Search results for: decision science

Authors: Danica Maljkovic, Igor Balen, Bojana Dalbelo Basic

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Following the regulation of EU Directive on Energy Efficiency, specifically Article 9, individual metering in district heating systems has to be introduced by the end of 2016. These directions have been implemented in member state’s legal framework, Croatia is one of these states. The directive allows installation of both heat metering devices and heat cost allocators. Mainly due to bad communication and PR, the general public false image was created that the heat cost allocators are devices that save energy. Although this notion is wrong, the aim of this work is to develop a model that would precisely express the influence of installation heat cost allocators on potential energy savings in each unit within multifamily buildings. At the same time, in recent years, a science of machine learning has gain larger application in various fields, as it is proven to give good results in cases where large amounts of data are to be processed with an aim to recognize a pattern and correlation of each of the relevant parameter as well as in the cases where the problem is too complex for a human intelligence to solve. A special method of machine learning, decision tree method, has proven an accuracy of over 92% in prediction general building consumption. In this paper, a machine learning algorithms will be used to isolate the sole impact of installation of heat cost allocators on a single building in multifamily houses connected to district heating systems. Special emphasises will be given regression analysis, logistic regression, support vector machines, decision trees and random forest method.

Keywords: district heating, heat cost allocator, energy efficiency, machine learning, decision tree model, regression analysis, logistic regression, support vector machines, decision trees and random forest method

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Authors: Mansoureh Ghiasabadi Farahani, Samira Kalantary, Sara Taghi-Pour, Mahboubeh Shamsi

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Nowadays, the development of computers technology and its recent applications provide access to new types of data, which have not been considered by the traditional data analysts. Two particularly interesting characteristics of such data sets include their huge size and streaming nature .Incremental learning techniques have been used extensively to address the data stream classification problem. This paper presents a concise survey on the obstacles and the requirements issues classifying data streams with using decision tree. The most important issue is to maintain a balance between accuracy and efficiency, the algorithm should provide good classification performance with a reasonable time response.

Keywords: big data, data streams, classification, decision tree

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Authors: N. Songumpai, W. Sumranwanich, S. Chatmaneerungcharoen

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This research aims to study the understanding of 8 aspects of nature of science (NOS). The research participants were 39 General Science student teachers who were selected by purposive sampling. In 2015 academic year, they enrolled in the course of Science Education Learning Management. Qualitative research was used as research methodology to understand how the student teachers propose on NOS. The research instruments consisted of open-ended questionnaires and semi-structure interviews that were used to assess students’ understanding of NOS. Research data was collected by 8 items- questionnaire and was categorized into students’ understanding of NOS, which consisted of complete understanding (CU), partial understanding (PU), misunderstanding (MU) and no understanding (NU). The findings reveal the majority of students’ misunderstanding of NOS regarding the aspects of theory and law(89.7%), scientific method(61.5%) and empirical evidence(15.4%) respectively. From the interview data, the student teachers present their misconceptions of NOS that indicate about theory and law cannot change; science knowledge is gained through experiment only (step by step); science is the things that are around humans. These results suggest that for effective science teacher education, the composition of design of NOS course needs to be considered. Therefore, teachers’ understanding of NOS is necessary to integrate into professional development program/course for empowering student teachers to begin their careers as strong science teachers in schools.

Keywords: nature of science, student teacher, no understanding, misunderstanding, partial understanding, complete understanding

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Authors: Mladen Dragičević, Mirjana Pejić Bach, Vanja Šimičević

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The purpose of this paper is to develop models that would enable predicting student success. These models could improve allocation of students among colleges and optimize the newly introduced model of government subsidies for higher education. For the purpose of collecting data, an anonymous survey was carried out in the last year of undergraduate degree student population using random sampling method. Decision trees were created of which two have been chosen that were most successful in predicting student success based on two criteria: Grade Point Average (GPA) and time that a student needs to finish the undergraduate program (time-to-degree). Decision trees have been shown as a good method of classification student success and they could be even more improved by increasing survey sample and developing specialized decision trees for each type of college. These types of methods have a big potential for use in decision support systems.

Keywords: data mining, knowledge discovery in databases, prediction models, student success

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Authors: Carla Silva

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Recent development of information and communication technology enables us to acquire, collect, analyse data in various fields of socioeconomic – technological systems. Along with the increase of economic globalization and the evolution of information technology, data mining has become an important approach for economic data analysis. As a result, there has been a critical need for automated approaches to effective and efficient usage of massive amount of educational data, in order to support institutions to a strategic planning and investment decision-making. In this article, we will address data from several different perspectives and define the applied data to sciences. Many believe that 'big data' will transform business, government, and other aspects of the economy. We discuss how new data may impact educational policy and educational research. Large scale administrative data sets and proprietary private sector data can greatly improve the way we measure, track, and describe educational activity and educational impact. We also consider whether the big data predictive modeling tools that have emerged in statistics and computer science may prove useful in educational and furthermore in economics. Finally, we highlight a number of challenges and opportunities for future research.

Keywords: data mining, research analysis, investment decision-making, educational research

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Authors: Mária Hudáková, Vladimír Míka, Katarína Hollá

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Making decisions is the core of management and a result of conscious activities which is under way in a particular environment and concrete conditions. The managers decide about the goals, procedures and about the methods how to respond to the changes and to the problems which developed. Their decisions affect the effectiveness, quality, economy and the overall successfulness in every organisation. In spite of this fact, they do not pay sufficient attention to the individual steps of the decision-making process. They emphasise more how to cope with the individual methods and techniques of making decisions and forget about the way how to cope with analysing the problem or assessing the individual solution variants. In many cases, the underestimating of the analytical phase can lead to an incorrect assessment of the problem and this can then negatively influence its further solution. Based on our analysis of the theoretical solutions by individual authors who are dealing with this area and the realised research in Slovakia and also abroad we can recognise an insufficient interest of the managers to assess the risks in the decision-making process. The goal of this paper is to assess the risks in the managers´ decision-making process relating to the conditions of the environment, to the subject’s activity (the manager’s personality), to the insufficient assessment of individual variants for solving the problems but also to situations when the arisen problem is not solved. The benefit of this paper is the effort to increase the need of the managers to deal with the risks during the decision-making process. It is important for every manager to assess the risks in his/her decision-making process and to make efforts to take such decisions which reflect the basic conditions, states and development of the environment in the best way and especially for the managers´ decisions to contribute to achieving the determined goals of the organisation as effectively as possible.

Keywords: risk, decision-making, manager, process, analysis, source of risk

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Authors: Masood Karamoozian, Zhang Hong

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The necessity and complexity of the decision-making process and the interference of the various factors to make decisions and consider all the relevant factors in a problem are very obvious nowadays. Hence, researchers show their interest in multi-criteria decision-making methods. In this research, the Analytical Hierarchy Process (AHP), Simple Additive Weighting (SAW), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods of multi-criteria decision-making have been used to solve the problem of optimal construction systems. Systems being evaluated in this problem include; Light Steel Frames (LSF), a case study of designs by Zhang Hong studio in the Southeast University of Nanjing, Insulating Concrete Form (ICF), Ordinary Construction System (OCS), and Prefabricated Concrete System (PRCS) as another case study designs in Zhang Hong studio in the Southeast University of Nanjing. Crowdsourcing was done by using a questionnaire at the sample level (200 people). Questionnaires were distributed among experts, university centers, and conferences. According to the results of the research, the use of different methods of decision-making led to relatively the same results. In this way, with the use of all three multi-criteria decision-making methods mentioned above, the Prefabricated Concrete System (PRCS) was in the first rank, and the Light Steel Frame (LSF) system ranked second. Also, the Prefabricated Concrete System (PRCS), in terms of performance standards and economics, was ranked first, and the Light Steel Frame (LSF) system was allocated the first rank in terms of environmental standards.

Keywords: multi-criteria decision making, AHP, SAW, TOPSIS

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Authors: Humayun Murshed, Shibly Abdullah

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The research conducted in early seventies apparently assumed the existence of a universal decision model for union negotiators and furthermore tended to regard financial information as a ‘neutral’ input into a rational decision-making process. However, research in the eighties began to question the neutrality of financial information as an input in collective bargaining rather viewing it as a potentially effective means for controlling the labour force. Furthermore, this later research also started challenging the simplistic assumptions relating particularly to union objectives which have underpinned the earlier search for universal union decision models. Despite the above developments there seems to be a dearth of studies in developing countries concerning the use of financial information in collective bargaining. This paper seeks to begin to remedy this deficiency. Utilising a case study approach based on two enterprises, one in the public sector and the other a multinational, the universal decision model is rejected and it is argued that the decision whether or not to use financial information is a contingent one and such a contingency is largely defined by the context and environment in which both union and management negotiators work. An attempt is also made to identify the factors constraining as well as promoting the use of financial information in collective bargaining, these being regarded as unique to the organizations within which the case studies are conducted.

Keywords: collective bargaining, developing countries, disclosures, financial information

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Authors: Erfan Izadpanah

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The present study was conducted to investigate the effect of the Trans-Cranial Direct Current Stimulation (tDCS) on cognitive flexibility and social decision-making in skilled, semi-skilled and novice football players. The present quasi-experimental pretest-posttest study was conducted on 60 randomly-selected subjects divided into trial and placebo groups (n=30 per group). The trial group received three 20-minute sessions of anodic stimulation at the intensity of 2 mA. The placebo group also received three sessions of sham anodic stimulation. Data were collected using the Wisconsin, Grant and Berg Card-Sorting Test (1948) and the ultimatum game and were then analyzed using the ANCOVA. The results showed significant differences between the skilled, semi-skilled and novice football players in the trial and placebo groups in terms of cognitive flexibility and social decision-making (P<0.01). TDCS appears to be able to improve cognitive flexibility and consequently social decision-making in football players and is recommended to sport psychologists and coaches as a useful intervention to increase cognitive flexibility and improve social decision-making in players.

Keywords: TDCS, cognitive flexibility, social decision-making, skilled, semi-skilled and novice football players

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Authors: Mogale Sabone, Thabiso Ntlole

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The pressure from globalization has made manufacturing organizations to move towards three major competitive arenas: quality, cost, and responsiveness. Quality is a universal value and has become a global issue. In order to survive and be able to provide customers with good products, manufacturing organizations’ supporting systems, tools, and structures it uses must grow or evolve. The majority of quality management concepts and strategies that are practiced recently are aimed at detecting and correcting problems which already exist and serve to limit losses. In agile manufacturing environment there is no room for defect and error so it needs a quality management which is proactively directed at problem prevention. This proactive quality management avoids losses by focusing on failure prevention, virtual elimination of the possibility of premature failure, mistake-proofing, and assuring consistently high quality in the definition and design of creation processes. To achieve this, a decision support tool for quality control and management is suggested. Current decision support tools/methods used by most manufacturing companies in Botswana for quality management and control are not integrated, for example they are not consistent since some tests results data is recorded manually only whilst others are recorded electronically. It is only a set of procedures not a tool. These procedures cannot offer interactive decision support. This point brings to light the aim of this research which is to develop a framework which will help manufacturing companies in Botswana build a decision support tool for quality control and management.

Keywords: decision support tool, manufacturing, quality control, quality management

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Authors: Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman

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Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original data set. In this paper, a new design of distributed intrusion detection system using a combination feature selection model based on bees and decision tree. Bees algorithm is used as the search strategy to find the optimal subset of features, whereas decision tree is used as a judgment for the selected features. Both the produced features and the generated rules are used by Decision Making Mobile Agent to decide whether there is an attack or not in the networks. Decision Making Mobile Agent will migrate through the networks, moving from node to another, if it found that there is an attack on one of the nodes, it then alerts the user through User Interface Agent or takes some action through Action Mobile Agent. The KDD Cup 99 data set is used to test the effectiveness of the proposed system. The results show that even if only four features are used, the proposed system gives a better performance when it is compared with the obtained results using all 41 features.

Keywords: distributed intrusion detection system, mobile agent, feature selection, bees algorithm, decision tree

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Authors: Nikita Valluri, Vatcharaporn Esichaikul

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With the popularity of data-driven decision making on the rise, this study focuses on providing an alternative outlook towards the process of decision-making. Combining quantitative and qualitative methods rooted in the social sciences, an integrated framework is presented with a focus on delivering a much more robust and efficient approach towards the concept of data-driven decision-making with respect to not only Big data but also 'Thick data', a new form of qualitative data. In support of this, an example from the retail sector has been illustrated where the framework is put into action to yield insights and leverage business intelligence. An interpretive approach to analyze findings from both kinds of quantitative and qualitative data has been used to glean insights. Using traditional Point-of-sale data as well as an understanding of customer psychographics and preferences, techniques of data mining along with qualitative methods (such as grounded theory, ethnomethodology, etc.) are applied. This study’s final goal is to establish the framework as a basis for providing a holistic solution encompassing both the Big and Thick aspects of any business need. The proposed framework is a modified enhancement in lieu of traditional data-driven decision-making approach, which is mainly dependent on quantitative data for decision-making.

Keywords: big data, customer behavior, customer experience, data mining, qualitative methods, quantitative methods, thick data

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Authors: Ayham Fattoum, Simos Chari, Duncan Shaw

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Volatile, uncertain, complex, and ambiguous (VUCA) conditions threaten systems viability with emerging and novel events requiring immediate and localized responses. Such responsiveness is only possible through devolved freedom and emancipated decision-making. The Viable System Model (VSM) recognizes the need and suggests maximizing autonomy to localize decision-making and minimize residual complexity. However, exercising delegated autonomy in VUCA requires confidence and knowledge to use intuition and guidance to maintain systemic coherence. This paper explores the role of intuition as an enabler of emancipated decision-making and autonomy under VUCA. Intuition allows decision-makers to use their knowledge and experience to respond rapidly to novel events. This paper offers three contributions to VSM. First, it designs a system model that illustrates the role of intuitive decision-making in managing complexity and maintaining viability. Second, it takes a black-box approach to theory development in VSM to model the role of autonomy and intuition. Third, the study uses a multi-stage discovery-oriented approach (DOA) to develop theory, with each stage combining literature, data analysis, and model/theory development and identifying further questions for the subsequent stage. We synthesize literature (e.g., VSM, complexity management) with seven months of field-based insights (interviews, workshops, and observation of a live disaster exercise) to develop a framework of intuitive complexity management framework and VSM models. The results have practical implications for enhancing the resilience of organizations and communities.

Keywords: Intuition, complexity management, decision-making, viable system model

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Authors: Haikal Achmad Thaha, Pujo Laksono, Dhamma Nibbana Putra

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Customer service is one of the core interest in a service sector of a company, whether as the core business or as service part of the operation. Most of the time, the people and the previous research in service industry is focused on finding the best business model solution for the service sector, usually to decide between total in house customer service, outsourcing, or something in between. Conventionally, to take this decision is some important part of the management job, and this is a process that usually takes some time and staff effort, meanwhile market condition and overall company needs may change and cause loss of income and temporary disturbance in the companies operation . However, in this paper we have offer a new concept model to assist decision making process in service industry. This model will featured information platform as central tool to integrate service industry operation. The result is service information model which would ideally increase response time and effectivity of the decision making. it will also help service industry in switching the service solution system quickly through machine learning when the companies growth and the service solution needed are changing.

Keywords: service industry, customer service, machine learning, decision making, information platform

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Authors: Xueqing Ivy Chen, Christo De Coning

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It is clear from practice that a gap exists between Result-Based Monitoring and Evaluation (RBME) as a discipline, and Knowledge Management (KM) on the other hand. Whereas various government departments have institutionalised these functions, KM and M&E has functioned in isolation from each other in a practical sense in the public sector. It’s therefore necessary to explore the relationship between KM and M&E and the necessity for integration, so that a convergence of these disciplines can be established. An integration of KM and M&E will lead to integration and improvement of evidence-based information and policy decision-making. M&E and KM process models are available but the complementarity between specific process steps of these process models are not exploited. A need exists to clarify the relationships between these functions in order to ensure evidence based information and policy decision-making. This paper will depart from the well-known policy process models, such as the generic model and consider recent on the interface between policy, M&E and KM.

Keywords: result-based monitoring and evaluation, RBME, knowledge management, KM, evident based decision making, public policy, information systems, institutional arrangement

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Authors: Sumalee Tientongdee

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The pre-service teacher training program at Suan Sunandha Rajabhat University, Bankgok Thailand has been provided for undergraduate students for more than 80 years. It was established as the first teacher college in the country. The pre-service teacher program in science education is considered as one of the new training programs to prepare pre-service teacher to teach science in secondary school level. The need of program assessment is strongly important. Therefore, this study was conducted to gain the opinions and recommendations from the principals, in-service teachers, and mentoring teachers from the partnership schools of Bangkok. The invited 120 participants for the annual meeting was hold in May 2017. The focus group discussion and questionnaires were used to collect the data during the reflection session. The content analysis was used to analyze the qualitative data. The results showed that the pre-service teacher training program in science education should improve students’ creative thinking skill, service mind, personality, and attitudes toward teaching science career. Also, the future science teachers must be able to teach in English to have more opportunities to teach science in Southeast Asian countries.

Keywords: pre-service teacher training program, reflection, science education, Suan Sunandha Rajabhat university

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Authors: Osagiede Mercy Afe

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After the internet explosion in the 90’s, Technology was immediately integrated into the school system. Technology which symbolizes advancement in human knowledge was seen as a setback by many educators many efforts have been made to help stem this erroneous believes and help educators realize the benefits of technology and ways of implementing it in the classrooms especially in the sciences. This advancement created a constantly expanding gap between the pupil’s perception on the use of technology within the learning atmosphere and the teacher’s perception and limitations hence the focus of this paper is on the need to refocus on the potentials of Science and Technology in enhancing children learning at school especially in science for sustainable development in Nigeria. The paper recommended measures for facilitating the sustenance of science and technology in Nigerian schools so as to enhance the potentials of our children in Science and Technology for a better tomorrow.

Keywords: children, information communication technology (ICT), potentials, sustainable development, science education

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Authors: Ahmed M. El-Araby, Ibrahim Sabry, Ahmed El-Assal

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The relationships between the industrial facilities, the capacity available for these facilities, and the costs involved are the main factors in deciding the correct selection of a facility layout. In general, an issue of facility layout is considered to be an unstructured problem of decision-making. The objective of this work is to provide a survey that describes the techniques by which a facility planning problem can be solved and also the effect of these techniques on the efficiency of the layout. The multi-criteria decision making (MCDM) techniques can be classified according to the previous researches into three categories which are the use of single MCDM, combining two or more MCDM, and the integration of MCDM with another technique such as genetic algorithms (GA). This paper presents a review of different multi-criteria decision making (MCDM) techniques that have been proposed in the literature to pick the most suitable layout design. These methods are particularly suitable to deal with complex situations, including various criteria and conflicting goals which need to be optimized simultaneously.

Keywords: facility layout, MCDM, GA, literature review

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Authors: Swati Kishor Zode, Rahul Ambekar

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Data mining is the extraction of fascinating examples on the other hand information from enormous measure of information and choice is made as indicated by the applicable information extracted. As of late, with the dangerous advancement in internet, stockpiling of information and handling procedures, privacy preservation has been one of the major (higher) concerns in data mining. Various techniques and methods have been produced for protection saving data mining. In the situation of Clinical Decision Support System, the choice is to be made on the premise of the data separated from the remote servers by means of Internet to diagnose the patient. In this paper, the fundamental thought is to build the precision of Decision Support System for multiple diseases for different maladies and in addition protect persistent information while correspondence between Clinician side (Client side) also, the Server side. A privacy preserving protocol for clinical decision support network is proposed so that patients information dependably stay scrambled amid diagnose prepare by looking after the accuracy. To enhance the precision of Decision Support System for various malady C5.0 classifiers and to save security, a Homomorphism encryption algorithm Paillier cryptosystem is being utilized.

Keywords: classification, homomorphic encryption, clinical decision support, privacy

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Authors: A. Luli, A. Santona

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It is rare for individuals that are involved in a relationship to think about the possibility of having procreation problems in the near present or in the future. However, infertility is a condition that affects millions of people all around the world. Often, infertile individuals have to deal with experiences of psychological, relational and social problems. In these cases, they have to review their choices and take into consideration, if it is necessary, new ones. Different studies have examined the different decisions that infertile individuals have to go through dealing with infertility and its treatment, but none of them is focused on the decision-making style used by infertile individuals to solve their problem and on the factors that influences it. The aim of this paper is to define the style of decision-making used by infertile persons to give a solution to the ‘problem’ and the potential predictive role of the attachment and of the dyadic adjustment. The total sample is composed by 251 participants, divided in two groups: the experimental group composed by 114 participants, 62 males and 52 females, age between 25 and 59 years, and the control group composed by 137 participants, 65 males and 72 females, age between 22 and 49 years. The battery of instruments used is composed by: the General Decision Making Style (GDMS), the Experiences in Close Relationships Questionnaire Revised (ECR-R), Dyadic Adjustment Scale (DAS), and the Symptom Checklist-90-R (SCL-90-R). The results from the analysis of the samples showed a prevalence of the rational decision-making style for both males and females. No significant statistical difference was found between the experimental and control group. Also the analyses showed a significant statistical relationship between the decision making styles and the adult attachment styles for both males and females. In this case, only for males, there was a significant statistical difference between the experimental and the control group. Another significant statistical relationship was founded between the decision making styles and the adjustment scales for both males and females. Also in this case, the difference between the two groups was founded to be significant only of males. These results contribute to enrich the literature on the subject of decision-making styles in infertile individuals, showing also the predictive role of the attachment styles and the adjustment, confirming in this was the few results in the literature.

Keywords: adjustment, attachment, decision-making style, infertility

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Authors: Babak Nematshahrbabaki, Fallahi Arezoo

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Objective: The aim of this study was to describe teenagers’ decisions to undergo orthodontic treatment through a qualitative study. Materials and methods: Twenty-three patients (12 girls), aged 12–18 years, at a dental clinic in Sanandaj the western part of Iran participated. Face-to-face and semi-structured interviews and two focus group discussions were held to gather data. Data analyzed by the grounded theory method. Results: ‘Decision-making’ was the core category. During the data analysis four main themes were developed: ‘being like everyone else’, ‘being diagnosed’, ‘maintaining the mouth’ and ‘cultural-social and environmental factors’. Conclusions: cultural- social and environmental factors have crucial role in decision-making to undergo orthodontic treatment. The teenagers were not fully conscious of these external influences. They thought their decision to undergo orthodontic treatment is independent while it is related to cultural- social and environmental factors.

Keywords: decision-making, qualitative study, teenager, orthodontic treatment

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Authors: Panwasn Mahalawalert

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The objective of this study was to meta-evaluation of Master Degree theses in Science Program of Evaluation Methodology at Srinakharinwirot University, published during 2008-2011. This study was summative meta-evaluation that evaluated all theses of Master Degree in Science Program of Evaluation Methodology. Data were collected using the theses characteristics recording form and the evaluation meta-evaluation checklist. The collected data were analyzed by two parts: 1) Quantitative data were analyzed by descriptive statistics presented in frequency, percentages, mean, and standard deviation and 2) Qualitative data were analyzed by content analysis. The results of this study were found the theses characteristics was results revealed that most of theses were published in 2011. The largest group of theses researcher were female and were from the government office. The evaluation model of all theses were Decision-Oriented Evaluation Model. The objective of all theses were evaluate the project or curriculum. The most sampling technique were used the multistage random sampling technique. The most tool were used to gathering the data were questionnaires. All of the theses were analysed by descriptive statistics. The meta-evaluation results revealed that most of theses had fair on Utility Standards and Feasibility Standards, good on Propriety Standards and Accuracy Standards.

Keywords: meta-evaluation, evaluation, master degree theses, Srinakharinwirot University

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Authors: Saba Ebrahimi, Hedieh Ashrafi

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Predicting the cover type of forests is a challenge for natural resource managers. In this project, we aim to perform a comprehensive comparative study of two well-known classification methods, support vector machine (SVM) and decision tree (DT). The comparison is first performed among different types of each classifier, and then the best of each classifier will be compared by considering different evaluation metrics. The effect of boosting and bagging for decision trees is also explored. Furthermore, the effect of principal component analysis (PCA) and feature selection is also investigated. During the project, the forest cover-type dataset from the remote sensing and GIS program is used in all computations.

Keywords: classification methods, support vector machine, decision tree, forest cover-type dataset

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Authors: Ismail El Gayar, Ossama Ismail, Yousri El Gamal

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This paper presented a technique to solve one of the transportation problems that faces us in real life which is the Bus Scheduling Problem. Most of the countries using buses in schools, companies and traveling offices as an example to transfer multiple passengers from many places to specific place and vice versa. This transferring process can cost time and money, so we build a decision support system that can solve this problem. In this paper, a genetic algorithm with the shortest path technique is used to generate a competitive solution to other well-known techniques. It also presents a comparison between our solution and other solutions for this problem.

Keywords: bus scheduling problem, decision support system, genetic algorithm, shortest path

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Authors: Sabyasachi Biswas, Indrajit Ghosh

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The random and heterogeneous behavior of vehicles in India puts up a greater challenge for researchers. Stop-and-go modeling at signalized intersections under heterogeneous traffic conditions has remained one of the most sought-after fields. Vehicles are often caught up in the dilemma zone and are unable to take quick decisions whether to stop or cross the intersection. This hampers the traffic movement and may lead to accidents. The purpose of this work is to develop a stop and go prediction model that depicts the drivers’ decision during the yellow time at signalised intersections. To accomplish this, certain traffic parameters were taken into account to develop surrogate model. This research investigated the Stop and Go behavior of the drivers by collecting data from 4-signalized intersections located in two major Indian cities. Model was developed to predict the drivers’ decision making during the yellow phase of the traffic signal. The parameters used for modeling included distance to stop line, time to stop line, speed, and length of the vehicle. A Kriging base surrogate model has been developed to investigate the drivers’ decision-making behavior in amber phase. It is observed that the proposed approach yields a highly accurate result (97.4 percent) by Gaussian function. It was observed that the accuracy for the crossing probability was 95.45, 90.9 and 86.36.11 percent respectively as predicted by the Kriging models with Gaussian, Exponential and Linear functions.

Keywords: decision-making decision, dilemma zone, surrogate model, Kriging

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Authors: Yousef Abu Nahleh, Arun Kumar, Fugen Daver, Reham Al-Hindawi

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Despite the availability of natural disaster related time series data for last 110 years, there is no forecasting tool available to humanitarian relief organizations to determine forecasts for emergency logistics planning. This study develops a forecasting tool based on identifying probability of disaster for each country in the world by using decision tree modeling. Further, the determination of aggregate forecasts leads to efficient pre-disaster planning. Based on the research findings, the relief agencies can optimize the various resources allocation in emergency logistics planning.

Keywords: decision tree modeling, forecasting, humanitarian relief, emergency supply chain

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Authors: Pachawo Bisani, Goodall Nyirenda

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The Malawi National Antiretroviral Therapy (NART) Electronic Medical Record (EMR) system was designed and developed with guidance from the Ministry of Health through the Department of HIV and AIDS (DHA) with the aim of supporting the management of HIV patient data and reporting in high prevalence ART clinics. As of 2021, the system has been scaled up to over 206 facilities across the country. The system is integrated with the clinical decision support system (CDSS) to assist healthcare providers in making a decision about an individual patient at a particular point in time. Despite NART EMR undergoing several evaluations and assessments, little has been done to evaluate the clinical decision support system in the NART EMR system. Hence, the study aimed to evaluate the use of CDSS in the NART EMR system in Malawi. The study adopted a mixed-method approach, and data was collected through interviews, observations, and questionnaires. The study has revealed that the CDSS tools were integrated into the ART clinic workflow, making it easy for the user to use it. The study has also revealed challenges in system reliability and information accuracy. Despite the challenges, the study further revealed that the system is effective and efficient, and overall, users are satisfied with the system. The study recommends that the implementers focus more on the logic behind the clinical decision-support intervention in order to address some of the concerns and enhance the accuracy of the information supplied. The study further suggests consulting the system's actual users throughout implementation.

Keywords: clinical decision support system, electronic medical record system, usability, antiretroviral therapy

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Authors: Isaias Teklia Berhe

Abstract:

A decision that deals with international disputes, be it arbitral or judicial, has to properly reflect objectivity and coherence with existing rules of international law. This paper shows the decision of the Ethio-Eritrea Claims Commission on the jus ad bellum case is bereft of objectivity and coherence, which contributed a disservice to international law on many aspects. The Commission’s decision that holds Eritrea in contravention to Art 2(4) of the UN Charter based on Ethiopia’s contention is flawed. It fails to consider: the illegitimacy of an actual authority established over contested territory through hostile acts, the proper determination of effectivites under international law, the sanctity of colonially determined boundaries, Ethiopia’s prior firm political recognition and undergirds to respect colonial boundary, and Ethio-Eritrea Border Commission’s decision. The paper will also argue that the Commission confused Eritrea’s right of self-defense with the rule against the non-use of force to settle territorial disputes; wherefore its decision sanitizes or sterilizes unlawful change of territory resulted through unlawful use of force to the effect of advantaging aggressions. The paper likewise argues that the decision is so sacrilegious that it disregards the ossified legal finality of colonial boundaries. Moreover, its approach toward armed attack does not reflect the peculiarity of the jus ad bellum case rather it brings about definitional uncertainties and sustains the perception that the law on self-defense is unsettled.

Keywords: armed attack, Eritrea, Ethiopia, self-defense, territorial integrity, use of force

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Authors: Asifa Amara, Prachi Singh, Kanishka, Debargho Pathak, Akshat Kumar, Jayakumar Eravelly

Abstract:

Today's generation is totally dependent on technology that uses data as its fuel. The present study is all about innovations and developments in data science and gives an idea about how efficiently to use the data provided. This study will help to understand the core concepts of data science. The concept of artificial intelligence was introduced by Alan Turing in which the main principle was to create an artificial system that can run independently of human-given programs and can function with the help of analyzing data to understand the requirements of the users. Data science comprises business understanding, analyzing data, ethical concerns, understanding programming languages, various fields and sources of data, skills, etc. The usage of data science has evolved over the years. In this review article, we have covered a part of data science, i.e., machine learning. Machine learning uses data science for its work. Machines learn through their experience, which helps them to do any work more efficiently. This article includes a comparative study image between human understanding and machine understanding, advantages, applications, and real-time examples of machine learning. Data science is an important game changer in the life of human beings. Since the advent of data science, we have found its benefits and how it leads to a better understanding of people, and how it cherishes individual needs. It has improved business strategies, services provided by them, forecasting, the ability to attend sustainable developments, etc. This study also focuses on a better understanding of data science which will help us to create a better world.

Keywords: data science, machine learning, data analytics, artificial intelligence

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Authors: Sayor Ajfar Aaron, Sajjat Hossain Abir, Ashif Newaz, Mushfiqur Rahman

Abstract:

Investigating the convergence of blockchain technology and artificial intelligence, this paper examines their synergistic effects on data integrity and decision-making within smart contracts. By implementing AI-driven analytics on blockchain-based platforms, the research identifies improvements in automated contract enforcement and decision accuracy. The paper presents a framework that leverages AI to enhance transparency and trust while blockchain ensures immutable record-keeping, culminating in significantly optimized operational efficiencies in various industries.

Keywords: artificial intelligence, blockchain, data integrity, smart contracts

Procedia PDF Downloads 14