Simulated annealing for SVM parameters optimization in student’s performance prediction

Mahareek, Esraa A.; Desuky, Abeer S.; El-Zhni, Habiba Abdullah

doi:10.11591/eei.v10i3.2855

Cited by 10 publications

(9 citation statements)

References 15 publications

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“…Our strategy for discovering human pose similarity and action recognition consists of three sequential tasks: starting with pose estimation from images based on the OpenPose framework, then classifying the actions using obtained pose key points as input and the support vector machine (SVM) classification algorithm [21], [22] and finally matching the poses between the input image and reference image to find the similarity between them based on cosine distance metrics. The OpenPose library is a human body recognition project built with the Caffe framework and based on convolutional neural networks (CNN).…”

Section: Methodsmentioning

confidence: 99%

Discover human poses similarity and action recognition based on machine learning

2023

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In the computer vision field, human action recognition depending on pose estimation recently made considerable progress, especially by using deep learning, which improves recognition performance. Therefore, it has been employed in various applications, including sports and physical activity follow-up. This paper presents a technique for recognizing the human posture in different images and matching their pose similarity. This aims to evaluate the viability of employing computer vision techniques to verify a person's body pose during exercise and determine whether the pose is executed properly. Exercise is one strategy we use to maintain our health throughout life. Gymnastics and yoga are two examples of this type of exercise. The proposed algorithm identifies human action by recognizing the body's key points. The OpenPose library has been used to detect 18 key points of the human body. The action classification task is performed using the support vector machine (SVM) algorithm. Then, the algorithm computes the similarity of the human pose by comparing a model image to a test image to determine the matching score. Evaluations show that our method can perform at a competitive or state-of-the-art performance on a number of body pose datasets.

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Section: Methodsmentioning

confidence: 99%

Discover human poses similarity and action recognition based on machine learning

2023

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“…Fuzzy c-mean converts each target into a cluster where the members with same characteristics are grouped in a same class. The cluster center is evaluated by measuring the distance between cluster and pixel as (4),…”

Section: Segmentationmentioning

confidence: 99%

A deep learning approach based on stochastic gradient descent and least absolute shrinkage and selection operator for identifying diabetic retinopathy

Ramanathan

Hossen

Sayeed

et al. 2022

IJEECS

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More than eighty-five to ninety percentage of the diabetic patients are affected with diabetic retinopathy (DR) which is an eye disorder that leads to blindness. The computational techniques can support to detect the DR by using the retinal images. However, it is hard to measure the DR with the raw retinal image. This paper proposes an effective method for identification of DR from the retinal images. In this research work, initially the Weiner filter is used for preprocessing the raw retinal image. Then the preprocessed image is segmented using fuzzy c-mean technique. Then from the segmented image, the features are extracted using grey level co-occurrence matrix (GLCM). After extracting the fundus image, the feature selection is performed stochastic gradient descent, and least absolute shrinkage and selection operator (LASSO) for accurate identification during the classification process. Then the inception v3-convolutional neural network (IV3-CNN) model is used in the classification process to classify the image as DR image or non-DR image. By applying the proposed method, the classification performance of IV3-CNN model in identifying DR is studied. Using the proposed method, the DR is identified with the accuracy of about 95%, and the processed retinal image is identified as mild DR.

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“…The deep learning model using global average polling (GAP) techniques with pre-trained CNN was developed for the earlier detection of diabetic retinopathy [23]. The SVM [24] and data mining [25] is used for predicting the performance of students in the online learning process. The framework for grid search model based on the walk-forward validation methodology [26].…”

Section: Supervised Learning and Unsupervised Learningmentioning

confidence: 99%

Application of multilayer perceptron to deep reinforcement learning for stock market trading and analysis

Sagiraju

Shashi

2021

IJEECS

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Trading strategies to maximize profits by tracking and responding to dynamic stock market variations is a complex task. This paper proposes to use a multilayer perceptron method (a part of artificial neural networks (ANNs)), that can be used to deploy deep reinforcement strategies to learn the process of predicting and analyzing the stock market products with the aim to maximize profit making. We trained a deep reinforcement agent using the four algorithms: proximal policy optimization (PPO), deep Q-learning (DQN), deep deterministic policy gradient (DDPG) method, and advantage actor critic (A2C). The proposed system, comprising these algorithms, is tested using real time stock data of two products: Dow Jones (DJIA-index), and Qualcomm (shares). The performance of the agent linked to the individual algorithms was evaluated, compared and analyzed using Sharpe ratio, Sortino ratio, Skew and Kurtosis, thus leading to the most effective algorithm being chosen. Based on the parameter values, the algorithm that maximizes profit making for the respective financial product was determined. We also extended the same approach to study and ascertain the predictive performance of the algorithms on trading under highly volatile scenario, such as the pandemic coronavirus disease 2019 (COVID-19).

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Simulated annealing for SVM parameters optimization in student’s performance prediction

Cited by 10 publications

References 15 publications

Discover human poses similarity and action recognition based on machine learning

Discover human poses similarity and action recognition based on machine learning

A deep learning approach based on stochastic gradient descent and least absolute shrinkage and selection operator for identifying diabetic retinopathy

Application of multilayer perceptron to deep reinforcement learning for stock market trading and analysis

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