Comparative Analysis of Facial Expression Recognition Methods by Denys - Florin COT

“…The research compares the performance of classical machine learning algorithms (such as K-Nearest Neighbors, Gaussian Naive Bayes, Support Vector Machines, Adaptive Boosting, Decision Tree, and Random Forest) with the modern deep learning methods (such as Convolutional Neural Networks, Deep Neural Networks, and Recursive Neural Networks) using standardized datasets. …”
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Identification of generators’ market power abuse based on hunter–prey optimisation and CatBoost by Qian Sun, Shan Deng, Yunyong Zhang, Jiayu Zhong, Yansong Wu

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On-Demand Gait-Synchronous Electrical Cueing in Parkinson's Disease Using Machine Learning and Edge Computing: A Pilot Study by Ardit Dvorani, Constantin Wiesener, Christina Salchow-Hommen, Magdalena Jochner, Lotta Spieker, Matej Skrobot, Hanno Voigt, Andrea Kuhn, Nikolaus Wenger, Thomas Schauer

“…While sensor technology and machine learning offer real-time detection for on-demand cueing, existing systems are limited by the usage of smartphones between the sensor(s) and cueing device(s) for data processing. …”
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A Novel Rubber Composite Sleeper-Deformation-Prediction Model Based on Response Surface Method (RSM) and Machine Learning (ML) Techniques by Abdulmumin Ahmed Shuaibu, Zhiping Zeng, Ibrahim Hayatu Hassan, Wang Weidong, Hassan Suleiman Otuoze, Suleiman Abdulhakeem, Bushrah Baba Abdulrahman

“…Simultaneously, lateral deformation was recorded using linear variable differential transducer (LVDT) displacement sensors. The temperature data were filtered to remove noise and normalized based on the Log-Pearson Type III outlier detection method and Box-Cox transformation, respectively, before being used to develop temperature-dependent models for sleeper deformation. …”
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Urban sentinel: advancing structural health monitoring for building damage measurement in districts through IoT integration and self-optimizing machine learning by Parsa Parsafar

“…These sensors transmit data using LoRaWAN wireless technology to a centralized management system, where a regression AI model harnesses the power of machine learning algorithms to analyze the data and predict the health status of the buildings. …”
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A Dynamic Kalman Filtering Method for Multi-Object Fruit Tracking and Counting in Complex Orchards by Yaning Zhai, Ling Zhang, Xin Hu, Fanghu Yang, Yang Huang

“…To address these challenges, this paper proposes a multi-object fruit tracking and counting method, which integrates an improved YOLO-based object detection algorithm with a dynamically optimized Kalman filter. …”
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Fault diagnosis model of rolling bearings based on the M-YOLO network by NING Shaohui, ZHANG Shaopeng, WU Yukun, DU Yue, FAN Xiaoning

“…ObjectiveThe algorithms developed for the combination of deep learning and bearing fault diagnosis have achieved initial results, but most of them are processed by processing one-dimensional vibration data and input into the network structure for diagnosis, while the research on fault diagnosis technology using two-dimensional signals as input is still on the surface, and the analysis of such methods is rarely reported. …”
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Assessment of binary prediction of fraudulent advertisements in ATS candidate tracking cloud systems by V. V. Ligi-Goryaev, G. A. Mankaeva, T. B. Goldvarg, S. S. Muchkaeva, V. V. Dzhakhnaev

“…The abstract describes the construction of a binary classification model for predicting the type of job advertisement in cloud-based ATS (Applicant Tracking Systems) as either legitimate or fraudulent. Various machine learning algorithms can be employed to address this issue. …”
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Diagnostic accuracy of artificial intelligence models in detecting congenital heart disease in the second-trimester fetus through prenatal cardiac screening: a systematic review an... by Lies Dina Liastuti, Lies Dina Liastuti, Yosilia Nursakina, Yosilia Nursakina

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Bayesian Q-learning in multi-objective reward model for homophobic and transphobic text classification in low-resource languages: A hypothesis testing framework in multi-objective... by Vivek Suresh Raj, Ruba Priyadharshini, Saranya Rajiakodi, Bharathi Raja Chakravarthi

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A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology: a cohort study by Jinsong Du, Jinsong Du, Jinsong Du, Xinru Tao, Le Zhu, Wenhao Qi, Xiaoqiang Min, Xiaoqiang Min, Hongyan Deng, Shujie Wei, Xiaoyan Zhang, Xiao Chang

“…This study innovatively proposed a visual risk prediction system for depressive symptoms and depression in middle-aged and older adults, rooted in machine learning and visualization technologies.MethodsUsing cohort data from the China Health and Retirement Longitudinal Study (CHARLS), involving 8,839 middle-aged and older adult participants, the study developed predictive models based on eight machine learning algorithms, primarily including LightGBM, XGBoost, and AdaBoost. …”
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A Novel Approach to Pine Nut Classification: Combining Near-Infrared Spectroscopy and Image Shape Features with Soft Voting-Based Ensemble Learning by Yueyun Yu, Xin Huang, Danjv Lv, Benjamin K. Ng, Chan-Tong Lam

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Comparing acoustic representations for deep learning-based classification of underwater acoustic signals: A case study on orca (Orcinus orca) vocalizations by Fabio Frazao, Ruth Joy, Michael Dowd

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Identification of multiomics and immune infiltration-associated biomarkers for early gastric cancer: a machine learning-based diagnostic model development study by Kewei Du, Wenfei Hu, Shan Gao, Jianxin Gan, Chongge You, Shangdi Zhang

“…Reliable diagnostic biomarkers that can detect early GC are critical to improve prognosis. …”
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Adopting TOGAF Framework for Sustainable and Scalable Robusta Coffee Leaf Rust Management by Thein Oak Kyaw Zaw, Kalaiarasi Sonai Muthu Anbananthen, Saravanan Muthaiyah, Baarathi Balasubramaniam, Suraya Mohammad, Yunus Yusoff, Khairul Shafee Kalid

“…The framework leverages enterprise architecture principles to integrate learning algorithms, image detection, and systematic plantation mapping within a structured approach that enhances data organization, rust severity visualization, and predictive analysis. …”
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DMSA-Net: a deformable multiscale adaptive classroom behavior recognition network by Chunyu Dong, Jing Liu, Shenglong Xie

“…Extensive experimentation reveals that our proposed method outperforms rival algorithms on two widely adopted benchmark datasets: SCB-Dataset3-S (the Student Classroom Behavior Dataset–https://github.com/Whiffe/SCB-dataset) and we created object detection dataset DataMountainSCB (https://github.com/Chunyu-Dong/DataFountainSCB1) containing six types of behaviors.…”
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AI Innovations in rPPG Systems for Driver Monitoring: Comprehensive Systematic Review and Future Prospects by Soha G. Ahmed, Katrien Verbert, Nazar Zaki, Ashraf Khalil, Hamad Aljassmi, Fady Alnajjar

“…A literature search up to April 2, 2024, across major databases, identified 344 studies; 29 were analyzed in depth, focusing on: 1) rPPG signal extraction and heart rate estimation, where deep learning improved accuracy; 2) fatigue detection, showing benefits of multimodal data fusion; 3) mental state monitoring, with machine learning classifying cognitive load and distraction; and 4) emotional state monitoring and dataset development, indicating a trend toward holistic driver assessment. …”
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Federated Learning Based on an Internet of Medical Things Framework for a Secure Brain Tumor Diagnostic System: A Capsule Networks Application by Roman Rodriguez-Aguilar, Jose-Antonio Marmolejo-Saucedo, Utku Köse

“…It enables machine learning or deep learning algorithms to establish a client–server relationship, whereby specific parameters are securely shared between models while maintaining the integrity of the learning tasks being executed. …”
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Advances in Surface-Enhanced Raman Spectroscopy for Urinary Metabolite Analysis: Exploiting Noble Metal Nanohybrids by Ningbin Zhao, Peizheng Shi, Zengxian Wang, Zhuang Sun, Kaiqiang Sun, Chen Ye, Li Fu, Cheng-Te Lin

“…We address the analytical challenges associated with SERS-based urinary metabolite analysis, including sample preparation, matrix effects, and data interpretation. Innovative solutions, such as the integration of SERS with microfluidic devices and the application of machine learning algorithms for spectral analysis, are highlighted. …”
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Integrating LiDAR Point Cloud Classification and Building Footprints for Enhanced 3D LOD Building Modeling: A Deep Learning Approach by L. Lakshmanan, S. Nagarajan

“…In this research, we use RandLA-Net, a cutting-edge deep learning algorithm to classify LiDAR point cloud data to distinguish building structures. …”
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