Search Results - Shap Mochan~

A model for enhancing pattern recognition in clinical narrative datasets through text-based feature selection and SHAP technique by Dalhatu, Sirajo Muhammad, Azmi Murad, Masrah Azrifah

“…Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF) models are trained and evaluated. …”
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A Field-Scale Framework for Assessing the Influence of Measure-While-Drilling Variables on Geotechnical Characterization Using a Boruta-SHAP Approach by Goldstein, D., Aldrich, Chris, Shao, Q., O’Connor, L.

“…Measure-While-Drilling data collected at the scale of an open-pit mine was used to characterize geotechnical properties using regression-based machine learning models. In contrast to previous studies using MWD data to recognize rock type using Principal Component Analysis (PCA), which only identifies the directions of maximum variance, the Boruta-SHAP method quantifies the individual contribution of each Measure-While-Drilling variable. …”
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Interpretable machine learning approach for predicting the workability and mechanical properties of betel nut husk fiber-reinforced concrete by Hasan, Mehedi, Hasan, Md Soumike, Hasan, Kamrul, Tushar, Fazlul Hoque, Khan, Majid, Putra Jaya, Ramadhansyah

“…The findings demonstrate that CatBoost and XGBoost can accurately predict the mechanical properties, offering a practical alternative to extensive laboratory testing and enabling time and cost savings in construction.…”
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Analysis of the mechanism of physical activity enhancing well-being among college students using artificial neural network by Cong, Yuxin, Dev, Roxana Dev Omar, Samsudin, Shamsulariffin, Yu, Kaihao

“…This study explores the impact mechanism of college students’ sports behavior on their well-being by constructing an Artificial Neural Network (ANN) model. …”
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The impact of macroeconomic variables on Brusa Stock Exchange using machine learning model by Kok, Yu Chen

“…Then, the machine learning method, SHapley Additive Explanation (SHAP) was used to calculate the impact value between stock price and macroeconomic variables. …”
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Optimization of machine learning models for predicting glutinous rice quality stored under various conditions by Dasore, Abhishek, Hashim, Norhashila, Shamsudin, Rosnah, Che Man, Hasfalina, Mohd Ali, Maimunah, Ageh, Opeyemi Micheal

“…The Random Forest (RF) machine learning model demonstrated high predictive performance (R2 > 0.9) with low error values for predicting quality attributes. …”
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Progressive kernel extreme learning machine for food image analysis via optimal features / Ghalib Ahmed Tahir by Ghalib Ahmed , Tahir

“…Moreover, during online learning, PKELM is equipped with a mechanism to label unlabeled instances and detect noisy samples. …”
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Predicting repurposed drugs targeting the NS3 protease of dengue virus using machine learning-based QSAR, molecular docking, and molecular dynamics simulations by Chongjun, Y., Nasr, A.M.S., Latif, M.A.M., Rahman, M.B.A., Marlisah, E., Tejo, B.A.

“…Our investigation employed three ML models–support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost)–for classifier development. …”
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Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

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Car dealership web application by Yap, Jheng Khin

“…Explainable models could enhance business value and application users’ trusts in machine learning with the aid of effective visualizations like beeswarm plots. …”
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Global spatial suitability mapping of wind and solar systems using an explainable aI-based approach by Sachit, Mourtadha Sarhan, Mohd Shafri, Helmi Zulhaidi, Abdullah, Ahmad Fikri, Mohd Rafie, Azmin Shakrine, Gibril, Mohamed Barakat A.

“…Real-world renewable energy experiences (more than 55,000 on-site wind and solar plants worldwide) are exploited to train three machine learning (ML) algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). …”
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AI for a positive web: Analyzing hate in social media by Chai, Yun Wai

“…This project aims to create an effective and user-friendly hate speech detection system using advanced machine learning and deep learning techniques. By developing various models, including Logistic Regression, Naive Bayes, Decision Trees, LSTM, BiLSTM, and CNN-LSTM, and incorporating an ensemble learning approach with a voting classifier, the system improves detection accuracy and reliability. …”
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State-of-the-art ensemble learning and unsupervised learning in fatigue crack recognition of glass fiber reinforced polyester composite (GFRP) using acoustic emission by Gholizadeh, S., Leman, Z., Baharudin, B.T.H.T.

“…The accuracy of these approaches demonstrates the reliability of various machine learning techniques in predicting the fatigue life of composite materials using acoustic emission.…”
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Modeling of human arm movement: a study on daily movement by Choudhury, Tasnuva Tabashhum, Rahman, Md. Mozasser, Khan, Md. Raisuddin, Khorshidtalab, Aida

“…Mathematical model is developed with the help of the robotic principle, mechanical system analysis method, and also with the help of biological data. …”
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Advanced AI-Powered Solutions for Predicting Blast-Induced Flyrock, Backbreak, and Rock Fragmentation by Nobahar, Pouya, Shirani Faradonbeh, Roohollah, Almasi, Seyed Najmedin, Bastami, Reza

“…In this study, based on the compiled 152 datasets from four different open-pit mines in Iran, six machine learning (ML) algorithms, including K-nearest neighbor (KNN), random forest (RF), eXtreme gradient boosting (XGBoost), support vector machine (SVM), decision tree (DT), and linear regression (LR), were used to develop robust models for predicting backbreak, flyrock, and rock fragmentation size. …”
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Transparent insights into alzheimer’s progression: a time-aware approach with explainable by Abraham Varghese, Ben George, Vinu Sherimon, Prashanth Gouda

“…The ADNI dataset, comprising 2980 observations, was employed for developing a prediction model using various machine learning classifiers. Among these classifiers, the Random Forest model emerged as the top performer, exhibiting superior accuracy, a high Coefficient of Determination (R2), and an impressive F1 score. …”
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XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection by Bouke, Mohamed Aly, Alramli, Omar Imhemed, Abdullah, Azizol

“…Spam detection is a critical cybersecurity and information management task with significant implications for security decision-making processes. Traditional machine learning algorithms such as Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Trees (DT), and Support Vector Machines (SVM) have been employed to mitigate this challenge. …”
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Experimental and AI-driven enhancements in gas-phase photocatalytic CO2 conversion over synthesized highly ordered anodic TiO2 nanotubes by Hossen, Md. Arif, Hasan, Md. Munirul, Ahmed, Yunus, Azrina, Abd Aziz, Nurashikin, Yaacof, Leong, Kah Hon

“…This study focuses on the prediction and optimization of CO2 conversion efficiency using machine learning (ML) approach over synthesized highly ordered TiO2 nanotube arrays (TNTAs) photocatalysts. …”
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Three essays on empirical corporate finance by Xu, Bo

“…The third essay (Chapter 4) explores the determinants of corporate risk-taking by deploying machine learning. Using a boarder series of linear and nonlinear machine learning models, results find evidence that machine learning can significantly predict and select the efficient determinants of firm’s risk-taking, as well as the heterogeneities of risk appetites across sub-samples. …”
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Shear performance of reinforced concrete (RC) beams strengthened with mortar-based composites under monotonic and fatigue loading by Liu, Xiangsheng

“…Shapley Additive Explanations (SHAP) and Partial Dependence Plots (PDP) were employed to enhance model interpretability and identify key factors influencing shear capacity, such as beam depth, concrete compressive strength, and mortar thickness. …”
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