Online-Journals.org (International Association of Online Engineering)
Not a member yet
9805 research outputs found
Sort by
E-assessment during the Coronavirus Outbreak from the Perspective of Undergraduate at the University of Sharjah, UAE
Get PDF— The aim of the current research was to detect how undergraduates from the faculties of medicine, dentistry, pharmacy, and health sciences felt about using electronic assessment at the time of COVID-19. Cross-sectional research was made at the University of Sharjah in UAE between January and April 2021. As a study tool, a questionnaire with 26 items was created using Google Forms and disseminated by the registration department via the learner's E-learning platforms. The study's data were analyzed using SPSS software. The outcomes demonstrated that students had a high level of acceptance of the computerized assessment. The overall arithmetic mean of the students' replies was (3.49) and had a standard deviation of (1.33), indicating that they accepted the electronic assessment to a high degree. Furthermore, there was a discernible difference between the acceptance of the internet-based assessment by male and female students, but the College of Medicine (COM) students benefited significantly from the variation in acceptance of the assessment depending on the variable of college. Students who had inadequate computer capacities, on the other hand, were more receptive to electronic testing. The study's findings may be helpful in developing academic methods, rearranging assessment alternatives, and changing the academic curriculum to address the problems and limitations of electronic assessment
A Novel SVM and K-NN Classifier Based Machine Learning Technique for Epileptic Seizure Detection
Get PDFAn EEG signal is used for capturing the signals from the brain, which helps in localization of epileptogenic region, thereby which plays a vital role for a successful surgery. The focal and non-focal signals are obtained from the epileptogenic region and normal region respectively. The localization of epileptic seizure with the help of focal signal is necessary while detecting seizures. Hence, the present article provides detailed analysis of EEG signals. The Focal and Non-focal signals are decomposed using EMD-DWT. A combination of EMD-DWT decomposition method in accordance with log-energy entropy gives an efficient accuracy in comparison to other entropy in differentiating the Focal from Non-focal signals. The extracted features are subjected to SVM and KNN classifiers whose performance will be calculated and verified with respect to accuracy, sensitivity and specificity. At the end, it will be shown that KNN produces the highest accuracy when compared to SVM classifier
Transfer Learning-Based Osteoporosis Classification Using Simple Radiographs
Get PDFOsteoporosis is a condition that affects the entire skeletal system, resulting in decreased density of bone mass and the weakening of bone tissue's micro-architecture. This leads to weaker bones that are more susceptible to fractures. Detecting and measuring bone mineral density has always been a critical area of focus for researchers in the diagnosis of bone diseases such as osteoporosis. However, existing algorithms used for osteoporosis diagnosis encounter challenges in obtaining accurate results due to X-ray image noise and variations in bone shapes, especially in low contrast conditions. Therefore, the development of efficient algorithms that can mitigate these challenges and improve the accuracy of osteoporosis diagnosis is essential. In this research paper, a comparative analysis was conducted Assessing the accuracy and efficiency of the latest deep learning CNN model, such as VGG16, VGG19, DenseNet121, Resnet50 and InceptionV3 in detecting to Classify Normal and Osteoporosis cases. The study employed 830 X-ray images of Spine, Hand, Leg, Knee, and Hip, comprising of Normal (420) and Osteoporosis (410) cases. Various performance metrics were utilized to evaluate each model, and the findings indicate that DenseNet121 exhibited superior performance with an accuracy rate of 93.4% with Achieving an error rate of 0.07 and a validation loss of only 0.57 in comparison with other models considered in this study
A Comparative Simulation Study of Classical and Machine Learning Techniques for Forecasting Time Series Data
Get PDFThis manuscript presents a simulation comparison of statistical classical methods and machine learning algorithms for time series forecasting notably the ARIMA model, K-Nearest Neighbors (KNN), The support Vector Regression (SVR), and Long-Short Term Memory (LSTM). The performance of the models was evaluated using different metrics especially Mean Squared Error (MSE), Mean Absolute Error (MAE), Median Absolute Error (Median AE), and Root Mean Squared Error (RMSE). The results of the simulations approve that KNN algorithm has better accuracy than the others models’ forecasting notably in the middle and long terms. The MAPE for the KNN model was around 4.976843 while SVR and LSTM architectures had a MAPE of 6.810311 and 13.992133 respectively. In the medium and long term, ML models are so powerful on big datasets. Paradoxically, Machine learning architectures outperform ARIMA for shorter-term predictions. Thus, ARIMA is most appropriate in the case of univariate small data sets, where deep learning algorithms are not yet at their best
An Efficient Preprocessing Technique for Multimodality Breast Cancer Images
Get PDFOn average, one in every eight women is diagnosed with breast cancer during their lifetime, and accounts for 14% of cancers in women. Since early diagnosis could improve treatment outcomes and longer survival times for patients, it is absolutely necessary to develop techniques to classify lesions within breast cancer mammograms and ultrasound images. The main goal is to determine the class of tumor present within the image, which is pivotal in diagnosing breast cancer patients. In this paper, we propose an Sobel-Canny-Gabor(SCG) model, which is a hybrid model that implements three different edge detection filters; Sobel filter, Gabor filter, and Canny filter. This model is used to enhance the appearance of the mammogram and ultrasound images, which is then fed into a classification model. Through classification, there could be a potential improvement in the results of the overall classification. Post-classification, the model is then evaluated using the metric Peak Signal-to-Noise Ratio (PSNR), which measures the quality between the original image and the compressed image.On average, one in every eight women is diagnosed with breast cancer during their lifetime, and accounts for 14% of cancers in women. Since early diagnosis could improve treatment outcomes and longer survival times for patients, it is absolutely necessary to develop techniques to classify lesions within breast cancer mammograms and ultrasound images. The main goal is to determine the class of tumor present within the image, which is pivotal in diagnosing breast cancer patients. In this paper, we propose an Sobel-Canny-Gabor(SCG) model, which is a hybrid model that implements three different edge detection filters; Sobel filter, Gabor filter, and Canny filter. This model is used to enhance the appearance of the mammogram and ultrasound images, which is then fed into a classification model. Through classification, there could be a potential improvement in the results of the overall classification. Post-classification, the model is then evaluated using the metric Peak Signal-to-Noise Ratio (PSNR), which measures the quality between the original image and the compressed image
Data Mining Application for the Spread of Endemic Butterfly Cenderawasih Bay using the K-Means Clustering Algorithm
Get PDFThe superfamily Papilionoidea day butterfly, which is endemic to the Cenderawasih Bay islands (Numfor, Supiori, Biak and Yapen), consists of 6 family species: the Papilionidae, Hesperiidae, Pieridae, Riodinidae, Lycaenidae and Nymphalidae families. This study aims to analyze the grouping of endemic butterflies of the Bay of Cendrawasih based on wings and colours in 4 Clusters, namely Numfor, Supiori, Biak and Yapen Islands, by applying the function of the K-Means Clustering algorithm data mining method. The grouping selection was carried out 7 times with the conclusion that Numfor had 13 types of Endemic Butterfly species, Biak had 7 Papuan Endemic Butterfly Species, Supiori had 9 Endemic Butterfly Species, and Yapen had 11 Endemic Butterfly Species. The analysis results were then retested in an application built using the Waterfall system development method and the PHP and MySQL programming languages. In addition to applying the K-Means Clustering algorithm for grouping endemic butterflies, the application created produces a butterfly distribution map that displays butterfly information based on family
Contribution of Online Tutoring in Promoting the Quality of Distance Learning for Moroccan Teachers
Get PDFThis paper aims at highlighting the contribution of online tutoring to the quality of distance learning (DL) and its effect on the progress and success of learners. The aim is, therefore, to judge the role of the tutor and the effect of his or her intervention on satisfying the real needs of Moroccan teachers in terms of learning during continuous training in DL mode. This study is grounded on a literature review regarding the effectiveness of tutoring and the qualitative analysis of data collected from a series of semi-structured interviews with actors (N=20) involved in the DL of Moroccan teachers. This study was achieved by the analysis of the results of a questionnaire returned by teachers (sample n=180) who were beneficiaries of the DL entitled “Communication in the Classroom,” which was provided by the Distance Learning for Students, Teachers and Professionals Association (FADEEP). The results emphasize the importance of factors relating to the effectiveness of tutoring in the quality of the DL and, more particularly, to the relevance of the tutor’s answers and his or her availability on the platform—and consequently, in the success of the learner—in addition to the material and technical, pedagogical, organizational, socio-political, and socio-cultural factors and factors linked to the learner of course
Explanatory Model on Academic Self-Efficacy in Engineering Students: Role of Anxiety, Dysthymia, and Negative Affect
Get PDFStudents in engineering tend to be loners, making interpersonal relationships and mental health issues more likely to arise. The COVID pandemic caused university students to experience anxiety and depression, which affected their academic performance and self-esteem. However, students’ mental health was rarely evaluated after the pandemic, especially engineering students, who showed higher levels of depression than students from other disciplines. The present study aims to establish an explanatory model of academic self-efficacy based on factors related to mental health, such as anxiety, depression, and negative emotions. The method used was quantitative cross-sectional, and a structural equation modeling was used. A sample of 561 students (54.4% males and 45.6% females) was analyzed. Instruments to measure self-efficacy, negative affect, depression, and anxiety, previously validated and with adequate reliability, were applied. The results showed that a state of anxiety affects academic self-efficacy negatively and directly; depression and negative affect indirectly affect academic self-efficacy mediated by a state of anxiety. These results show that a student with depression problems and a predominance of negative emotions is vulnerable to present anxiety in an academic setting. This anxiety causes his efficacy beliefs to decrease. As a result, he does not feel capable of facing academic challenges
Early Prediction of Monkeypox Virus Outbreak Using Machine Learning
Get PDFAt the onset of an infectious disease, such as the monkeypox virus (MPXV), surveillance data is crucial in keeping track of the outbreak’s progression. The surveillance data for MPXV received considerable attention after multiple European countries recorded cases. Historical data obtained from May 9, 2022, to August 10, 2022, were used to model the cumulative case trajectories of MPXV in five countries. Our study employed autoregressive integrated moving averages (ARIMA), neural network autoregression (NNETAR), exponential smoothing (ETS), and seasonal naïve regression (SNAÏVE) for training and evaluation. The paper makes the following contributions: (1) enhanced model stability with the Box-Cox transformation as a preprocessing step, (2) experimentation with both linear and non-linear models, and (3) simulation of the top five countries during the impulsive rise in cases of MPXV. The results were evaluated using three metrics: root mean square error (RMSE), mean square error (MAE), and mean absolute percentage error (MAPE). The ARIMA (0,1,3) (1,0,0)[7] model yielded the lowest percentage error of 5.16 in the holdout set for MAPE in France observations. The ETS (A, A, A) model, the lowest percentage error in the holdout set for MAE was 7.35 in Germany. Regarding the NNETAR (1,1,2) [7] model, the lowest percentage error in the holdout observations for RMSE was 8.33 in Spain, 2.75 in the United Kingdom (UK), and 8.05 in the United States of America (USA) in that order. Based on these findings, we can conclude that while the transformation proved crucial for model performance, it was not necessary for all experiments, as ARIMA remained dominant in France and the ETS model in Germany. At the same time, NNETAR model outperformed in cumulative case counts in Spain, the UK, and the USA. Our experimentation allows for early identification and contributes to a better understanding of forecasting MPXV cases using combinations of both linear and nonlinear models
EEG Spectral Feature Markers as an Indicator of Human Cognitive Process
Get PDFInformation technologies allow using modern and timely effective analyses of EEG waves and the methods of data processing that allows effective usage of this method into pedagogically and psychologically oriented researches. Aim of this study was to develop and validate method of EEG signal spectral properties usage in the investigations of the process of cognition in the process of the perception of music by the choice of professional studies. 23 research participants took part in the research – the students of the University of Latvia, the division of participants "non-musician" and "musician". The EEG recording synchronized with the musical signal using the generated synchronization signal that given to one of the unipolar input channels of the EEG equipment. The research analyses the basic rhythm of EEG the changes of the maximum frequency and the wave frequency power in the processes connected with the perception and cognition of music for 15 seconds long intervals. During the time of listening to the chorus songs, the range frequency of the range rhythm of alpha and beta does not change to the musicians but during the time of listening to the instrumental music it increases but it was more vivid in the range of beta frequency. Non-musicians reacted differently - while listening to chorus songs and instrumental music the frequency of alfa waves of EEG increased, but the beta wave frequency decreased. EEG as a method of investigation is recommended for pedagogical research to evaluate the neurological functions in the cognitive processes