NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

A Survey on Feature Selection Algorithms

One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and feature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations. DOI: 10.17762/ijritcc2321-8169.16043

An improved multiple classifier combination scheme for pattern classification

Combining multiple classifiers are considered as a new direction in the pattern recognition to improve classification performance. The main problem of multiple classifier combination is that there is no standard guideline for constructing an accurate and diverse classifier ensemble. This is due to the difficulty in identifying the number of homogeneous classifiers and how to combine the classifier outputs. The most commonly used ensemble method is the random strategy while the majority voting technique is used as the combiner. However, the random strategy cannot determine the number of classifiers and the majority voting technique does not consider the strength of each classifier, thus resulting in low classification accuracy. In this study, an improved multiple classifier combination scheme is proposed. The ant system (AS) algorithm is used to partition feature set in developing feature subsets which represent the number of classifiers. A compactness measure is introduced as a parameter in constructing an accurate and diverse classifier ensemble. A weighted voting technique is used to combine the classifier outputs by considering the strength of the classifiers prior to voting. Experiments were performed using four base classifiers, which are Nearest Mean Classifier (NMC), Naive Bayes Classifier (NBC), k-Nearest Neighbour (k-NN) and Linear Discriminant Analysis (LDA) on benchmark datasets, to test the credibility of the proposed multiple classifier combination scheme. The average classification accuracy of the homogeneous NMC, NBC, k-NN and LDA ensembles are 97.91%, 98.06%, 98.09% and 98.12% respectively. The accuracies are higher than those obtained through the use of other approaches in developing multiple classifier combination. The proposed multiple classifier combination scheme will help to develop other multiple classifier combination for pattern recognition and classification

Convergence as Evidence

The comparative method grants epistemic access to the biological past. Comparing lineages provides empirical traction on both hypotheses about particular lineages and models of trait evolution. Understanding this evidential role is important. Although philosophers have recently turned their attention to relations of descent (homology), very little work exists exploring the status of evidence from convergences (analogy). I argue that, where they exist, convergences play a central role in the confirmation of adaptive hypotheses. I focus on ‘analogous inferences’ (inferences which take a trait/environment dyad from one lineage and project it to another), show how such inferences ought to be analyzed and suggest three methods for strengthening their evidential weight

Belief rule-base expert system with multilayer tree structure for complex problems modeling

Belief rule-base (BRB) expert system is one of recognized and fast-growing approaches in the areas of complex problems modeling. However, the conventional BRB has to suffer from the combinatorial explosion problem since the number of rules in BRB expands exponentially with the number of attributes in complex problems, although many alternative techniques have been looked at with the purpose of downsizing BRB. Motivated by this challenge, in this paper, multilayer tree structure (MTS) is introduced for the first time to define hierarchical BRB, also known as MTS-BRB. MTS- BRB is able to overcome the combinatorial explosion problem of the conventional BRB. Thereafter, the additional modeling, inferencing, and learning procedures are proposed to create a self-organized MTS-BRB expert system. To demonstrate the development process and benefits of the MTS-BRB expert system, case studies including benchmark classification datasets and research and development (R&D) project risk assessment have been done. The comparative results showed that, in terms of modelling effectiveness and/or prediction accuracy, MTS-BRB expert system surpasses various existing, as well as traditional fuzzy system-related and machine learning-related methodologie

Intelligent Robotics Navigation System: Problems, Methods, and Algorithm

This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments

Assessment of student learning in a business internship

Internships, like other forms of cooperative education, involve students undertaking work as an integrated component of their tertiary education programme. It is only relatively recently that research has been undertaken to consider what it is that students actually learn when undertaking such work. This is because workplace learning is complex, informal, and subject to the contextual influences of the particular workplace. Such complexities are heightened in some disciplines, like business, where the work occurs in diverse workplace settings, with the work requirements being unique to each student. Not surprisingly, there is even less research that may assist practitioners to find ways of assessing such learning. Most forms of summative assessment are based on adherence to the principles of criterion-referencing, which require using the same criteria and set of standards for all students. But when the learning takes place away from the formal, structured environment of an educational setting, underpinned by a fixed and 'known' curriculum, adoption of such principles is problematic, and can create the conditions for assessment to be inherently unfair (and therefore invalid). This is because they can fail to take account of the individual and variable nature of the work, the contextual influences involved, and the conscious and unconscious biases of the assessors. So how does one assess student performance and learning in cooperative education? This thesis sets out to address this question in relation to a business internship that is part of an undergraduate degree programme in a large New Zealand polytechnic. A multi-theoretical approach was taken to the study, which provided valuable frames of reference for viewing assessment of learning. By adopting an interpretive methodology, primarily driven through participatory action research, the contextual complexities involved were able to be incorporated into the research design. Through engagement with the practitioner, a self-assessed, evidence-based portfolio model of assessment was created. A key feature of the model is that the 'truth' of students' performance emerges through consensus, based on an informed understanding of the subjective elements and contextual influences present. An important contribution is the on-going dialogue that occurs, throughout the work placement, between the stakeholders (employers, students and academics). The study has found that the assessment model developed was able to address the complexities involved. The stakeholders supported and valued the portfolio assessment model, and it was apparent that the formative aspects of the portfolio contributed positively to its summative outcome, without seemingly compromising the nature of either. The portfolio also had a high 'backwash' effect on learning, contributing to its consequential validity. Such learning included students' increased awareness of the important competencies required in the workplace and how such competencies contribute to effective performance. In addition, the self-assessed nature of the model contributed to students' development as lifelong assessors of their own learning; preparing them to become self-regulating professionals. Finally, it was apparent that informal, emergent learning, derived from the sociocultural influences present, was an important feature of students' workplace experiences