scholarly journals Artificial Intelligence and Deep Learning based Information Retrieval Framework for Assessing Students Performance

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Performance Indicators ◽  
Information Gain ◽  
Educational Institution ◽  
Academic Quality ◽  
Self Assessment ◽  
Continuous Quality ◽  
Assessment Approach

Improving the quality of education is a challenging activity in every educational institution. Through this research paper, a model has been proposed representing the challenges in order to manage the trade-off to maintain the philosophy of continuous quality improvement and strict control based on Higher Education Institutions (HEIs). Several standards criteria, performance parameters, and Key Performance Indicators are studied and suggested for a quality self-assessment approach. After the data is collected, the significant features are selected for analysis of data using dedicated gain, which are designed by integrating the information gain and the dedicated weight constants. After that, deep learning methodologies like regression analysis, the artificial neural network, and the Matlab model are used for evaluating the academic quality of institutions. Finally, areas of development have been recommended using the probabilistic model to the administrators of the institutions based on the prediction made using a deep neural network.

scholarly journals Real-Time Automated Classification of Sky Conditions Using Deep Learning and Edge Computing

2021 ◽  
Vol 13 (19) ◽  
pp. 3859
Author(s):  
Joby M. Prince Czarnecki ◽  
Sathishkumar Samiappan ◽  
Meilun Zhou ◽  
Cary Daniel McCraine ◽  
Louis L. Wasson
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
Precision Agriculture ◽  
Edge Computing ◽  
Training Data ◽  
Learning Approaches ◽  
Sky Conditions

The radiometric quality of remotely sensed imagery is crucial for precision agriculture applications because estimations of plant health rely on the underlying quality. Sky conditions, and specifically shadowing from clouds, are critical determinants in the quality of images that can be obtained from low-altitude sensing platforms. In this work, we first compare common deep learning approaches to classify sky conditions with regard to cloud shadows in agricultural fields using a visible spectrum camera. We then develop an artificial-intelligence-based edge computing system to fully automate the classification process. Training data consisting of 100 oblique angle images of the sky were provided to a convolutional neural network and two deep residual neural networks (ResNet18 and ResNet34) to facilitate learning two classes, namely (1) good image quality expected, and (2) degraded image quality expected. The expectation of quality stemmed from the sky condition (i.e., density, coverage, and thickness of clouds) present at the time of the image capture. These networks were tested using a set of 13,000 images. Our results demonstrated that ResNet18 and ResNet34 classifiers produced better classification accuracy when compared to a convolutional neural network classifier. The best overall accuracy was obtained by ResNet34, which was 92% accurate, with a Kappa statistic of 0.77. These results demonstrate a low-cost solution to quality control for future autonomous farming systems that will operate without human intervention and supervision.

scholarly journals Analysis of COVID-19 Infections on a CT Image Using DeepSense Model

2020 ◽  
Vol 8 ◽  
Author(s):  
Adil Khadidos ◽  
Alaa O. Khadidos ◽  
Srihari Kannan ◽  
Yuvaraj Natarajan ◽  
Sachi Nandan Mohanty ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Input Data ◽  
Training Data ◽  
Learning Framework ◽  
Learning Classifiers ◽  
Tomography Image ◽  
Mining Model ◽  
Deep Learning Model

In this paper, a data mining model on a hybrid deep learning framework is designed to diagnose the medical conditions of patients infected with the coronavirus disease 2019 (COVID-19) virus. The hybrid deep learning model is designed as a combination of convolutional neural network (CNN) and recurrent neural network (RNN) and named as DeepSense method. It is designed as a series of layers to extract and classify the related features of COVID-19 infections from the lungs. The computerized tomography image is used as an input data, and hence, the classifier is designed to ease the process of classification on learning the multidimensional input data using the Expert Hidden layers. The validation of the model is conducted against the medical image datasets to predict the infections using deep learning classifiers. The results show that the DeepSense classifier offers accuracy in an improved manner than the conventional deep and machine learning classifiers. The proposed method is validated against three different datasets, where the training data are compared with 70%, 80%, and 90% training data. It specifically provides the quality of the diagnostic method adopted for the prediction of COVID-19 infections in a patient.

scholarly journals Analysis of Implementation Total Quality Management at Educational Institutions in Indonesia

2021 ◽  
Vol 5 (1) ◽  
pp. 101
Author(s):  
Mercy Hosang ◽  
Deitje A. Katuuk ◽  
Viktory N.J. Rotty ◽  
Jeffry S.J. Lengkong
Keyword(s):  
Quality Management ◽  
Total Quality Management ◽  
Cultural Change ◽  
Educational Institution ◽  
Quality Standard ◽  
Educational Institutions ◽  
Quality Of Education ◽  
Total Quality ◽  
Continuous Quality

The implementation of total quality management is applied to every educational institution as an effort to improve the quality of education. In this case, it is important to carry out management functions in each educational institution. This article discusses the implementation of total quality management in educational institutions to seek to improve the quality of education in achieving a quality standard in education. Every educational institution should show better quality and be able to compete. This is intended so that educational institutions continue to gain the trust of the public and stakeholders. To continue to get this, it must be improved continuously, both physically and non-physically. So as to make a quality educational institution and guaranteed quality. There are several main things that need to be considered in implementing total quality management in the world of education, namely: continuous quality improvement; determine quality standards, cultural change; changes in the organization; and maintain relationships with other agencies and customers and evaluate the system if anything is not appropriate

A Learning Outcome Inspired Survey Instrument for Assessing the Quality of Continuous Improvement Cycle

2019 ◽  
Vol 15 (2) ◽  
pp. 108-129 ◽  
Author(s):  
Abdallah Namoun ◽  
Ahmad Taleb ◽  
Mohammed Al-Shargabi ◽  
Mohamed Benaida
Keyword(s):  
Quality Improvement ◽  
Continuous Quality Improvement ◽  
Delphi Study ◽  
Assessment Tool ◽  
Survey Instrument ◽  
Assessment Instrument ◽  
Self Assessment ◽  
Continuous Quality ◽  
System Requirements

Measuring the effectiveness of a continuous quality improvement cycle in education is a cumbersome and sophisticated process. This article contributes a comprehensive self-assessment instrument for identifying the strengths and weaknesses of all phases of a continuous quality improvement cycle, including planning, data collection, analysis and reporting, and implementation of improvements. To this end, a four round Delphi study soliciting a total of 23 program quality experts from four universities was conducted. The produced survey instrument contains a total of 50 questions. The instrument may be used by quality experts in education to judge the quality of their continuous quality improvement cycle that endeavours to assess the attainment of learning outcomes in various undergraduate educational programs. Moreover, the instrument could be exploited to infer relevant user and system requirements and guide the development of an automated self-assessment tool aimed at identifying the shortcomings in educational continuous quality improvement cycles.

scholarly journals Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China

2020 ◽  
Vol 10 (16) ◽  
pp. 5640
Author(s):  
Jingyu Yao ◽  
Shengwu Qin ◽  
Shuangshuang Qiao ◽  
Wenchao Che ◽  
Yang Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Supervised Learning ◽  
Landslide Susceptibility ◽  
Deep Neural Network ◽  
Spatial Information ◽  
Information Gain ◽  
Jilin Province ◽  
Landslide Susceptibility Mapping ◽  
Support Vector

Accurate and timely landslide susceptibility mapping (LSM) is essential to effectively reduce the risk of landslide. In recent years, deep learning has been successfully applied to landslide susceptibility assessment due to the strong ability of fitting. However, in actual applications, the number of labeled samples is usually not sufficient for the training component. In this paper, a deep neural network model based on semi-supervised learning (SSL-DNN) for landslide susceptibility is proposed, which makes full use of a large number of spatial information (unlabeled data) with limited labeled data in the region to train the mode. Taking Jiaohe County in Jilin Province, China as an example, the landslide inventory from 2000 to 2017 was collected and 12 metrological, geographical, and human explanatory factors were compiled. Meanwhile, supervised models such as deep neural network (DNN), support vector machine (SVM), and logistic regression (LR) were implemented for comparison. Then, the landslide susceptibility was plotted and a series of evaluation tools such as class accuracy, predictive rate curves (AUC), and information gain ratio (IGR) were calculated to compare the prediction of models and factors. Experimental results indicate that the proposed SSL-DNN model (AUC = 0.898) outperformed all the comparison models. Therefore, semi-supervised deep learning could be considered as a potential approach for LSM.

scholarly journals Convolutional neural-network-based classification of retinal images with different combinations of filtering techniques

2021 ◽  
Vol 11 (1) ◽  
pp. 480-490
Author(s):  
Asha Gnana Priya Henry ◽  
Anitha Jude
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Performance Metrics ◽  
Input Image ◽  
Application Techniques ◽  
Enhancement Method ◽  
Neural Network Input

Abstract Retinal image analysis is one of the important diagnosis methods in modern ophthalmology because eye information is present in the retina. The image acquisition process may have some effects and can affect the quality of the image. This can be improved by better image enhancement techniques combined with the computer-aided diagnosis system. Deep learning is one of the important computational application techniques used for a medical imaging application. The main aim of this article is to find the best enhancement techniques for the identification of diabetic retinopathy (DR) and are tested with the commonly used deep learning techniques, and the performances are measured. In this article, the input image is taken from the Indian-based database named as Indian Diabetic Retinopathy Image Dataset, and 13 filters are used including smoothing and sharpening filters for enhancing the images. Then, the quality of the enhancement techniques is compared using performance metrics and better results are obtained for Median, Gaussian, Bilateral, Wiener, and partial differential equation filters and are combined for improving the enhancement of images. The output images from all the enhanced filters are given as the convolutional neural network input and the results are compared to find the better enhancement method.

scholarly journals An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Umashankar Subramaniam ◽  
M. Monica Subashini ◽  
Dhafer Almakhles ◽  
Alagar Karthick ◽  
S. Manoharan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Left Lung ◽  
X Ray ◽  
Detection Algorithms ◽  
Learning Techniques ◽  
Accuracy Of Results

The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X-ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X-ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X-ray can improve the accuracy of results in COVID-19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X-ray image results in better accuracy in classification for all three classes (normal/COVID-19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy.

A Learning Outcome Inspired Survey Instrument for Assessing the Quality of Continuous Improvement Cycle

2021 ◽  
pp. 702-724
Author(s):  
Abdallah Namoun ◽  
Ahmad Taleb ◽  
Mohammed Al-Shargabi ◽  
Mohamed Benaida
Keyword(s):  
Quality Improvement ◽  
Continuous Quality Improvement ◽  
Delphi Study ◽  
Assessment Tool ◽  
Survey Instrument ◽  
Assessment Instrument ◽  
Self Assessment ◽  
Continuous Quality ◽  
System Requirements

Measuring the effectiveness of a continuous quality improvement cycle in education is a cumbersome and sophisticated process. This article contributes a comprehensive self-assessment instrument for identifying the strengths and weaknesses of all phases of a continuous quality improvement cycle, including planning, data collection, analysis and reporting, and implementation of improvements. To this end, a four round Delphi study soliciting a total of 23 program quality experts from four universities was conducted. The produced survey instrument contains a total of 50 questions. The instrument may be used by quality experts in education to judge the quality of their continuous quality improvement cycle that endeavours to assess the attainment of learning outcomes in various undergraduate educational programs. Moreover, the instrument could be exploited to infer relevant user and system requirements and guide the development of an automated self-assessment tool aimed at identifying the shortcomings in educational continuous quality improvement cycles.

scholarly journals Deep learning approach to obtain collaborative filtering neighborhoods

2021 ◽  
Author(s):  
Jesús Bobadilla ◽  
Ángel González-Prieto ◽  
Fernando Ortega ◽  
Raúl Lara-Cabrera
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Nearest Neighbors ◽  
Learning Approach ◽  
Localization Algorithm ◽  
Generative Process ◽  
Open Datasets

AbstractIn the context of recommender systems based on collaborative filtering (CF), obtaining accurate neighborhoods of the items of the datasets is relevant. Beyond particular individual recommendations, knowing these neighbors is fundamental for adding differentiating factors to recommendations, such as explainability, detecting shilling attacks, visualizing item relations, clustering, and providing reliabilities. This paper proposes a deep learning architecture to efficiently and accurately obtain CF neighborhoods. The proposed design makes use of a classification neural network to encode the dataset patterns of the items, followed by a generative process that obtains the neighborhood of each item by means of an iterative gradient localization algorithm. Experiments have been conducted using five popular open datasets and five representative baselines. The results show that the proposed method improves the quality of the neighborhoods compared to the K-Nearest Neighbors (KNN) algorithm for the five selected similarity measure baselines. The efficiency of the proposed method is also shown by comparing its computational requirements with that of KNN.

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