Rule Interaction Conversion Operations

Different types of interactions between pairs of phonological rules can be converted into one another using three formal operations that we discuss in this article. One of these conversion operations, rule re-ordering (here called swapping), is well-known; another, flipping, is a more recent finding (Hein et al., 2014). We introduce a third conversion operation that we call cropping. Formal relationships among the members of the set of rule interactions, expanded by cropping beyond the classical four (feeding, bleeding, counterfeeding, and counterbleeding) to include four more (mutual bleeding, seeding, counterseeding, and merger), are identified and clarified. We show that these conversion operations exhaustively delimit the set of possible pairwise rule interactions predicted by conjunctive rule ordering (Chomsky & Halle, 1968), and that each interaction is related to each of the others by the application of at most two conversion operations.

Diagnosis of Coronary Artery Diseases using Classification Algorithms based on Wavelet Transforms

One of the primary drivers of the death in the world is Coronary Artery Diseases (CAD) which is a major threat in developing and developed countries. The fundamental drivers in CAD leads to blockage of the coronary lumen subsequently blood clot and that prompts to damage of heart muscles or unexpected heart attack which causes death. It is difficult to ascertain that a certain person has been affected by CAD, since there are bunch of parameters has been involved to ascertain the conclusion. Classification has been done using wavelet transform to classify the certain parameters. We analyzed following methods such as NB, Logistic, SMO, RBF Network, K-star, Multiclass Classifier, Conjunctive rule, Decision table, LMT, NB Tree, DTNB, LAD Tree, Random Tree and Random Forest calculations has been associated with extensive fragment of the surveys. This database has been generated from UCI machine learning database. In this paper, we used k-fold cross validation with k values as 10, with 14 properties and calculations of Accuracy, Precision, TPR, FPR, Recall, F-measure and ROC are analyzed practically. The experimental evaluation shows the improvement in accuracy rate of 77.0%, by using the Logistic, SMO and LMT algorithms than the traditional method.

Consideration-Constrained Engineering Design for Strategic Insights

Consumer behavior can be modeled using a decision-making process termed “consideration” in which consumers form requirements, “consideration rules,” in order to narrow their options for further evaluation. One type of consideration rule is the conjunctive rule, where a consumer makes a list of requirements and a product must meet all of the requirements in order to be considered for purchase, such as “the vehicle must get 25 miles per gallon or more”; “it must be priced at $22,000 or less”; and “it must be a standard-sized sedan.” This paper offers a design framework for linking these consideration rules with design. We demonstrate the use of our framework with a case study, namely the Volkswagen (VW) “clean diesel” scandal, which investigates the design strategies used in response to the scandal by capturing considerations within the marketing product planning subproblem and assuring engineering feasibility within the engineering design subproblem.

Developing an Effective Classification Model for Medical Data Analysis

The sparse nature of medical data makes knowledge discovery and prediction a complex task for analysis. Machine learning algorithms have produced promising results for diversified data. This chapter constructs the effective classification model for medical data analysis. In particular, nine classification models, namely Naïve Bayes, decision tree (i.e., J48 and Random Forest), multilayer perceptron, radial bias function, k-nearest neighbors, single conjunctive rule learner, support vector machine, and simple logistics have been applied for developing an effective model. Besides, classification models have also been used in conjunction with ensemble learning methods, since ensemble methods significantly increase the predictive outcomes of the classification models. The evaluation of classification models has been measured using accuracy, f-measure, precision, and recall metrics. The empirical results revealed that the combination of ensemble learning methods with classification models produces better predictions in comparison with sole classification model for the medical data.

Cue Use in Distal Autism Spectrum Assessment: A Lens Model Analysis of the Efficacy of Telehealth Technologies

Recent developments in the field of telehealth suggest that novel technologies may ameliorate patients’ limited access to clinicians capable of conducting ASD assessments (Koch, 2006). Specifically, studies have shown that parents can capture informative behaviors that aid in autism assessment by using phone-based applications, and use of these videos result in diagnoses that are consistent with those of clinicians who interact with the same child in person (Nazneen et al., 2015; Smith et al., In press). It is yet unknown how clinicians make use of the information gleaned from videos uploaded to a store-and-forward system. Given that clinicians and physicians often exhibit bias in their use of available information, we sought to understand how cues were utilized when direct contact or observation of the patient is not possible. We used lens model analyses to evaluate one store-and-forward approach: the Naturalistic Observation Diagnostic Assessment (NODA; Smith et al., 2009). Brunswik’s (1952, 1955) lens model provides a computational approach to evaluating use of information while formulating decisions (Karelaia & Hogarth, 2008), such as in the assessment and diagnosis of ASD in children. The parents of 51 children used the NODA procedure to upload four 10-minute long videos depicting the child’s behavior in familiar in-home scenarios. Eleven children were typically developing, and the remaining 40 were seeking an Autism evaluation. Each child was observed twice: One clinician performed a standard in-person assessment (IPA), while the other performed an assessment via videos uploaded to the NODA tool. Observations for 65 classes of behavior (e.g., limited conversation, speaking volume too loud, lack of peer play, echolalia, lining up toys, preoccupation with activity) were clustered into eight nominal variables representing the seven sub-criteria associated with ASD (American Psychiatric Association, 2013) and an additional criterion for behavior labeled as typical. We computed a count for each ASD variable that represented the frequency with which the NODA clinician used the label when tagging the videos. Three pairs of linear regressions were run to estimate the weight clinicians placed on observations associated with each sub-criterion for ASD. Each pair of regressions consisted of one analysis where NODA tag counts were regressed onto the decision made by the IPA clinician and another that regressed NODA tag counts onto the NODA clinician’s decision. The three sets of regressions modeled the clinicians’ use of cues as an equal weight strategy, a conjunctive strategy, and a disjunctive strategy respectively. Our results suggest that clinicians consistently derive their decisions from a limited number of the cues available to them, as no analysis found more than two classes of observation to be predictive of diagnosis. Specifically, we found that IPA and NODA clinicians appeared to adopt a conjunctive rule, and relied most heavily on the number of typical behaviors observed. We also found a high level of agreement between the IPA and NODA clinicians with respect to use of information and diagnosis. These findings suggests that there is no dearth of information available to clinicians for distal ASD assessment when observations are made through pre-recorded video provided by parents via the NODA system as compared to IPA. The results of the reported study illustrate the promise of telehealth technology adoption for distal patient assessment and diagnosis.

Cognitive Changes in Conjunctive Rule-Based Category Learning: An ERP Approach

PreprintWhen learning rule-based categories, sufficient cognitive resources are needed to test hypotheses, maintain the currently active rule in working memory, update rules after feedback, and to select a new rule if necessary. Prior research has demonstrated that conjunctive rules are more complex than unidimensional rules and place greater demands on executive functions like working memory. In our study, event-related potentials (ERPs) were recorded while participants performed a conjunctive rule-based category learning task with trial-by-trial feedback. In line with prior research, correct categorization responses resulted in a larger stimulus-locked late positive complex compared to incorrect responses, possibly indexing the updating of rule information in memory. Incorrect trials elicited a pronounced feedback-locked P300 elicited which suggested a disconnect between perception, and the rule-based strategy. We also examined the differential processing of stimuli that were able to be correctly classified by the suboptimal single-dimensional rule (“easy” stimuli) versus those that could only be correctly classified by the optimal, conjunctive rule (“difficult” stimuli). Among strong learners, a larger, late positive slow wave emerged for difficult compared to easy stimuli, suggesting differential processing of category items even though strong learners performed well on the conjunctive category set. Overall, the findings suggest that ERP combined with computational modelling can be used to better understand the cognitive processes involved in rule-based category learning

Adaptive and robust evidence theory with applications in prediction of floor water inrush in coal mine

The Internet of Things generates rich information either from different sources or the same source via different measurement methods. This demands data fusion for decision making. Despite the progress in data fusion, existing data fusion techniques, such as the classic Dempster–Shafer evidence Theory, face challenges when dealing with highly conflicting sources of evidence. To address this problem, an Adaptive and Robust evidence Theory (ART) is presented in this paper through a robust combination of conjunctive and disjunctive rules. It is capable of handling both conflicting and reliable sources of evidence. When the sources of evidence are reliable, the conjunctive rule plays a predominant role, whereas if the sources of evidence are in high conflict the disjunctive rule is critical. Our ART approach was compared with existing representative evidence theory methods through two examples, and was further applied in the prediction of floor water inrush in coal mines. The ART approach presented in this paper was demonstrated to behave better than the existing methods.

Dopaminergic Genetic Polymorphisms Predict Rule-based Category Learning

Dopaminergic genes play an important role in cognitive function. DRD2 and DARPP-32 dopamine receptor gene polymorphisms affect striatal dopamine binding potential, and the Val158Met single-nucleotide polymorphism of the COMT gene moderates dopamine availability in the pFC. Our study assesses the role of these gene polymorphisms on performance in two rule-based category learning tasks. Participants completed unidimensional and conjunctive rule-based tasks. In the unidimensional task, a rule along a single stimulus dimension can be used to distinguish category members. In contrast, a conjunctive rule utilizes a combination of two dimensions to distinguish category members. DRD2 C957T TT homozygotes outperformed C allele carriers on both tasks, and DARPP-32 AA homozygotes outperformed G allele carriers on both tasks. However, we found an interaction between COMT and task type where Met allele carriers outperformed Val homozygotes in the conjunctive rule task, but both groups performed equally well in the unidimensional task. Thus, striatal dopamine binding may play a critical role in both types of rule-based tasks, whereas prefrontal dopamine binding is important for learning more complex conjunctive rule tasks. Modeling results suggest that striatal dopaminergic genes influence selective attention processes whereas cortical genes mediate the ability to update complex rule representations.