Composite Deep Belief Network approach for enhanced Antepartum foetal electrocardiogram signal

Antepartum Foetal surveillance is the most vital epoch of investigation during the pregnancy period. This surveillance would provide an opening to plan and manage the Foetus during Intrapartum and Antepartum stages of pregnancy. Moreover, it will help to identify high risk Foetuses during pregnancies which are complicated by maternal health conditions like diabetes mellitus, intrauterine growth restriction, etc. The foetal electrocardiogram (fECG) signal can be detected in the course of pregnancy from the Antepartum stage. Generally, fECG signal analysis is not carried out for Foetal surveillance. Rather, the traditional methodologies like phonocardiogram, etc. are being utilized. The reason is the unavailability of an effective methodology for providing good quality fECG signal. The proposal of a hybrid tactic called Bayesian Deep Belief Network (BDBN) for fECG signal enhancement is presented in this article. The proposed BDBN technique involves Baye’s filtering methodology in amalgamation with Deep Belief Network. The Baye’s filtering was employed to eliminate undesired signal components. Deep learning (DL) technique was utilized with Deep belief network (DBN) to extract high quality fECG signal. The methodology resulted with good quality fECG signal which is indeed valuable for timely Physician analysis.     

Parallel distributed processing and neuropsychology: A neural network model of Wisconsin card sorting and verbal fluency

Neural networks can be used as a tool in the explanation of neuropsychological data. Using the Hebbian Learning Rule and other such principles as competition and modifiable interlevel feedback, researchers have successfully modeled a widely used neuropsychological test, the Wisconsin Card Sorting Test. One of these models is reviewed here and extended to a qualitative analysis of how verbal fluency might be modeled, which demonstrates the importance of accounting for the attentional components of both tests. Difficulties remain in programming sequential cognitive processes within a parallel distributed processing (PDP) framework and integrating exceedingly complex neuropsychological tests such as Proverbs. PDP neural network methodology offers neuropsychologists co-validation procedures within narrowly defined areas of reliability and validity.     

Connectionist models of conditioning: A tutorial

Models containing networks of neuron-like units have become increasingly prominent in the study of both cognitive psychology and artificial intelligence. This article describes the basic features of connectionist models and provides an illustrative application to compound-stimulus effects in respondent conditioning. Connectionist models designed specifically for operant conditioning are not yet widely available, but some current learning algorithms for machine learning indicate that such models are feasible. Conversely, designers for machine learning appear to have recognized the value of behavioral principles in producing adaptive behavior in their creations.     

Order effects in belief updating with consistent and inconsistent evidence

The current study tests for the presence of differential order effects in evaluation tasks with consistent and inconsistent evidence as predicted by the Hogarth and Einhorn (1992) belief-adjustment model. The results, based on both between-subjects and within-subjects experiments, demonstrate that there were significant recency effects with inconsistent evidence as predicted, larger recency effects when the inconsistent evidence was farther apart in subjective value as predicted, and significant recency effects even when subjects were given training designed to both help them understand the task as completely as possible and to be better able to assess the pieces of evidence. By including a within-subjects design, we were able to demonstrate that the difference in subjective value between two pieces of evidence is the primary factor influencing the magnitude of the recency effect, regardless of whether the evidence is consistent or inconsistent. This latter finding is unique and contrary to previous research and theory.     

An Ecologically Differentiated, Multifactor Model of Adolescent Network Orientation

The paper presents a test of an ecologically differentiated model of social network orientation for adolescents that distinguished between different social network reference groups (family, peers, and nonfamily adults). The model was tested in two consecutive studies. Study 1 describes initial model development (N = 120). Study 2 presents a confirmatory factor analysis with a second sample (N = 430) to replicate the factor structure developed in Study 1. Results supported a three-factor model of network orientation that differentiated between network reference groups. Analyses of concurrent and predictive validity indicated that orientation to network reference groups was differentially related to the perceived quality and frequency of support from members of respective social network groups. Group differences (gender, race) regarding network orientation to different network reference groups were consistent with studies of other social network processes. Implications for the study of the network orientation and the study of social networks more generally are discussed.     

Belief and Survival

Our ability to survive in a world beset by looming global perils depends ultimately on our collective will to harness our intellects and change our behaviors. In order to respond appropriately, people must first believe that serious problems exist, that there are potential solutions, and that they have a role to play in finding and implementing them. Without such beliefs, individual change is unlikely. In order to promote belief change, it is important to understand how beliefs are learned, what their functions are, and why they are so often resistant to change. These issues are discussed in this article, along with the role that social dilemmas play in inhibiting individually prosocial behavior.     

Single-Stage Prediction Models Do Not Explain the Magnitude of Syntactic Disambiguation Difficulty

The disambiguation of a syntactically ambiguous sentence in favor of a less preferred parse can lead to slower reading at the disambiguation point. This phenomenon, referred to as a garden-path effect, has motivated models in which readers initially maintain only a subset of the possible parses of the sentence, and subsequently require time-consuming reanalysis to reconstruct a discarded parse. A more recent proposal argues that the garden-path effect can be reduced to surprisal arising in a fully parallel parser: words consistent with the initially dispreferred but ultimately correct parse are simply less predictable than those consistent with the incorrect parse. Since predictability has pervasive effects in reading far beyond garden-path sentences, this account, which dispenses with reanalysis mechanisms, is more parsimonious. Crucially, it predicts a linear effect of surprisal: the garden-path effect is expected to be proportional to the difference in word surprisal between the ultimately correct and ultimately incorrect interpretations. To test this prediction, we used recurrent neural network language models to estimate word-by-word surprisal for three temporarily ambiguous constructions. We then estimated the slowdown attributed to each bit of surprisal from human self-paced reading times, and used that quantity to predict syntactic disambiguation difficulty. Surprisal successfully predicted the existence of garden-path effects, but drastically underpredicted their magnitude, and failed to predict their relative severity across constructions. We conclude that a full explanation of syntactic disambiguation difficulty may require recovery mechanisms beyond predictability.     

Cognitive computing models for estimation of reference evapotranspiration: A review

Irrigation practices can be advanced by the aid of cognitive computing models. Repeated droughts, population expansion and the impact of global warming collectively impose rigorous restrictions over irrigation practices. Reference evapotranspiration (ET₀) is a vital factor to predict the crop water requirements based on climate data. There are many techniques available for the prediction of ET₀. An efficient ET₀ prediction model plays an important role in irrigation system to increase water productivity. In the present study, a review has been carried out over cognitive computing models used for the estimation of ET₀. Review exhibits that artificial neural network (ANN) approach outperforms support vector machine (SVM) and genetic programming (GP). Second order neural network (SONN) is the most promising approach among ANN models.     

Crafting Mosaics: Person-Centered Religious Influence and Selection in Adolescent Friendships

This research addresses the intersection of two key domains of adolescents’ lives: religion and peer networks. Religion scholars argue that religion is multi-faceted and better understood by focusing on combinations of indicators (i.e., mosaics), versus a variable-centered approach. We adopt this framework and investigate the interplay between religion and peer networks, both in how religious mosaics are shaped by friends and how religious profiles affect friend selection dynamics. With data from two schools in the National Longitudinal Study of Adolescent Health, we estimate religious mosaics using latent class analysis (LCA) to identify profiles consisting of combinations of commonly available survey-based measures of religious attitudes, behaviors, and identities. Finding evidence of theoretically expected profiles, we then use stochastic actor–based models (SABMs) to investigate network dynamics for these LCA-based religious profiles. We demonstrate how the profile data can be integrated within the SABM framework to evaluate processes of friend selection and influence. Results show evidence of adolescents influencing one another's religious mosaics, but not selecting friends on that basis.