Search results for: data driven decision making

Authors: Salma Akter, Mohammad M. Haque, Lawrence Akwetey

Abstract:

An empirical research design will analyze different factors/predictors children use to influence their parents in the family buying decision process in the unexplored area of British Bangladeshi children in the United Kingdom. The proposed conceptual model of factors- buying decision making process will be tested by the Structure Equation Model. A structured Questionnaire and secondary sources will employ to collect data and analyse and measure the validity by Statistical tools (SPSS) and Microsoft Excel. The Contemporary research aims to use the deductive approach developing the research questions and testing the hypothesis to identify the impact of different strategies British Bangladeshi children used to influence their parents in the family buying decision which was overlooked in the previous research.

Keywords: British Bangladeshi children, buying decision process, children influence, influential factors

Procedia PDF Downloads 244

Authors: Li-Wei Wu, Chung-Yu Wang

Abstract:

Recently, interest has been generated in the search for a new reference framework for value creation that is centered on the co-creation process. Co-creation implies cooperative value creation between service firms and customers and requires the building of experiences as well as the resolution of problems through the combined effort of the parties in the relationship. For customers, values are always co-created through their participation in services. Customers can ultimately determine the value of the service in use. This new approach emphasizes that a customer’s participation in the service process is considered indispensable to value co-creation. An important feature of service in the context of exchange is co-production, which implies that a certain amount of participation is needed from customers to co-produce a service and hence co-create value. Co-production no doubt helps customers better understand and take charge of their own roles in the service process. Thus, this proposal is to encourage co-production, thus facilitating value co-creation of that is reflected in both customers and service firms. Four determinants of co-production are identified in this study, namely, commitment, trust, asset specificity, and decision-making uncertainty. Commitment is an essential dimension that directly results in successful cooperative behaviors. Trust helps establish a relational environment that is fundamental to cross-border cooperation. Asset specificity motivates co-production because this determinant may enhance return on asset investment. Decision-making uncertainty prompts customers to collaborate with service firms in making decisions. In other words, customers adjust their roles and are increasingly engaged in co-production when commitment, trust, asset specificity, and decision-making uncertainty are enhanced. Although studies have examined the preceding effects, to our best knowledge, none has empirically examined the simultaneous effects of all the curvilinear relationships in a single study. When these determinants are excessive, however, customers will not engage in co-production process. In brief, we suggest that the relationships of commitment, trust, asset specificity, and decision-making uncertainty with co-production are curvilinear or are inverse U-shaped. These new forms of curvilinear relationships have not been identified in existing literature on co-production; therefore, they complement extant linear approaches. Most importantly, we aim to consider both the bright and the dark sides of the determinants of co-production.

Keywords: co-production, commitment, trust, asset specificity, decision-making uncertainty

Procedia PDF Downloads 172

Authors: Thewodros K. Geberemariam

Abstract:

The rapid physical expansion of urbanization coupled with aging infrastructure presents a unique decision and management challenges for many big city municipalities. Cities must therefore upgrade and maintain the existing aging urban drainage infrastructure systems to keep up with the demands. Given the overall contribution of assets to municipal revenue and the importance of infrastructure to the success of a livable city, many municipalities are currently looking for a robust and smart urban drainage infrastructure asset management solution that combines management, financial, engineering and technical practices. This robust decision-making shall rely on sound, complete, current and relevant data that enables asset valuation, impairment testing, lifecycle modeling, and forecasting across the multiple asset portfolios. On this paper, predictive computational intelligence (CI) and multi-class machine learning (ML) coupled with online, offline, and historical record data that are collected from an array of multi-parameter sensors are used for the extraction of different operational and non-conforming patterns hidden in structured and unstructured data to determine and produce actionable insight on the current and future states of the network. This paper aims to improve the strategic decision-making process by identifying all possible alternatives; evaluate the risk of each alternative, and choose the alternative most likely to attain the required goal in a cost-effective manner using historical and near real-time urban drainage infrastructure data for urban drainage infrastructures assets that have previously not benefited from computational intelligence and machine learning advancements.

Keywords: computational intelligence, machine learning, urban drainage infrastructure, machine learning, classification, prediction, asset management space

Procedia PDF Downloads 134

Authors: Cristóbal Moënne-Loccoz, Rodrigo C. Vergara, Vladimir López, Domingo Mery, Diego Cosmelli

Abstract:

Our daily interaction with computational interfaces is plagued of situations in which we go from inexperienced users to experts through self-motivated exploration of the same task. In many of these interactions, we must learn to find our way through a sequence of decisions and actions before obtaining the desired result. For instance, when drawing cash from an ATM machine, choices are presented in a step-by-step fashion so that a specific sequence of actions must be performed in order to produce the expected outcome. But, as they become experts in the use of such interfaces, do users adopt specific search and learning strategies? Moreover, if so, can we use this information to follow the process of expertise development and, eventually, predict future actions? This would be a critical step towards building truly adaptive interfaces that can facilitate interaction at different moments of the learning curve. Furthermore, it could provide a window into potential mechanisms underlying decision-making behavior in real world scenarios. Here we tackle this question using a simple game interface that instantiates a 4-level binary decision tree (BDT) sequential decision-making task. Participants have to explore the interface and discover an underlying concept-icon mapping in order to complete the game. We develop a Hidden Markov Model (HMM)-based approach whereby a set of stereotyped, hierarchically related search behaviors act as hidden states. Using this model, we are able to track the decision-making process as participants explore, learn and develop expertise in the use of the interface. Our results show that partitioning the problem space into such stereotyped strategies is sufficient to capture a host of exploratory and learning behaviors. Moreover, using the modular architecture of stereotyped strategies as a Mixture of Experts, we are able to simultaneously ask the experts about the user's most probable future actions. We show that for those participants that learn the task, it becomes possible to predict their next decision, above chance, approximately halfway through the game. Our long-term goal is, on the basis of a better understanding of real-world decision-making processes, to inform the construction of interfaces that can establish dynamic conversations with their users in order to facilitate the development of expertise.

Keywords: behavioral modeling, expertise acquisition, hidden markov models, sequential decision-making

Procedia PDF Downloads 234

Authors: Diego De Castro Rodrigues, Marcelo B. Nery, Sergio Adorno

Abstract:

The project aims to develop a framework for social data analysis, particularly by mobilizing criminal records and applying descriptive computational techniques, such as associative algorithms and extraction of tree decision rules, among others. The methods and instruments discussed in this work will enable the discovery of patterns, providing a guided means to identify similarities between recurring situations in the social sphere using descriptive techniques and data visualization. The study area has been defined as the city of São Paulo, with the structuring of social data as the central idea, with a particular focus on the quality of the information. Given this, a set of tools will be validated, including the use of a database and tools for visualizing the results. Among the main deliverables related to products and the development of articles are the discoveries made during the research phase. The effectiveness and utility of the results will depend on studies involving real data, validated both by domain experts and by identifying and comparing the patterns found in this study with other phenomena described in the literature. The intention is to contribute to evidence-based understanding and decision-making in the social field.

Keywords: social data analysis, criminal records, computational techniques, data mining, big data

Procedia PDF Downloads 59

Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

Abstract:

In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: electrocardiogram, dictionary learning, sparse coding, classification

Procedia PDF Downloads 359

Authors: Muhammad Asim, Mehvish Amjad

Abstract:

Background: Less than half of women in South Asia (SA) use any modern contraceptive method which leads to a huge burden of unintended pregnancies, unsafe abortions, maternal deaths, and socioeconomic loss. Women empowerment plays a pivotal role in improving various health seeking behaviours, including contraceptive use. The objective of this study to explore the association between women's empowerment and modern contraceptive, among rich and poor segment of population in SA. Methods: We used the most recent, large-scale, demographic health survey data of five South Asian countries, namely Afghanistan, Pakistan, Bangladesh, India, and Nepal. The outcome variable was the current use of modern contraceptive methods. The main exposure variable was a combination (interaction) of socio-economic status (SES) and women’s level of empowerment (low, medium, and high), where SES was bifurcated into poor and rich; and women empowerment was divided into three categories: decision making, attitude to violence and social independence. Moreover, overall women empowerment indicator was also created by using three dimensions of women empowerment. We applied both descriptive statistics and multivariable logistic regression techniques for data analyses. Results: Most of the women possessed ‘medium’ level of empowerment across South Asian Countries. The lowest attitude to violence empowerment was found in Afghanistan, and the lowest social independence empowerment was observed in Bangladesh across SA. However, Pakistani women have the lowest decision-making empowerment in the region. The lowest modern contraceptive use (22.1%) was found in Afghanistan and the highest (53.2%) in Bangladesh. The multivariate results depict that the overall measure of women empowerment does not affect modern contraceptive use among poor and rich women in most of South Asian countries. However, the decision-making empowerment plays a significant role among both poor and rich women to use modern contraceptive methods across South Asian countries. Conclusions: The effect of women’s empowerment on modern contraceptive use is not consistent across countries, and among poor and rich segment of population. Of the three dimensions of women’s empowerment, the autonomy of decision making in household affairs emerged as a stronger determinant of mCPR as compared with social independence and attitude towards violence against women.

Keywords: women empowerment, modern contraceptive use, South Asia, socio economic status

Procedia PDF Downloads 56

Authors: Katharina Fellnhofer

Abstract:

The more information and knowledge that technology provides, the more important are profoundly human skills like intuition, the skill of using nonconscious information. As our world becomes more complex, shaken by crises, and characterized by uncertainty, time pressure, ambiguity, and rapidly changing conditions, intuition is increasingly recognized as a key human asset. However, due to methodological limitations of sample size or time frame or a lack of real-world or cross-cultural scope, precisely how to measure intuition when facing risks on a nonconscious level remains unclear. In light of the measurement challenge related to intuition’s nonconscious nature, a technique is introduced to measure intuition via hidden images as nonconscious additional information to trigger intuition. This technique has been tested in a within-subject fully online design with 62,721 real-world investment decisions made by 657 subjects in Europe and the United States. Bayesian models highlight the technique’s potential to measure skill at using nonconscious information for conscious decision making. Over the long term, solving the mysteries of intuition and mastering its use could be of immense value in personal and organizational decision-making contexts.

Keywords: cognition, intuition, investment decisions, methodology

Procedia PDF Downloads 69

Authors: Mohammad Al-Mousawi

Abstract:

This academic article explores the indispensable role of critical thinking in the process of diagnosing diseases. Employing a multidisciplinary approach, we delve into the cognitive skills and analytical mindset that clinicians, researchers, and healthcare professionals must employ to navigate the complexities of disease identification. By examining the integration of critical thinking within the realms of medical education, diagnostic decision-making, and technological advancements, this article aims to underscore the significance of cultivating and applying critical thinking skills in the ever-evolving landscape of healthcare.

Keywords: critical thinking, medical education, diagnostic decision-making, fostering critical thinking

Procedia PDF Downloads 45

Authors: Wipanee Maen-In

Abstract:

The objectives of the research are i) to study the motivations of european and scandinavian tourists who select Ranong province as their destinations ii) to study their perception towards the Ranong Province and iii) to study the visitors’ decision making while visiting Ranong Province. The samples of the study are 220 European and Scandinavian tourists’ visitors at the Ranong by accidental sampling and in clouding online questionnaires for 53 sampling. The data analysis includes Percentage, Frequency and One-way ANOVA. The findings from the research are the motivation level of the visitors is considered prominent, the average score of the motivational factors ranks higher than the average of the pull factors to visit the Ranong province when considering the factors analysis, the research shows that the reason that most tourists visit the Ranong is for relaxation while the purity of the natural mineral hot springs is the most important pull factor.

Keywords: European and Scandinavian, Ranong province, tourists’ perceptions, visitors’ decision making

Procedia PDF Downloads 211

Authors: Cathryn Robinson, Bernard McKenna, David Rooney

Abstract:

This paper argues that the application of values-based leadership increasingly challenges leaders in rules-based organisations, particularly in bureaucratic organisations such as the military, public service, police, and emergency services. Leaders are grappling to reconcile how to enact values-based leadership and decision-making when they are bound by rules, policies, and procedures. This interpretive study used a multi-faceted vignette (critical incident) as the basis of an interview with air force officers at three levels: executive, senior, and junior. In this way, practice is forced to intersect with discourse. The findings revealed a shared set of discourse themes (legal; rules; safety and risk; operational practice/theatre discourses), but also clear dialectical tensions. These tensions were evident in executive officers and senior leaders emphasizing rules and information themes, whereas junior officers emphasized decision making, collateral, and situation. These findings reveal discourse and practice incommensurability that could have grave implications in the conduct of war.

Keywords: critical incident, discourse analysis, rules-based, values-based

Procedia PDF Downloads 164

Authors: D.Mendes, J. Henriques, P. Carvalho, T. Rocha, S. Paredes, R. Cabiddu, R. Trimer, R. Mendes, A. Borghi-Silva, L. Kaminsky, E. Ashley, R. Arena, J. Myers

Abstract:

The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption).

Keywords: cardiorespiratory fitness, data-driven models, knowledge extraction, machine learning

Procedia PDF Downloads 264

Authors: Mariella Pinna

Abstract:

Sustainable entrepreneurship is an innovative corporate approach to create value combining economic, social and environmental goals over time. In the last two decades, the interest in sustainable entrepreneurship has flourished thanks to its potential to answer the current challenges of sustainable development. As a result, scholars are increasingly interested in understanding the determinants of the intentions to become a sustainable entrepreneur and consistent behavior. To date, prior studies provided empirical evidence for the influence of attitudes, perceived feasibility and desirability, values, and personality traits on the decision-making process of becoming a sustainable entrepreneur. Conversely, scant effort has been provided to understand which factors inhibit sustainable entrepreneurial intentions and behaviors. Therefore a global understanding of the sustainable entrepreneurship decision-making process is missing. This paper contributes to the debate on sustainable entrepreneurship by proposing a conceptual model that combines the factors which are predicted to facilitate and hinder the proclivity of individuals to become sustainable entrepreneurs. More in particular, the proposed framework theorizes about the role of the characteristics of the prospective sustainable entrepreneur (e.g., socio-demographic, psychological, cultural), the positive antecedents (e.g., attitude, social feasibility and desirability, among others) and the negative precursors (e.g., neutralization) in influencing sustainable entrepreneurship intentions and subsequent behavior. The proposed framework is expected to shed further light on the decision-making process of becoming a sustainable entrepreneur, which in turn, is of practical relevance for public policy institutions and the society as a whole to enhance the favorable conditions to create new sustainable ventures.

Keywords: sustainable entrepreneurship, entrepreneurial intentions, entrepreneurial decision-making, antecedents of entrepreneurial intention and behavior

Procedia PDF Downloads 187

Authors: Michelle Hartanto, Risheka Suthantirakumar

Abstract:

Introduction Staff redeployment, increased numbers of acutely unwell patients requiring resuscitation decision-making conversations, visiting restrictions, and varying guidance regarding resuscitation for patients with COVID-19 disrupted clinicians’ management of resuscitation and escalation decision-making processes. While it was generally accepted that the COVID-19 pandemic disturbed numerous aspects of the Recommended Summary Plan for Emergency Care and Treatment (ReSPECT) process in the United Kingdom, a process which establishes a patient’s CPR status and treatment escalation plans, the impact of the pandemic on clinicians’ attitudes towards these resuscitation and decision-making conversations was unknown. This was the first study to examine the impact of the COVID-19 pandemic on clinicians’ knowledge, skills, and attitudes towards the ReSPECT process. Methods A cross-sectional survey of clinicians at one acute teaching hospital in the UK was conducted. A questionnaire with a defined five-point Likert scale was distributed and clinicians were asked to recall their pre-pandemic views on ReSPECT and report their current views at the time of survey distribution (May 2020, end of the first COVID-19 wave in the UK). Responses were received from 171 clinicians, and self-reported views before and during the pandemic were compared. Results Clinicians reported they found managing ReSPECT conversations more challenging during the pandemic, especially when conducted over the telephone with relatives, and they experienced an increase in negative emotions before, during, and after conducting ReSPECT conversations. Our findings identified that due to the pandemic there was now a need for clinicians to receive training and support in conducting resuscitation and escalation decision-making conversations over the telephone with relatives and managing these processes.

Keywords: cardiopulmonary resuscitation, COVID-19 pandemic, DNACPR discussion, education, recommended summary plan for emergency care and treatment, resuscitation order

Procedia PDF Downloads 89

Authors: Hyeon-Kyo Lim, Sang-Hun Byun

Abstract:

Risk assessment is carried out in most industrial plants for accident prevention, but there exists insufficient data for statistical decision making. It is commonly said that risk can be expressed as a product of consequence and likelihood of a corresponding hazard factor. Eventually, therefore, risk assessment involves human decision making which cannot be objective per se. This study was carried out to comprehend perceptive characteristics of human beings in industrial plants. Subjects were shown a set of illustrations describing scenes of industrial plants, and were asked to assess the risk of each scene with not only linguistic variables but also numeric scores in the aspect of consequence and likelihood. After that, their responses were formulated as fuzzy membership functions, and compared with those of university students who had no experience of industrial works. The results showed that risk level of industrial workers were lower than those of any other groups, which implied that the workers might generally have a tendency to neglect more hazard factors in their work fields.

Keywords: fuzzy, hazard, linguistic variable, risk assessment

Procedia PDF Downloads 237

Authors: Prakash Singh

Abstract:

The question that arises is whether a theory such as the Collegial Leadership Model of Emancipation (COLME) has the potency to introduce leadership change by empowering and emancipating their employees. It is a fallacy to simply assume that experience alone, in the absence of theory, will contribute to this knowledge base to develop collegial leaders. The focus of this study is to therefore ascertain whether the COLME can serve as a conceptual framework to transform traditional bureaucratic management practices (TBMPs) in order to promote shared decision-making in organizations such as schools. All the respondents in this exploratory qualitative study embraced collegiality to transform TBMPs in their organizations. For the positive effects to be sustained, the collegial practices need to be evolutionary and emancipatory in order to evoke the values of collegial leadership as elucidated by the findings of this study. Interviewees affirmed that the COLME provides an astute framework to develop commendable collegial leadership practices as it clearly outlines procedures to develop and use the leadership potential of all the employees in order to foster joint accountability. They acknowledged that when the principles of collegiality are flexibly applied, they contribute to the creation of a holistic milieu in which all employees are able to express themselves freely, without fear of failure, and thus feel that they are part of the democratic decision-making process. Evidently, a conceptual framework such as the COLME can serve as a benchmark for leadership effectiveness because organizational outcomes need to be measured against standards of excellence in meeting both employee and customer expectations.

Keywords: collegial leadership model, employee empowerment, shared decision-making, traditional bureaucratic management practices

Procedia PDF Downloads 473

Authors: B. Vikram, Y. Anu Leena, Y. Anu Neena, M. V. Krishna Rao, V. S. S. Kumar

Abstract:

The industry is often criticized for inefficiencies in outcomes such as time and cost overruns, low productivity, poor quality and inadequate customer satisfaction. To enhance the chances for construction projects to be successful, selecting an able contractor is one of the fundamental decisions to be made by clients. The selection of the most appropriate contractor is a multi-criteria decision making (MCDM) process. In this paper, multi-attribute utility theory (MAUT) is employed utilizing the multiplicative form of utility function for ranking the prequalified contractors. Performance assessment criteria covering contracting company attributes, experience record, past performance, performance potential, financial stability and project specific criteria are considered for contractor evaluation. A case study of multistoried building for which four contractors submitted bids is considered to illustrate the applicability of multiplicative approach of MAUT to rank the prequalified contractors. The proposed MAUT decision making methodology can also be employed to other decision making situations.

Keywords: multi-attribute utility theory, construction industry, prequalification, contractor

Procedia PDF Downloads 416

Authors: Anli Du Preez, James Bekker

Abstract:

Data is currently one of the most critical and influential emerging technologies. However, the true potential of data is yet to be exploited since, currently, about 1% of generated data are ever actually analyzed for value creation. There is a data gap where data is not explored due to the lack of data analytics infrastructure and the required data analytics skills. This study developed a decision support framework for data analytics by following Jabareen’s framework development methodology. The study focused on machine learning algorithms, which is a subset of data analytics. The developed framework is designed to assist data analysts with little experience, in choosing the appropriate machine learning algorithm given the purpose of their application.

Keywords: Data analytics, Industrial engineering, Machine learning, Value creation

Procedia PDF Downloads 147

Authors: Adandé Belarmain Fandohan

Abstract:

Climate change has become a major concern globally, particularly in rural communities that have to find rapid coping solutions. Several vulnerability assessment approaches have been developed in the last decades. This comes along with a higher risk for different methods to result in different conclusions, thereby making comparisons difficult and decision-making non-consistent across areas. The effect of methods and computational approaches on estimates of people’s vulnerability was assessed using data collected from the Gambia. Twenty-four indicators reflecting vulnerability components: (exposure, sensitivity, and adaptive capacity) were selected for this purpose. Data were collected through household surveys and key informant interviews. One hundred and fifteen respondents were surveyed across six communities and two administrative districts. Results were compared over three computational approaches: the maximum value transformation normalization, the z-score transformation normalization, and simple averaging. Regardless of the approaches used, communities that have high exposure to climate change and extreme events were the most vulnerable. Furthermore, the vulnerability was strongly related to the socio-economic characteristics of farmers. The survey evidenced variability in vulnerability among communities and administrative districts. Comparing output across approaches, overall, people in the study area were found to be highly vulnerable using the simple average and maximum value transformation, whereas they were only moderately vulnerable using the z-score transformation approach. It is suggested that assessment approach-induced discrepancies be accounted for in international debates to harmonize/standardize assessment approaches to the end of making outputs comparable across regions. This will also likely increase the relevance of decision-making for adaptation policies.

Keywords: maximum value transformation, simple averaging, vulnerability assessment, West Africa, z-score transformation

Procedia PDF Downloads 83

Authors: Ali Abbas

Abstract:

Industrial Engineering is a broad multidisciplinary field with intersections and applications in numerous areas. In out current environment, the path from a phenomenon to value involves numerous people with expertise in various areas including domain knowledge of a field and the ability to make decisions within an operating environment that lead to value creation. We propose some skills that industrial engineering programs should focus on, and argue that an industrial engineer is a decision maker instead of a problem solver.

Keywords: decision analysis, problem-solving, value creation, industrial engineering

Procedia PDF Downloads 353

Authors: Manita Ale, Ganesh P. Shivakoti, Ram C. Bastakoti

Abstract:

Irrigation system, a system for enhancing agricultural productivity, requires regular maintenance in order to avoid irregular allocation of water. For maintenance of the system in long run, farmers’ participation plays a key role increasing the performance of system. The performance of any irrigation system mainly relies on various factors which affect collective action plus decision making, as well as their shared impacts. The paper consists of system level information that were collected from 12 Irrigation Systems (IS) from three-sampled districts of Nepal and the household information that were collected from 160 irrigation water users. The results reveal that, out of 12 sampled irrigation systems, only 4 systems shows high performance levels. The high performance level of those systems was characterized on the basis of adequate availability of water, good maintenance of system infrastructure, and conformance to existing rules followed. In addition, the paper compares different irrigation systems based on trust, reciprocity, cropping intensity, command area and yield as tools to indicate the importance of collective action in performance of irrigation system.

Keywords: collective action, decision-making, farmers’ participation, performance

Procedia PDF Downloads 384

Authors: Badr A. Al-Jehani

Abstract:

Nowadays, contractor selection is a critical activity of the project owner. Selecting the right contractor is essential to the project manager for the success of the project, and this cab happens by using the proper selecting method. Traditionally, the contractor is being selected based on his offered bid price. This approach focuses only on the price factor and forgetting other essential factors for the success of the project. In this research paper, the Analytic Network Process (ANP) method is used as a decision tool model to select the most appropriate contractor. This decision-making method can help the clients who work in the construction industry to identify contractors who are capable of delivering satisfactory outcomes. Moreover, this research paper provides a case study of selecting the proper contractor among three contractors by using ANP method. The case study identifies and computes the relative weight of the eight criteria and eleven sub-criteria using a questionnaire.

Keywords: contractor selection, project management, decision-making, bidding

Procedia PDF Downloads 75

Authors: Ewa M. Laskowska, Jorn Vatn

Abstract:

Industry 4.0 focuses on digital optimization of industrial processes. The idea is to use extracted data in order to build a decision support model enabling use of those data for real time decision making. In terms of predictive maintenance, the desired decision support tool would be a model enabling prognostics of system's health based on the current condition of considered equipment. Within area of system prognostics and health management, a commonly used health indicator is Remaining Useful Lifetime (RUL) of a system. Because the RUL is a random variable, it has to be estimated based on available health indicators. Health indicators can be of different types and come from different sources. They can be process variables, equipment performance variables, data related to number of experienced failures, etc. The aim of this study is the analysis of performance variables of emergency shutdown valves (ESV) used in oil and gas industry. ESV is inspected periodically, and at each inspection torque and time of valve operation are registered. The data will be analyzed by means of machine learning or statistical analysis. The purpose is to investigate whether the available data could be used as a health indicator for a prognostic purpose. The second objective is to examine what is the most efficient way to incorporate the data into predictive model. The idea is to check whether the data can be applied in form of explanatory variables in Markov process or whether other stochastic processes would be a more convenient to build an RUL model based on the information coming from registered data.

Keywords: emergency shutdown valves, health indicator, prognostics, remaining useful lifetime, RUL

Procedia PDF Downloads 70

Authors: Ying-Lun Tseng Chiu-Ying Chen

Abstract:

Taiwan's Mental Health Act was amended at the end of 2022; they added regulations regarding refusing compulsory treatment by patients with mental illnesses. In addition, not only by an examination committee, the judge must also assess the patient's need for compulsory treatment. Additionally, the maximum of compulsory hospitalization has been reduced from an unlimited period to a maximum of 60 days. They aim to promote the healthcare autonomy of individuals with mental illnesses in Taiwan and prevent their silenced voice in medical decision-making while they still possess rationality. Furthermore, they plan to use community support and social care networks to replace the current practice of compulsory treatment in Taiwan. This study uses qualitative research methodology, utilizing interview guidelines to inquire about the experiences of Taiwanese who have undergone compulsory hospitalization, compulsory community treatment, and compulsory medical care. The interviews aimed to explore their feelings when they were subjected to compulsory medical intervention, the inside of their illness, their opinions after treatments, and whether alternative medical interventions proposed by them were considered. Additionally, participants also asked about their personal life history and their support networks in their lives. We collected 12 Taiwanese who had experienced compulsory medical interventions and were interviewed 14 times. The findings indicated that participants still possessed rationality during the onset of their illness. However, when they have other treatments to replace compulsory medical, they sometimes diverge from those of the doctors and their families. Finally, doctors prefer their professional judgment and patients' families' option. Therefore, Taiwanese mental health patients' power of decision-making still needs to improve. Because this research uses qualitative research, so difficult to find participants, and the sample size rate was smaller than Taiwan's population, it may have biases in the analysis. So, Taiwan still has significant progress in enhancing the decision-making rights of participants in the study.

Keywords: medical decision making, compulsory treatment, medical ethics, mental health act

Procedia PDF Downloads 55

Authors: Adepoju A. A., Olapade-Ogunwole F., Ganiyu M. O.

Abstract:

This study examined women's empowerment in cassava production in southwest Nigeria. The contributions of the five domains namely decision about agricultural production, decision-making power over productive resources, control of the use of income, leadership and time allocation to women disempowerment, profiled the women based on their socio-economics features and determined factors influencing women's disempowerment. Primary data were collected from the women farmers and processors through the use of structured questionnaires. Purposive sampling was used to select the LGAs and villages based on a large number of cassava farmers and processors, while cluster sampling was used to select 360 respondents in the study area. Descriptive statistics such as bar charts and percentages, Women Empowerment in Agriculture (WEAI), and the Logit regression model were used to analyze the data collected. The results revealed that 63.88% of the women were disempowered. Lack of decision-making power over productive resources; 36.47% and leadership skills; 33.26% contributed mostly to the disempowerment of the women. About 85% of the married women were disempowered, while 76.92% of the women who participated in social group activities were more empowered than their disempowered counterparts. The findings showed that women with more years of processing experience have the probability of being disempowered while those who engage in farming as a primary livelihood activity, and participate in social groups among others have the tendency to be empowered. In view of this, it was recommended that women should be encouraged to farm and contribute to social group activities.

Keywords: cassava, production, empowerment, southwest, Nigeria

Procedia PDF Downloads 34

Authors: Louis Nwachi

Abstract:

Urban planning systems in most countries still rely on expert-driven, top-down technocratic plan-making processes rather than a public and people-led process. This paper set out to evaluate the need for public participation in the plan-making process and to highlight the factors that affect public participation in the plan-making process. In doing this, it adopted a qualitative approach based on document review and interviews taken from real-world phenomena. A case study strategy using the Metropolitan Area of Abuja, the capital of Nigeria, as the study sample was used in carrying out the research. The research finds that participation is an important tool in the plan-making process and that public engagement in the process contributes to the identification of key urban issues that are unique to the specific local areas, thereby contributing to the establishment of priorities and, in turn, to the mobilization of resources to meet the identified needs. It also finds that the development of a participation model by city authorities encourages public engagement and helps to develop trust between those in authority and the different key stakeholder groups involved in the plan-making process.

Keywords: plan-making, participation, urban planning, city

Procedia PDF Downloads 74

Authors: Syed Iftikhar Hussain Shah, Vasilis Peristeras, Ioannis Magnisalis

Abstract:

Data that is high in volume, velocity, veracity and comes from a variety of sources is usually generated in all sectors including the government sector. Globally public administrations are pursuing (big) data as new technology and trying to adopt a data-centric architecture for hosting and sharing data. Properly executed, big data and data analytics in the government (big) data ecosystem can be led to data-driven government and have a direct impact on the way policymakers work and citizens interact with governments. In this research paper, we conduct a systematic literature review. The main aims of this paper are to highlight essential aspects of the government (big) data ecosystem and to explore the most critical socio-technical factors that contribute to the successful implementation of government (big) data ecosystem. The essential aspects of government (big) data ecosystem include definition, data types, data lifecycle models, and actors and their roles. We also discuss the potential impact of (big) data in public administration and gaps in the government data ecosystems literature. As this is a new topic, we did not find specific articles on government (big) data ecosystem and therefore focused our research on various relevant areas like humanitarian data, open government data, scientific research data, industry data, etc.

Keywords: applications of big data, big data, big data types. big data ecosystem, critical success factors, data-driven government, egovernment, gaps in data ecosystems, government (big) data, literature review, public administration, systematic review

Procedia PDF Downloads 205

Authors: Chhavi Rana

Abstract:

The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, Healthcare, and business intelligence contain voluminous and incremental data, which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organization's decision-making strategy can be enhanced using big data analytics and applying different machine learning techniques and statistical tools on such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates on various frameworks in the process of Analysis using different machine-learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

Procedia PDF Downloads 59

Authors: Chhavi Rana

Abstract:

The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, healthcare, and business intelligence contain voluminous and incremental data which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organisation decision-making strategy can be enhanced by using big data analytics and applying different machine learning techniques and statistical tools to such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates various frameworks in the process of analysis using different machine learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

Procedia PDF Downloads 76

Authors: Kunyanuth Kularbphettong, Cholticha Tongsiri

Abstract:

This paper aims to create the model for student in choosing an emphasized track of student majoring in computer science at Suan Sunandha Rajabhat University. The objective of this research is to develop the suggested system using data mining technique to analyze knowledge and conduct decision rules. Such relationships can be used to demonstrate the reasonableness of student choosing a track as well as to support his/her decision and the system is verified by experts in the field. The sampling is from student of computer science based on the system and the questionnaire to see the satisfaction. The system result is found to be satisfactory by both experts and student as well.

Keywords: data mining technique, the decision support system, knowledge and decision rules, education

Procedia PDF Downloads 404