Mechanisms and modelling of diffusion in solids: a multiscale framework with industrial case studies and AI enhancements by Obinna Onyebuchi Barah, Faith Natukunda, Ige Bori, Kelechi John Ukagwu

“…Case studies in carburization, alloy homogenization, and high-temperature coating systems illustrate how diffusion modelling informs real-world process optimization. …”
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Review of Icing Prediction Model and Algorithm for Overhead Transmission Lines Considering Time Cumulative Effects by Chuanqi WANG, Liwen WU, Zhibin DENG, Weifeng DENG, Bin YANG

“…Secondly, the combination and intercrossing mode of support vector machine, hybrid swarm intelligence optimization algorithm, genetic algorithm in the model focus on the identification and modeling of icing process. …”
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Predicting Relative Density of Pure Magnesium Parts Produced by Laser Powder Bed Fusion Using XGBoost by Kristijan Šket, Snehashis Pal, Janez Gotlih, Mirko Ficko, Igor Drstvenšek

“…The focus of the presented work is on the prediction of the relative product density using the machine learning model XGBoost to improve the production process and thus the usability of the material for practical use. …”
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Proposed Comprehensive Methodology Integrated with Explainable Artificial Intelligence for Prediction of Possible Biomarkers in Metabolomics Panel of Plasma Samples for Breast Canc... by Cemil Colak, Fatma Hilal Yagin, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…The SHapley Additive Descriptions (SHAP) analysis evaluated the optimal prediction model for interpretability. <i>Results</i>: The RF algorithm showed improved accuracy (0.963 ± 0.043) and sensitivity (0.977 ± 0.051); however, LightGBM achieved the highest ROC AUC (0.983 ± 0.028). …”
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Application of the Random Forest Algorithm for Accurate Bipolar Disorder Classification by Miguel Suárez, Ana M. Torres, Pilar Blasco-Segura, Jorge Mateo

“…This study explores the use of the Random Forest (RF) algorithm as a machine learning approach to classify patients with BD and healthy controls based on electroencephalogram (EEG) data. …”
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Application of a methodological framework for the development and multicenter validation of reliable artificial intelligence in embryo evaluation by D. Gilboa, Akhil Garg, M. Shapiro, M. Meseguer, Y. Amar, N. Lustgarten, N. Desai, T. Shavit, V. Silva, A. Papatheodorou, A. Chatziparasidou, S. Angras, J. H. Lee, L. Thiel, C. L. Curchoe, Y. Tauber, D. S. Seidman

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Arsitektur Sistem Percakapan Otomatis Berbahasa Indonesia dengan Normalisasi Bahasa Informal Menjadi Baku by Muhammad Fathur Rahman Khairul, Rizal Setya Perdana

“…Pendekatan yang dilakukan akan melatih model pre-trained GPT-2 berbahasa Indonesia dengan data parallel corpus untuk memahami makna dari bahasa informal dan mampu menerjemahkannya ke dalam bentuk baku. …”
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Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry by Miguel Mascarenhas, Francisco Mendes, Joana Mota, Tiago Ribeiro, Pedro Cardoso, Miguel Martins, Maria João Almeida, João Rala Cordeiro, João Ferreira, Guilherme Macedo, Cecilio Santander

“…This is the first worldwide study proving the accuracy of a ML model for differentiation of motility patterns in HR-ARM, demonstrating the value of artificial intelligence models in optimizing HR-ARM availability while reducing interobserver variability and increasing accuracy.…”
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A novel and efficient personalized stress detection technique using a deep learning model by Ulligaddala Srinivasarao, Gopisetty Rathnamma, M. Satish Kumar, Lakshmipathi Anantha, Rakesh Kumar Donthi, T. Jhansi Rani

“…Several techniques were introduced to detect the stress level through text from social media using both machine learning (ML) and deep learning (DL) models. …”
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Modeling of Chemiresistive Gas Sensors: From Microscopic Reception and Transduction Processes to Macroscopic Sensing Behaviors by Zhiqiao Gao, Menglei Mao, Jiuwu Ma, Jincheng Han, Hengzhen Feng, Wenzhong Lou, Yixin Wang, Teng Ma

“…However, the gap between experimental observations and fundamental sensing mechanisms hinders systematic optimization. Despite the critical role of modeling in explaining atomic-scale interactions and offering predictive insights beyond experiments, existing reviews on chemiresistive gas sensors remain predominantly experimental-centric, with a limited systematic exploration of the modeling approaches. …”
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Dark Side of the Web: Dark Web Classification Based on TextCNN and Topic Modeling Weight by Gun-Yoon Shin, Younghoan Jang, Dong-Wook Kim, Sungjin Park, A-Ran Park, Younghwan Kim, Myung-Mook Han

“…This optimization was facilitated by leveraging the anonymity characteristic of the Dark Web and employing topic-modeling-based weight generation. …”
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An XGBoost-SHAP Model for Energy Demand Prediction With Boruta&#x2013;Lasso Feature Selection by Yiwen Wang, Weibin Cheng, Yuting Jin, Jifei Li, Yantian Yang, Shaobing Hu

“…This study proposes an interpretable ML framework for energy demand prediction based on the Boruta-Lasso two-stage feature selection model, extreme gradient boosting (XGBoost) regression model, grid search optimization algorithm, and Shapley additive explanations (SHAP) algorithm. …”
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Smart indoor monitoring for disabled individuals using an ensemble of deep learning models in an IoT environment by Faisal S. Alsubaei, Abdulrahman A. Alshdadi, Mohammed Rizwanullah

“…Currently, numerous studies focus on human activity recognition (HAR) using deep learning (DL) and machine learning (ML) methods, but more effort is needed to enhance HAR models for disabled individuals. …”
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Data‐Driven Feature Decomposition Integrated Prediction Model for Dust Concentration in Open‐Pit Mines by Shuangshuang Xiao, Jin Liu, Qing Yang, Zhiguo Chang, Yonggui Zhang

“…Combining the characteristics of dust concentration data and the concept of multimodal information integration modeling, a support vector machine (SVM)‐long short‐term memory (LSTM) network was chosen to build a data feature‐driven dust concentration combination prediction model. …”
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Mechanisms and modelling of diffusion in solids: a multiscale framework with industrial case studies and AI enhancements by Barah, Obinna Onyebuchi, Natukunda, Faith, Bori, Ige, Ukagwu, Kelechi John

“…Case studies in carburization, alloy homogenization, and high-temperature coating systems illustrate how diffusion modelling informs real-world process optimization. …”
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A Solution for Risk Management of a Distribution Company in the Retail Market by Forough Taki, Ali Shishebori, Ramtin Sadeghi

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Immune Evasion Mechanism Mediated by ITPRIPL1 and Its Prognostic Implications in Glioma by Zou Xiaoyun, Ye Wenhao, Wu Huan, Yang Yuanyuan, Liu Changqing, Wen Hebao, Ma Caiyun

“…This included evaluating ITPRIPL1 expression levels in glioma, its association with clinicopathological features, prognostic significance, immune landscape, targeted drug sensitivity, and underlying biological functions. Ninety‐eight machine learning algorithm combinations were screened to identify the optimal predictive model. …”
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Impact of imbalanced features on large datasets by Waleed Albattah, Rehan Ullah Khan

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An effective heuristic for developing hybrid feature selection in high dimensional and low sample size datasets by Hyunseok Shin, Sejong Oh

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