8 February 2026
Large language models (LLMs) are now routine writing tools across various domains, intensifying questions about when text should be treated as human-authored, artificial intelligence (AI)-generated, or collaboratively produced. This rapid review aims...
7 February 2026
The enhanced long-range navigation (eLoran) system serves as an important backup method for the global navigation satellite system (GNSS) system. In long-distance transmission scenarios, the signal propagation delay of the eLoran system is affected b...
Active athletes represent a specific target for learning and development. Their schedules, including training sessions and competitions, leave little time for education. However, athletes still need skills beyond sports to ensure they are prepared fo...
Although multi-agent reinforcement learning (MARL) has achieved significant success in various domains, its deployment in real-world scenarios remains challenging, particularly in communication-constrained environments involving multi-task coupling....
In logistics systems, the organization of information retrieval plays a key role in human interaction with technical systems to ensure decision-making speed, route optimization, planning, and resource allocation. At the same time, the efficiency of t...
This paper proposes a novel method for transferable adversarial attacks from Image Quality Assessment (IQA) to Video Quality Assessment (VQA) models. Attacking modern VQA models is challenging due to their high complexity and the temporal nature of v...
Sentiment analysis of Arabic text, particularly on social media platforms, presents a formidable set of unique challenges that stem from the language’s complex morphology, its numerous dialectal variations, and the frequent and nuanced use of e...
To overcome the limitations of current link prediction methods in effectively leveraging topological information and node importance, this paper introduces a new model called AMPS (Adaptive Multi-scale Potential-enhanced Path Similarity). The model i...
In practical well-logging datasets, severe missing values, anomalous disturbances, and highly imbalanced lithology classes are pervasive. To address these challenges, this study proposes a well-logging lithology identification framework that combines...
In the big data era, understanding and influencing consumer behavior in digital marketing increasingly relies on large-scale data and AI-driven analytics. This narrative, concept-driven review examines how big data technologies and machine learning r...
News data have become essential resources across various disciplines. Still, access to full-text news corpora remains challenging due to high costs and the limited availability of free alternatives. This paper presents a novel Python package (gdeltne...
In this paper, an explainable decision-making and guidance integration method is developed based on dynamic Bayesian network and the optimized control method. The proposed method can be applied for the autonomous decision-making and guidance in the g...
Speaker anonymization effectively conceals speaker identity in speech signals to protect privacy. To address issues in existing anonymization systems, including reduced voice distinguishability, limited anonymized voices, reliance on an external spea...
Cybersecurity has become one of the top priorities in Saudi Arabia, playing a key role in achieving Vision 2030 and advancing the kingdom’s position in digital transformation. This study investigates how cybersecurity knowledge, attitudes, and...
This paper introduces LLM4ATS, a framework integrating large language models, RAG, and closed-loop verification to automatically generate highly reliable automotive automated test scripts from natural language descriptions. Addressing the complex lin...
In the context of a new wave of scientific and technological revolution and industrial transformation, this study proposes an emerging technology identification framework that integrates a High-Value Patent Knowledge Graph with Social Network Analysi...
Renewable energy sources like wind turbines and solar panels are integrated into modern power grids as Distributed Energy Resources (DERs). These DERs can operate independently or as part of microgrids. Interconnecting multiple microgrids creates Net...
Large Language Models (LLMs) have demonstrated impressive capabilities in structured reasoning and symbolic tasks, with coding emerging as a particularly successful application. This progress has naturally motivated efforts to extend these models to...
With the rapid growth of visual content, automated aesthetic evaluation has become increasingly important. However, existing research faces three key challenges: (1) the absence of datasets combining Image Aesthetic Assessment (IAA) scores and Image...
Abundant evidence shows that there is a core component within a complex system, referred to as the metasystem, that fundamentally shapes the structural and dynamical characteristics of a complex system. The limitations of existing techniques for anal...
The aim of this study is to systematically capture, synthesize, and evaluate current research trends related to Automated Multiple-Choice Question Generation as they emerge within the broader landscape of natural language processing (NLP) and large l...
The rapid spread of climate change misinformation across digital platforms undermines scientific literacy, public trust, and evidence-based policy action. Advances in Natural Language Processing (NLP) and Large Language Models (LLMs) create new oppor...
The integration of large models and multimodal foundation models into the low-altitude economy is driving a transformative shift, enabling intelligent, autonomous, and efficient operations for low-altitude vehicles (LAVs). This article provides a com...
Air conditioners are a critical adaptation measure against heat- and cold-related risks under climate change. However, their electricity use and refrigerant leakage increase greenhouse gas (GHG) emissions. This study developed a data-driven life-cycl...
Information diffusion models developed for Twitter, Reddit, and Facebook assume network contagion and competition for shared attention. Telegram operates differently. It is built around channels rather than social graphs, and users receive posts dire...
Retrieval-Augmented Generation (RAG) is widely used for long-text summarization due to its efficiency and scalability. However, standard RAG methods flatten documents into independent chunks, disrupting sequential flow and thematic structure, resulti...
Within the scope of the study, the parameters of friction pendulum-type (FPS) isolators used or planned to be used in different projects were evaluated specifically for the project and its location. The evaluations were conducted within a performance...
Data prefetching is essential for modern file storage systems operating in large-scale cloud and data-intensive environments, where high performance increasingly depends on intelligent, adaptive mechanisms. Traditional rule-based methods and recently...
Deep neural networks are vulnerable and susceptible to adversarial examples, which can induce erroneous predictions by injecting imperceptible perturbations. Transferability is a crucial property of adversarial examples, enabling effective attacks un...
The journal Big Data and Cognitive Computing (BDCC) is a scholarly online journal which provides a platform for big data theories with emerging technologies on smart clouds and exploring supercomputers with new cognitive applications [...]
AI-based audio generation has advanced rapidly, enabling deepfake audio to reach levels of naturalness that closely resemble real recordings and complicate the distinction between authentic and synthetic signals. While numerous CNN- and Transformer-b...
Background: This study explores an innovative integration of big data analytics techniques aimed at enhancing predictive modeling in financial markets. It investigates how combining sentiment analysis with latent profile analysis (LPA) can accurately...
With the rapid development of mobile internet technology, the explosive growth of image–text multimodal data generated by social networking platforms has provided rich practical scenarios and theoretical research value for multimodal sentiment...
Fuzzy Cognitive Maps (FCMs) are a type of recurrent neural network with built-in meaning in their architecture, originally devoted to modeling and scenario simulation tasks. These knowledge-based neural systems support feedback loops that handle stat...
Managing risk in drifting complex systems is hindered by the weak integration of unstructured incident narratives into quantitative, decision-ready models. We present a phenomena-centric semantic factor framework that closes the data–model&ndas...
Acquired brain injury (ABI) often results in cognitive and motor impairments that can compromise driving ability, an essential aspect of independence and social participation. This study utilized a custom-designed driving simulator to compare driving...
AI enhances aquatic resource management by automating species detection, optimizing feed, forecasting water quality, protecting species interactions, and strengthening the detection of illegal, unreported, and unregulated fishing activities. However,...
In the context of the convergence of multiple energy systems, the risk of power quality degradation across different stages of energy generation and distribution has become increasingly significant. Accurate identification of power quality disturbanc...
Recent advances in large language models (LLMs) have driven substantial progress in knowledge base question answering (KBQA), particularly under few-shot settings. However, symbolic program generation remains challenging due to its strict structural...
Artificial Intelligence (AI)-enabled diagnosis has emerged as a promising avenue for revolutionizing medical image analysis, such as X-ray analysis, across a wide range of healthcare disciplines, including dentistry, consequently offering swift, effi...
High concentrations of tropospheric ozone (
Accurate forecasting of China’s Consumer Price Index (CPI) is crucial for effective macroeconomic policymaking, yet remains challenging due to structural breaks and nonlinear dynamics inherent in the inflation process. Traditional linear models...
Recent advances in machine learning have enabled highly effective ciphertext-based cryptographic algorithm identification, posing a potential threat to encrypted communication. Inspired by adversarial example techniques, we present CSPM (Class-Specif...
Background: Quantum Machine Learning (QML) has attracted significant attention in recent years. With quantum computing achievements in computationally costly domains, discovering its potential in improving the performance and efficiency of deep learn...
Real-time fine-grained human activity recognition (HAR) remains a challenging problem due to rapid spatial–temporal variations, subtle motion differences, and dynamic environmental conditions. Addressing this difficulty, we propose NovAc-DL, a...
In cloud-based distributed systems, trace anomaly detection plays a vital role in maintaining system reliability by identifying early signs of performance degradation or faults. However, existing methods often fail to capture the complex temporal and...
The dynamic flexible job shop scheduling problem (DFJSP) with machine faults, considering the recovery condition and variable processing time, is studied to determine the rescheduling scheme when machine faults occur in real time. The Monte Carlo Tre...
Artificial intelligence (AI) has been increasingly embedded within data-driven financial decision-making; however, its effectiveness was found to remain dependent upon the maturity of data governance frameworks. This systematic review was conducted i...
The proliferation of big data applications across various industries has led to a paradigm shift in data architecture, with traditional approaches giving way to more agile and scalable frameworks. The evolution of big data architecture began with the...
This interdisciplinary pilot study examines the use of Natural Language Processing (NLP) techniques, specifically Large Language Models (LLMs) with Prompt Engineering (PE), to analyze economic vulnerability from qualitative self-narratives. Seventy n...
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