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modeling algorithm » scheduling algorithm (Expand Search)
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coding algorithm » cosine algorithm (Expand Search), colony algorithm (Expand Search), scheduling algorithm (Expand Search)
data modeling » data models (Expand Search), spatial modeling (Expand Search)
modeling algorithm » scheduling algorithm (Expand Search)
method algorithm » mould algorithm (Expand Search)
coding algorithm » cosine algorithm (Expand Search), colony algorithm (Expand Search), scheduling algorithm (Expand Search)
data modeling » data models (Expand Search), spatial modeling (Expand Search)
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261
Scatter search metaheuristic for homology based protein structure prediction. (c2015)
Published 2015“…We assess our algorithm on a total of 11 proteins whose structures are present in the Protein Data Bank (PDB) and which has been used in previous literature. …”
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masterThesis -
262
Machine learning based approaches for intelligent adaptation and prediction in banking business processes. (c2018)
Published 2018“…Companies, nowadays, rely on systems and applications to automate their business processes and data management. In this context, the notion of integrating machine learning techniques in banking business processes has emerged, where trainable computational algorithms can be improved by learning. …”
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Get full text
masterThesis -
263
Frontiers and trends of supply chain optimization in the age of industry 4.0: an operations research perspective
Published 2024“…Furthermore, we recommend four promising research avenues in this interplay: (1) incorporating new decisions relevant to data-enabled SC decisions, (2) developing data-enabled modeling approaches, (3) preprocessing parameters, and (4) developing data-enabled algorithms. …”
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264
Automatic and Intelligent Stressor Identification Based on Photoplethysmography Analysis
Published 2021“…This work leverages the output of wearable technology to provide automatic stress and stressor identification model. In particular, this study proposes a novel algorithm that first detects instances of stress and then classifies the stressor type using photoplethysmography (PPG) data from wearable smartwatches. …”
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265
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266
Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: application to children with asthma
Published 2019“…<h3>Background</h3><p dir="ltr">Predictive modeling with longitudinal electronic health record (EHR) data offers great promise for accelerating personalized medicine and better informs clinical decision-making. …”
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267
A Cyber-Physical System and Graph-Based Approach for Transportation Management in Smart Cities
Published 2021“…In this paper, we proposed a transport-control model that exploits cyber-physical systems (CPS) and sensor-technology to continuously monitor and mine the big city data for smart decision-making. …”
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268
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269
Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast d...
Published 2020“…<h3>Objective</h3><p dir="ltr">To develop a machine-based algorithm from clinical and demographic data, physical activity and glucose variability to predict hyperglycaemic and hypoglycaemic excursions in patients with type 2 diabetes on multiple glucose lowering therapies who fast during Ramadan.…”
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270
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271
Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes
Published 2025“…Feature selection was done using a Random Forest algorithm to identify the top 20 features for the SAH severity prediction. …”
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272
Transmission Line Fault Location Using Unsynchronized Measurements
Published 2013Get full text
doctoralThesis -
273
Performance Analysis of Artificial Neural Networks in Forecasting Financial Time Series
Published 2013Get full text
doctoralThesis -
274
The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review
Published 2021“…We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). …”
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275
A multi-pretraining U-Net architecture for semantic segmentation
Published 2025“…For the validation of the proposed model, we used data from 21,000 cell nuclei at a resolution of 1000 by 1000 pixels. …”
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276
Prediction of biogas production from chemically treated co-digested agricultural waste using artificial neural network
Published 2020“…An Artificial neural network (ANN) algorithm was developed to model and optimize the cumulative methane production (CMP) from ASWs, CM, and their mixture under mesophilic and thermophilic conditions. …”
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277
Ensemble Deep Random Vector Functional Link Neural Network for Regression
Published 2022“…The esc-edRVFL is identified as the best-performing algorithm through a comprehensive evaluation of 31 UCI datasets.…”
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278
A Survey of Machine Learning Innovations in Ambulance Services: Allocation, Routing, and Demand Estimation
Published 2024“…By thoroughly reviewing the existing literature and methodologies, this paper provides a comprehensive overview of the approaches used in ambulance allocation, routing, demand estimation and simulation models. We discuss the challenges faced by these methods, emphasizing the need for innovative solutions that can adapt to real-time data and changing emergency patterns. …”
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279
Framework for rapid design and optimisation of immersive battery cooling system
Published 2025“…A conjugate heat transfer model for a 3S2P pouch cell module (20 Ah LiFePO₄) is developed and validated against experimental data (< 2% error). …”
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280
Fast Transient Stability Assessment of Power Systems Using Optimized Temporal Convolutional Networks
Published 2024“…In a postfault scenario, a copula of processing blocks is implemented to ensure the reliability of the proposed method where high-importance features are incorporated into the TCN-GWO model. The proposed algorithm unlocks scalability and system adaptability to operational variability by adopting numeric imputation and missing-data-tolerant techniques. …”