Learning to mean

This paper sets out to consider the nature of research or enquiry that is appropriate to the study of the design curriculum. It is suggested that the nature of educational enquiry is different from that of research in the natural sciences, which has often been mistakenly adopted as a paradigm. Educational enquiry must be based on educational practice and must start from the experience of educational action. Design activity is identified as a strand of human action, specifically concerned with ill-defined problems, and exercised through the cognitive modelling capacity of the mind. There is a fundamental semantic dimension to design activity and design educational activity, and thus enquiry into these activities is necessarily a semantic enquiry.     

Differences in brain networks of children with ADHD: Whole-brain analysis of resting-state fMRI

The aim of this study was to investigate changes of functional connectivity (FC) in resting state networks (RSNs) in medication-naive children with attention-deficit/hyperactivity disorder (ADHD). Twenty children with a diagnosis of ADHD (11.8 ± 2.29 years; 16 boys) and 20 age-matched typically developing (TD) children (13 ± 1.78 years; 10 boys) were included. It was found that brain FC differences in children with ADHD are not only in the well known RSNs such as default mode, visual, sensory motor, attentional, frontal, central executive, and frontal networks but also involve interaction among whole brain functional networks. In addition, basal ganglia and cerebellum networks which later gained importance were examined in this study. All RSNs has been shown significant differences in special regions which belongs related RSN. The highest positive (HPC) and negative (HNC) correlation were calculated between 14 independent components including 11 different RSNs. We observed different FC changes (decreased/increased) according to the RSNs between ADHD and control children. The HPC was defined between the visual and cerebellum network in ADHD children and between the dorsal attentional network and sensory motor network in TD children. Also, the HNC was detected between the visual and basal ganglia network in both groups. Investigating intra and inter network FC could provide a framework to better understand the neural basis that underlies core symptom dimensions in ADHD.     

Learning Considerations with Economic Order Quantity

A method for the consideration of learning impacts on economic order quantity is presented for the simple inventory model. The developed techniques can be applied to the analysis of more complex and generalized inventory systems.     

具有机器学习能力的智能车间调度系统

基于规则的车间调度系统与专家系统的结合，使智能调度系统得到了广泛的应用。但是知识的欠缺制约了应用效果。本文介绍一种具有机器学习能力的智能车间调度系统的设计。     

The impact of distractions on young adult drivers with attention deficit hyperactivity disorder (ADHD)

Young adults with attention deficit hyperactivity disorder (ADHD) are at higher risk for being involved in automobile crashes. Although driving simulators have been used to identify and understand underlying behaviors, prior research has focused largely on single-task, non-distracted driving. However, in-vehicle infotainment and communications systems often vie for a driver's attention, potentially increasing the risk of collision. This paper explores the impact of secondary tasks on individuals with and without ADHD, a medical condition known to affect the regulation of attention. Data are drawn from a validated driving simulation representing periods before, during, and after participation in a secondary cognitive task. A hands-free phone task was employed in a high stimulus, urban setting and a working memory task during low stimulus, highway driving. Drivers with ADHD had more difficulty on the telephone task, yet did not show an increased decrement in driving performance greater than control participants. In contrast, participants with ADHD showed a larger decline in driving performance than controls during a secondary task in a low demand setting. The results suggest that the interaction of the nature of the driving context and the secondary task has a significant influence on how drivers with ADHD allocate attention and, in-turn, on the relative impact on driving performance. Drivers with ADHD appear particularly susceptible to distraction during periods of low stimulus driving.     

Alzheimer Disease Detection Empowered with Transfer Learning

Alzheimer's disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia. Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread. Alzheimer's is most common in elderly people in the age bracket of 65 and above. An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes. Deep learning and machine learning techniques are used to solve many medical problems like this. The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining (MRI) working to classify the images in four stages, Mild demented (MD), Moderate demented (MOD), Non-demented (ND), Very mild demented (VMD). Simulation results have shown that the proposed system model gives 91.70% accuracy. It also observed that the proposed system gives more accurate results as compared to previous approaches.     

Machine Learning Applied to Problem-Solving in Medical Applications

Physical health plays an important role in overall well-being of the human beings. It is the most observed dimension of health among others such as social, intellectual, emotional, spiritual and environmental dimensions. Due to exponential increase in the development of wireless communication techniques, Internet of Things (IoT) has effectively penetrated different aspects of human lives. Healthcare is one of the dynamic domains with ever-growing demands which can be met by IoT applications. IoT can be leveraged through several health service offerings such as remote health and monitoring services, aided living, personalized treatment, and so on. In this scenario, Deep Learning (DL) models are employed in proficient disease diagnosis. The current research work presents a new IoT-based physical health monitoring and management method using optimal Stacked Sparse Denoising Autoencoder (SSDA) technique i.e., OSSDA. The proposed model utilizes a set of IoT devices to collect the data from patients. Imbalanced class problem poses serious challenges during disease diagnosis process. So, the OSSDA model includes Synthetic Minority Over-Sampling Technique (SMOTE) to generate artificial minority class instances to balance the class distribution. Further, the hyperparameter settings of the OSSDA model exhibit heavy influence upon the classification performance of SSDA technique. The number of hidden layers, sparsity, and noise count are determined by Sailfish Optimizer (SFO). In order to validate the effectiveness and performance of the proposed OSSDA technique, a set of experiments was conducted on diabetes and heart disease datasets. The simulation results portrayed a proficient diagnostic outcome from OSSDA technique over other methods. The proposed method achieved the highest accuracy values i.e., 0.9604 and 0.9548 on the applied heart disease and diabetes datasets respectively.     

Using Parameter Prediction Models to Forecast Post-Interruption Learning

Industrial learning curves are well-established tools for estimation of labor times and costs during the start-up, or learning, phase of a production activity. Their accuracy is impressive given a stable environment and continuous production. One of the problems that users face, however, is dealing with interruptions of the learning process. An improved ability to predict the course of learning after interruptions of production would enable manufacturers, service companies and others to improve their cost estimates and plan for resource requirements. Recent research has addressed the effects of interruption (and forgetting) as well as the nature of the relearning curve. This project represents an attempt to predict the parameters of relearning curves based on information available at the time that production resumes—including the original learning curve parameters, the amount of original learning, and the length of the production interruption. We develop and test parameter prediction models (PPMs) for estimating the parameters of relearning curves, then compare the predictive ability of such “estimated relearning curves” to the predictive ability of relearning curves (RLCs) that use only the relearning data. The PPMs perform well, showing lower mean absolute percentage errors than the best RLC model for this task until several relearning data points become available.     

Thirty-day self-reported risky driving behaviors of ADHD and non-ADHD drivers

The present study aims to compare differences in reported risky driving behaviors of drivers – males and females – having and not having Attention Deficit Hyperactivity Disorder (ADHD), by using a checklist of driving behaviors based on the Driving Behavior Questionnaire (DBQ). Unlike the studies which employ the DBQ by asking the subjects to fill the questionnaire once, in this present study, the participants were asked to report their behaviors on a daily basis for 30 consequent days. The checklist included two factors of risky driving behavior: Violation and Faults. Thirty-eight drivers – 10 males and 9 females with ADHD, and 9 males and 10 females without ADHD (N-ADHD) as control groups – participated in the study. The results showed that the mean of the unsafe behaviors of ADHD was higher, i.e., less safe driving, compared to that of N-ADHD. However, a statistically significant effect was found only between male ADHD and male N-ADHD for the Faults. In order to check the effect of the length of the study, the 30 days duration of the research was divided into three consecutive periods. The reported driving habits of the female ADHD showed safer behaviors than those of the males. Unlike the findings of N-ADHD of both genders, which showed a tendency towards safer driving reports in the three periods, both genders of the ADHD showed higher rates of Faults, i.e., a decrease in safety driving reports, in the three periods. The findings suggest that ADHD drivers differ from the N-ADHD drivers in making driving mistakes, i.e., Faults, due to their lack of sustained attention, but not in making Violations. However, some of the results in the present study were not very strong. Possible explanations for this as well as methodological considerations are discussed, and further research is suggested.     

An Artificial Sensory Neuron with Tactile Perceptual Learning

Sensory neurons within skin form an interface between the external physical reality and the inner tactile perception. This interface enables sensory information to be organized identified, and interpreted through perceptual learning—the process whereby the sensing abilities improve through experience. Here, an artificial sensory neuron that can integrate and differentiate the spatiotemporal features of touched patterns for recognition is shown. The system comprises sensing, transmitting, and processing components that are parallel to those found in a sensory neuron. A resistive pressure sensor converts pressure stimuli into electric signals, which are transmitted to a synaptic transistor through interfacial ionic/electronic coupling via a soft ionic conductor. Furthermore, the recognition error rate can be dramatically decreased from 44% to 0.4% by integrating with the machine learning method. This work represents a step toward the design and use of neuromorphic electronic skin with artificial intelligence for robotics and prosthetics.