An automatic recognition of target product and multiparameter collaborative regulation-based machine learning framework for dimethyl oxalate hydrogenation …
Q Yang, J Zhou, R Bao, D Rong, Z Wang - Chemical Engineering Science, 2025 - Elsevier
Conventional machine learning depends on the development of catalyst descriptors and
optimization models that are tailored to match specific product. Therefore, an automatic …
optimization models that are tailored to match specific product. Therefore, an automatic …
[HTML][HTML] Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food
Z Wang, W An, J Wang, H Tao, X Wang, B Han, J Wang - Toxins, 2024 - mdpi.com
Zearalenone (ZEN) has been detected in both pet food ingredients and final products,
causing acute toxicity and chronic health problems in pets. Therefore, the early detection of …
causing acute toxicity and chronic health problems in pets. Therefore, the early detection of …
[HTML][HTML] Kolmogorov–Arnold Network in the Fault Diagnosis of Oil-Immersed Power Transformers
Instabilities in energy supply caused by equipment failures, particularly in power
transformers, can significantly impact efficiency and lead to shutdowns, which can affect the …
transformers, can significantly impact efficiency and lead to shutdowns, which can affect the …
[HTML][HTML] Real-time earthquake magnitude prediction using designed machine learning ensemble trained on real and CTGAN generated synthetic data
The earthquake early warning (EEW) system provides advance notice of potentially
damaging ground shaking. In EEW, early estimation of magnitude is crucial for timely rescue …
damaging ground shaking. In EEW, early estimation of magnitude is crucial for timely rescue …
COVID-19 IgG antibodies detection based on CNN-BiLSTM algorithm combined with fiber-optic dataset
The urgent need for efficient and accurate automated screening tools for COVID-19
detection has led to research efforts exploring various approaches. In this study, we present …
detection has led to research efforts exploring various approaches. In this study, we present …
Impact of imbalanced datasets on ML algorithms for malware classification
This paper discusses the impact of imbalanced datasets on ML models for malware
classification and whether disproportionate distribution of various families affects the ability …
classification and whether disproportionate distribution of various families affects the ability …
[HTML][HTML] Exploring Asymmetric Gender-Based Satisfaction of Delivery Riders in Real-Time Crowdsourcing Logistics Platforms
D Li, Y Zhang - Symmetry, 2024 - mdpi.com
This study investigates gender-based differences in the satisfaction ranking of riders on real-
time crowdsourcing logistics platforms, using online reviews from the Ele. me platform …
time crowdsourcing logistics platforms, using online reviews from the Ele. me platform …
[HTML][HTML] Assessing Huanglongbing Severity and Canopy Parameters of the Huanglongbing-Affected Citrus in Texas Using Unmanned Aerial System-Based Remote …
Huanglongbing (HLB), also known as citrus greening disease, is a devastating disease of
citrus. However, there is no known cure so far. Recently, under Section 24 (c) of the Federal …
citrus. However, there is no known cure so far. Recently, under Section 24 (c) of the Federal …
[HTML][HTML] Sales Forecasting with LSTM, Custom Loss Function, and Hyperparameter Optimization: A Case Study
Forecasting sales trends is a valuable activity for companies of all types and sizes, as it
enables more efficient decision making to avoid unnecessary expenses from excess …
enables more efficient decision making to avoid unnecessary expenses from excess …
[PDF][PDF] Travel Vlog Reviews: Support Vector Machine Performance in Sentiment Classification
This research investigates the combination of the Support Vector Machine algorithm with the
Synthetic Minority Over-sampling Technique to improve classification performance in …
Synthetic Minority Over-sampling Technique to improve classification performance in …