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Scopus
ISSN 2590-0056 · Elsevier · 938 artikel di indeks
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Artikel Jurnal
VLMs using language-guided inference capture context-sensitivity of human object recognition behavior
Human vision is a context-sensitive process that interprets objects in relation to their surroundings. While behavioral research has long shown that scene context facilitates object recognition, the underlying computational mechanisms—and the extent to which artificial vision models replicate this…
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Artikel Jurnal
Multimodal artificial intelligence for human–computer interaction enhanced breast lesion diagnosis
This study aims to enhance breast ultrasound diagnosis by developing a multimodal artificial intelligence (AI) system that integrates lesion detection, diagnostic fusion, and human–computer interaction. We propose KIDNet, a deep learning model for detecting and scoring lesions. A Transformer-based…
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Artikel Jurnal
DeiTFake: Deepfake detection model using DeiT multi-stage training
Deepfakes are major threats to the integrity of digital media. We propose DeiTFake, a DeiT-based transformer and a two-stage progressive training strategy with increasing augmentation complexity. The approach applies an initial transfer-learning phase with standard augmentations, followed by a…
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Artikel Jurnal
Technology of discovering physical equipment connection relationship
Traditional network topology discovery methods rely heavily on specific protocols or are limited to single subnets, making them inadequate for large-scale, protocol-heterogeneous, and dynamic data center networks. To address this, we propose a framework that discovers physical device connections…
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Artikel Jurnal
Developing an efficient supplier performance evaluation and selection process by integrating the MARCOS method
Supplier selection is a key element of trading and supply chain management, with direct implications for cost, operational efficiency, and long-term performance. The evaluation process involves multiple and often conflicting criteria, and traditional multi-criteria decision-making (MCDM) methods…
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Artikel Jurnal
A deep semantic analysis based on sparse feature fusion and knowledge distillation in urban planning
To comprehensively analyze the deep semantic meaning, morphology, and attributes of targets across various scenarios and conditions, this study explores sparse feature fusion and knowledge distillation techniques in deep semantic analysis. The proposed architecture integrates these two technologies…
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Artikel Jurnal
MCDA4AI: A framework for managing n>2 criteria problems in decisions about artificial intelligence
Human decisions about the design, deployment, and governance of AI systems are increasingly complex, involving multiple desiderata such as accuracy, fairness, transparency, cost, and environmental impact. This paper proposes a novel and scalable framework for managing this complexity through…
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Artikel Jurnal
On the power and limits of foundation model image embeddings for privacy-preserving federated learning
While machine learning models generally benefit from including more training data, aggregating data, in particular medical data, can be difficult due to data privacy regulations. In response, federated learning has emerged in the last few years to train models on distributed data without the need…
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Optimizing student job placements with NLP and Explainable AI: A fair and transparent hiring framework
Background and Objectives:: AI enabled recruitment systems suffered with the challenges like bias, transparency, and fairness in student placement processes. Traditional manual hiring methods are always found slow and subjective, leaves doubt of unintentional discrimination. This study proposes a…
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Artikel Jurnal
Small target detection in remote sensing images based on multi-scale self-attention aggregation and coordinate attention enhancement
Small targets in remote sensing images suffer from degraded texture and edges due to network pooling, with further challenges from scale variations and arbitrary orientations. SuperYOLO has two key limitations: its pixel-level symmetric Multi-modal Fusion (MF) module fails to distinguish the…
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Artikel Jurnal
Reinforcement-learned unequal error protection for quantized semantic embeddings
This paper tackles the pressing challenge of preserving semantic meaning in communication systems constrained by limited bandwidth. We introduce a novel reinforcement learning framework that achieves per-dimension unequal error protection (UEP) via adaptive repetition coding. Central to our…
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Artikel Jurnal
Integrating agentic AI and digital twins for intelligent decision-making systems
The integration of agentic artificial intelligence (agentic AI) and digital twins (DTs) enables decision-making systems that are intelligent, adaptive, and goal-oriented. This paper advances that convergence through a multilayer integration framework that organizes perception, knowledge and data…
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Artikel Jurnal
CleanNav: Deep learning and reinforcement strategies for smart robot exploration
The developing trend in autonomous cleaning solutions for our homes has highlighted some challenges such as efficient navigation through spaces, avoiding obstacles, and interacting with objects in an intelligent way. Conventional vacuum cleaners adhere to set cleaning paths and thus do not adapt so…
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Artikel Jurnal
Label-efficient sleep staging from multi-channel EEG with self-supervised contrastive learning and iterative self-distillation
Manual sleep stage classification from polysomnography (PSG) is labor-intensive and subject to expert variability, motivating automated and deployment-oriented solutions for clinical use. We present a multi-channel self-supervised learning (SSL) contrastive framework combined with iterative…
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Artikel Jurnal
Automated quality assessment of online medical science popularization Videos: A framework based on large language models
Background: Unverified information in online medical science popularization videos poses significant public health risks. In the era of user-generated content, the sheer volume of daily video uploads overwhelms conventional human moderation, necessitating scalable automated assessment technologies…
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Artikel Jurnal
Design space exploration for layer-specific efficiency enhancement in CNN accelerators
Deploying specific convolutional neural networks (CNNs) on target FPGA boards presents a significant challenge: fully utilizing on-chip resources while maintaining efficient accelerator operation under bandwidth and resource constraints. This paper presents an automated hardware/software co-design…
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DAPFAM: A Domain-Aware Family-level Dataset to benchmark cross domain patent retrieval
Patent prior-art retrieval becomes especially challenging when relevant disclosures cross technological boundaries. Existing benchmarks lack explicit domain partitions, making it difficult to assess how retrieval systems cope with such shifts. We introduce DAPFAM, a family-level benchmark with…
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Artikel Jurnal
HXTS: An efficient tweakable block cipher solution for enhancing sector data security
Aiming at the problems of low encryption efficiency and weak security of HCTR and XTS, this paper designs an efficient tweakable block cipher solution—HXTS to enhance the security of sector data. HXTS is designed on a new structure of Hash-XTS-Hash, which reduces the computation amount of the hash…
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Artikel Jurnal
EEG-based prediction of driving performance using spectrograms and vision transformers
Cognitive lapses during driving are a leading cause of road accidents, underscoring the need for systems capable of online monitoring of attention and performance. While previous studies have used EEG to assess driver fatigue or focus, most approaches rely on raw signals or handcrafted features…
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Artikel Jurnal
An enhanced diabetic retinopathy blindness detection using deep learning and image preprocessing techniques
Diabetic retinopathy (DR) is a gradual and severe consequence of diabetes that increases the risk of visual impairment and blindness globally. Early and precise identification of DR is crucial for successful care, but availability to specialist diagnostic tools is restricted, especially in…