Results 211 to 220 of about 93,556 (252)

Cross‐Modal Characterization of Thin‐Film MoS2 Using Generative Models

Advanced Intelligent Systems, EarlyView.
Cross‐modal learning is evaluated using atomic force microscopy (AFM), Raman spectroscopy, and photoluminescence spectroscopy (PL) through unsupervised learning, regression, and autoencoder models. Autoencoder models are used to generate spectroscopy data from the microscopy images.
Isaiah A. Moses, Chen Chen, Joan M. Redwing, Wesley F. Reinhart   +3 more
wiley   +1 more source

Optimized CNN framework with VGG19, EfficientNet, and Bayesian optimization for early colon cancer detection. [PDF]

Sci Rep
Rahman T, Deb N, Larguech S, Moniruzzaman M, Kumer Ghosh A, Ahmed Sami A, Abd Aziz AB, Salem Al-Bawri S.   +7 more
europepmc   +1 more source

Edge Information‐Augmented Auxiliary Diagnosis Method for Cervical Cancer in Medical Decision‐Making Systems

Advanced Intelligent Systems, EarlyView.
To address the problems of insufficient utilization of multiscale features and inefficient feature sharing between tasks in the model, this study proposes an edge‐enhanced intelligent cervical cancer screening method that achieves feature reuse and improves efficiency by jointly optimizing nucleolus segmentation and lesion classification.
Li Wen, Xiaoqing Zhang, Tingyi Dai, Fangfang Gou, Jia Wu   +4 more
wiley   +1 more source

Explainable machine learning for early diagnosis of esophageal cancer: A feature-enriched Light Gradient Boosting Machine framework with Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretations. [PDF]

J Int Med Res
Ridwan AM, Mohi Uddin KM.
europepmc   +1 more source

SmartDetectAI: An AI‐Powered Web App for Real‐Time Colorimetric Detection of Heavy Metals in Water

Advanced Intelligent Systems, EarlyView.
SmartDetectAI integrates silver nanoparticle‐based colorimetric sensing with an AI‐powered web app for rapid, on‐site detection of toxic heavy metals in water. By combining aggregation‐driven optical changes with machine learning analysis of red ‐ green ‐ blue values, the platform achieves portable, low‐cost, and accurate monitoring of Hg‐ and Cd‐based
Nishat Tasnim, Mirza M. A. Tunur, Fahad Arefin, Shahad Saroar, Md. Wahadoszamen, Taslim Ur Rashid, Aminul I. Talukder, Md. Nurul Amin, Mahmudur Rahman, Ahsan Habib   +9 more
wiley   +1 more source

Improving Visible Light Positioning Accuracy Using Particle Swarm Optimization (PSO) for Deep Learning Hyperparameter Updating in Received Signal Strength (RSS)-Based Convolutional Neural Network (CNN). [PDF]

Sensors (Basel)
Chang CM, Lin YZ, Chow CW.
europepmc   +1 more source

Real‐Time Sampling‐Based Model Predictive Control Based on Reverse Kullback–Leibler Divergence and Its Adaptive Acceleration

Advanced Intelligent Systems, EarlyView.
This study presents a new sampling‐based model predictive control minimizing reverse Kullback‐Leibler divergence to quickly find a local optimum. In addition, a modified Nesterov's acceleration method is introduced for faster convergence. The method is effective for real‐time simulations and real‐world operability improvement on a force‐driven mobile ...
Taisuke Kobayashi, Kota Fukumoto
wiley   +1 more source

Tree-based learning for high-fidelity prediction of chaos. [PDF]

Sci Rep
Giammarese A, Rana K, Bollt EM, Malik N.
europepmc   +1 more source

Synthetic Electrocardiogram Spectrogram Generation Using Generative Adversarial Network‐Based Models: A Comparative Study

Advanced Intelligent Systems, EarlyView.
Cardiovascular diseases are leading death causes; electrocardiogram (ECG) analysis is slow, motivating machine learning and deep learning. This study compares deep convolutional generative adversarial network, conditional GAN, and Wasserstein GAN with gradient penalty (WGAN‐GP) for synthetic ECG spectrograms; Fréchet Inception Distance (FID) and ...
Giovanny Barbosa‐Casanova, Norelli Schettini, Winston Percybrooks, Begoña García‐Zapirain   +3 more
wiley   +1 more source

Reply to Pastore, E.P. Comment on "Korkmaz et al. A Deep Learning and Explainable AI-Based Approach for the Classification of Discomycetes Species. Biology 2025, 14, 719". [PDF]

Biology (Basel)
Korkmaz AF, Ekinci F, Altaş Ş, Kumru E, Güzel MS, Akata I.   +5 more
europepmc   +1 more source