The growing presence of malicious bot accounts on social media poses a threat to the authenticity of online communities, as they amplify misinformation, spread spam, and manipulate engagement. Reliable detection of these accounts is therefore essential to protect the integrity of platforms such as Instagram. This study introduces a deep learning–based detection framework built on the CrossGatedTabular (CGT) architecture, designed to learn complex patterns in user activity. To strengthen evaluation, two publicly available datasets of Instagram accounts were merged into a comprehensive benchmark representing diverse user behaviors. Natural language processing (NLP) was applied to refine text... More >

This paper presents a novel hybrid copy–move forgery detection method that combines the efficiency of FAST-BRIEF (for rapid keypoint detection and binary descriptors) with the robustness of SIFT (for scale- and rotation-invariant feature matching). The proposed framework employs g2NN matching for accurate feature correspondence, followed by morphological processing and LSC-SSIM superpixel segmentation for precise localization of tampered regions. The method is evaluated on 30 diverse test images from benchmark datasets comprising over 700 images, achieving a 95% F-measure with an average CPU time of 6.02 seconds. It demonstrates strong resilience to geometric transformations (rotation, sca... More >

The growing demand for secondary use of electronic health records (EHRs) in clinical research has amplified the importance of effective de-identification of protected health information (PHI) to comply with privacy regulations such as HIPAA. Manual annotation remains error-prone, time-consuming, and inconsistent across healthcare institutions, while existing automated systems often face trade-offs between accuracy, interpretability, and computational cost. This study proposes a novel hybrid de-identification framework that integrates neural, statistical, and rule-based approaches to achieve high recall, operational efficiency, and deployment feasibility in real-world healthcare settings. More >

This innovative study analyzes the emotional reaction of 27 individuals from Generation X to flirtatious behaviors exhibited by conversational artificial intelligence. Using a quantitative methodology based on Sentiment Analysis, testimonies and experiences with three models—ChatGPT, Grok, and Gemini—were collected, focusing on phrases or linguistic gestures that could be considered seductive, empathetic, or emotionally warm. The results show that, although there is a clear awareness that no person is behind the AI, several responses generated feelings of companionship, affective validation, and even mild attachment, especially in moments of emotional vulnerability—very difficult to ex... More >

Accurate traffic flow prediction plays a critical role in intelligent transportation systems, providing essential support for urban planning, traffic control, and congestion mitigation. To address the challenges of spatial heterogeneity and temporal dynamics inherent in traffic data, this paper proposes AST-GNNFormer, an adaptive spatio-temporal graph neural network that integrates graph attention mechanisms with temporal convolution. The model introduces three key components to enhance predictive accuracy and generalization: (1) a Layer-aware Information Preservation mechanism that mitigates over-smoothing in deep GNNs by retaining original node features across layers; (2) an Inter-Layer At... More >

Object detection is a fundamental problem in computer vision, with applications spanning self-driving cars, surveillance systems, medical imaging, robotics, and smart cities. Among the myriad of algorithms developed for this task, the You Only Look Once (YOLO) family stands out for its ability to perform real-time and accurate object detection. This article provides a comprehensive analysis of the YOLO algorithm series, from YOLOv1 to YOLOv8, evaluating them across key performance metrics, including precision, recall, mean Average Precision (mAP), frames per second (FPS), and overall effectiveness. Unlike traditional two-stage detectors such as R-CNN, YOLO formulates object detection as a si... More >

Accurate forecasting of reference evapotranspiration (ET$_o$) is essential for sustainable water resource management and precision agriculture, yet systematic comparisons across extended forecasting horizons remain limited. This study evaluates three ET$_o$ prediction methods---Random Forest (RF), Cartesian Genetic Programming (CGP), and Convolutional Neural Network accelerated by Graphics Processing Unit (CNN-GPU)---across six time intervals ranging from 1 to 364 days, using data from two semi-arid stations in northwestern Iran. Model performance was assessed via dispersion analysis (scatter and violin plots) and four accuracy metrics (RMSE, MAE, R$^2$, SI). Results indicate that RF and CNN... More >

The growing prominence of prompt engineering as a means of controlling large language models has given rise to a diverse set of methods, ranging from handcrafted templates to embedding-level tuning. Yet, as prompts increasingly serve not merely as input scaffolds but as adaptive interfaces between users and models, the question of how to systematically optimize them remains unresolved. Reinforcement learning, with its capacity for sequential decision-making and reward-driven adaptation, has been proposed as a possible framework for discovering effective prompting strategies. This survey explores the emerging intersection of RL and prompt engineering, organizing existing research along three... More >