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This paper presents a comprehensive comparative analysis of machine learning predictive models for early-stage Parkinson's disease detection. Using a multi-class supervised learning approach with Random Forest and SVM classifiers on clinical and biomarker datasets, the study identifies the most discriminative features for diagnosis. The proposed system achieved improved accuracy through feature engineering pipelines and cross-validation, contributing a reproducible framework for clinical decision support in neurodegenerative disease screening.

This paper proposes a neuroplasticity-inspired AI architecture for autonomous spacecraft operations in deep space environments where real-time human intervention is infeasible. The system integrates neural monitoring with adaptive radiation protection mechanisms, using sequence modeling and predictive pipelines to support mission-critical decisions across 10+ simulated scenarios. The framework demonstrated a 20% improvement in pattern recognition accuracy and a 15% reduction in signal processing latency over baseline approaches.

This paper presents a comprehensive review of cloud storage architecture and its role in modern data management systems. The study examines the evolution of cloud computing, key storage models, and architectural components including distributed file systems, service interfaces, and storage resource pools. It also analyzes different deployment models such as public, private, hybrid, and community clouds while discussing advantages like scalability, cost efficiency, and high availability. The review highlights the architectural layers of cloud storage and the technologies that enable secure, reliable, and efficient data storage in distributed environments.

This paper explores the growing role of generative artificial intelligence, specifically ChatGPT, in modern business environments. The study reviews how large language models can support organizational functions such as customer service, business analytics, marketing automation, and decision support. It evaluates the benefits of AI-driven communication systems in improving operational efficiency, scalability, and data-driven decision-making. The paper also discusses challenges including ethical considerations, data privacy, and reliability of AI-generated outputs, providing insights into the future integration of generative AI technologies in business ecosystems.

This paper proposes a unified machine learning framework for predicting multiple health conditions within a single deployable system. The framework integrates supervised classification models to detect four clinically significant conditions: type 2 diabetes, coronary heart disease, Parkinson’s disease, and occupational burnout. Each module employs a classifier selected based on dataset characteristics, including Support Vector Machines for diabetes, Parkinson’s disease, and burnout prediction, and Logistic Regression for heart disease classification. The system incorporates tailored preprocessing pipelines, probability calibration, and physiologically bounded input validation to improve prediction reliability. Evaluation across held-out datasets demonstrates competitive performance with F1 scores ranging from 69.6% to 91.3%. The complete framework is deployed as a lightweight Streamlit web application enabling accessible multi-disease screening without external infrastructure.