Search results for: business Intelligence

Authors: Prat Yotyawilai, Taratip Suwannasart

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Business Process Model and Notation (BPMN) is more important in the business process and creating functional models, and is a standard for OMG, which becomes popular in various organizations and in education. Researches related to software testing based on models are prominent. Although most researches use the UML model in software testing, not many researches use the BPMN Model in creating test cases. Therefore, this research proposes a design of a tool for generating test cases from the BPMN. The model is analyzed and the details of the various components are extracted before creating a flow graph. Both details of components and the flow graph are used in generating test cases.

Keywords: software testing, test case, BPMN, flow graph

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Gezahegn Gizaw

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The FDRE Tourism Training Institute organizes forums for debates, best practices exchange and focus group discussions to forge a sustainable and growing tourism sector while minimizing negative impacts on the environment, communities, and cultures. This study aimed at applying empirical research method to identify and quantify relative importance of success factors and individual engagement indicators that were identified in these forums. Response to the 12-question survey was collected from a total of 437 respondents in academic training institutes (212), business executive and employee (204) and non-academic government offices (21). Overall, capacity building was perceived as the most important driver of success for stakeholder engagement. Business executive and employee category rated capacity building as the most important driver of success (53%), followed by decision-making process (27%) and community participation (20%). Among educators and students, both capacity building and decision-making process were perceived as the most important factors (40% of respondents), whereas community participation was perceived as the most important success factor only by 20% of respondents. Individual engagement score in capacity building, decision-making process and community participation showed highest variability by educational level of participants (variance of 3.4% - 5.2%, p<0.001). Individual engagement score in capacity building was highly correlated to perceived benefit of training on improved efficiency, job security, higher customer satisfaction and self-esteem. On the other hand, individual engagement score in decision making process was highly correlated to its perceived benefit on lowering business costs, improving ability to meet the needs of a target market, job security, self-esteem and more teamwork. The study provides a set of recommendations that help educators, business executives and policy makers to maximize the individual and synergetic effect of training, decision making process on sustainability and growth of the tourism sector in Ethiopia.

Keywords: engagement score, driver of success, capacity building, tourism

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Authors: Manjitsingh Rajput

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Visual communication plays an important role in artificial intelligence (AI) because it enables machines to understand and interpret visual information, similar to how humans do. This abstract explores the importance of visual communication in AI and emphasizes the importance of various applications such as computer vision, object emphasis recognition, image classification and autonomous systems. In going deeper, with deep learning techniques and neural networks that modify visual understanding, In addition to AI programming, the abstract discusses challenges facing visual interfaces for AI, such as data scarcity, domain optimization, and interpretability. Visual communication and other approaches, such as natural language processing and speech recognition, have also been explored. Overall, this abstract highlights the critical role that visual communication plays in advancing AI capabilities and enabling machines to perceive and understand the world around them. The abstract also explores the integration of visual communication with other modalities like natural language processing and speech recognition, emphasizing the critical role of visual communication in AI capabilities. This methodology explores the importance of visual communication in AI development and implementation, highlighting its potential to enhance the effectiveness and accessibility of AI systems. It provides a comprehensive approach to integrating visual elements into AI systems, making them more user-friendly and efficient. In conclusion, Visual communication is crucial in AI systems for object recognition, facial analysis, and augmented reality, but challenges like data quality, interpretability, and ethics must be addressed. Visual communication enhances user experience, decision-making, accessibility, and collaboration. Developers can integrate visual elements for efficient and accessible AI systems.

Keywords: visual communication AI, computer vision, visual aid in communication, essence of visual communication.

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Authors: Michael Smalenberger

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Intelligent tutoring systems (ITS) are computer-based platforms which can incorporate artificial intelligence to provide step-by-step guidance as students practice problem-solving skills. ITS can replicate and even exceed some benefits of one-on-one tutoring, foster transactivity in collaborative environments, and lead to substantial learning gains when used to supplement the instruction of a teacher or when used as the sole method of instruction. A common facet of many ITS is their use of Bayesian Knowledge Tracing (BKT) to estimate parameters necessary for the implementation of the artificial intelligence component, and for the probability of mastery of a knowledge component relevant to the ITS. While various techniques exist to estimate these parameters and probability of mastery, none directly and reliably ask the user to self-assess these. In this study, 111 undergraduate students used an ITS in a college-level introductory statistics course for which detailed transaction-level observations were recorded, and users were also routinely asked direct questions that would lead to such a self-assessment. Comparisons were made between these self-assessed values and those obtained using commonly used estimation techniques. Our findings show that such self-assessments are particularly relevant at the early stages of ITS usage while transaction level data are scant. Once a user’s transaction level data become available after sufficient ITS usage, these can replace the self-assessments in order to eliminate the identifiability problem in BKT. We discuss how these findings are relevant to the number of exercises necessary to lead to mastery of a knowledge component, the associated implications on learning curves, and its relevance to instruction time.

Keywords: Bayesian Knowledge Tracing, Intelligent Tutoring System, in vivo study, parameter estimation

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Authors: Xingyu Gao, Qiang Wu, Yuanyuan Liu, Yue Yang

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In the era of the knowledge economy, the relationship between scientific knowledge and patents has garnered significant attention. Understanding the intricate interplay between the foundations of science and technological innovation has emerged as a pivotal challenge for both researchers and policymakers. This study establishes a coupled network of artificial intelligence patents based on co-cited scientific papers. Leveraging centrality metrics from network analysis offers a fresh perspective on understanding the influence of information flow and knowledge sharing within the network on patent impact. The study initially obtained patent numbers for 446,890 granted US AI patents from the United States Patent and Trademark Office’s artificial intelligence patent database for the years 2002-2020. Subsequently, specific information regarding these patents was acquired using the Lens patent retrieval platform. Additionally, a search and deduplication process was performed on scientific non-patent references (SNPRs) using the Web of Science database, resulting in the selection of 184,603 patents that cited 37,467 unique SNPRs. Finally, this study constructs a coupled network comprising 59,379 artificial intelligence patents by utilizing scientific papers co-cited in patent backward citations. In this network, nodes represent patents, and if patents reference the same scientific papers, connections are established between them, serving as edges within the network. Nodes and edges collectively constitute the patent coupling network. Structural characteristics such as node degree centrality, betweenness centrality, and closeness centrality are employed to assess the scientific connections between patents, while citation count is utilized as a quantitative metric for patent influence. Finally, a negative binomial model is employed to test the nonlinear relationship between these network structural features and patent influence. The research findings indicate that network structural features such as node degree centrality, betweenness centrality, and closeness centrality exhibit inverted U-shaped relationships with patent influence. Specifically, as these centrality metrics increase, patent influence initially shows an upward trend, but once these features reach a certain threshold, patent influence starts to decline. This discovery suggests that moderate network centrality is beneficial for enhancing patent influence, while excessively high centrality may have a detrimental effect on patent influence. This finding offers crucial insights for policymakers, emphasizing the importance of encouraging moderate knowledge flow and sharing to promote innovation when formulating technology policies. It suggests that in certain situations, data sharing and integration can contribute to innovation. Consequently, policymakers can take measures to promote data-sharing policies, such as open data initiatives, to facilitate the flow of knowledge and the generation of innovation. Additionally, governments and relevant agencies can achieve broader knowledge dissemination by supporting collaborative research projects, adjusting intellectual property policies to enhance flexibility, or nurturing technology entrepreneurship ecosystems.

Keywords: centrality, patent coupling network, patent influence, social network analysis

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Authors: Marine Kobalava

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Modern forms of public relations function in an integrated manner together with marketing communication in business companies. This ensures continuity of communication, elimination of duplication in activities, reduction of costs, and strengthening and efficient use of communication means. There exist a number of challenges in implementing integrated forms of public relations in Georgia, especially in terms of marketing communications and strategies. Objectives: The goal of the study is to reveal public relations challenges in Georgian business companies and to develop recommendations along with perfecting marketing communications and strategies. Methodologies: Bibliographic and empirical research has been conducted. Analysis, induction, synthesis, and other methods have been used. Contributions: The challenges of Public relations in Georgia are identified; the perception of different population groups on integrated forms of PR is determined; effective forms of marketing communication are defined; mechanisms for developing marketing strategies are proposed.

Keywords: public relations, challenges, marketing communication, strategy

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Authors: Yating Yang, Xue Zhang, Chengli Zhao

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In the increasingly interconnected global environment brought about by globalization, it is crucial for countries to timely grasp the development motivations in relevant research fields of other countries and seize development opportunities. Motivation, as the intrinsic driving force behind actions, is abstract in nature, making it difficult to directly measure and evaluate. Drawing on the ideas of social influence network theory, the research motivations of a country can be understood as the driving force behind the development of its science and technology sector, which is simultaneously influenced by both the country itself and other countries/regions. In response to this issue, this paper improves upon Friedkin's social influence network theory and applies it to motivation description, constructing a dynamic alliance network and hostile network centered around the United States and China, as well as a sensitivity matrix, to remotely assess the changes in national research motivations under the influence of international relations. Taking artificial intelligence as a case study, the research reveals that the motivations of most countries/regions are declining, gradually shifting from a neutral attitude to a negative one. The motivation of the United States is hardly influenced by other countries/regions and remains at a high level, while the motivation of China has been consistently increasing in recent years. By comparing the results with real data, it is found that this model can reflect, to some extent, the trends in national motivations.

Keywords: influence network theory, remote assessment, relation matrix, dynamic sensitivity matrix

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Authors: Mohsen Akhlaghi, Tahereh Ebrahimi

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Pricing strategy is a vital component in achieving the balance between customer value and business profitability. The aim of this study is to provide insights into the factors, techniques, and approaches involved in pricing decisions. The study utilizes a descriptive approach to discuss various aspects of pricing strategy in marketing, drawing on concepts from market research, consumer psychology, competitive analysis, and adaptability. This approach presents a comprehensive view of pricing decisions. The result of this exploration is a framework that highlights key factors influencing pricing decisions. The study examines how factors such as market positioning, product differentiation, and brand image shape pricing strategies. Additionally, it emphasizes the role of consumer psychology in understanding price elasticity, perceived value, and price-quality associations that influence consumer behavior. Various pricing techniques, including charm pricing, prestige pricing, and bundle pricing, are mentioned as methods to enhance sales by influencing consumer perceptions. The study also underscores the importance of adaptability in responding to market dynamics through regular price monitoring, dynamic pricing, and promotional strategies. It recognizes the role of digital platforms in enabling personalized pricing and dynamic pricing models. In conclusion, the study emphasizes that effective pricing strategies strike a balance between customer value and business profitability, ultimately driving sales, enhancing brand perception, and fostering lasting customer relationships.

Keywords: business, customer benefits, marketing, pricing

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Authors: Md. Awal Hossain Mollah

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Corporate governance is playing a crucial role for ensuring social accountability and responsibility of business organization through Corporate Social Responsibility (CSR) for the last two decades. In Bangladesh, CSR is a growing and popular concept and a recent development. Various business and corporate organizations are playing crucial role for helping vulnerable sections of our society now. For instance, Dutch Bangla Bank has been providing scholarship for under graduate and graduate students in our country which is very helpful for promoting poor and meritorious students in Bangladesh. In this study, how far CSR is playing its role for ensuring human right in Bangladesh will be examined with specific case studies. The study focus will reflect on both developed and developing nations based on literature review and possible empirical evidence.

Keywords: CSR, corporate governance, social security, Bangladesh, scholarships, graduate students, Dutch angla Bank

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Authors: Parisa Mansour

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Cystic fibrosis (CF) is one of the most common autosomal recessive diseases among whites. It mostly affects the lungs, causing infections and inflammation that account for 90% of deaths in CF patients. Because of this high variability in clinical presentation and organ involvement, investigating treatment responses and evaluating lung changes over time is critical to preventing CF progression. High-resolution computed tomography (HRCT) greatly facilitates the assessment of lung disease progression in CF patients. Recently, artificial intelligence was used to analyze chest CT scans of CF patients. In this paper, we propose a convolutional neural network (CNN) approach to classify CF lung patterns in HRCT images. The proposed network consists of two convolutional layers with 3 × 3 kernels and maximally connected in each layer, followed by two dense layers with 1024 and 10 neurons, respectively. The softmax layer prepares a predicted output probability distribution between classes. This layer has three exits corresponding to the categories of normal (healthy), bronchitis and inflammation. To train and evaluate the network, we constructed a patch-based dataset extracted from more than 1100 lung HRCT slices obtained from 45 CF patients. Comparative evaluation showed the effectiveness of the proposed CNN compared to its close peers. Classification accuracy, average sensitivity and specificity of 93.64%, 93.47% and 96.61% were achieved, indicating the potential of CNNs in analyzing lung CF patterns and monitoring lung health. In addition, the visual features extracted by our proposed method can be useful for automatic measurement and finally evaluation of the severity of CF patterns in lung HRCT images.

Keywords: HRCT, CF, cystic fibrosis, chest CT, artificial intelligence

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Authors: Irsanti Hasyim, Christian Wibisono, Angela Teressia

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Social enterprises, as one of the way of doing business are on the rise and emerging from the innovations of decades of social initiatives. In simple way, social enterprises use the business platform to achieve social and/or environmental objectives whilst simultaneously seeking a financial return. In Indonesia, the number of social enterprises rapidly grows and most of them were using social media as their business platform. Social Media are perceived as tools for creating online communities of users who share interests, activities, and objectives. Many companies view the use of online communities in social media as a profitable marketing tool from which they can derive several benefits. Through social media, consumers share experiences with and suggest ideas to others while developing new relationships within their communities. Therefore, this study intends to identify the benefits that participants in online communities seek and examine the relationships between members’ levels of participation, brand trust, and brand commitment. Using convenience sampling method, 236 fully answered questioner was collected and used as a sample of this research. The sample of this research consisted of member or follower in several social media from selected social enterprise in Indonesia. Data collected in this research were process by using Partial Least Square and came up with the result that functional benefit and monetary benefit, are only two from five benefits that proposed were the only variable that has significant result even though from APC, ARS and AARS outcomes show that the model can be claimed to be significant.

Keywords: brand trust, brand commitment, social enterprise, social media

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Authors: Hiba El Assibi

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This study aims to weakly solve Kalah, a two-player board game, by developing a start-to-finish winning strategy using an optimized Minimax algorithm with Alpha-Beta Pruning. In weakly solving Kalah, our focus is on creating an optimal strategy from the game's beginning rather than analyzing every possible position. The project will explore additional enhancements like symmetry checking and code optimizations to speed up the decision-making process. This approach is expected to give insights into efficient strategy formulation in board games and potentially help create games with a fair distribution of outcomes. Furthermore, this research provides a unique perspective on human versus Artificial Intelligence decision-making in strategic games. By comparing the AI-generated optimal moves with human choices, we can explore how seemingly advantageous moves can, in the long run, be harmful, thereby offering a deeper understanding of strategic thinking and foresight in games. Moreover, this paper discusses the evaluation of our strategy against existing methods, providing insights on performance and computational efficiency. We also discuss the scalability of our approach to the game, considering different board sizes (number of pits and stones) and rules (different variations) and studying how that affects performance and complexity. The findings have potential implications for the development of AI applications in strategic game planning, enhancing our understanding of human cognitive processes in game settings, and offer insights into creating balanced and engaging game experiences.

Keywords: minimax, alpha beta pruning, transposition tables, weakly solving, game theory

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Authors: Emad A. Mohammed

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The concept of hydraulic flow units (HFU) has been used for decades in the petroleum industry to improve the prediction of permeability. This concept is strongly related to the flow zone indicator (FZI) which is a function of the reservoir rock quality index (RQI). Both indices are based on reservoir porosity and permeability of core samples. It is assumed that core samples with similar FZI values belong to the same HFU. Thus, after dividing the porosity-permeability data based on the HFU, transformations can be done in order to estimate the permeability from the porosity. The conventional practice is to use the power law transformation using conventional HFU where percentage of error is considerably high. In this paper, neural network technique is employed as a soft computing transformation method to predict permeability instead of power law method to avoid higher percentage of error. This technique is based on HFU identification where Amaefule et al. (1993) method is utilized. In this regard, Kozeny and Carman (K–C) model, and modified K–C model by Hasan and Hossain (2011) are employed. A comparison is made between the two transformation techniques for the two porosity-permeability models. Results show that the modified K-C model helps in getting better results with lower percentage of error in predicting permeability. The results also show that the use of artificial intelligence techniques give more accurate prediction than power law method. This study was conducted on a heterogeneous complex carbonate reservoir in Oman. Data were collected from seven wells to obtain the permeability correlations for the whole field. The findings of this study will help in getting better estimation of permeability of a complex reservoir.

Keywords: permeability, hydraulic flow units, artificial intelligence, correlation

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Authors: Olena Kovalchuk

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How to define the understanding of social responsibility issues by Ukrainian companies is a contravention question. Thus, one of the practical uses of social responsibility is a diagnostic tool development for educational, business or scientific purposes. So the purpose of this research is to develop a tool for measurement of social responsibility in organization. Methodology: A 21-item questionnaire “Organization Social Responsibility Scale” was developed. This tool was adapted for the Ukrainian sample and based on the questionnaire “Perceived Role of Ethics and Social Responsibility” which connects ethical and socially responsible behavior to different aspects of the organizational effectiveness. After surveying the respondents, the factor analysis was made by the method of main compounds with orthogonal rotation VARIMAX. On the basis of the obtained results the 21-item questionnaire was developed (Cronbach’s alpha – 0,768; Inter-Item Correlations – 0,34). Participants: 121 managers at all levels of Ukrainian organizations (57 males; 65 females) took part in the research. Results: Factor analysis showed five ethical dilemmas concerning the social responsibility and profit compatibility in Ukrainian organizations. Below we made an attempt to interpret them: — Social responsibility vs profit. Corporate social responsibility can be a way to reduce operational costs. A firm’s first priority is employees’ morale. Being ethical and socially responsible is the priority of the organization. The most loaded question is "Corporate social responsibility can reduce operational costs". Significant effect of this factor is 0.768. — Profit vs social responsibility. Efficiency is much more important to a firm than ethics or social responsibility. Making the profit is the most important concern for a firm. The dominant question is "Efficiency is much more important to a firm than whether or not the firm is seen as ethical or socially responsible". Significant effect of this factor is 0.793. — A balanced combination of social responsibility and profit. Organization with social responsibility policy is more attractive for its stakeholders. The most loaded question is "Social responsibility and profitability can be compatible". Significant effect of this factor is 0.802. — Role of Social Responsibility in the successful organizational performance. Understanding the value of social responsibility and business ethics. Well-being and welfare of the society. The dominant question is "Good ethics is often good business". Significant effect of this factor is 0.727. — Global vision of social responsibility. Issues related to global social responsibility and sustainability. Innovative approaches to poverty reduction. Awareness of climate change problems. Global vision for successful business. The dominant question is "The overall effectiveness of a business can be determined to a great extent by the degree to which it is ethical and socially responsible". Significant effect of this factor is 0.842. The theoretical contribution. The perspective of the study is to develop a tool for measurement social responsibility in organizations and to test questionnaire’s adequacy for social and cultural context. Practical implications. The research results can be applied for designing a training programme for business school students to form their global vision for successful business as well as the ability to solve ethical dilemmas in managerial practice. Researchers interested in social responsibility issues are welcome to join the project.

Keywords: corporate social responsibility, Cronbach’s alpha, ethical behaviour, psychodiagnostic tool

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Authors: Nurullah Ekmekci, Ömer Akkaya, Kazım Karaboğa, Mahmut Tekin

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Job selection is one of the most important decisions that affect their lives in the name of being more useful to themselves and the society. There are many criteria to consider in the job selection. The amount of criteria in the job selection makes it a multi-criteria decision-making (MCDM) problem. In this study; job selection has been discussed as multi-criteria decision-making problem and has been solved by Analytic Hierarchy Process (AHP), one of the multi-criteria decision making methods. A survey, contains 5 different job selection criteria (finding a job friendliness, salary status, job , social security, work in the community deems reputation and business of the degree of difficulty) within many job selection criteria and 4 different job alternative (being academician, working at the civil service, working at the private sector and working at in their own business), has been conducted to the students of Selcuk University Faculty of Economics and Administrative Sciences. As a result of pairwise comparisons, the highest weighted criteria in the job selection and the most coveted job preferences were identified.

Keywords: analytical hierarchy process, job selection, multi-criteria, decision making

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Authors: Sharda Kumari, Saiman Shetty

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Autonomous trucking is just one of the numerous significant shifts impacting fleet management services. The Society of Automotive Engineers (SAE) has defined six levels of vehicle automation that have been adopted internationally, including by the United States Department of Transportation. On public highways in the United States, organizations are testing driverless vehicles with at least Level 4 automation which indicates that a human is present in the vehicle and can disable automation, which is usually done while the trucks are not engaged in highway driving. However, completely driverless vehicles are presently being tested in the state of California. While autonomous trucking can increase safety, decrease trucking costs, provide solutions to trucker shortages, and improve efficiencies, logistics, too, requires advancements to keep up with trucking innovations. Given that artificial intelligence, machine learning, and automated procedures enable people to do their duties in other sectors with fewer resources, CRM (Customer Relationship Management) can be applied to the autonomous trucking business to provide the same level of efficiency. In a society witnessing significant digital disruptions, fleet management is likewise being transformed by technology. Utilizing strategic alliances to enhance core services is an effective technique for capitalizing on innovations and delivering enhanced services. Utilizing analytics on CRM systems improves cost control of fuel strategy, fleet maintenance, driver behavior, route planning, road safety compliance, and capacity utilization. Integration of autonomous trucks with automated fleet management, yard/terminal management, and customer service is possible, thus having significant power to redraw the lines between the public and private spheres in autonomous trucking logistics.

Keywords: autonomous vehicles, customer relationship management, customer experience, autonomous trucking, digital transformation

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Authors: Xiaoping Qiu, Binci You, Yue Hu

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The executive time of the workflow network has an important effect on the efficiency of the business process. In this paper, the activity executive time is divided into the service time and the waiting time, then the lead time can be extracted from the waiting time. The executive time formulas of the three basic structures in the workflow network are deduced based on the activity lead time. Taken the process of e-commerce logistics as an example, insert appropriate lead time for key activities by using Petri net, and the executive time optimization model is built to minimize the waiting time with the time-cost constraints. Then the solution program-using VC++6.0 is compiled to get the optimal solution, which reduces the waiting time of key activities in the workflow, and verifies the role of lead time in the timeliness of e-commerce logistics.

Keywords: electronic business, execution time, lead time, optimization model, petri net, time workflow network

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Authors: Pooria Norouzi, Nicholas Tsang, Adam van der Goes, Joseph Yu, Douglas Zheng, Sirine Maleej

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In this study, virtual meters will be designed and used for energy balance measurements of an air handling unit (AHU). The method aims to replace traditional physical sensors in heating, ventilation, and air conditioning (HVAC) systems with simulated virtual meters. Due to the inability to manage and monitor these systems, many HVAC systems have a high level of inefficiency and energy wastage. Virtual meters are implemented and applied in an actual HVAC system, and the result confirms the practicality of mathematical sensors for alternative energy measurement. While most residential buildings and offices are commonly not equipped with advanced sensors, adding, exploiting, and monitoring sensors and measurement devices in the existing systems can cost thousands of dollars. The first purpose of this study is to provide an energy consumption rate based on available sensors and without any physical energy meters. It proves the performance of virtual meters in HVAC systems as reliable measurement devices. To demonstrate this concept, mathematical models are created for AHU-07, located in building NE01 of the British Columbia Institute of Technology (BCIT) Burnaby campus. The models will be created and integrated with the system’s historical data and physical spot measurements. The actual measurements will be investigated to prove the models' accuracy. Based on preliminary analysis, the resulting mathematical models are successful in plotting energy consumption patterns, and it is concluded confidently that the results of the virtual meter will be close to the results that physical meters could achieve. In the second part of this study, the use of virtual meters is further assisted by artificial intelligence (AI) in the HVAC systems of building to improve energy management and efficiency. By the data mining approach, virtual meters’ data is recorded as historical data, and HVAC system energy consumption prediction is also implemented in order to harness great energy savings and manage the demand and supply chain effectively. Energy prediction can lead to energy-saving strategies and considerations that can open a window in predictive control in order to reach lower energy consumption. To solve these challenges, the energy prediction could optimize the HVAC system and automates energy consumption to capture savings. This study also investigates AI solutions possibility for autonomous HVAC efficiency that will allow quick and efficient response to energy consumption and cost spikes in the energy market.

Keywords: virtual meters, HVAC, artificial intelligence, energy consumption prediction

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Authors: Udeh Nneka Evelyn

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The shortcoming of traditional costing system in terms of validity, accuracy, consistency, and Relevance increased the need for modern management accounting system. Activity –Based Costing (ABC) can be used as a modern tool for planning, Control and decision making for management. Past studies on ABC system have focused on manufacturing firms thereby making the studies on service firms scanty to some extent. This paper reviewed the application and relevance of activity-based costing technique in service oriented business organizations by employing a qualitative research method which relied heavily on literature review of past and current relevant articles focusing on ABC. Findings suggest that ABC is not only appropriate for use in a manufacturing environment; it is also most appropriate for service organizations such as financial institutions, the healthcare industry and government organization. In fact, some banking and financial institutions have been applying the concept for years under other names. One of them is unit costing, which is used to calculate the cost of banking services by determining the cost and consumption of each unit of output of functions required to deliver the service. ABC in very basic terms may provide very good payback for businesses. Some of the benefits that relate directly to the financial services industry are: identification the most profitable customers: more accurate product and service pricing: increase product profitability: Well organized process costs.

Keywords: business, costing, organizations, planning, techniques

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Authors: Yasser F. Hassan

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Social networking involves use of an online platform or website that enables people to communicate, usually for a social purpose, through a variety of services, most of which are web-based and offer opportunities for people to interact over the internet, e.g. via e-mail and ‘instant messaging’, by analyzing the voting behavior and ratings of judges in a popular comments in social networks. While most of the party literature omits the electorate, this paper presents a model where elites and parties are emergent consequences of the behavior and preferences of voters. The research in artificial intelligence and psychology has provided powerful illustrations of the way in which the emergence of intelligent behavior depends on the development of representational structure. As opposed to the classical voting system (one person – one decision – one vote) a new voting system is designed where agents with opposed preferences are endowed with a given number of votes to freely distribute them among some issues. The paper uses ideas from machine learning, artificial intelligence and soft computing to provide a model of the development of voting system response in a simulated agent. The modeled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structure. We employ agent-based computer simulation to demonstrate the formation and interaction of coalitions that arise from individual voter preferences. We are interested in coordinating the local behavior of individual agents to provide an appropriate system-level behavior.

Keywords: voting system, rough sets, multi-agent, social networks, emergence, power indices

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Authors: Pranav Ajmera, Amit Kharat, Tanveer Gupte, Richa Pant, Viraj Kulkarni, Vinay Duddalwar, Purnachandra Lamghare

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The cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR, that is, a value greater than 0.55, is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR from chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. We propose a deep learning-based model for automatic CTR calculation that can assist the radiologist with the diagnosis of cardiomegaly and optimize the radiology flow. The study population included 1012 posteroanterior (PA) CXRs from a single institution. The Attention U-Net deep learning (DL) architecture was used for the automatic calculation of CTR. A CTR of 0.55 was used as a cut-off to categorize the condition as cardiomegaly present or absent. An observer performance test was conducted to assess the radiologist's performance in diagnosing cardiomegaly with and without artificial intelligence (AI) assistance. The Attention U-Net model was highly specific in calculating the CTR. The model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. During the analysis, we observed that 51 out of 1012 samples were misclassified by the model when compared to annotations made by the expert radiologist. We further observed that the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Our segmentation-based AI model demonstrated high specificity and sensitivity for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows.

Keywords: cardiomegaly, deep learning, chest radiograph, artificial intelligence, cardiothoracic ratio

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Authors: Siti Nor Amira Bt. Mohamad, Mohamad Yazis B. Ali Basah, Muhammad Ridhwan B. Ab. Aziz, Khairil Faizal B. Khairi, Mazlynda Bt. Md. Yusuf, Hisham B. Sabri

Abstract:

The understanding of risk and the concept of it occurs associated in Islamic financing was well-known in the financial industry by the using of Profit-and-Loss Sharing (PLS). It was presently in any Islamic financial transactions in order to comply with shariah rules. However, the existence of risk in Murabahah contract of financing is an ability that the counterparty is unable to complete its obligations within the agreed terms. Therefore, it is called as credit or default risk. Credit risk occurs when the client fails to make timely payment after the bank makes complete delivery of assets. Thus, it affects the growth of the bank as the banking business is in no position to have appropriate measures to cover the risk. Therefore, the bank may impose penalty on the outstanding balance. This paper aims to highlight the credit risk determinant and issues surrounding in Islamic bank in Malaysia in terms of Murabahah financing and how to manage it by using the proper techniques. Finally, it explores the credit risk management concept that might solve the problems arise. The study found that the credit risk can be managed properly by improving the use of comprehensive reference checklist of business partners on their character and past performance as well as their comprehensive database. Besides that, prevention of credit risk can be done by using collateral as security against the risk and we also argue on the Shariah guidelines and procedures should be implement coherently by the banking business because so that the risk would be control by having an effective instrument for Islamic modes of financing.

Keywords: Islamic banking, credit risk, Murabahah financing, risk mitigation

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Authors: Hsiao-Chuan Huang, King-Lung Kuo, Mei-Hsin Lo, Hsiao-Yun Chou, Yusen Lin

Abstract:

The most robust and economical method for laboratory diagnosis of TB is to identify mycobacterial bacilli (AFB) under acid-fast staining despite its disadvantages of low sensitivity and labor-intensive. Though digital pathology becomes popular in medicine, an automated microscopic system for microbiology is still not available. A new AI-assisted automated microscopic system, consisting of a microscopic scanner and recognition program powered by big data and deep learning, may significantly increase the sensitivity of TB smear microscopy. Thus, the objective is to evaluate such an automatic system for the identification of AFB. A total of 5,930 smears was enrolled for this study. An intelligent microscope system (TB-Scan, Wellgen Medical, Taiwan) was used for microscopic image scanning and AFB detection. 272 AFB smears were used for transfer learning to increase the accuracy. Referee medical technicians were used as Gold Standard for result discrepancy. Results showed that, under a total of 1726 AFB smears, the automated system's accuracy, sensitivity and specificity were 95.6% (1,650/1,726), 87.7% (57/65), and 95.9% (1,593/1,661), respectively. Compared to culture, the sensitivity for human technicians was only 33.8% (38/142); however, the automated system can achieve 74.6% (106/142), which is significantly higher than human technicians, and this is the first of such an automated microscope system for TB smear testing in a controlled trial. This automated system could achieve higher TB smear sensitivity and laboratory efficiency and may complement molecular methods (eg. GeneXpert) to reduce the total cost for TB control. Furthermore, such an automated system is capable of remote access by the internet and can be deployed in the area with limited medical resources.

Keywords: TB smears, automated microscope, artificial intelligence, medical imaging

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Authors: Ladaporn Pithuk

Abstract:

The purposes of the study were to ascertain the customer requirement, to identify the factors related to online business in Thailand. The sample of this study consisted of 400 customers who are purchasing product and service on E-commerce. To get primary sources, a questionnaire consisting of 31 questions was designed and adapted from previous studies. The data from the questionnaires were collected and analyzed in descriptive forms and (ONE-WAY ANOVA) was conducted. The majority of the respondents showed customer requirement by stating “moderately agree” for questions asking them about customization, connection, content, commerce, context, communication and community, however, they also displayed negative attitudes by identifying “moderately disagree” for security concerns and after-sales services. These important issues need to be improved immediately since it can encourage customers to buy goods and services through the Internet or discourage them, and businesses should offer more channels of payment methods for customers for instance, e-payment.

Keywords: customer requirement, customization, connection, online business

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Authors: Sook Ching Yee, Angela Siew Hoong Lee

Abstract:

Big data technologies have become a trend to exploit business opportunities and provide valuable business insights through the analysis of big data. However, there are still many organizations that have yet to adopt big data technologies especially small and medium organizations (SME). This study uses the technology acceptance model (TAM) to look into several constructs in the TAM and other additional constructs which are positive affect, negative affect, organizational factor and motivational factor. The conceptual model proposed in the study will be tested on the relationship and influence of positive affect, negative affect, organizational factor and motivational factor towards the intention to use big data technologies to produce an outcome. Empirical research is used in this study by conducting a survey to collect data.

Keywords: big data technologies, motivational factor, negative affect, organizational factor, positive affect, technology acceptance model (TAM)

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Authors: Asma Ben Abdallah

Abstract:

Language has a powerful influence over people and their actions. The language of advertising has a very great impact on the consumer. It makes use of different features from the linguistic continuum. The present paper attempts to apply the theories of stylistics to the analysis of advertising texts. In order to decipher the stylistic features of the advertising discourse, 30 advertising text samples designed by MA Business students have been selected. These samples have been analyzed at the level of design and content. The study brings insights into the use of stylistic devices in advertising, and it reveals that both linguistic and non-linguistic features of advertisements are frequently employed to develop a well-thought-out design and content. The practical significance of the study is to highlight the specificities of the advertising genre so that people interested in the language of advertising (Business students and ESP teachers) will have a better understanding of the nature of the language used and the techniques of writing and designing ads. Similarly, those working in the advertising sphere (ad designers) will appreciate the specificities of the advertising discourse.

Keywords: the language of advertising, advertising discourse, ad design, stylistic features

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Authors: Paulina Pietrzyk-Kowalec

Abstract:

This paper presents the phenomenon of multilingualism becoming the norm rather than the exception in the European Union. It also seeks to describe the correlation between the command of foreign languages and employability. It is evident that the challenges of today's societies when it comes to employability and to the reality of the current labor market are more and more diversified. Thus, it is one of the crucial tasks of higher education to prepare its students to face this kind of complexity, understand its nuances, and have the capacity to adapt effectively to situations that are common in corporations based in the countries belonging to the EU. From this point of view, the assessment of the impact that the command of foreign languages of European university students could have on the numerous business sectors becomes vital. It also involves raising awareness of future professionals to make them understand the importance of mastering communicative skills in foreign languages that will meet the requirements of students' prospective employers. The direct connection between higher education institutions and the world of business also allows companies to realize that they should rethink their recruitment and human resources procedures in order to take into account the importance of foreign languages. This article focuses on the objective of the multilingualism policy developed by the European Commission, which is to enable young people to master at least two foreign languages, which is crucial in their future careers. The article puts emphasis on the existence of a crucial connection between the research conducted in higher education institutions and the business sector in order to reduce current qualification gaps.

Keywords: cross-cultural communication, employability, human resources, language attitudes, multilingualism

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Authors: Guadalupe Fuentes-Lombardo, Manuel Carlos Vallejo-Martos, Rubén Fernández-Ortiz, Miriam Cano-Rubio

Abstract:

The different ways in which companies enter foreign markets, exporting their products and direct investment and using strategic alliances or not, are influenced by a series of peculiarities specific to family businesses. In these companies, different systems, such as the family, property, and business overlap; giving them unique and specific characteristics which on occasions can enhance the development of cooperation agreements and in other situations can hinder them. Previous research has shown that these companies are more likely to enter into strategic alliances with certain specific features, and are more reluctant to take part in others in which some of the advantages of the family business are put at risk, such as control of ownership and decision-making over the company by the family, among others. These arguments show that there is a wide range of interesting aspects and peculiarities in the process of internationalization of the family business, although the research objectives of this paper focus on three in particular. Our first objective will be to discover why family businesses decide to establish or not strategic alliances in their internationalization processes in comparison with other companies that are not family owned. Secondly we will be identifying the idiosyncratic aspects of family businesses that favor or hinder the use of strategic alliances as a means of entering foreign markets. Our third and final objective will be to define the types of strategic alliance most commonly used by family businesses and the reasons why they choose these particular forms of alliance rather than others. We chose these research objectives for three main reasons. Firstly because research on this subject shows that alliances are the best way to begin the international expansion process, among other reasons because they provide the partners with different kinds of resources and capacity, so increasing the probability of successful internationalization. Secondly, because family and non-family businesses are often equipped with different types of resources and strategic alliances, offer them the chance to acquire resources less frequently found in family businesses. Thirdly, because the strengths and weaknesses of these companies could affect their decisions whether or not to use strategic alliances in their international expansion process and the success achieved in these alliances. As a result, these companies prefer to enter into cooperation agreements with conditions that do not put their specific status as family companies at risk.

Keywords: family business, internationalization, strategic alliances, olive-oil and wine industry

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Authors: Anette Alén

Abstract:

Intellectual property (IP) regimes have traditionally been designed to integrate various conflicting elements stemming from private entitlement and the public good. In IP laws and regulations, this design takes the form of specific uses of protected subject-matter without the right-holder’s consent, or exhaustion of exclusive rights upon market release, and the like. More recently, the pursuit of ‘balance’ has gained ground in the conceptualization of these conflicting elements both in terms of IP law and related policy. This can be seen, for example, in European Union (EU) copyright regime, where ‘balance’ has become a key element in argumentation, backed up by fundamental rights reasoning. This development also entails an ever-expanding dialogue between the IP regime and the constitutional safeguards for property, free speech, and privacy, among others. This study analyses the concept of ‘balance’ in EU copyright law: the research task is to examine the contents of the concept of ‘balance’ and the way it is operationalized and pursued, thereby producing new knowledge on the role and manifestations of ‘balance’ in recent copyright case law and regulatory instruments in the EU. The study discusses two particular pieces of legislation, the EU Digital Single Market (DSM) Copyright Directive (EU) 2019/790 and the finalized EU Artificial Intelligence (AI) Act, including some of the key preparatory materials, as well as EU Court of Justice (CJEU) case law pertaining to copyright in the digital era. The material is examined by means of document analysis, mapping the ways ‘balance’ is approached and conceptualized in the documents. Similarly, the interaction of fundamental rights as part of the balancing act is also analyzed. Doctrinal study of law is also employed in the analysis of legal sources. This study suggests that the pursuit of balance is, for its part, conducive to new battles, largely due to the advancement of digitalization and more recent developments in artificial intelligence. Indeed, the ‘balancing act’ rather presents itself as a way to bypass or even solidify some of the conflicting interests in a complex global digital economy. Indeed, such a conceptualization, especially when accompanied by non-critical or strategically driven fundamental rights argumentation, runs counter to the genuine acknowledgment of new types of conflicting interests in the copyright regime. Therefore, a more radical approach, including critical analysis of the normative basis and fundamental rights implications of the concept of ‘balance’, is required to readjust copyright law and regulations for the digital era. Notwithstanding the focus on executing the study in the context of the EU copyright regime, the results bear wider significance for the digital economy, especially due to the platform liability regime in the DSM Directive and with the AI Act including objectives of a ‘level playing field’ whereby compliance with EU copyright rules seems to be expected among system providers.

Keywords: balance, copyright, fundamental rights, platform liability, artificial intelligence

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