An enhanced machine learning approach with stacking ensemble learner for accurate liver cancer diagnosis using feature selection and gene expression data by Amena Mahmoud, Eiko Takaoka

“…This approach reduced the dimensionality of the data while preserving crucial biological information. …”
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New online in-air signature recognition dataset and embodied cognition inspired feature selection by Yuheng Guo, Yuhan Zhou, Yifan Ge, Junwei Yu, Gen Li, Hiroyuki Sato

“…Our methodology includes a thorough statistical analysis with domain descriptors and DTW algorithms, alongside a comparative evaluation of seven deep-learning models on both the MIAS-427 and smartwatch datasets. …”
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A Data-Driven Comparative Analysis of Machine-Learning Models for Familial Hypercholesterolemia Detection by Tomasz Kocejko

“…This study presents an assessment of familial hypercholesterolemia (FH) probability using different algorithms (CatBoost, XGBoost, Random Forest, SVM) and its ensembles, leveraging electronic health record data. …”
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Gravity experiments with radio pulsars by Paulo C. C. Freire, Norbert Wex

“…So far, GR has passed all pulsar tests with flying colours, while at the same time many alternative gravity theories have either been strongly constrained or even falsified. …”
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Transformer and Convolutional Neural Network: A Hybrid Model for Multimodal Data in Multiclass Classification of Alzheimer’s Disease by Abdulaziz Alorf

“…The proposed method was also compared to benchmark algorithms and outperformed them; it was substantially less computationally expensive while maintaining its accuracy.…”
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Integrating health equity in artificial intelligence for public health in Canada: a rapid narrative review by Samantha Ghanem, Marielle Moraleja, Danielle Gravesande, Jennifer Rooney

“…Several health equity considerations for applying AI in public health were identified, including gaps in AI epistemology, algorithmic bias, accessibility of AI technologies, ethical and privacy concerns, unrepresentative training datasets, lack of transparency and interpretability of AI models, and challenges in scaling technical skills.ConclusionWhile AI has the potential to advance public health in Canada, addressing equity is critical to preventing inequities. …”
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Multi-Objective Optimization of Machinability and Energy Consumption of Cast Iron Depending on Cooling Rate by Burak Öztürk, Fuat Kara

“…GA results indicated that a cutting speed of 200 m/min and a feed rate of 0.15 mm/rev minimized surface roughness to 1.359 Ra while reducing Specific Energy Consumption (SEC) from 3.25 to 2.83 Wh/mL. …”
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Automatic Scan-to-BIM—The Impact of Semantic Segmentation Accuracy by Jidnyasa Patil, Mohsen Kalantari

“…Given the rapid advancement of deep learning algorithms in recent years, it is crucial to analyze how their accuracy impacts reconstruction quality. …”
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Machine learning models for predicting the bearing capacity of shallow foundations: A Comparative study and sensitivity analysis by Hamid Mohammadnezhad, Seyedmohammad Eslami

“…With the development of new methods such as Machine Learning (ML) algorithms in recent decades, a resolution to these challenges has been identified. …”
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The Synergy of Minds and Machines: Rethinking the AI-HI Relationship through Dialectical Reconstruction by Ridwan Ishola MOGAJI, Adewale Oluwaseun MOTADEGBE

“…Artificial intelligence on the other hand is the simulation of human cognitive functions by machines, especially in tasks such as problem-solving, pattern recognition, and decision-making, which often operates based on algorithms and data, both of which are unique and important in themselves. …”
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Enhanced CLIP-GPT Framework for Cross-Lingual Remote Sensing Image Captioning by Rui Song, Beigeng Zhao, Lizhi Yu

“…Remote Sensing Image Captioning (RSIC) aims to generate precise and informative descriptive text for remote sensing images using computational algorithms. Traditional “encoder-decoder” approaches face limitations due to their high training costs and heavy reliance on large-scale annotated datasets, hindering their practical applications. …”
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STATE-OF-THE-ART OF MACHINE LEARNING IN NEURO DEVELOPMENT DISORDER: A SYSTEMATIC REVIEW by Lilian Yen Wei Lee, Ag Asri Ag Ibrahim, Rayner Alfred

“…This paper presents a comprehensive literature review focusing on the utilization of machine learning (ML) and deep learning (DL) methods for predicting and detecting Neurodevelopmental Disorders (NDDs), such as Intellectual Disability (ID), Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), Dyslexia, among others. While existing reviews often lack detailed discussions on the specific ML algorithms, datasets, and performance metrics employed in NDD prediction and detection, this study aims to address this gap by examining two primary aspects: prediction and detection. …”
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Assessing the Impact of Mixed Pixel Proportion Training Data on SVM-Based Remote Sensing Classification: A Simulated Study by Jianyu Gu, Russell G. Congalton

“…Support vector machine (SVM) algorithms have been widely utilized in the remote sensing community due to their high performance with small training datasets. …”
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AAD-YOLO: An Improved YOLOv8 Model for Complex Remote Sensing Scenarios by Yue Hong, Yi Shu, Shuo Guo

“…A comparative analysis with several state-of-the-art detection algorithms further validates the effectiveness and robustness of the proposed method.…”
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Research on Security Enhancement for Abnormal Content Release in Unified Audio Video Information service Systems by FENG Junhao

“…The system efficiently detects prohibited content while ensuring that legitimate content remains unaffected. …”
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Detecting Unbalanced Network Traffic Intrusions With Deep Learning by S. Pavithra, K. Venkata Vikas

“…To overcome these challenges, this project proposes a novel hybrid Intrusion Detection System using machine learning algorithms, which includes XGBoost, Long Short-Term Memory (LSTM), Mini-VGGNet, and AlexNet, which is used to handle the unbalanced network traffic data. …”
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Comparative Analysis of Machine Learning Models for Predicting Innovation Outcomes: An Applied AI Approach by Marko Martinović, Kristian Dokic, Dalibor Pudić

“…The results showed that tree-based boosting algorithms consistently outperformed other models in accuracy, precision, F1-score, and ROC-AUC, while the kernel-based approach excelled in recall. …”
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Lung Cancer Detection Utilizing Mixed Sensor Based Electronic Nose by Umit Ozsandikcioglu, Ayten Atasoy, Yusuf Sevim

“…Data dimension reduction was achieved using linear discriminant analysis and principal component analysis algorithms. The individual classification accuracies for the metal oxide semiconductor and quartz crystal microbalance sensor data are 81.54% and 73.18%, respectively. …”
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Long-Short Term Memory Networks and Synthetic Data for Heavy Vehicle Rollover Prevention by Guido Perboli, Antonio Tota, Filippo Velardocchia

“…In details, an heavy vehicle model was designed in IPG Carmaker®, while experimental data on its physical alter ego were acquired. …”
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Evaluative representations of the student audience in the context of current processes in Donbas: comparison of studies by L. B. Vnukova

“…Accordingly, a dichotomy of opinions appears: some share the belief that Russia is a strong power with an independent path of development, while others see themselves as part of a European (global) civilization. …”
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