Breast cancer presents a substantial health obstacle since it is the most widespread invasive cancer and the second most common cause of death in women. Prompt identification is essential for effective intervention, rendering breast cancer screening a critical component of healthcare. Although mammography is frequently employed for screening purposes, the manual diagnosis performed by pathologists can be laborious and susceptible to mistakes. Regrettably, the majority of research prioritizes mass classification over mass localization, resulting in an uneven distribution of attention. In response to this problem, we suggest a groundbreaking approach that seeks to identify and pinpoint cancers in breast mammography pictures. This will allow medical experts to identify tumors more quickly and with greater precision. This paper presents a complex deep convolutional neural network design that incorporates advanced deep learning techniques such as U-Net and YOLO. The objective is to enable automatic detection and localization of breast lesions in mammography pictures. To assess the effectiveness of our model, we carried out a thorough review that included a range of performance criteria. We specifically evaluated the accuracy, precision, recall, F1-score, ROC curve, and R-squared error using the publicly available MIAS dataset. Our model performed exceptionally well, with an accuracy rate of 93.0% and an AUC (area under the curve) of 98.6% for the detection job. Moreover, for the localization task, our model achieved a remarkably high R-squared value of 97%. These findings highlight that deep learning can boost the efficiency and accuracy of diagnosing breast cancer. The automation of breast lesion detection and classification offered by our proposed method bears substantial benefits. By alleviating the workload burden on pathologists, it facilitates expedited and accurate breast cancer screening processes. As a result, the proposed approach holds promise for improving healthcare outcomes and bolstering the overall effectiveness of breast cancer detection and diagnosis.

Breast cancer is a significant health issue for women, being one of the most common types of cancer and the second leading cause of death among them. Detecting breast cancer early is crucial for effective treatment. While ultrasound is often used for screening, diagnosing cancer manually takes a lot of time and is not always accurate. This study introduces a new way to find breast cancer in ultrasound images using a deep-learning model called U-net. This study used techniques like binary cross-entropy and Adam optimization to make the method strong and reliable. The BCE Dice Loss, which combines binary cross-entropy and the Dice Coefficient, helps the authors precisely locate the cancer. This method isn't just effective for ultrasound images – it can also work with mammography and MRI images. Plus, it's not limited to breast cancer; it can help detect other medical issues, too. When the researchers tested this method with ultrasound images, they found it had a training accuracy of 99.37% and a test accuracy of 96.2%, showing how well it performs.

Online shopping provides an excellent opportunity and platform for today's traditional businesses. Because of the advancement of online purchasing systems, students often prefer online shopping. Thus, students' involvement in online purchasing has become an important trend. The research aims to determine university students' purchase intentions toward Bangladeshi clothing brands using several machine learning approaches. An online questionnaire survey was conducted with 1000 university students, and the study goal is to understand their attitudes to online shopping from a different perspective. This paper represents a comparative study of different machine-learning techniques that have been applied to the problem of customer purchasing intention. The experiments were conducted using supervised machine learning techniques like linear regression, logistic regression, and Support Vector Machine (SVM) was also used to predict university students' purchase intentions. This study found that students' age, quality of cloth, purchase discount, and price positively impacted student purchase intentions, but the buying risk negatively affected students' purchase intentions. Linear regression gives the highest accuracy with maximum features, and the accuracy is 89.2%.