2026 July - 46 articles
Diffusion-based trajectory planners achieve strong nominal performance in autonomous driving, but sparse safety intervention remains difficult to evaluate and realize effectively. This study addresses this problem by proposing a safety-aware event-tr...
Selecting appropriate healthcare waste (HW) treatment technologies is a challenging multi-criteria decision-making problem characterized by uncertainty, conflicting evaluation criteria, and limited decision-support information. Existing approaches of...
Marine monitoring records collected from buoys and nearshore sensors are often affected by missing values, abrupt spikes, and short-term fluctuations. These errors are difficult to remove with a single detection or interpolation rule, especially when...
The paper presents a model for the design and management of metadata that enables the efficient compilation of specialised datasets from large, heterogeneous data collections. The metadata are represented as a typed property graph that facilitates th...
We introduce the MERI (Market Evolutionary Resilience Index), a cognitive big data framework operationalising jamming transition physics into a daily operational regime detector (low-latency inference, 8 ms per observation). Opaque models cannot be d...
Real-world IoT network security generates traffic at big-data scale with extreme class imbalance, temporal non-stationarity, and continuously evolving attack strategies that overwhelm static supervised classifiers. This paper presents a cognitive com...
This study investigates how artificial intelligence (AI) is represented within Korean-language recommendation algorithm discourse on TikTok and YouTube. To examine the structural characteristics and discourse tendencies of AI-related discussions, the...
Scene classification aims to identify scene categories by analyzing environmental information. In real-world scenes, scene features are primarily captured through acoustic and visual modalities. However, environmental complexity and information diver...
Real-time retail credit scoring is a data-intensive cognitive computing task. Each decision must fuse heterogeneous signals, execute a non-linear model, return a calibrated probability of default (PD), and emit a regulator-compliant local explanation...
The convergence of the World Wide Web and artificial intelligence (AI) has fundamentally reconfigured digital ecosystems. Yet conventional generational models—Web 1.0 through 4.0—remain inadequate for capturing the recursive, mutually con...
This work offers an extensive performance evaluation of video-based pothole detection algorithms utilizing a unique dataset of 619 high-resolution movies recorded in South Kalimantan, Indonesia. Seven distinct models were assessed: three multi-frame-...
The rapid evolution of software complexity demands more efficient and autonomous testing mechanisms. Artificial intelligence (AI) has emerged as a solution to the limitations of traditional manual testing in software development, which is time-consum...
Diagnosis coding is a large-scale multi-label task in which each clinical encounter may require one or more coding labels from a large label space. Conventional top-k and threshold-based classifiers provide practical coding suggestions but do not dir...
Temporal Action Detection (TAD) localizes and classifies action instances within long videos and underlies many downstream video understanding applications. Transformer-based detectors scale poorly to long sequences due to quadratic self-attention, w...
Continuous and complete meteorological observations are essential for reliable climate analysis and environmental assessment. However, missing values caused by sensor malfunctions and transmission failures can introduce systematic biases and increase...
Artificial Intelligence (AI) is increasingly embedded in public governance, raising new challenges for anticipating its societal implications while safeguarding democratic accountability within expanding computational infrastructures. This article ex...
Existing cache replacement strategies in large-scale spatiotemporal data systems struggle to cope with complex and dynamic access patterns characterized by long-tail distributions and periodic behaviors. Traditional heuristic-based methods such as Le...
Fall-from-height (FFH) detection is a critical component in wearable safety systems, particularly in environments where high-intensity movements can lead to frequent false positives. Conventional approaches based on simple thresholding of acceleratio...
Construction companies, petrochemical enterprises, and airports are examples of large-scale organizational–technical systems (OTSs) and are characterized by a distributed structure, numerous parallel technological and business processes, and su...
Accurate change detection in high-resolution remote sensing imagery is essential for urban planning, land-use monitoring, and disaster response. This study introduces CTA-Net, a Cross-Temporal Attention Network for binary change detection in bi-tempo...
Large Language Models (LLMs) have rapidly become central components of cognitive computing systems and AI-assisted knowledge work. However, the effectiveness of LLM-generated outputs depends not only on the model’s capabilities but also on the...
Seasonal variation in heart rate variability (HRV) reflects multiple interacting determinants rather than a single underlying determinant. In this study, we aimed to examine subgroup-level seasonal HRV variation in relation to physical activity (PA)...
This paper proposes a novel approach for multiple-view
The proliferation of large-scale interaction datasets, from scientific collaboration networks and legislative records to online communication platforms, has made the analysis of group-based, time-varying systems one of the central challenges of moder...
The rapid development of the low-altitude economy demands comprehensive electromagnetic spectrum awareness. However, constructing a comprehensive radio environment map (REM) in this scenario is challenging, as spectrum sensing data collected by unman...
Artificial intelligence is rapidly changing education. However, many learning chatbots are still reactive tools, which respond to arbitrary questions without leading learners through a meaningful pedagogical journey. This article presents a determini...
Spiking Neural P systems provide a rule-based model of distributed computation inspired by membrane computing, while kernel P systems use guarded transformations and structured control of rule applicability. This paper introduces Convolutive Kernel-G...
Extracting composition expressions from materials science patent documents is essential for patent document searches. Composition expressions describing a single unit of elements and quantities (e.g., “Al: 0.02% or more and 0.08% or less”...
Next-generation communication technologies are increasingly shaping not only network infrastructure and digital services, but also public expectations, risk perceptions, and policy debates. As 5G deployment continues and 6G research accelerates, soci...
Lean manufacturing has historically focused on eliminating waste from physical production processes; however, increasing digitalization has shifted a substantial portion of operational effort toward information processing and decision making. Existin...
The temporal dynamics of shot quality in elite football remain poorly understood, despite well-documented declines in physical and technical performance during matches. This study aimed to analyze the evolution of advanced shot metrics across match h...
Career growth path planning is still dominated by statistical association models that summarize historical transitions but do not explicitly represent the causal mechanisms linking capability development, project exposure, policy support, performance...
Retrieval grounding is crucial for high-stakes administrative applications, since large language models remain prone to hallucinations when addressing legal questions. This problem is particularly relevant in Moroccan territorial governance, where of...
Phishing detection models often report strong benchmark performance, yet their reliability under realistic deployment conditions remains uncertain. This study examines this problem by investigating three failure modes of cross-dataset phishing email...
Industrial digitalization increasingly requires automated tools capable of extracting structured knowledge from complex engineering documentation, such as Piping and Instrumentation Diagrams (P&IDs). This work proposes an integrated framework tha...
Predicting near-future monetization in virtual livestreaming remains methodologically challenging because paid-support events are sparse, temporally dependent, and vulnerable to leakage under inappropriate evaluation designs. This study develops a le...
The widespread deployment of Internet of Things (IoT) environments has led to an increasing number of cyberattacks, highlighting the need for efficient and accurate intrusion detection systems. Over the last few decades, Intrusion Detection Systems (...
Unsupervised learning often relies on non-negative matrix factorization (NMF) for extracting low-dimensional features. Standard deep NMF models, however, tend to miss complex hierarchical patterns and may warp the intrinsic geometry of high-dimension...
Sequential resource allocation has long been a central problem in operations research, yet ongoing technological developments, particularly in cloud and high-performance computing and in multi-channel marketing, are giving rise to new structural cons...
Compressed sensing (CS) enables signal reconstruction from fewer measurements when the signal is sparse in a transform domain. However, executing
Traditional acupoint localization methods rely heavily on manual operation, resulting in high subjectivity and limited accuracy. To improve the precision and stability of acupoint detection, this study integrates machine vision technology with in sit...
Artificial intelligence (AI) is increasingly applied to healthcare decision-making; however, many persistent patient safety risks arise from sociotechnical conditions such as communication breakdowns, coordination failures, and organisational culture...
Words occurring in similar contexts have been observed to have similar meanings. A natural and established method within computational linguistics implements this observation by representing words as vectors with dimensions determined by words that a...
This paper extends a previous classification study by examining clustering methods on the same synthetic datasets and comparing their behavior with the previously obtained classification results. This study investigates the performance of selected ti...
Forecasting cryptocurrency prices remains difficult because market dynamics are highly volatile, non-stationary, and regime-dependent. This study investigates whether combining a spiking-inspired recurrent architecture with the Grey Wolf Optimizer (G...
The increasing complexity and volume of technical documentation, including requirements specifications, patents, and engineering reports, create significant challenges for manual analysis and knowledge extraction. This paper includes a systematic rev...
