Enhancing blockchain transaction classification with ensemble learning approaches by Amrutanshu Panigrahi, Abhilash Pati, Bibhuprasad Sahu, Rourab Paul, Ajit Kumar Nayak, Subrata Chowdhury, Ramya Govindaraj, J Shreyas

“…Various ML classification algorithms are applied to the selected features from two ensemble feature selection algorithms as the base learners to make initial predictions. …”
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Robust Fault Classification in Permanent Magnet Synchronous Machines Under Dynamic and Noisy Conditions by Mikal Laursen, Van-Van Huynh, Duy-Hung Ha, Mahmoud S. Mahmoud, van Khang Huynh

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Real-Time Common Rust Maize Leaf Disease Severity Identification and Pesticide Dose Recommendation Using Deep Neural Network by Zemzem Mohammed Megersa, Abebe Belay Adege, Faizur Rashid

“…We used a convolutional neural network (CNN), specifically the ResNet50 model, for this purpose. To evaluate its performance, ResNet50 was compared with other state-of-the-art algorithms, including VGG19, VGG16, and AlexNet, using similar parameters. …”
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Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images by Anand Kumar Gupta, Asadi Srinivasulu, Kamal Kant Hiran, Goddindla Sreenivasulu, Sivaram Rajeyyagari, Madhusudhana Subramanyam

“…In this research work, two learning algorithms, namely, deep learning (DL) and machine learning (ML), were developed to forecast the Omicron virus infections. …”
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Assessing Computational Thinking in Engineering and Computer Science Students: A Multi-Method Approach by Farman Ali Pirzado, Awais Ahmed, Sadam Hussain, Gerardo Ibarra-Vázquez, Hugo Terashima-Marin

“…The results show that students excel in pattern recognition and abstraction but face decomposition and algorithmic thinking challenges. Cross-sectional analyses were conducted between CTT, CTS and the open-ended part to compare CT skills across different demographic groups (e.g., age, gender, academic disciplines), showing clear differences based on age, gender, and academic disciplines, with Computer Science students performing better than engineering students. …”
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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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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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