Results 51 to 60 of about 432 (243)
AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling
Advanced Science, EarlyView.This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi +4 more
wiley +1 more source
Enhancing Fuzzy C-Means Clustering with a Novel Standard Deviation Weighted Distance Measure
مجلة بغداد للعلومThe aim of this paper is to present a new approach to address the Fuzzy C Mean algorithm, which is considered one of the most important and famous algorithms that addressed the phenomenon of uncertainty in forming clusters according to the overlap ratios.
Ahmed Husham Mohammed +1 more
doaj +1 more source
Illuminating the Intracellular World: Breakthroughs in Nanoscale Optoelectronics
Advanced Electronic Materials, EarlyView.This perspective explores optoelectronic biointerfaces spanning macroscale flexible devices to nanoscale intracellular systems, emphasizing their integration across dimensions. It examines capacitive, Faradaic, and photothermal mechanisms that enable light‐driven control of cellular activity and highlights key material and design challenges in ...
Tania Assaf, Menahem Y. Rotenberg
wiley +1 more source
ODDFUZZ: Discovering Java Deserialization Vulnerabilities via Structure-Aware Directed Greybox Fuzzing [PDF]
, 2023 Java deserialization vulnerability is a severe threat in practice. Researchers have proposed static analysis solutions to locate candidate vulnerabilities and fuzzing solutions to generate proof-of-concept (PoC) serialized objects to trigger them ...
Li, Bin +11 more
core +1 more source
BugMiner: Mining the Hard-to-Reach Software Vulnerabilities through the Target-Oriented Hybrid Fuzzer [PDF]
, 2020 Greybox Fuzzing is the most reliable and essentially powerful technique for automated software testing. Notwithstanding, a majority of greybox fuzzers are not effective in directed fuzzing, for example, towards complicated patches, as well as towards ...
Fayozbek Rustamov +4 more
core +1 more source
AIChE Journal, EarlyView.ABSTRACT Hybrid modeling combines first‐principles equations with a data‐driven subcomponent. Training for the data‐driven part is sensitive to measurement noise when training targets are constructed using pointwise time derivatives. Beyond differentiation errors, hybrid models involve solving an inverse problem to estimate the data‐driven term, which ...
Hangjun Cho +4 more
wiley +1 more source
DLF: A Deep Active Ensemble Learning Framework for Test Case Generation
InformationHigh-quality test cases are vital for ensuring software reliability and security. However, existing symbolic execution tools generally rely on single-path search strategies, have limited feature extraction capability, and exhibit unstable model ...
Yaogang Lu, Yibo Peng, Dongqing Zhu
doaj +1 more source
Deep Learning-Based Hybrid Fuzz Testing [PDF]
International Journal of Software and Informatics, 2021 Fengjuan Gao +3 more
openaire +1 more source
Advanced Intelligent Discovery, EarlyView.Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz +2 more
wiley +1 more source
Autonomous Vehicle Security: Hybrid Threat Modeling Approach
IEEE Open Journal of Vehicular TechnologyAutonomous vehicles (AVs) are poised to revolutionize modern transportation, offering enhanced safety, efficiency, and convenience. However, AV architectures' increasing connectivity and complexity have introduced significant cybersecurity risks ...
Amal Yousseef +7 more
doaj +1 more source