bluuebunny/crossref_metadata_2025 huggingface dataset:
Factors Shaping the Patterns of Online Shopping Behavior
Authors: Khanam Fahima , Ullah Mahmud , Al-Zadid Muhammad Omar
Published: January 2023

The rapid growth of online shopping in almost each of the countries around the world has generated lots of interest among the academicians and marketing professionals to search for up-to-date research-based knowledge on shopping behavioral patterns of the online shoppers to help the decision makers make data-based marketing decisions. The behavioral patterns of the online shoppers among the different countries in the world certainly differ a lot. Apparently, digital divide may be blamed as the main culprit for it, but these differences are there not only because of the nuances of the digital phenomena. There are many other social, cultural, technological, technical, economic, political, legal, educational, religious, etc. factors as well. The article explores the most influential factors, and a number of other factors too, to contribute new contents to the existing literature. It provides some useful perspectives of the various aspects of online shopping by carefully probing into consumers' intention toward online purchase in more depth and detail.
Research Data Management
Authors: Koltay Tibor
Published: January 2023

This chapter explains the main aspects and the importance of being familiar with research data management (RDM), stressing the centrality of the research data lifecycle and the importance of data sharing, whereby the ideas of the FAIR principles are characterized as a main driver of reuse. The importance of data management plans is emphasized as well. RDM processes such as adding metadata, citing, retrieving, and curating datasets are portrayed. The need for cooperating between disciplinary researchers and different data professionals, such as data librarians and data curators, is highlighted. Moreover, it is underlined that a number of educational programs to data science also contain data management.
An Agent and Pattern-Oriented Approach to Data Visualization
Authors: Pang Chung-Yeung , Pang Severin K. Y.
Published: January 2023

In order to analyze and visualize big data with a large variety of data, we need an agile and flexible software system. Following the traditional standard programming paradigm, such a system cannot easily be built. This article introduces a programming approach that combines generic programming, pattern-oriented programming, and agent-oriented programming. Reflection techniques that allow components and patterns to change their behavior depending on the data context are also presented. The main motivation of aspect-oriented programming, the separation of concerns, is also discussed. This approach provides guidelines for building flexible, extensible, and maintainable software systems. However, a paradigm shift in programming is required, which is also presented in this article.
Big Data Visualization of Association Rules and Frequent Patterns
Authors: Leung Carson K.
Published: January 2023

Big data and machine learning are driving Industry 4.0. In the current era of big data, numerous rich data sources are generating huge volumes of a wide variety of valuable data at a high velocity. Embedded in these big data are implicit, previously unknown, and potentially useful information and knowledge. This calls for data science, which makes good use of big data mining and analytics, machine learning, visualization, and related techniques to discover and visualize hidden gems. This may maximize the citizens' wealth and/or promote all society's health. As an important big data mining and analytics task, frequent pattern mining aims to discover interesting knowledge in the forms of frequently occurring sets of merchandise items or events. To enable users to get better understanding of the discovered patterns in a comprehensive manner, several data visualization and visual analytics tools have been proposed. This encyclopedia article focuses on visualization of big data, as well as association rules and frequent patterns discovered from these big data.
Data Mining for Visualizing Polluted Gases
Authors: Alsultanny Yas A.
Published: January 2023

Knowledge discovery from big data is one of the important issues. Big data mining needs many steps, which must be implemented carefully to get accurate results. Visualization is one of the 10 Vs characteristics of big data, and it is the final step in summarizing the results numerically. This article aims to mining the big data recorded by environmental station. These stations are recording the concentrations of pollution gases and meteorological parameters. The 2D and 3D data visualization are used to evaluate the capability of visualization in determining the effect of meteorological parameters on some gases that caused pollution. The results showed the visualization is a very important tool, and visualization can be used in mining big data by simply showing decision makers the pollution gases concentrations graphically. This article recommended using big data visualization periodically as an alarming tool with IoT for monitoring the levels of pollution gases concentration.
A Systems Analysis for Air Quality in Urban Ecology
Authors: Ahamer Gilbert , Aschemann Ralf , Pilch Birgit
Published: January 2023

In order to describe the mode of operation of a networked system, a systems dynamics algorithm was designed for the topic of air pollution control. With the help of a matrix multiplication, this systems dynamics approach facilitates assessment of necessary measures for improved air quality (and its effects) to be simulated and assessed in a practical-minded quick overview. It turns out that for a desired long-term improvement in air quality measures that can be derived from the field of social sciences are at least as effective as technological measures that address the emitter directly. This is due to the fact that control loops become effective in a networked system which, in the case of amplification, can unleash a great effect while necessitating only relatively little effort.
Data-Driven Clinical Decision Support Systems Theory and Research
Authors: Noteboom Cherie , Zeng David , Sutrave Kruttika , Behrens Andrew , Godasu Rajesh , Chauhan Akhilesh
Published: January 2023

Computerized clinical decision support systems (CDSS) have evolved rapidly and are causing a radical transformation in healthcare. These systems are integrating artificial intelligence-enabled mechanisms to improve the quality of care, health of populations, and reduce the costs of healthcare. Some of these mechanisms include descriptive, predictive, and prescriptive analytics to assist with clinical decision making. However, there are significant barriers to CDSS adoption, implementation, and acceptance among physicians and clinicians who utilize the systems to provide high-quality patient care. The authors used systematic literature review guidelines to the research questions: “To what extent is data-driven decision making applied in CDSS?” “What prevalent theories are used in data-driven CDSS research?” and “What major system attributes contribute to the theoretical frameworks?”
Machine Learning Approach to Art Authentication
Authors: Dobbs Bryan Todd , Ras Zbigniew W.
Published: January 2023

The popularity of machine learning algorithms produced numerous applications in computer vision in the past 10 years. One application is art authentication, which assures that a piece of art is created by an artist. The models produced by machine learning algorithms provide an objective measure to authenticate an artist to their artwork collection. This article discusses an experiment using the residual neural network machine learning algorithm. This experiment demonstrates how a computer can distinguish between 34 and 958 artists with various degrees of confidence.
Artificial Intelligence in E-Learning Systems
Authors: Marmo Roberto
Published: January 2023

E-learning is conducted via electronic media, typically the internet, in order to transfer knowledge to students. Many problems are involved in content creation, assessment and grading, learner profiles, MOOC, personalize learning, etc. This article describes some technologies regarding artificial intelligence that can help teacher and student and can improve student engagement, learning outcomes, teaching abilities, assessment of the quality of learning materials, content creation. It also discusses background, knowledge, challenges, and critical factors necessary for the complex nature of present education. Future trends are described.
Online Educational Video Recommendation System Analysis
Authors: R. Parvathi , Siri Agarwal Aarushi , Singh Urmila
Published: January 2023

Most online platforms that provide video content, including TEDx, usually use various recommendation systems to gather more viewers. These videos are recommended based on various criteria. They can be either based on the user behavior and history of watched videos or on the basis of generally liked videos. The aim of this work is to conduct an in-depth analysis of the education platform called TEDx. This analysis will help in deriving the current protocols and thresholds this platform follows to curate and recommend videos to new users of the platform. The end goal is to figure out the various correlations between different parameters pertaining to these videos and on this basis to derive concrete illustrative representations of said relations and also to build a framework around these facts to find the exact relation between various videos on the platform.
Change Management Science Innovation Methodologies
Authors: Geada Nuno
Published: January 2023

A question of central importance for researchers and practitioners is how information technology can help organizations to survive and thrive in turbulent and constantly changing business environments. The objective of this study is to identify the leading trends that have influenced the research and the prominent themes published using a scientific approach. Major themes discussed in this article include change management, digital transformation, digitalization, sustainability. Some areas of research have been extensively studied, while others are starting recently. In addition, trends are identified, and different future research themes are proposed.
Emerging Tools and Technologies in Data Science
Authors: Ullah Mahmud , Rahman Nayem
Published: January 2023

Business intelligence (BI) is inevitable to the business organizations to make the most effective decisions with the highest level of efficiency, and to find ways to make continuous improvement in their decision-making processes based on data science (DS). So, it is very important for them to search for the latest BI and DS tools and technologies. They expect to get increasingly improved performance from these emerging tools and technologies in the field of DS, which consequently helps them to drive down their information technology (IT) budget to run their businesses. The advent of computing technologies, software engineering, DS technologies, cloud computing, etc., and very rapid emergence and adoption of smartphones around the globe, have made the modern day business environment much more competitive than ever before, and have resulted into enormous increase in the use of the advanced BI and DS tools and technologies, which will be steadily increasing further in the future. This article attempts to make an analytical discussion of these ever-improving DS tools and technologies.
Fairness Challenges in Artificial Intelligence
Authors: Chakrobartty Shuvro , El-Gayar Omar F.
Published: January 2023

Fairness is a highly desirable human value in day-to-day decisions that affect human life. In recent years many successful applications of AI systems have been developed, and increasingly, AI methods are becoming part of many new applications for decision-making tasks that were previously carried out by human beings. Questions have been raised: 1) Can the decision be trusted? 2) Is it fair? Overall, are the AI-based systems making fair decisions, or are they increasing the unfairness in society? This article presents a systematic literature review (SLR) of existing works on AI fairness challenges. Towards this end, a conceptual bias mitigation framework for organizing and discussing AI fairness-related research is developed and presented. The systematic review provides a mapping of the AI fairness challenges to components of a proposed framework based on the suggested solutions within the literature. Future research opportunities are also identified.
Ensemble Methods and Their Applications
Authors: Govindarajan M.
Published: January 2023

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. This article mainly focused on distinguishing between non-generative ensemble methods and generative ensembles. Non-generative ensemble methods embrace a large set of different approaches to combine learning machines. The subdivisions of non-generative strategies are ensemble fusion and ensemble selection methods. Generative ensemble methods generate sets of base learners acting on the base learning algorithm or on the structure of the data set and try to actively improve diversity and accuracy of the base learners. The subdivisions of generative strategies are resampling methods, feature selection methods, and output coding methods. The main aim of this article is to explain the detailed characteristics of each ensemble method and to provide an overview of the main application areas of ensemble methods.
Stock Price Prediction
Authors: Tekin Ahmet Tezcan , Çebi Ferhan , Kaya Tolga
Published: January 2023

In this study, the last two years' hourly opening and closing prices of the banks' stocks traded on BIST-30 were used as the dataset. The research is aimed to predict the closing prices of these stocks in the light of machine learning. In this context, the authors propose a new method containing ensemble learning algorithms and fuzzy clustering technics for predicting stock prices. With this method, authors aim to find stocks which are similar characteristics with test sets and model them together. Thanks to this method, authors aim to improve modelling success. For comparing the results, authors also create models with classical machine learning methods such as support vector machines, random forest, and boosting type new generation algorithms such as extreme gradient boosting and catboost.
Bio-Inspired Algorithms for Feature Selection
Authors: Kaleche Rachid , Bendaoud Zakaria , Bouamrane Karim , Yachba Khadidja
Published: January 2023

Feature selection is an important process of machine learning, especially when facing the high dimensionality challenges due the unprecedent increase of data, namely big data. The main objective of the feature selection process is to find the smaller feature subset which optimizes a learning algorithm performance providing by this a better readability. The feature selection problem is known to be an NP-hard problem, and classical approaches tackling it reached their limits. Therefore, tackling feature selection problem by bio-inspired algorithms has gained an increased interest due to the improved obtained results. This study presents an overview of feature selection problem and bio-inspired algorithms as a background. Based on this background, bio-inspired algorithms modeling elements for feature selection are described, followed by application domains and bio-inspired algorithms approach samples for feature selection problems. In addition, challenges and issues are discussed aiming to open future research opportunities.
Fuzzy Complex System of Linear Equations
Authors: Temelcan Gizem , Köçken Hale Gonce , Albayrak İnci
Published: January 2023

The complex system of linear equations (CSLE) has a wide range of applications in engineering, optimization, operational research, such as circuit analysis and wave equations in quantum mechanics. Since variables and/or parameters of the CSLE are generally unknown, uncertain, or imprecise in real-life applications, Fuzzy CSLE (FCSLE) arises. The fuzziness enables the modeling of the CSLE in a more natural and direct way, and thus, the FCSLE has attracted the attention of many researchers and become a significant area both in theory and application. With this motivation, a short review of the FCSLE has been conducted to guide the future studies of new researchers in this area. This review will give a general framework about the progression of the area, its solution approaches, and provide a bibliography on the topic.
Class Discovery, Comparison, and Prediction Methods for RNA-Seq Data
Authors: Cephe Ahu , Koçhan Necla , Ertürk Zararsız Gözde , Eldem Vahap , Zararsız Gökmen
Published: January 2023

Gene-expression studies have been studied using microarray data for many years, and numerous methods have been developed for these data. However, microarray technology is old technology and has some limitations. RNA-sequencing (RNA-seq) is a new transcriptomics technique capable of coping with these limitations, using the capabilities of new generation sequencing technologies, and performing operations quickly and cheaply based on the principle of high-throughput sequencing technology. Compared to microarrays, RNA-seq offers several advantages: (1) having less noisy data, (2) being able to detect new transcripts and coding regions, (3) not requiring pre-determination of the transcriptomes of interest. However, RNA-seq data has several features that pose statistical challenges. Thus, one cannot directly use methods developed for microarray analyses, which has a discrete and overdispersed nature of data, quite different from the continuous data structure of microarrays. This article aims to provide an overview and practical guidance to researchers working with RNA-seq data for different purposes.
Big Data and Sustainability Innovation
Authors: Harsanto Budi , Firmansyah Egi Arvian
Published: January 2023

Today, big data has become an inseparable part of the advancement of information and communication technology. This is in line with the development of sustainability innovation, which has been developed, particularly in the last decade. The purpose of this article is to understand the development of big data in sustainability innovation. The analysis was carried out using descriptive statistics and co-occurrence analysis on published documents obtained through a systematic search in the Web of Science academic database. According to the findings, there was an increase in publications, particularly beginning around 2014, with contributions from the fields of environmental sciences, science technology, business economics, engineering, and computer sciences. Co-occurrence analysis yields 116 most frequently occurring keywords, which are divided into six clusters. This article contributes to a better understanding of the role of big data in fostering more sustainable innovation in business and society.
Digital Transformation and Circular Economy for Sustainability
Authors: Payer Renan Carriço , Quelhas Osvaldo Luiz Gonçalves , Lima Gilson Brito Alves , Bergiante Níssia Carvalho Rosa
Published: January 2023

The article analyses how technologies from Industry 4.0 may step up circular economy initiatives inside organizations to provide a sustainable digital transformation. The authors present advances found in the literature towards convergencies between circular economy and digital transformation, and also point out the main challenges and restraints that need to be overcome through more scientific studies, or through the best practices inside the organizational environment. This article also presents an integration between the principles and fundamentals from circular economy and the main technologies from Industry 4.0 using the framework ReSOLVE. Thus, it aims to show how technological resources may cooperate in reaching a circular economy inside organizations.
Evolving From Predictive to Liquid Maintenance in Postmodern Industry
Authors: Lozano Rodriguez Manuel , Torres Carlos E.
Published: January 2023

PdM is unready to face the near-future incoming challenges since it is anchored in an obsolete paradigm alien to the incoming cyber-physical reality and unfit for unbelievable data density. In addition to this, PdM is wormed by philosophical hiddenness around time and taxonomies abuse; it is not the sound subdiscipline it appears to be. So, we are doomed to dialogue and get along with AIs if we want to break our human predictiveness ceiling glass and keep PdM improving. In this article, the authors explain not only the turn of the tide but how to flow towards a non-essentialist PdM paradigm.
Industrial Revolution 4.0 With a Focus on Food-Energy-Water Sectors
Authors: Ghosh Abichal , Kamesh Reddi , Moulik Siddhartha , Roy Anirban
Published: January 2023

In this article, the authors explore how sustainable goals can be achieved with artificial intelligence (AI) and machine learning (ML) with a focus on food, energy, and water sectors. This article explores the possibilities of intervention in the above three sectors by exploring the production, processing, storage, and distribution. The challenge lies in the rapid screening of materials, processes, optimized usage of resources, minimization of wastage, and availability of the same to the users. All these challenges can be achieved by AI-ML interventions in the food, energy, and water sectors and can be an expected norm during this Industrial Revolution 4.0 era. From recent reported studies, it is clear that for achieving sustainable goals in the food, energy, and water sectors application of AI-ML methods plays a critical role in modelling, prediction, design, optimization, monitoring, and control of processes in the above sectors. In this article, the authors focus on all these techniques for better understanding and reaching sustainable goals across the three sectors.
Industry Revolution 4.0 and Its Impact on Education
Authors: Minasi Riccardo
Published: January 2023

The purpose of this article is to discuss what will happen to the education system in the area of Industrial Revolution 4.0. Information management is the most challenging problem faced by any organization in the developing world. What we realize in our education won't persist for an entire career as technologies accelerate. The fourth industrial revolution has changed the landscape of educational innovation. The rapid innovation revolution has brought another educantional model for the future – Education 4.0. To prepare graduates for the future lives and jobs achived by IR 4.0, where more intelligent robots will replace humans in certain operational departments, education should tap into information and capabilities that robots cannot replace. The innovation disruption created Education 4.0, which focuses on education and skills developments, such as artifical intelligence, information and test giant, distributed computing and mobile orchestration, online networking, and internet of things.
Structure Implementation of Online Streams
Authors: N. Ambika
Published: January 2023

The previous contribution is rank-based monitoring and sampling methodology. It is based on data growth. It instantly discovers the mean variations in a means when only an insufficient division of searches is obtainable online. The measurement sequence will automatically enlarge knowledge for unobservable variables based on the online remarks. It wisely earmarks the monitoring sources to the most questionable input streams. The architecture can precisely gather the variables based on several noticeable variables and completely assemble a global monitoring statistic with the proposed augmented vector, which leads to a quick apprehension of the out-of-control state even if limited changed variables in real-time. It quickens the disclosure of method transfers in the circumstances of unfinished measurements by growing the unobservable learning with the dimensions of the marked ones. The suggestion aims to construct a structure based on the fed data. The present suggestion conserves 10.77% more energy and availability by 27.58% compared previous contribution.
Exploration of Research Challenges and Potential Applications in IoT
Authors: Mewada Shivlal
Published: January 2023

IoT is rapidly growing. As the next phase of the internet's growth, it's more essential than ever to recognize the various possible areas for IoT applications, as well as the research challenges that come with them. IoT is anticipated to penetrate virtually all aspects of daily life, from smart cities to healthcare, smart agriculture, logistics and retail, and even smart living and smart surroundings. Despite the fact that current IoT-enabling technologies have vastly improved in recent years, there are still a slew of issues that need to be addressed. Many research problems are expected to develop since the IoT idea is based on heterogeneous technology. IoT is a key study subject for studies in several related domains such as information technology and computer science since it is so wide and affects almost every aspect of our lives. As a result, the internet of things is paving the way for new types of research to be conducted. This article addresses future uses and research difficulties as well as the current progress of IoT technology.
Evaluation of Tourism Sustainability in La Habana City
Authors: Korstanje Maximiliano Emanuel , Robert Beatón Martha Omara , Echarri Chávez Maite , Martínez Carballo Massiel , Martinez Robert Victor
Published: January 2023

The article is aimed at evaluating the sustainability of a tourist destination with a focus on processes, having as its foundation the experiences in the business sector referred to in the consulted scientific literature. The main research techniques used to conduct the study were a comparative analysis, descriptive statistics analysis, and the Delphi method. The main results obtained are related to understanding and recognizing destinations as a systemic organization, which supports the justification of adapting and conceptualizing the foundations of the process approach. This led to the proposal of a procedure that allows for the integration of multiple actors of different natures and the stakeholders in the evaluation of tourism sustainability in destinations. The evaluation process was carried out in the Historic Center of Havana and made it possible to identify the current weaknesses in achieving the sustainability of the model based on the processes evaluated, defining areas of action in an environment of improvement.
The Contribution of Benefit Management to Improve Organizational Maturity
Authors: Gomes Jorge Vareda , Romão Mário José Batista
Published: January 2023

The business challenges that some companies face require enterprise-wide solutions that call for an integrated approach and an effective management of organizational resources to achieve business objectives with an acceptable level of risk. A maturity model is an improvement approach which provides organizations with the essential elements for effective change. The maturity models process helps to integrate traditionally separate organizational functions, enhances goals and priorities, supplies guidance for quality processes, and shares benchmarks for appraising current outcomes. The benefits management approach emerges as a complement to traditional management practices and proposes a continuous mapping of business benefits and the implementation and monitoring of intermediate results. Benefits management reinforces the distinction between project results and business benefits.
Balanced Scorecard as a Tool to Evaluate Digital Marketing Activities
Authors: Fatin Tasnia , Ullah Mahmud , Rahman Nayem
Published: January 2023

Digital marketing has become significantly important to the industry and academia as one of the most effective marketing media ever since its inception in the early 1990s. Business-related people have discovered a new marketing framework by the advent and quick expansion of internet and information technology. Most of the companies are creating new dimensions in their businesses by taking numerous actions to switch to digital marketing from or add digital marketing to their traditional marketing strategies. But the evaluation practices of these newly developed digital marketing activities do not seem to have developed proportionately. This article is an attempt to examine digital marketing media and its campaigns and provide a framework to evaluate the digital marketing activities. To evaluate the performance of digital marketing campaigns, this article uses the balanced scorecard approach devised by Kaplan and Norton. Managers and researchers are expected to get insights into the different aspects of digital marketing and its effective evaluation tools from the outcome of this article.
An Approach for a Multi-Objective Capacitated Transportation Problem
Authors: Kara Nurdan , Köçken Hale Gonce
Published: January 2023

The multi-objective solid transportation problem is a special class of vector-minimum linear programming problems in which the objectives are often in conflict. Furthermore, the supply, demand, and conveyance constraints in MSTP may not be only of equality type but also of inequality type. The fuzzy programming approach is one of the most common solution methods for multi-objective programming problems. In this approach, linear membership functions are generally used in the literature. In this study, the fuzzy programming approach is applied by utilizing a special type of non-linear (exponential) membership functions to solve the multi-objective capacitated solid transportation problem (MCSTP) and a Pareto-optimal solution is obtained. Finally, an application from the literature is provided to illustrate the efficiency of the exponential membership function. Also, a comparison is presented with the solution obtained by using a linear membership function.
One vs.Two vs. Multidimensional Searches for Optimization Methods
Authors: Vitor Fabio
Published: January 2023

Optimization is an important tool for decision makers to make better and more informed decisions. Most algorithms designed to solve optimization models are considered one-dimensional search methods. That is, an improved solution is obtained at each iteration by moving along a single search direction and solving a one-dimensional subproblem. In contrast, multidimensional search methods consider more than one search direction and solve a multidimensional subproblem at each step. This article presents an extensive review of existing multidimensional search algorithms to solve optimization problems. The article also describes a modified and improved version of the slope algorithm, a technique to perform multidimensional searches. This version aims to improve the numerical stability of the slope algorithm. Some computational experiments show that the modified version is still effective and more reliable.
Challenges and Opportunities in Knowledge Representation and Reasoning
Authors: Dadure Pankaj , Pakray Partha , Bandyopadhyay Sivaji
Published: January 2023

Knowledge representation is of immense importance in the field of artificial intelligence and natural language processing. The representation of knowledge goes hand in hand with automated reasoning as one of the key goals of representing knowledge effectively is being able to reason about it. Researchers of knowledge representation and reasoning have built techniques and methods that are the main source of development in computer science and have made tremendous progress in a wide variety of real-life applications, ranging from natural language processing to robotics and software engineering. Further research is required in order to allow a more active role in guiding the reasoning process through the knowledge representation framework. This article has discussed knowledge representation and reasoning and analyzed the major challenges and new opportunities where novel knowledge representation and reasoning research have had a major impact.
Spatial Audio Coding and Machine Learning
Authors: Dabbabi Karim
Published: January 2023

Spatial audio encoding plays a fundamental role in the ultra-high-definition TV (UHDTV) and the latest generation of television broadcasting, as well as other technological devices by providing a three-dimensional (3D) audio content to consumers. In this article, the fundamental concepts of the spatial audio coding including its techniques, standards, and applications are exhibited. The object-based audio reproduction system will be presented and compared to the traditional channel-based system in order to offer a good understanding of this system to the users and to give them more flexibility in their preferred audio composition. Moreover, the MPEG standard for encoding multi-channel audio signals will be exposed. Machine learning (ML) methods and their applications in acoustics and spatial audio scenes will then be offered. Ultimately, further research directions will be illustrated and discussed.
Analyzing the Significance of Learner Emotions Using Data Analytics
Authors: Hariharan Shanmugasundaram , D. Magdalene Delighta Angeline , Perumal Ramasubramanian , I. Samuel PeterJames
Published: January 2023

Research on emotional intelligence (EI) in the learning process enhances the learner progress to attain the aspirations that have been set. The proposed work helps to recognize the learners' emotional behavior owing to the hectic situation due to COVID-19. Concentrating on the motivational and psychometric characteristics of the learners, this study scrutinized the involvement of EI in learner's learning. For this, three learning environments (LEs) are defined that adopt different methodologies of learning to analyze the increased performance of different types of learners. The face emotions of several learners were captured, recognized, and grouped using naïve bayes algorithm. The learners with negative emotions are identified from the algorithm, and the inputs are assessed with cross-tabulation techniques like chi-square and ANOVA. The results confirm that the learning environment along with the teaching methodologies and emotional happenings in and around decide the performance of the learner.
Machine Learning and Sensor Data Fusion for Emotion Recognition
Authors: Younis Eman M. G. , Zaki Someya Mohsen , Houssein Essam Halim
Published: January 2023

In this article, the authors investigate the development of sensor data fusion-based emotion detection models. They use direct and continuous sensor data to construct emotion prediction models. They use sensor data fusion involving the environmental and physiological signals. This article integrates on-body physiological markers, surrounding sensory data, and emotion measurements to achieve the following goals: 1) collecting in the wild data set of multiple sensors; 2) using data fusion, feature fusion, and decision fusion for emotion recognition; 3) prediction of emotional states based on fusing environmental and physiological variables; 4) developing subject-dependent emotion detection models. To achieve that, they have done a real-world study “in the wild” with physiological and mobile sensors. The datasets come from participants walking around Minia University campus. The authors compared the obtained results to choose the best-performing model. Results show that D Tand RF outperforms SVM and KNN significantly by 1% or 2% (p <0.01) with an average accuracy of 0.97%.
Relative Relations in Biomedical Data Classification
Authors: Czajkowski Marcin
Published: January 2023

Advances in data science continue to improve the precision of biomedical research, and machine learning solutions are increasingly enabling the integration and exploration of molecular data. Recently, there is a strong need for “white box,” a comprehensive machine learning model that may actually reveal and evaluate patterns with diagnostic or prognostic value in omics data. In this article, the authors focus on algorithms for biomedical analysis in the field of explainable artificial intelligence. In particular, they present computational methods that address the concept of relative expression analysis (RXA). The classification algorithms that apply this idea access the interactions among genes/molecules to study their relative expression (i.e., the ordering among the expression values, rather than their absolute expression values). One then searches for characteristic perturbations in this ordering from one phenotype to another. They cover the concept of RXA, challenges of biomedical data analysis, and the innovations that the use of relative relationship-based algorithms brings.
Foundational Recommender Systems for Business
Authors: Aeron Prageet
Published: January 2023

Given the importance of recommender systems, a detailed understanding of the collaborative filtering systems is imperative. The standard formulation of most collaborative recommender algorithms is either based on prediction of a missing user-item rating or it tends to derive topmost k, users, or items. Collaborative methods could be further divided into user-based filtering and item-based filtering. Regression-based approach can help in combining the advantages of both user-based and content-based methods. Machine learning-based models on the other hand offer further generalization of above models and formally segregate the data modeling, training, and prediction phases. Latent factor models further the same idea by formally incorporating the dimensionality reduction concept and have been found to be very effective. The article is likely to be well received by the academics, especially the doctoral students/researchers in the field of recommender systems as well as the practitioners either utilizing or trying to procure recommendation systems for their organizations.
Recommendation Systems
Authors: El Bouhissi Houda
Published: January 2023

With the increasing amount of data produced daily, it becomes very difficult for users to find resources suitable to their needs. Recommendation systems, which are capable of providing individualized suggestions or guiding the user to interesting or relevant resources within a large data space, are proposed for this purpose. In this article, the authors do a comprehensive assessment of recommendation models, propose a categorization scheme, analyze challenges, and explore unresolved issues. In addition, they highlight new trends and future visions in this field of study, emphasizing the need of merging ontologies and machine-learning algorithms to increase the accuracy and efficiency of the recommender systems.
A Simulation Model for Application Development in Enterprise Data Platforms
Authors: Rahman Nayem
Published: January 2023

Behind the scheduled delivery of poor-quality products overrunning the cost or budget are the major blames the software development industry has to handle even today, at its apparent peak period of excellence at present. In spite of continuous refinement over the last four-five decades, it is still not free from many genuine criticisms like these. To get rid of these criticisms gradually, proper evaluation of different software development controls and measures, and timely elimination of code defects in each stage of data platform application development are required to ensure better management of software development life cycle and improved performance of the launched software. This article presents a simulation model of a data platform using code inspection and code scorecard and comparing the removal rate of code defects. Results of the simulation model prepared by the authors show a significant reduction in code defects, improved software development processes, and flawless production execution, which is expected to enable the software developers to improve their current processes.
Death Analytics and the Potential Role in Smart Cities
Authors: Braman James , Brown Alexis D.
Published: January 2023

With all of the means available now for collecting massive amounts of data, many are made possible through advancements in the internet of things (IoT). As more devices are connected and brought online, the ability to analyze rich data points will continue to grow. In this article, the authors discuss how smart cities can use this data to address concerns associated with increasing urbanization, resource allocation, infrastructure, energy, and various economic and social needs by examining potential data collection through IoT devices. As more people populate and consider moving to smart cities, one major area of concern is the potential for impacts of the human element of death and loss. With large populations living in these smart cities, a corresponding increase in the number of deaths will undoubtedly follow. The concept of death analytics and the computational analysis of related data is discussed in the context of smart cities to better leverage technology to help the needs of the bereaved, the terminally ill and to protect the final wishes and content of the dead.
A Bio-Inspired Approach to Solve the Problem of Regular Carpooling
Authors: Yachba Khadidja , Bendaoud Zakaria , Bouamrane Karim , Kaleche Rachid
Published: January 2023

Carpooling involves sharing your personal vehicle to make a common trip in order to share the costs of fuel, toll, or simply to exchange. The goal of this work is to adopt an ideal strategy for carpooling. The problem is to find the best groups between a fixed set of individuals who make the same trip every day and in a regular way. In order to reach the goal, the authors adapted a bio-inspired meta-heuristic (firefly algorithm). This technique allowed them to have very satisfactory results.
A Review on the Use of Artificial Intelligence in Reverse Logistics
Authors: Sinha Abhishek Kumar , John Sajan T. , Sridharan R.
Published: January 2023

This study investigates the role of machine learning (ML) and artificial intelligence (AI) in reverse logistics (RL) within the scope of e-commerce and e-waste. RL is a process to plan and manage the flow of products from consumer to provider with the purpose to generate value. From the bottom line of sustainability, it is important to manage and improve the product return and end-of-life product. Past research studies have presented several techniques to optimize the cost, schedule, and route. However, the integration of ML and AI tools in information technology systems is limited and insufficient. The purpose is to present the research gap for further study. The article is organized as follows: Section 1 provides an introduction to reverse logistics, ML, AI, e-commerce, and e-waste. Section 2 presents a comprehensive review of the current research, and Section 3 concludes the article.
Knowledge-Based Artificial Intelligence
Authors: Batsakis Sotiris , Matsatsinis Nikolaos
Published: January 2023

Knowledge-based artificial intelligence has been extensively used in numerous application areas leading to the development of a vast number of methods and tools. In recent years, focus has shifted on non-symbolic approaches, and neural networks in particular have achieved human-level performance in various applications where accountability is a very important issue, closely related to the interpretability of artificial intelligence methods in general. Lack of interpretability of neural networks and various machine learning methods has led to the adoption of knowledge-based methods instead, which offer models compliant with explainability and interpretability requirements. In this article, an overview of knowledge-based methods is presented along with the state of the art in this area, offering to the AI practitioner guidance for applying these important methods in practice.
Employee Classification in Reward Allocation Using ML Algorithms
Authors: Dharmapala Parakramaweera Sunil
Published: January 2023

This work discussed an application of machine learning algorithms in predicting employee categories in reward allocation based on input features determined from survey responses. The results reported in this article are primarily based on beliefs and perceptions of the survey respondents about the four categories of employees, namely performer, needy, starter, and senior. The authors considered two classification models—full model with 10 input features and the reduced model with seven input features—and the results show that the reduced model performed better than the full model, indicating that three qualitative input features bear no relevance to predicting the employee categories. Both models selected optimizable ensemble and optimizable SVM as best machine learning classifiers, based on accuracy rates and AUC scores. Finally, using the reduced model on out-of-sample observations, employee categories were correctly predicted matching the actual categories.
Salivary and serum irisin in healthy adults before and after exercise
Authors: Missaglia Sara , Tommasini Ester , Vago Paola , Pecci Claudio , Galvani Christel , Silvestrini Andrea , Mordente Alvaro , Tavian Daniela
Published: January 2023

Irisin is an exercise-induced cytokine mainly secreted by myocytes. Circulating level of irisin can increase in response to acute exercise, promoting pleiotropic effects on health. Generally, irisin is evaluated in blood, however, its collection is invasive. Saliva sample would not have any risk associated with blood collection and would represent a less invasive method for irisin detection. Until now, there are only a few studies that have analyzed irisin levels in saliva. In the present research, five healthy male adults performed an incremental exercise until exhaustion on cycle ergometer. Serum and saliva samples were collected before exercise and 15min, 24h and 48h after reaching the exhaustion. Irisin was detected by ELISA assay. Serum and salivary irisin levels increased from baseline to 24h post exercise and reverted to basal levels after 48h of rest. A significant rise of both serum and salivary irisin level at 24h (p≤0.05) compared to baseline levels was found. Furthermore, a significant correlation between irisin percentage change in serum and saliva from baseline to 24h post exercise was detected (r=0.92, p<0.05). Despite the relatively limited sample, this research suggests that collecting saliva samples might represent a valid and less invasive method to detect irisin level changes induced by exercise.
Quercetin modulates ovarian autophagy–related molecules and stereological parameters in a rat model of PCOS
Authors: Neisy Asma , Koohpeyma Farhad , Khorchani Majid Jafari , Karimi Fatemeh , Zal Fatemeh
Published: January 2023

Objective:

To examine the effect of quercetin on stereological parameters and autophagy-related genes in ovaries of polycystic ovary syndrome (PCOS) rats.

Methods:

Fifty female Sprague-Dawley rats were randomly divided into five groups: the control group, the ethanol group, the quercetin group (15 mg/kg/day), the PCOS group, as well as the PCOS + quercetin group. After the induction of PCOS, quercetin was administered orally for 30 days. Histological, stereological and real-time PCR analyses were carried out to evaluate the effect of quercetin on PCOS rats.

Results:

Stereological analysis revealed that quercetin significantly increased the number of ovarian follicles and the volume of corpus luteum and induced a significant decrease in atretic follicles in comparison to the PCOS group. In addition, quercetin markedly increased mTOR gene expression while decreasing Beclin-1 and LC3 gene expression.

Conclusions:

Quercetin strongly modulates the expression of ovarian autophagy-related genes and stereological parameters in PCOS rats. Therefore, it can be considered as an ameliorative component for ovarian follicular impairments.