Prediction of alkali-silica reaction expansion of concrete using explainable machine learning methods by Yasitha Alahakoon, Hirushan Sajindra, Ashen Krishantha, Janaka Alawatugoda, Imesh U. Ekanayake, Upaka Rathnayake

“…Each model was evaluated based on the model performance and XGBoost shows the most effective model for predicting the ASR expansion with R2 of 0.99 for training and R2 of 0.98 for testing. …”
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Enhancing piezoelectric control of cracks in thin solid aluminum plates using finite element data and a neural networks approach by Abdul Aabid, Muneer Baig, Mohammad Abdul Malik

“…These simulations have been executed considering the different parameters and their ranges. Later, these data have been arranged to train the algorithms. …”
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Predictive framework of vegetation resistance in channel flow by Fengcong Jia, Weijie Wang, Yu Han, Jiayu Du, Yue Zhang, Zihan Liu, Hairong Gao

“…To improve predictive performance, optimization algorithms such as PSO, WSO, and RIME were applied. …”
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Reinforcement Learning-Based Control for Robotic Flexible Element Disassembly by Benjamín Tapia Sal Paz, Gorka Sorrosal, Aitziber Mancisidor, Carlos Calleja, Itziar Cabanes

“…An adaptive reward function is tailored to account for varying material properties, ensuring robust performance across different operational scenarios. The RL-based approach is evaluated in a simulation using soft actor–critic (SAC), deep deterministic policy gradient (DDPG), and proximal policy optimization (PPO) algorithms, benchmarking their effectiveness in dynamic environments. …”
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Bone diagenesis and stratigraphic implications from Pleistocene karst systems by Héctor Del Valle, Alejandro B. Rodríguez-Navarro, Abel Moclán, Paula García-Medrano, Isabel Cáceres

“…Eleven chemometric indices considering the different bone components (phosphates, carbonates, organic phase), together with the apatite unit cell parameters and cell volume were evaluated by 9 machine learning algorithms for bone diagenesis/stratigraphic classification. …”
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Deep Residual Learning Image Recognition Model for Skin Cancer Disease Detection and Classification by Jamal Mustafa Al-Tuwaijari, Naeem Th. Yousir, Nafea Ali Majeed Alhammad, Salama Mostafa

“…However, due to the variety of skin cancer tumour shapes and colours, deep learning algorithms misclassify whether a tumour is cancerous or benign. …”
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A real-world pharmacovigilance study of lorazepam based on the FDA adverse event reporting system database by Chunyue Fang, Xiaoyan Xu, Jianyi Li, Yuanyuan Zhong, Wei Dai, Jin Wen, Qionghui Yang, Ruixiang Chen

“…Abstract Lorazepam is extensively used to treat anxiety disorders and anxiety associated with depression. This study evaluates the safety of lorazepam based on real-world data from the U.S. …”
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AI-driven model for optimized pulse programming of memristive devices by Benjamin Spetzler, Markus Fritscher, Seongae Park, Nayoun Kim, Christian Wenger, Martin Ziegler

“…The synaptic weight-update behavior of bilayer HfO2/TiO2 memristive devices is characterized over a range of pulse parameters to provide experimental data for the AI model. Three different artificial neural network (ANN) configurations are trained and evaluated regarding the amount of training data required for accurate predictions and the computational costs. …”
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Research on the Integration of Sensing and Communication Based on Fractional-Order Fourier Transform by Mingyan Qi, Yuelong Su, Zhaoyi Wang, Kun Lu

“…The simulation evaluated the detection performance of the two methods under different signal-to-interference ratios (SIR) and filter widths. …”
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Novel Cuproptosis-Related Gene Signature for Precise Identification of High-Risk Populations in Low-Grade Gliomas by Ping Chen, Hailing Han, Xuejie Wang, Bing Wang, Zhanfeng Wang

“…In addition, LGG immune cell infiltration was viewed using CIBERSORT and ssGSEA algorithms and correlation analysis was done with cuproptosis-related genes. …”
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Contrastive Disentangled Variational Autoencoder for Collaborative Filtering by Woo-Seong Yun, Seong-Min Kang, Yoon-Sik Cho

“…The proposed contrastive disentanglement framework is generic and thus adaptable to VAE-based recommender algorithms. We comprehensively evaluate our proposed method on popular datasets such as MovieLens20M and Netflix and show that it consistently outperforms corresponding VAE models, achieving superior performance across all metrics. …”
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Machine Learning Unveils the Impacts of Key Elements and Their Interaction on the Ambient-Temperature Tensile Properties of Cast Titanium Aluminides Employing SHAP Analysis by Shiqiu Liu, Li Liang

“…Comparative analysis of three algorithms within the training dataset proved the random forest regression (RFR) as the optimal modeling approach. …”
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AIoT-Driven Human Activity Recognition for Versatile Framework on Multipurpose Applications by Nanik Triwahyuni, Eni Wardihani, Aminuddin Rizal, Samuel Beta, Ricky Sambora, Rindang Oktaviani

“…Before real-time performance was taken, we evaluated 5 different machine learning model and choose which one is more optimized. …”
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Utilizing Artificial Intelligence for Microbiome Decision-Making: Autism Spectrum Disorder in Children from Bosnia and Herzegovina by Džana Bašić-Čičak, Jasminka Hasić Telalović, Lejla Pašić

“…Model accuracy was evaluated, and an external dataset was introduced to test model generalizability. …”
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Multivariate determinants of self-management in Health Care: assessing Health Empowerment Model by comparison between structural equation and graphical models approaches by Filippo Trentini, Matteo Malgaroli, Anne Linda Camerini, Clelia Di Serio, Peter Schulz

“…Graphical modeling algorithms were implemented in a R software environment.…”
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Physical education and sport activity assessment tool-based machine learning predictive analysis for planification of training sessions by Mohamed Rebbouj, Said Lotfi

“…Conclusions Incorporating the Machine Learning techniques may encourage the change in the way we teach physical education and sport activities; otherwise, the assessment based on ML techniques will give a different overview on how to start a learning cycle and follow it up. …”
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Impact of Hypoglycemia on Glucose Variability over Time for Individuals with Open-Source Automated Insulin Delivery Systems by Arsalan Shahid, Dana M. Lewis

“…The experimental results are further validated on T1DEXI data (n = 222), originating from commercial AID systems. Different hypoglycemia categorization approaches did not show significant differences in glycemic variability outcomes. …”
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Binary program taint analysis optimization method based on function summary by Pan YANG, Fei KANG, Hui SHU, Yuyao HUANG, Xiaoshao LYU

“…Taint analysis is a popular software analysis method, which has been widely used in the field of information security.Most of the existing binary program dynamic taint analysis frameworks use instruction-level instrumentation analysis methods, which usually generate huge performance overhead and reduce the program execution efficiency by several times or even dozens of times.This limits taint analysis technology’s wide usage in complex malicious samples and commercial software analysis.An optimization method of taint analysis based on function summary was proposed, to improve the efficiency of taint analysis, reduce the performance loss caused by instruction-level instrumentation analysis, and make taint analysis to be more widely used in software analysis.The taint analysis method based on function summary used function taint propagation rules instead of instruction taint propagation rules to reduce the number of data stream propagation analysis and effectively improve the efficiency of taint analysis.For function summary, the definition of function summary was proposed.And the summary generation algorithms of different function structures were studied.Inside the function, a path-sensitive analysis method was designed for acyclic structures.For cyclic structures, a finite iteration method was designed.Moreover, the two analysis methods were combined to solve the function summary generation of mixed structure functions.Based on this research, a general taint analysis framework called FSTaint was designed and implemented, consisting of a function summary generation module, a data flow recording module, and a taint analysis module.The efficiency of FSTaint was evaluated in the analysis of real APT malicious samples, where the taint analysis efficiency of FSTaint was found to be 7.75 times that of libdft, and the analysis efficiency was higher.In terms of accuracy, FSTaint has more accurate and complete propagation rules than libdft.…”
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Debris Flow Susceptibility Prediction Using Transfer Learning: A Case Study in Western Sichuan, China by Tiezhu Li, Qidi Huang, Qigang Chen

“…In this study, debris flow susceptibility models were developed using three machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). …”
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Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM. by Sadia Nazim, Muhammad Mansoor Alam, Syed Safdar Rizvi, Jawahir Che Mustapha, Syed Shujaa Hussain, Mazliham Mohd Suud

“…Nowadays, AI is significantly employed for evaluations in cybersecurity that find it challenging to justify their proceedings; this absence of accountability is alarming. …”
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