Hierarchical Recognition for Urban Villages Fusing Multiview Feature Information by Zhenkang Wang, Nan Xia, Song Hua, Jiale Liang, Xiankai Ji, Ziyu Wang, Jiechen Wang

“…The spectral, textural, and structural features were extracted from Google RSI by machine-learning classifiers for each segmented block. …”
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Decision support systems for waste-to-energy technologies: A systematic literature review of methods and future directions for sustainable implementation in Ghana by Theophilus Frimpong Adu, Lena Dzifa Mensah, Mizpah Ama Dziedzorm Rockson, Francis Kemausuor

“…Future research directions identified by this study include the development of Ghana-specific DSS models, integration of real-time data collection methodologies, creation of user-friendly interfaces for local decision-makers, and exploration of emerging technologies such as blockchain and IoT or Machine learning (ML) for enhancing DSS in WtE management.…”
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Information Security and Artificial Intelligence–Assisted Diagnosis in an Internet of Medical Thing System (IoMTS) by Pi-Yun Chen, Yu-Cheng Cheng, Zi-Heng Zhong, Feng-Zhou Zhang, Neng-Sheng Pai, Chien-Ming Li, Chia-Hung Lin

“…For a symmetric cryptography scheme, this study proposed a key generator combining a chaotic map and Bell inequality and generating unordered numbers and unrepeated 256 secret keys in the key space. Then, a machine learning - based model was employed to train the encryptor and decryptor for both biosignals and image infosecurity. …”
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Multi-Feature Driver Variable Fusion Downscaling TROPOMI Solar-Induced Chlorophyll Fluorescence Approach by Jinrui Fan, Xiaoping Lu, Guosheng Cai, Zhengfang Lou, Jing Wen

“…Using the Random Forest (RF) model, we downscaled SIF data from 0.05° to 1 km based on invariant spatial scaling theory, focusing on the winter wheat growth cycle. Various machine learning models, including CNN, Stacking, Extreme Random Trees, AdaBoost, and GBDT, were compared, with Random Forest yielding the best performance, achieving R² = 0.931, RMSE = 0.052 mW/m²/nm/sr, and MAE = 0.031 mW/m²/nm/sr for 2018–2019 and R² = 0.926, RMSE = 0.058 mW/m²/nm/sr, and MAE = 0.034 mW/m²/nm/sr for 2019–2020. …”
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Integrating Explainable Artificial Intelligence With Advanced Deep Learning Model for Crowd Density Estimation in Real-World Surveillance Systems by Sultan Refa Alotaibi, Hanan Abdullah Mengash, Mohammed Maray, Faiz Abdullah Alotaibi, Abdulwhab Alkharashi, Ahmad A. Alzahrani, Moneerah Alotaibi, Mrim M. Alnfiai

“…The system assists in detecting and analyzing crowd density in real-time by utilizing artificial intelligence and machine learning (ML) models on surveillance videos. It detects crowded areas, manages crowd flow, and combines automated analysis with human oversight for improved public safety and early intervention. …”
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Comparison of diet and exercise on cardiometabolic factors in young adults with overweight/obesity: multiomics analysis and gut microbiota prediction, a randomized controlled trial by Zongyu Lin, Tianze Li, Fenglian Huang, Miao Wu, Lewei Zhu, Yueqin Zhou, Ying‐An Ming, Zhijun Lu, Wei Peng, Fei Gao, Yanna Zhu

“…Additionally, we used machine learning algorithms to further predict individual responses based on baseline gut microbiota composition, with specific microbial genera guiding targeted intervention selection. …”
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Towards PErsonalised PRognosis for children with traumatic brain injury: the PEPR study protocol by Jaap Oosterlaan, Marsh Königs, Job B M van Woensel, Marc Engelen, Marjan E Steenweg, Petra J W Pouwels, Cece C Kooper, Hilgo Bruining, Arne Popma, Dennis R Buis, Maayke Hunfeld

“…In addition, the potential added value of advanced neuroimaging data and the use of machine learning algorithms in the development of prognostic models will be assessed.Methods and analysis 210 children aged 4–18 years diagnosed with mild-to-severe TBI will be prospectively recruited from a research network of Dutch hospitals. …”
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Dataset of aerial photographs acquired with UAV using a multispectral (green, red and near-infrared) camera for cherry tomato (Solanum lycopersicum var. cerasiforme) monitoringDrya... by Osiris Chávez-Martínez, Sergio Alberto Monjardin-Armenta, Jesús Gabriel Rangel-Peraza, Zuriel Dathan Mora-Felix, Antonio Jesus Sanhouse-García

“…However, this multispectral imagery dataset can also have various uses, such as creating training datasets with accurate labels or classes which can then be used to develop, train, and/or validate machine learning algorithms for image classification, object detection tasks, or change detection analysis.…”
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Marine ecological information prediction by using adjacent location spatiotemporal deep learning model with ensemble learning techniques by Yue-Shan Chang, Shu-Ting Huang, Basanta Haobijam, Satheesh Abimannan, Takayuki Kushida

“…Recently, many studies have used machine learning or deep learning to predict SST and TH. …”
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Compact Quantum Cascade Laser-Based Noninvasive Glucose Sensor Upgraded with Direct Comb Data-Mining by Liying Song, Zhiqiang Han, Hengyong Nie, Woon-Ming Lau

“…The sensor data-mines 164 sets of critical singularity strengths, each comprising 4 critical singularity strengths directly from the 9840 million raw signal datapoints, and the 656 critical singularity strengths are subjected to a machine-learning regression model analysis, which yields 164 glucose concentrations. …”
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Diagnosis of lung cancer using salivary miRNAs expression and clinical characteristics by Negar Alizadeh, Hoda Zahedi, Maryam Koopaie, Mahnaz Fatahzadeh, Reza Mousavi, Sajad Kolahdooz

“…Receiver operating characteristic (ROC) curve was utilized to assess the potential significance of miRNAs in saliva for lung cancer diagnosis with the use of multiple logistic regression (MLR), principal component analysis, and machine learning methods. Results Diagnostic odds ratio (DOR) of miR-20a in lung adenocarcinoma diagnosis versus healthy control was higher than miR-221, and DOR of miR-221 was higher than let-7a-2. miR-20a demonstrated a higher DOR for small cell lung carcinoma versus healthy control compared to let-7a-2, which in turn exhibited a higher DOR than miR-221. …”
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Design of an Integrated Model Combining CycleGAN, PPO, and Vision Transformer for Adaptive Scene Rendering in the Metaverse by Durga Prasad Kavadi, Kailash Chandra Mishra, Sai Babu Veesam, Mudimela Madhusudhan, Palacharla Ravi Kumar, Pannangi Naresh, Yuvaraju Chinnam

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Advancements and trends in mangrove species mapping based on remote sensing: A comprehensive review and knowledge visualization by Yuqi Wu, Chunyan Lu, Kexin Wu, Wenna Gao, Nuocheng Yang, Jingwen Lin

“…Classification algorithm development has evolved four stages, from pixel-based methods to object-oriented approaches, progressing to approaches incorporating machine learning algorithms, and currently advancing towards ensemble learning and deep learning. …”
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Accuracy and Usability of Smartphone-Based Distance Estimation Approaches for Visual Assistive Technology Development by Giles Hamilton-Fletcher, Mingxin Liu, Diwei Sheng, Chen Feng, Todd E. Hudson, John-Ross Rizzo, Kevin C. Chan

“…<italic>Methods:</italic> We tested five smartphone-based distance-estimation approaches in the image center and periphery at 1&#x2013;3 meters, including machine learning (CoreML), infrared grid distortion (IR_self), light detection and ranging (LiDAR_back), and augmented reality room-tracking on the front (ARKit_self) and back-facing cameras (ARKit_back). …”
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Automatic Recognition of Authors Identity in Persian based on Systemic Functional Grammar by Fatemeh Soltanzadeh, Azadeh Mirzaei, Mohammad Bahrani, Shahram Modarres Khiabani

“…Subsequent feature selection identified the most effective features for the machine learning phase. The results indicated that the relative frequency of function words outperformed SFG-based attributes in terms of effectiveness. …”
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Contrast quality control for segmentation task based on deep learning models—Application to stroke lesion in CT imaging by Juliette Moreau, Juliette Moreau, Laura Mechtouff, Laura Mechtouff, David Rousseau, Omer Faruk Eker, Omer Faruk Eker, Yves Berthezene, Yves Berthezene, Tae-Hee Cho, Tae-Hee Cho, Carole Frindel, Carole Frindel

“…IntroductionAlthough medical imaging plays a crucial role in stroke management, machine learning (ML) has been increasingly used in this field, particularly in lesion segmentation. …”
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Algorithm for Cloud Particle Phase Identification Based on Bayesian Random Forest Method by Fu Tao, Yang Zhipeng, Tao Fa, Hu Shuzhen, Lu Yuxiang, Fu Changqing

“…Results in a high rate of misclassification when employing machine learning techniques for identifying the phase state of cloud particles.To accurately identify phases of cloud particles, a Bayesian Random Forest Method is employed, utilizing co-located millimeter-wave cloud radar and microwave radiometer observations. …”
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The association of origin and environmental conditions with performance in professional IRONMAN triathletes by Beat Knechtle, Mabliny Thuany, David Valero, Elias Villiger, Pantelis T. Nikolaidis, Marilia S. Andrade, Ivan Cuk, Thomas Rosemann, Katja Weiss

“…Data was analyzed using descriptive statistics and machine learning (ML) regression models. The models considered gender, country of origin, event location, water, and air temperature as independent variables to predict the final race time. …”
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Establishment and validation of predictive model of ARDS in critically ill patients by Senhao Wei, Hua Zhang, Hao Li, Chao Li, Ziyuan Shen, Yiyuan Yin, Zhukai Cong, Zhaojin Zeng, Qinggang Ge, Dongfeng Li, Xi Zhu

“…This study aimed to observe the incidence of ARDS among high-risk patients and develop and validate an ARDS prediction model using machine learning (ML) techniques based on clinical parameters. …”
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Differences in Characteristics of Peripartum Patients Who Did and Did Not Require an Upgrade to the Intensive Care Unit: A Propensity Score Matching Study by Jennifer A. Walker, Jerry M. Yang, Saad Pirzada, Manahel Zahid, Samantha Asuncion, Amanda Tuchler, Gillian Cooper, Allison Lankford, Emad Elsamadicy, Quincy K. Tran

“…The Classification And Regression Tree, a machine learning algorithm, was used to identify significant predictors of ICU upgrade. …”
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