Search results for: data driven decision making

Authors: Georges Bou Ghantous

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This literature review examines the transformative impact of Artificial Intelligence (AI) on higher education, highlighting both the potential benefits and challenges associated with its adoption. The review reveals that AI significantly enhances personalized learning by tailoring educational experiences to individual student needs, thereby boosting engagement and learning outcomes. Automated grading systems streamline assessment processes, allowing educators to focus on improving instructional quality and student interaction. AI's data-driven insights provide valuable analytics, helping educators identify trends in at-risk students and refine teaching strategies. Moreover, AI promotes enhanced instructional innovation through the adoption of advanced teaching methods and technologies, enriching the educational environment. Administrative efficiency is also improved as AI automates routine tasks, freeing up time for educators to engage in research and curriculum development. However, the review also addresses the challenges that accompany AI integration, such as data privacy concerns, algorithmic bias, dependency on technology, reduced human interaction, and ethical dilemmas. This balanced exploration underscores the need for careful consideration of both the advantages and potential hurdles in the implementation of AI in higher education.

Keywords: administrative efficiency, data-driven insights, data privacy, ethical dilemmas, higher education, personalized learning

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Authors: Priyal Motwani, Garvit Goel

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In this study, we have studied and analysed the transaction data of investors of a mutual fund investment company based in India. Based on the data available, we have identified the top four biases that affect the investors of the emerging market economies through regression analysis and three uniquely defined ratios. We found that the four most prominent biases that affected the investment making decisions in India are– Chauffer Knowledge, investors tend to make ambitious decisions about sectors they know little about; Bandwagon effect – the response of the market indices to macroeconomic events are more profound and seem to last longer compared to western markets; base-rate neglect – judgement about stocks are too much based on the most recent development ignoring the long-term fundamentals of the stock; availability bias – lack of proper communication channels of market information lead people to be too reliant on limited information they already have. After segregating the investors into six groups, the results have further been studied to identify a correlation among the demographics, gender and unique cultural identity of the derived groups and the corresponding prevalent biases. On the basis of the results obtained from the derived groups, our study recommends six methods, specific to each group, to educate the investors about the prevalent biases and their role in investment decision making.

Keywords: Bandwagon effect, behavioural biases, Chauffeur knowledge, demographics, investor literacy, mutual funds

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Authors: Assem El-Ansary

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This paper illustrates the event-oriented Behavioral Pattern Analysis (BPA) modeling approach using the Healthcare Multi-Agent System. The Event defined in BPA is a real-life conceptual entity that is unrelated to any implementation. The major contributions of this research are: The Behavioral Pattern Analysis (BPA) modeling methodology. The development of an interactive software tool (DECISION), which is based on a combination of the Analytic Hierarchy Process (AHP) and the ELECTRE Multi-Criteria Decision Making (MCDM) methods.

Keywords: analysis, modeling methodology, software modeling, event-oriented, behavioral pattern, use cases, Healthcare Multi-Agent System

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Authors: Maryam Mostafaee, Hassan Sadeghi Naeini, Sara Mostowfi

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Today’s Knowledge management (KM), plays an important role in organizations. Basically, knowledge management is in the relation of using it for taking advantage of work forces in an organization for forwarding the goals and demand of that organization used at the most. The purpose of knowledge management is not only to manage existing documentation, information, and Data through an organization, but the most important part of KM is to control most important and key factor of those information and Data. For sure it is to chase the information needed for the employees in the right time of needed to take from genuine source for bringing out the best performance and result then in this matter the performance of organization will be at most of it. There are a lot of definitions over the objective of management released. Management is the science that in force the accurate knowledge with repeating to the organization to shape it and take full advantages for reaching goals and targets in the organization to be used by employees and users, but the definition of Knowledge based on Kalinz dictionary is: Facts, emotions or experiences known by man or group of people is ‘ knowledge ‘: Based on the Merriam Webster Dictionary: the act or skill of controlling and making decision about a business, department, sport team, etc, based on the Oxford Dictionary: Efficient handling of information and resources within a commercial organization, and based on the Oxford Dictionary: The art or process of designing manufactured products: the scale is a beautiful work of industrial design. When knowledge management performed executive in universities, discovery and create a new knowledge be facilitated. Make procedures between different units for knowledge exchange. College's officials and employees understand the importance of knowledge for University's success and will make more efforts to prevent the errors. In this strategy, is explored factors and affective trends and manage of it in University. In this research, Iranian universities for a time being analyzed that over usage of knowledge management, how they are behaving and having understood this matter: 1. Discovery of knowledge management in Iranian Universities, 2. Transferring exciting knowledge between faculties and unites, 3. Participate of employees for getting and using and transferring knowledge, 4.The accessibility of valid sources, 5. Researching over factors and correct processes in the university. We are pointing in some examples that we have already analyzed which is: -Enabling better and faster decision-making, -Making it easy to find relevant information and resources, -Reusing ideas, documents, and expertise, -Avoiding redundant effort. Consequence: It is found that effectiveness of knowledge management in the Industrial design field is low. Based on filled checklist by Education officials and professors in universities, and coefficient of effectiveness Calculate, knowledge management could not get the right place.

Keywords: knowledge management, industrial design, educational curriculum, learning performance

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Authors: Panpan Feng

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The concept of green infrastructure originated in Europe and the United States. It aims to ensure smart growth of urban and rural ecosystems and achieve sustainable urban and rural ecological, social, and economic development by combining it with gray infrastructure in traditional planning. Based on the literature review of the theoretical origin, value connotation, and measurement methods of green infrastructure, this study summarizes the research content of green infrastructure at different scales from the three spatial levels of region, city, and block and divides it into functional dimensions, spatial dimension, and strategic dimension. The results show that in the functional dimension, from region-city-block, the research on green infrastructure gradually shifts from ecological function to social function. In the spatial dimension, from region-city-block, the research on the spatial form of green infrastructure has shifted from two-dimensional to three-dimensional, and the spatial structure of green infrastructure has shifted from single ecological elements to multiple composite elements. From a strategic perspective, green infrastructure research is more of a spatial planning tool based on land management, environmental livability and ecological psychology, providing certain decision-making support.

Keywords: green infrastructure, multi-scale, social and ecological functions, spatial strategic decision-making tools

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Authors: Daniil Chernov

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In the modern world, the number of conventional interstate wars is declining while the number of military interventions is rising. States no longer initiate conflicts by declaring war but actively intervene in existing military confrontations, often using a comparable number of coercive means. According to existing scholarly understanding, the decision to use force in international relations (in any form) is influenced by roughly the same set of factors: the dynamics of domestic political processes, national interests, international law, and ethical considerations. In the database on armed intervention to be presented in the report, the multifactor model of decision-making is developed. The database describes more than 200 different parameters for armed interventions between 1992 and 2022. The report will present the structure of the database, descriptive statistics, and its key advantages over other sources.

Keywords: conflict resolution, international relations, military intervention, database

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Authors: Alaa Abdullah Altaee

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This paper presents an adaptive decision feedback equalizer (ADFE) for multi-Gbps serial links utilizing a fix-step error signal extracted from cross-points of received data symbols. The extracted signal is generated based on violation of received data symbols with minimum detection requirements at the clock and data recovery (CDR) stage. The iterations of the adaptation process search for the optimum feedback tap coefficients to maximize the data eye-opening and minimize the adaptation convergence time. The effectiveness of the proposed architecture is validated using the simulation results of a serial link designed in an IBM 130 nm 1.2V CMOS technology. The data link with variable channel lengths is analyzed using Spectre from Cadence Design Systems with BSIM4 device models.

Keywords: adaptive DFE, CMOS equalizer, error detection, serial links, timing jitter, wire-line communication

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Authors: Phan Nghiem Vu, Le Tuan Vu, Ta Quang Tai

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The optimal construction design used during the execution of a construction project is a key factor in determining high productivity and customer satisfaction, however, this management process sometimes is carried out without care and the systematic method that it deserves, bringing negative consequences. This study proposes a knowledge management (KM) approach that will enable the intelligent use of experienced and acknowledged engineers to improve the management of construction design works for a project. Then a knowledge-based application to support this decision-making process is proposed and described. To define and design the system for the application, semi-structured interviews were conducted within five construction consulting organizations with the purpose of studying the way that the method’ optimizing process is implemented in practice and the knowledge supported with it. A system of an optimizing construction design works (OCDW) based on knowledge was developed then validated with construction experts. The OCDW was liked as a valuable tool for construction design works’ optimization, by supporting organizations to generate a corporate memory on this issue, reducing the reliance on individual knowledge and also the subjectivity of the decision-making process. The benefits are described as provided by the performance support system, reducing costs and time, improving product design quality, satisfying customer requirements, expanding the brand organization.

Keywords: optimizing construction design work, construction consultant organization, knowledge management, knowledge-based application

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Authors: Assem El-Ansary

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This paper illustrates the event-oriented Behavioral Pattern Analysis (BPA) modeling approach in developing an Intelligent Agent Reservation System (IARS). The Event defined in BPA is a real-life conceptual entity that is unrelated to any implementation. The major contributions of this research are developing the Behavioral Pattern Analysis (BPA) modeling methodology, and developing an interactive software tool (DECISION) which is based on a combination of the Analytic Hierarchy Process (AHP) and the ELECTRE Multi-Criteria Decision Making (MCDM) methods.

Keywords: analysis, intelligent agent, reservation system, modeling methodology, software modeling, event-oriented, behavioral pattern, use cases

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Authors: Arkaprabha Bhattacharyya, Makarand Hastak

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The COVID-19 pandemic is unprecedented in terms of its global reach and economic impacts. Historically, investment in infrastructure development projects has been touted to boost the economic growth of a nation. The State and Local governments responsible for delivering infrastructure assets work under tight budgets. Therefore, it is important to understand which infrastructure projects have the highest potential of boosting economic growth in the post-pandemic era. This paper presents relationships between infrastructure projects and economic growth. Statistical relationships between investment in different types of infrastructure projects (transit, water and wastewater, highways, power, manufacturing etc.) and indicators of economic growth are presented using historic data between 2002 and 2020 from the U.S. Census Bureau and U.S. Bureau of Economic Analysis (BEA). The outcome of the paper is the comparison of statistical correlations between investment in different types of infrastructure projects and indicators of economic growth. The comparison of the statistical correlations is useful in ranking the types of infrastructure projects based on their ability to influence economic prosperity. Therefore, investment in the infrastructures with the higher rank will have a better chance of boosting the economic growth. Once, the ranks are derived, they can be used by the decision-makers in infrastructure investment related decision-making process.

Keywords: economic growth, infrastructure development, infrastructure projects, strategic investment

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Authors: Winita Kitisak, Boontarika Panyomoon, Siriyakorn Nilpoun, Nithit Yosit, Peeraya Somsak

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The purpose of this study will affect the marketing mix that influences the consumers’ behavior towards beverage purchasing from retail stores. It aims to study the consumers and to better understand their behaviors and factors influencing their decision making on buying beverage in retail stores. We study the example of 400 consumers in Chiang Mai. The study shows that most of the respondents were male, 50 percent is 20-30 year old, and 36.66 percent is 31-40 year old, only 2.66 percent is upper 50 years old, bachelor’s degree holders, working in business field and student with 10,001-15,000 Baht income. Most buyers spend 4-6 times a week buying cheap beverage from retail stores. The consumer bought alcoholic beverages, green tea drinks, and soft drinks, but the mainly purchased product was beer. The results indicate that the brand of the product motivates more on consumers’ demand. While shelf displays, products presentation, and sales promotion affect the most on the consumers’ decision to purchase from the retail stores, the promotions moderately impact the consumers’ decision on purchasing from retail stores.

Keywords: consumer behavior, beverage, retail stores, convenience store

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Authors: Suarez-Puello Alejandro, Baquero-Ruiz Andrés F, Suarez-Puello Rodrigo

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This paper discusses the results of a foresight exercise which analyzes Bogota’s fruit, vegetable and tuber supply chain strategy- described at the Food Supply and Security Master Plan (FSSMP)-to provide the inhabitants of Bogotá, Colombia, with basic food products at a fair price. The methodology consisted of using quantitative and qualitative foresight tools such as system dynamics and variable selection methods to better represent interactions among stakeholders and obtain more integral results that could shed light on this complex situation. At first, the Master Plan is an input to establish the objectives and scope of the exercise. Then, stakeholders and their relationships are identified. Later, system dynamics is used to model product, information and money flow along the fruit, vegetable and tuber supply chain. Two scenarios are presented, discussing actions by the public sector and the reactions that could be expected from the whole food supply system. Finally, these impacts are compared to the Food Supply and Security Master Plan’s objectives suggesting recommendations that could improve its execution. This foresight exercise performed at a governmental level is intended to promote the widen the use of foresight as an anticipatory, decision-making tool that offers solutions to complex problems.

Keywords: decision making, foresight, public policies, supply chain, system dynamics

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Authors: Ahmad Haidar

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Modern economic systems are characterized by growing complexity, and addressing their challenges requires innovative approaches. This study examines the implications of artificial intelligence (AI) on unemployment in Europe from a macroeconomic perspective, employing data modeling techniques to understand the relationship between AI integration and labor market dynamics. To understand the AI-unemployment nexus comprehensively, this research considers factors such as sector-specific AI adoption, skill requirements, workforce demographics, and geographical disparities. The study utilizes a panel data model, incorporating data from European countries over the last two decades, to explore the potential short-term and long-term effects of AI implementation on unemployment rates. In addition to investigating the direct impact of AI on unemployment, the study also delves into the potential indirect effects and spillover consequences. It considers how AI-driven productivity improvements and cost reductions might influence economic growth and, in turn, labor market outcomes. Furthermore, it assesses the potential for AI-induced changes in industrial structures to affect job displacement and creation. The research also highlights the importance of policy responses in mitigating potential negative consequences of AI adoption on unemployment. It emphasizes the need for targeted interventions such as skill development programs, labor market regulations, and social safety nets to enable a smooth transition for workers affected by AI-related job displacement. Additionally, the study explores the potential role of AI in informing and transforming policy-making to ensure more effective and agile responses to labor market challenges. In conclusion, this study provides a comprehensive analysis of the macroeconomic implications of AI on unemployment in Europe, highlighting the importance of understanding the nuanced relationships between AI adoption, economic growth, and labor market outcomes. By shedding light on these relationships, the study contributes valuable insights for policymakers, educators, and researchers, enabling them to make informed decisions in navigating the complex landscape of AI-driven economic transformation.

Keywords: artificial intelligence, unemployment, macroeconomic analysis, european labor market

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Authors: Shameka Stanford

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This presentation will discuss how cognitive and communication disorders in the areas of executive functioning, receptive and expressive language can impact the problem-solving and decision making of individuals with such impairments. More specifically, this presentation will discuss approaches the legal defense team of capital case lawyers can add to their experience when servicing individuals who have a history of educational decline, special education, and limited intervention and treatment. The objective of the research is to explore and identify the correlations between impaired executive function skills and decision making and competency for individuals facing death penalty charges. To conduct this research, experimental design, randomized sampling, qualitative analysis was employed. This research contributes to the legal and criminal justice system related to how they view, defend, and characterize, and judge individuals with documented cognitive and communication disorders who are eligible for capital case charges. More importantly, this research contributes to the increased ability of death penalty lawyers to successfully defend clients with a history of academic difficulty, special education, and documented disorders that impact educational progress and academic success.

Keywords: communication disorders, cognitive disorders, capital murder, death penalty, executive function

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Authors: Roopa Singh, Anurag Singh, Ajay

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Agriculture contributes a major share in the national economy of India. A major portion of Indian economy (about 70%) depends upon agriculture as it forms the main source of income. About 43% of India’s geographical area is used for agricultural activity which involves 65-75% of total population of India. The given work deals with the Fast moving Consumer Goods (FMCG) industries and their inventories which use agricultural produce as their raw material or input for their final product. Since the beginning of inventory practices, many developments took place which can be categorised into three phases, based on the review of various works. The first phase is related with development and utilization of Economic Order Quantity (EOQ) model and methods for optimizing costs and profits. Second phase deals with inventory optimization method, with the purpose of balancing capital investment constraints and service level goals. The third and recent phase has merged inventory control with electrical control theory. Maintenance of inventory is considered negative, as a large amount of capital is blocked especially in mechanical and electrical industries. But the case is different in food processing and agro-based industries and their inventories due to cyclic variation in the cost of raw materials of such industries which is the reason for selection of these industries in the mentioned work. The application of electrical control theory in inventory control makes the decision-making highly instantaneous for FMCG industries without loss in their proposed profits, which happened earlier during first and second phases, mainly due to late implementation of decision. The work also replaces various inventories and work-in-progress (WIP) related errors with their monetary values, so that the decision-making is fully target-oriented.

Keywords: control theory, inventory control, manufacturing sector, EOQ, feedback, FMCG sector

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Authors: Preeti Nakhat, Neeta Sinha

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Career plays an indispensable role in shaping one’s outlook on life. Choosing right career adds 'feathers to the life' whereas wrong career decision 'takes a toll 'in one’s life. It is pivotal for the students to know the career opportunities related to their field where they can escalate and excel. With the aim to comprehend certainty and indecisiveness in career decision among college students, a study will be conducted. The study focuses to gain insight on decisiveness and indecisiveness of career among the students. The hypotheses for the study are (1) There is no relation between the medium of education (vernacular/English medium) and career decisiveness among the college students. (2) There is no relation between the faculty(science, commerce, arts)chosen and career decisiveness. (3)There is no relation between father’s qualification and career decisiveness. To test the aforementioned hypotheses, a survey questionnaire will be used. The questionnaire is 'Career decision scale' by Samuel H. Osipow. This study will include 200 college going students. The data will be collected from first, second, third, and fourth year students. Statistical analysis of the data collected with be done through SPSS/Excel calculation and then the hypotheses will be tested.

Keywords: career decisiveness, career indecisiveness, college students, career

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Boumaraf Malak

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The development of intelligent transportation systems (ITS) and artificial intelligence (AI) are spurring us to pave the way for the widespread adoption of autonomous vehicles (AVs). This is open again opportunities for smart roads, smart traffic safety, and mobility comfort. A highly intelligent decision-making system is essential for autonomous driving around dense, dynamic objects. It must be able to handle complex road geometry and topology, as well as complex multiagent interactions, and closely follow higher-level commands such as routing information. Autonomous vehicles have become a very hot research topic in recent years due to their significant ability to reduce traffic accidents and personal injuries. Using new artificial intelligence-based technologies handles important functions in scene understanding, motion planning, decision making, vehicle control, social behavior, and communication for AV. This paper focuses only on deep reinforcement learning-based methods; it does not include traditional (flat) planar techniques, which have been the subject of extensive research in the past because reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. The DRL algorithm used so far found solutions to the four main problems of autonomous driving; in our paper, we highlight the challenges and point to possible future research directions.

Keywords: deep reinforcement learning, autonomous driving, deep deterministic policy gradient, deep Q-learning

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Authors: Sevde D. Karayel, Ediz Atmaca

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Supplier selection subject is vital because of development competitiveness and performance of firms which have right, rapid and with low cost procurement. Considering the fact that competition between firms is no longer on their supply chains, hence it is very clear that performance of the firms’ not only depend on their own success but also success of all departments in supply chain. For this purpose, firms want to work with suppliers which are cost effective, flexible in terms of demand and high quality level for customer satisfaction. However, diversification and redundancy of their expectations from suppliers, supplier selection problems need to be solved as a hard problem. In this study, supplier selection problem is discussed for critical piece, which is using almost all production of products in and has troubles with lead time from supplier, in a firm that produces medical devices. Analyzing policy in the current situation of the firm in the supplier selection indicates that supplier selection is made based on the purchasing department experience and other authorized persons’ general judgments. Because selection do not make based on the analytical methods, it is caused disruptions in production, lateness and extra cost. To solve the problem, AHP and TOPSIS which are multi-criteria decision making techniques, which are effective, easy to implement and can analyze many criteria simultaneously, are used to make a selection among alternative suppliers.

Keywords: AHP-TOPSIS methods, multi-criteria decision making, supplier selection problem, supply chain management

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Authors: Shanshan Zhu, Mohammad Nasim

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Financial fraud continues to be a major threat to financial institutions across the world, causing colossal money losses and undermining public trust. Fraud prevention techniques, based on hard rules, have become ineffective due to evolving patterns of fraud in recent times. Against such a background, the present study probes into distinct methodologies that exploit emergent AI-driven techniques to further strengthen fraud detection. We would like to compare the performance of generative adversarial networks and graph neural networks with other popular techniques, like gradient boosting, random forests, and neural networks. To this end, we would recommend integrating all these state-of-the-art models into one robust, flexible, and smart system for real-time anomaly and fraud detection. To overcome the challenge, we designed synthetic data and then conducted pattern recognition and unsupervised and supervised learning analyses on the transaction data to identify which activities were fishy. With the use of actual financial statistics, we compare the performance of our model in accuracy, speed, and adaptability versus conventional models. The results of this study illustrate a strong signal and need to integrate state-of-the-art, AI-driven fraud detection solutions into frameworks that are highly relevant to the financial domain. It alerts one to the great urgency that banks and related financial institutions must rapidly implement these most advanced technologies to continue to have a high level of security.

Keywords: AI-driven fraud detection, financial security, machine learning, anomaly detection, real-time fraud detection

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Authors: Kaleb Shiferaw, Berhanu Geberemedhin, Dereje Legesse

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Access to credit is often viewed as a key to transform semi-subsistence smallholders into market oriented producers. However, only a few studies have examined factors that affect farmers’ decision to allocate credit on farm activities in general and livestock production in particular. A trivariate probit model with double selection is employed to identify factors that affect farmers’ decision to allocate credit on livestock production using data collected from smallholder farmers in Ethiopia. After controlling for two sample selection bias – taking credit for the production season and decision to allocate credit on farm activities – land ownership and access to a livestock centered extension service are found to have a significant (p<0.001) effect on farmers decision to use credit for livestock production. The result showed farmers with large land holding, and access to a livestock centered extension services are more likely to utilize credit for livestock production. However since the effect of land ownership squared is negative the effect of land ownership for those who own a large plot of land lessens. The study highlights the fact that improving access to credit does not automatically translate into more productive households. Improving farmers’ access to credit should be followed by a focused extension services.

Keywords: livestock production, credit access, credit allocation, household decision, double sample selection

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Authors: Luís Pina

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The conservation of marine biodiversity keeps ecosystems in balance and ensures the sustainable use of resources. In this context, technological resources have been used for monitoring marine species to allow biologists to obtain data in real-time. There are different mobile applications developed for data collection for monitoring purposes, but these systems are designed to be utilized only on third-generation (3G) phones or smartphones with Internet access and in rural parts of the developing countries, Internet services and smartphones are scarce. Thus, the objective of this work is to develop a system to monitor marine turtles using Unstructured Supplementary Service Data (USSD), which users can access through basic mobile phones. The system aims to improve the data collection mechanism and enhance the effectiveness of current systems in monitoring sea turtles using any type of mobile device without Internet access. The system will be able to report information related to the biological activities of marine turtles. Also, it will be used as a platform to assist marine conservation entities to receive reports of illegal sales of sea turtles. The system can also be utilized as an educational tool for communities, providing knowledge and allowing the inclusion of communities in the process of monitoring marine turtles. Therefore, this work may contribute with information to decision-making and implementation of contingency plans for marine conservation programs.

Keywords: GSM, marine biology, marine turtles, unstructured supplementary service data (USSD)

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Authors: Ruilian Zhao, Shukai Zhang, Yan Wang, Weiwei Wang

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Repairing broken test cases is an expensive and challenging task in evolving software systems. Although an automated repair technique with intent preservation has been proposed, but it does not take into account the association between test repairs and assertions, leading to a large number of irrelevant candidates and decreasing the repair capability. This paper proposes an assertion-driven test repair approach. Furthermore, an intent-oriented priority criterion is raised to guide the repair candidate generation, making the repairs closer to the intent of the test. In more detail, repair targets are determined through post-dominance relations between assertions and the methods that directly cause compilation errors. Then, test repairs are generated from the target in a bottom-up way, guided by the intent-oriented priority criteria. Finally, the generated repair candidates are prioritized to match the original test intent. The approach is implemented and evaluated on the benchmark of 4 open-source programs and 91 broken test cases. The result shows that the approach can fix 89% (81/91) of broken test cases, which is more effective than the existing intentpreserved test repair approach, and our intent-oriented priority criteria work well.

Keywords: test repair, test intent, software test, test case evolution

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Authors: Slim Namouchi, Bruno Vallet, Imed Riadh Farah

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3D urban models provide powerful tools for decision making, urban planning, and smart city services. The accuracy of this 3D based systems is directly related to the quality of these models. Since manual large-scale modeling, such as cities or countries is highly time intensive and very expensive process, a fully automatic 3D building generation is needed. However, 3D modeling process result depends on the input data, the proprieties of the captured objects, and the required characteristics of the reconstructed 3D model. Nowadays, producing 3D real-world model is no longer a problem. Remotely sensed data had experienced a remarkable increase in the recent years, especially data acquired using unmanned aerial vehicles (UAV). While the scanning techniques are developing, the captured data amount and the resolution are getting bigger and more precise. This paper presents a literature review, which aims to identify different methods of automatic 3D buildings extractions either from LiDAR or the combination of LiDAR and satellite or aerial images. Then, we present open source technologies, and data models (e.g., CityGML, PostGIS, Cesiumjs) used to integrate these models in geospatial base layers for smart city services.

Keywords: CityGML, LiDAR, remote sensing, SIG, Smart City, 3D urban modeling

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Authors: Eugene Y. C. Wong, Henry Y. K. Lau, Mardjuki Raman

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CO2 emissions from maritime transport operations represent a substantial part of the total greenhouse gas emission. Vessels are designed with better energy efficiency. Minimizing CO2 emission in maritime operations plays an important role in supply chain decarbonisation. This paper reviews the initiatives on slow steaming operations towards the reduction of carbon emission. It investigates the relationship and impact among slow steaming cost reduction, carbon emission reduction, and shipment delay. A scenario-based cost-driven decision support model is developed to facilitate the selection of the optimal slow steaming options, considering the cost on bunker fuel consumption, available speed, carbon emission, and shipment delay. The incorporation of the social cost of cargo is reviewed and suggested. Additional measures on the effect of vessels sizes, routing, and type of fuels towards decarbonisation are discussed.

Keywords: slow steaming, carbon emission, maritime logistics, sustainability, green supply chain

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Authors: Shivi Agarwal, Trilok Mathur

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This paper proposes the method to measure the total factor productivity (TFP) change by credibility theory for fuzzy input and output variables. Total factor productivity change has been widely studied with crisp input and output variables, however, in some cases, input and output data of decision-making units (DMUs) can be measured with uncertainty. These data can be represented as linguistic variable characterized by fuzzy numbers. Malmquist productivity index (MPI) is widely used to estimate the TFP change by calculating the total factor productivity of a DMU for different time periods using data envelopment analysis (DEA). The fuzzy DEA (FDEA) model is solved using the credibility theory. The results of FDEA is used to measure the TFP change for fuzzy input and output variables. Finally, numerical examples are presented to illustrate the proposed method to measure the TFP change input and output variables. The suggested methodology can be utilized for performance evaluation of DMUs and help to assess the level of integration. The methodology can also apply to rank the DMUs and can find out the DMUs that are lagging behind and make recommendations as to how they can improve their performance to bring them at par with other DMUs.

Keywords: chance-constrained programming, credibility theory, data envelopment analysis, fuzzy data, Malmquist productivity index

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Authors: Archana Singh, Jyoti Agarwal, Ajay Rana

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Data Mining is an efficient technology to discover patterns in large databases. Association Rule Mining techniques are used to find the correlation between the various item sets in a database, and this co-relation between various item sets are used in decision making and pattern analysis. In recent years, the problem of finding association rules from large datasets has been proposed by many researchers. Various research papers on association rule mining (ARM) are studied and analyzed first to understand the existing algorithms. Apriori algorithm is the basic ARM algorithm, but it requires so many database scans. In DIC algorithm, less amount of database scan is needed but complex data structure lattice is used. The main focus of this paper is to propose a new optimized algorithm (Friendly Algorithm) and compare its performance with the existing algorithms A data set is used to find out frequent itemsets and association rules with the help of existing and proposed (Friendly Algorithm) and it has been observed that the proposed algorithm also finds all the frequent itemsets and essential association rules from databases as compared to existing algorithms in less amount of database scan. In the proposed algorithm, an optimized data structure is used i.e. Graph and Adjacency Matrix.

Keywords: association rules, data mining, dynamic item set counting, FP-growth, friendly algorithm, graph

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Authors: Lior Davidovitch

Abstract:

Traditional training based on static models and case studies is the backbone of most teaching and training programs of engineering education. However, project management learning is characterized by dynamics models that requires new and enhanced learning method. The results of empirical experiments evaluating the effectiveness and efficiency of using cognitive simulator as a new training technique are reported. The empirical findings are focused on the impact of keeping and reviewing learning history in a dynamic and interactive simulation environment of engineering education. The cognitive simulator for engineering project management learning had two learning history keeping modes: manual (student-controlled), automatic (simulator-controlled) and a version with no history keeping. A group of industrial engineering students performed four simulation-runs divided into three identical simple scenarios and one complicated scenario. The performances of participants running the simulation with the manual history mode were significantly better than users running the simulation with the automatic history mode. Moreover, the effects of using the undo enhanced further the learning process. The findings indicate an enhancement of engineering students’ learning and decision making when they use the record functionality of the history during their engineering training process. Furthermore, the cognitive simulator as educational innovation improves students learning and training. The practical implications of using simulators in the field of engineering education are discussed.

Keywords: cognitive simulator, decision making, engineering learning, project management

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Authors: Recep Sait Arpat, Hakan Güreşci

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Emergency, disaster and crisis management terms are generally perceived as the same processes. This conflict effects the approach and delegating policy of the political order. Crisis management starts in the aftermath of the mismanagement of disaster and emergency. In the light of the information stated above in this article Turkey and United Kingdom(UK)’s crisis management systems are analyzed. This article’s main aim is to clarify the main points of the emergency management system of United Kingdom and Turkey’s disaster management system by comparing them. To do this: A prototype model of the political decision making processes of the countries is drawn, decision making mechanisms and the planning functions are compared. As a result it’s found that emergency management policy in Turkey is reactive whereas it’s proactive in UK; as the delegating policy Turkey’s system is similar to UK; levels of emergency situations are similar but not the same; the differences are stemming from the civil order and nongovernmental organizations effectiveness; UK has a detailed government engagement model to emergencies, which shapes the doctrine of the approach to emergencies, and it’s successful in gathering and controlling the whole state’s efforts; crisis management is a sub-phase of UK emergency management whereas it’s accepted as a outmoded management perception and the focal point of crisis management perception in UK is security crisis and natural disasters while in Turkey it is natural disasters. In every anlysis proposals are given to Turkey.

Keywords: crisis management, disaster management, emergency management, turkey, united kingdom

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Authors: Bronwen Wade

Abstract:

Over the last half-century, child welfare systems have increasingly relied on quantitative risk assessment tools, such as actuarial or predictive risk tools. These tools are developed by performing statistical analysis of how attributes captured in administrative data are related to future child maltreatment. Some scholars argue that attributes in administrative data can serve as proxies for race and that quantitative risk assessment tools reify racial bias in decision-making. Others argue that these tools provide more “objective” and “scientific” guides for decision-making instead of subjective social worker judgment. This study performs a systematic review of the literature on the impact of quantitative risk assessment tools on racial disproportionality; it examines methodological biases in work on this topic, summarizes key findings, and provides suggestions for further work. A search of CINAHL, PsychInfo, Proquest Social Science Premium Collection, and the ProQuest Dissertations and Theses Collection was performed. Academic and grey literature were included. The review includes studies that use quasi-experimental methods and development, validation, or re-validation studies of quantitative risk assessment tools. PROBAST (Prediction model Risk of Bias Assessment Tool) and CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) were used to assess the risk of bias and guide data extraction for risk development, validation, or re-validation studies. ROBINS-I (Risk of Bias in Non-Randomized Studies of Interventions) was used to assess for bias and guide data extraction for the quasi-experimental studies identified. Due to heterogeneity among papers, a meta-analysis was not feasible, and a narrative synthesis was conducted. 11 papers met the eligibility criteria, and each has an overall high risk of bias based on the PROBAST and ROBINS-I assessments. This is deeply concerning, as major policy decisions have been made based on a limited number of studies with a high risk of bias. The findings on racial disproportionality have been mixed and depend on the tool and approach used. Authors use various definitions for racial equity, fairness, or disproportionality. These concepts of statistical fairness are connected to theories about the reason for racial disproportionality in child welfare or social definitions of fairness that are usually not stated explicitly. Most findings from these studies are unreliable, given the high degree of bias. However, some of the less biased measures within studies suggest that quantitative risk assessment tools may worsen racial disproportionality, depending on how disproportionality is mathematically defined. Authors vary widely in their approach to defining and addressing racial disproportionality within studies, making it difficult to generalize findings or approaches across studies. This review demonstrates the power of authors to shape policy or discourse around racial justice based on their choice of statistical methods; it also demonstrates the need for improved rigor and transparency in studies of quantitative risk assessment tools. Finally, this review raises concerns about the impact that these tools have on child welfare systems and racial disproportionality.

Keywords: actuarial risk, child welfare, predictive risk, racial disproportionality

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