Development and Internal Validation of Machine Learning Algorithms for Predicting Subsequent Contralateral Slipped Capital Femoral Epiphysis in Patients With Unilateral Slips by David P. VanEenenaam, Jr., BS, Carter Hall, BS, Daniel A. Maranho, MD, PhD, Christopher J. DeFrancesco, MD, Eduardo N. Novais, MD, Wudbhav N. Sankar, MD

“…Key Concepts: (1) This study is, to our knowledge, the first to apply emerging machine learning (ML) techniques to address the indications for prophylactic pinning of the unaffected side in patients presenting with unilateral slipped capital femoral epiphysis (SCFE). (2) Our preliminary ML model predicts development of a contralateral slip with reasonable accuracy. …”
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Artificial Intelligence in Action (2019-2024): A Review on Parkinson’s Disease Detection Using Non-Invasive Procedures by Peetabas Patro, Tapan Kumar Das

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Exploring the State-of-the-Art Algorithms for Brain Tumor Classification Using MRI Data by Joice J Anish, D Ajitha

“…In this paper, cutting-edge machine learning (ML) and deep learning (DL) techniques are used to show how BT is currently being diagnosed with MRI images. …”
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Unraveling the Nexus of ML and 6G: Challenges, Opportunities, and Future Directions by Mohammed Anis Oukebdane, A. F. M. Shahen Shah, Abul Kalam Azad, John Ekoru, Milka Madahana

“…Machine learning (ML) and sixth-generation (6G) wireless networks together present a revolutionary paradigm ready to meet hitherto unheard-of needs in connection, scalability, and intelligence. …”
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A reinforcement learning approach to effective forecasting of pediatric hypoglycemia in diabetes I patients using an extended de Bruijn graph by Mert Onur Cakiroglu, Hasan Kurban, Lilia Aljihmani, Khalid Qaraqe, Goran Petrovski, Mehmet M. Dalkilic

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Multimodal Particulate Matter Prediction: Enabling Scalable and High-Precision Air Quality Monitoring Using Mobile Devices and Deep Learning Models by Hirokazu Madokoro, Stephanie Nix

“…In response to persistent air pollution challenges across Japan, we developed a system that utilizes cutting-edge transformer-based deep learning architectures to estimate PM values from imagery captured by smartphone cameras. …”
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Attack resilient IoT security framework using multi head attention based representation learning with improved white shark optimization algorithm by Jawhara Aljabri

“…Abstract At present, the internet of things (IoT) plays a vital part in the growth of programmed electrical power stations while presenting magnificent chances, particularly cybersecurity. …”
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DeepBioFusion: Multi-modal deep learning based above ground biomass estimation using SAR and optical satellite images by Abdul Hanan, Mehak Khan, Nieves Fernandez-Anez, Reza Arghandeh

“…Traditional methods, such as manual field surveys, are labor-intensive and limited in scope. This study presents DeepBioFusion, a multi-modal deep learning framework that first estimates AGB for validation as ground truth generation by using LiDAR-derived tree heights and a Tree Species map, employing allometry equations to relate tree height to Diameter at Breast Height (DBH). …”
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Gesture recognition for hearing impaired people using an ensemble of deep learning models with improving beluga whale optimization-based hyperparameter tuning by Mohammed Assiri, Mahmoud M. Selim

“…This study proposes a Gesture Recognition for Hearing Impaired People Using an Ensemble of Deep Learning Models with Improving Beluga Whale Optimization (GRHIP-EDLIBWO) model. …”
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Enhanced IoT-Based Face Mask Detection Framework Using Optimized Deep Learning Models: A Hybrid Approach With Adaptive Algorithms by Parul Dubey, Pushkar Dubey, Celestine Iwendi, Cresantus N. Biamba, Deepak Dasaratha Rao

“…Often, existing methods require some kind of pre-installed equipment or difficult-to-manipulate environmental conditions, and computational resource constraints essentially put an end to them. In the present study, a hybrid Flame-Sailfish Optimization (HFSO)-based deep learning framework is proposed. …”
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Deep Learning and Transformer Models for Groundwater Level Prediction in the Marvdasht Plain: Protecting UNESCO Heritage Sites—Persepolis and Naqsh-e Rustam by Peyman Heidarian, Franz Pablo Antezana Lopez, Yumin Tan, Somayeh Fathtabar Firozjaee, Tahmouras Yousefi, Habib Salehi, Ava Osman Pour, Maria Elena Oscori Marca, Guanhua Zhou, Ali Azhdari, Reza Shahbazi

“…Groundwater level monitoring is crucial for assessing hydrological responses to climate change and human activities, which pose significant threats to the sustainability of semi-arid aquifers and the cultural heritage they sustain. This study presents an integrated remote sensing and transformer-based deep learning framework that combines diverse geospatial datasets to predict spatiotemporal variations across the plain near the Persepolis and Naqsh-e Rustam archaeological complexes—UNESCO World Heritage Sites situated at the plain’s edge. …”
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A Hybrid LMD–ARIMA–Machine Learning Framework for Enhanced Forecasting of Financial Time Series: Evidence from the NASDAQ Composite Index by Jawaria Nasir, Hasnain Iftikhar, Muhammad Aamir, Hasnain Iftikhar, Paulo Canas Rodrigues, Mohd Ziaur Rehman

“…The stochastic components are modeled using ARIMA to represent linear temporal dynamics, while the deterministic components are projected using cutting-edge machine learning methods, including XGBoost, Random Forest (RF), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs). …”
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Eeg-based detection of epileptic seizures in patients with disabilities using a novel attention-driven deep learning framework with SHAP interpretability by Tawfeeq Shawly, Ahmed A. Alsheikhy

“…In this research, we introduce a cutting-edge deep learning methodology for the automated prediction of epilepsy, incorporating a Novel Attention Module (NAM) into a new Convolutional Neural Network (CNN) to improve the extraction of features from EEG signals. …”
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Adaptive Energy Management for MATTER-Enabled Smart Homes by Navodit Bhardwaj, Pallavi Joshi

“…The framework is evaluated across multiple metrics: energy consumption, detection accuracy, system resilience, and computational efficiency under edge-device constraints. A comprehensive analysis of hardware footprint, latency sensitivity, and data loss impact is presented, along with detailed anomaly detection criteria and threshold tuning to minimize false positives/negatives. …”
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Artificial Intelligence-Driven Prognostics and Health Management for Centrifugal Pumps: A Comprehensive Review by Salman Khalid, Soo-Ho Jo, Syed Yaseen Shah, Joon Ha Jung, Heung Soo Kim

“…This review investigates common fault types in CPs, while placing a specific emphasis on artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) techniques, for fault diagnosis and prognosis. …”
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N-DriverMotion: Driver Motion Learning and Prediction Using an Event-Based Camera and Directly Trained Spiking Neural Networks on Loihi 2 by Hyo Jong Chung, Byungkon Kang, Yoon Seok Yang

“…Driver motion recognition is a key factor in ensuring the safety of driving systems. This paper presents a novel system for learning and predicting driver motions, along with an event-based (720 × 720) dataset, N-DriverMotion, newly collected to train a neuromorphic vision system. …”
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From Continent to Ocean: Investigating the Multi-Element and Precious Metal Geochemistry of the Paraná-Etendeka Large Igneous Province Using Machine Learning Tools by J. J. Lindsay, H. S. R. Hughes, C. M. Yeomans, J. C. Ø. Andersen, I. McDonald

“…The PELIP presents an opportunity to observe magma geochemistry as the continent and sub-continental lithospheric mantle (SCLM) are progressively removed from a melting environment. …”
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Towards a smarter battery management system: A critical review on deep learning-based state of charge estimation of lithium-ion batteries by Jiaqi Yao, Julia Kowal

“…A systematic walk-through of the existing deep learning-based SOC estimation approaches, together with the frequently applied optimization strategies, is presented, where we also appeal for a standardized evaluation protocol in this field. …”
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Design and Implementation of a Deep Learning-Based Hand Gesture Recognition System for Rehabilitation Internet-of-Things (RIoT) Environments Using MediaPipe by Nurul Hanis Mohd Dhuzuki, Ahmad Anwar Zainuddin, Nur Anis Sofea Kamarul Zaman, Alin Nur Maisarah Ahmad Razmi, Wonderful Shammah Kaitane, Asmarani Ahmad Puzi, Mohd Naqiuddin Johar, Maslina Yazid, Nor Azlin Mohd Nordin, Shahrul Naim Sidek, Hasan Firdaus Mohd Zaki

“… Frequent hospital visits for hand rehabilitation exercises, such as strengthening and opposition exercises, present significant challenges, especially for patients in remote areas. …”
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A study on how to improve students' continuance intention in landscape architecture conservation course within a virtual learning environment based on SVVR by Yang Li, Yi Yang, Silu Bao

“…While university curricula begin to focus on this area, the shortcomings of conventional teaching methods are apparent, especially in technology-supported learning. Therefore, it is crucial to incorporate cutting-edge technologies to devise novel educational strategies that improve students' learning experiences and achievements. …”
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