(1) Background: An approach for skin cancer recognition and classification by implementation of a novel combination of features and two classifiers, as an auxiliary diagnostic method, is proposed. (2) Methods: The predictions are made by k-nearest neighbor with a 5-fold cross validation algorithm and a neural network model to assist dermatologists in the diagnosis of cancerous skin lesions. As a main contribution, this work proposes a descriptor that combines skin surface fractal dimension and relevant color area features for skin lesion classification purposes. The surface fractal dimension is computed using a 2D generalization of Higuchi’s method. A clustering method allows for the selection of the relevant color distribution in skin lesion images by determining the average percentage of color areas within the nevi and melanoma lesion areas. In a classification stage, the Higuchi fractal dimensions (HFDs) and the color features are classified, separately, using a kNN-CV algorithm. In addition, these features are prototypes for a Radial basis function neural network (RBFNN) classifier. The efficiency of our algorithms was verified by utilizing images belonging to the 7-Point, Med-Node, and PH2 databases; (3) Results: Experimental results show that the accuracy of the proposed RBFNN model in skin cancer classification is 95.42% for 7-Point, 94.71% for Med-Node, and 94.88% for PH2, which are all significantly better than that of the kNN algorithm. (4) Conclusions: 2D Higuchi’s surface fractal features have not been previously used for skin lesion classification purpose. We used fractal features further correlated to color features to create a RBFNN classifier that provides high accuracies of classification.

(1) Background: In this research, we aimed to identify and validate a set of relevant features to distinguish between benign nevi and melanoma lesions. (2) Methods: Two datasets with 70 melanomas and 100 nevi were investigated. The first one contained raw images. The second dataset contained images preprocessed for noise removal and uneven illumination reduction. Further, the images belonging to both datasets were segmented, followed by extracting features considered in terms of form/shape and color such as asymmetry, eccentricity, circularity, asymmetry of color distribution, quadrant asymmetry, fast Fourier transform (FFT) normalization amplitude, and 6th and 7th Hu’s moments. The FFT normalization amplitude is an atypical feature that is computed as a Fourier transform descriptor and focuses on geometric signatures of skin lesions using the frequency domain information. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were employed to ascertain the relevance of the selected features and their capability to differentiate between nevi and melanoma. (3) Results: The ROC curves and AUC were employed for all experiments and selected features. A comparison in terms of the accuracy and AUC was performed, and an evaluation of the performance of the analyzed features was carried out. (4) Conclusions: The asymmetry index and eccentricity, together with F₆ Hu’s invariant moment, were fairly competent in providing a good separation between malignant melanoma and benign lesions. Also, the FFT normalization amplitude feature should be exploited due to showing potential in classification.

This study proposes a transparent, data-driven framework for behavior recognition based exclusively on IMU measurements, hypothesizing that vehicular jerk-based features can help in differentiating driving behavior. Unlike studies relying on direct jerk values, our approach derives novel findings from jerk-based features. For rolling windows of 300 samples, a comprehensive set of statistical and dynamic descriptors is extracted, including amplitude, variance, standard deviation, coefficient of variation, standard error, skewness, and kurtosis, as well as jerk-based features such as jerk_std, jerk_variance, jerk_amplitude, and jerk_spikes. Statistical analysis is used to identify features with strong discriminative power. The selected features are used to compute the Driving Score (DS) and, along with the Kernel Density Estimation (KDE) and associated statistics, provide a driver’s profile. Low DS values are consistently associated with increased jerk variability, whereas high DS values correspond to smoother and more controlled motion profiles. The robustness of the proposed framework is evaluated using several machine learning classifiers as baselines, with the jerk-based features as inputs. For the aggressive driver class, the Driving Behavior Score (DBS) model reports a Recall of 0.952 and an F1 of 0.925. For the normal driver class, the DBS model reports a Recall of 0.839 and an F1 of 0.879. The model has a total accuracy of 0.907. Also, Logistic Regression and ensemble models like Extreme Gradient Boosting (XGB) and Random Forest (RF) perform well. The proposed framework offers an explainable, computationally efficient alternative to conventional machine-learning classifiers for identifying aggressive drivers. It relies on lightweight statistical computations being suitable for real-time implementation.

Spatial chromatic instability is currently one of the most robust methods for improving solutions proposed for image-based fire detection systems. Real flames exhibit erratic, turbulent local color variations, providing a more reliable discriminative signal than global color information alone, especially in visually ambiguous non-fire situations. This study proposes a generalizable feature representation based on the Spatial Chromatic Instability Index (ICCS) to measure local RGB variations (ICCS_R, ICCS_G, ICCS_B, and ICCS_T). Two public datasets comprising both fire image files and non-fire imagery were used. The Hilbert–Schmidt Independence Criterion (HSIC) and Silhouette coefficient analysis were used to quantify the statistical dependence between feature sets and the resulting cluster separation. To evaluate the practical discriminatory performance of spatial chromatic instability, three classifiers, i.e., Logistic Regression, Linear SVM, and Random Forest, were employed. To verify the proposed approach’s effectiveness, three deep learning models, Swin Transformer, MobileViT, and ViT-Base-16, were also employed for cross-checking. Performance metrics demonstrated that integrating ICCS features into global color features improved classification. Logistic Regression performed best overall on the Kaggle dataset when local ICCS features were included, achieving an accuracy of 0.935 and an F1-score of 0.958. For the Mendeley dataset, Linear SVM achieved an accuracy of 0.862 and an F1-score of 0.881. The ICCS is a robust, easy-to-understand, and fast approach for identifying fires. It has real potential in early warning systems, mainly due to its limited requirements for computing power.

This study proposes a transparent, data-driven framework for behavior recognition based exclusively on IMU measurements, hypothesizing that vehicular jerk-based features can help in differentiating driving behavior. Unlike studies relying on direct jerk values, our approach derives novel findings from jerk-based features. For rolling windows of 300 samples, a comprehensive set of statistical and dynamic descriptors is extracted, including amplitude, variance, standard deviation, coefficient of variation, standard error, skewness, and kurtosis, as well as jerk-based features such as jerk_std, jerk_variance, jerk_amplitude, and jerk_spikes. Statistical analysis is used to identify features with strong discriminative power. Effect sizes, measured by Cohen’s d, quantify the difference between normal and aggressive driving styles. The selected features are used to compute the Driving Score (DS) and provide a driver’s profile. Experimental results reveal a correlation between lower DS scores (< 50) and windows characterized by high jerk variability, large amplitude fluctuations, and frequent spikes. Conversely, higher DS scores (>70) indicate smooth and stable motion patterns. The robustness of the proposed framework is evaluated using several machine learning classifiers as baselines, with the most important jerk-based features as inputs. For the aggressive driver class, the DBS model reports a Recall of 0.952 and an F1 of 0.925. For the normal driver class, the DBS model reports a Recall of 0.839 and an F1 of 0.879. The model has a total accuracy of 0.907. Also, Logistic Regression and ensemble models like XGB and RF perform well. The proposed framework offers an explainable, computationally efficient alternative to conventional machine-learning classifiers for identifying aggressive drivers.