Online-Journals.org (International Association of Online Engineering)
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The Role of Interactive Mobile Learning in Enhancing University Students' Foreign Language Writing Skills
Get PDFThis study aims to investigate the effectiveness of interactive mobile learning (IML) in improving the foreign language writing skills of college writers. Utilising the pervasive and practical nature of mobile devices, IML’s engaging and enjoyable features make it an exceptional instrument for language acquisition. The study is grounded in cognitive load theory, constructivism, and social theory. These assumptions offer a strong foundation for examining the effectiveness of mobile learning applications in improving students’ writing skills. The study replicated an experimental design and consisted of 200 college students who were enrolled in English as a Foreign Language (EFL) courses. The participants were segregated into two cohorts: one cohort employed the mobile application Write Better to enhance their writing skills, while the other cohort depended on traditional methods of teaching. Data was collected through pre- and post-tests, tracking of app usage, and analysis of qualitative remarks. The students in the experimental group exhibited much bigger enhancements in their writing skills compared to those in the control group. Enhanced written output was further correlated with increased use of the mobile application. Although there were some technical challenges, a qualitative investigation found that students liked the app’s captivating attributes, such as the ability to receive immediate feedback and collaborate on projects. This study enhances the current body of literature by presenting data that supports the effectiveness of IML in EFL writing teaching. Furthermore, it offers instructors clear instructions on how to incorporate mobile devices into the classroom setting
An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification
Get PDFIn order to effectively treat skin diseases, an accurate and prompt diagnosis is required. In this article, a novel method for classifying skin disorders using a multimodal classifier is presented. The proposed classifier utilizes multiple information sources to enhance the accuracy of disease classification. It incorporates images of skin lesions and patient-specific data. The multimodal classifier simultaneously classifies diseases by combining image and structured data inputs. The effectiveness of the proposed classifier was evaluated using the ISIC 2018 dataset, which includes images and clinical data for seven categories of skin diseases. The results indicate that the proposed model outperforms conventional single-modal and single-task classifiers, achieving an accuracy of 98.66% for image classification and 94.40% for clinical data classification. In addition, we compare the performance of the proposed model with that of other methodologies, demonstrating its superiority. Despite yielding promising results, the proposed method has limitations in terms of data requirements and generalizability. Future research directions include incorporating additional information sources, investigating genetic data integration, and applying the method to various medical conditions. This study illustrates the potential of integrating multimodal techniques with transfer learning in deep neural networks to enhance the classification accuracy of cutaneous diseases
Enhancing Classification Performance through FeatureBoostThyro: A Comparative Study of Machine Learning Algorithms and Feature Selection
Get PDFEarly-stage prediction of a disease is an important and challenging task. The application of machine learning techniques is playing an important role in this era. Thyroid is one of the chronic endocrine diseases, and approximately 42 million people in India are affected by this disease. This paper presents a comprehensive investigation into the enhancement of classification performance through the novel ‘FeatureBoostThyro’ (FBT) model. The study evaluates various machine learning algorithms, including stochastic gradient descent (SGD), K nearest neighbor (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM), in conjunction with diverse feature selection methods. The research systematically explores the impact of feature selection techniques such as information gain, relief F, chi-square, gini index, forward selection, backward selection, recursive feature elimination, and LASSO on model performance across the chosen algorithms. The analysis reveals notable variations in performance metrics, including accuracy, precision, recall, and F1-score, providing valuable insights into the interplay between algorithm and feature selection. One main contribution of this research is the introduction of the FBT model, which consistently outperforms other models across various feature selection methods, making it a promising tool for addressing complex classification tasks. The findings contribute to a broader understanding of model selection and optimization in machine learning applications. The proposed model undergoes evaluation using two distinct datasets: the primary dataset acquired from Lata Mangeshkar Hospital in Nagpur and the secondary dataset obtained from the UCI dataset
Advancing Brain Tumor Segmentation in MRI Scans: Hybrid Attention-Residual UNET with Transformer Blocks
Get PDFAccurate segmentation of brain tumors is vital for effective treatment planning, disease diagnosis, and monitoring treatment outcomes. Post-surgical monitoring, particularly for recurring tumors, relies on MRI scans, presenting challenges in segmenting small residual tumors due to surgical artifacts. This emphasizes the need for a robust model with superior feature extraction capabilities for precise segmentation in both pre- and post-operative scenarios. The study introduces the Hybrid Attention-Residual UNET with Transformer Blocks (HART-UNet), enhancing the U-Net architecture with a spatial self-attention module, deep residual connections, and RESNET50 weights. Trained on BRATS’20 and validated on Kaggle LGG and BTC_ postop datasets, HART-UNet outperforms established models (UNET, Attention UNET, UNET++, and RESNET 50), achieving Dice Coefficients of 0.96, 0.97, and 0.88, respectively. These results underscore the model’s superior segmentation performance, marking a significant advancement in brain tumor analysis across pre- and post-operative MRI scans
A Proposed Approach for Object Detection and Recognition by Deep Learning Models Using Data Augmentation
Get PDFObject detection and recognition play a crucial role in computer vision applications, ranging from security systems to autonomous vehicles. Deep learning algorithms have shown remarkable performance in these tasks, but they often require large, annotated datasets for training. However, collecting such datasets can be time-consuming and costly. Data augmentation techniques provide a solution to this problem by artificially expanding the training dataset. In this study, we propose a deep learning approach for object detection and recognition that leverages data augmentation techniques. We use deep convolutional neural networks (CNNs) as the underlying architecture, specifically focusing on popular models such as You Only Look Once version 3 (YOLOv3). By augmenting the training data with various transformations, such as rotation, scaling, and flipping, we can effectively increase the diversity and size of the dataset. Our approach not only improves the robustness and generalization of the models but also reduces the risk of overfitting. By training on augmented data, the models can learn to recognize objects from different viewpoints, scales, and orientations, leading to improved accuracy and performance. We conduct extensive experiments on benchmark datasets and evaluate the performance of our approach using standard metrics such as precision, recall, and mean average precision (mAP). The experimental results demonstrate that our data augmentation-based deep learning approach achieves superior object detection and recognition accuracy compared to traditional training methods without data augmentation. We compare the average accuracy of the YOLOv3-SPP model with two other variants of the YOLOv3 algorithm: one with a feature extraction network consisting of 53 convolutional layers and the other with 13 convolutional layers. The average accuracy of the proposed model (YOLOv3-SPP) is reported as accuracy of 97%, F1-score of 96%, precision of 94%, and average Intersection over Union (IoU) of 78.04%
The Impact of Blockchain Technology Effectiveness in Indonesia's Learning System
Get PDFThis research investigates the integration of blockchain technology in vocational education in Indonesia, evaluating its impact on database system learning outcomes. Involving 86 Indonesian students, this study aims to assess the effectiveness of blockchain in improving students’ understanding, skills, and academic performance. Using a quasi-experimental design, the findings of this study showed a significant improvement in learning outcomes, as evidenced by the analysis of pre-test and post-test scores. The measure of the effectiveness of the improvement in learning outcomes, with a substantial increase of approximately 46.50%, emphasizes the positive influence of blockchain technology on student achievement in the database systems course. The findings of this study will contribute valuable information to educational institutions, policy makers, and educators who wish to incorporate new technologies such as blockchain into their learning systems. The implication of this research is to provide recommendations in optimizing the integration of blockchain technology, which significantly affects learning outcomes in database systems. This is realized through facilitating fast and efficient access to learning materials, fostering a more interactive and engaging learning environment, and enhancing students’ understanding of industry practices. The integration of blockchain into database systems learning not only improves the quality of learning but also equips students for a successful career in information technology
Prototype Realtime Detection Of Abnormal Heart Beat Using Multiple Back Propagation Neural Network (BPNN)
Get PDFReal-time heart rate monitoring and early detection of heart abnormalities are vital to determine heart health before it worsens. To achieve this goal, this project uses the backpropagation neural network (BPNN) method including its capability to classify heartbeats into normal or abnormal by inputting heartbeat values in BPM units derived from prototypes utilizing sensors like Sensor Easy Pulse and NodeMCU, along with considerations of age and sports activity. All data from sensors will be stored in Firebase. Then Firebase will connect to Android, and the normal and abnormal heart classification results will be displayed on the Android system. Simulation results successfully examined 40 people as a sample and provided information from real-time heart rate monitoring, age, and sports activity as input. This research seeks to contribute to improving health services at various public health service centers and independently in detecting heart health early
High Performance for Predicting Diabetic Nephropathy Using Stacking Regression of Ensemble Learning Method
Get PDFDiabetes may lead to several problems, one of the most prevalent and deadly of which is diabetic nephropathy. Therefore, the condition represents a significant threat to one’s health since it has the potential to cause irreversible harm to the kidneys’ ability to operate. A significant portion of the research that is being conducted now is focused on determining how accurately diabetic people may be predicted to develop kidney illness. Considering this, the research suggests a regression stacking approach for predicting albumin levels. These albumin values will serve as a reference for the incidence of diabetic nephropathy disease. They will be derived from the medical records of patients. The utilization of stacking regression from three different ensemble approaches, using Random Forest and CatBoost regressors, while the Huber algorithm is used as a meta-learner. The accuracy with which the combination of parameters that are employed is determined is a significant factor. It contributes to the high degree of performance that the ensemble approach achieves. Therefore, in this investigation, a grid search was carried out to tune the hyperparameters of both regressor models. We evaluated the performance of the proposed model using accuracy, MAPE, RMSE, and MSE values. The experimental findings demonstrate great performance. Three selected variables including quantitative UACR, semi-quantitative UACR, and urinary creatinine, achieved high performance. Overall, the performance obtained an accuracy rate of more than 98% with an error rate (MAPE, RMSE, and MSE values) of less than 1%. In conclusion, the stack regressor model can be implemented to predict diabetic nephropathy using clinical datasets
Blockchain of Things for Securing and Managing Water 4.0 Applications
Get PDFThe design of a smart water monitoring and control system in urban areas plays a pivotal role in providing efficient distribution mechanisms to reduce leakage, especially in regions facing water scarcity and limited resources. The convergence of the Internet of Things (IoT) and blockchain technology to improve the system’s performance, enhance its security, and provide a decentralized and tamper-proof environment presents an excellent opportunity to evolve the system further and form a state-of-the-art Water 4.0 ecosystem. The proposed Blockchain of Things (BCoT) water system is introduced as a pilot to explore its potential in delivering and managing Water 4.0 applications. An Ethereum platform formed the heart of the BCoT system, while a Raspberry Pi 4 acted as a node to the blockchain that collected data from various sensors and microcontrollers via MQTT programmed by Node-Red. LabVIEW software also provided supervisory control and data acquisition (SCADA). The BCoT system was tested, and its functionality was verified, showing good promise to take smart water systems to a new level of innovation that may resolve the many challenges faced by countries with limited water resources and address the challenges of the 21st-century “Water 4.0” ecosystem
A Novel Validation Study of a Wrist Orthosis for the Objective Evaluation of Rigidity in Parkinson’s Disease
Get PDFParkinson’s disease (PD) is a neurological condition affecting millions, marked by mobility issues and characterized by motor and non-motor symptoms, including tremors, bradykinesia, postural instability, and rigidity. Diagnosis often relies on subjective assessments such as the Movement Disorder Society Unified PD Rating Scale (MDS-UPDRS). This study focuses on validating a wrist orthosis designed to quantify rigidity in PD patients objectively. Developed at the Center for Innovation and Technological Evaluation in Health (NIATS), the orthosis integrates a Faulhaber linear motor (LM 2070-080-11) and microcontroller (MCLM 3006 S RS). Calibration experiments, including varied mass assessments, established the orthosis’s reliability. Results indicated a newly calculated force constant of 14.28 N/A, 18.49% higher than the manufacturer’s value, with a strong Pearson correlation coefficient (0.9997189). The orthosis detected masses ranging from 39.07 to 812.64 grams without yielding. Angular displacement calibration, utilizing a GP10 goniometer and Myosystem-Br1 software, demonstrated linearity, supported by Pearson coefficients of 0.9995091 and 0.995259. These findings underscore the orthosis’s potential as a reliable tool for measuring rigidity in PD patients, promising advancements in physiotherapy and disease monitoring