Brain tumors affect the normal functioning of the brain and if not treated in time these cancerous cells may affect the other tissues, blood vessels, and nerves surrounding these cells. Today, a large population worldwide is affected by the precarious disease of the brain tumor. Healthy tissues of the brain are suspected to be damaged because of tumors that become the most significant reason for a large number of deaths nowadays. Therefore, their early detection is necessary to prevent patients from unfortunate mishaps resulting in loss of lives. The manual detection of brain tumors is a challenging task due to discrepancies in appearance in terms of shape, size, nucleus, etc. As a result, an automatic system is required for the early detection of brain tumors. In this paper, the detection of tumors in brain cells is carried out using a deep convolutional neural network with stochastic gradient descent (SGD) optimization algorithm. The multi-classification of brain tumors is performed using the ResNet-50 model and evaluated on the public Kaggle brain-tumor dataset. The method achieved 99.82% and 99.5% training and testing accuracy, respectively. The experimental result indicates that the proposed model outperformed baseline methods, and provides a compelling reason to be applied to other diseases.
With the emergence of the COVID-19 pandemic, access to physical education on campus became difficult for everyone. Therefore, students and universities have been compelled to transition from in-person to online education. During this pandemic, online education, the use of unfamiliar digital learning tools, the lack of internet access, and the communication barriers between teachers and students made precision education more difficult. Customizing models from previous studies that only consider a single course in order to make a prediction reduces the predictive power of the model because it only considers a small subset of the attributes of each possible course. Due to a lack of data for each course, overfitting often occurs. It is challenging to obtain a comprehensive understanding of the student’s participation during the semester system or in a broader context. In this paper, a model that is flexible and more generalizable is developed to address these issues. This model resolves the problem of generalized models and overfitting by using a large number of responses from college and university students as a dataset that considered a broader range of attributes, regardless of course differences. CatBoost, an advanced type of gradient boosting algorithm, was used to conduct this research, and enabled the developed model to perform effectively and produce accurate results. The model achieved a 96.8% degree of accuracy. Finally, a comparison was made with other related work to demonstrate the concept, and the experimental results proved that the Catboost model is a viable, accurate predictor of students’ performance.
Background To estimate the determinants of anxiety and depression among university teachers in Lahore, Pakistan, during COVID-19. Methods A cross-sectional study was conducted by enrolling 668 teachers from the universities of Lahore, Pakistan. Data were collected using a questionnaire. Chi-square for significance and logistic regression for the association were used. Results Majorly, the university teachers, with an average age of 35.29 years, had regular jobs (72.8%), job experience of > 6 years (51.2%) and good self-reported health (55.4%). The majority of the teachers were working as lecturers (59.6%), lecturing in arts (33.5%) or general science (42.5%) departments, having MPhil (37.9%) or master (28.9%) degrees, and teaching via synchronous video (59.3%) mode. Anxiety and depression, severe and extremely severe, were higher among lecturers, MPhil or master degree holders, teachers lecturing arts and general science subjects, and in those on contract employment. Anxiety was significantly associated with academic departments; arts (OR;2.5, p = 0.001) and general science (OR;2.9, p = 0.001), poor health status (OR;4.4, p = 0.018), and contractual employment (OR;1.8, p = 0.003). Depression was associated with academic departments; arts (OR;2.7, p = 0.001) and general science (OR;2.5, p = 0.001), and health status (OR;2.3, p = 0.001). Conclusion Among university teachers, anxiety and depression, severe and extremely severe, were prevalent among lecturers having MPhil or master degrees, belonging to arts and general science departments, and among contract employees. Anxiety and depression were significantly associated with academic disciplines, lower cadre, and poor health status.
Institutions of higher learning have made persistent efforts to provide students with a high-quality education. Educational data mining (EDM) enables academic institutions to gain insight into student data in order to extract information for making predictions. COVID-19 represents the most catastrophic pandemic in human history. As a result of the global pandemic, all educational systems were shifted to online learning (OL). Due to issues with accessing the internet, disinterest, and a lack of available tools, online education has proven challenging for many students. Acquiring accurate education has emerged as a major goal for the future of this popular medium of education. Therefore, the focus of this research was to identifying attributes that could help in students’ performance prediction through a generalizable model achieving precision education in online education. The dataset used in this research was compiled from a survey taken primarily during the academic year of COVID-19, which was taken from the perspective of Pakistani university students. Five machine learning (ML) regressors were used in order to train the model, and its results were then analyzed. Comparatively, SVM has outperformed the other methods, yielding 87.5% accuracy, which was the highest of all the models tested. After that, an efficient hybrid ensemble model of machine learning was used to predict student performance using NB, KNN, SVM, decision tree, and logical regression during the COVID-19 period, yielding outclass results. Finally, the accuracy obtained through the hybrid ensemble model was obtained as 98.6%, which demonstrated that the hybrid ensemble learning model has performed better than any other model for predicting the performance of students.
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