Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities by Munya A. Arasi, Hussah Nasser AlEisa, Amani A. Alneil, Radwa Marzouk

“…They are efficient in certifying functions of detection of actions, observing crucial functions, and tracking. Conventional machine learning and deep learning approaches effectively detect human activity. …”
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Integrating Drone Truthing and Functional Classification of Remote Sensing Time Series for Supervised Vegetation Mapping by Giacomo Quattrini, Simone Pesaresi, Nicole Hofmann, Adriano Mancini, Simona Casavecchia

“…Unlike traditional ground truthing activities, drone truthing enabled the generation of large, spatially balanced reference datasets, which are critical for machine learning classification systems. These datasets improved classification accuracy by ensuring a comprehensive representation of vegetation spectral variability, enabling the classifier to identify the key phenological patterns that best characterize and distinguish different vegetation types across the landscape. …”
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Data from Emergency Medical Service Activities: A Novel Approach to Monitoring COVID-19 and Other Infectious Diseases by Daniele del Re, Luigi Palla, Paolo Meridiani, Livia Soffi, Michele Tancredi Loiudice, Martina Antinozzi, Maria Sofia Cattaruzza

“…<b>Conclusions</b>: This novel approach, combined with a machine learning predictive approach, could be a powerful public health tool to signal the start of disease outbreaks and monitor the spread of infectious diseases.…”
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Climate change impact assessment on groundwater level changes: A study of hybrid model techniques by Stephen Afrifa, Tao Zhang, Xin Zhao, Peter Appiahene, Mensah Samuel Yaw

“…The HM is made up of a Bayesian model averaging (BMA) and three machine learning models: random forest (RF), support vector machine (SVM), and artificial neural network. …”
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Time-series forecasting of microbial fuel cell energy generation using deep learning by Adam Hess-Dunlop, Harshitha Kakani, Stephen Taylor, Dylan Louie, Jason Eshraghian, Colleen Josephson

“…Very little work currently exists attempting to model and predict the relationship between soil conditions and SMFC energy generation, and we are the first to use machine learning to do so. In this paper, we train Long Short Term Memory (LSTM) models to predict the future energy generation of SMFCs across timescales ranging from 3 min to 1 h, with results ranging from 2.33 to 5.71% Mean Average Percent Error (MAPE) for median voltage prediction. …”
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Deep Learning and Radiomics in Triple-Negative Breast Cancer: Predicting Long-Term Prognosis and Clinical Outcomes by Cheng C, Wang Y, Zhao J, Wu D, Li H, Zhao H

“…Deep learning and radiomics techniques represent advanced machine learning methodologies and are also emerging outcomes in the medical-engineering field in recent years. …”
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Identification of Bacterial Lipopolysaccharide-Associated Genes and Molecular Subtypes in Autism Spectrum Disorder by He Y, He Y, Cheng B

“…This research aims to explore bacterial lipopolysaccharide (LPS)- and immune-related (BLI) molecular subgroups in ASD to enhance understanding of the disorder.Methods: We analyzed 89 control samples and 157 ASD samples from the GEO database, identifying BLI signatures using least absolute shrinkage and selection operator regression (LASSO) and logistic regression machine learning algorithms. A nomogram prediction model was developed based on these signatures, and we performed Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), and immune cell infiltration analysis to assess the impact of BLI subtypes and their underlying mechanisms.Results: Our findings revealed 17 differentially expressed BLI genes in children with ASD, with BLNK, MAPK8, PRKCQ, and TNFSF12 identified as potential biomarkers. …”
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Precision livestock farming applied to the dairy sector: 50 years of history with a text mining and topic analysis approach by Lucia Trapanese, Giovanna Bifulco, Alfio Calanni Macchio, Francesca Aragona, Sissy Purrone, Giuseppe Campanile, Angela Salzano

“…A comprehensive search on the Scopus® bibliometric database was carried out using various related keywords such as: “precision livestock farming, sensors, machine learning and dairy”. The research identified 5362 papers published from January 1976 to April 2024 that, after filtering, became 1794 eligible records. …”
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A Deep Learning-Based Approach for Two-Phase Flow Pattern Classification Using Void Fraction Time Series Analysis by Jefferson Dos Santos Ambrosio, Marco Jose da Silva, Andre Eugenio Lazzaretti

“…Flow regime classification is essential for analyzing and modeling two-phase flows, as it demarcates the flow behavior and influences the selection of appropriate predictive models. Machine learning-based approaches have gained relevance in flow regime classification research in the last few years. …”
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Pengenalan Jalan Berlubang Berbasis Vision Menggunakan Pyramid Histogram Of Oriented Gradients by Ahmad Habib Fitriansyah, Ema Rachmawati, Risnandar Risnandar

“…This article presents a new approach using image processing and machine learning to identify potholes on roads. The proposed system uses shape features extracted from Pyramid Histogram of Oriented Gradients (PHOG) and a Support Vector Machine (SVM) with polynomial kernels for classification. …”
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Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data by Youssra Cheriguene, Wael Jaafar, Halim Yanikomeroglu, Chaker Abdelaziz Kerrache

“…Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. …”
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The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic by M. Poongodi, Mohit Malviya, Mounir Hamdi, Hafiz Tayyab Rauf, Seifedine Kadry, Orawit Thinnukool

“…Large data sets need to be advanced so that extensive models related to deep analysis can be used to combat Coronavirus infection, which can be done by applying Artificial intelligence techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Computer vision to varying processing files. …”
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Automated Cattle Monitoring System for Calving Time Prediction Using Trajectory Data Embedded Time Series Analysis by Wai Hnin Eaindrar Mg, Thi Thi Zin, Pyke Tin, Masaru Aikawa, Kazayuki Honkawa, Yoichiro Horii

“…Furthermore, the system accurately classifies cattle as either normal or abnormal and predicts calving events a 4-h in advance using the EFS feature, comparing its performance with various machine learning algorithms. The system&#x0027;s seamless integration significantly enhances farm management and animal welfare.…”
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Distinct Urinary Metabolite Signatures Mirror In Vivo Oxidative Stress-Related Radiation Responses in Mice by Yaoxiang Li, Shivani Bansal, Baldev Singh, Meth M. Jayatilake, William Klotzbier, Marjan Boerma, Mi-Heon Lee, Jacob Hack, Keisuke S. Iwamoto, Dörthe Schaue, Amrita K. Cheema

“…By Day 30, the WBI HDR group showed persistent metabolic dysregulation, while the WBI LDR and PBI BM2.5 groups were similar to control mice. Machine learning models identified metabolites that were predictive of the type of radiation exposure with high accuracy, highlighting their potential use as biomarkers for radiation damage and oxidative stress.…”
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ML-Based Self-Optimization Handover Technique for Beyond 5G Mobile Network by Saddam Alraih, Rosdiadee Nordin, Asma Abu-Samah, Ibraheem Shayea, Nor Fadzilah Abdullah

“…The technique utilizes Machine Learning (ML), particularly leveraging the Regression Tree (RT) model. …”
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The effect of consuming bread contaminated with heavy metals on cardiovascular disease and calculating its risk assessment by Kambiz Ahmadi Angali, Majid Farhadi, Abdolkazem Neisi, Bahman Cheraghian, Mehdi Ahmadi, Afshin Takdastan, Abdolah Dargahi

“…The association between CVD and HMs has been evaluated utilizing seven machine-learning techniques. The results showed that the effect coefficient (β) of bread consumption in the incidence of heart disease is 4.6908 × 10–02. …”
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Source Characteristics Influence AI-Enabled Orthopaedic Text Simplification by Saman Andalib, BS, Sean S. Solomon, BS, Bryce G. Picton, BS, Aidin C. Spina, BS, John A. Scolaro, MD, Ariana M. Nelson, MD

“…Statistical and machine learning methods evaluated the correlations and predictive capacity of these features for transformation success. …”
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A Review of Agricultural Film Mapping: Current Status, Challenges, and Future Directions by Mengmeng Zhang, Jinwei Dong, Quansheng Ge, Hasituya, Pengyu Hao

“…Deep learning has apparent advantages than traditional machine learning algorithms in extracting PGs details, rarely used for mapping PMF. …”
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Multi-Temporal Image Fusion-Based Shallow-Water Bathymetry Inversion Method Using Active and Passive Satellite Remote Sensing Data by Jie Li, Zhipeng Dong, Lubin Chen, Qiuhua Tang, Jiaoyu Hao, Yujie Zhang

“…Finally, ICESat-2 laser altimeter data are fused with multi-temporal Sentinel-2 satellite data to construct a machine learning framework for coastal bathymetry. The bathymetric control points are extracted from ICESat-2 ATL03 products rather than from field measurements. …”
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Causal Inference for Hypertension Prediction With Wearable E lectrocardiogram and P hotoplethysmogram Signa... by Ke Gon g, Yifan Chen, Xinyue Song, Zhizhong Fu, Xiaorong Ding

“…Finally, we used these features to detect hypertension via machine learning algorithms. Results We validated the proposed method on 405 subjects. …”
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