Brain tumors are life-threatening conditions that require accurate diagnosis for effective treatment, and magnetic Resonance Imaging plays a significant role in the diagnosis of brain tumors.  Categorization of the tumor type is essential for making necessary medical decisions. Brain Tumor is commonly classified as Normal, Benign, or Malignant. There is a publicly available dataset in Kaggle for brain tumor classification with classes such as meningioma, pituitary gland, glioma, and no tumor. In our work, we proposed a novel method, Smote Tomek with Support Vector Machine (SVM), for brain tumor classification.The Combined sampling technique of smote from oversampling and Tomek from Undersampling was applied to compensate for the imbalance in the data.First, we implemented the combined technique of SMOTETomek to clear this data imbalance, leading to an improvement in the results.We then fitted the balanced data to the SVM classifier. Hence, our proposed method produces the best result with an accuracy of 95% for categorizing the data as pituitary tumor or no tumor. It also provides better results in terms of other metrics such as sensitivity and specificity. This method was also compared with other competent classifiers and was found to be an effective method for the classification of brain tumor data.

Purpose: To determine if the use of health information technology contributes to burnout among radiologic technologists. Methods: Searches for original research on burnout and the effects of health information technology among radiologic technologists were conducted in the Pub Med and Cochrane databases and the Radiology and Radiologic Technology journals from 2011 – 2024. Results: Moderate to high levels of burnout were seen predominantly among radiographers between thirty to forty years of age and with at least ten years of experience.  The studies did not assess the effects of health information technology, and a correlation could not be established. Conclusion: Radiologic technologists, specifically women in radiography, are affected by burnout. Dissatisfaction with employer incentives and operations and COVID-19 mitigation strategies were contributors. Health information technology usage on burnout among radiologic technologists was not assessed, identifying opportunities to explore this field.

Cervical cancer is one of the leading causes of cancer death among females worldwide and its behavior epidemiologically likes a venereal disease of low infectiousness. Early age at first intercourse and multiple sexual partners have been shown to exert strong effects on risk. Genital Human papilloma virus is the central risk factor for developing cervical cancer. Ethiopia has a population of 36.9 million women ages 15 years and older who are at risk of developing cervical cancer. Its incidence is 31.0 per 100,000 women with 7,445 annual number of new cases and 5,338 deaths every year. In order to investigate the epidemiology of cervical cancer associated with the human papillomavirus in Ethiopia, I construct mathematical model for progression of human papilloma virus with vaccination intervention; and I took into consideration nonlinear ordinary differential equations. I used a nonlinear stability analysis method for proving the local and global stability of the existing equilibrium point. I proved that the disease free equilibrium point is locally asymptotically stable and also globally asymptotically stable. Using second generation matrix, I obtained the effective and basic reproduction number for the dynamical system.Using standard parameter estimation I found that the numerical value of effective reproduction number is  and the basic reproduction number is . From this numerical value it is possible to conclude that vaccination intervention strategy is effective to control the spread of human papilloma virus diseases. The most sensitive parameter is  the proportion rate of vaccinated Individuals. I also conduct numerical simulations which support the finding in the sensitivity analysis. 

Consumption of locally brewed alcoholic beverages is considered a customary practice by the indigenous communities in India. However, unrestrained alcohol use by members of the tribal communities in India has been widely reported. While most studies focus on the cultural and economic significance of ethnic beverages, lesser number of studies focuses on the impact of alcohol consumption on these populations. There is a further dearth of systematic study on the adverse social impact of alcohol consumption on the tribal communities. The present study attempts to determine the prevalence of alcohol consumption and its social impact from some identified Lodha tribal villages of Paschim Medinipur district of West Bengal. Data for the study was collected through in-depth interviews and focus group discussion. The theoretical framework of thematic analysis has been used for data analysis. With the application of the framework three adverse social impact of inordinate alcohol consumption were identified namely domestic abuse, child care negligence and impaired work productivity.

The up surging role of artificial intelligence (AI)towards orchestrated marketing approach assist resonating target customers from different touch points. Technologies such as augmented reality (AR) and virtual reality(VR) play a crucial role by inducing a nexus of sensory stimuli in enhancing buyers’ experiential value and determining their purchase behaviour. Through physical mapping, AR applications recognize and track physical space and objects followed by the placement of virtual objects in the real world.  Thus, AR leverage machine learning to enable human-AI interaction. In this line, the present study intended to develop an AR experience model to determine buyers’ behaviour in B2C e-commerce. Partial Least Square based Structural Equation Modeling was incorporated to witness the significance of the hypothesized relationships between the variables; while, bootstrapping technique was utilized to analyze the higher order effects in the proposed model. Findings revealed that the AR-induced perceived augmentation in e-retail enhances the experiential value of customers which positively lead to user engagement and purchase intentions. Further, the study witnessed the role of spatial presence as a mediating variable in perceived augmentation and experiential value relationship. The proposed model is unique to accompany the behavioural consequences predicted by the experiential value gained by the customers during human-AI inter action via AR application in B2C e-commerce. The study may assist marketers to augment their AR-based applications and sensory marketing stimuli in line with customers’ expectations; thus, enriching their experiential value, engagement and purchase intentions in online retail.