An empirical evaluation of imbalanced data strategies from a practitioner's point of view

This research tested the following well known strategies to deal with binary imbalanced data on 82 different real life data sets (sampled to imbalance rates of 5%, 3%, 1%, and 0.1%): class weight, SMOTE, Underbagging, and a baseline (just the base classifier). As base classifiers we used SVM with RBF kernel, random forests, and gradient boosting machines and we measured the quality of the resulting classifier using 6 different metrics (Area under the curve, Accuracy, F-measure, G-mean, Matthew's correlation coefficient and Balanced accuracy). The best strategy strongly depends on the metric used to measure the quality of the classifier. For AUC and accuracy class weight and the baseline perform better; for F-measure and MCC, SMOTE performs better; and for G-mean and balanced accuracy, underbagging

Volume 16 Number 3

https://thekeep.eiu.edu/eej/1035/thumbnail.jp

Eating Disorders: A Treatment Apart. The Unique Use of the Therapist\u27s Self in the Treatment of Eating Disorders

Treatment skills that serve general mental health practice, though applicable to eating disorder care, by themselves will not suffice to meet the uniquely pressing demands and requirements of treating these life‐threatening disorders. Eating disorders adversely influence every aspect of human functioning, demanding a comprehensive and integrative approach to care. Because eating disorders disrupt the patient\u27s relationship with self and others, the quality of the therapist\u27s versatile and integrative use of self within the therapeutic relationship can become the single most significant intervention in achieving successful healing outcomes. The intensity of professional challenges within the treatment process reflects the urgency behind the patient\u27s need to heal. Treatment efficacy is achieved through the therapist\u27s commitment to a timely, intentional, and practicable fulfillment of clearly established goals, uniquely tailored to each patient and eating disorder. The self‐integrated psychotherapist, as case manager, is required to manage a complex landscape of pathology and strengths, regression and healing, diverse professional and familial resources, transference and countertransference phenomena and, with skillful proficiency, traditional as well as nontraditional (neurophysiological) treatment interventions and approaches to care. This chapter highlights key elements in the therapist’s V.I.A.B.L.E. (Versatile, Integrative, Action-oriented, outcome-Based, Loving, and Educative) use of self in facilitating the healing of the eating disordered patient and malnourished brain

Sports Result Prediction Based on Machine Learning and Computational Intelligence Approaches: A Survey

In the current world, sports produce considerable statistical information about each player, team, games, and seasons. Traditional sports science believed science to be owned by experts, coaches, team managers, and analyzers. However, sports organizations have recently realized the abundant science available in their data and sought to take advantage of that science through the use of data mining techniques. Sports data mining assists coaches and managers in result prediction, player performance assessment, player injury prediction, sports talent Identification and game strategy evaluation. Predicting the results of sports matches is interesting to many, from fans to punters. It is also interesting as a research problem, in part due to its difficulty: the result of a sports match is dependent on many factors, such as the morale of a team (or a player), skills, coaching strategy, etc. So even for experts, it is very hard to predict the exact results of individual matches. The present study reviews previous research on data mining systems to predict sports results and evaluates the advantages and disadvantages of each system

Professional power and the state: A study of five professions in state welfare agencies in the UK

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The thesis defines a profession as a group of workers who have been authorised by the state to determine aspects of their own work, training and organisation,usually,but not necessarily,as a result of their specialist expertise. It argues that knowledge about professions would be advanced by examining the social structures and processes of regulation and management of different professions, rather than by concentrating on the particular characteristics of the work or of the workers. Following this approach the thesis presents research into the different national regulatory structures, and local management structures of five "welfare service" professions in the U.K. In explaining the differences in structure the thesis shows how each occupation exploited characteristics which provided power in particular situations to establish organisation and control advantageous to its interests,and how characteristics such as specialist knowledge, status, and income were stabilised and further developed as a result. It also examines the complex involvement of the state in legitimating, advancing and limiting professional power. The main contribution of the thesis is to develop Freidson's theory of professions through logical critique and by reference to empirical evidence about five U.K. welfare professions, and by, - showing that national regulatory structures do not define a division of labour or provide the absolute autonomy which Freidson proposed, - showing that different types of professional autonomy are institutionalised in local management structures, usually on central government recommendation, and by providing a typology of professional autonomy based on empirical research, - showing that characteristics of professions are related to, but not, as proposed by Freidson, determined by professional autonomy, - developing Freidson's general perspective to accommodate the empirical evidence by reconceptualising the nature of professions in terms of professional authority, rather than autonomy, and by developing a model of the authorisation of professional power. In developing Freidson's theory the thesis also contributes, - to knowledge about professional organisation within state welfare bureaucracies, mainly by providing detailed descriptions of differences and changes in management structures, - to the methodology of action research by developing the theoretical basis of a method for investigating the legitimation of authority in establishing management structures, - to knowledge about the details of the relationship between the state and welfare professions, mainly by providing evidence of the involvement of the state at national and local levels in decisions and structures which profoundly shape the nature of practice, relationships with clients, and futures of welfare occupations

Enhancing remanufacturing automation using deep learning approach

In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces.In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces

Fieldwork quality of life: addressing the Occupational Therapy Level II fieldwork student/supervisor relationship

Thesis (O.T.D.)--Boston UniversityOccupational therapy Level II fieldwork (FW) students face contemporary stressors that may interfere with the learning process. Academic programs have a greater number of non-traditional students who must find a balance between academic, family and financial obligations. FW environments have become more stressful as increased productivity levels, shorter treatment durations, and budget cuts control clinic operations. These stressors may result in the FW student expressing higher degrees of anxiety and decreased confidence in performing entry-level skills at graduation. While physician and nursing professions have addressed best practices to manage clinical student stressors and training strategies for supervisors, a review of the occupational therapy (OT) literature reveals no study focusing on FW student well-being. This is surprising given the value that OT places on quality of life and establishing therapeutic relationships with our clients. This doctoral project describes a program directed to OT practitioners who have little or no experience in supervising OT Level II FW students. The program, given as a continuing education course, will provide the participants with training and tools to establish therapeutic relationships with their FW students. The program will use elements of the Intentional Relationship Model (Taylor, 2008) to educate the OT supervisor to use the therapeutic use of self (TUOS) to improve the quality of the student/supervisor relationship. The participants will also be introduced to the Fieldwork Quality of Life (FWQoL), a theoretical framework developed for this doctoral project, which will provide guidelines to assist the OT supervisor in determining if the FW student is having a positive FW experience. The program will use a small group format incorporating lecture, group discussions, video simulations, and provision of standardized questionnaires to assist the FW supervisor in monitoring the student's confidence and anxiety levels. A follow-up program, composed of volunteers from the program, will track their supervision of a FW student to determine program effectiveness

The client, counsellor and organisational components of an external workplace counselling service : an evaluation

This is a study of work place counselling. It responded to four key stimulants: (1) the paucity of workplace counselling evaluations; (2) the need to more fully incorporate the client and organisation into evaluations; (3) the call for an increased qualitative focus in counselling research; (4) the need for practitioners to become research-minded.The site was the Northern Ireland Fire Brigade's (NIFB) external counselling service. The study was inspired by the concept of the workplace counselling triangle - of client, counsellor and organisation. It explores the degree of congruence across the aims, needs, expectations and evaluations of each of the three facets; the aim being to ascertain whether the NIFB 's counselling provision meets the needs of its three primary stakeholders?A qualitative methodology was adopted, with stakeholder perspectives captured by semi-structured interviews. Counselling process assessments, sick absence analysis and a workforce awareness survey were also conducted.The counselling service was dramatically effective from the client perspective. Counsellors, while satisfied with their client work had reservations about organisational links. Their wish for greater primary intervention was matched by the NIFB being surprised that they were not more proactive at this level! A need for effective organisational induction and terms of engagement were identified, so as to allow counsellors to move beyond the personal counselling role. The observed reduction in absenteeism post-counselling was a dividend for the organisation.Although the NIFB counselling service does not currently meet all stakeholder needs, it has been shown to be significantly effective in both human and financial terms. The service is needed, period

Impact of Dynamic Capabilities on Small and Medium-sized Enterprises (SMEs) Performance in Oman

Drawing on dynamic capabilities’ theory, this study examines the impact of entrepreneurial knowledge (EK) on firm performance in Oman, a member nation of the Gulf Cooperation Council (GCC). In addition, two aspects of dynamic capabilities: (1) opportunities recognition and (2) opportunities exploitation, were explored as potential mediators of the indirect relationship between entrepreneurial knowledge and firm performance. Using a cross-sectional survey design (N=102), the study found entrepreneurial knowledge has a direct, positive, statistically significant relationship on firm performance, as measured by customer satisfaction and market effectiveness. At the same time, the sequential mediation of opportunity recognition and opportunity exploitation was positive and significant. In contrast, the proposed mediated relationship from EK through opportunity exploitation to firm performance was not significant. A subsequent analysis proposing business IT dependency (ITD) of SMEs in Oman (tech firms versus non-tech firms) as a moderator of the relationships between entrepreneurial knowledge and the sequential order mediation of opportunity recognition and opportunity exploitation to firm performance was not significant. The relatively small sample size of this study or other underlying factors, such as cultural factors, may have influenced the proposed mediated moderated results. Therefore, based on the literature, further investigation is needed to better understand these relationships. Overall, the xiii findings provide an initial understanding of potential relationships between EK and firm performance in less developed countries