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Skin cancer is one of the widespread diseases among existing cancer types. More importantly, the detection of lesions in early diagnosis has tremendously attracted researchers' attention. Thus, artificial intelligence (AI)-based techniques have supported the early diagnosis of skin cancer by investigating deep-learning-based convolutional neural networks (CNN). However, the current methods remain challenging in detecting melanoma in dermoscopic images. Therefore, in this paper, we propose an ensemble model that uses the vision of both EfficientNetV2S and Swin-Transformer models to detect the early focal zone of skin cancer. Hence, we considerthat the former architecture leads to greater accuracy, while the latter model has the advantage of recognizing dark parts in an image. We have modified the fifth block of the EfficientNetV2S model and have included the Swin-Transformer model. Our experiments demonstrate that the constructed ensemble model has attained a higher level of accuracy over the individual models and has also decreased the losses as compared to traditional strategies. The proposed model achieved an accuracy score of 99.10%, a sensitivity of 99.27%, and a specificity score of 99.80%. [ABSTRACT FROM AUTHOR]

Background & Objective: Antimicrobial resistance is a worldwide issue. According to United Kingdom Government Commissioned Review on Antimicrobial Resistance, antimicrobial resistance might cause mortality of 10 million people each year by 2050. Microbes developed resistance to antimicrobial agents, largely due to irrational antibiotic use. The presence of a certified pharmacist, as well as prescriber's level of education and experience, are all linked to proper medication use. We aimed to find the differentiation in knowledge, attitude and practices of the pharmacists regarding the differences in experience, education level, graduation institute and working sector. Another area of our study was to ascertain the commonly prescribed antibiotics. Methodology: It was a quantitative, cross-sectional and multicenter study performed in Lahore, Pakistan. The sample size for prescriptions was 324, calculated by Daniel’s equation and 230 for questionnaires by convenient sampling. Questionnaires were administered to clinical, community, industrial and academic pharmacists with consent. A data collection form was utilized for finding prescription patterns. Results: According to 72% community, 71% clinical, 53% industrial, and 69% academic pharmacists, samples for culture and sensitivity must be taken before starting antibiotics. Contrarily, 28% community, 19 % clinical, 10% industrial and 23% academic pharmacists disagreed upon the use of broad-spectrum antibiotics, where narrow spectrum antibiotics were effective. The most commonly prescribed antibiotic was ceftriaxone (38.7%), followed by ciprofloxacin (6.8%) and meropenem (5.8%). Conclusion: The study found good knowledge, attitude and practices among pharmacists regarding antibiotic use. Negligible significant differences were observed in scores among pharmacists working under various set-ups. Irrational prescribing can be avoided by introducing training for physicians and pharmacists. [ABSTRACT FROM AUTHOR]