The Implementation of Literacy Culture Programs in Elementary School

This study aims to describe the implementation of literacy culture programs of elementary school. This study used phenomenology of a qualitative approach. The subjects were the headmaster, teachers, students who were selected by using purposive sampling technique. The validity data test used triangulation techniques by comparing the results of observations, interviews, and documentation. The results showed that: 1) The policy for implementing a literacy culture at elementary school by using the School Literacy Movement program of the government. It is based on Permendikbud No. 23, 2015 focusing on character development. (2) The implementation of literacy culture of elementary school. It is based on the habituation stage that carried out 10 minutes of reading Al Qur’an and storybooks before lessons. The development stage of the activities carried out the respond to the contents of reading books by reading independently, giving appreciation for student literacy achievements, and having a school literacy team. The learning stage is by getting used to reading textbooks and using various strategies that support learning.

Lesson Study as an Effort to Improve Teacher's Pedagogical Competence in SMP and MTS 2 Muhammadiyah Aimas in the time of Covid-19

This study aims to explain lesson study as an effort to improve teacher pedagogy at SMP Muhammadiyah Aimas and MTs teachers. Muhammadiyah 2 Aimas. The research method used is descriptive qualitative. The research subjects were 10 teachers who were involved from two schools. Data collection techniques used are observation, interviews and documentation. The data analysis technique used Mills and Huberman. The results showed that the principle of lesson study increased the pedagogical competence of teachers of SMP and MTs 2 Muhammadiyah Aimas compared to conventional learning principles. The planning stage (Plan) shows increased collaboration between teachers, creative teachers in choosing and using media that are closer to students' lives, and teachers are getting better at creating a didactic atmosphere in the learning planning process. The learning stage (Do) shows an increase in student activity in learning, the teacher is maximal as a facilitator, the teacher feels challenged by the presence of the observer, the teacher feels helped by the observer, the teacher is more creative, and student responses are higher when carrying out learning. The evaluation stage (See) shows the increasing ability of teachers in evaluating learning outcomes. There are collaborative efforts from the planning, implementation stages to make the evaluation stage maximal and measurable. and higher student response when carrying out learning. The evaluation stage (See) shows the increasing ability of teachers in evaluating learning outcomes. There are collaborative efforts from the planning, implementation stages to make the evaluation stage maximal and measurable. and higher student response when carrying out learning. The evaluation stage (See) shows the increasing ability of teachers in evaluating learning outcomes. There are collaborative efforts from the planning, implementation stages to make the evaluation stage maximal and measurable.

Pill Detection Model for Medicine Inspection Based on Deep Learning

This paper proposes a deep learning algorithm that can improve pill identification performance using limited training data. In general, when individual pills are detected in multiple pill images, the algorithm uses multiple pill images from the learning stage. However, when there is an increase in the number of pill types to be identified, the pill combinations in an image increase exponentially. To detect individual pills in an image that contains multiple pills, we first propose an effective database expansion method for a single pill. Then, the expanded training data are used to improve the detection performance. Our proposed method shows higher performance improvement than the existing algorithms despite the limited imaging and data set size. Our proposed method will help minimize problems, such as loss of productivity and human error, which occur while inspecting dispensed pills.

A Semi-Supervised Transfer Learning with Dynamic Associate Domain Adaptation for Human Activity Recognition Using WiFi Signals

Human activity recognition without equipment plays a vital role in smart home applications, freeing humans from the shackles of wearable devices. In this paper, by using the channel state information (CSI) of the WiFi signal, semi-supervised transfer learning with dynamic associate domain adaptation is proposed for human activity recognition. In order to improve the CSI quality and denoising of CSI, we carried out missing packet filling, burst noise removal, background estimation, feature extraction, feature enhancement, and data augmentation in the data pre-processing stage. This paper considers the problem of environment-independent human activity recognition, also known as domain adaptation. The pre-trained model is trained from the source domain by collecting a complete labeled dataset of all of the CSI of human activity patterns. Then, the pre-trained model is transferred to the target environment through the semi-supervised transfer learning stage. Therefore, when humans move to different target domains, a partial labeled dataset of the target domain is required for fine-tuning. In this paper, we propose a dynamic associate domain adaptation called DADA. By modifying the existing associate domain adaptation algorithm, the target domain can provide a dynamic ratio of labeled dataset/unlabeled dataset, while the existing associate domain adaptation algorithm only allows target domains with the unlabeled dataset. The advantage of DADA is that it provides a dynamic strategy to eliminate different effects on different environments. In addition, we further designed an attention-based DenseNet model, or AD, as our training network, which is modified by an existing DenseNet by adding the attention function. The solution we proposed was simplified to DADA-AD throughout the paper. The experimental results show that for domain adaptation in different domains, the accuracy of human activity recognition of the DADA-AD scheme is 97.4%. It also shows that DADA-AD has advantages over existing semi-supervised learning schemes.

Classification of Brain MRI Tumor Images Based on Deep Learning PGGAN Augmentation

The wide prevalence of brain tumors in all age groups necessitates having the ability to make an early and accurate identification of the tumor type and thus select the most appropriate treatment plans. The application of convolutional neural networks (CNNs) has helped radiologists to more accurately classify the type of brain tumor from magnetic resonance images (MRIs). The learning of CNN suffers from overfitting if a suboptimal number of MRIs are introduced to the system. Recognized as the current best solution to this problem, the augmentation method allows for the optimization of the learning stage and thus maximizes the overall efficiency. The main objective of this study is to examine the efficacy of a new approach to the classification of brain tumor MRIs through the use of a VGG19 features extractor coupled with one of three types of classifiers. A progressive growing generative adversarial network (PGGAN) augmentation model is used to produce ‘realistic’ MRIs of brain tumors and help overcome the shortage of images needed for deep learning. Results indicated the ability of our framework to classify gliomas, meningiomas, and pituitary tumors more accurately than in previous studies with an accuracy of 98.54%. Other performance metrics were also examined.

Self-Supervised Denoising Image Filter Based on Recursive Deep Neural Network Structure

The purpose of this paper is to propose a novel noise removal method based on deep neural networks that can remove various types of noise without paired noisy and clean data. Because this type of filter generally has relatively poor performance, the proposed noise-to-blur-estimated clean (N2BeC) model introduces a stage-dependent loss function and a recursive learning stage for improved denoised image quality. The proposed loss function regularizes the existing loss function so that the proposed model can better learn image details. Moreover, the recursive learning stage provides the proposed model with an additional opportunity to learn image details. The overall deep neural network consists of three learning stages and three corresponding loss functions. We determine the essential hyperparameters via several simulations. Consequently, the proposed model showed more than 1 dB superior performance compared with the existing noise-to-blur model.

Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review

The main goal of this paper is to review and evaluate how we can take advantage of state-of-the-art machine learning techniques and apply them in wind energy operation conditions monitoring and fault diagnosis, boosting wind turbines’ availability. To accomplish this, we focus our work on analysing the current techniques in predictive maintenance, which are aimed at acting before a major failure occurs using condition monitoring. In particular, we start framing the predictive maintenance problem as an ML problem to detect patterns that indicate a fault on turbine generators. Then, we extend the problem to detect future faults. Therefore, this review will consist of analysing techniques to tackle the challenges of each machine learning stage, such as data pre-processing, feature engineering, and the selection of the best-suited model. By using specific evaluation metrics, the expected final result of using these techniques will be an improvement in the early prediction of a future fault. This improvement will have an increase in the availability of the turbine, and therefore in energy production.

A Study of Needle Stick Injuries among Nursing College Students in a Tertiary Care Hospital in Chengalpattu District, Tamil Nadu

Introduction: Needle stick injuries (NSIs) are the most potential occupational hazards among nursing personnel with possible transmission of blood borne pathogens. As nursing students are in the learning stage, they might be at higher risk of acquiring the injuries.Objectives: Todetermine the prevalence of needle stick injuries and to assess the awareness, attitude and practices followed with regard to NSIs.Methodology: An online cross-sectional study was conducted from October to December 2020 among 175 students in a nursing college in Chengalpattu district Tamil Nadu, using a pretested semi- structured questionnaire, adopting universal sampling technique. Data was analyzed using SPSS version 23. Categorical variables were summarized as Percentages and chi square test was used for statistical analysis. Results: The overall prevalence of needle stick injury among nursing college students was 16%.Final year studentsweremore exposed to the injuries(35.7%). Majority (96.57%) of the students were aware about universal precaution guidelines, 57% of students were aware about the diseases transmitted by NSI, 97.71% were aware about safety devices and 67.43% of students were aware about the post-exposure prophylaxis in management of NSIs.Among the students, 71.43% had used gloves regularly, 72% were immunized against Hepatitis B, only 25.71% of students attended Integrated Counselling Testing Centre(ICTC) and more than half of the students always practiced recapping needle after giving injections. Conclusion: Majority of the students in this study were aware about NSIs, their attitude towards NSIs was agreeable. The practices reported though assessed through online survey was found to be deficient.Periodic education and training need to be done to avoid injuries in future.