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 >

The rapid advancement of generative AI has created a critical gap between technological innovation and accountability frameworks. Traditional responsibility mechanisms fail structurally when confronted with AI's black box nature, emergent behaviors, and capacity for autonomous decision-making—characteristics that sever the causal chains upon which legal and ethical liability depends. This Perspective argues that resolving this accountability paradox requires not a single regulatory fix but a distributed responsibility model spanning four interconnected domains: the proactive obligations of technology developers across the AI lifecycle, the paradigm shifts required in legal frameworks inclu... More >

The rapid expansion of edge computing and Internet of Things (IoT) ecosystems has introduced new cybersecurity challenges, particularly in decentralized, resource-constrained environments where traditional security models often fall short. This paper proposes an immune-inspired artificial intelligence framework (I3AI) that draws on core principles of biological immune systems including self-organization, local learning, and immune memory to enable adaptive, privacy-preserving defense mechanisms across distributed edge nodes. The architecture incorporates federated learning to maintain a decentralized threat intelligence network while ensuring data privacy and minimal communication overhead.... More >

Depression affects approximately 280 million people worldwide, yet remains substantially underdiagnosed due to the limitations of clinical assessment tools. This systematic review synthesizes 38 studies (2013--2025) on AI-based depression detection across voice, facial expression, physiological signals, and social media modalities, following PRISMA 2020 guidelines. Multimodal fusion consistently outperforms unimodal approaches, with reported F1 gains of 5-15% on benchmark datasets; however, no reviewed system has undergone prospective clinical validation, and reported accuracy figures should be interpreted as upper bounds on clinical utility. Critical open challenges include cross-cultural... More >

In the medical field, efficient and accurate classification of sequential and structured data is crucially important and useful for early diagnosis and treatment. Traditional machine learning models struggle with the complexity and nonlinearity of dynamic datasets, whereas deep learning models, despite their effectiveness, require extensive resources and lack transparency. This paper proposes a novel lightweight ensemble framework integrating a parameterized SoftMax function with a non-parametric Random Forest method through a soft voting mechanism, supported by the Nonlinear AutoRegressive eXogenous (NARX) model and optimized using a forward orthogonal search and selection (FOSS) algorithm... More >

Topic modeling is a widely used unsupervised natural language processing (NLP) technique aimed at discovering latent themes within documents. Since traditional methods fall short in capturing contextual meaning, approaches based on large language models (LLMs)—such as BERTopic—hold the potential to generate more meaningful and diverse topics. However, systematic comparative studies of these models, especially in domains requiring high accuracy and interpretability such as healthcare, remain limited. This study compares ten different LLMs (GPT, Claude, Gemini, LLaMA, Qwen, Phi, Zephyr, DeepSeek, NVIDIA-LLaMA, Gemma) using a dataset of 9,320 medical article abstracts. Each model was tasked... More >