A Machine Learning-Based Risk Prediction Model During Pregnancy in Low-Resource Settings by Kapil Tomar, Chandra Mani Sharma, Tanisha Prasad, Vijayaraghavan M. Chariar

“…Various ICTs have been incorporated into the healthcare industry to diagnose the issue as quickly as is feasible and an appropriate remedy can be initiated to treat diseases. Machine Learning (ML) techniques have the potential to predict the probable risk factors for timely interventions; however, challenge arises when the data are limited and unstructured. …”
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Spectroscopic and Imaging Technologies Combined with Machine Learning for Intelligent Perception of Pesticide Residues in Fruits and Vegetables by Haiyan He, Zhoutao Li, Qian Qin, Yue Yu, Yuanxin Guo, Sheng Cai, Zhanming Li

“…This review has proposed an innovative method that integrates spectral and image data, enhancing the accuracy of pesticide residue detection through the construction of interpretable machine learning algorithms, and providing support for the intelligent sensing and analysis of agricultural and food products.…”
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Identification of sweetpotato virus disease-infected leaves from field images using deep learning by Ziyu Ding, Fanguo Zeng, Haifeng Li, Jianyu Zheng, Junzhi Chen, Biao Chen, Wenshan Zhong, Xuantian Li, Zhangying Wang, Lifei Huang, Xuejun Yue, Xuejun Yue

“…The proposed method also exhibits excellent detection results in simulated scenarios. In summary, this study successfully deploys a deep learning framework to segment SPVD lesions from field images of sweetpotato foliage, which will contribute to the rapid and intelligent detection of sweetpotato diseases.…”
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Using Drones and Artificial Intelligence to Assess Damage in Aircraft Assembly Joints by Filip Kubik, Marcin Krysiak

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Predicting weather-related power outages in large scale distribution grids with deep learning ensembles by L. Prieto-Godino, C. Peláez-Rodríguez, J. Pérez-Aracil, J. Pastor-Soriano, S. Salcedo-Sanz

“…An optimization-based feature selection approach has been considered for selecting the optimal Reanalysis node locations used as predictors. To overcome the data imbalance challenge and enhance prediction accuracy, we propose a Deep Learning-based (DL) ensemble algorithm. …”
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Deep Learning dengan Teknik Early Stopping untuk Mendeteksi Malware pada Perangkat IoT by Iwang Moeslem Andika Surya, Triawan Adi Cahyanto, Lutfi Ali Muharom

“…Although CNN was initially designed for image processing, this algorithm also effectively detects complex patterns in non-image data, such as IoT network traffic, due to its ability to extract hierarchical features. …”
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Continuous and Unconstrained Tremor Monitoring in Parkinson’s Disease Using Supervised Machine Learning and Wearable Sensors by Fernando Rodriguez, Philipp Krauss, Jonas Kluckert, Franziska Ryser, Lennart Stieglitz, Christian Baumann, Roger Gassert, Lukas Imbach, Oliver Bichsel

“…We developed and implemented a supervised machine learning algorithm, trained and tested on tremor scores. …”
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Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study by Maik JM Beuken, Melanie Kleynen, Susy Braun, Kees Van Berkel, Carla van der Kallen, Annemarie Koster, Hans Bosma, Tos TJM Berendschot, Alfons JHM Houben, Nicole Dukers-Muijrers, Joop P van den Bergh, Abraham A Kroon, Iris M Kanera

“…The factor probabilistic distance clustering algorithm was applied in order to detect clusters within this high-dimensional data. …”
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Domain Adaptation in Application to Gravitational Lens Finding by Hanna Parul, Sergei Gleyzer, Pranath Reddy, Michael W. Toomey

“…To discover these rare objects, efficient automated detection methods need to be developed. In this work, we assess the performance of three domain adaptation (DA) techniques—adversarial discriminative DA, Wasserstein distance guided representation learning (WDGRL), and supervised domain adaptation (SDA)—in enhancing lens-finding algorithms trained on simulated data when applied to observations from the Hyper Suprime-Cam Subaru Strategic Program. …”
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Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao by Jiawei Gao, Bochao Chen, Su-Kit Tang

“…The collected data were analyzed and validated using machine learning algorithms, including Isolation Forest, Random Forest, Logistic Regression, and Local Outlier Factor. …”
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A Fully Integrated Orthodontic Aligner With Force Sensing Ability for Machine Learning‐Assisted Diagnosis by Hao Feng, Wenhao Song, Ruyi Li, Linxin Yang, Xiaoxuan Chen, Jiajun Guo, Xuan Liao, Lei Ni, Zhou Zhu, Junyu Chen, Xibo Pei, Yijun Li, Jian Wang

“…Integrated with machine learning algorithm, this fully integrated aligner can also identify and track adverse oral habits that can cause/exacerbate malocclusion, such as lip biting, thumb sucking, and teeth grinding. …”
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Development and Internal Validation of a Machine Learning-Based Colorectal Cancer Risk Prediction Model by Deborah Jael Herrera, Daiane Maria Seibert, Karen Feyen, Marlon van Loo, Guido Van Hal, Wessel van de Veerdonk

“…<b>Methods:</b> We analyzed data from 154,887 adults, aged 55–74 years, who participated in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. …”
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Enhancing Crop Health: Advanced Machine Learning Techniques for Prediction Disease in Palm Oil Tree by Nandy Manish, Kumar Yalakala Dinesh

“…This study builds predictive models by using a palmd database comprised of the large datasets of palm oil tree health indicators, environmental factors and historical disease outbreaks to identify early signs of disease with high accuracy.To analyze both structured as well as unstructured data multiple machine learning algorithms were used such as Random Forest, Support Vector Machines, Convolution Neural Networks. …”
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Privacy-Preserving Machine Learning for IoT-Integrated Smart Grids: Recent Advances, Opportunities, and Challenges by Mazhar Ali, Moharana Suchismita, Syed Saqib Ali, Bong Jun Choi

“…Recent studies have provided valuable insights into the potential of machine learning algorithms in SGs, covering areas such as generation, distribution, microgrids, consumer energy market, and cyber security. …”
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The interpretable machine learning model for depression associated with heavy metals via EMR mining method by Site Xu, Mu Sun

“…Data were derived from the US National Health and Nutrition Examination Survey (NHANES) spanning from 2013 to March 2020. …”
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The Talent Cultivation Model of Study Travel Majors in Universities Based on the Internet of Things and Deep Learning by Yanjie Zhan

“…The system utilizes IoT devices to collect real-time data on students&#x2019; learning behaviors, social interactions, and environmental perceptions, which are then analyzed through multi-level feature extraction and deep learning algorithms. …”
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MULTI-MODEL STACK ENSEMBLE DEEP LEARNING APPROACH FOR MULTI-DISEASE PREDICTION IN HEALTHCARE APPLICATION by Bhaskar Adepu, T. Archana

“…Leveraging deep learning models, it becomes feasible to detect and forecast the early stages of numerous diseases based on individual health conditions. …”
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Smart indoor monitoring for disabled individuals using an ensemble of deep learning models in an IoT environment by Faisal S. Alsubaei, Abdulrahman A. Alshdadi, Mohammed Rizwanullah

“…Initially, the SIMDP-EDLIoT approach uses linear scaling normalization (LSN) to ensure that the input data is scaled appropriately. Besides, the Improved Osprey Optimization Algorithm (IOOA)-based feature selection is employed to classify the most relevant features, enhancing the efficiency of the system by reducing dimensionality. …”
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Exploring interconnections among atoms, brain, society, and cosmos with network science and explainable machine learning by Daniele Caligiore, Daniele Caligiore, Anna Monreale, Anna Monreale, Giulio Rossetti, Angela Bongiorno, Giuseppe Fisicaro

“…For instance, if common aspects of criticality in neuroscience and cosmology are identified, an algorithm trained on brain data could be repurposed to detect critical states in cosmic systems, even with limited cosmic data. …”
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Multi‐sensor missile‐borne LiDAR point cloud data augmentation based on Monte Carlo distortion simulation by Luda Zhao, Yihua Hu, Fei Han, Zhenglei Dou, Shanshan Li, Yan Zhang, Qilong Wu

“…Comparative experiments between the proposed point cloud DA algorithm and the current state‐of‐the‐art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3% and 17.9%, surpassing SOTA performance of current point cloud DA algorithms.…”
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