Online-Journals.org (International Association of Online Engineering)
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An Efficient Autism Spectrum Disorder Classification in Different Age Groups using Machine Learning Models
Get PDFThe current world has witnessed the emergence of various illnesses, such as autism spectrum disorder (ASD), that are not yet medically recognized. It impacts multiple behavioral domains, such as repetitive and stereotyped behavior, social competence, and linguistic skills. This condition is a severe neurodevelopmental disorder. It Identifying and classifying ASD is challenging and time-consuming due to its symptoms being remarkably similar to those of many other mental illnesses. Machine learning-based models are increasingly being used to predict a wide range of human diseases, leveraging various physiological and other characteristics. Our study aims to develop a classification model that can predict the likelihood of ASD in various age groups, such as toddlers, children, adolescents, and adults. We have utilized several machine learning (ML) algorithms, including support vector machine (SVM), Naive Bayes (NB), random forest (RF), extra trees classifier (ET), k-nearest neighbor (K-NN), decision tree (DT), Ada boost classifier (AB), and stochastic gradient descent (SGD) classifiers. These models are tested using four unique non-clinical ASD screening datasets that are publicly available from Kaggle and the UCI library. In the first dataset, there are 1054 instances and 19 features related to toddlers. The remaining ones consist of 21 traits and, for children, adolescents, and adults, 292, 104, and 704 cases, respectively. The outcomes of the experimentation have shown that the SDG, DT, and ET classifiers are the most commonly used models and have achieved results with almost 100% accuracy
Improving the Accuracy of Oncology Diagnosis: A Machine Learning-Based Approach to Cancer Prediction
Get PDFCancer ranks among the most lethal illnesses worldwide, and predicting its onset can be a crucial factor in enhancing people’s quality of life by taking preventive measures to improve treatment and survival. This study conducted comparative research to determine the machine learning model with the highest accuracy for tumor type classification, distinguishing between malignant (cancer) and benign tumors. The models evaluated include decision tree (DT), naive bayes (NB), extra trees classifier (ETM), random forest (RF), K-means clustering (K-means), logistic regression (LR), adaptive boosting (AdaBoost), gradient boosting (GB), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost) to identify the one with the best accuracy. The models were trained using a dataset of 569 records and a total of 32 variables, containing patient information and tumor characteristics. The study was structured into sections, such as related studies, descriptions of the models, case study development, results, discussion, and conclusions. The models’ performance was evaluated based on metrics of precision, sensitivity, accuracy, and F1 score. Following the training, the results positioned the XGBoost model as having the best performance, achieving 98% precision, accuracy, sensitivity, and F1 score
Fabrication of TiO2 Nanoparticle Coating on Stainless Steel 316L and Its Assessment for Orthopaedic Applications
Get PDFThe study aims to investigate the efficacy of titanium dioxide (TiO2) nanoparticle coating on stainless steel 316L (SS 316L) orthopaedic implants to enhance their biocompatibility, osseointegration, and durability. The TiO2 nanoparticles were synthesized via the hydrothermal method and extensively characterized for composition, crystallinity, and morphology using techniques such as X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) with energy dispersive X-ray analysis (EDX), corroborated by elemental mapping. SEM and XRD analyses revealed the synthesized nanoparticles have a spherical shape and an average size of approximately 23 nanometres. The synthesized TiO2 nanoparticles were uniformly coated on SS 316L substrates using the spin coating technique, as confirmed by SEM images. Cell viability of the synthesized TiO2 nanoparticles, as well as uncoated and TiO2 nanoparticle-coated SS 316L substrates, was evaluated using the MTT (3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide) assay against the NIH-3T3 mouse embryonic fibroblast cell line. The results demonstrated that the TiO2 nanoparticle-coated SS 316L substrate showed a significant increase of 22.87% in cell viability as compared to the uncoated SS 316L substrate. A ball-on-disc tribometer was employed to assess wear and friction resistance at various speeds, viz., 150 rpm, 300 rpm, and 450 rpm, under 30N load conditions for five minutes. The results collectively indicate a substantial improvement in the performance of TiO2 nanoparticle-coated SS 316L substrates for orthopaedic applications
Lung Sound Classification for Respiratory Disease Identification Using Deep Learning: A Survey
Get PDFIntegrating artificial intelligence (AI) into lung sound classification has markedly improved respiratory disease diagnosis by analysing intricate patterns within audio data. This study is driven by the widespread issue of lung diseases, which affect around 500 million people globally. Early detection of respiratory diseases is crucial for delivering timely and effective treatment. Our study consists of a comprehensive survey of lung sound classification methodologies, exploring the advancements made in leveraging AI to identify and classify respiratory diseases. This survey thoroughly investigates lung sound classification models, along with data augmentation, feature extraction, explainable techniques and support tools to improve systems for diagnosing respiratory conditions. Our goal is to provide meaningful insights for healthcare professionals, researchers and technologists who are dedicated to developing methodologies for the early detection of pulmonary diseases. The paper provides a summary of the current status of lung sound classification research, highlighting both advancements and challenges in the use of AI for more accurate and efficient diagnostic methods in respiratory healthcare
Federated-Learning Intrusion Detection System Based Blockchain Technology
Get PDFThis study presents the implementation of a blockchain-based federated-learning (FL) intrusion detection system. This approach utilizes machine learning (ML) instead of traditional signature-based methods, enabling the system to detect new attack types. The FL technique ensures the privacy of sensitive data while still utilizing the large amounts of data distributed across client devices. To achieve this, we employed the federated averaging method and incorporated a custom preprocessing stage for data standardization. The use of blockchain technology in combination with FL created a fully decentralized and open learning system capable of overcoming new security challenges
Considering the Development and Evaluation of Engineers as Teachers
Get PDFEngineering faculty are required to teach but are rarely trained in evidence-based practices in teaching and learning. While many faculty often mirror how they were taught and slowly develop their curriculum and skills over time, the methods for their development cannot keep pace with the rapidly changing landscape of engineering classrooms. This includes changes in students’ needs, pedagogical advances, and the technology accessible to students. In response to the need for training, development, and continuous improvement of engineering faculty’s teaching beyond the limited feedback that student and peer evaluations provide, our department piloted a program offering 18 different options for teaching development activities. Faculty could choose from these options to enhance their teaching practices. Three focus groups were conducted with a total of 12 faculty participants to iterate and improve upon the piloted program. Qualitative analysis of these focus groups, involving multiple phases of coding and general theming of the focus group transcript data, revealed more than just the faculty’s impressions of the piloted program. Our analysis revealed that two value systems exist when it comes to teaching and teaching development: what is valued personally by a faculty member and what is valued systemically by the department, college, university, or field they work within. The identification of these two distinct value systems revealed that misalignments exist between them. What faculty personally value about their teaching and teaching development might not be valued by the systems in place, and vice versa. We present examples of how faculty discuss these two value systems and explore the implications of conflicting value systems, as well as opportunities for departments to enhance alignment between these value systems to boost faculty motivation for regular engagement with teaching development practices
Problem-Based Learning and Knowledge of Digital Electronics among Engineering Students
Get PDFThis paper aims to explore the relationship between problem-based learning (PBL) and the digital electronics course for engineering students at a private university with a population of 97 students. The study utilized a deductive and correlational hypothetical method with a non-experimental and quantitative design. As a result, the statistically significant relationship between PBL and the digital electronics course is verified. A relationship coefficient equal to Rho = 0.780 and p < 0.01, confirms the proposed alternative hypothesis. This result provides a robust quantitative perspective on how the integration of PBL positively influences engineering students’ acquisition of digital electronics knowledge. The contribution lies in empirical support for the effectiveness of the PBL approach, providing educators and engineering professionals with a solid foundation for enhancing pedagogical strategies and fostering more effective and practical learning in the discipline
A Framework Model for Exploring Factors for Measuring E-Learning Systems and Its Relevant Outcomes via AHP
Get PDFUniversities have focused on learning outcomes as a metric for evaluating the quality assurance of educational systems. However, due to the absence of a clear guide on measuring outcomes and analysing them accordingly to improve the educational cycle, educators struggled to efficiently and effectively address shortcomings. By proposing a new framework, this study fills this gap and provides a guided approach to measuring learning outcomes in the context of university e-learning systems. Facilitated by a comprehensive research literature review of 102 articles, filtered from 271 articles using the PRISMA method, the opinions of five experts regarding e-learning systems and their outcomes were analysed using the analytical hierarchy process (AHP) to provide priority rankings. Teaching methods, teaching quality, learning environment, and students emerged as the main factors, along with their sub-factors. Moreover, our experts from diverse educational and geographical backgrounds provide added value, enabling the framework to be implemented across various environments and fields. Ultimately, this framework accurately measures sub-factors to identify the strengths and weaknesses of educational variables. The proposed framework is a step in the right direction, enabling the design of a suitable system that takes into account users’ needs when assessing e-learning outcomes
Integration of GeoGebra Calculator 3D with Augmented Reality in Mathematics Education for an Immersive Learning Experience
Get PDFThe use of augmented reality (AR) with GeoGebra allows for the contextualization of mathematical operations in real-world situations. In this approach, the teacher presents questions or problems that students solve using visualization and experimentation software. The objective of this work is to evaluate the impact of integrating the GeoGebra 3D calculator with AR. For the development of this study, the quasi-experimental method was employed, involving the comparison of results between two groups: the experimental group (EG) and the control group (CG). We worked with a population of 78 students. The study conducted confirms the use of the GeoGebra calculator in 3D with AR. AR effectively enhances mathematical learning. Seventy percent of the students in the EG achieved an outstanding level of performance, while 30% reached an expected level. In addition, a positive attitude towards mathematics was observed in 100% of the students. These results demonstrate that using the GeoGebra calculator in 3D with AR has a positive impact on mathematics learning. While in CG, 10% achieved the expected level of performance, 85% were in progress, and 5% were at the initial stage. Finally, it was concluded that the GeoGebra calculator in 3D with AR is very useful. It helps enhance the teaching and learning (TL) of mathematics and motivates students, making the development of class sessions more dynamic
Didactic Design of Teaching Materials Created by Future Teachers in the Czech Republic
Get PDFThis paper describes research on the didactic design of teaching materials created by Czech future teachers in their university theses. The results of the research indicate that the teaching materials for future teachers are far from achieving the highest level of didactic design. The average value of the coefficient of the total didactic design is only 26%. The weakest aspect of the teaching materials is the component of the learning management system. This fact implies a significant underestimation of the learning process. Students preparing for the teaching profession have not demonstrated the ability to compile didactically well-developed teaching material in their final theses. They use only a limited number of structural components, and therefore the basic function of teaching materials, the didactic function, is not fully fulfilled. The didactic function ensures the effective usability of textbooks and similar texts in actual education