Search results for: decision to export

Authors: Soha A. Bahanshal, Vaibhav Verdhan, Bayong Kim

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Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time.

Keywords: prediction, decision trees, machine learning, support vector machine, ensemble model, student graduation, GOT graduate on time

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Authors: Cyrillus Prabandana

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Supply chain optimisation requires multiple factors as consideration or constraints. These factors are including but not limited to demand forecasting, raw material fulfilment, production capacity, inventory level, facilities locations, transportation means, and manpower availability. By knowing all manageable factors involved and assuming the uncertainty with pre-defined percentage factors, an integrated supply chain model could be developed to manage various business scenarios. This paper analyse the utilisation of geographical point of view to develop an integrated supply chain network model to optimise the distribution of finished product appropriately according to forecasted demand and available supply. The supply chain optimisation model shows that small change in one supply chain constraint is possible to largely impact other constraints, and the new information from the model should be able to support the decision making process. The model was focused on three areas, i.e. raw material fulfilment, production capacity and finished products transportation. To validate the model suitability, it was implemented in a project aimed to optimise the concrete supply chain in a mining location. The high level of operations complexity and involvement of multiple stakeholders in the concrete supply chain is believed to be sufficient to give the illustration of the larger scope. The implementation of this geographical supply chain network modeling resulted an optimised concrete supply chain from raw material fulfilment until finished products distribution to each customer, which indicated by lower percentage of missed concrete order fulfilment to customer.

Keywords: decision making, geographical supply chain modeling, supply chain optimisation, supply chain

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Authors: Sıdıka Kaya, Gülay Sain Güven, Seda Karsavuran, Onur Toka

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This study is part of an ongoing research project supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Project Number 114K404, and participation to this conference was supported by Hacettepe University Scientific Research Coordination Unit under Project Number 10243. Evaluation of hospital readmissions is gaining importance in terms of quality and cost, and is becoming the target of national policies. In Turkey, the topic of hospital readmission is relatively new on agenda and very few studies have been conducted on this topic. The aim of this study was to determine 30-day readmission rates and risk factors for readmission. Whether readmission was planned, related to the prior admission and avoidable or not was also assessed. The study was designed as a ‘prospective cohort study.’ 472 patients hospitalized in internal medicine departments of a university hospital in Turkey between February 1, 2015 and April 30, 2015 were followed up. Analyses were conducted using IBM SPSS Statistics version 22.0 and SPSS Modeler 16.0. Average age of the patients was 56 and 56% of the patients were female. Among these patients 95 were readmitted. Overall readmission rate was calculated as 20% (95/472). However, only 31 readmissions were unplanned. Unplanned readmission rate was 6.5% (31/472). Out of 31 unplanned readmission, 24 was related to the prior admission. Only 6 related readmission was avoidable. To determine risk factors for readmission we constructed Chi-square automatic interaction detector (CHAID) decision tree algorithm. CHAID decision trees are nonparametric procedures that make no assumptions of the underlying data. This algorithm determines how independent variables best combine to predict a binary outcome based on ‘if-then’ logic by portioning each independent variable into mutually exclusive subsets based on homogeneity of the data. Independent variables we included in the analysis were: clinic of the department, occupied beds/total number of beds in the clinic at the time of discharge, age, gender, marital status, educational level, distance to residence (km), number of people living with the patient, any person to help his/her care at home after discharge (yes/no), regular source (physician) of care (yes/no), day of discharge, length of stay, ICU utilization (yes/no), total comorbidity score, means for each 3 dimensions of Readiness for Hospital Discharge Scale (patient’s personal status, patient’s knowledge, and patient’s coping ability) and number of daycare admissions within 30 days of discharge. In the analysis, we included all 95 readmitted patients (46.12%), but only 111 (53.88%) non-readmitted patients, although we had 377 non-readmitted patients, to balance data. The risk factors for readmission were found as total comorbidity score, gender, patient’s coping ability, and patient’s knowledge. The strongest identifying factor for readmission was comorbidity score. If patients’ comorbidity score was higher than 1, the risk for readmission increased. The results of this study needs to be validated by other data–sets with more patients. However, we believe that this study will guide further studies of readmission and CHAID is a useful tool for identifying risk factors for readmission.

Keywords: decision tree, hospital, internal medicine, readmission

Procedia PDF Downloads 256

Authors: Ju Young Kim, Mohammad Zahirul Islam, Mahmuda Akter Mele, Su Jeong Han, Hyuk Sung Yoon, In-Lee Choi, Ho-Min Kang

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Strawberry and tomato fruits were harvested at the red ripens maturity stage in the Republic of Korea. The fruits were dipped in fungi solution and afterwards were sterilized with sodium hypochlorite (NaOCl) and chlorine dioxide (ClO2) gas. Some fruits were dipped in 150μL/L NaOCl solution for 10 minutes, and others were treated with 5μL/L ClO2 gas for 12 hours and packed with 20,000 cc OTR (oxygen transmission rate) film, the rest were packed in 10,000 cc OTR film inserted with 5μL/L ClO2 gas. 5μL/L ClO2 gas insert treatment showed the lowest carbon dioxide and ethylene, and the highest oxygen concentration was on the final storage day (15th day) in both strawberry and tomato fruits. Tomato fruits showed the lowest fresh weight loss in 5μL/L ClO2 gas insert treatment. The visual quality as well as shelf life showed the highest in 5μL/L ClO2 gas insert treatment of both strawberry and tomato fruits. In addition, the fungal incidence of strawberry and tomato fruits were the most suppressed in 5μL/L ClO2 gas insert treatment. 5μL/L ClO2 gas insert treatment showed higher firmness and soluble solids in both strawberry and tomato fruits. So, 5μL/L ClO2 gas insert treatment may be useful to prevent the fungal incidence as well as retaining the postharvest quality, and increase the shelf life of strawberry and tomato fruits for long term storage. This study was supported by Export Promotion Technology Development Program (314027-03), IPET, Ministry of Agriculture, Food and Rural Affairs, Republic of Korea.

Keywords: chlorine dioxide, ethylene, fungi, sodium hypochlorite

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Authors: Nadama Kumar, P. Parthiban, T. Niranjan

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In the present paper, we propose an incorporated approach for 3PL supplier choice that suits the distinctive strategic needs of the outsourcing organization in southern part of India. Four fundamental criteria have been used in particular Performance, IT, Service and Intangible. These are additionally subdivided into fifteen sub-criteria. The proposed strategy coordinates Structural Equation Modeling (SEM) and Non-additive Fuzzy Integral strategies. The presentation of fluffiness manages the unclearness of human judgments. The SEM approach has been used to approve the determination criteria for the proposed show though the Non-additive Fuzzy Integral approach uses the SEM display contribution to assess a supplier choice score. The case organization has a exclusive vertically integrated assembly that comprises of several companies focusing on a slight array of the value chain. To confirm manufacturing and logistics proficiency, it significantly relies on 3PL suppliers to attain supply chain superiority. However, 3PL supplier selection is an intricate decision-making procedure relating multiple selection criteria. The goal of this work is to recognize the crucial 3PL selection criteria by using the non-additive fuzzy integral approach. Unlike the outmoded multi criterion decision-making (MCDM) methods which frequently undertake independence among criteria and additive importance weights, the nonadditive fuzzy integral is an effective method to resolve the dependency among criteria, vague information, and vital fuzziness of human judgment. In this work, we validate an empirical case that engages the nonadditive fuzzy integral to assess the importance weight of selection criteria and indicate the most suitable 3PL supplier.

Keywords: 3PL, non-additive fuzzy integral approach, SEM, fuzzy

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Authors: Richa Grover, Rahul Grover, V. Balaji Rao, Kavish Kejriwal

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Selection of suppliers is a crucial problem in the supply chain management. On top of that, sustainable supplier selection is the biggest challenge for the organizations. Environment protection and social problems have been of concern to society in recent years, and the traditional supplier selection does not consider about this factor; therefore, this research work focuses on introducing sustainable criteria into the structure of supplier selection criteria. Sustainable Supply Chain Management (SSCM) is the management and administration of material, information, and money flows, as well as coordination among business along the supply chain. All three dimensions - economic, environmental, and social - of sustainable development needs to be taken care of. Purpose of this research is to maximize supply chain profitability, maximize social wellbeing of supply chain and minimize environmental impacts. Problem statement is selection of suppliers in a sustainable supply chain network by ranking the suppliers against sustainable criteria identified. The aim of this research is twofold: To find out what are the sustainable parameters that can be applied to the supply chain, and to determine how these parameters can effectively be used in supplier selection. Multicriteria decision making tools will be used to rank both criteria and suppliers. AHP Analysis will be used to find out ratings for the criteria identified. It is a technique used for efficient decision making. TOPSIS will be used to find out rating for suppliers and then ranking them. TOPSIS is a MCDM problem solving method which is based on the principle that the chosen option should have the maximum distance from the negative ideal solution (NIS) and the minimum distance from the ideal solution.

Keywords: sustainable supply chain management, sustainable criteria, MCDM tools, AHP analysis, TOPSIS method

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Authors: Jean Berger, Nassirou Lo, Martin Noel

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Perfectly suited for natural or man-made emergency and disaster management situations such as flood, earthquakes, tornadoes, or tsunami, multi-target search path planning for a team of rescue agents is known to be computationally hard, and most techniques developed so far come short to successfully estimate optimality gap. A novel mixed-integer linear programming (MIP) formulation is proposed to optimally solve the multi-target multi-agent discrete search and rescue (SAR) path planning problem. Aimed at maximizing cumulative probability of successful target detection, it captures anticipated feedback information associated with possible observation outcomes resulting from projected path execution, while modeling agent discrete actions over all possible moving directions. Problem modeling further takes advantage of network representation to encompass decision variables, expedite compact constraint specification, and lead to substantial problem-solving speed-up. The proposed MIP approach uses CPLEX optimization machinery, efficiently computing near-optimal solutions for practical size problems, while giving a robust upper bound obtained from Lagrangean integrality constraint relaxation. Should eventually a target be positively detected during plan execution, a new problem instance would simply be reformulated from the current state, and then solved over the next decision cycle. A computational experiment shows the feasibility and the value of the proposed approach.

Keywords: search path planning, search and rescue, multi-agent, mixed-integer linear programming, optimization

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Authors: P. Larpsomboonchai

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Agro-industry is one of major industries that has strong impacts on national economic incomes, growth, stability, and sustainable development. Moreover, this industry also has strong influences on social, cultural and political issues. Furthermore, this industry, as producing primary and secondary products, is facing challenges from such diverse factors such as demand inconsistency, intense international competition, technological advancements and new competitors. In order to maintain and to improve industry’s competitiveness in both domestics and international markets, science and technology are key factors. Besides hard sciences and technologies, modern industrial engineering concepts such as Just in Time (JIT) Total Quality Management (TQM), Quick Response (QR), Supply Chain Management (SCM) and Lean can be very effective to supportant to increase efficiency and effectiveness of these agricultural products on world stage. Onion is one of Thailand’s major export products which brings back national incomes. But, it also facing challenges in many ways. This paper focused its interests in onion packing process and its related activities such as storage and shipment from one of major packing plant and storage in Mae Wang District, Chiang Mai, Thailand, by applying Toyota Production System (TPS) or Lean concepts, to improve process capability throughout the entire packing and distribution process which will be profitable for the whole onion supply chain. And it will be beneficial to other related agricultural products in Thailand and other ASEAN countries.

Keywords: packing process, Toyota Production System (TPS), lean concepts, waste reduction, lean in agro-industries activities

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Authors: Usman Jilani, Ibad Khurram, Irshad Hussain

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Environmentally sustainable waste management is a challenging task as it involves multiple and diverse economic, environmental, technical and regulatory issues. Municipal Solid Waste Management (MSWM) is more challenging in developing countries like Pakistan due to lack of awareness, technology and human resources, insufficient funding, inefficient collection and transport mechanism resulting in the lack of a comprehensive waste management system. This work presents an overview of current MSWM practices in Peshawar, the provincial capital of Khyber Pakhtunkhwa, Pakistan and proposes a better and sustainable integrated solid waste management system with incineration (Waste to Energy) option. The diverted waste would otherwise generate revenue; minimize land fill requirement and negative impact on the environment. The proposed optimized solution utilizing scientific techniques (like mathematical modeling, optimization algorithms and GIS) as decision support tools enhances the technical & institutional efficiency leading towards a more sustainable waste management system through incorporating: - Improved collection mechanisms through optimized transportation / routing and, - Resource recovery through incineration and selection of most feasible sites for transfer stations, landfills and incineration plant. These proposed methods shift the linear waste management system towards a cyclic system and can also be used as a decision support tool by the WSSP (Water and Sanitation Services Peshawar), agency responsible for the MSWM in Peshawar.

Keywords: municipal solid waste management, incineration, mathematical modeling, optimization, GIS, Peshawar

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Authors: ChunYi Peng, Wei Hsuan CHeng, Shyh Kuang Ueng

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: Hyeon-Seung Kim, Sang-Mi Park, Sun-Ju Han, Leen-Seok Kang

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As BIM (Building Information Modeling) application continually expands, the visual simulation techniques used for facility design and construction process information are becoming increasingly advanced and diverse. For building structures, BIM application is design - oriented to utilize 3D objects for conflict management, whereas for civil engineering structures, the usability of nD object - oriented construction stage simulation is important in construction management. Simulations of 5D and 6D objects, for which cost and resources are linked along with process simulation in 4D objects, are commonly used, but they do not provide a decision - making function for process management problems that occur on site because they mostly focus on the visual representation of current status for process information. In this study, an nD CAD system is constructed that facilitates an optimized schedule simulation that minimizes process conflict, a construction duration reduction simulation according to execution progress status, optimized process plan simulation according to project cost change by year, and optimized resource simulation for field resource mobilization capability. Through this system, the usability of conventional simple simulation objects is expanded to the usability of active simulation objects with which decision - making is possible. Furthermore, to close the gap between field process situations and planned 4D process objects, a technique is developed to facilitate a comparative simulation through the coordinated synchronization of an actual video object acquired by an on - site web camera and VR concept 4D object. This synchronization and simulation technique can also be applied to smartphone video objects captured in the field in order to increase the usability of the 4D object. Because yearly project costs change frequently for civil engineering construction, an annual process plan should be recomposed appropriately according to project cost decreases/increases compared with the plan. In the 5D CAD system provided in this study, an active 5D object utilization concept is introduced to perform a simulation in an optimized process planning state by finding a process optimized for the changed project cost without changing the construction duration through a technique such as genetic algorithm. Furthermore, in resource management, an active 6D object utilization function is introduced that can analyze and simulate an optimized process plan within a possible scope of moving resources by considering those resources that can be moved under a given field condition, instead of using a simple resource change simulation by schedule. The introduction of an active BIM function is expected to increase the field utilization of conventional nD objects.

Keywords: 4D, 5D, 6D, active BIM

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Authors: Mónica Arroyo

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South America shows increasing decline in regional export values in the last years, after a strong growth of trade flows especially with China up to 2013. This change is due to the end of the commodity price boom, the slowing of the Chinese economy and the effects of the 2008 economic crisis. This paper examines the integration of regional economies in this context, particularly the situation in Brazil and Argentina. Based on transformations over the last two decades, the analysis is focused on the spatial circuits of production linked to foreign markets, contributing to the understanding of the different uses of territory and the within-country inequality. The South American regional exports, consisting basically of raw materials, are concentrated in a few companies. Large areas are intended for the production of agriculture and mining commodities, under the command of major economic groups, both domestic and foreign, relegating the local population to less productive places or, in most cases, forcing them to change their activity and to migrate to other regions in search of some source of income. On the other hand, the dynamics of these commodities’ spatial circuits of production print requirements in territories in terms of infrastructure and regulation. Capturing this movement requires understanding businesses and government’s role in territorial regulation, and consequently how regional systems are changing – for instance, economic specialisation, growing role of services, investment in roads, railways, ports, and airports. This paper aims to highlight topics for discussion on regional economic dynamics and their different degrees of internationalisation. The intention is to contribute to the debate about the relations between trade, globalization, and development.

Keywords: regional inequality, international trade, developing world, South America

Procedia PDF Downloads 260

Authors: Mario Spremic

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Despite the financial crisis and ongoing need for cost cutting, companies all around the world heavily invest in information systems (IS) and underlying information technology (IT). Information systems (IS) play very important role in modern business organizations supporting its organizational efficiency or, under certain circumstances, fostering business model innovation and change. IS can influence organization competitiveness in two ways: supporting operational efficiency (IS as a main infrastructure for the current business), or differentiating business through business model innovation and business process change. In either way, IS becomes very important to the business and needs to be aligned with strategic objectives in order to justify massive investments. A number of studies showed that investments in IS and underlying IT resulted in added business value if they are truly connected with strategic business objectives. In that sense proliferation of governance of enterprise IT helps companies manage, or rather, governs IS as a primary business function with executive management involved in making a decision about IS and IT. The quality of IT governance is rising with the large number of decisions about IS made by executive management, not IT departments. The more executive management is engaged in making a decision about IS and IT, the IT governance is of better quality. In this paper, the practice of governing the enterprise IT will be investigated on a sample of the largest 100 Croatian companies. Research questions posed here will reveal if there are some formal IT governance mechanisms, are there any differences in perceived role of IS and IT between CIOs (Chief Information Officers) and CEOs (Chief Executive Officers) of the sampled companies and what are the mechanisms to govern massive investment in enterprise IT.

Keywords: IT governance, governance of enterprise IT, information system auditing, operational efficiency

Procedia PDF Downloads 304

Authors: Wittaya Apai, Satippong Rattanakam, Suttinee Likhittragulrung, Nuttanai Tungmunkongvorakul, Sompetch Jaroensuk

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The objective of this study was to find some alternative ways to decrease sulfur dioxide (SO₂) residue problem and prolong storage life in fresh longan for export. Office of Agricultural Research and Development Region 1, Chiang Mai province conducted the research and development from 2016-2018. A grade longan cv. Daw fruit with panicle attached was placed in 11.5 kg commercial perforated plastic basket. They had 5 selected treatments comprising of 3 baskets as replication for each treatment, i.e. 1.5% SO₂ fumigation prior to insert SO₂-generated pads (Uvasys®) (1.5% SO₂+SO₂ pad), dipping in 5% hydrochloric acid (HCl) mixed with 1% sodium metabisulfite (SMS) for 5 min (5% HCl +1% SMS), ozone (O₃) fumigation for 1 hours (h) prior to 1.5% SO₂ fumigation (O₃ 1 h+1.5% SO₂), 1.5% SO₂ fumigation prior to O₃ fumigation for 1 h (1.5% SO₂+O₃ 1 h) and 1.5% SO₂ fumigation alone as commercial treatment (1.5% SO₂). They were stored at 5 ˚C, 90% relative humidity (RH) for 40-80 days. The results found that the possible treatments were 1.5% SO₂+O₃ 1 h and 5% HCl +1% SMS respectively and prevented pericarp browning for 80 days at 5 ºC. There were no significant changes in some parameters in any treatments; 1.5% SO₂+O₃ 1 h and 1.5% SO₂ during storage, i.e., pericarp browning, flesh discoloration, disease incidence (%) and sensory evaluation during storage. Application 1.5% SO₂+O₃ 1 h had a tendency less both SO₂ residue in fruit and disease incidence (%) including brighter pericarp color as compared with commercial 1.5% SO₂ alone. Moreover, HCl 5%+SMS 1% showed the least SO₂ residue in whole fruit below codex tolerance at 50 mg/kg throughout period of time. The fruit treated with 1.5% SO₂+O₃ 1 h, 1.5% SO₂, 5% HCl+1% SMS, O₃ 1 h+1.5% SO₂, and 1.5% SO₂+SO₂ pad could prolong storage life for 40, 40, 40, 30 and 30 days respectively at 5°C, 90% RH. Thus, application 1.5% SO₂+O₃ 1 h and/or 5% HCl +1% SMS could be used for extending shelf life fresh longan exported to restricted countries due to less SO₂ residue and fruit quality was maintained as compared with the conventional method.

Keywords: longan, sulfur dioxide, ozone fumigation, sodium metabisulfite

Procedia PDF Downloads 126

Authors: Peng Chun-Yi, Chen Wei-Hsuan, Ueng Shyh-Kuang

Abstract:

This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: Eda Orhun

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Purpose: This paper analyzes a target firm’s decision to voluntarily disclose information during a takeover event and the effect of such disclosures on the outcome of the takeover. Such voluntary disclosures especially in the form of earnings forecasts made around takeover events may affect shareholders’ decisions about the target firm’s value and in return takeover result. This study aims to shed light on this question. Design/methodology/approach: The paper tries to understand the role of voluntary disclosures by target firms during a takeover event in the likelihood of takeover success both theoretically and empirically. A game-theoretical model is set up to analyze the voluntary disclosure decision of a target firm to inform the shareholders about its real worth. The empirical implication of model is tested by employing binary outcome models where the disclosure variable is obtained by identifying the target firms in the sample that provide positive news by issuing increasing management earnings forecasts. Findings: The model predicts that a voluntary disclosure of positive information by the target decreases the likelihood that the takeover succeeds. The empirical analysis confirms this prediction by showing that positive earnings forecasts by target firms during takeover events increase the probability of takeover failure. Overall, it is shown that information dissemination through voluntary disclosures by target firms is an important factor affecting takeover outcomes. Originality/Value: This study is the first to the author's knowledge that studies the impact of voluntary disclosures by the target firm during a takeover event on the likelihood of takeover success. The results contribute to information economics, corporate finance and M&As literatures.

Keywords: takeovers, target firm, voluntary disclosures, earnings forecasts, takeover success

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Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

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Authors: R. Tariq, R. Lee

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Background: Additional demands placed on senior clinical teams with ongoing COVID-19 management has accelerated the need to harness the wider healthcare professional resources and upskill them to take on greater clinical responsibility safely. The UK NHS Long Term Plan (2019)¹ emphasises the importance of expanding Advanced Practitioners’ (APs) roles to take on more clinical diagnostic responsibilities to cope with increased demand. In acute settings, APs are often the first point of care for patients and require training to take on initial triage responsibilities efficiently and safely. Critically, their roles include determining which onward services the patients may require, and assessing whether they can be treated at home, avoiding unnecessary admissions to the hospital. Dem Dx is a Clinical Reasoning Platform (CRP) that claims to help frontline healthcare professionals independently assess and triage patients. It guides the clinician from presenting complaints through associated symptoms to a running list of differential diagnoses, media, national and institutional guidelines. The objective of this study was to compare the clinical referral rates and guidelines adherence registered by the HMR Urgent Community Care Team (UCCT)² and Dem Dx recommendations using retrospective cases. Methodology: 192 cases seen by the UCCT were anonymised and reassessed using Dem Dx clinical pathways. We compared the UCCT’s performance with Dem Dx regarding the appropriateness of onward referrals. We also compared the clinical assessment regarding adherence to NICE guidelines recorded on the clinical notes and the presence of suitable guidance in each case. The cases were audited by two medical doctors. Results: Dem Dx demonstrated appropriate referrals in 85% of cases, compared to 47% in the UCCT team (p<0.001). Of particular note, Dem Dx demonstrated an almost 65% (p<0.001) improvement in the efficacy and appropriateness of referrals in a highly experienced clinical team. The effectiveness of Dem Dx is in part attributable to the relevant NICE and local guidelines found within the platform's pathways and was found to be suitable in 86% of cases. Conclusion: This study highlights the potential of clinical decision support, as Dem Dx, to improve the quality of onward clinical referrals delivered by a multidisciplinary team in primary care. It demonstrated that it could support healthcare professionals in making appropriate referrals, especially those that may be overlooked by providing suitable clinical guidelines directly embedded into cases and clear referral pathways. Further evaluation in the clinical setting has been planned to confirm those assumptions in a prospective study.

Keywords: advanced practitioner, clinical reasoning, clinical decision-making, management, multidisciplinary team, referrals, triage

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Authors: Erine van Niekerk, Jason Holland

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In Māori culture water is considered to be the foundation of all life and has its own mana (spiritual power) and mauri (life force). Water classification for cultural values therefore includes categories like waitapu (sacred water), waimanawa-whenua (water from under the land), waipuna (freshwater springs), the relationship between water quantity and quality and the relationship between surface and groundwater. Particular rivers and lakes have special significance to iwi and hapu for their rohe (tribal areas). The Mararoa River, including its freshwater springs and wetlands, is an example of such an area. There is currently little information available about the sources, characteristics and behavior of these important water resources and this study on the water quality of the Mararoa River and adjacent freshwater springs will provide valuable information to be used in informed decisions about water management. The regional council of Southland, Environment Southland, is required to make changes under their water quality policy in order to comply with the requirements for the New National Standards for Freshwater to consult with Maori to determine strategies for decision making. This requires an approach that includes traditional knowledge combined with scientific knowledge in the decision-making process. This study provided the scientific data that can be used in future for decision making on fresh water springs combined with traditional values for this particular area. Several parameters have been tested in situ as well as in a laboratory. Parameters such as temperature, salinity, electrical conductivity, Total Dissolved Solids, Total Kjeldahl Nitrogen, Total Phosphorus, Total Suspended Solids, and Escherichia coli among others show that recorded values of all test parameters fall within recommended ANZECC guidelines and Environment Southland standards and do not raise any concerns for the water quality of the springs and the river at the moment. However, the destruction of natural areas, particularly due to changes in farming practices, and the changes to water quality by the introduction of Didymosphenia geminate (Didymo) means Māori have already lost many of their traditional mahinga kai (food sources). There is a major change from land use such as sheep farming to dairying in Southland which puts freshwater resources under pressure. It is, therefore, important to draw on traditional knowledge and spirituality alongside scientific knowledge to protect the waters of the Mararoa River and waipuna. This study hopes to contribute to scientific knowledge to preserve the cultural values of these significant waters.

Keywords: cultural values, freshwater springs, Maori, water quality

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Authors: Godian Asami Mabindah

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The convergence of knowledge management (KM) and competitive intelligence (CI) has gained significant attention in recent years as organizations seek to enhance their competitive advantage in an increasingly complex and dynamic business environment. This research study aims to explore and understand the synergistic relationship between KM and CI and its impact on organizational performance. By investigating how the integration of KM and CI practices can contribute to decision-making, innovation, and competitive advantage, this study seeks to unlock the potential benefits and challenges associated with this integration. The research employs a mixed-methods approach to gather comprehensive data. A quantitative analysis is conducted using survey data collected from a diverse sample of organizations across different industries. The survey measures the extent of integration between KM and CI practices and examines the perceived benefits and challenges associated with this integration. Additionally, qualitative interviews are conducted with key organizational stakeholders to gain deeper insights into their experiences, perspectives, and best practices regarding the synergistic relationship. The findings of this study are expected to reveal several significant outcomes. Firstly, it is anticipated that organizations that effectively integrate KM and CI practices will outperform those that treat them as independent functions. The study aims to highlight the positive impact of this integration on decision-making, innovation, organizational learning, and competitive advantage. Furthermore, the research aims to identify critical success factors and enablers for achieving constructive interaction between KM and CI, such as leadership support, culture, technology infrastructure, and knowledge-sharing mechanisms. The implications of this research are far-reaching. Organizations can leverage the findings to develop strategies and practices that facilitate the integration of KM and CI, leading to enhanced competitive intelligence capabilities and improved knowledge management processes. Additionally, the research contributes to the academic literature by providing a comprehensive understanding of the synergistic relationship between KM and CI and proposing a conceptual framework that can guide future research in this area. By exploring the synergies between KM and CI, this study seeks to help organizations harness their collective power to gain a competitive edge in today's dynamic business landscape. The research provides practical insights and guidelines for organizations to effectively integrate KM and CI practices, leading to improved decision-making, innovation, and overall organizational performance.

Keywords: Competitive Intelligence, Knowledge Management, Organizational Performance, Incusivity, Optimum Performance

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Authors: Gulnaz Erkomaishvili

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Introduction: The pandemic and the current economic crisis have created problems for entrepreneurship and, therefore for start-up entrepreneurs. The paper presents the challenges of start-up entrepreneurs in Georgia in the time of pandemic and the analysis of the state economic policy measures. Despite many problems, the study found that in 54.2% of start-ups surveyed under the pandemic, innovation opportunities were growing. It can be stated that the pandemic was a good opportunity to increase the innovative capacity of the enterprise. 52% of the surveyed start-up entrepreneurs managed to adapt to the current situation and increase the sale of their products/services through remote channels. As for the assessment of state support measures by start-up entrepreneurs, a large number of Georgian start-ups do not assess the measures implemented by the state positively. Methodology: The research process uses methods of analysis and synthesis, quantitative and qualitative, interview/survey, grouping, relative and average values, graphing, comparison, data analysis, and others. Main Findings: Studies have shown that for the start-up entrepreneurs, the main problem remains: inaccessible funding, workers' qualifications gap, inflation, taxes, regulation, political instability, inadequate provision of infrastructure, amount of taxes, and other factors. Conclusions: The state should take the following measures to support business start-ups: create an attractive environment for investment, availability of soft loans, creation of an insurance system, infrastructure development, increase the effectiveness of tax policy (simplicity of the tax system, clarity, optimal tax level ); promote export growth (develop strategy for opening up international markets, build up a broad marketing network, etc.).

Keywords: start-up entrepreneurs, startups, start-up entrepreneurs support programs, start-up entrepreneurs support economic policy

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Authors: Mehrdad Akbarzadeh, Afsaneh Abrisham

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Introduction: Critical thinking skills are crucial for nurses, particularly those working in neonatal care, where quick and informed decision-making is essential. This study aims to evaluate the effectiveness of critical thinking skills on job performance among neonatal nurses. Methods: A cross-sectional study was conducted with 450 neonatal nurses from a hospital in Mashhad. Participants were assessed using the Critical Thinking Questionnaire (CThQ) to measure their critical thinking abilities across various subscales, including Analyzing, Evaluating, Creating, Remembering, Understanding, and Applying. Additionally, a custom Job Performance Checklist completed by supervising nurses, was used to evaluate job performance across several dimensions. Data were collected and analyzed using SPSS V.23. Correlation analysis was conducted to determine the relationship between critical thinking skills and job performance. Results: The mean age of the nurses was 33.46 ± 14.2 years, with 79.15% being female. The nurses demonstrated high proficiency in critical thinking, with notable scores in the Creating (23.98 ± 4.8), Applying (17.35 ± 3.2), and Evaluating (16.67 ± 3.4) subscales. The results indicate a significant positive correlation between several critical thinking subscales and job performance. The Creating subscale exhibited the strongest correlation (R = 0.79, p < 0.001), followed by Overall CThQ (R = 0.68, p = 0.039) and Evaluating (R = 0.67, p = 0.041). Analyzing (R = 0.45, p = 0.013) and Understanding (R = 0.41, p = 0.015) also showed significant correlations with job performance. Remembering (R = 0.29, p = 0.061) and Applying (R = 0.43, p = 0.057) were not significantly correlated with job performance. Conclusion: The findings indicate that critical thinking skills, especially in creating and evaluating, are strongly associated with job performance in neonatal nurses. Enhancing these skills through targeted training programs could improve job performance, particularly in decision-making and time management. This study underscores the importance of critical thinking in neonatal care and its impact on nursing efficacy and patient outcomes.

Keywords: critical thinking, job performance, neonatal nurses, healthcare quality

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Authors: Fayza M. Obied-Allah

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Customer’ needs, quality, and value creation while reducing costs through supply chain management provides challenges and opportunities for companies and researchers. In the light of these challenges, modern ideas must contribute to counter these challenges and exploit opportunities. Perhaps this paper will be one of these contributions. This paper discusses the impact of the quality cost on revenue sharing as a most important incentive to configure business networks. No doubt that the costs directly affect the size of income generated by a business network, so this paper investigates the impact of quality costs on business networks revenue, and their impact on the decision to participate the revenue among the companies in the supply chain. This paper develops the quality cost approach to align with the modern era, the developed model includes five categories besides the well-known four categories (namely prevention costs, appraisal costs, internal failure costs, and external failure costs), a new category has been developed in this research as a new vision of the relationship between quality costs and innovations of industry. This new category is Recycle Cost. This paper is organized into six sections, Section I shows quality costs overview in the supply chain. Section II discusses revenue sharing between the parties in supply chain. Section III investigates the impact of quality costs in revenue sharing decision between partners in supply chain. The fourth section includes survey study and presents statistical results. Section V discusses the results and shows future opportunities for research. Finally, Section VI summarizes the theoretical and practical results of this paper.

Keywords: quality cost, recycle cost, revenue sharing, supply chain management

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Authors: Sakshi V. Tantak, Vishap K. Malik, Neelanjney Pilarisetty

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A patent is an exclusive right granted for an invention. It can be a product or a process that provides an innovative method of doing something, or offers a new technical perspective or solution to a problem. A patent can be obtained by making the technical information and details about the invention publicly available. The patent owner has exclusive rights to prevent or stop anyone from using the patented invention for commercial uses. Any commercial usage, distribution, import or export of a patented invention or product requires the patent owner’s consent. It has been observed that the central and important parts of patents are scripted in idiosyncratic and complex linguistic structures that can be difficult to read, comprehend or interpret for the masses. The abstracts of these patents tend to obfuscate the precise nature of the patent instead of clarifying it via direct and simple linguistic constructs. This makes it necessary to have an efficient access to this knowledge via concise and transparent summaries. However, as mentioned above, due to complex and repetitive linguistic constructs and extremely long sentences, common extraction-oriented automatic text summarization methods should not be expected to show a remarkable performance when applied to patent documents. Other, more content-oriented or abstractive summarization techniques are able to perform much better and generate more concise summaries. This paper proposes an efficient summarization system for patents using artificial intelligence, natural language processing and deep learning techniques to condense the knowledge and essential information from a patent document into a single summary that is easier to understand without any redundant formatting and difficult jargon.

Keywords: abstractive summarization, deep learning, natural language Processing, patent document

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Authors: Mesfin Nigussie Ibido

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The study is designed to explore the effects of land certification in securing women’s land rights of two rural villages in Robe district at Arsi Zone of Oromia regional state. The land is very critical assets for human life survival and the backbone for rural women livelihood. Equal access and control power to the land have given a chance for rural women to participate in different economic activities and improve their bargaining ability for decision making on their rights. Unfortunately, women were discriminated and marginalized from access and control of land for centuries through customary practices. However, in many countries, legal reform is used as a powerful tool for eliminating discriminatory provisions in property rights. Among other equity and efficiency concerns, the land certification program in Ethiopia attempts to address gender bias concerns of the current land-tenure system. The existed rural land policy was recognizing a women land rights and benefited by strengthened wives awareness of their land rights and contribute to the strong involvement of wives in decision making. However, harmful practices and policy implementation problems still against women do not fully exercise a provision of land rights in a different area of the country. Thus, this study is carried out to examine the effect of land certification in securing women’s land rights by eliminating the discriminatory nature of cultural abuses of study areas. Probability and non-probability sampling types were used, and the sample size was determined by using the sampling distribution of the proportion method. Systematic random sampling method was applied by taking the nth element of the sample frame. Both quantitative and qualitative research methods were applied, and survey respondents of 192 households were conducted and administering questionnaires in the quantitative method. The qualitative method was applied by interviews with focus group discussions with rural women, case stories, Village, and relevant district offices. Triangulation method was applied in data collection, data presentation and in the analysis of findings. Study finding revealed that the existence of land certification is affected by rural women positively by advancing their land rights, but still, some women are challenged by unsolved problems in the study areas. The study forwards recommendation on the existed problems or gaps to ensure women’s equal access to and control over land in the study areas.

Keywords: decision making, effects, land certification, land right, tenure security

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Authors: Cristina Schreiner, Romeo Ciobanu, Marius Pislaru

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This paper aims to build a system based on fuzzy models that can be implemented in the assessment of ecological systems, to determine appropriate methods of action for reducing adverse effects on environmental and implicit the population. The model proposed provides new perspective for environmental assessment, and it can be used as a practical instrument for decision-making.

Keywords: ecosystem model, environmental security, fuzzy logic, sustainability of habitable regions

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Authors: Raed Alsufyani, Hissam Tawfik, Victor Chang, Muthu Ramachandran

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This study investigates the factors that influence the decision by players in the healthcare sector to embrace Cloud Business Intelligence Technology with a focus on healthcare organizations in Saudi Arabia. To bring this matter into perspective, this study primarily considers the Technology-Organization-Environment (TOE) framework and the Human Organization-Technology (HOT) fit model. A survey was hypothetically designed based on literature review and was carried out online. Quantitative data obtained was processed from descriptive and one-way frequency statistics to inferential and regression analysis. Data were analysed to establish factors that influence the decision to adopt Cloud Business intelligence technology in the healthcare sector. The implication of the identified factors was measured, and all assumptions were tested. 66.70% of participants in healthcare organization backed the intention to adopt cloud business intelligence system. 99.4% of these participants considered security concerns and privacy risk have been the most significant factors in the adoption of cloud Business Intelligence (CBI) system. Through regression analysis hypothesis testing point that usefulness, service quality, relative advantage, IT infrastructure preparedness, organization structure; vendor support, perceived technical competence, government support, and top management support positively and significantly influence the adoption of (CBI) system. The paper presents quantitative phase that is a part of an on-going project. The project will be based on the consequences learned from this study.

Keywords: cloud computing, business intelligence, HOT-fit model, TOE, healthcare and innovation adoption

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

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Authors: Hiba El Assibi

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

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

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Authors: Bliss Singhal

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Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer.

Keywords: breast cancer, principal component analysis, genetic algorithm, k-nearest neighbors, decision tree classifier, logistic regression

Procedia PDF Downloads 82