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233 articles for “Model Training”
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Research ArticleOpen accessIn the abstractCapsule Defect Detection and Classification Using Enhanced MobileNet ModelWenqing Bian, Yongzhi Huang, Baolian Shan, Yanting ChangThe quality of capsules has a direct impact on human health. However, defects inevitably arise during the capsule manufacturing process, and manual sorting is required. Most studies on capsule defects focus on detecting surface imperfections and those on internal capsule defects remain relatively limited. This study classifies four types of capsules using the deep learning model MobileNet, aiming to accurately … …American Journal of Biochemistry and Biotechnology, Vol. 20, Issue 4 · 5 Nov 2024 · 5,203 accessesView PDFCite
Review ArticleOpen accessIn the abstractMachine Learning Integration for Precise Facial Micro-Expression RecognitionViola Bakiasi Shtino, Markela Muça, Senada BushatiThis study advances facial micro-expression recognition through innovative machine-learning techniques, addressing critical needs in psychology, security, and human-computer interaction. The purpose of this study is to improve micro-expression recognition through the optimization of feature transformation and machine learning algorithms. We introduce a novel approach combining Kernel Principal Component Analysis (KPCA) and Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction, … …Journal of Computer Science, Vol. 20, Issue 11 · 22 Oct 2024 · 5,592 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing IoT RPL Protocol Security Against Black Hole Attacks with Deep Learning TechniquesKrari Ayoub, Hajami Abdelmajid, Toubi Ayoub, Mihi SoukainaAs the Internet of Things (IoT) extends its reach into critical infrastructures, it encounters escalating security threats, particularly black hole attacks that jeopardize network communication. The growing use of IoT in essential services underscores the need for robust network integrity. Driven by this urgency, our research has developed a sophisticated detection framework that effectively identifies black hole attacks within IoT … …Journal of Computer Science, Vol. 20, Issue 11 · 18 Oct 2024 · 5,000 accessesView PDFCite
Research ArticleOpen accessIn the abstractRadial Basis Function Neural Networks Towards Electronic Trust QuantificationSmitha Zacaria, Tiny du ToitTrust is a broad term applicable in various contexts. Trust between electronic entities is complex to quantify, particularly in intricate networks. Traditional trust algorithms rely on historical trust values, requiring storage and access to past transaction details. Contextual variations further complicate trust calculation. Challenges in calculating trust include heightened computational complexities, managing information storage, and securing access to extensive datasets … …Journal of Computer Science, Vol. 20, Issue 11 · 26 Sep 2024 · 4,791 accessesView PDFCite
Research ArticleOpen accessIn the abstractPerformance Analysis of Garbage Classification Using Balanced and Unbalanced Dataset with EfficientNetV2B1 ArchitectureRetinderdeep Singh, Chander Prabha, Ayman Noor et al.This research concentrates on EfficientNetV2B1 deep learning model to classify garbage collection tasks, with both balanced and unbalanced dataset configurations. The dataset includes 7,260 images for the balanced dataset and 15,515 for the unbalanced one, both of the datasets are used to train and evaluate the model. Training and evaluation of the deep learning model with the standard performance variables: … …Journal of Computer Science, Vol. 20, Issue 10 · 27 Aug 2024 · 5,091 accessesView PDFCite
Research ArticleOpen accessIn the abstractAutism Spectrum Disorder Prediction Using Hybrid Deep Learning Model and a Recommendation System Application for Autistic PatientBiplov Paneru, Bishwash Paneru, Krishna Bikram Shah et al.The goal of this study is to create machine learning models that use a big dataset to predict Autism Spectrum Disorder (ASD). To achieve optimal performance, a number of algorithms were employed and refined, including Support Vector Machines (SVM), Random Forest, XGBoost, Multi-Layer Perceptron (MLP) and a hybrid model that combines MLP and SVM. To evaluate the durability of the … …Journal of Computer Science, Vol. 20, Issue 9 · 6 Jul 2024 · 6,219 accessesView PDFCite
Research ArticleOpen accessIn the abstractA New Hybrid Model to Predict the Performance of Trainee Teachers Based on Clustering and ClassificationYissam Lakhdar, Khawla EL-Bendadi, Brahim BakkasThis article explores how artificial intelligence, particularly machine learning, can be used to assess and predict the performance of trainee teachers in Morocco. Considering the country's focus on integrating technology into education and the challenges posed by the COVID-19 pandemic, the authors propose a novel hybrid model that combines clustering and classification algorithms. This model aims to understand the Information … …Journal of Computer Science, Vol. 20, Issue 9 · 26 Jun 2024 · 5,100 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Multi-Split Cross-Strategy for Enhancing Machine Learning Algorithms Prediction Results with Data Generated by Conditional Generative Adversarial NetworkAbdelfattah Abassi, Brahim Bakkas et al.In this study, we present a Multi-Split Cross-Strategy (MSC-Strategy) designed to leverage synthetic tabular data generated by a Conditional Generative Adversarial Network (CGAN). Our study aims to investigate the potential of synthetic data in comparison to real-world data for improving machine learning predictive results. Firstly, we develop a CGAN architecture tailored to generate synthetic tabular data, trained on a comprehensive … …Journal of Computer Science, Vol. 20, Issue 7 · 9 Apr 2024 · 6,695 accessesView PDFCite
Research ArticleOpen accessIn the abstractNon-Hodgkin Lymphoma Risk Grading Through the Pathological Data by Using the Optimized Convolutional Lymphnet ModelSivaranjini Nagarajan, Gomathi MuthuswamyDiagnosing Non-Hodgkin Lymphoma (NHL) is difficult and often requires specialised training and expertise as well as extensive morphological investigation and, in certain cases, costly immunohistological and genetic techniques. Computational approaches enabling morphological-based decision making are necessary for bridging the existing gaps. Histopathological images can be accurately classified using deep learning approaches, however data on NHL subtyping is limited. However, there … …Journal of Computer Science, Vol. 20, Issue 5 · 28 Feb 2024 · 4,545 accessesView PDFCite
Research ArticleOpen accessIn the abstractMachine Learning Oceanographic Data for Prediction of the Potential of Marine ResourcesDenny Arbahri, Oky Dwi Nurhayati, Imam MuditaMarine data and information are very important for human survival, therefore this data and information is attractive to investors because of the potential economic value. This data and information has been difficult to obtain, the solution to overcome this is by analyzing oceanographic data for 2009-2019 collected from the marine database belonging to the Agency for the Study and Application … …Journal of Computer Science, Vol. 20, Issue 2 · 8 Jan 2024 · 5,970 accessesView PDFCite