scholarly journals Repertoar Musik Keroncong Dengan Menggunakan Idiom Musik Sunda: Implementasi Model Pembelajaran Kolaborasi pada Mata Kuliah Sejarah Analisis Musik Indonesia di Departemen Pendidikan Musik FPSD UPI Bandung

2021 ◽  
Vol 21 (3) ◽  
pp. 127-137
Author(s):  
Hery Supiarza ◽  
Harry Tjahjodiningrat
Keyword(s):  
Collaborative Learning ◽  
Music Education ◽  
Traditional Approach ◽  
Future Research ◽  
Learning Models ◽  
Student Work ◽  
Student Collaboration ◽  
The Arts ◽  
Song Production ◽  
Department Of Music

Analysis at the Department of Music Education, FPSD, Universitas. Pendidikan Indonesia This study discusses the Implementation of Collaborative Learning Models in the History course of Indonesian music analysis at the Department of Music Education, UPI Baandung FPSD. The researcher as a lecturer in this course intends to add to the repertoire of keroncong songs, which since the 1980s keroncong song production has stalled due to competition in the Indonesian music industry. The Action Research method was used in this study to develop students' abilities in creating keroncong songs. 7 stages are used, starting from initial observation, analysis, combining ideas and ideas into big themes, evaluation exercises 1, recording and mastering. The results of this study created 10 keroncong music recordings as a product of a pure repertoire of student collaboration with the Sundanese traditional approach as the basis for creation. Future research will improve the 10 products into a more professional recording result. This research can be a reference for the application of collaborative learning models to create student work and creations in the arts.

scholarly journals Striatal and hippocampal contributions to flexible navigation in rats and humans

2020 ◽  
Vol 4 ◽  
pp. 239821282097977
Author(s):  
Christoffer J. Gahnstrom ◽  
Hugo J. Spiers
Keyword(s):  
Caudate Nucleus ◽  
Spatial Navigation ◽  
Dorsal Striatum ◽  
Future Research ◽  
Neural Responses ◽  
Learning Models ◽  
Transition Structure ◽  
Cortical Areas ◽  
Reinforcement Learning Models ◽  
Human Neuroimaging

The hippocampus has been firmly established as playing a crucial role in flexible navigation. Recent evidence suggests that dorsal striatum may also play an important role in such goal-directed behaviour in both rodents and humans. Across recent studies, activity in the caudate nucleus has been linked to forward planning and adaptation to changes in the environment. In particular, several human neuroimaging studies have found the caudate nucleus tracks information traditionally associated with that by the hippocampus. In this brief review, we examine this evidence and argue the dorsal striatum encodes the transition structure of the environment during flexible, goal-directed behaviour. We highlight that future research should explore the following: (1) Investigate neural responses during spatial navigation via a biophysically plausible framework explained by reinforcement learning models and (2) Observe the interaction between cortical areas and both the dorsal striatum and hippocampus during flexible navigation.

scholarly journals Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7911
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Similarities And Differences ◽  
Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

Assessment of Teacher Attitudes Toward Inclusion of Students with Disabilities: A Confirmatory Factor Analysis

1997 ◽  
Vol 14 (1) ◽  
pp. 51-64 ◽  
Author(s):  
Georgios D. Sideridis ◽  
Judy P. Chandler
Keyword(s):  
Physical Education ◽  
Music Education ◽  
Students With Disabilities ◽  
Structural Equation ◽  
Structural Model ◽  
Equation Modeling ◽  
Future Research ◽  
Physical Education Teachers ◽  
The Status ◽  
Fit Index

The Teacher Integration Attitudes Questionnaire (TIAQ) was developed in order to assess the attitudes and beliefs of teachers (n = 110) with regard to the inclusion of students with disabilities in regular education settings. Using Structural Equation Modeling, the final structural model of the TIAQ comprised four constructs, namely, “Skills,” “Benefits,” “Acceptance,” and “Support.” The final model was fully supported by the derivation sample of music education teachers (n = 54) and produced a Comparative Fit Index (CFI = 1.00). The replication sample of physical education teachers (n = 56) partially supported the generality of the TIAQ, (CFI = .844). Further, the internal consistency properties of the TIAQ (Cronbach’s alpha was .77 for both samples) were satisfactory. We conclude that the psychometric properties of the TIAQ were adequate, and it can be used as a valid assessment in evaluating the status of inclusion for students with disabilities as perceived by music education and physical education teachers. However, future research is needed to support its generality with other groups of teachers and professionals.

scholarly journals EyeHealer: A large-scale anterior eye segment dataset with eye structure and lesion annotations

2021 ◽  
Author(s):  
Wenjia Cai ◽  
Jie Xu ◽  
Ke Wang ◽  
Xiaohong Liu ◽  
Wenqin Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Image Annotation ◽  
Clinical Care ◽  
Significant Proportion ◽  
Anterior Segment ◽  
Future Research ◽  
Learning Models ◽  
Anterior Eye Segment ◽  
Segmentation Models

Abstract Anterior segment eye diseases account for a significant proportion of presentations to eye clinics worldwide, including diseases associated with corneal pathologies, anterior chamber abnormalities (e.g. blood or inflammation) and lens diseases. The construction of an automatic tool for the segmentation of anterior segment eye lesions will greatly improve the efficiency of clinical care. With research on artificial intelligence progressing in recent years, deep learning models have shown their superiority in image classification and segmentation. The training and evaluation of deep learning models should be based on a large amount of data annotated with expertise, however, such data are relatively scarce in the domain of medicine. Herein, the authors developed a new medical image annotation system, called EyeHealer. It is a large-scale anterior eye segment dataset with both eye structures and lesions annotated at the pixel level. Comprehensive experiments were conducted to verify its performance in disease classification and eye lesion segmentation. The results showed that semantic segmentation models outperformed medical segmentation models. This paper describes the establishment of the system for automated classification and segmentation tasks. The dataset will be made publicly available to encourage future research in this area.

scholarly journals Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shelly Soffer ◽  
Eyal Klang ◽  
Orit Shimon ◽  
Yiftach Barash ◽  
Noa Cahan ◽  
...  
Keyword(s):  
Systematic Review ◽  
Pulmonary Embolism ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Future Research ◽  
Learning Models ◽  
Summary Receiver Operating Characteristic ◽  
Pulmonary Angiogram

AbstractComputed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803–0.927) and 0.86 (95% CI 0.756–0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.

scholarly journals Methods for image denoising using convolutional neural network: a review

2021 ◽  
Author(s):  
Ademola E. Ilesanmi ◽  
Taiwo O. Ilesanmi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Denoising ◽  
Speckle Noise ◽  
Future Research ◽  
The Arts

AbstractImage denoising faces significant challenges, arising from the sources of noise. Specifically, Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in imaging. Convolutional neural network (CNN) has increasingly received attention in image denoising task. Several CNN methods for denoising images have been studied. These methods used different datasets for evaluation. In this paper, we offer an elaborate study on different CNN techniques used in image denoising. Different CNN methods for image denoising were categorized and analyzed. Popular datasets used for evaluating CNN image denoising methods were investigated. Several CNN image denoising papers were selected for review and analysis. Motivations and principles of CNN methods were outlined. Some state-of-the-arts CNN image denoising methods were depicted in graphical forms, while other methods were elaborately explained. We proposed a review of image denoising with CNN. Previous and recent papers on image denoising with CNN were selected. Potential challenges and directions for future research were equally fully explicated.

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