence in document, similarity between sentences, numerical data, title overlap, subject object verb (SOV) qualifier, lexical similarity) and three ancillary features (proper nouns, hindi cue phrase, thematic words). It was seen that applying fuzzy technique with eleven features gave better results for summarization than the same using eight features. The precision increases in the range of 3–5% for different datasets. Datasets used were Hindi news articles from online sources.
Archana N. Gulati, Sudhir D. Sawarkar

RC-Network and Comm-Network for Improvement of Research Collaboration and Communication Among Delhi University Teachers

Abstract
Research collaboration studies have become significant indicators of research, productivity, and developments of organizations. In the research work, the authors have proposed and construct research collaboration network (RC-network) and communication network (Comm-network) for the Delhi University teachers. Teachers are treated as vertices, and two vertices are connected if they have written a research document together in the case of RC-network, whereas if they communicate (talk about education, politics, rights, or other socially relevant issues) with each other, then it is a case of Comm-network. In the research work carried out, the researchers have collected the data through different resources and questionnaires. There are 712 teachers’ records of research document and 214 teacher’s records from questionnaire in the collected data. Two networks are constructed as RC-network and Comm-network. The researchers have found through the study that both networks follow scale-free degree distribution as reported by the other similar study. These networks unpacked several hidden characteristics of collaboration and communication among teachers of Delhi University. Constructed networks are sparse and partitioned in huge number of connected components. Moreover, they have identified the key clusters of research collaborations and communications among teachers. These clusters consist of teachers mostly from the same college or department or center. This indicates that the research collaborations are not of interdisciplinary nature in Delhi University. Even the communications among the teachers from different disciplines or colleges take place less frequently than those among the teachers from the same discipline or college. This study may prove to be helpful in improving and understanding the research and communication among teachers of Delhi University.
Narender Kumar, Sapna Malhotra, Chitra Rajora, Ravins Dohare
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