Artificial neural networking for computational assessment of ternary hybrid nanofluid flow caused by a stretching sheet: implications of machine-learning approach by Imad Khan, M. Waleed Ahmed Khan

“…Backpropagation neural networks, one of the supervised learning algorithms, is commonly used to train data networks by optimizing the error between actual and predicted values. …”
Get full text

Enhance differential privacy mechanisms for clinical data analysis using CNNs and reinforcement learning by Rakesh Batchala, Priyank Jain, Manasi Gyanchandani, Sanyam Shukla, Rajesh Wadhvani

“…The primary emphasis is on predicting and optimizing ventilation and sedation strategies for patients in Intensive Care Units. …”
Get full text

Real-Time Typical Urodynamic Signal Recognition System Using Deep Learning by Xin Liu, Ping Zhong, Di Chen, Limin Liao

“…This resulted in a total of 2,655 images to train, validate and test the DL algorithm to predict the urdynamic signals. Results Yolov5l had the best detection performance and the highest comprehensive index score (F1, 0.81; mean average precision, 0.83). …”
Get full text

Modern Methods for Diagnosing Faults in Rotor Systems: A Comprehensive Review and Prospects for AI-Based Expert Systems by Oleksandr Roshchupkin, Ivan Pavlenko

“…Some techniques like the vibration signal analysis method, spectral analysis, thermography, ultrasound diagnosis, and machine learning algorithms for predicting failure are of particular interest among them. …”
Get full text

Analyzing the compressive performance of lightweight foamcrete and parameter interdependencies using machine intelligence strategies by Wang Guoyuan, Fan Wenbo, Shi Qingbin, Luo Yingqi

“…A sensitivity analysis was conducted to determine how important certain aspects were. For predicting foamcrete’s compressive strength, MEP was better than GEP. …”
Get full text

An enhanced machine learning approach with stacking ensemble learner for accurate liver cancer diagnosis using feature selection and gene expression data by Amena Mahmoud, Eiko Takaoka

“…The selected features were then used to train a stacking ensemble model, which combined multiple base learners, including Multi-Layer Perceptron (MLP), Random Forest (RF) model, K-nearest neighbor (KNN) model, and Support vector machine (SVM), with a meta-learner Extreme Gradient Boosting (Xgboost) model to make final predictions. The stacking ensemble achieved an accuracy of (97%), outperforming individual machine learning algorithms and traditional diagnostic methods. …”
Get full text

A Data-Driven Comparative Analysis of Machine-Learning Models for Familial Hypercholesterolemia Detection by Tomasz Kocejko

“…The accuracy of the DLCN score in predicting FH was first evaluated by examining the proportion of patients with positive DNA tests relative to those with a DLCN score of 6 and above, the threshold for genetic testing. …”
Get full text

Water Quality Monitoring Using Landsat 8 OLI in Pleasant Bay, Massachusetts, USA by Haley E. Synan, Brian L. Howes, Sara Sampieri, Steven E. Lohrenz

“…Satellite-derived estimates of chlorophyll-a and Secchi depth were acquired using various algorithms including the “Case-2 Regional/Coast Color” (C2RCC), “Case-2 Extreme” (C2X), l2gen processor, and a random forest machine learning algorithm. …”
Get full text

Classification and Regression Trees analysis identifies patients at high risk for kidney function decline following hospitalization. by Weihao Wang, Wei Zhu, Janos Hajagos, Laura Fochtmann, Farrukh M Koraishy

“…Estimated glomerular filtration rate (eGFR) decline is associated with negative health outcomes, but the use of decision tree algorithms to predict eGFR decline is underreported. …”
Get full text

Paint shop vehicle sequencing based on quantum computing considering color changeover and painting quality by Jing Huang, Qing Chang, Hua-Tzu Fan

“…We use a machine learning model pretrained on historical data to predict the probability of painting defect. The problem is formulated as a combinational optimization problem with two cost components, i.e., color changeover cost and repair cost. …”
Get full text

Long-Short Term Memory Networks and Synthetic Data for Heavy Vehicle Rollover Prevention by Guido Perboli, Antonio Tota, Filippo Velardocchia

“…Considering the same and other connected implications, the necessity for techniques able to estimate and predict overturning eventualities appears evident. …”
Get full text

Medication Adherence: does Patient Participation in Randomized Clinical Trials Affect on it? by N. O. Vasyukova, Yu. V. Lukina, N. P. Kutishenko, S. Yu. Martsevich, O. I. Zvonareva

“…., the absence of a “gold standard” for assessing adherence in clinical practice makes it difficult to predict and significantly improve it among patients. …”
Get full text

An exploration of machine learning approaches for early Autism Spectrum Disorder detection by Nawshin Haque, Tania Islam, Md Erfan

“…This study explores the application of Logistic Regression, Support Vector Classifier, K-Nearest Neighbour, Decision Tree, and Random Forest for predicting Autism in children and toddlers by leveraging advancements in machine learning. …”
Get full text

The Potential of Unsupervised Induction of Harmonic Syntax for Jazz by Ruben Cartuyvels, John Koslovsky, Marie-Francine Moens

“…Experiments show that our methods can model harmony in datasets of jazz pieces, often resulting in realistic parse trees that overlap with expert annotations, without access to these annotations during training at all. Code, models, and predictions are publicly available.1…”
Get full text

A Review of Embodied Grasping by Jianghao Sun, Pengjun Mao, Lingju Kong, Jun Wang

“…Then, the embodied algorithms are introduced, starting from pre-trained models, with three main research goals: (1) embodied perception, using data captured by visual sensors to perform point cloud extraction or 3D reconstruction, combined with pre-trained models, to understand the target object and external environment and directly predict the execution of actions; (2) embodied strategy: In imitation learning, the pre-trained model is used to enhance data or as a feature extractor to enhance the generalization ability of the model. …”
Get full text

Artificial Intelligence in Glioblastoma—Transforming Diagnosis and Treatment by Alen Rončević, Nenad Koruga, Anamarija Soldo Koruga, Robert Rončević

“…They can also enhance prognostication by predicting survival, recurrence, and treatment responses, helping clinicians to make more informed decisions. …”
Get full text

Modern Approach in Pattern Recognition Using Circular Fermatean Fuzzy Similarity Measure for Decision Making with Practical Applications by Revathy Aruchsamy, Inthumathi Velusamy, Prasantha Bharathi Dhandapani, Suleman Nasiru, Christophe Chesneau

“…Machine learning algorithm utilizes pattern recognition as an instrument for identifying patterns and also similarity measure (SM) is a beneficial pattern recognition tool used to classify items, discover variations, and make future predictions for decision making. …”
Get full text

Global miniaturization of broadband antennas by prescreening and machine learning by Slawomir Koziel, Anna Pietrenko-Dabrowska, Ubaid Ullah

“…Our technique includes parameter space pre-screening and the iterative refinement of kriging surrogate models using the predicted merit function minimization as an infill criterion. …”
Get full text

Comprehensive multi-omics analysis of histone acetylation modulators identifies ASH1L as a novel aggressive marker for osteosarcoma by Chenlie Ni, Qiwen Sun, Haibo Yin

“…The effectiveness of HAMRPs in predicting the immune landscape of osteosarcoma was confirmed using CIBERSORT, ssGSEA, and ESTIMATE algorithms. …”
Get full text

ULOTrack: Underwater Long-Term Object Tracker for Marine Organism Capture by Ju He, Yang Yu, Hongyu Wei, Hu Xu

“…Therefore, existing tracking algorithms face difficulty in direct application to underwater object tracking. …”
Get full text