We introduce local matching stability and furthest matchable frame as quantitative measures for evaluating the success of underwater image enhancement. This enhancement process addresses visual degradation caused by light absorption, scattering, mari...
The choice of a symmetric encryption algorithm in practice is rarely as straightforward as it may appear from theoretical comparisons alone. In addition to security considerations, real-world selection often depends on execution time, reliability, en...
Automatic detection of deceptive intent in negotiation dialogue remains difficult because deceptive utterances are rare, context-dependent, and pragmatically subtle. This study develops a deployment-oriented evaluation pipeline for negotiation analyt...
Being aware of a personal context is a promising task for various applications, such as biometry, human-computer interactions, telemonitoring, remote care, mobile marketing and security. The task can be formally defined as the classification of a per...
Making internet-of-things (IoT)-based applications context-aware demands large amounts of raw data to be collected, interpreted, stored, and reused or repurposed if needed from many domains and applications. Context is transient but interpreted data...
Visual tracking from the unmanned aerial vehicle (UAV) perspective has been at the core of many low-altitude remote sensing applications. Most of the aerial trackers follow “tracking-by-detection” paradigms or their temporal-context-embed...
Vehicular Ad-Hoc Networks (VANETs) play a critical role in Intelligent Transportation Systems (ITS), enabling communication between vehicles and roadside infrastructure. This paper proposes an Adaptive Context-Aware VANET Routing (ACAVR) protocol des...
Mental health discourse may reflect social relationships, cultural norms, and religious factors that shape how individuals express and interpret distress. Existing NLP research on mental health has advanced the detection of depression, anxiety, suici...
Large language model (LLM)-powered web agents combine privileged instructions with user requests, webpages, retrieved documents, tool outputs, memory, and conversation history, creating prompt-injection risks that static text classification may not c...
The research and innovation related to fifth-generation (5G) networks that has been carried out in recent years has decided on the fundamentals of the smart slice in radio access networks (RANs), as well as the autonomous fixed network operation. One...
Detecting small targets in UAV aerial imagery is inherently difficult because these objects occupy only a small number of pixels and are highly susceptible to cluttered backgrounds, dense spatial arrangements, and pronounced scale variation. To addre...
JPEG-LS (a lossless (LS) compression standard developed by the Joint Photographic Expert Group) compressed image restoration is a significant problem in remote sensing applications. It faces the following two challenges: first, bridging small pixel-v...
The goal of visual place recognition (VPR) is to determine the location of a query image by identifying its place in a collection of image databases. Visual sensor technologies are crucial for visual place recognition as they allow for precise identi...
As brain organoids and organoid-based computational models grow in complexity, they increasingly exhibit electrophysiological patterns consistent with plasticity and information processing. This article explores a central question at the intersection...
The growing adoption of context-aware reasoning in Large Language Model (LLM)-based applications, particularly through retrieval-augmented and prompt-enriched architectures, has substantially broadened the utility of LLMs in specialized, real-world s...
Accurate visual tracking is a challenging issue in computer vision. Correlation filter (CF) based methods are sought in visual tracking based on their efficiency and high performance. Nonetheless, traditional CF-based trackers have insufficient conte...
Online monitoring and water quality analysis of lakes are urgently needed. A feasible and effective approach is to use a Wireless Sensor Network (WSN). Lake water environments, like other real world environments, present many changing and unpredictab...
With the recent technological advances, it is possible to monitor vital signs using Bluetooth-enabled biometric mobile devices such as smartphones, tablets or electric wristbands. In this manuscript, we present a system to estimate the risk of cardio...
An increasing number of special Internet of Things (IoT) applications are being deployed within federated and zero-trust (ZT) environments. These ad-hoc networks consist of heterogeneous, resource-constrained devices from various administrative domai...
Problem: Medical image segmentation faces critical challenges in balancing global context modeling and computational efficiency. While conventional neural networks struggle with long-range dependencies, Transformers incur quadratic complexity. Althou...
This study proposes EQAS (Empowered Question-Answering System), a hybrid framework designed to support context-aware question-answering and intelligent feedback generation in domain-specific knowledge environments. EQAS integrates fine-tuned transfor...
To improve the accuracy of stereo matching, the multi-scale dense attention network (MDA-Net) is proposed. The network introduces two novel modules in the feature extraction stage to achieve better exploit of context information: dual-path upsampling...
In order to enable a robust 24-h monitoring of traffic under changing environmental conditions, it is beneficial to observe the traffic scene using several sensors, preferably from different modalities. To fully benefit from multi-modal sensor output...
Visually impaired users face significant challenges in navigating complex indoor environments due to limited spatial awareness and lack of real-time semantic guidance. This paper proposes a multimodal navigation system integrating environmental perce...
Rich and real-world variants of the well-known vehicle routing problem (VRP) involve customer demands that are uncertain and influenced by contextual factors. Classical metaheuristics rely on deterministic demand estimates, while simheuristics incorp...
The launch of sixth-generation (6G) mobile networks is expected to introduce significant variability in Quality of Service (QoS), driven by environmental conditions, traffic heterogeneity, device diversity, and network slicing policies. Existing clus...
Accurate crack segmentation is critical for automated infrastructure inspection but remains challenging due to the inherent conflict between preserving fine-grained geometric details and modeling global semantic context. Existing deep learning approa...
Water scarcity, climate variability, and increasing water demands are placing growing pressure on water supply and distribution systems, particularly in water-scarce environments. Optimization-based approaches have become central to improving system...
Sign language understanding for automated vehicles sits at the intersection of accessibility, intelligent transportation, and safety-critical human–machine interaction. The existing sign-language recognition systems are largely confined to cont...
Semantic segmentation of remote sensing images plays a crucial role in accurate land-cover classification and environmental monitoring. However, existing semantic segmentation networks still struggle with multiscale feature extraction and context mod...
The Low-Strain Pile Integrity Test (LSPIT), standardized in ASTM D5882, is widely used as a rapid and economical non-destructive technique for assessing pile continuity in deep foundation systems. However, interpretation of LSPIT reflectograms remain...
The task of image captioning in low-resource languages like Tamil is fraught with challenges due to limited linguistic resources and complex semantic structures. This paper addresses the problem of generating contextually and linguistically coherent...
After the advent of Glove and Word2vec, the dynamic development of language models (LMs) used to generate word embeddings has enabled the creation of better text classifier frameworks. With the vector representations of words generated by newer LMs,...
The digital transformation of low-voltage distribution networks demands a renewed perspective on both regulatory frameworks and metering technologies. This article explores the intersection between incentive structures and metering technologies, focu...
Federated learning (FL) is emerging as a pivotal paradigm for environmental monitoring, enabling decentralized model training across edge devices without exposing raw data. This review provides the first structured synthesis of 361 peer-reviewed stud...
Advances in artificial intelligence (AI) and intelligent computing have significantly accelerated drug discovery by enabling accurate modeling of complex biomedical relationships. Among these efforts, drug repositioning—identifying novel therap...
Low-light image enhancement (LLIE) requires modeling spatially extensive and interdependent degradations across large pixel regions, while directly equipping diffusion-based LLIE with heavy global modules inside the iterative denoising backbone leads...
Aspect-based sentiment analysis (ABSA) plays a significant role in the field of big data and aims to distinguish the sentiment polarity of specific aspects in given sentences; however, the previous works on ABSA had two limitations. They mainly consi...
Decision Support Systems (DSSs) are increasingly shaping high-performance sport by translating complex time series data into actionable insights for coaches and practitioners. This paper outlines a structured, five-stage DSS development pipeline, gro...
Few-shot multimodal sentiment analysis (MSA), constrained by limited annotated data, often suffers from large cross-modal semantic alignment gaps and difficulty in capturing fine-grained sentiment cues, making it challenging to fully exploit the comp...
In high-altitude regions of the Global South, social housing programs are essential for mitigating vulnerability to low temperatures, but their standardized designs often fail to meet thermal performance codes. This study evaluates a “Sumaq Was...
Digitalization is globally transforming the world with profound implications. It has enormous potential to foster progress toward sustainability. However, in its current form, digitalization also continues to enable and encourage practices with numer...
This paper presents a lightweight semantic retrieval framework driven by an in-memory knowledge graph (IMKG) to overcome the limitations of traditional keyword matching and the prohibitive hardware costs of deep learning models in digitizing ancient...
In contemporary manned–unmanned teaming (MUM-T) systems, the accurate detection of aircraft in Synthetic Aperture Radar (SAR) imagery is crucial for battlefield surveillance and target identification. However, challenges such as background clut...
Industrial anomaly detection methods based on reverse distillation (RD) have shown significant potential. However, existing RD approaches struggle to achieve an effective balance between constraining the feature consistency of the teacher–stude...
In 2024, a total of 361 billion emails were sent and received by businesses and consumers each day. Email remains the preferred method of communication for work-related matters, with knowledge workers spending two to five hours a day managing their i...
As a distributed approach to Artificial Intelligence (AI) model construction over wireless networks, federated learning (FL) based on multi-device collaborative training can protect data privacy, as well as increase the computing load of local model...
The rapid expansion of the tourism industry underscores the necessity for sustainable hotel recommendation systems that guide user choices while safeguarding the long-term viability of the tourism ecosystem. However, existing methods often struggle t...
Cross-view image matching between UAV and satellite platforms is critical for geographic localization but remains challenging due to domain gaps caused by disparities in imaging sensors, viewpoints, and illumination conditions. To address these chall...
Background and Objectives: The accurate segmentation of pulmonary nodules in computed tomography (CT) remains a critical yet challenging task due to variations in nodule size, shape, and boundary ambiguity. This study proposes CAAF-ResUNet (Context-A...
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