A Supervised Approach for Land Use Identification in Trento Using Mobile Phone Data as an Alternative to Unsupervised Clustering Techniques by Manuel Mendoza-Hurtado, Gonzalo Cerruela-García, Domingo Ortiz-Boyer

“…By analyzing spatiotemporal patterns in CDRs, we trained and evaluated several classification algorithms, including k-nearest neighbors (kNN), support vector machines (SVM), and random forests (RF), to map land use categories, such as home, work, and forest. …”
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The Chinese Air-Sea heat Flux (CASFlux) gridded dataset: description and validation by Rongwang Zhang, Weihao Guo, Fenghua Zhou, Jiyuan Yin, Jian Huang, Xin Wang

“…Machine learning approaches and improved parameterization algorithms were employed in the production of CASFlux. …”
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Machine Learning with Voting Committee for Frost Prediction by Vinícius Albuquerque de Almeida, Juliana Aparecida Anochi, José Roberto Rozante, Haroldo Fraga de Campos Velho

“…After calculating the two indices using the ML model and the RWNM, a voting committee (VC) was trained to select between the computed outputs. The AdaBoostClassifier algorithm was employed to implement the voting committee. …”
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Hybrid Darknet53-SVM model with random grid search optimization for enhanced colorectal cancer histological image classification by Pragati Patharia, Prabira Kumar Sethy, K. Lakshmipathi Raju, Anita Khanna, Ashoka Kumar Ratha, Santi Kumari Behera, Aziz Nanthaamornphong

“…This study proposes an advanced deep learning-based framework for classifying CRC histological images into eight categories: Adipose, Complex, Debris, Empty, Lympho, Mucosa, Stroma, and Tumor. Initially, 18 pre-trained Convolutional Neural Networks (CNNs) were evaluated using transfer learning techniques. …”
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Automatic classification of fungal-fungal interactions using deep leaning models by Marjan Mansourvar, Jonathan Funk, Søren Dalsgård Petersen, Sajad Tavakoli, Jakob Blæsbjerg Hoof, David Llorente Corcoles, Sabrina M. Pittroff, Lars Jelsbak, Niels Bjerg Jensen, Ling Ding, Rasmus John Normand Frandsen

“…We used our model to categorize the outcome of interactions between the plant pathogen Fusarium graminearum and individual isolates from a collection of 38,400 fungal strains. The authors trained multiple deep learning architectures and evaluated their performance. …”
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Dynamic Error Compensation Control of Direct-Driven Servo Electric Cylinder Terminal Positioning System by Mingwei Zhao, Lijun Liu, Zhi Chen, Qinghua Yang, Xiaowei Tu

“…The length and error prediction models were trained and validated using opposite and mixed datasets tested on the experimental platform, to observe dynamic errors and evaluate and optimize the prediction models. …”
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Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach by Illia Mushta, Sulev Koks, Anton Popov, Oleksandr Lysenko

“…This study aims to identify a biomarker from DATSCAN images and develop a machine learning (ML) algorithm for PD diagnosis. Using 13 DATSCAN-derived parameters and patient handedness from 1309 individuals in the Parkinson’s Progression Markers Initiative (PPMI) database, we trained an AdaBoost classifier, achieving an accuracy of 98.88% and an area under the receiver operating characteristic (ROC) curve of 99.81%. …”
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Comprehensive Performance Comparison of Signal Processing Features in Machine Learning Classification of Alcohol Intoxication on Small Gait Datasets by Muxi Qi, Samuel Chibuoyim Uche, Emmanuel Agu

“…However, until now, no work has systematically evaluated the performance of various categories of gait features for alcohol intoxication detection task using traditional machine learning algorithms. …”
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Deep learning signature to predict postoperative anxiety in patients receiving lung cancer surgery by Qingqing Ji, Guohua Zhou, Xiangxiang Sun

“…Preoperative MRI-T1WI images were collected to train the deep learning signature utilized the ResNet-152 algorithm. …”
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An Optical Sensor for Measuring In-Plane Linear and Rotational Displacement by Suhana Jamil Ahamed, Michael Aaron McGeehan, Keat Ghee Ong

“…Variations in reflected R, G, B and C light intensities, caused by displacements, were used to predict linear and rotational motion via a polynomial regression algorithm. To train and validate this model, we employed a custom-built positioning stage that produced controlled displacement and rotation while recording corresponding changes in light intensity. …”
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Analysis of Microbiome for AP and CRC Discrimination by Alessio Rotelli, Ali Salman, Leandro Di Gloria, Giulia Nannini, Elena Niccolai, Alessio Luschi, Amedeo Amedei, Ernesto Iadanza

“…Subsequently, the synthesised data quality was evaluated using a logistic regression model in parallel with an optimised support vector machine algorithm (polynomial kernel). …”
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A novel classification method for balance differences in elite versus expert athletes based on composite multiscale complexity index and ranking forests. by Yuqi Cheng, Dawei Wu, Ying Wu, Youcai Guo, Xinze Cui, Pengquan Zhang, Jie Gao, Yanming Fu, Xin Wang

“…A classic train-test split method was applied, and the performance of different classifiers was evaluated using Receiver Operating Characteristic(ROC) analysis. …”
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Fatigue Damage Assessment of a Metro Vehicle Bogie Frame Based on Measured Field Load by Yangmin Wu, Zunsong Ren

“…As the operating conditions of metro vehicles become more complex, the fatigue damage of metro bogie frames under actual operating conditions becomes increasingly difficult to evaluate realistically. After a period of operation of metro trains, the load excitation on the frame and its vibrations become more intense, which causes elastic resonance and leads to fatigue damage. …”
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Advancing thyroid diagnosis: integrating AI-driven CAD framework with numerical data and ultrasound images by Saleh Ateeq Almutairi

“…The framework achieved promising performance, with models trained without data augmentation consistently outperforming their augmented counterparts. …”
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Automating hock wound detection in dairy cattle by W. Flanders, P.S. Basran, M. Wieland

“…Hock scoring in dairy cattle is a crucial welfare assessment tool used to evaluate the condition of a cow's hocks, particularly for signs of injury, swelling, or lesions. …”
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Crash severity prediction using a virtual geometry-group-based deep learning approach with images-based feature representation by Nanon Sonnatthanon, Kasem Choocharukul

“…To address class imbalance, a combination of the Synthetic Minority over Sampling technique, Edited Nearest Neighbor, and Tomek Link algorithms are applied. A Virtual Geometry Group deep learning model, optimized using Bayesian optimization, is trained to maximize the F1-score and Area Under the Curve, ensuring high prediction performance. …”
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Using N-Version Architectures for Railway Segmentation with Deep Neural Networks by Philipp Jaß, Carsten Thomas

“…Autonomous trains require reliable and accurate environmental perception to take over safety-critical tasks from the driver. …”
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Bearing Response Prediction in Hydrothermal Aged Carbon Fiber Reinforced Epoxy Composite Joints Using Machine Learning Techniques by Mohit Kumar, Govind Vashishtha, Babita Dhiman, Sumika Chauhan

“…The machine learning technique, support vector regression is trained and evaluated to assess their accuracy and reliability in predicting bearing response. …”
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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

“…Later, these data have been arranged to train the algorithms. As a result, this combined approach highlights the feasibility of active crack repair using piezoelectric actuators for improved structural integrity. …”
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Mitigating Catastrophic Forgetting in Pest Detection Through Adaptive Response Distillation by Hongjun Zhang, Zhendong Yin, Dasen Li, Yanlong Zhao

“…Our method effectively reduces catastrophic forgetting during incremental learning, enabling the student detector to maintain memory of old classes while assimilating new pest categories. Experimental evaluations on the large-scale IP102 pest dataset demonstrate that the proposed ARD method consistently outperforms existing state-of-the-art algorithms across various class-incremental learning scenarios, significantly narrowing the performance gap compared to fully trained models.…”
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