Results 191 to 200 of about 84,699 (302)

Optimizing 3D Bin Packing of Heterogeneous Objects Using Continuous Transformations in SE(3)

Advanced Intelligent Systems, EarlyView.
This article presents a method for solving the three‐dimensional bin packing problem for heterogeneous objects using continuous rigid‐body transformations in SE(3). A heuristic optimization framework combines signed‐distance functions, neural network approximations, point‐cloud bin modeling, and physics simulation to ensure feasibility and stability ...
Michele Angelini, Marco Carricato
wiley   +1 more source

Comparative evaluation of machine learning and deep learning approaches for compressive strength prediction of geopolymer concrete. [PDF]

Sci Rep
Ezz H, Bakr SM.
europepmc   +1 more source

Explainable AI‐Driven Optimization of Electrode Activation Reduces Power Consumption While Preserving Object Recognition in Retinal Prostheses

Advanced Intelligent Systems, EarlyView.
Explainable artificial intelligence (XAI) guides selective electrode activation in retinal prostheses by emphasizing visually informative regions. XAI‐assisted phosphene generation maintains object recognition performance while significantly reducing stimulation power.
Sein Kim, Hamin Shim, Maesoon Im
wiley   +1 more source

Deep learning reconstruction improves detection of focal liver lesions in hepatobiliary phase compared to conventional EOB-MRI. [PDF]

Front Med (Lausanne)
Peng X, Cheng Q, Tian R, Lang L, Li C, Tao R, Xu T, Pylypenko D, Zuo L, Yu D, Lv W.   +10 more
europepmc   +1 more source

Electroencephalogram‐Driven Recognition of Parkinson's Disease Through a Mycelium‐Inspired Memristive Reservoir Computing Circuit

Advanced Intelligent Systems, EarlyView.
This work presents a bio‐inspired computing framework for Parkinson's disease analog recognition using electroencephalogram signals. Temporally encoded EEG features stimulate a mycelium‐inspired memristive reservoir, where disease‐related patterns emerge through physical spatiotemporal dynamics.
Ioannis K. Chatzipaschalis, Ioannis Tompris, Pantelis Fraidakis, Iosif‐Angelos Fyrigos, Antonio Rubio, Georgios Ch. Sirakoulis   +5 more
wiley   +1 more source

Recent advancements in the application of artificial intelligence-based approaches for screening, diagnosis, prognosis and treatment of cervical cancer. [PDF]

Oncol Rev
Tulimilli SV, Karnik M, Cockroft S, Bettadapura ADS, Nataraj SM, Mohideen HS, Dovat S, Sharma A, Madhunapantula SV.   +8 more
europepmc   +1 more source

Towards Advanced Intelligent and Perceptive Soft Grippers

Advanced Intelligent Systems, EarlyView.
Implementing soft yet strong and intelligent soft grippers request innovative and creative solutions in designing soft bodies and seamlessly integrating actuated systems with hierarchical sensing. This review systematically analyses soft grippers with a deep understanding of core components, from fundamental design principles to actuation and sensing ...
Haneul Kim, Hayeon Shin, Seik Park, Changbeom Shim, Seonggun Joe   +4 more
wiley   +1 more source

An interpretable ultrasound-based deep learning system for early breast cancer in a Chinese population. [PDF]

Insights Imaging
Wu S, Wang S, Liang D, Shi J, Shang X, Zhang L, Liu Y, Su X, Wang Y, Lu Q, Li Z, Zhao Z, Ji X, Li D, He Y.   +14 more
europepmc   +1 more source

Shapley Additive Explanation for Local Class Differentiation: Local Explainability for Class Differentiation in Classification Models

Advanced Intelligent Systems, EarlyView.
An instance‐level, model‐agnostic explanation of class differentiation is introduced through SHAP‐LCD, linking probability shifts to feature‐wise Shapley contributions. The method operates on tabular and image data and is released in a fully reproducible implementation, offering a transparent way to examine, at each instance, why predictive models ...
Roxana M. Romero Luna, Hugo A. Ordoñez Erazo, Carlos A. Cobos Lozada   +2 more
wiley   +1 more source

MDSC: Unsupervised Multipath Decomposition and Synergistic Correction for Efficient Low‐Light Image Enhancement with Detail‐Aware Smoothing

Advanced Intelligent Systems, EarlyView.
This work proposes MDSC, an unsupervised low‐light enhancement framework integrating three core innovations: detail‐aware smoothing, multipath decomposition, and synergistic correction. It suppresses noise, handles rapid illumination variations, and prevents reflectance‐contrast amplification inherent to Retinex separation.
Yong Cheng, De Yu, Yongqi Yuan, Mengjie Ye, Huan Wang, Qian Zhang, Ge Jin   +6 more
wiley   +1 more source