According to the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder among older adults, surpassed only by Alzheimer’s disease. PD is characterized by a heterogeneous combination of motor and non-motor symptoms, with involuntary movements constituting a central clinical defining feature. The prodromal phase presents substantial diagnostic challenges, as early symptoms are often mild, nonspecific, and commonly misinterpreted because overt motor signs are absent. Significantly, these early alterations can precede formal diagnosis by up to two decades, highlighting the urgent need for effective early detection strategies. In this work, we propose a functional Magnetic Resonance Imaging (fMRI)-based image-processing and machine learning framework for early detection of PD. Statistical features are extracted to train a set of 25 machine learning kernels, from which the most accurate model is selected. We then apply the Minimum Redundancy Maximum Relevance (mRMR) algorithm to identify the most descriptive fMRI frames for each subject. We introduce a hierarchical classification strategy: an initial binary classification to distinguish control subjects from prodromal + PD cases, followed by a second binary classification to discriminate between Prodromal and PD. The experimental results demonstrate a maximum precision of $96.3$% with 16 axial slices and $86.4$% with a single slice, indicating the effectiveness of the preprocessing strategy and its potential as a non-invasive biomarker for early PD detection, even with reduced input data. These findings suggest that high classification performance can be achieved with a limited number of fMRI slices, facilitating data acquisition and reducing subject burden in clinical studies.

Although the Space Phasor Model (SPM) is widely used to analyze induction motors, this study presents a new framework that applies SPM with geometric parameter extraction via a metaheuristic optimization algorithm. Differential Evolution was chosen for its simplicity, reliability, and effectiveness in exploring the parameter space. The Space Vector is constructed from the three-phase current signals. The approach captures spatial trajectories: healthy operation exhibits circular patterns, while inter-turn short-circuit (ITSC) faults deform them into ellipses, revealing both the affected phase and fault severity. The ellipse parameters are normalized using Z-score and organized into a structured fault database that includes both no-load and full-load conditions, supporting load-aware classification of 25 operating states and substantially extending the 13-class scope of previous studies. Tests with multiple classifiers (including Random Forests, Extra Trees, and gradient boosting) confirm the approach’s reliability, achieving 98.7% accuracy across 25 classes, 99.5% for the 13 most representative faults, and 94.1% under full-load for seven classes. Overall, this approach offers a phase-specific, severity-oriented diagnostic tool, highlighting its potential for industrial motor fault analysis and setting a new benchmark for multi-class classification.