The COVID-19 pandemic has enhanced the adoption of virtual learning after the urgent suspension of traditional teaching. Different online learning strategies were established to face this learning ...crisis. The present descriptive cross-sectional study was conducted to reveal the different digital procedures implemented by the College of Medicine at Qassim University for better student performance and achievement.
The switch into distance-based learning was managed by the digitalization committee. Multiple online workshops were conducted to the staff and students about the value and procedures of such a shift. New procedures for online problem-based learning (PBL) sessions were designed. Students’ satisfaction was recorded regarding the efficiency of live streaming educational activities and online assessment.
The students were satisfied with the overall shift into this collaborative e-learning environment and the new successful procedures of virtual PBL sessions. The digital learning tools facilitated the performance of the students and their peer sharing of knowledge. The role of informatics computer technologies was evident in promoting the students, research skills, and technical competencies.
The present work elaborated on the procedures and privileges of the transformation into digitalized learning, particularly the PBL sessions, which were appreciated by the students and staff. It recommended the adoption of future online theoretical courses as well as the development of informatics computer technologies.
Objective: To investigate the medical students’ performance with and perception towards different multimedia medical imaging tools.
Method: The cross-sectional study was conducted at the College of ...Medicine, Qassim University, Saudi Arabia, from 2019 to 2020, and comprised third year undergraduate medical students during the academic year 2019-2020. The students were divided into tow groups. Those receiving multimedia-enhanced problem-based learning sessions were in intervention group A, while those receiving traditional problem-based learning sessions were in control group B. Scores of the students in the formative assessment at the end of the sessions were compared between the groups. Students’ satisfaction survey was also conducted online and analysed. Data was analysed using SPSS 21.
Result: Of the 130 medical students, 75(57.7%) were males and 55(42.3%) were females. A significant increase in the mean scores was observed for both male and female students in group A compared to those in group B (p<0.05). The perception survey was filled up by 100(77%) students, and open-ended comments were obtained from 88(88%) of them. Overall, 69(74%) subjects expressed satisfaction with the multimedia-enhanced problem-based learning sessions.
Conclusions: Radiological and pathological images enhanced the students’ understanding, interaction and critical thinking during problem-based learning sessions.
Key Words: Radiological images, PBL sessions, Medical students, Qassim University, Medical imaging.
BACKGROUND: Road Traffic Injuries (RTIs) is a vital concern that affect mutually both developed and undeveloped countries. In Saudi Arabia the death rate from traffic accidents is approximately 28.8 ...per 100,000 people. In the year 2018, the Kingdom of Saudi Arabia finally set an end to its legal ban on car driving for women, providing the way for millions of new drivers to steer across the country. Conversely, gender has a statistically momentous impact on driving behavior.
AIM: In this study we studied about the principal attitudes and behaviors of female drivers in Capital City Riyadh.
METHODS: This is a cross-sectional study in which we analyzed female’s behavior which they are living in Riyadh City using the “Dulla index” instrument to identify whether aggressive driving behavior is expected in females in Riyadh.
RESULTS: Using DDDI, we found that aggressive and dangerous driving behavior is not common among female drivers in Riyadh City. However, aggressive behavior was found three times more among employees when they drive than students, as well as participants with the educational level of (diploma/bachelor), (Singles/Divorced/widows), and those who (employees) were more likely associated with the behaviors of risky driving than their counterpart.
CONCLUSION: This study revealed that the women who reside in Riyadh city are well-educated about the traffic laws, and the rate of aggressive, dangerous driving behavior was uncommon among them. Further studies are required to augment knowledge and condense the hazardous driving behaviors in Saudi Arabia.
Accurate multi-time scale prediction of groundwater level (GWL) is important for water resources planning and management. But it is difficult to achieve reliable and robust GWL predictive even by the ...use of soft-computing techniques which considers the uncontrollable error, indefinite input, and unneglectable uncertainty at the time of modeling process. Soft computing approaches integrated into the data pre-treatment approach, input selection model, or uncertainty analysis can be employed to resolve this issue. The design of extensive deterministic and uncertainty analysis of automated models for the prediction of GWL is yet to be extensively explored. In this aspect, this study focuses on the design of type-II fuzzy logic system with uncertainty handling (T2FLS-UH) for GWL prediction. The goal of the T2FLS-UH technique is for predicting the GWL with the consideration of uncertainty. The T2FLS-UH technique encompasses different stages of operations such as data preparation, feature selection, prediction, and membership function selection. Besides, an ensemble empirical mode decomposition (EEMD) technique is involved for the decomposition of the original signals as to various intrinsic mode functions (IMFs). In addition, the Boruta approach is used for the selection of appropriate input variables for the prediction process. Moreover, the T2FLS model is applied for the GWL prediction process to estimate the value for every IMF. To improvise the predictive performance, the membership function of the T2FLS technique is chosen optimally by seagull optimization (SGO) algorithm. A wide range of simulations was carried to highlight the enhanced predictive performance of the T2FLS-UH technique. The experimental values pointed out the supremacy of the T2FLS-UH technique over the recent state of art prediction techniques.
The ability to predict the radioactive soil radon gas concentration is important for human beings because it serves as a precursor to earthquakes. Several studies have been conducted across the globe ...to confirm the correlation of radon emission dynamics and earthquakes, and concluded that the soil radon gas is the witness of anomalous behaviour before the occurrences of several earthquakes. This anomalous behavior can help to construct a better prediction model for earthquake forecasting. This paper aims at employing different ensemble and individual machine learning methods on real time radon time series data with different scenarios to predict anomalies in data caused by the seismic activities.The ensemble methods include boosted tree, bagged cart and boosted linear model while standalone machine learning methods include support vector machine with linear and radial kernels and k-nearest neighbors (<inline-formula> <tex-math notation="LaTeX">{K} </tex-math></inline-formula>-NN). We tested the methods on a dataset recorded on the fault line located in Muzaffarabad. Time series data was collected over a period ranging from March 1, 2017 to May 11, 2018 including nine(09) earthquakes. The methods are tested in four different settings with 10 times 10 folds cross validation procedure over the time window of 1 to 4. The repeated 10 fold cross validation is performed to reduce the noise in the model performance estimation by replicating the 10 fold cross validation procedure 10 times. Statistical performance evaluation measures viz. root mean square error (RMSE), root mean squared log error (RMSLE), mean absolute percentage error (MAPE), percentage bias (PB), and mean squared error (MSE) have been calculated for the assessment of performance. In setting 1, the support vector machine with radial kernel performs better with the minimum RMSE score of 1381.023 when compared to other prediction models. In setting 3, it can be observed through different performance metrics such as RMSE, the value in the range 1262.864, 1409.616 which is minimum when other prediction models for predicting soil radon gas concentration dataset. For setting 4, the boosted tree model yielded the minimum RMSE and MAPE scores of 1573.174 and 0.056 respectively. Findings of the study shows that boosted tree and support vector machine with radial kernel proved to be better regression models for the prediction of anomalies in soil radon gas concentration during seismic activities. An important finding of this study suggests that by employing boosted tree ensemble method make us able to accurately predict soil radon gas concentration automatically from environmental parameters.
In recent times, numerous decision-making procedures are not only based on the decision-making of choices, but also public perceptions of possible solutions. In a multi-criteria-based decision-making ...system, user preferences have been deeply considered. Sentiment analysis, on either side, is similar to natural language processing dedicated to the creation of methods capable of assessing evaluations and determining their intensity. The main aim of this research is to make efficient decisions using social media tweets. The proposed method uses the SentiRank method and neutrosophic set theory to make decisions and rank the reviews. Novel multi-criteria-based neutrosophic theory is used in this research for decision-making. An assembled neutral vocabulary, and the adapted VADER, are used to create Neutro-VADER, a novel version. Every evaluation of a product feature is given a positive, neutral, or negative scores of sentiment by the Neutro-VADER. A unique idea at this level is to use the positive, neutral, and negative scores on emotion to represent reality, uncertainty, and falsehood participation levels of a neutrosophic number. The testing findings support the value of sentiment data through reviews in the ranking procedure. The performance metrics used in the systems are precision, recall, and F1 measures and accuracy for evaluating the aspect detection module. The system performs better in food, service, and pricing categories, whereas the anecdotes group gives bad results. F1 and accuracy level shows better results in the proposed system by using SentiRank and the neutrosophic set theory method. KCI Citation Count: 0
In the present era, cancer is the leading cause of demise in both men and women worldwide, with low survival rates due to inefficient diagnostic techniques. Recently, researchers have been devising ...methods to improve prediction performance. In medical image processing, image enhancement can further improve prediction performance. This study aimed to improve lung cancer image quality by utilizing and employing various image enhancement methods, such as image adjustment, gamma correction, contrast stretching, thresholding, and histogram equalization methods. We extracted the gray-level co-occurrence matrix (GLCM) features on enhancement images, and applied and optimized vigorous machine learning classification algorithms, such as the decision tree (DT), naïve Bayes, support vector machine (SVM) with Gaussian, radial base function (RBF), and polynomial. Without the image enhancement method, the highest performance was obtained using SVM, polynomial, and RBF, with accuracy of (99.89%). The image enhancement methods, such as image adjustment, contrast stretching at threshold (0.02, 0.98), and gamma correction at gamma value of 0.9, improved the prediction performance of our analysis on 945 images provided by the Lung Cancer Alliance MRI dataset, which yielded 100% accuracy and 1.00 of AUC using SVM, RBF, and polynomial kernels. The results revealed that the proposed methodology can be very helpful to improve the lung cancer prediction for further diagnosis and prognosis by expert radiologists to decrease the mortality rate.
Unmanned aerial vehicles (UAVs) are assumed to be a promising model of automatic emergency tasks in dynamic marine ecosystems. But, the real-time communication efficacy betwixt UAVs and base ...platforms is developing a serious challenge. The compact-sized powerful flying robots can be wirelessly controlled and accomplish end tasks with and without human involvement. UAVs still face severe challenges that limit the dream of completely autonomous unmanned flying machines. The main difficulties contain path planning and hindrance avoidance of such unmanned flying robots, which are mandatory but carry out the application-specific functionality in either indoor or outdoor environments. This study introduces a new Dispersal Foraging Strategy with Cuckoo Search Optimization based Path Planning (DFSCSO-PP) technique for UAV networks. In the presented DFSCSO-PP technique, the identification of optimal paths for data transmission is performed in the UAV network. In addition, the presented DFSCSO-PP technique involves the optimal allocation of resources while finding the optimal paths in the network. Moreover, the DFSCSO technique can be designed by integrating the DFS concept into the CSO method to avoid local optima problems. A widespread simulation analysis is performed to exhibit the enhanced outcome of the DFSCSO-PP approach. A detailed set of comparative studies assured the improved performance of the DFSCSO-PP technique over other approaches.
Wearable devices such as smartwatches, wristbands, and GPS shoes are commonly employed for fitness and wellness as they enable people to observe their day-to-day health status. These gadgets ...encompass sensors to accumulate data related to user activities. Clinical act graph devices come under the class of wearables worn on the wrist to compute the sleep parameters by storing sleep movements. Sleep is very important for a healthy lifestyle. Inadequate sleep can obstruct physical, emotional, and mental health, and could result in several illnesses such as insulin resistance, high blood pressure, heart disease, stress, etc. Recently, deep learning (DL) models have been employed for predicting sleep quality depending upon the wearables data from the period of being awake. In this aspect, this study develops a new wearables-assisted smart health monitoring for sleep quality prediction using optimal deep learning (WSHMSQP-ODL) model. The presented WSHMSQP-ODL technique initially enables the wearables to gather sleep-activity-related data. Next, data pre-processing is performed to transform the data into a uniform format. For sleep quality prediction, the WSHMSQP-ODL model uses the deep belief network (DBN) model. To enhance the sleep quality prediction performance of the DBN model, the enhanced seagull optimization (ESGO) algorithm is used for hyperparameter tuning. The experimental results of the WSHMSQP-ODL method are examined under different measures. An extensive comparison study shows the significant performance of the WSHMSQP-ODL model over other models.
Currently, there are many limitations to classify images of small objects. In addition, there are limitations such as error detection due to external factors, and there is also a disadvantage that it ...is difficult to accurately distinguish between various objects. This paper uses a convolutional neural network (CNN) algorithm to recognize and classify object images of very small moths and obtain precise data images. A convolution neural network algorithm is used for image data classification, and the classified image is transformed into image data to learn the topological structure of the image. To improve the accuracy of the image classification and reduce the loss rate, a parameter for finding a fast-optimal point of image classification is set by a convolutional neural network and a pixel image as a preprocessor. As a result of this study, we applied a convolution neural network algorithm to classify the images of very small moths by capturing precise images of the moths. Experimental results showed that the accuracy of classification of very small moths was more than 90%.
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