2026 February - 28 articles
As network environments become increasingly interconnected, ensuring robust cyber-security has become critical, particularly with the growing sophistication of modern cyber threats. Intrusion detection systems (IDSs) play a vital role in identifying...
Support Vector Machine (SVM) is a popular kernel-based method for data classification that has demonstrated high efficiency across a wide range of practical applications. However, SVM suffers from several limitations, including the potential failure...
In recent years, agriculture has begun to transform thanks to the arrival of robots and autonomous vehicles capable of performing complex operations such as weeding and spraying in an intelligent and targeted manner. In fact, new-generation agricultu...
The rise of toxic content, particularly in the form of hateful memes, poses a significant challenge to social media platforms. This paper presents an empirical comparative study of unimodal and multimodal architectures for toxic content detection. Ra...
Large Language Models are increasingly used for high-level robotic reasoning, yet their latency and stochasticity complicate their direct use in low-level control. Moreover, extracting actionable navigation cues from multimodal context incurs inferen...
A popular method for projecting high-dimensional data onto a lower-dimensional space while preserving the integrity of its structure is t-distributed Stochastic Neighbor Embedding (t-SNE). This technique minimizes the Kullback–Leibler (
The transition of Large Language Models (LLMs) from centralized clouds to edge environments is critical for addressing privacy concerns, latency bottlenecks, and operational costs. However, existing edge benchmarking frameworks remain tailored to dis...
Perception in trellised orchards is often challenged by dense canopy occlusion and overhead plastic coverings, which cause pronounced variations in sky visibility at row terminals. Accurately recognizing row terminals, including both row head and row...
Adverse weather removal aims to restore images degraded by haze, rain, or snow. However, existing unified models often rely on implicit degradation cues, making them vulnerable to inaccurate weather perception and insufficient semantic guidance, whic...
Semantic segmentation and deep learning methods have rarely been applied to fractional vegetation cover (FVC) segmentation tasks due to the lack of publicly available datasets for training deep learning models. FVC is a key indicator for assessing ve...
Modern Autonomous Vehicles generate large volumes of heterogeneous in-vehicle data, making cybersecurity a critical challenge as adversarial attacks become increasingly adaptive, stealthy, and multi-protocol. Traditional intrusion detection systems o...
This paper presents a scalable machine learning pipeline for extracting actionable, product-related insights from user-generated social media comments. Leveraging sentence embeddings from SBERT and unsupervised clustering (k-Means and agglomerative),...
The use of large language models (LLMs) to automate the generation of medical case-based multiple-choice questions (MCQs) is increasing, but their accuracy, reliability, and educational validity are still not well understood. This study in a comparat...
The early and accurate identification of breast cancer is a significant healthcare issue, largely because the traditional machine learning approaches rely on handcrafted features that are unable to fully capture the spatial and textural complexity fo...
Dense foliage, severe illumination variations, and interference from multiple individuals with similar appearances in complex orchard environments pose significant challenges for vision-based following robots in maintaining persistent target percepti...
Guidelines provide specific recommendations based on the best available medical knowledge, summarizing and balancing the advantages and disadvantages of various diagnostic and treatment options. Currently, consensus methods are the best and most comm...
This study investigates how Vision Language Models (VLMs) can be used and methodically configured to extract Environmental, Social, and Governance (ESG) metrics from corporate sustainability reports, addressing the limitations of existing text-only a...
The increasing integration of artificial intelligence (AI) in decision-making processes has amplified discussions surrounding algorithmic authority—the perceived epistemic legitimacy of AI systems over human judgment. This study investigates ho...
Migraine and epilepsy are common neurological disorders that share overlapping symptoms, such as visual disturbances and altered consciousness, making accurate diagnosis challenging. Although their underlying mechanisms differ, both conditions involv...
Accurate lung tumor segmentation using computed tomography (CT) scans is needed for efficient tumor treatment. However, the development of deep learning models is often constrained by strict patient privacy regulations that limit direct data sharing....
In an e-learning platform, information retrieval plays an enormous role through efficient processing. Recently, the education sector has increased its trend in online learning systems by generating a large amount of educational content based on stude...
Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide, and emphysema is present in the majority of affected patients and can be identified on computed tomography (CT). This study investigated whether radiomic feat...
The automated and accurate results of classifying histopathology images are necessary in the early detection of cancer, especially the common cancers such as Colorectal Cancer (CRC) and Lung Cancer (LC). Nonetheless, classical deep learning framework...
Large Language Model (LLM) agents depend heavily on multiple external tools such as APIs, databases and computational services to perform complex tasks. However, these tool executions create latency and introduce costs, particularly when agents handl...
Ontology learning from unstructured text has become a critical task for knowledge-driven applications in Big Data and Artificial Intelligence. While significant advances have been made in the automatic extraction of concepts and relations using neura...
Conversational agents are transforming digital interactions across various domains, including healthcare, education, and customer service, thanks to advances in large language models (LLMs). As these systems become more autonomous and ubiquitous, und...
Artificial intelligence (AI), particularly machine learning and deep learning architectures, has been widely applied to support thyroid cancer diagnosis, but existing evidence on its performance and limitations remains scattered across techniques, ta...
Prostate cancer is diagnosed through a comprehensive study of histopathology slides, which takes time and requires professional interpretation. To minimize this load, we developed a semi-supervised learning technique that combines transformer-based r...
