Results 51 to 60 of about 983,800 (290)

Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif, Diane Demailly, Gabriella A. Horvàth, Juan Dario Ortigoza Escobar, Nathalie Dorison, Mayté Castro Jiménez, Cécile A. Hubsch, Thomas Wirth, Gun‐Marie Hariz, Sophie Huby, Morgan Dornadic, Zohra Souei, Muhammad Mushhood Ur Rehman, Simone Hemm, Mehdi Boulaymen, Eduardo M. Moraud, Jocelyne Bloch, Xavier Vasques   +17 more
wiley   +1 more source

Hyperparameter Tuning in Machine Learning: A Comprehensive Review

Journal of Engineering Research and Reports
Hyperparameter tuning is essential for optimizing the performance and generalization of machine learning (ML) models. This review explores the critical role of hyperparameter tuning in ML, detailing its importance, applications, and various optimization ...
Justus A Ilemobayo, O. Durodola, Oreoluwa Alade, Opeyemi J Awotunde, Adewumi T Olanrewaju, Olumide Babatope Falana, Adedolapo Ogungbire, A. Osinuga, Dabira Ogunbiyi, Ark O. Ifeanyi, Ikenna E Odezuligbo, O. E. Edu   +11 more
semanticscholar   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Metaheuristic-Based Hyperparameter Tuning for Recurrent Deep Learning: Application to the Prediction of Solar Energy Generation

Axioms, 2023
As solar energy generation has become more and more important for the economies of numerous countries in the last couple of decades, it is highly important to build accurate models for forecasting the amount of green energy that will be produced ...
C. Stoean, M. Zivkovic, Aleksandra Bozovic, Nebojša Bačanin, Roma Strulak-Wójcikiewicz, Milos Antonijevic, R. Stoean   +6 more
semanticscholar   +1 more source

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

Evaluasi Performa XGBoost dengan Oversampling dan Hyperparameter Tuning untuk Prediksi Alzheimer

Techno.Com
Alzheimer adalah gangguan neurodegeneratif yang mempengaruhi kemampuan kognitif dan memori, deteksi dini sangat penting untuk pengobatan yang tepat. Namun, untuk mendeteksi Alzheimer memerlukan biaya yang tinggi, sehingga penggunaan machine learning bisa
Furqon Nurbaril Yahya, Mochammad Anshori, Ahsanun Naseh Khudori   +2 more
doaj   +1 more source

Comparative additive manufacturing defect prediction accuracy with a few transfer learning implementations of deep learning models [PDF]

EPJ Web of Conferences
This paper addresses the problem of a comprehensive quality assurance strategy for additively manufactured components with integrated in-situ inspection and artificial intelligence and machine learning (AIML) models.
Bajpai Anamol, Regalla Srinivasa Prakash, Sahoo Subhendu Kumar, Yenishetti Pranav   +3 more
doaj   +1 more source

I Choose You: Automated Hyperparameter Tuning for Deep Learning-Based Side-Channel Analysis

IEEE Transactions on Emerging Topics in Computing
Today, the deep learning-based side-channel analysis represents a widely researched topic, with numerous results indicating the advantages of such an approach.
Li-Chao Wu, Guilherme Perin, S. Picek
semanticscholar   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

Advanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare, Y. Beideler, D. Chune, R. De Guio, T. Roland, H. Chibane   +5 more
wiley   +1 more source