Multi-input multi-output machine learning predictive model for engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline blends

Zandie, Mohammad; Ng, Hoon Kiat; Gan, Suyin; Said, Mohd Farid Muhamad; Cheng, Xinwei

doi:10.1016/j.energy.2022.125425

Cited by 20 publications

(4 citation statements)

References 39 publications

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“…To address the former limitation, a potential solution lies in employing a multioutput machine learning algorithm, which can predict multiple response variables simultaneously. , These output/response variables need not be mutually exclusive, even in classification problems. The application of “filters”, which predict solubilities, absorption, toxicities, metal chelation, significant interactions with plasma proteins, and other unfavorable physiochemical properties, could be used to further select for the best potential in vivo inhibitors.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase

Cheng,

Aitha,

Thomas

et al. 2024

J. Chem. Inf. Model.

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The worldwide spread of the metallo-β-lactamases (MBL), especially New Delhi metallo-β-lactamase-1 (NDM-1), is threatening the efficacy of β-lactams, which are the most potent and prescribed class of antibiotics in the clinic. Currently, FDA-approved MBL inhibitors are lacking in the clinic even though many strategies have been used in inhibitor development, including quantitative high-throughput screening (qHTS), fragment-based drug discovery (FBDD), and molecular docking. Herein, a machine learningbased prediction tool is described, which was generated using results from HTS of a large chemical library and previously published inhibition data. The prediction tool was then used for virtual screening of the NIH Genesis library, which was subsequently screened using qHTS. A novel MBL inhibitor was identified and shown to lower minimum inhibitory concentrations (MICs) of Meropenem for a panel of E. coli and K. pneumoniae clinical isolates expressing NDM-1. The mechanism of inhibition of this novel scaffold was probed utilizing equilibrium dialyses with metal analyses, native state electrospray ionization mass spectrometry, UV−vis spectrophotometry, and molecular docking. The uncovered inhibitor, compound 72922413, was shown to be 9-hydroxy-3-[(5-hydroxy-1-oxa-9-azaspiro[5.5]undec-9-yl)carbonyl]-4Hpyrido[1,2-a]pyrimidin-4-one.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase

Cheng,

Aitha,

Thomas

et al. 2024

J. Chem. Inf. Model.

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“…In order to produce multiple predicted variables simultaneously in one cycle of learning, this study construct the proposed architecture using multi-input multi-output (MIMO) principle. MIMO principal is a modification of ML architecture which reconstruct the prediction models to produces multiple outputs [10]. The proposed LUTanh activation function has been embedded into sequence learning block of both LSTM and Bi-LSTM as shown in Fig.…”

Section: Model Architecturementioning

confidence: 99%

“…The MIMO principle used neural network (NN) architecture to produce several outputs, where each individual output node in output layers of NN is considered as individual predicted variable. The MIMO concept has been applied in several studies such as stock price prediction [9], engine performance [10], and pollution forecasting [11]. The implementation of MIMO principle opens up possibilities in predicting latitude, longitude, magnitude, and the depth of earthquake simultaneously in one cycle of model prediction.…”

Section: Introductionmentioning

confidence: 99%

LUTanh Activation Function to Optimize BI-LSTM in Earthquake Forecasting

2024

IJIES

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Earthquakes, as unpredictable and potentially catastrophic events, have long captured the attention of geophysicists due to their profound impact on communities. The devastating consequences of these events underscore the critical need for early earthquake prediction systems capable of forecasting location, magnitude, and depth. With the rapid advancements in technology, particularly in the fields of data science, earthquake prediction methods have undergone significant evolution. This study introduces a proposed method that modified ReLU (Rectified Linear Unit) called LUTanh (Linear Unit Hyperbolic Tangent) which combine both benefits of ReLU and Tanh activation functions. This study applied and compared the proposed method performance in long short-term memory (LSTM) and Bidirectional LSTM (Bi-LSTM) algorithms for predicting earthquake disaster. Furthermore, this proposed method was tested in architecture of multiple input multiple output (MIMO) principle in predicting the occurrence of earthquake disaster. The evaluation results decisively reveal that the LUTanh applied in Bi-LSTM model, particularly when optimized with the Adam optimizer, consistently outperforms the LSTM counterpart. Error assessments of LUTanh in Bi-LSTM consistently demonstrate lower average error scores compared to the origin ReLU activation function up to 4% of mean absolute error (MAE) and 3% of mean square error (MSE).

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“…Before the commencement of training, bias and weight values are assigned randomly[20][21][22][23][24].These values are then iteratively updated by the TrainLM function, which employs the gradient descent method to optimize the network. The training of the multilayer perceptron (MLP) model adheres to specifi c stopping criteria, namely a minimum gradient of 10-7 and a maximum of 10,000 epochs[25][26][27][28][29][30]. The ideal hyper-parameters for the ANN model are identifi ed in TableA1.…”

mentioning

confidence: 99%

Performance assessment of packed bed systems for humidity control in greenhouse applications: An experimental-based AI modeling approach

Mrinal

2024

Int J Agric Sc Food Technol

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Optimal humidity control is essential for enhancing crop yields and ensuring favorable growth conditions in greenhouse agriculture. Packed bed systems have emerged as effective tools for regulating humidity levels, yet accurately assessing their performance remains unexplored, especially for temperate oceanic climates. This paper presents a packed-bed system with water as the working fluid to increase the humidity level during the winter for greenhouse cultivation. Accordingly, an experimental setup is developed, and a detailed parametric study is conducted. Further, an artificial intelligence (AI)-based modeling approach is developed for evaluating the performance of packed bed systems for greenhouse applications under varying environmental conditions with various inlet air flowrates, such as 176 m³/hr, 286 m³/hr, 383 m³/hr, and 428 m³/hr. Operating with water at an average temperature of 15.7 °C and a flow rate of 12.8 kg/min, the system achieves a significant 50% increase in humidity ratio, transitioning from an inlet humidity ratio of 6 g/kgda to an outlet ratio of 9 g/kgda, indicating its efficiency in elevating air humidity levels. The multi-layer perceptron neural network, trained with 112 non-repeated datasets and employing a 2-10-10-1 topology, demonstrates high accuracy and resilience in estimating Δωa values for the packed bed system, with predictions closely aligning with experimental data and exhibiting a maximum discrepancy within ± 2.5%. This research contributes to the advancement of precision agriculture practices by providing a comprehensive framework for assessing and improving humidity control in greenhouse environments.

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Multi-input multi-output machine learning predictive model for engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline blends

Cited by 20 publications

References 39 publications

Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase

Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase

LUTanh Activation Function to Optimize BI-LSTM in Earthquake Forecasting

Performance assessment of packed bed systems for humidity control in greenhouse applications: An experimental-based AI modeling approach

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