IJAMSR - INTERNATIONAL JOURNAL OF ADVANCED MULTIDISCIPLINARY SCIENTIFIC RESEARCH

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Past Issues

The Symbolism of The Candle in Freemasonry. Reflections on Light, The Four Elements and The Three Journeys of The Apprentice

Ferenc Sebök

CrossRef DOI : 10.31426/ijamsr.2025.8.3.8111

CrossRef DOI URL : https://doi.org/10.31426/ijamsr.2025.8.3.8111

Download PDF Google Search Abstract

Improving English Speaking Skills through the "Role-Play" Technique

Tambiabu Kagitala, Mr. M. Veeraiah

CrossRef DOI : 10.31426/ijamsr.2025.8.3.8112

CrossRef DOI URL : https://doi.org/10.31426/ijamsr.2025.8.3.8112

Download PDF Google Search Abstract

The Purpose of this study is to get the empirical knowledge of the Role-play technique to improve speaking ability, to know if the role-play effective in teaching speaking, to find the answer if the role is effective to improve English speaking ability. The technique of the research is qualitative. The data from field was done through observation in the classroom. Here the writer directly taught the students and observed students’ activities in speaking class. The writer explains the finding and gives some samples of role-play technique, activities in teaching and how to teach speaking through role-play technique.

Predictive Modelling of Cancer Using Machine Learning and Data Mining Techniques: A Performance-Based Comparative Analysis Approach

Vimmi Kochher, Dr. Ankit Kumar, Dr. Shivani Sharma

CrossRef DOI : 10.31426/ijamsr.2025.8.3.8113

CrossRef DOI URL : https://doi.org/10.31426/ijamsr.2025.8.3.8113

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Cancer continues to be a leading cause of global mortality, prompting the urgent need for accurate and early diagnostic tools. This study explores the application of data mining techniques such as Support Vector Machine (SVM), Decision Tree, k-Nearest Neighbours (KNN), Random Forest, and Artificial Neural Network (ANN) in predicting cancer disease using clinical datasets. A comparative analysis of these models was performed using metrics like accuracy, precision, recall, and F1-score. Results indicate that ANN outperforms other models in accuracy and precision, while KNN achieves the highest recall. These findings emphasize the potential of data mining to enhance early detection and decision-making in oncology.

Impact of Auditory, Visual, and Kinesthetic Learning Styles on English Achievement in High School Students

Joel Vas, Dr. Ankur Sharma

CrossRef DOI : 10.31426/ijamsr.2025.8.3.8114

CrossRef DOI URL : https://doi.org/10.31426/ijamsr.2025.8.3.8114

Download PDF Google Search Abstract

This study investigates the relationship between the linguistic style of learning and academic achievement in English among high school students. Recognizing the diversity in learning preferences auditory, visual, kinaesthetic/tactile, and multimodal the research explores how these styles influence students’ performance in English language tasks, including reading, writing, speaking, and comprehension.

Machine Learning for Dynamic Earned Value Management (EVM): A 2025 Perspective

Yaswanth Battula, Madhuri Pydi, Samuel Raj Kumar Paul

CrossRef DOI : 10.31426/ijamsr.2025.8.3.8115

CrossRef DOI URL : https://doi.org/10.31426/ijamsr.2025.8.3.8115

Download PDF Google Search Abstract

This research paper explores the integration of machine learning technologies with Earned Value Management (EVM) methodologies, offering a comprehensive analysis of how these technologies are transforming project performance measurement and forecasting in 2025. The paper examines the fundamental principles of EVM, identifies its traditional limitations, and investigates how machine learning algorithms and AI-driven approaches address these challenges. Through an analysis of current applications, emerging trends, and future directions, the research provides insights into the evolving landscape of project performance management in an increasingly digital and data-driven environment. The findings suggest that machine learning enhances EVM capabilities through improved data collection and analysis, more accurate forecasting, dynamic baseline adjustments, and enhanced decision support. These advancements address longstanding limitations of traditional EVM while creating new possibilities for project performance optimization in complex, uncertain environments.

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