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The overall volume of data available on the Internet is growing rapidly while finding relevant documents is becoming increasingly difficult. Moreover, queries entered by users are unique, unstructured and often ambiguous while the process has changed dramatically from standard query languages that governed by strict syntax rules to unstructured strings. In Web information retrieval, search paradigms used term occurrences to weight document content prior to any boosting stage. PageRank algorithm, for instance, was used integrated techniques to enhance post retrieval document relevancy to adequately compromise the overall process in two stages. Nevertheless, hypertexts in Web have been used for improving the quality of search results for the most common type of queries. Our main premise is that hypertexts play an important role for ranking documents in IR such as margining between user queries and consensus hypertext. We propose a new algorithm that uses term impact technology for compromising hypertext weighting in Web along with Wikipedia for efficiently find most relevant documents among large set of results. Our experimental results showed that Wikipedia could efficiently improve document relevancy rank when combined with hypertexts for exhibit robust and very good short-term process capability.

Nowadays business operates in a turbulent environment, demanding quality products at low cost in short delivery times. Bearing this in mind, entrepreneurs devote special attention to define strategies, monitor entrepreneurial indicators and recruiting qualified employees. In this sense, organizations need to rely on an efficient measurement system with vast and complex information. Considering this need, the Balanced Scorecard (BSC) embraces a short-, medium- and long-term time horizon and guides entrepreneurs in the measurement of their Key Performance Indicators (KPI), as acknowledged by the well-known company like Apple, which is using BSC for planning and performance measurement. Furthermore, as the KPIs need to reflect organisational performance, the interconnection of BSC with SAP Enterprise Resource Planning (ERP) is considered of benefit. By means of this interlinkage, performance is monitored in an integrated way, as the flow of material and information of the whole chain is integrated and interlinked via ERP. Stressing out that several BSC implementations have failed, this research, intends to detail how ERP implementation can leverage the BSC’s features, namely to monitor entrepreneur’s strategy. As several studies have focused their attention in an isolated way and several BSC implementations failed, this research sheds light on entrepreneur’s strategy leveraged and monitored by the interrelation of ERP and BSC.

A finite zero-sum game defined on a subset of the unit square is considered. The game is a generalized progressive discrete silent duel, in which the kernel is skew-symmetric, and the players, referred to as duelists, have identical linear accuracy functions featured with an accuracy proportionality factor. As the duel starts, time moments of possible shooting become denser by a geometric progression. Apart from the duel beginning and end time moments, every following time moment is the partial sum of the respective geometric series. Due to the skew-symmetry, both the duelists have the same optimal strategies and the game optimal value is 0. If the accuracy factor is not less than 1, the duelist’s optimal strategy is the middle of the duel time span. If the factor is less than 1, the duel solution is not always a pure strategy saddle point. In a boundary case, when the accuracy factor is equal to the inverse numerator of the ratio that is the time moment preceding the duel end moment, the duel has four pure strategy saddle points which are of the mentioned time moments. For a trivial game, where the duelist possesses just one moment of possible shooting between the duel beginning and end moments, and the accuracy factor is 1, any pure strategy situation, not containing the duel beginning moment, is optimal.

Zero-trust security is a security model that assumes no entity is implicitly trusted, regardless of its origin or scope of access. This approach requires continuous verification of all users, devices, and applications before granting access to resources. Cloud computing is a model for delivering IT resources and applications as a service over the Internet. Cloud computing offers many benefits, including scalability, agility, and cost savings. However, cloud computing also introduces new security challenges. This paper proposes a survey-based research methodology to evaluate the usage of clouds in zero-trust security strategies. The paper identifies different zero-trust security solutions, their key features, and the benefits and challenges of implementing these solutions in the cloud. The paper will also discuss the costs and benefits of different zero-trust security solutions. The findings of this research will be valuable for organizations that are considering implementing a zero-trust security strategy in the cloud. The paper will provide guidance on how to choose the best zero-trust security solution for organizational needs and how to implement it effectively.

In the realm of investment decisions, the influence of behavioral biases has emerged as a captivating area of exploration. This article embarks on a comprehensive journey through the landscape of behavioral biases in investment choices, delving into their profound impact on financial markets. Contrary to traditional finance theories assuming rationality, a multitude of empirical evidence attests to the pervasive effects of cognitive and emotional biases. Through an extensive literature review, this article elucidates the intricacies of key biases such as overconfidence, loss aversion, anchoring, confirmation bias, herding behavior, disposition effect, framing effects, and regret aversion. By examining the distinct ways these biases distort investors' judgment and decision-making processes, we unveil the often unexpected deviations from rationality. Each bias, rooted in human psychology, can lead to suboptimal investment behaviors, portfolio misalignments, and heightened market volatility. However, recognizing the impact of these biases provides opportunities for transformative insights. As investment professionals, policymakers, and individuals alike comprehend the subtle nuances of behavioral biases, tailored interventions, educational initiatives, and adaptive strategies can be devised to mitigate their adverse effects. This article not only synthesizes the prevailing research but also charts a course for future investigations. The implications of understanding and addressing behavioral biases extend beyond financial realms, offering a bridge between finance and psychology. As interdisciplinary collaboration gains momentum, pathways for future research become evident, beckoning scholars to delve deeper into the uncharted territories of human behavior and its intricate relationship with investment decisions. Through the exploration of these biases and their potential remedies, this article illuminates the evolving landscape of investment decision-making in a world where cognitive fallacies intersect with financial choicest.

Current supply chain management (SCM) requires the control of physical and information flows in order to satisfy the customer, i.e. deliver the right product to the customer at the right place, at the right time, at the right price and at the lowest cost. SCM is inseparable from traceability which makes reliable the said flows, accelerates the transmission of information on these flows, allows to access a detailed knowledge of the movements, and makes the flows visible. In order to streamline and monitor, if possible, in real time and permanently these logistical processes, we propose the design and implementation of an Ontology-based traceability system based on an architectural model for the physical Internet using computing resources such as Cloud computing, Fog computing and Internet of Things (IoT) to achieve efficiency and sustainability goals. To evaluate our system, we were able to carry out all the queries that the user can express whether he is a customer, a supplier or a manager.

Education data mining (EDM) applies data mining techniques to extract insights from educational data, enabling educators to evaluate their teaching methods and improve student outcomes. Feature selection algorithms play a crucial role in improving classifier accuracy by reducing redundant features. However, a detailed and diverse comparative analysis of feature selection algorithms on multiclass educational datasets is missing. This paper presents a study that compares ten different feature selection algorithms for predicting student grades. The goal is to identify the most effective feature selection technique for multi-class student grades prediction. Five classifiers, including Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Gradient Boosting (GB), and k-Nearest Neighbors (KNN), are trained and tested on ten different feature selection algorithms. The results show that SelectFwe(SFWE-F) performed best, achieving an accuracy of 74.3% with Random Forests (RT) across all ten feature selection algorithms. This algorithm selects features based on their relationship with the target variable while controlling the family-wise error rate.

We revisit the old but formally still unresolved debate on the time efficiency of accessing the elements of 1D arrays via indices versus accessing them via pointers. To analyze that, we have programmed benchmarks of minimal complexity in the C++ language and inspected the machine code of their compilation in the x86 assembly language. Before exploring the performance, we briefly compared a few methods used for the execution time measurements. The results on the Wintel platform show no significant advantage in using pointers over indices except for some benchmarks and array (data) types. In other cases, the exact opposite may be true. The cause of this inconsistency lies in the compilation of the source code into the rather nonorthogonal x86 instruction set. Furthermore, the execution speed does not clearly relate to the instruction length. The parallel aim of this work is to provide a ground for further analysis and measurements of this kind using different compilers, languages, and computer platforms.

Globally, cardiovascular diseases stand as the primary cause of mortality. In response to the imperative to enhance operational efficiency and reduce expenses, healthcare organizations are currently undergoing a transformation. The incorporation of analytics into their IT strategy is vital for the successful execution of this transition. The approach involves consolidating data from various sources into a data lake, which is then leveraged with analytical models to revolutionize predictive analytics. The deployment of IoT-based predictive systems is aimed at diminishing mortality rates, particularly in the domain of coronary heart disease prognosis. However, the abundant and diverse nature of data across various disciplines poses significant challenges in terms of data analysis, extraction, management, and configuration within these large-scale data technologies and tools. In this context, a multi-level fuzzy rule generation approach is put forward to identify the characteristics necessary for heart disease prediction. These features are subsequently trained using an optimized recurrent neural network. Medical professionals assess and categorize the features into labeled classes based on the perceived risk. This categorization allows for early diagnosis and prompt treatment. In comparison to conventional systems, the proposed method demonstrates superior performance.

The purpose of this study is to analyze the use of information technologies for the development of the digital economy. Digitalization of education is a necessary condition for sustainable development of modern society. The article is devoted to analyzing the factors of digitalization of education, among which information technologies are the main ones. For the analysis, the authors used a model for the development of the digital economy, which was also developed in the training of a specialist in the agro-industrial complex. Education is viewed as a subsystem of the digital economy. It is shown that the digitalization of education has both positive and negative consequences not so much for the development of industry as for human development. The article draws conclusions about the features of the socio-anthropological crisis in the context of the digitalization of education. As conclusions, the article presents forecasts for the development of digitalization of education, which include the improvement of the development and methods of introducing information and communication technologies in education, a constant change in the forms of knowledge assessment, the successful use of information technologies in the training of a specialist.

Financial Technology (FinTech) has sparked widespread interest and is fast spreading. As a result of its continual growth, new terminology in this domain has been introduced. The name 'FinTech' is one such example. This term covers a wide range of practices that are repeatedly used in the financial technology industry. This processes were typically accomplished in careers or organizations to supply required services through the use of information technology-based applications. The word covers a wide range of delicate subjects, including security, privacy, threats, cyberattacks, and others. Several cutting-edge technologies, including those associated with a mobile embedded system, mobile networks, mobile cloud computing, big data, data analytics techniques, and cloud computing, among others, must be mutually integrated for FinTech to thrive. To be approved by its users, this new technology must overcome serious security and privacy flaws. This research gives a thorough analysis of FinTech by discussing the present as well as expected confidentiality and safety problems facing the financial sector to protect FinTech. Finally, it examines potential obstacles to ensuring financial technology application security and privacy.

This study examines the complex interactions in organizational structures using Bohm's ‘wholeness’ and Prigogine’s equilibrium theories. Wave analogies and quantum principles like superposition, non-locality, and entanglement explain fluctuations in these systems. Thus, this research suggests a paradigm shift in organizational methods towards a balanced, scientific approach. Organizations need flexible tactics and behave like dissipative structures to maintain internal coherence in chaos. Heightened through mindful techniques, corporate consciousness provides insights into temporal dynamics, improving decision-making, market resilience, and an expanded organizational ethos founded in present awareness. This heightened consciousness and demand for organizational alignment and coherence empowers the corporate entities to succeed in present conditions and anticipate and address future obstacles. This study introduces ‘Mindful Corporate Entity’ (‘MCE’), emphasizing mindfulness as a critical tool for organizational well-being and sustainability. This change is proposed to close management gaps.

Proper risk assessment and management are considered important issues in design improvement. This tendency has been due to the continuing uncertainty of the global economy and the advancement of information technology. Despite the most important and valuable benefits, developed design topics are more vulnerable and can expose the organization to higher levels of risk. Achieving sustainability is recognized as a growing and effective strategy to meet today's global design challenges. Design in the oil, gas and petrochemical industry is important due to its characteristics and high risk in different countries, especially Iran, due to its different effects on the environmental dimension. The issue of identifying and evaluating design risks has also been neglected in recent research. Therefore, the purpose of this study is to identify and evaluate design risks in Iran’s oil, gas and petrochemical industry. The present research is descriptive-survey and is qualitative and quantitative in terms of technique and data used. Based on the literature review, 13 risk factors affecting the design were identified, in two stages, in-depth interviews were conducted with the opinions of experts, and by collecting opinions through a questionnaire, and 10 key risk factors were finally confirmed. In the first stage, the validity of the questionnaire was finally confirmed based on the opinions of 5 experts and in the second stage, using the opinions of 8 experts in this industry. Cronbach's alpha coefficient of the risk questionnaire is higher than 0.7, which indicates the reliability of the research tool. According to the research findings, two factors such as weak technology / knowledge sustainability and environmental pollution, compared to other risk factors, have a higher priority in design projects in Iran's oil, gas and petrochemical industry. It is suggested that industry owners take serious and continuous measures in the field of "improving the level of process knowledge".

One of the research issues that researchers are interested in is unbalanced data classification techniques. Boosting approaches like Wang's Boosting and Modified Boosted SVM (MBSVM) have been demonstrated to be more effective for unbalanced data. Our proposal The Modified Boosted Random Forest (MBRF) classifier is a Random Forest classifier that uses the Boosting approach. The main motivation of the study is to analyze sentiment of geotagged tweets understanding the state of mind of people at FIFA and Olympics datasets. Tree based model Random Forest algorithm using boosting approach classifies the tweets to build a recommendation system with an idea of providing commercial suggestions to participants, recommending local places to visit or perform activities. MBRF employs various strategies: i) a distance-based weight-update method based on K-Medoids ii) a sign-based classifier elimination technique. We have equally partitioned the datasets as 70% of data allocated for training and the remaining 30% data as test data. Our imbalanced data ratio measured 3.1666 and 4.6 for FIFA and Olympics datasets. We looked at accuracy, precision, recall and ROC curves for each event. The average AUC achieved by MBRF on FIFA dataset is 0.96 and Olympics is 0.97. A comparison of MBRF and Decision tree model using 'Entropy' proved MBRF better.

Conventionally, the Internet consists of three parts: Surface, Deep, and Dark Webs. In the last two decades, a massive increase in illicit activities took place on the different platforms of the Dark Web. Moreover, social networks on Dark Web implicate extremism dissemination on a wide scale. In this paper, we propose an approach to generate textual patterns from discussions on Dark Web terrorist forums employing Data Mining techniques. The discovered patterns help identify the influential members and extract critical topics. We describe our system modules that perform data preprocessing, text preprocessing with TF-IDF weighting, outlier detection, clustering evaluation, clustering, and clustering validation, implemented with the RapidMiner tool. We apply K-Means as the Clustering method with different distance metrics, evaluate the clustering process using Elbow and Silhouette methods, and validate it using Davies-Bouldin Index. Furthermore, we investigate the effects of altering the distance metrics for outlier detection on the Clustering results.

Working from home and work flexibility have been highly researched academic topics over the past forty years, and their importance was further amplified during the COVID-19 pandemic. Numerous qualitative and quantitative studies were aimed at better understanding this work arrangement, but they often encountered the challenges of limited scope and the inability to generalize their results. The goal of this study was to test the consistency of the instrument used in measuring the experiences of working from home for Croatian engineers, check the presence of its latent dimensions, and finally determine whether these dimensions could predict engineers’ job satisfaction. Three factors were extracted by exploratory factor analysis (productivity and work quality, work-life balance, and organizations’ distrust and control), while the multiple linear regression analysis ran on those factors as predictors proved that, although work-life balance does not influence job satisfaction, productivity and work quality positively predict it, while organizations’ distrust and control do so negatively.

The purpose of this research is to measure the mediating role of teamwork and participation (T&P) on the relationship between two human resource (HR) practices – “training and development” (T&D) and “performance appraisal” (PA) – and operational performance (OP). Employing the contingency approach, this study develops a research model that validates the assumption that appropriate internal orchestration of HR practices improves firm performance. Using AMOS, data from 157 manufacturing firms are analyzed through structural equation modeling (SEM). The results show that T&P fully mediates the relationship between T&D and OP, and partially mediates the relationship between PA and OP. This study, which provides empirical support for the importance of OP to firm effectiveness, finds that OP is positively related to financial performance (FP). The study develops a theoretical logic and empirically demonstrates that T&P is an appropriate practice for mediating the impact of T&D and PA on OP. The inclusion of T&P contributes to the theory and shows that appropriate internal orchestration of HR practices can realize effective OP and consequently greater FP of the firm.

The subject of this research is to determine the level of digital technology acceptance in micro and small organizations. There is a lack of research in the existing literature that would move away from existing models and theories and explain the reasons for digital technology acceptance by micro and small organizations. It was noticed that research on the intention to accept digital technologies in micro and small organizations needs to focus on moderating factors, the influence of which has been neglected in existing research. For this reason, a model for digital technology acceptance by micro and small organizations was created, which explored the effect of moderating factors and encompassed the key characteristics of micro and small organizations. The effect of perceived financial risk, perceived security risk, perceived loss of time, perceived government pressure, and the level of knowledge of decision-makers on the intention to accept digital technologies are examined. As well the relationship between external pressure (market participants' pressure and crisis circumstances) and the level of knowledge of decision-makers in organizations was explored. The focus of the research is to explore the moderating effect of the organization's digital maturity and competitive priorities on the relationship of factors (perceived security risk, perceived loss of time, perceived government pressure) and intent to accept digital technologies. The moderating effect of the decision-makers decision-making style on the relationship between external pressure (market participants' pressure and crisis circumstances) and the level of knowledge of decision-makers are explored as well.

Query, expressing the user need and requirement, has an important role, in an information retrieval system, for reaching a high accuracy search. In this paper, we present an overview of the different refinement operations that the query may undergo, in the sake to enhance performance of an information retrieval system, such as: automatic query formulation through words prevision, query reformulation, query expansion, and query optimization.

Data-centric is a newly explored concept, where the attention is given to data optimization methodologies and techniques to improve model performance, rather than focusing on machine learning models and hyperparameter tunning. This paper suggests an effective data optimization methodology for optimizing imbalanced small datasets that improves machine learning model performance. This paper is focused on providing an effective solution when the number of observations is not enough to construct a machine learning model with high values of the estimated magnitudes. For example, the majority of the observations are labeled as one class (majority class), and the rest as the other, commonly considered as the class of interest (minority class). The proposed methodology does not depend on the applied classification models, rather it is based on the properties of the data resampling approach to systematically enhance and optimize the training dataset. The paper examines numerical experiments applying the data centric optimization methodology, and compares with previously obtained results by other authors.

The word metaverse is resurfacing and becoming viral after Facebook changed its company name to Meta. A lot of opportunities are offered by the rise of metaverse concept. For example, a library can be presented in the form of metaverse so that visitors do not need to come physically. Metaverse means beyond the universe or in this case beyond our universe. Companies around the world including Microsoft, Facebook, Epic Games, Apple, and Nvidia already invested in the metaverse as a business opportunity, and the top 3 business sectors are technology, education, and finance. Most companies also believe there are many benefits of the metaverse that can overcome the current obstacles om this universe, such as removing the physical limitation to do various activities. This paper will give a fundamental understanding of the metaverse and the opportunity of the metaverse in practice and research in the future. The result of this paper can be a guide for any scholars and practitioners to understand the metaverse starting from the definition to the things that are related to the metaverse.

This study is aimed at solving such a fundamental task as the use of modern information technologies in the development of electronic or digital democracy in Russia, as the main direction of the strategy for supporting democratic institutions and democratic processes and spreading democratic values. E-democracy is the basic foundation of the information society, accumulating a number of traditional and innovative tools that are successfully used in the democratic institutions of the modern state in the regulation of socially significant relations. The development of modern democratic processes is based not so much on public authorities, but on civil society, which is gradually transforming into a digital society. For this reason, the issue of searching for and actively implementing mechanisms that contribute to the development of information technology infrastructures, communication systems, and increasing the information literacy of the population seems to be relevant.

The framework for assessing the company's digital capability includes six key areas that can be individually evaluated to consider the overall maturity of a company's digital capability. According to this framework key areas are innovation capability, transformation capability and IT excellence as digital transformation enablers and customer centricity, effective knowledge worker and operational excellence as digital transformation goals. The focus of this research is the area of operational excellence. The purpose of the paper is to assess how the manufacturing company manages the digital transformation of the operational excellence. In the paper, the digital capability maturity model (DCMM) and its corresponding business transformation management methodology (BTM2) are used for this assessment. The BTM2 includes nine management disciplines and each of them is individually evaluated for the case of the operational excellence. These are the following disciplines: strategy management, value management, risk management, business process management, IT management, change management, training management, project management and meta management. According to the obtained assessment results the maturity of management disciplines for the case of operational excellence within the examined company is mostly reactive (maturity level 2). In order to improve the digital transformation of the company's operational excellence, it is necessary to define digital use cases based on the new technologies and map them to the existing maturity model of operational excellence of the company. It is necessary to consider the requirements of stakeholders regarding the benefits and risks of new digital technologies to improve the operational excellence of the company. Accordingly, the company should to define a new strategic plan and align it with the new IT strategy. For this purpose, COBIT 5 framework is used in the paper. The contribution of this research is in the proposed and described three-step approach to managing the operational excellence of companies and improving the level of digital capabilities of companies.

The term "cloud computing" refers to a variety of tools and applications used by organizations to manage their work processes online. Apart from providing the expected benefits, the gadgets and applications of the cloud are helpful for education. The purpose of this study is not to examine the outcomes of cloud computing in universities, but to examine how students use cloud applications. A survey research design was used to examine how students of Kurukshetra University use cloud computing applications. 200 students from the streams of science, management, social science, and arts were randomly selected to answer a structured questionnaire. A response rate of 95% was achieved by 48 students in Science, 50 in Management, 48 in Social Science, and 44 in Arts who completed and returned the questionnaire on time. The analyzed data is presented in a table that includes the Likert scale mean, standard deviation, and the cross-table Chi-square test. In the study, most students highly preferred G Suite Cloud (57%) and Microsoft (40%) learning applications. It can be seen from the study that WhatsApp (98%), Facebook (96%), and YouTube (97%) are the most preferred cloud-based social media among the students, most students preferred Google Meet (95%) and Zoom (91%) meeting application, and most students preferred Google Drive (68%) and pCloud (67%) for data storage.

Within this work we analyze and compare ten Petri net supporting tools for system modeling and analysis. A short summary of several available overviews of Petri net tools is performed before the tools analysis itself to evaluate the current state in this field. Principal parts of the paper are focused on analysis and evaluation of the selected tools. We provide detailed and practical description of selection of up to date tools, their evaluation based on various criteria, and recommendations for several use cases.

Today, customer retention and growth is the first goal. Companies have taken a heavy toll on each customer, and competitors are constantly seeking to steal them. Satisfied customers have many benefits for the company, they are less sensitive to prices, will be a customer for a long period of time, and give a favorable view of the company and its products and services to others. Paying attention to the factors that are less important from the customer's point of view and evaluating these factors leads to spending resources in areas that have less impact on customer behavior, so understanding the factors that have the greatest impact on customer satisfaction to the organization to move in this will help. In this study, we intend to use the operational data of the lines and customer satisfaction scales to evaluate the relative efficiency of product lines using the network data envelopment analysis method. In order to achieve this goal and quantify the data obtained from the questionnaire, another linear programming method called multi-criteria satisfaction analysis will be used. Another innovation of this research is the application of customer expectations in the data envelopment analysis mole because most researchers that focus on quality do not consider this component in their evaluation model. The results indicate that the distribution system is the most effective factor and resources and supply is the most effective factor affecting customer satisfaction.

Gender disparity and gender imbalance are globally pervasive in academia. Research into gender disparity characterizing gender differences uses metrics such as publication rates, author contributions, citation rates and research impact. Previous studies are contradictory in their findings and are limited in scope. As such, the current study examines the research contribution of both men and women in Shimla, Himachal Pradesh, India. A Scopus search was used to identify all articles published over a 20-year period (1999-2018). For each article in the final sample of 2,881 publications, the gender the first author was identified. Both the number and the growth rate of publications by women were lower than those by men. Conversely, the growth in citation rate was higher for women in 2009 and 2016. Gender disparity was found to be highest at the Indian Institute of Wheat and Barley Research and less severe at the HP Govt. Dental College.

Kosovo’s private sector business is led by small and medium enterprises and shows a crucial role in the economic development and promoting the growth of this sector. Due to the lack of studies in Kosova, related to marketing strategy and export performance, the research aims to explore growth determinants of SMEs in Kosovo by emphasizing exports, revealing the importance of marketing strategy related to the performance of SMEs. Since exporting is becoming more important, it is necessary to understand all the factors that are involved in this booming trade. This research analyses explore the literature dealing with small and medium enterprises' performance to inspect the traditional and modern academic standpoints on estimating the impact of export performance, based on secondary data of five hundred small and medium enterprises, as trade, manufacturing, and services sectors in Kosovo. The data were analysed using both descriptive statistics and inferential techniques, such as logistic regression analysis, and probit model using the SPSS software. Results show a significant linkage of marketing strategy and export performance; managers and policymakers can take some action to improve export performance. On the other hand, the results measure the impact of price, production, promotion and place in export performance incorporate expected future performance. It is argued that enterprises in Kosovo must pay more attention to strategies, and attempt to design and implement better strategies that will be beneficial for developing international trade, especially exports, as one way of trading by SMEs.

The paper examines traditional knowledge about the role of financial and physical resources in entrepreneurship. Based on Bourdieu's theory, we search for an answer to the question of which forms of economic capital are used by Croatian entrepreneurs in the computer programming industry and what their value in the context of accessing other forms of capital is. The study is based on a qualitative methodological approach. In-depth interview technique accompanied by unstructured observations was used in data collection. In addition to primary data, research includes the use of qualitative and quantitative secondary data. Research suggests that in the computer programming industry, economic capital is just one tool in the conversion game of entrepreneurial capital. Moreover, the interviewed entrepreneurs attach the least importance to it in business, favoring the value of intangible resources such as specialized knowledge, business connections and personal acquaintances. The company's start-up phase is characterized by the entrepreneur's reliance on personal savings, which is replaced by financing based on retained earnings in the later stages of business. Other forms of growth financing, such as bank loans and recapitalization of external investors, are used by a minority of larger companies, with a good base of symbolic capital. The results on capital conversions indicate relatively easy conversion of economic capital into cultural capital and symbolic capital, and less frequent use of economic capital to create social capital. Fresh insights into the entrepreneurs' perceptions provided by the study expand existing knowledge about entrepreneurship within the computer programming industry, suggesting that it is an industry with huge potential for young talents without a personal financial base that is commonly considered a precondition for entering entrepreneurship.

This study examines the impact of transformation in higher education, in particular on the introduction of new technology to students in Malaysia. This study is unique as it focuses on usage and technology resistance. Another uniqueness of the study is that technology resistance is measured by non-technology and post-technology antecedents, which have yet to be discovered by existing studies. These factors could potentially influence students to refrain from using technology in their studies, as many previous studies only focus on usage. Furthermore, prior research should have classified different types of resistance that cause significant knowledge gaps in technology education. A survey of 156 respondents concluded that antecedents of non-technology usage do lead to resistance. Post-technology usage, similarly, does influence resistance. As expected, the usage of technology influenced students' performances. Finally, resistance to technology did not affect students' performance. This is likely the first study to examine the impact of transformation in education and classify the antecedents of technology resistance into two categories: non-technology usage and post-technology usage. This research will help policymakers understand how students react to educational transformations. Adding technology could improve how well students do in university, but it could also hurt how well they do in university.

In the digital world, information is exposed anytime, anywhere, to everybody, hence its privacy is a crucial matter. No matter how complicated the encryption algorithm is, it requires a strong and hard-to-break encryption key. Since the three-dimensional protein sequence structures are usually highly conserved, even better than DNA sequences, this work presents an innovative scheme for implementing the protein sequence and structural to build protein data tables that are then used to generate extremely strong encryption-keys. An image-encryption scheme designed to implement such encryption keys is developed and produced compatible security strength with existing encryption schemes. Prototype experiments resulted in an average normalized mean absolute error of 66.84%, an average peak signal to noise ratio of 6.85 dB, and comparable entropy with other cryptosystems. The obtained results make this scheme a promising color image protection technique for various applications.

Search engines are now the main source for information retrieval due to the huge expansion of data on the internet over the last ten years. Providing users with the most relevant results for their queries poses a significant challenge for search engines. Semantic search engines, which go beyond traditional keyword-based searches, have appeared as advanced information retrieval systems to address this problem. These search engines produce more precise and pertinent search results because they understand the meanings of words and their relationships. They play a pivotal role in managing the vast amount of internet data, with a primary aim of enhancing search precision and user satisfaction. However, improving search precision remains as an important goal for natural language processing researchers. The main objective of our research is to improve the search engine results. We present a novel approach for measuring the similarity between a user’s query and a list of documents within a search engine. This approach provides a new fuzzy recommendation system using a syntactic and semantic similarity. Our results indicate that our method outperforms several existing approaches from the literature, achieving a high level of accuracy.

ICT products like Academic Information Systems (AIS) and LMS are commonly utilized in Indonesian universities. These two ICT tools will be of tremendous use to higher education institutions in managing their academic business procedures. The two systems are still not connected in many higher institutions, nevertheless, for example when it comes to handling academic data. If the management and integration process is carried out conventionally, it will be challenging and inefficient due to the massive volume of data and complexity of these two systems. Observing these issues, the researcher plans to study the two REST-based Web Service systems' connection and make use of the External Database functionality, which will be utilized to synchronize data in real-time from AIS to E-Learning. It is hoped that the integration model resulting from research can be a solution for integration between AIS and LMS and can be implemented in many higher education institutions.

Strategic management of SMEs is perceived as crucial backbone of their business, as it impacts their business models and internal processes all the way up to digital innovation measured by the level of digital maturity. How do the strategic management factors influence the digital maturity? Through quantitative analysis of 76 respondents representing SMEs in the Czech Republic, the research data was collected and statistically tested. Results imply that strategic management factors affect the level of digital maturity. This paper contributes to prior literature by practical implementation of modified digital maturity model and by addressing the correlation of strategic management factors and the level of digital maturity. Limitations springing from sample site and environment are addressed and discussion on the results is conducted. Author proposes a conclusion that strategic management factors can be perceived as a driver of digital transformation, emphasizing the need for future research and practical discussion.

Social media has developed into a symbolic channel that affects consumer behavior due to the remarkable marketing and ecommerce opportunities that the internet has provided. This study is based on segmentation of consumers into different categories in the world of fashion using social media. This paper proposes two conceptual models (the FC-CBR model of consumer brand relationship and the FC-CBP model of consumer brand perception) for exploring further into these constructs and developing a more coherent theoretical framework. The study identifies factors of Consumer Brand Relationship (CBR) and Consumer Brand Perception (CBP) using social media in the fashion world. Subsequently, the manuscript groups fashion consumers into clusters using K-means cluster analysis based on consumer brand relationship and consumer brand perception. The manuscript demonstrates how the clusters can be used in the development of efficient targeting and positioning strategies by practitioners.

In this scholar, we compare three metrics for machine translation, from English to French and vice versa, and we give some combination formulas based on some schemes, algorithms, and machine learning tools. As an experimental dataset, we consider 10 English and French theses abstracts published in the web with four free in charge machine translation systems. Five combinations, with the same implicit weights, are considered namely: (BLEU+NIST), (BLEU+ (1-WER)), (NIST+(1-WER)), (BLEU+NIST+(1-WER)), and (FR(BLEU)+FR(NIST)+FR(WER)). These combinations are also considered differently through generating weights parameters on the basis of regression. The results of 12 formulas are computed and compared then in total. According to the obtained results, average regression combinations based on machine learning step are the best, especially with the three basic metrics, followed by average WER metric in the case of English to French. For French to English, (FR(BLEU)+FR(NIST)+FR(WER)) combination is the best followed respectively by the average regression combination with both first parameters (Reg(α,β)) and average BLEU basic metric. Another performance criterion is considered here, in the second position, namely: the number of times, over the 10 abstracts, where the formula is the best. Based on the obtained results, combination with regression based on the first and the last parameters (Reg(α,γ)) outperforms the others, in the case of English to French, with 3 times followed by Reg(β,γ), Reg(α,β,γ), NIST+(1-WER), and the basic metrics (BLEU, NIST, and WER) with 2 times for each of them. For French to English, the basic WER metric outperforms the others with three times followed by BLEU, (BLEU+ (1-WER)), (FR(BLEU)+FR(NIST)+FR(WER)), and Reg(α,γ) with 2 times for each of them. To note that there is a room of improvement for the combinations with1.0914 in the case of English to French and 1.01 in the case of French to English.

Concerning the human aspect of organizational sustainability, this study aims to comprehensively examine organizational support (OS), employee attitude (EA), psychological empowerment (PE), and innovative work behavior (IWB). This study analyzes the relationship between OS, EA, and IWB in flexible manufacturing systems (FMS) and focuses on PE's role as a mediator between OS and EA. By dividing OS into perceived supervisor support (PSS) and procedural justice (PJ) and dividing EA into a sense of belonging (SB) and sense of awe (SA). A questionnaire was used to survey 341 participants from 23 teams employed in 9 large manufacturing enterprises in Indonesia. Eight Hypotheses were examined with Structural Equation Modeling (SEM). Results demonstrated that OS significantly affects EA and IWB, and PE mediates OS and EA through IWB. In this article, we seek to empirically test the entire belonging dimension of the OS as it relates to EA and IWB. These variables were chosen because they have well-documented pragmatic value for organizations. They also have reliable and valid relationships with various organizational support concepts. Future studies should include more variables for determining OS and EA to provide further context for organizational sustainability studies, particularly in FMS-transitioning industries.

Machine learning has been useful for prediction in the various sectors of the economy. The research work proposed an ensemble SA-CCT machine learning algorithm that gives early and accurate prediction of blackpod disease to farmers and agricultural extension officers in South-West, Nigeria. Since data mining put into consideration the types of pattern in a given dataset, the study considered the pattern in climatic dataset retrieved from Nigeria Meteorological agency (NIMET). The proposed model uses climatic parameters (Rainfall and Temperature) to predict the outbreak of blackpod disease. The ensemble SA-CCT model was formulated by hybridizing a linear algorithm Seasonal Auto Regressive Integrated Moving Average (SARIMA) and a nonlinear algorithm Compact Classification Tree (CCT), the implementation was done with python programming. The proposed SA-CCT model gives the following results after evaluation. Precision: 0.9429, Recall 0.9167, Mean Square Error: 0.2357, Accuracy: 0.9444

Scientometrics is one of the most important scales for evaluating scientific outputs. Co-word and social network analysis (SNA) were used to investigate relationships in the field of social accounting researches indexed on Scopus. The findings of the study indicate that the most frequent topics and vocabulary in this area is economic analysis, economic and social effects. Moreover, the most valuable subject areas were identified: based on closeness, they are water resources, tourism economics and structural adjustment. Based on centrality, economic impact, economic analysis, environmental economics and income distribution. The results of the study indicate that many important social and environmental issues can be assessed through social accounting. Co-authorship analysis revealed that the co-authorship network in this domain was discrete and with low density. By utilizing the co-occurrence map, researchers and policymakers can make policy making more effective through being aware of the research trends and hot topics in social accounting.

Information Technology is a key factor in eliminating the limitations posed by time and location and gives a much better and a faster access to informaition. In other words, technology has revolutionized work methods as a result of which, the paper which was used as a substrate for writing the work upon it has now been replaced by an electronic substrate. The changes created by technology include the use of Blockchain, IoT (Internet of Things), Cloud Accounting and Big Data to do accounting in automated form.The statistical sample of this study is given on the basis of the 171 person Cochran formulae consisting of financial managers, accountants and auditors of the Tehran city. Empirical results show that the use of Information Technology has a very significant role on the quality of Accounting Information and all these factors are influential in the quality of accounting system.

The study analysed Croatian journals covered by Web of Science (WoS) Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) over three time periods. The first two periods were the periods of inclusion of Croatian journals in SCIE/SSCI, and the third period was the period of exclusion of journals. In 1999, there were 12 Croatian journals indexed, and in 2011, mostly as a result of the WoS Regional Expansion Programme, there were 50 Croatian journals covered by SCIE/SSCI. The IF range of the Croatian journals showed an increase over the years from 1999 to 2011, but most journals remained in the fourth quartile (Q4). Although Croatia is considered as a country from the scientific periphery, in comparison with neighbouring Italy, Hungary, Slovenia, and Serbia in 2005, 2011, and 2018, Croatia had a permanently strong journal representation in JCR.

In spite of threatening external factors such as uncertain environmental structure and market pitfalls, the companies struggling with the difficulty of retaining the current customer and finding the potential ones should first realize the existence of “Achilles Tendons” within themselves. To prevent this potential problem, they should focus on the labor which can be a very substantial weak point and an important asset as well. Regarding serious problems arisen from labor force, this study proposes a new term called as “Achilles Tendon Syndrome” to be used in organizational behavior literature. Various theories from different disciplines such as medicine, psychology, physics, economics and sociology support development process of the term. Similarities and differences with other close concepts are also provided throughout the paper. The current study aims to inspire future studies by filling an important gap in the organization and management literature.

One of the predominant challenges facing banks in low resource countries is the management of Digital Financial Services (DFS) risks. Many banks are making worthwhile efforts to boost the factors that make them come on top of the challenges, unfortunately they have fallen short. This article sought to develop an e-risk management framework for DFS in Ugandan banks. Design Science Research paradigm, a mono-method qualitative research method and a case study research strategy was adopted. Questionnaires, interviews and document review were the main data collection methods. Findings from this study indicate that banks in Uganda use a handful of DFS business models and face a number of DFS risks including; talent for DFS, technology, strategy, governance, product, client acquisition, crime/fraud, regulation, and agent management among others. Notwithstanding, Ugandan banks should carefully balance IT spend across customer expectations, improve cybersecurity and internal process and regularly check its IT security.

This study aims to investigate the organizational factors affecting knowledge concealment in scientific contexts. This study has taken a mixed approach to identify these factors, so what makes it unique is the pluralism of data collection methods. Also, using the "Delphi" method, a conceptual model of organizational factors affecting knowledge concealment in scientific contexts is presented. The statistical sample included 314 employees of the Islamic Azad University of Khorasan Razavi who were selected through the random sampling method. They filled knowledge concealment questionnaire by Demirkasimoglu (2016) and a scholar-made questionnaire. Data were analyzed using the structural equation method (SEM) in AMOS software. Findings revealed that the coefficient correlation of organizational culture, reward system, organizational structure, organizational support, organizational justice, quality of work-life, and leadership style with knowledge concealment was -0.62, -0.27, -0.49, -1.30, -0.77, -0.39, and -0.88, respectively. The results indicate that organizational factors have an inverse and significant relationship with knowledge concealment.

This paper elaborates the design of a National Learning Analytics (LA) and Educational Data Mining System for pre-tertiary education in Croatia. The described approach consists of the following five phases: 1) objectives setting, 2) user needs analysis, 3) data availability analysis, 4) dashboards pre-design and 5) validation of functionalities. There is an evident research gap, as well as the lack of practical examples about the development of a Learning Analytics System (LAS) for pre-tertiary education on the national and/or regional level. Therefore, this research aims to make a scientific contribution by filling the recognized gap, but also contribute to solving the practical issues of national schools’ LAS development. To increase the usability of an LAS, the involvement of all end users is essential. In the described case, six main user groups were identified: 10-18 years old students, teachers, school management and support staff, regional and national authorities responsible for education, strategic bodies and researchers and, finally, project partners that work on system development. For each of them, separate dashboard functionalities have been designed through several rounds of consultations. Consultations with users started with focus groups and panels, to brainstorm the most important issues they would like to answer in order to enhance learning and teaching. Between the two rounds of consultation, there was an evaluation phase of the relevance of the gathered questions. Finally, targeted users provided feedback on the pre-production functionalities of each dashboard and validated them. In this process, several challenges were detected, including data gathering and protection, ethical issues and interpretation of results for students who are underage, and continuous adjustments to the users’ needs.

Forecasting the future trends is of utmost importance for managers and decision makers in different sectors. Scholars thus have introduced various techniques to the service industry aiming at employing a prediction model with ultimate accuracy and high efficiency. The literature proves that adaptive neuro-fuzzy inference systems (ANFIS) are the most efficiency models. However, the literature lacks reports on how ANFIS parameters may affect the accuracy of the system. Employing tourist arrival records to Cyprus between 2015 and 2019, this study has developed an ANFIS system to evaluate the accuracy performance of different prediction models with varied number of inputs and number or type of membership functions. Results show that the forecasting accuracy of a model with four inputs and four membership functions when the type of membership functions is Gaussian is relatively better than other models. In other words, it can be concluded that the forecast model with four inputs and four Gaussian membership functions is ultimate with the most accurate prediction record with reference to MAE, RMSE, and MAPE. The results of this study may be significant for senior managers and decision-makers of the tourism industry.

This study aims to evaluate the ways that the actual usage of a platform for digital competence acquisition, evaluation and certification contributes to satisfaction and perceived success of students in primary and secondary schools. A cross-sectional survey was implemented online to collect 1725 students’ answers in six European countries. The analysis of collected data was carried out by employing Pearson correlation, Partial least squares structural equation modelling (PLS-SEM) and Importance-performance map analysis (IPMA). Findings indicated that the usage of such a platform has greater effects on the impacts than on students’ satisfaction. Detailed analysis of correlations revealed that students’ decision on whether they will use the platform in the future greatly depends on how it contributes to the success of their learning processes. Results also suggest that teachers are seen as an inevitable part of such a process and are mandatory to achieve the full potential of the platform.

This paper presents a formulated mobile-based location-awareness model that was implemented into a Location-awareness System (L-aS hereafter) for finding the pharmacy location where prescribed drugs and their prices are available for sale. The scenario that inspired the model formulation was formalized using the unified modeling language. The model was implemented within the android studio integrated development environment with the L-aS database created through SQL lite database. The system was tested using user experience based testing technique. Based on core system performance testing, the system demonstrated a normal response time, resource utilization (i.e. storage and memory usage), and data use potentials of 414.6ms, 4.964mb and 1.9116 kb/secs, and 3.0296mb respectively. Therefore, the system performed well under ordinary conditions as an android application running on small memory devices. The study concluded that the developed mobile based pharmacy store location-aware system was useful to provide information to purchase prescribed drugs especially in perplexed situation(s).

Work life balance is the relationship between, how much time and effort someone spends to work and how much time and effort spend to other elements of their life, such as family, children, friends etc., HR policies are the organization’s continuing guidelines for managing its employees. As in this study, it mainly focuses on what are all roles played by the human resource department on balancing work and life in the Information Technology sector of women professionals. For this research, descriptive research was done with a sample of 200 women professionals from selected IT companies in Chennai using simple random sampling technique. The questionnaire was created using the independent variable of HR policies on work life balance and mediator of continuance commitment with a dependent variable of work life balance. The statistical tools used in this study are CFA, Discriminant validity and Composite reliability with the assistance of SPSS and AMOS software. The research found that, all the item used in the study are reflect a good measure of the variables.

The composition of supervisory and board of directors in companies is extremely important for the successful operation of companies. Nowadays, women in the workplace, and especially in management positions, encounter a form of discrimination that has its own professional name - the phenomenon of "glass ceiling". Therefore, it is very important to analyze the representation of women and men on the company’s supervisory board and board of directors. This paper will explain women's leadership style, differences between men's and women's leadership style, and the problems that women in employment, and especially in promotion. The aim off this paper is to determine whether the “glass ceiling” phenomenon is prominent in Croatian companies and to what extent. In addition, women’s representation in senior positions will be related to business performance criteria.