Bone cancer is considered a serious health problem, and, in many cases, it causes patient death. The X-ray, MRI, or CT-scan image is used by doctors to identify bone cancer. The manual process is time-consuming and required expertise in that field. Therefore, it is necessary to develop an automated system to classify and identify the cancerous bone and the healthy bone. The texture of a cancer bone is different compared to a healthy bone in the affected region. But in the dataset, several images of cancer and healthy bone are having similar morphological characteristics. This makes it difficult to categorize them. To tackle this problem, we first find the best suitable edge detection algorithm after that two feature sets one with hog and another without hog are prepared. To test the efficiency of these feature sets, two machine learning models, support vector machine (SVM) and the Random forest, are utilized. The features set with hog perform considerably better on these models. Also, the SVM model trained with hog feature set provides an -score of 0.92 better than Random forest -score 0.77.

Breast cancer is the most frequently encountered medical hazard for women in their forties, affecting one in every eight women. It is the greatest cause of death worldwide, and early detection and diagnosis of the disease are extremely challenging. Breast cancer currently exceeds all other female cancers, including ovarian cancer. Researchers can use access to healthcare records to find previously unknown healthcare trends. According to the National Cancer Institute (NCI), breast cancer mortality rates can be lowered if the disease is detected early. The novelty of our work is to develop an optimized stacking ensemble learning (OSEL) model capable of early breast cancer prediction. A dataset from the University of California, Irvine repository was used, and comparisons to modern classifier models were undertaken. The implementation analyses reveal the unique approach’s efficacy and superiority when compared to existing contemporary categorization models (AdaBoostM1, gradient boosting, stochastic gradient boosting, CatBoost, and XGBoost). In every classification task, predictive models may be used to predict the class level, and the current research explores a range of predictive models. It is better to integrate multiple classification algorithms to generate a set of prediction models capable of predicting each class level with 91–99% accuracy. On the breast cancer Wisconsin dataset, the suggested OSEL model attained a maximum accuracy of 99.45%, much higher than any single classifier. Thus, the study helps healthcare professionals find breast cancer and prevent it from happening.

One of the most challenging tasks in the education sector in India is to predict student's academic performance due to a huge volume of student data. In the Indian context, we don't have any existing system by which analyzing and monitoring can be done to check the progress and performance of the student mostly in Higher education system. Every institution has their own criteria for analyzing the performance of the students. The reason for this happing is due to the lack of study on existing prediction techniques and hence to find the best prediction methodology for predicting the student academics progress and performance. Another important reason is the lack in investigating the suitable factors which affect the academic performance and achievement of the student in particular course. So to deeply understand the problem, a detail literature survey on predicting student's performance using data mining techniques is proposed. The main objective of this article is to provide a great knowledge and understanding of different data mining techniques which have been used to predict the student progress and performance and hence how these prediction techniques help to find the most important student attribute for prediction. Actually, we want to improve the performance of the student in academic by using best data mining techniques. At last, it could also provide some benefits for faculties, students, educators and management of the institution.

This paper highlights important issues of higher education system such as predicting student's academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an indepth awareness about how these algorithms predict student the performance of different student and enhance their skill.

Robotic Process Automation (RPA) is transforming the education sector by automating repetitive, rule-based tasks previously carried out by humans. This paper investigates the applications, benefits, challenges, and implications of RPA in education, focusing on its impact on administrative processes, student support services, and teaching methodologies. By analyzing 54 studies conducted between 2020 and 2024, this research explores how educational institutions have adopted RPA to enhance operational efficiency. Case studies reveal common success factors, including significant time savings, error reduction, and improved satisfaction for both students and staff. RPA has enabled institutions to streamline administrative workflows, optimize resource management, and offer personalized learning experiences. However, challenges such as implementation costs, data security, and the need for effective change management are also addressed. Additionally, this paper examines ethical concerns, particularly those surrounding job displacement and privacy. The findings underscore the transformative potential of RPA while highlighting the complexities of its integration into educational settings. The methodology followed PRISMA guidelines, with a systematic search of academic databases including IEEE Xplore, Scopus, Web of Science, and Google Scholar. Inclusion criteria focused on studies published between 2020 and 2024 that addressed RPA applications in education. Data analysis was conducted using thematic synthesis to identify key trends across the selected studies.

The growing field of educational data mining seeks to analyse educational data in order to develop models for improving education and the effectiveness of educational institutions. Educational data mining is utilised to develop novel approaches for extracting information from educational databases, enabling improved decision-making within the educational system. The main objective of this research paper is to investigate recent advancements in data mining techniques within the field of educational research, while also analysing the methodologies employed by previous researchers in this area. The predictive capabilities of various machine learning algorithms, namely Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Random Forest, K-Nearest Neighbour, and XGBoost Classifier, were evaluated and compared for their effectiveness in determining students' academic performance. The utilisation of Random Forest and XGBoost classifiers in analysing scholastic, behavioural, and additional student features has demonstrated superior accuracy compared to other algorithms. The training and testing of these classification models achieved an impressive accuracy rate of approximately (96.46% & 87.50%) and (95.05% & 84.38%), respectively. Employing this technique can provide educators with valuable insights into students' motivations and behaviours, ultimately leading to more effective instruction and reduced student failure rates. Students' achievements significantly influence the delivery of education.