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Background and Objectives: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. Materials and Methods: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images’ slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. Results: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. Conclusions: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance.

The COVID-19 pandemic has created significant challenges for higher education teachers, especially their well-being. A study was conducted to investigate the well-being of teachers in higher education institutions and to comprehend the pandemic's impact. The study took a quantitative approach, surveying and interviewing the teachers and analyzing the data with PLS-SEM and CB-SEM. The results revealed three key factors impacting teacher well-being: accomplishment, physical health, and relationships. The study emphasizes the importance of supporting teacher well-being during the pandemic by prioritizing physical health, building relationships, and engaging in meaningful activities. The findings can help to shape policies and programs that promote physical and emotional health in higher education institutions. Finally, this study provides valuable insights into teachers' experiences during the COVID-19 pandemic and emphasizes the need for increased support for their well-being.

Staff members use tried-and-true procedures when completing workplace visits, delivering services, and completing tasks for clients. However, the COVID-19 pandemic compelled employers to change the work styles of individual employees to ensure good communication, work-life balance, and flexibility for employees while maintaining optimal work productivity levels. In addition, the World Health Organization established social separation guidelines to combat COVID-19. The pandemic thus, challenged the work culture and also resulted in employees being quarantined in their homes. As a result of this transformation, employees were being encouraged to use digital tools to facilitate work-from-home opportunities. The current study aims to analyze employees' psychological and productive effects of work-from-home culture. It also looks for coworker bonding that is threatened by this transformation and suggests a way to keep it intact. Through a thorough review of the literature, the authors developed a comprehensive model to assess the pandemic's impact on employees' lifestyles. The conceptual model was empirically tested by applying the model to data collected from 233 employees from various backgrounds. The model result was validated using Partial Least Squares Methods- Structural Equation Modeling. The inferences highlight the factors influencing employee morale and work culture, as well as the parameters closely related to employee functioning in the organization that should not be affected.

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