Parkinson’s disease (PD) is a chronic neurological condition that progresses over time. People start to have trouble speaking, writing, walking, or performing other basic skills as dopamine-generating neurons in some brain regions are injured or die. The patient’s symptoms become more severe due to the worsening of their signs over time. In this study, we applied state-of-the-art machine learning algorithms to diagnose Parkinson’s disease and identify related risk factors. The research worked on the publicly available dataset on PD, and the dataset consists of a set of significant characteristics of PD. We aim to apply soft computing techniques and provide an effective solution for medical professionals to diagnose PD accurately. This research methodology involves developing a model using a machine learning algorithm. In the model selection, eight different machine learning techniques were adopted: Namely, Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Naïve Bayes (NB), Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbours (KNN), Extreme Gradient Boosting (XGBoost), and Logistic Regression (LR). Subsequently, the concentrated models were validated through 10-fold Cross-Validation and Receiver Operating Characteristic (ROC)—Area Under the Curve (AUC). In addition, GridSearchCV was utilised to measure each algorithm’s best parameter; eventually, the models were trained through the hyperparameter tuning approach. With 98% accuracy, LightGBM had the highest accuracy in this study. RF, KNN, and SVM came in second with 96% accuracy. Furthermore, the performance scores of NB and LR were recorded to be 76% and 83%, respectively. It is to be mentioned that after applying 10-fold cross-validation, the average performance score of LightGBM accounted for 93%. At the same time, the percentage of ROC-AUC appeared at 0.92, which indicates that this LightGBM model reached a satisfactory level. Finally, we extracted meaningful insights and figured out potential gaps on top of PD. By extracting meaningful insights and identifying potential gaps, our study contributes to the significance and impact of PD research. The application of advanced machine learning algorithms holds promise in accurately diagnosing PD and shedding light on crucial aspects of the disease. This research has the potential to enhance the understanding and management of PD, ultimately improving the lives of individuals affected by this condition.

Unmanned Aerial Vehicles (UAVs) are increasingly employed in traffic surveillance, urban planning, and infrastructure monitoring due to their cost-effectiveness, flexibility, and high-resolution imaging. However, vehicle detection and classification in aerial imagery remain challenging due to scale variations from fluctuating UAV altitudes, frequent occlusions in dense traffic, and environmental noise, such as shadows and lighting inconsistencies. Traditional methods, including sliding-window searches and shallow learning techniques, struggle with computational inefficiency and robustness under dynamic conditions. To address these limitations, this study proposes a six-stage hierarchical framework integrating radiometric calibration, deep learning, and classical feature engineering. The workflow begins with radiometric calibration to normalize pixel intensities and mitigate sensor noise, followed by Conditional Random Field (CRF) segmentation to isolate vehicles. YOLOv9, equipped with a bi-directional feature pyramid network (BiFPN), ensures precise multi-scale object detection. Hybrid feature extraction employs Maximally Stable Extremal Regions (MSER) for stable contour detection, Binary Robust Independent Elementary Features (BRIEF) for texture encoding, and Affine-SIFT (ASIFT) for viewpoint invariance. Quadratic Discriminant Analysis (QDA) enhances feature discrimination, while a Probabilistic Neural Network (PNN) performs Bayesian probability-based classification. Tested on the Roundabout Aerial Imagery (15,474 images, 985K instances) and AU-AIR (32,823 instances, 7 classes) datasets, the model achieves state-of-the-art accuracy of 95.54% and 94.14%, respectively. Its superior performance in detecting small-scale vehicles and resolving occlusions highlights its potential for intelligent traffic systems. Future work will extend testing to nighttime and adverse weather conditions while optimizing real-time UAV inference.