Bahareh Rahmani

Bahareh Rahmani, Harsha Reddy Bindela, Rama Kanth Reddy Gosula et al.

A combination of traditional image processing methods with advanced neural networks concretes a predictive and preventive healthcare paradigm. This study offers rapid, accurate, and non-invasive diagnostic solutions that can significantly impact patient outcomes, particularly in areas with limited access to radiologists and healthcare resources. In this project, deep learning methods apply in enhancing the diagnosis of respiratory diseases such as COVID-19, lung cancer, and pneumonia from chest x-rays. We trained and validated various neural network models, including CNNs, VGG16, InceptionV3, and EfficientNetB0, with high accuracy, precision, recall, and F1 scores to highlight the models' reliability and potential in real-world diagnostic applications.

Mohammad Amir Salari, Bahareh Rahmani

Machine learning (ML) transforms healthcare by enabling predictive analytics, personalized treatments, and improved patient outcomes. However, traditional ML workflows often require specialized skills, infrastructure, and resources, limiting accessibility for many healthcare professionals. This paper explores how BigQuery ML Cloud service helps healthcare researchers and data analysts to build and deploy models using SQL, without need for advanced ML knowledge. Our results demonstrate that the Boosted Tree model achieved the highest performance among the three models making it highly effective for diabetes prediction. BigQuery ML directly integrates predictive analytics into their workflows to inform decision-making and support patient care. We reveal this capability through a case study on diabetes prediction using the Diabetes Health Indicators Dataset. Our study underscores BigQuery ML's role in democratizing machine learning, enabling faster, scalable, and efficient predictive analytics that can directly enhance healthcare decision-making processes. This study aims to bridge the gap between advanced machine learning and practical healthcare analytics by providing detailed insights into BigQuery ML's capabilities. By demonstrating its utility in a real-world case study, we highlight its potential to simplify complex workflows and expand access to predictive tools for a broader audience of healthcare professionals.

Abbas Maazallahi, Sreehari Thota, Naga Prasad Kondaboina et al.

This study analyzes crop yield prediction in India from 1997 to 2020, focusing on various crops and key environmental factors. It aims to predict agricultural yields by utilizing advanced machine learning techniques like Linear Regression, Decision Tree, KNN, Naïve Bayes, K-Mean Clustering, and Random Forest. The models, particularly Naïve Bayes and Random Forest, demonstrate high effectiveness, as shown through data visualizations. The research concludes that integrating these analytical methods significantly enhances the accuracy and reliability of crop yield predictions, offering vital contributions to agricultural data science.