Extraction, Validation, And Evaluation Of Motivational Tactics Rules In A Web-Based Intelligent Tutoring System (WITS)

Kajian ini memberi tumpuan terhadap cara menlestarikan serta meningkatkan motivasi pelajar semasa proses pembelajaran dalam persekitaran Sistem Pentutoran Cerdas Berasaskan Web (Web-Based Intelligent Tutoring System, WITS) The current study focuses on finding a way to sustain or enhance the learners’ motivation during the learning process within a Web-Based Intelligent Tutoring System (WITS) environmen

Brain Pathology Classification of MR Images Using Machine Learning Techniques

A brain tumor is essentially a collection of aberrant tissues, so it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification from brain MRI scans using machine learning techniques are widely recognized as challenging and important tasks. The potential applications of machine learning in diagnostics, preoperative planning, and postoperative evaluations are substantial. Accurate determination of the tumor’s location on a brain MRI is of paramount importance. The advancement of precise machine learning classifiers and other technologies will enable doctors to detect malignancies without requiring invasive procedures on patients. Pre-processing, skull stripping, and tumor segmentation are the steps involved in detecting a brain tumor and measurement (size and form). After a certain period, CNN models get overfitted because of the large number of training images used to train them. That is why this study uses deep CNN to transfer learning. CNN-based Relu architecture and SVM with fused retrieved features via HOG and LPB are used to classify brain MRI tumors (glioma or meningioma). The method’s efficacy is measured in terms of precision, recall, F-measure, and accuracy. This study showed that the accuracy of the SVM with combined LBP with HOG is 97%, and the deep CNN is 98%

Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review

The adoption of deep learning (DL) and machine learning (ML) has surged in recent years because of their imperative practicalities in different disciplines. Among these feasible workabilities are the noteworthy contributions of ML and DL, especially ant colony optimization (ACO) and whale optimization algorithm (WOA) ameliorated with neural networks (NNs) to identify specific categories of skin lesion disorders (SLD) precisely, supporting even high-experienced healthcare providers (HCPs) in performing flexible medical diagnoses, since historical patient databases would not necessarily help diagnose other patient situations. Unfortunately, there is a shortage of rich investigations respecting the contributory influences of ACO and WOA in the SLD classification, owing to the recent adoption of ML and DL in the medical field. Accordingly, a comprehensive review is conducted to shed light on relevant ACO and WOA functionalities for enhanced SLD identification. It is hoped, relying on the overview findings, that clinical practitioners and low-experienced or talented HCPs could benefit in categorizing the most proper therapeutical procedures for their patients by referring to a collection of abundant practicalities of those two models in the medical context, particularly (a) time, cost, and effort savings, and (b) upgraded accuracy, reliability, and performance compared with manual medical inspection mechanisms that repeatedly fail to correctly diagnose all patients

Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning Contexts

Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses massive volumes of unlabeled data to train neural networks. SSL techniques have evolved in response to the poor classification performance of conventional and even modern machine learning (ML) and DL models of enormous unlabeled data produced periodically in different disciplines. However, the literature does not fully address SSL’s practicalities and workabilities necessary for industrial engineering and medicine. Accordingly, this thorough review is administered to identify these prominent possibilities for prediction, focusing on industrial and medical fields. This extensive survey, with its pivotal outcomes, could support industrial engineers and medical personnel in efficiently predicting machinery faults and patients’ ailments without referring to traditional numerical models that require massive computational budgets, time, storage, and effort for data annotation. Additionally, the review’s numerous addressed ideas could encourage industry and healthcare actors to take SSL principles into an agile application to achieve precise maintenance prognostics and illness diagnosis with remarkable levels of accuracy and feasibility, simulating functional human thinking and cognition without compromising prediction efficacy

Proteinase 3 is an IL-32 binding protein

IL-32, a recently discovered proinflammatory cytokine with four isoforms, induces IL-1β, TNF-α, IL-6, and chemokines. Here, we used ligand (IL-32α) affinity chromatography in an attempt to isolate an IL-32α soluble receptor or binding protein. Recombinant IL-32α was covalently immobilized on agarose, and preparations of concentrated crude human urinary proteins were applied for chromatographic separation. A specific 30-kDa protein eluted from the column during acid washing and was identified by mass spectrometry as proteinase 3 (PR3) and confirmed by N-terminal microsequencing. PR3, a neutrophil granule serine protease, exists in a soluble or membrane form and is the major autoantigen for autoantibodies in the systemic vasculitic disease, Wegener's granulomatosis. The affinity of IL-32α to PR3 was determined by surface plasmon resonance. The dissociation constants were 2.65 ± 0.4 nM for urinary PR3 and 1.2 ± 0.05 nM for neutrophil-derived PR3. However, irreversible inactivation of PR3 enzymatic activity did not significantly change binding to the cytokine. Nevertheless, limited cleavage of IL-32 yielded products consistent with PR3 enzyme activity. Moreover, after limited cleavage by PR3, IL-32α was more active than intact IL-32α in inducing macrophage inflammatory protein-2 in mouse macrophages and IL-8 in human peripheral blood mononuclear cells. We suggest that PR3 is a specific IL-32α binding protein, independent of its enzymatic activity. However, limited cleavage of IL-32α by PR3 enhances activities of the cytokine. Therefore, specific inhibition of PR3 activity to process IL-32 or neutralization of IL-32 by inactive PR3 or its fragments may reduce the consequences of IL-32 in immune regulated diseases