bluuebunny/crossref_metadata_2025 huggingface dataset:
Formation and Open Access of Institutional Electronic Research Corners for Promotion of Communication Facilities and Quality Research
Authors: Swain Sukanta Chandra
Published: March 2020

Indian higher education institutions (HEIs) are struggling hard to get a berth among top-rated HEIs in the world. One of the important parameters that restrict our HEIs from the list of top-notch global HEIs is ‘contribution to research’. It does not mean that Indian HEIs don’t have quality researchers to produce world class research output. Had that been the case, Indian researchers doing research abroad would not have done miracles with their unique research output in varied domains. The hindrance for researchers in India could be owing to improper communication facility that fails to exchange ideas and outcomes of research among peers. Thus, the urge for formation of sound institutional electronic repositories, i.e., electronic research corners (e-research corners) has crop up. Mere formation of e-research corners won’t suffice to address the issue. Besides formation, e-research corners need to be open accessed. University Grants Commission (UGC) of India has established an Inter-University Center called Information and Library Network Center (Inflibnet), which is the best example of open access institutional repositories. While some HEIs of India do have their research corners that are accessed by only the internal stakeholders, some other HEIs don’t have any such repositories as such. On the backdrop of the necessity of formation and open access of institutional electronic repositories, this paper focuses on unfolding how open access to sound e-research corners promote quality education. Besides, it will also design a framework that will assist the HEIs to build sound e-research centers. For the purpose, in-depth interviews will be conducted with 50 researchers working in reputed HEIs in Bhubaneswar to ascertain how open access e-research centers facilitate quality research. Focus Group Discussion will also be conducted among experts in the field like research heads of selected HEIs, Chief Librarians of established Libraries and reputed educationists of the State for designing the framework to form e-research centers. The findings of this research will help in communicating ideas and findings of researchers to all the corners that matter for quality research.
Transforming English Language Education in Saudi Arabia: Why Does Technology Matter?
Authors: Al-Shehri Saleh
Published: March 2020

Several studies have discussed the potential of reforming the Saudi educational system in line with Vision 2030 announced in 2016. While some studies highlighted factors that can make transformation successful, reviewed studies proposed future plans on how educational reform can take place, i.e., studies that investigated the Vision’s educational opportunities are mostly proposals, meaning that no experimental research has yet been conducted on the actual learning practice, particularly with English education and the incorporation of technology into learning. Thus, the current longitudinal study is an attempt to investigate English language teachers’ perceptions of and attitudes about the Vision and whether they have experienced any remarkable educational changes in their own behavior and teaching practices. The study was conducted with 15 female in-service English language teachers. Data was collected in two phases to reveal any change in teachers perceptions over time. The study concluded that almost all participants held positive attitudes toward Vision 2030. However, there was not a correlation between teachers' perceptions and actual teaching experience in terms of pedagogical and technological perceptions and the Vision's implications.
Curriculum Data Association Organization and Knowledge Management Method for Unstructured Learning Resources
Authors: Li Na
Published: March 2020

To solve the problems in the curriculum of unstructured learning resources, data association organizations and knowledge management methods were proposed. First, according to functions and requirements, the associated course data system was designed. Then, the table conversion test and the storage index test were performed. Finally, the merger test of the entity was carried out. Results showed that the data association organization and knowledge management methods effectively solved the problem of the curriculum of unstructured learning resources. In summary, the online learning environment provides conditions for unstructured learning resources.
Diversity, Indigenous Knowledge, and LIS Pedagogy
Authors: Alenzuela Reysa , Terry Margaret Austrai
Published: March 2020

While the rest of the world discusses how Library and Information Studies (LIS) can best cope with the emerging trends in technology and global information platforms, there are parts of the world that do not offer LIS programs. This chapter provides insights on the development of LIS qualification that recognizes the needs of librarians in Vanuatu. The Ni-Vanuatu competency index serves as a reference for the skills required to deliver services and resources to their clients. The importance of having a functional program that integrates the uniqueness of Vanuatu's cultural diversity and indigenous knowledge is the primary discussion for this chapter. It is too early to discuss the steps for meeting the criteria for the qualifications framework as the program is not in place. Still, the focus of what kind of LIS Education suits Ni-Vanuatu librarians is the beginning of the conversation.
Globalization in LIS Education
Authors: Alenzuela Reysa , Kim Heesop
Published: March 2020

Globalization can be seen to impact Library and Information Science (LIS) education as global processes and practices influence policies and structures, pedagogy, faculty, research, and collaboration. Globalization and internationalization are employed interchangeably in much professional literature, but the latter is a more prominent concept in LIS education. Internationalization in LIS has been discussed in Europe and the US in light of its impact on learning, academic mobility, research collaboration, and international partnerships. This chapter focuses on iSchools being in the frontier of internationalization in addressing the need in higher education to reach across national borders, specifically in South Korea and the Philippines. The discussion focuses on the development shown by two countries in integrating international and global dimensions into the curriculum, faculty composition, research, student support, and global linkages/partnership.
Internationalization of Library and Information Science Education in Central Asia
Authors: Lapo Piotr , Davletyarova Nurila
Published: March 2020

In the Central Asian republics of the former Soviet Union (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan) with the total population over 50 million people, there are more than 25 thousand libraries. Kazakhstan and Kyrgyzstan are two of the countries that made strides in economic reforms along with other areas of development, including the libraries. This chapter, which emphasizes the development of Library and Information Science education, provides an overview of the current policies of the higher education internationalization and the current state of library development and library education. From the current initiatives, challenges and opportunities are drawn, and a summary on similar internationalization practices are presented.
Libraries, Archives, and Museums in Guam
Authors: Perez Dante
Published: March 2020

Knowledge, tradition, culture, and arts are reflected in how the information is shared in the knowledge society. The new millennium looks into the practices of libraries, archives, and museums and moves forward to the issues, challenges, and opportunities. While competencies of the staff play an important role, information and communication technologies undeniably have also brought about the changes. This chapter provides an overview of the libraries, archives, and museums (LAMs) on the island of Guam, emphasizing the traditional roles and examining deeper into the issues and challenges on the impact of information and communication technologies. Convergence, specialized knowledge and a better framework to be adopted for development and growth that can contribute to the global knowledge society are the key points in the discussion.
Corporate Social Responsibility and Its Application to Social and Business Systems
Authors: Rahim Emad
Published: March 2020

This chapter provides an overview of contemporary aspects of change management and social responsibility and social and business systems. The topic of sustainability describes the foundation of the subject, as well as theory, concepts, and principles. The author provides various case examples and perspectives on the triple bottom line, scale-free networks, social and business systems, and delivers to readers a blueprint for developing a change management strategy for fostering a socially responsible environment.
Marginalization of Women and Mass Markets
Authors: Hernández R. José G. , García G. María J. , Hernández G. Gilberto J.
Published: March 2020

This chapter relates to gender equality in the free market, particularly the mass market. The objective of this work is to review if the woman is marginalized in the mass market through a decision tree structure. To achieve this overall objective, the integrated-adaptable methodology for the development of decision support system (MIASAD) is used. There are not enough elements to make a generalization, but, from the results obtained with the decision tree structure used in this investigation, it can be seen that there is no marginalization of women in the mass products market. This implies that no additional effort is necessary to minimize the marginalization of women in the mass market. As a consequence, efforts can focus on better segmentation of the female market. Thus, products that are more suitable for each of the sectors found can be generated.
Digital Transformation of Diplomacy
Authors: Goundar Sam , Chandra Bettylyn , Bhardwaj Akashdeep , Saber Fatemeh , Appana Subhash
Published: March 2020

This chapter seeks to examine the digital transformation (digitalisation) of diplomacy and how such digital transformations can be used to positively influence and improve a country's foreign services. The chapter further explores how the country's diplomats and their Foreign Service counterparts at Ministry of Foreign Affairs (MFA) can utilize the tools provided by digitalisation to advance the country's interests. Given the critical intelligence data, diplomatic protocols, and confidential information exchanged at the diplomatic level between countries, it is equally important to evaluate and assess the cyber security measures that are being taken to secure the digital network of the diplomatic missions. Scholarly research was initially conducted to position the field of research amongst pertinent literature to ascertain the use of digital tools in diplomacy and present key deliberations that exist.
Malicious Application Detection and Classification System for Android Mobiles
Authors: Malik Sapna , Khatter Kiran
Published: March 2020

The Android Mobiles constitute a large portion of mobile market which also attracts the malware developer for malicious gains. Every year hundreds of malwares are detected in the Android market. Unofficial and Official Android market such as Google Play Store are infested with fake and malicious apps which is a warning alarm for naive user. Guided by this insight, this paper presents the malicious application detection and classification system using machine learning techniques by extracting and analyzing the Android Permission Feature of the Android applications. For the feature extraction, the authors of this work have developed the AndroData tool written in shell script and analyzed the extracted features of 1060 Android applications with machine learning algorithms. They have achieved the malicious application detection and classification accuracy of 98.2% and 87.3%, respectively with machine learning techniques.
Machine Learning Approaches for Supernovae Classification
Authors: Agrawal Surbhi , Bora Kakoli , Routh Swati
Published: March 2020

In this chapter, authors have discussed few machine learning techniques and their application to perform the supernovae classification. Supernovae has various types, mainly categorized into two important types. Here, focus is given on the classification of Type-Ia supernova. Astronomers use Type-Ia supernovae as “standard candles” to measure distances in the Universe. Classification of supernovae is mainly a matter of concern for the astronomers in the absence of spectra. Through the application of different machine learning techniques on the data set authors have tried to check how well classification of supernovae can be performed using these techniques. Data set used is available at Riess et al. (2007) (astro-ph/0611572).
Best Features Selection for Biomedical Data Classification Using Seven Spot Ladybird Optimization Algorithm
Authors: Bidi Noria , Elberrichi Zakaria
Published: March 2020

This article presents a new adaptive algorithm called FS-SLOA (Feature Selection-Seven Spot Ladybird Optimization Algorithm) which is a meta-heuristic feature selection method based on the foraging behavior of a seven spot ladybird. The new efficient technique has been applied to find the best subset features, which achieves the highest accuracy in classification using three classifiers: the Naive Bayes (NB), the Nearest Neighbors (KNN) and the Support Vector Machine (SVM). The authors' proposed approach has been experimented on four well-known benchmark datasets (Wisconsin Breast cancer, Pima Diabetes, Mammographic Mass, and Dermatology datasets) taken from the UCI machine learning repository. Experimental results prove that the classification accuracy of FS-SLOA is the best performing for different datasets.
Feature Selection Algorithms for Classification and Clustering
Authors: Tiwari Arvind Kumar
Published: March 2020

Feature selection is an important topic in data mining, especially for high dimensional dataset. Feature selection is a process commonly used in machine learning, wherein subsets of the features available from the data are selected for application of learning algorithm. The best subset contains the least number of dimensions that most contribute to accuracy. Feature selection methods can be decomposed into three main classes, one is filter method, another one is wrapper method and third one is embedded method. This chapter presents an empirical comparison of feature selection methods and its algorithm. In view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enable to adequately decide which algorithm to use in certain situation. This chapter reviews several fundamental algorithms found in the literature and assess their performance in a controlled scenario.
Methods and Processes for District-Wide Literacy Evaluation
Authors: Lawrence Salika A. , English Minkie O.
Published: March 2020

This study examined how quantitative and qualitative data collection methods helped evaluators learn about classroom, school, and district level practices during a school district evaluation. Findings indicate (a) qualitative methods provide more accurate information about micro level, every day practices, while macro level data are useful for comparative, cross-context review of practice to inform program and/or administrative decisions; and (b) comprehensive evaluation of literacy programs require stakeholders to collaborate across the spectrum, working with a wide range of varied data collection processes at both macro and micro levels. Dove-tailing quantitative and qualitative data collection methods can reveal macro level information about practice that can align with micro level classroom-based practice or reveal discrepancies across contexts. Recommendations are identified for collecting data and developing an action plan with stakeholders.
User-Oriented Video Streaming Service Based on Passive Aggressive Learning
Authors: Oide Makoto , Takahashi Akiko , Abe Toru , Suganuma Takuo
Published: March 2020

The authors propose a method to dynamically determine appropriate quality of service (QoS) required by users for video streaming services. In the proposed method, the video bit rate as the QoS parameter is determined based on the passive aggressive learning, which is an online learning algorithm for a regression problem, according to the user requirements, computational/network resources and service provisioning environments. Moreover, the method makes it possible to provide appropriate QoS by using optimization solution. In this paper, the authors describe the design and implementation of the method, then confirm the feasibility of the proposed method through the experiments.
Model-Driven Multi-Domain IoT
Authors: Lengyel László , Ekler Péter , Tömösvári Imre , Balogh Tamás , Mezei Gergely , Forstner Bertalan , Charaf Hassan
Published: March 2020

The Internet of Things (IoT) is the network of physical objects embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data. The chapter introduces the Model-driven Multi-Domain IoT concept and provides a method and a supporting framework. Multi-Domain IoT as the actual frontier for innovation, competition, and productivity. The method supports effective service and application development and therefore covers connected devices, data collection, data access and complex analytics. The efficiency of the method and the framework is confirmed by several projects. Selected parts of these projects are introduced as innovation projects and case studies.
Digital Forensic and Machine Learning
Authors: Mariappan Poonkodi , B. Padhmavathi , Teja Talluri Srinivasa
Published: March 2020

Digital Forensic as it sounds coerce human mind primarily with exploration of crime. However in the contemporary world, digital forensic has evolved as an essential source of tools from data acquisition to legal action. Basically three stages are involved in digital forensic namely acquisition, analysis and reporting. Digital Forensic Research Workshop (DFRW) defined digital forensic as “Use of Scientifically derived and proven method towards the identification, collection, analysis, interpretation, documentation and presentation of digital evidence derived from digital sources for the purpose of facilitating or furthering the reconstruction of event to be criminal”. The hard problem in digital forensic is such that the acquired data need to be cleaned and is required to be intelligible for reading by human. As a solution to this complexity problem a number of tools are present which may be repeated until relevant data is obtained.
Improving Logging Prediction on Imbalanced Datasets
Authors: Lal Sangeeta , Sardana Neetu , Sureka Ashish
Published: March 2020

Logging is an important yet tough decision for OSS developers. Machine-learning models are useful in improving several steps of OSS development, including logging. Several recent studies propose machine-learning models to predict logged code construct. The prediction performances of these models are limited due to the class-imbalance problem since the number of logged code constructs is small as compared to non-logged code constructs. No previous study analyzes the class-imbalance problem for logged code construct prediction. The authors first analyze the performances of J48, RF, and SVM classifiers for catch-blocks and if-blocks logged code constructs prediction on imbalanced datasets. Second, the authors propose LogIm, an ensemble and threshold-based machine-learning model. Third, the authors evaluate the performance of LogIm on three open-source projects. On average, LogIm model improves the performance of baseline classifiers, J48, RF, and SVM, by 7.38%, 9.24%, and 4.6% for catch-blocks, and 12.11%, 14.95%, and 19.13% for if-blocks logging prediction.
Comparative Study of Classification Models with Genetic Search Based Feature Selection Technique
Authors: Sahu Sanat Kumar , Shrivas A. K.
Published: March 2020

Feature selection plays a very important role to retrieve the relevant features from datasets and computationally improves the performance of a model. The objective of this study is to evaluate the most important features of a chronic kidney disease (CKD) dataset and diagnose the CKD problem. In this research work, the authors have used a genetic search with the Wrapper Subset Evaluator method for feature selection to increase the overall performance of the classification model. They have also used Bayes Network, Classification and Regression Tree (CART), Radial Basis Function Network (RBFN) and J48 classifier for classification of CKD and non-CKD data. The proposed genetic search based feature selection technique (GSBFST) selects the best features from CKD dataset and compares the performance of classifiers with proposed and existing genetic search feature selection techniques (FSTs). All classification models give the better result with proposed GSBFST as compared to without FST and existing genetic search FSTs.
Data Mining Approach for Predicting the Likelihood of Infertility in Nigerian Women
Authors: Idowu Peter Adebayo , Balogun Jeremiah Ademola , Alaba Olumuyiwa Bamidele
Published: March 2020

According to WHO, there are 60 - 80 million infertile couples worldwide with the highest incidence in some regions of Sub-Saharan Africa. The social stigma of infertility weighs especially heavily on women, who bear the sole blame for barren marriages in many developing countries and may face divorce as a result. Interviews were conducted with gynecologists at one of the Teaching Hospitals in Nigeria in order to identify likelihood variables for infertility. 14 risk factors were identified and data collected from 39 patients from the hospital was pre-processed and the variables used to formulate the predictive model for the likelihood of infertility in women using three different decision trees algorithms. The predictive model was simulated using WEKA environment. The results revealed that C4.5 algorithm had the highest accuracy of 74.4% while the least performance was for the random tree algorithm with a value of 53.8%. This chapter presents a predictive model which can assist gynecologists in making more objective decisions concerning infertility likelihood.
A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect
Authors: Ibañez Rodrigo , Soria Alvaro , Teyseyre Alfredo Raul , Berdun Luis , Campo Marcelo Ricardo
Published: March 2020

Progress and technological innovation achieved in recent years, particularly in the area of entertainment and games, have promoted the creation of more natural and intuitive human-computer interfaces. For example, natural interaction devices such as Microsoft Kinect allow users to explore a more expressive way of human-computer communication by recognizing body gestures. In this context, several Supervised Machine Learning techniques have been proposed to recognize gestures. However, scarce research works have focused on a comparative study of the behavior of these techniques. Therefore, this chapter presents an evaluation of 4 Machine Learning techniques by using the Microsoft Research Cambridge (MSRC-12) Kinect gesture dataset, which involves 30 people performing 12 different gestures. Accuracy was evaluated with different techniques obtaining correct-recognition rates close to 100% in some results. Briefly, the experiments performed in this chapter are likely to provide new insights into the application of Machine Learning technique to facilitate the task of gesture recognition.
R4 Model for Case-Based Reasoning and Its Application for Software Fault Prediction
Authors: Rashid Ekbal
Published: March 2020

Making R4 model effective and efficient I have introduced some new features, i.e., renovation of knowledgebase (KBS) and reducing the maintenance cost by removing the duplicate record from the KBS. Renovation of knowledgebase is the process of removing duplicate record stored in knowledgebase and adding world new problems along with world new solutions. This paper explores case-based reasoning and its applications for software quality improvement through early prediction of error patterns. It summarizes a variety of techniques for software quality prediction in the domain of software engineering. The system predicts the error level with respect to LOC and with respect to development time, and both affects the quality level. This paper also reviews four existing models of case-based reasoning (CBR). The paper presents a work in which I have expanded our previous work (Rashid et al., 2012). I have used different similarity measures to find the best method that increases reliability. The present work is also credited through introduction of some new terms like coefficient of efficiency, i.e., developer's ability.
Feedback-Driven Refinement of Mandarin Speech Recognition Result Based on Lattice Modification and Rescoring
Authors: Wang Xiangdong , Yang Yang , Liu Hong , Qian Yueliang , Jia Duan
Published: March 2020

In real world applications of speech recognition, recognition errors are inevitable, and manual correction is necessary. This paper presents an approach for the refinement of Mandarin speech recognition result by exploiting user feedback. An interface incorporating character-based candidate lists and feedback-driven updating of the candidate lists is introduced. For dynamic updating of candidate lists, a novel method based on lattice modification and rescoring is proposed. By adding words with similar pronunciations to the candidates next to the corrected character into the lattice and then performing rescoring on the modified lattice, the proposed method can improve the accuracy of the candidate lists even if the correct characters are not in the original lattice, with much lower computational cost than that of the speech re-recognition methods. Experimental results show that the proposed method can reduce 24.03% of user inputs and improve average candidate rank by 25.31%.
Supervised Machine Learning for Plants Identification Based on Images of Their Leaves
Authors: Rahmani Mohamed Elhadi , Amine Abdelmalek , Hamou Reda Mohamed
Published: March 2020

Botanists study in general the characteristics of leaves to give to each plant a scientific name; such as shape, margin...etc. This paper proposes a comparison of supervised plant identification using different approaches. The identification is done according to three different features extracted from images of leaves: a fine-scale margin feature histogram, a Centroid Contour Distance Curve shape signature and an interior texture feature histogram. First represent each leaf by one feature at a time in, then represent leaves by two features, and each leaf was represented by the three features. After that, the authors classified the obtained vectors using different supervised machine learning techniques; the used techniques are Decision tree, Naïve Bayes, K-nearest neighbour, and neural network. Finally, they evaluated the classification using cross validation. The main goal of this work is studying the influence of representation of leaves' images on the identification of plants, and also studying the use of supervised machine learning algorithm for plant leaves classification.
Gene Selection from Microarray Data for Alzheimer's Disease Using Random Forest
Authors: Nishiwaki Kazutaka , Kanamori Katsutoshi , Ohwada Hayato
Published: March 2020

A significant amount of microarray gene expression data is available on the Internet, and researchers are allowed to analyze such data freely. However, microarray data includes thousands of genes, and analysis using conventional techniques is too difficult. Therefore, selecting informative gene(s) from high-dimensional data is very important. In this study, the authors propose a gene selection method using random forest as a machine learning technique. They applied this method to microarray data on Alzheimer's disease and conducted an experiment to rank genes. The authors' results indicated some genes that have been investigated for their relevance to Alzheimer's disease, proving that their proposed cognitive method was successful in finding disease-related genes using microarray data.
Mixed Methods Research
Authors: Ghosh Rajashi
Published: March 2020

Mixed methods research (MMR) is increasingly becoming a popular methodological approach in several fields due to the promise it holds for comprehensive understanding of complex problems being researched. However, researchers interested in MMR often lack reference to a guide that can explain the key issues pertaining to the paradigm wars influencing MMR, different objectives of MMR, choice of MMR designs, and articulation of research questions in MMR. This paper addresses that gap through providing a peek into these issues through illustrative examples. This brief introduction to MMR is meant to encourage readers to delve deeper into the MMR literature and make informed decisions in designing and implementing MMR studies.
Can Citizen Science Seriously Contribute to Policy Development?
Authors: Chapman Colin , Hodges Crona
Published: March 2020

This chapter considers the potential for citizen science to contribute to policy development. A background to evidence-based policy making is given, and the requirement for data to be robust, reliable and, increasingly, cost-effective is noted. The potential for the use of ‘co-design' strategies with stakeholders, to add value to their engagement as well as provide more meaningful data that can contribute to policy development, is presented and discussed. Barriers to uptake can be institutional and the quality of data used in evidence-based policy making will always need to be fully assured. Data must be appropriate to the decision making process at hand and there is potential for citizen science to fill important, existing data-gaps.
An Insight into State-of-the-Art Techniques for Big Data Classification
Authors: Bansal Neha , Singh R.K. , Sharma Arun
Published: March 2020

This article describes how classification algorithms have emerged as strong meta-learning techniques to accurately and efficiently analyze the masses of data generated from the widespread use of internet and other sources. In particular, there is need of some mechanism which classifies unstructured data into some organized form. Classification techniques over big transactional database may provide required data to the users from large datasets in a more simplified way. With the intention of organizing and clearly representing the current state of classification algorithms for big data, present paper discusses various concepts and algorithms, and also an exhaustive review of existing classification algorithms over big data classification frameworks and other novel frameworks. The paper provides a comprehensive comparison, both from a theoretical as well as an empirical perspective. The effectiveness of the candidate classification algorithms is measured through a number of performance metrics such as implementation technique, data source validation, and scalability etc.
Mobile Sink with Mobile Agents
Authors: Borawake-Satao Rachana , Prasad Rajesh Shardanand
Published: March 2020

Wireless Sensor Networks (WSN) has been drawing considerable attention and discussion in recent years due to its potential applications in various fields. In modern applications for future internet the MSN (Mobile Sensor Network) is a key factor. Mobility allows the applications of Wireless Sensor Network to be compatible with IoT (Internet of Things) applications. As mobility enhances capability of the network it also affects the performance of the network at each layer. In recent years the various methodologies are proposed to handle mobility. Most of them use mobility for efficient data collection in WSNs. The purpose of this paper is to study effects of mobility on various performance parameters of the network and to explore the effective techniques to handle mobility in network. This paper proposes Mobile Sink with Mobile Agent mobility model for WSN which will increase the lifetime of the network using sink and agent node mobility.
Puzzling the Picture using Grounded Theory
Authors: Bennett Elisabeth E.
Published: March 2020

Since the first publication by Glaser and Strauss in 1967, Grounded Theory has become a highly influential research approach in the social sciences. The approach provides techniques and coding strategies for building theory inductively from the “ground up” as concepts within the data earn relevance into an evolving substantive theory. Over time, Grounded Theory has undergone development and adaptations, and the first phases of analysis have been successfully applied to other types of inductive approaches, such as basic qualitative and case studies. The methodological literature can be difficult to navigate for new researchers as well as experienced analysts using the approach for the first time. This article synthesizes the work of various seminal scholars to address the value of grounded theorizing and it builds a picture of what it means to do grounded theory.
Applying Machine Learning in Optical Music Recognition of Numbered Music Notation
Authors: Wu Fu-Hai Frank
Published: March 2020

Although research of optical music recognition (OMR) has existed for few decades, most of efforts were put in step of image processing to approach upmost accuracy and evaluations were not in common ground. And major music notations explored were the conventional western music notations with staff. On contrary, the authors explore the challenges of numbered music notation, which is popular in Asia and used in daily life for sight reading. The authors use different way to improve recognition accuracy by applying elementary image processing with rough tuning and supplementing with methods of machine learning. The major contributions of this work are the architecture of machine learning specified for this task, the dataset, and the evaluation metrics, which indicate the performance of OMR system, provide objective function for machine learning and highlight the challenges of the scores of music with the specified notation.
A Relative Study About Mobile Ad-Hoc Network (MANET)
Authors: Khatri Preety , Rajvanshi Priti Rani
Published: March 2020

This chapter includes a relative study of mobile ad-hoc networks (MANET), vehicular ad hoc networks (VANET), and Flying ad-hoc networks (FANET). The approaches and protocol applicable to MANET are equally applicable to VANET or FANET. Authors discuss several emerging application and the future trends of MANET, VANET, and FANET. The common attacks on ad hoc networks are also introduced. The chapter enhances the overall concepts relative to MANET, VANET, and FANET. Authors compare mobile ad-hoc networks (MANET), vehicular ad hoc networks (VANET), and flying ad-hoc networks (FANET) in all aspects with the help of several examples. The chapter includes a relative and detailed study of mobile ad-hoc networks (MANET), vehicular ad hoc networks (VANET), and Flying ad-hoc networks (FANET).
Challenges for Pakistani SMEs in Industry 4.0
Authors: Iqbal Qaisar , Ahmad Noor Hazlina
Published: March 2020

Small and medium enterprises (SMEs) play a vital role in the development of any economy. The disruptive technologies of the fourth industrial revolution have ignited the flexibility and dynamic nature of the market. Industry 4.0 requires firms to deliver highly customized and high-quality products at low price and on time. However, the SMEs sector is not performing up to the standard, which is quite disturbing in the current economic situation of the country. This chapter has elaborated the application of advanced information and communication technologies of Industry 4.0 in the context of SMEs. Based on the requirements of Industry 4.0, this chapter has also explored the challenges faced by SMEs in Pakistan. The SMEs in Pakistan are presently coping the challenges such as limited access to finance, the undue compliance and excessive paperwork imposed by regulators, lacking mechanism of talent management, lacking skilled workforce, absence of networking and collaboration, mismanagement of raw material, and insufficient infrastructure.
A Fuzzy Decision-Making Model for the Key Performance Indicators of Hospital Service Quality Evaluation
Authors: Yucesan Melih , Mete Suleyman , Gul Muhammet , Celik Erkan
Published: March 2020

One of the major concerns of the healthcare industry throughout the world is to provide better hospital service quality. Management and delivery of hospital healthcare services are achieved in a competitive environment in Turkey. For this reason, to make better decisions, the services provided by the public and private hospitals should be monitored and evaluated according to the viewpoint of medical stakeholders. This chapter presents a cause-and-effect, decision-making model in evaluating hospital service quality criteria. Since the decision-making process involves the vagueness of human judgments, a combination of fuzzy sets and decision-making trial and evaluation laboratory (DEMATEL) is used. Results of the study demonstrate that medical equipment level of the hospital, the attitude of nurses and medical staff to patients, pharmacists' advice on medicine preservation, medical staff with professional abilities, outpatient waiting time for medical treatment, and number and quality of the bathrooms available have more impact on the entire hospital service quality. In conclusion, the proposed approach will contribute to better providing of healthcare services at a higher quality level.
Deep Learning in the Healthcare Industry
Authors: Shirazi Zahra A. , de Souza Camila P. E. , Kashef Rasha , Rodrigues Felipe F.
Published: March 2020

Artificial Neural networks (ANN) are composed of nodes that are joint to each other through weighted connections. Deep learning, as an extension of ANN, is a neural network model, but composed of different categories of layers: input layer, hidden layers, and output layers. Input data is fed into the first (input) layer. But the main process of the neural network models is done within the hidden layers, ranging from a single hidden layer to multiple ones. Depending on the type of model, the structure of the hidden layers is different. Depending on the type of input data, different models are applied. For example, for image data, convolutional neural networks are the most appropriate. On the other hand, for text or sequential and time series data, recurrent neural networks or long short-term memory models are the better choices. This chapter summarizes the state-of-the-art deep learning methods applied to the healthcare industry.
The Implementation of Industry 4.0 by Using Industrial and Service Robots in the Production Processes
Authors: Karabegović Isak , Karabegović Edina , Mahmic Mehmed , Husak Ermin
Published: March 2020

From the very knowledge of Industry 4.0, its implementation is carried out in all segments of society, but we still do not fully understand the breadth and speed of its implementation. We are currently witnessing major changes in all industries, so new business methods are emerging. There is a transformation of production systems, a new form of consumption, delivery, and transportation, all thanks to the implementation of new technological discoveries that cover robotics and automation, the internet of things (IoT), 3D printers, smart sensors, radio frequency identification (RFID), etc. Robotic technology is one of the most important technologies in Industry 4.0, so that the robot application in the automation of production processes with the support of information technology brings us to smart automation (i.e., smart factories). The changes are so deep that, from the perspective of human history, there has never been a time of greater promise or potential danger.
The Importance of Implementing Big Data Analytics Concepts in Companies
Authors: Stupar Savo , Bičo Ćar Mirha , Šahić Elvir
Published: March 2020

The unimaginable speed of the development of information and communication technologies has led to the fact that in the time period from 2009 to 2012, human society generated and saved more information than in the entire history until then. As estimated in 2013, approximately 1,200 exabytes were generated around the world, and this vast amount of information is doubled every three years. This would mean that estimated amount of generated data for 2019 is approximately 4800 exabytes, that is, three times more than the total amount information generated throughout the history. Since such a large amount of information cannot be stored or processed (and especially not in real or reasonable time) using the common methods of storing and processing data, a new paradigm of storage, processing and management of large quantities, variety and data processing speed has emerged under the name big data concept.
Application of Cloud Computing in Companies
Authors: Stupar Savo , Bičo Ćar Mirha , Šahić Elvir
Published: March 2020

Cloud computing is a new technology that represents the realization of an old idea that computer data processing is executed and charged as a service. The goal of this chapter is to define the basics of the cloud computing concept and how this technology works, and then explain where is all the data that cloud computing uses, how they are distributed, how and to what extent are available, who are the ultimate beneficiaries, what are the advantages and the disadvantages of applying this concept. This cloud computing model consists of five essential features, three service models, and four application models, and the elaboration of these concepts will be an integral part of this chapter. Particular attention will be paid to the possibility of data abuse in cloud computing and regarding that data protection against manipulation from service providers as well as the financial aspect of cloud computing.
Integrating Cyber and Physical Environments for Adaptive Process Control in Work Systems
Authors: Hozdić Elvis
Published: March 2020

Modern industrial production conditioned by the overall globalization, scientific, and technological achievements requires a new and systematic approach to the management and control of production systems. The traditional concepts of production systems, their hierarchical control, and management structure are not able to cope with the challenges brought by new production philosophy: Industry 4.0. In this chapter, a new approach was proposed in the structuring of production systems through the integration of cyber and physical environment in the elementary functional unit: elementary socio-cyber-physical work system (ESCPWS).
Redesign of the Workplace for Toolmakers Towards Industry 4.0
Authors: Radić Ivana , Rupnik Bojan , Šinko Simona , Kramberger Tomaž , Gajšek Brigita
Published: March 2020

This chapter presents a workspace redesign of a toolmaker position in a tooling industry towards Industry 4.0. In general, the theory is lacking studies that would pinpoint concrete methodology to present the redesign of a company specific workplace in a way that would follow guidelines of the Industry 4.0 systematically. In this research, the authors have primarily focused on a digital readiness and identification of potential areas and tasks suitable for the implementation of enabling technologies. Collected data are based on the case study conducted in a tooling company. The result is a procedure to generate a systematic approach, a roadmap, towards Industry 4.0. To achieve the redesign of toolmaker's workplace, the authors combined the AS-IS state analysis and use Toolbox Industry 4.0. The effects of a redesigned process manifest in reduced laborious, repetitive manual work, errors, and toolmaker workload.
New Challenge of School Principals
Authors: Karataş İbrahim Hakan
Published: March 2020

Increasing diversity in schools has made the culture of living together an important issue in schools and forces education, schools, and school administrations to transform. The roles, responsibilities, and tasks of school principals are also transforming. However, the culture of living together does not fully coincide with the values of the nation-state. Laws of nation states, social understanding, and beliefs and values of school principals create significant obstacles for school principals who are expected to lead the culture of living together. On the other hand, school principals-preparing programs keep their traditional approach, are theory-oriented, and lack appropriate educational strategies. This new challenge to school principals needs to be understood, and the preparing of aspiring and incumbent school principals needs to be reconsidered in this new framework.
The Role of Leaders in Peace Building
Authors: Cerit Yusuf
Published: March 2020

Since the existence of humanity, there have been various conflicts and wars. Conflict and war setting causes people to lose their lives and deteriorate their living spaces and sociological environment. In order to prevent this kind of negativity, it can be stated that the disputes between people or societies must be solved before they turn into conflict or war. Leadership is vital to the success of efforts to create peace by ending the conflict or war. In this regard, it is important to identify community leaders who take actions to create peace. In the literature, the concept of peace leadership emerged as a relatively new phenomenon. Therefore, this chapter examines how leadership can be effective in creating and sustaining peace, the concept of peace leadership, and the characteristics, skills, and roles of peace leaders.
Peace Education and Empathy
Authors: Polat Soner , Halçe Binasa
Published: March 2020

In this experimental study, the effect of the peace education program on empathy tendencies of fourth-grade students was examined. The research was designed in an experimental design. In the 2016-2017 academic year, all of the fourth grades (eight classes) of Şirinköy Elementary School in Gölcük province of Kocaeli were determined as the experimental group, and all of the fourth grades (seven classes) of Barbaros Elementary School were determined as the control group. The sample of the study included 275 fourth grade students. One hundred thirty-nine of these students are in the experimental group where the program is applied, and 136 students are in the control group where the program is not implemented. The peace education program was implemented for a period of 14 weeks, one lesson per week. SPSS 21.0 program was used to analyze the quantitative data. The analysis of the data was based on .05 significance level. When the findings obtained in the study are examined, it is seen that peace education is an effective program in increasing the empathy tendencies of fourth-grade students.
Digital Learning Management Systems Case Study
Authors: Al Amoush Abdeleh Bassam , Sandhu Kamaljeet
Published: March 2020

Digital learning management systems (DLMSs) are an important and necessary educational tool for learning activities. Universities are using the Digital LMS tools that are unique to their environment. Different universities have different DLMS tools that are software driven and allow the users (management, instructors, and students) to use them for regular tasks. This research discusses the qualitative data collected using a case study methodology. In this research, a case study design was selected for the qualitative methodology and semi-structured interviews were employed as the data collection method. This case study is based on a deanship of a university implementing a digital learning management system in the country of Jordan. The research provides an analysis of the interviews to gain insights into instructors' perspectives regarding the factors influencing the implementation of the digital learning management system in a higher education sector.