Cyberbullying, which manifests in various forms, is a growing challenge on social media, mainly when it involves threats of violence through images, especially those featuring weapons. This study introduces a computational framework to identify such content using convolutional neural networks of weapon-related images. By integrating artificial intelligence techniques with image analysis, our model detects visual patterns associated with violent threats, creating safer digital environments. The development of this work involved analyzing images depicting scenes with weapons carried by children or adolescents. Images were sourced from social media and spatial repositories. The statistics were processed through a 225-layer convolutional neural network, achieving an 86% accuracy rate in detecting weapons in images featuring children, adolescents, and young adults. The classifier method reached an accuracy of 17.86% with training over only 25 epochs and a recall of 14.2%. Weapon detection is a complex task due to the variability in object exposures and differences in weapon shapes, sizes, orientations, colors, and image capture methods. Segmentation issues and the presence of background objects or people further compound this complexity. Our study demonstrates that convolutional neural networks can effectively detect weapons in images, making them a valuable tool in addressing cyberbullying involving weapon imagery. Detecting such content contributes to creating safer digital environments for young people.

In this article, we propose a robust star classification methodology leveraging light curves collected from 15 datasets within the Kepler field in the visible optical spectrum. By employing a Bagging neural network ensemble approach, specifically an Bagging-Performance Approach Neural Network (BAPANN), which integrates three supervised neural network architectures, we successfully classified 760 samples of curves which represent 9 type of stars. Our method demonstrated a high classification accuracy of up to 97% using light curve datasets containing 13, 20, 50, 150, and 450 points per star. The BAPANN achieved a minimum error rate of 0.1559 and exhibited efficient learning, requiring an average of 29 epochs. Additionally, nine types of stellar variability were classified through 45 conducted tests, taking into account error margins of 0, 5, and 10 for the light curve samples. These results highlight the BAPANN model’s robustness against uncertainty and ability to converge quickly in terms of iterations needed for learning, training, and validation.

The automated detection of HI holes in galaxy disks has been a long-standing challenge in extragalactic radio astronomy and has become increasingly urgent due to the large data volumes generated by current and forthcoming large-area HI surveys. Conventional approaches based on visual inspection are subjective, difficult to reproduce, and fundamentally incompatible with the scale of the data delivered by instruments such as MeerKAT and the Square Kilometer Array. We present a detection pipeline based on a convolutional neural network, which was specifically designed for the identification of HI holes in moment-0 maps and applied to NGC 6946, a nearby spiral galaxy for which one of the best-characterized HI hole catalogs is available. This approach combines Gaussian high-pass and Gabor filtering, as preprocessing steps tailored to the elliptical morphology of HI holes, with a convolutional neural network trained on 48 visually identified holes from the reference catalog of Boomsma et al. The model achieves a precision of 0.997 and a recall of 1.000 on the validation set, and a mean average precision (mAP50) of 83.2% when applied to the full NGC 6946 moment-0 image evaluated against the 69 cataloged holes used for training and validation. The pipeline also recovers 12 candidate HI holes that are not listed in the reference catalog, with eccentricities between 0.88 and 0.94 and aspect ratios between 2.1 and 3.0. The limitations of the proposed approach, such as the small training set and validation on a single-galaxy, are discussed. Our work establishes a reproducible baseline for automated HI hole detection and provides a foundation for future development toward survey-scale application.