Deep Neural Network for Multi-Class Prediction of Student Performance in Educational Data

Prediction of student performance is the significant part in processing the educational data. Machine learning algorithms are leading the role in this process. Deep learning is one of the important concepts of machine learning algorithm. In this paper, we applied the deep learning technique for prediction of the academic excellence of the students using R Programming. Keras and Tensorflow libraries utilized for making the model using neural network on the Kaggle dataset. The data is separated into testing data training data set. Plot the neural network model using neuralnet method and created the Deep Learning model using two hidden layers using ReLu activation function and one output layer using softmax activation function. After fine tuning process until the stable changes; this model produced accuracy as 85%.

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Convolutional Neural Network

10.4018/978-1-6684-2408-7.ch077 ◽

2022 ◽

pp. 1559-1575

Author(s):

Mário Pereira Véstias

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Model ◽

Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

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Micro Clustering Methodology for Document Objects using Deep Learning Techniques

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b3716.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 1546-1552

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Recurrent Neural Network ◽

Large Data ◽

Heterogeneous Data ◽

Machine Learning Algorithms ◽

Volume Data ◽

Data Set ◽

Clustering And Classification

Large data clustering and classification is a very challenging task in data mining. Various machine learning and deep learning systems have been proposed by many researchers on a different dataset. Data volume, data size and structure of data may affect the time complexity of the system. This paper described a new document object classification approach using deep learning (DL) and proposed a recurrent neural network (RNN) for classification with a micro-clustering approach.TF-IDF and a density-based approach are used to store the best features. The plane work used supervised learning method and it extracts features set called as BK of the desired classes. once the training part completed then proceeds to figure out the particular test instances with the help of the planned classification algorithm. Recurrent Neural Network categorized the particular test object according to their weights. The system can able to work on heterogeneous data set and generate the micro-clusters according to classified results. The system also carried out experimental analysis with classical machine learning algorithms. The proposed algorithm shows higher accuracy than the existing density-based approach on different data sets.

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Convolutional Neural Network

Encyclopedia of Information Science and Technology, Fifth Edition - Advances in Information Quality and Management ◽

10.4018/978-1-7998-3479-3.ch002 ◽

2021 ◽

pp. 12-26

Author(s):

Mário Pereira Véstias

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Model ◽

Artificial Neural

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DETECTION AND CLASSIFICATION OF SYMBOLS IN PRINCIPLE SKETCHES USING DEEP LEARNING

Proceedings of the Design Society ◽

10.1017/pds.2021.118 ◽

2021 ◽

Vol 1 ◽

pp. 1183-1192

Author(s):

Sebastian Bickel ◽

Benjamin Schleich ◽

Sandro Wartzack

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Machine Learning Algorithms ◽

Training Data ◽

Product Development Process ◽

Data Set ◽

Deep Learning Network ◽

The Cost ◽

Early Phases

AbstractData-driven methods from the field of Artificial Intelligence or Machine Learning are increasingly applied in mechanical engineering. This refers to the development of digital engineering in recent years, which aims to bring these methods into practice in order to realize cost and time savings. However, a necessary step towards the implementation of such methods is the utilization of existing data. This problem is essential because the mere availability of data does not automatically imply data usability. Therefore, this paper presents a method to automatically recognize symbols from principle sketches, which allows the generation of training data for machine learning algorithms. In this approach, the symbols are created randomly and their illustration varies with each generation. . A deep learning network from the field of computer vision is used to test the generated data set and thus to recognize symbols on principle sketches. This type of drawing is especially interesting because the cost-saving potential is very high due to the application in the early phases of the product development process.

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Retinal blood vessels detection for diabetic retinopathy with Ridgelet transform and convolution neural network

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691320500484 ◽

2020 ◽

Vol 18 (06) ◽

pp. 2050048

Author(s):

Nirmal Yadav

Keyword(s):

Neural Network ◽

Machine Learning ◽

Diabetic Retinopathy ◽

Deep Learning ◽

Blood Vessels ◽

Machine Learning Algorithms ◽

Convolution Neural Network ◽

Transform Method ◽

Ridgelet Transform ◽

Study Results

Applying machine learning in life sciences, especially diagnostics, has become a key area of focus for researchers. Combining machine learning with traditional algorithms provides a unique opportunity of providing better solutions for the patients. In this paper, we present study results of applying the Ridgelet Transform method on retina images to enhance the blood vessels, then using machine learning algorithms to identify cases of Diabetic Retinopathy (DR). The Ridgelet transform provides better results for line singularity of image function and, thus, helps to reduce artefacts along the edges of the image. The Ridgelet Transform method, when compared with earlier known methods of image enhancement, such as Wavelet Transform and Contourlet Transform, provided satisfactory results. The transformed image using the Ridgelet Transform method with pre-processing quantifies the amount of information in the dataset. It efficiently enhances the generation of features vectors in the convolution neural network (CNN). In this study, a sample of fundus photographs was processed, which was obtained from a publicly available dataset. In pre-processing, first, CLAHE was applied, followed by filtering and application of Ridgelet transform on the patches to improve the quality of the image. Then, this processed image was used for statistical feature detection and classified by deep learning method to detect DR images from the dataset. The successful classification ratio was 98.61%. This result concludes that the transformed image of fundus using the Ridgelet Transform enables better detection by leveraging a transform-based algorithm and the deep learning.

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A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic data

Geophysics ◽

10.1190/geo2019-0405.1 ◽

2020 ◽

Vol 85 (4) ◽

pp. WA41-WA52 ◽

Cited By ~ 3

Author(s):

Dario Grana ◽

Leonardo Azevedo ◽

Mingliang Liu

Keyword(s):

Neural Network ◽

Machine Learning ◽

Monte Carlo ◽

Monte Carlo Methods ◽

Seismic Data ◽

Transition Probabilities ◽

Machine Learning Algorithms ◽

Data Set ◽

Facies Classification ◽

The Neural Network

Among the large variety of mathematical and computational methods for estimating reservoir properties such as facies and petrophysical variables from geophysical data, deep machine-learning algorithms have gained significant popularity for their ability to obtain accurate solutions for geophysical inverse problems in which the physical models are partially unknown. Solutions of classification and inversion problems are generally not unique, and uncertainty quantification studies are required to quantify the uncertainty in the model predictions and determine the precision of the results. Probabilistic methods, such as Monte Carlo approaches, provide a reliable approach for capturing the variability of the set of possible models that match the measured data. Here, we focused on the classification of facies from seismic data and benchmarked the performance of three different algorithms: recurrent neural network, Monte Carlo acceptance/rejection sampling, and Markov chain Monte Carlo. We tested and validated these approaches at the well locations by comparing classification predictions to the reference facies profile. The accuracy of the classification results is defined as the mismatch between the predictions and the log facies profile. Our study found that when the training data set of the neural network is large enough and the prior information about the transition probabilities of the facies in the Monte Carlo approach is not informative, machine-learning methods lead to more accurate solutions; however, the uncertainty of the solution might be underestimated. When some prior knowledge of the facies model is available, for example, from nearby wells, Monte Carlo methods provide solutions with similar accuracy to the neural network and allow a more robust quantification of the uncertainty, of the solution.

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Can Short and Partial Observations Reduce Model Error and Facilitate Machine Learning Prediction?

Entropy ◽

10.3390/e22101075 ◽

2020 ◽

Vol 22 (10) ◽

pp. 1075

Author(s):

Nan Chen

Keyword(s):

Machine Learning ◽

Model Error ◽

Machine Learning Algorithms ◽

Training Data ◽

Conditional Sampling ◽

Data Set ◽

Partial Observations ◽

Sampling Algorithm ◽

Highly Nonlinear ◽

Non Gaussian

Predicting complex nonlinear turbulent dynamical systems is an important and practical topic. However, due to the lack of a complete understanding of nature, the ubiquitous model error may greatly affect the prediction performance. Machine learning algorithms can overcome the model error, but they are often impeded by inadequate and partial observations in predicting nature. In this article, an efficient and dynamically consistent conditional sampling algorithm is developed, which incorporates the conditional path-wise temporal dependence into a two-step forward-backward data assimilation procedure to sample multiple distinct nonlinear time series conditioned on short and partial observations using an imperfect model. The resulting sampled trajectories succeed in reducing the model error and greatly enrich the training data set for machine learning forecasts. For a rich class of nonlinear and non-Gaussian systems, the conditional sampling is carried out by solving a simple stochastic differential equation, which is computationally efficient and accurate. The sampling algorithm is applied to create massive training data of multiscale compressible shallow water flows from highly nonlinear and indirect observations. The resulting machine learning prediction significantly outweighs the imperfect model forecast. The sampling algorithm also facilitates the machine learning forecast of a highly non-Gaussian climate phenomenon using extremely short observations.

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Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network

Diagnostics ◽

10.3390/diagnostics9030104 ◽

2019 ◽

Vol 9 (3) ◽

pp. 104 ◽

Cited By ~ 11

Author(s):

Ahmed ◽

Yigit ◽

Isik ◽

Alpkocak

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Nearest Neighbor ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

K Nearest Neighbor ◽

Data Set ◽

Leukemia Data

Leukemia is a fatal cancer and has two main types: Acute and chronic. Each type has two more subtypes: Lymphoid and myeloid. Hence, in total, there are four subtypes of leukemia. This study proposes a new approach for diagnosis of all subtypes of leukemia from microscopic blood cell images using convolutional neural networks (CNN), which requires a large training data set. Therefore, we also investigated the effects of data augmentation for an increasing number of training samples synthetically. We used two publicly available leukemia data sources: ALL-IDB and ASH Image Bank. Next, we applied seven different image transformation techniques as data augmentation. We designed a CNN architecture capable of recognizing all subtypes of leukemia. Besides, we also explored other well-known machine learning algorithms such as naive Bayes, support vector machine, k-nearest neighbor, and decision tree. To evaluate our approach, we set up a set of experiments and used 5-fold cross-validation. The results we obtained from experiments showed that our CNN model performance has 88.25% and 81.74% accuracy, in leukemia versus healthy and multiclass classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other wellknown machine learning algorithms.

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Utilizing the Road Mark Training Set from Ground-Based Mapping System to Airborne Imagery in Deep Learning Framework

Abstracts of the ICA ◽

10.5194/ica-abs-1-364-2019 ◽

2019 ◽

Vol 1 ◽

pp. 1-1

Author(s):

Tee-Ann Teo

Keyword(s):

Neural Network ◽

Deep Learning ◽

Spatial Resolution ◽

Image Features ◽

Training Data ◽

Training Set ◽

Data Set ◽

The Road ◽

Mapping System ◽

Close Range

Abstract. Deep Learning is a kind of Machine Learning technology which utilizing the deep neural network to learn a promising model from a large training data set. Convolutional Neural Network (CNN) has been successfully applied in image segmentation and classification with high accuracy results. The CNN applies multiple kernels (also called filters) to extract image features via image convolution. It is able to determine multiscale features through the multiple layers of convolution and pooling processes. The variety of training data plays an important role to determine a reliable CNN model. The benchmarking training data for road mark extraction is mainly focused on close-range imagery because it is easier to obtain a close-range image rather than an airborne image. For example, KITTI Vision Benchmark Suite. This study aims to transfer the road mark training data from mobile lidar system to aerial orthoimage in Fully Convolutional Networks (FCN). The transformation of the training data from ground-based system to airborne system may reduce the effort of producing a large training data set.This study uses FCN technology and aerial orthoimage to localize road marks on the road regions. The road regions are first extracted from 2-D large-scale vector map. The input aerial orthoimage is 10&thinsp;cm spatial resolution and the non-road regions are masked out before the road mark localization. The training data are road mark’s polygons, which are originally digitized from ground-based mobile lidar and prepared for the road mark extraction using mobile mapping system. This study reuses these training data and applies them for the road mark extraction using aerial orthoimage. The digitized training road marks are then transformed to road polygon based on mapping coordinates. As the detail of ground-based lidar is much better than the airborne system, the partially occulted parking lot in aerial orthoimage can also be obtained from the ground-based system. The labels (also called annotations) for FCN include road region, non-regions and road mark. The size of a training batch is 500&thinsp;pixel by 500&thinsp;pixel (50&thinsp;m by 50&thinsp;m on the ground), and the total number of training batches for training is 75 batches. After the FCN training stage, an independent aerial orthoimage (Figure 1a) is applied to predict the road marks. The results of FCN provide initial regions for road marks (Figure 1b). Usually, road marks show higher reflectance than road asphalts. Therefore, this study uses this characteristic to refine the road marks (Figure 1c) by a binary classification inside the initial road mark’s region.To compare the automatically extracted road marks (Figure 1c) and manually digitized road marks (Figure 1d), most road marks can be extracted using the training set from ground-based system. This study also selects an area of 600&thinsp;m&thinsp;&times;&thinsp;200&thinsp;m in quantitative analysis. Among the 371 reference road marks, 332 can be extracted from proposed scheme, and the completeness reached 89%. The preliminary experiment demonstrated that most road marks can be successfully extracted by the proposed scheme. Therefore, the training data from the ground-based mapping system can be utilized in airborne orthoimage in similar spatial resolution.

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Rotor Unbalance Kind and Severity Identification by Current Signature Analysis with Adaptative Update to Multiclass Machine Learning Algorithms

Studies in Engineering and Technology ◽

10.11114/set.v8i1.5213 ◽

2021 ◽

Vol 8 (1) ◽

pp. 28

Author(s):

S. L. Ávila ◽

H. M. Schaberle ◽

S. Youssef ◽

F. S. Pacheco ◽

C. A. Penz

Keyword(s):

Machine Learning ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Signature Analysis ◽

Data Set ◽

Learning Techniques ◽

Environmental Variations ◽

Current Signature

The health of a rotating electric machine can be evaluated by monitoring electrical and mechanical parameters. As more information is available, it easier can become the diagnosis of the machine operational condition. We built a laboratory test bench to study rotor unbalance issues according to ISO standards. Using the electric stator current harmonic analysis, this paper presents a comparison study among Support-Vector Machines, Decision Tree classifies, and One-vs-One strategy to identify rotor unbalance kind and severity problem – a nonlinear multiclass task. Moreover, we propose a methodology to update the classifier for dealing better with changes produced by environmental variations and natural machinery usage. The adaptative update means to update the training data set with an amount of recent data, saving the entire original historical data. It is relevant for engineering maintenance. Our results show that the current signature analysis is appropriate to identify the type and severity of the rotor unbalance problem. Moreover, we show that machine learning techniques can be effective for an industrial application.

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