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2,076 papers
Introduction: Near-perfect crop-label accuracy can conceal uncertainty, perturbation sensitivity, and weak explanations. This study evaluates these reliability dimensions without treating benchmark classification as an agronomic recommendation. Materials and methods: Ten classifiers were evaluated using five matched st...
Digital Signal Processing (DSP), cryptography, and Machine Learning (ML) applications require multipliers that consume low power while delivering high speed and occupying minimal physical space, as computational demands continue to increase. Conventional multiplier architectures are limited due to high power consumptio...
Recently, with the rapid growth of Point of Interest (POIs) and increasing of social activities, more and more individuals tend to join parties or trips with groups formed randomly (named random groups, RGs). Although some works have proposed wonderful POI recommendation methods for RGs, they failed to consider how to...
Traffic accidents pose severe risks to public safety, yet existing injury severity prediction models often overlook spatial-temporal dynamics. This study proposes a two-stage framework integrating spatial-temporal analysis with machine learning to enhance prediction accuracy and interpretability. In the first stage, a...
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Vehicular Terahertz (THz) Open Radio Access Networks (ORAN) face severe challenges due to ultra-high mobility, rapid channel dynamics, spectrum scarcity, and frequent handovers, which limit the effectiveness of existing reactive and centralized management strategies. This paper proposes a Federated Blockchain-Assisted...
Mathematical modelling is necessary to understand complex biological systems, since it provides quantitative means to investigate disease transmission, population dynamics, ecological interactions, biological networks, and physiological processes. When biological mechanisms are expressed in mathematical terms, models c...
This study examines the development and intellectual structure of artificial intelligence (AI)-based financial fraud detection through a bibliometric analysis of 2200 Scopus- and Web of Science-indexed journal articles published between 1992 and 2026. The literature has expanded rapidly, with 89.6% of publications appe...
In the early-stage design phase, disassembly sequence planning must often rely on an incomplete engineering bill of materials rather than on detailed CAD geometry. This paper proposes a circularity-based hybrid learning framework that combines a genetic algorithm, Q-learning, and a graph neural network to generate and...
Standard digital Transformer architectures rely on dense matrix-matrix multiplications (GEMM) characterized by continuous-valued activations, generating massive thermal energy footprints and memory bandwidth bottlenecks during inference and training. In contrast, biological brains process complex spatio-temporal inform...
Generative models learn the distribution of data and draw new samples from it. Deep generative models parameterize this distribution with neural networks, usually conditioned on an application-specific input. They are used in many domains, such as drug discovery, chat and coding assistants, robotics, and animation. Thi...
Emotion recognition is a difficult task in the real world because of the interrelationship that exists between facial expression, scene setting, and social interaction.Conventional methods usually use single modality or simple fusion methods and do not provide rich multimodal relationships, and contextual dependencies....
Future sixth-generation (6G) Internet of Everything (IoE) networks require zero-touch management systems capable of autonomously allocating communication and computational resources, satisfying service-level constraints, and providing transparent explanations for their decisions. Existing neuro-symbolic explainable art...
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
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