Workshop on Fuzzy Control Systems and Space Station Applications

The Workshop on Fuzzy Control Systems and Space Station Applications was held on 14-15 Nov. 1990. The workshop was co-sponsored by McDonnell Douglas Space Systems Company and NASA Ames Research Center. Proceedings of the workshop are presented

A Hybrid Artificial Neural Network Model For Data Visualisation, Classification, And Clustering [QP363.3. T253 2006 f rb].

Tesis ini mempersembahkan penyelidikan tentang satu model hibrid rangkaian neural buatan yang boleh menghasilkan satu peta pengekalan-topologi, serupa dengan penerangan teori bagi peta otak, untuk visualisasi, klasifikasi dan pengklusteran data. In this thesis, the research of a hybrid Artificial Neural Network (ANN) model that is able to produce a topology-preserving map, which is akin to the theoretical explanation of the brain map, for data visualisation, classification, and clustering is presented

Towards a consolidation of worldwide journal rankings - A classification using random forests and aggregate rating via data envelopment analysis

AbstractThe question of how to assess research outputs published in journals is now a global concern for academics. Numerous journal ratings and rankings exist, some featuring perceptual and peer-review-based journal ranks, some focusing on objective information related to citations, some using a combination of the two. This research consolidates existing journal rankings into an up-to-date and comprehensive list. Existing approaches to determining journal rankings are significantly advanced with the application of a new classification approach, ‘random forests’, and data envelopment analysis. As a result, a fresh look at a publication׳s place in the global research community is offered. While our approach is applicable to all management and business journals, we specifically exemplify the relative position of ‘operations research, management science, production and operations management’ journals within the broader management field, as well as within their own subject domain

Reconciling Contemporary Approaches to School Attendance and School Absenteeism: Toward Promotion and Nimble Response, Global Policy Review and Implementation, and Future Adaptability (Part 1)

School attendance is an important foundational competency for children and adolescents, and school absenteeism has been linked to myriad short- and long-term negative consequences, even into adulthood. Many efforts have been made to conceptualize and address this population across various categories and dimensions of functioning and across multiple disciplines, resulting in both a rich literature base and a splintered view regarding this population. This article (Part 1 of 2) reviews and critiques key categorical and dimensional approaches to conceptualizing school attendance and school absenteeism, with an eye toward reconciling these approaches (Part 2 of 2) to develop a roadmap for preventative and intervention strategies, early warning systems and nimble response, global policy review, dissemination and implementation, and adaptations to future changes in education and technology. This article sets the stage for a discussion of a multidimensional, multi-tiered system of supports pyramid model as a heuristic framework for conceptualizing the manifold aspects of school attendance and school absenteeism

Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is the most common type of leukemia. The Cancer Genome Atlas Research Network has demonstrated the increasing genomic complexity of acute myeloid leukemia (AML). In addition, the network has facilitated our understanding of the molecular events leading to this deadly form of malignancy for which the prognosis has not improved over past decades. AML is a highly heterogeneous disease, and cytogenetics and molecular analysis of the various chromosome aberrations including deletions, duplications, aneuploidy, balanced reciprocal translocations and fusion of transcription factor genes and tyrosine kinases has led to better understanding and identification of subgroups of AML with different prognoses. Furthermore, molecular classification based on mRNA expression profiling has facilitated identification of novel subclasses and defined high-, poor-risk AML based on specific molecular signatures. However, despite increased understanding of AML genetics, the outcome for AML patients whose number is likely to rise as the population ages, has not changed significantly. Until it does, further investigation of the genomic complexity of the disease and advances in drug development are needed. In this review, leading AML clinicians and research investigators provide an up-to-date understanding of the molecular biology of the disease addressing advances in diagnosis, classification, prognostication and therapeutic strategies that may have significant promise and impact on overall patient survival

The role of AI classifiers in skin cancer images

Background: The use of different imaging modalities to assist in skin cancer diagnosis is a common practice in clinical scenarios. Different features representative of the lesion under evaluation can be retrieved from image analysis and processing. However, the integration and understanding of these additional parameters can be a challenging task for physicians, so artificial intelligence (AI) methods can be implemented to assist in this process. This bibliographic research was performed with the goal of assessing the current applications of AI algorithms as an assistive tool in skin cancer diagnosis, based on information retrieved from different imaging modalities. Materials and methods: The bibliography databases ISI Web of Science, PubMed and Scopus were used for the literature search, with the combination of keywords: skin cancer, skin neoplasm, imaging and classification methods. Results: The search resulted in 526 publications, which underwent a screening process, considering the established eligibility criteria. After screening, only 65 were qualified for revision. Conclusion: Different imaging modalities have already been coupled with AI methods, particularly dermoscopy for melanoma recognition. Learners based on support vector machines seem to be the preferred option. Future work should focus on image analysis, processing stages and image fusion assuring the best possible classification outcome.info:eu-repo/semantics/publishedVersio

Decision tree learning for intelligent mobile robot navigation

The replication of human intelligence, learning and reasoning by means of computer algorithms is termed Artificial Intelligence (Al) and the interaction of such algorithms with the physical world can be achieved using robotics. The work described in this thesis investigates the applications of concept learning (an approach which takes its inspiration from biological motivations and from survival instincts in particular) to robot control and path planning. The methodology of concept learning has been applied using learning decision trees (DTs) which induce domain knowledge from a finite set of training vectors which in turn describe systematically a physical entity and are used to train a robot to learn new concepts and to adapt its behaviour. To achieve behaviour learning, this work introduces the novel approach of hierarchical learning and knowledge decomposition to the frame of the reactive robot architecture. Following the analogy with survival instincts, the robot is first taught how to survive in very simple and homogeneous environments, namely a world without any disturbances or any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex environments by adding further worlds to its existing knowledge. The repertoire of the robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered decision trees (DTs) accommodating a number of primitives. To classify robot perceptions, control rules are synthesised using symbolic knowledge derived from searching the hierarchy of DTs. A second novel concept is introduced, namely that of multi-dimensional fuzzy associative memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs. In this thesis, the feasibility of the developed techniques is illustrated in the robot applications, their benefits and drawbacks are discussed

The UP AMIGOS project: Testing the predictive validity of the 2007 Pediatric Expert Committee Recommendations in Latinos

Background. Mexicans are disproportionately affected by cardiovascular disease and there is mounting evidence that Mexicans may be genetically prone to the development of cardiovascular disease (CVD) risk factors. Objective. There were three aims of study. The first aim was to identify the prevalence of three CVD risk factors in Mexican young adults: (1) non-alcoholic fatty liver disease (NAFLD), (2) dyslipidemia, and (2) impaired fasting glucose. The second aim was to test the sensitivity and specificity of the Pediatric Expert Committee Recommendations (PECR) in identifying Mexicans with these three cardiovascular disease risk factors. Finally, the third aim was to explore ways to improve the clinical screening algorithm. Methods. In this cross-sectional study, data for UP AMIGOS were collected from 9,974 participants (age 18- to 21-years-old) living in Central Mexico. Participants underwent a health screen that included: a questionnaire, anthropometric measurements (i.e. height, weight, waist circumference, blood pressure), a physician-conducted history and physical, and venipuncture for blood biomarkers. Analysis. In order to determine prevalence of CVD risk factors, descriptive statistics were run making comparisons in prevalence by sex and weight category: normal weight, overweight, or obese. The value of the PECR was measured with sensitivity, specificity, and positive predictive value, with additional tests for significant associations. Alternative algorithms were explored using classification and regression tree analysis. Results. NALFD (17.1 to 45.5%) and dyslipidemia (44.8%) were fairly prevalent. In contrast, impaired fasting glucose (IFG) was rare (4.0%). Each CVD risk factor increased with increasing levels of adiposity. The PECR provided a reasonable clinical screen for NALFD, but was fairly insensitive in detecting those with dyslipidemia or IFG. Multiple exploratory analyses revealed more sensitive screening solutions for each individual CVD disease risk factor, but at the cost of having a less parsimonious clinical screen. Significance. Mexican adolescents and young adults already have a high prevalence of CVD risk factors. These risk factors may go unnoticed and eventually convert to irreversible disease, unless a valid, predictive screening protocol is established. Based on this analysis, screening recommendations are three-fold: (1) Universal screening for dyslipidemia is recommended for Mexican young adults, (2) IFG screening is not recommended in adolescents or young adults, (3) the PECR may be a reasonable clinical screen for NALFD, but more data is needed

Analysis of workers\u27 compensation claims data for improving safety outcomes in agribusiness industries

Occupational injuries continue to be a major issue for non-farm agricultural workplaces such as commercial grain elevators and ethanol plants. For preventing these injuries and improving workplace safety outcomes requires learning from past incidents, and identify the most significant causes and implement targeted prevention strategies. However, obtaining detailed records of past incidents is a challenge acknowledged by investigators across several industrial sectors including agribusiness. Previous researchers suggest workers ’ compensation claims as an excellent data source to address the existing informational gaps about safety incidents and injuries in the workplace. In this study, workers’ compensation claims obtained from a leading private insurance company were investigated using statistical techniques such as chi-square tests, regression analysis, and data mining techniques such as decision trees. The study objective was to analyze these claims, identify injury causes, risks, and problem areas so supervisors and safety professionals can make decisions needed to improve safety outcomes in the workplace. The findings of this study are documented in three separate manuscripts. Since safety incidents that cause injuries and fatalities have a widespread impact, therefore mitigating these incidents using a proactive data-driven approach rather than just compliance can benefit the worker, the organization, and society-at-large