Deep Learning for Detection of Underwater Aircraft Wrecks from US Conflicts by Leila Character, Mark Moline, Matt W. Breece, Erik White, Dan Davis, Colin Colbourn

“…Using high-resolution sidescan sonar and a YOLOv7 model, we present a deep learning approach to expedite the search for these individuals. …”
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Mapping Bus and Stream Travel Time Using Machine Learning Approaches by Gopikrishnan Nair, B. Anil Kumar, Lelitha Vanajaskshi

“…So, a viable solution is to model and link the stream and bus travel times. Based on this, the present study proposes a method to map bus travel times with stream travel times using machine learning techniques, namely, Gradient Boosting Method and Support Vector Machines. …”
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Heart and Brain behind E-Learning amidst COVID19 Pandemic by Reshma Ramakrishnan, Mamta Agrawal, Priyanka Gandhi, Saurabh Shrivastava, Ayushi Choudhary, Syed Faraaz Hussain

“…Introduction: COVID-19 pandemic presented us with a window to introduce digital learning in ophthalmology. …”
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Domain generalization for image classification based on simplified self ensemble learning. by Zhenkai Qin, Xinlu Guo, Jun Li, Yue Chen

“…The introduction of focal loss and complex sample loss weight further fine-tunes the model's sensitivity to hard-to-learn instances, enhancing generalization to difficult samples. …”
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Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks by Apostolos Avranas, Philippe Ciblat, Marios Kountouris

“…Furthermore, we present a novel way to use a Dueling Network, which leads to further performance improvement. …”
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RF-Based UAV Detection and Identification Enhanced by Machine Learning Approach by Yash Vasant Ahirrao, Rana Pratap Yadav, Sunil Kumar

“…The system processes the signal data to extract key features and aiding in differentiating emitter types such as Wi-Fi, Bluetooth, or UAV control signals. Machine learning algorithms are trained to make decisions based on these extracted features. …”
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Optimization of microwave components using machine learning and rapid sensitivity analysis by Slawomir Koziel, Anna Pietrenko-Dabrowska

“…Verification experiments demonstrate the remarkable efficacy of the presented approach and its advantages over the benchmark methods that include machine learning in full-dimensionality space and population-based metaheuristics.…”
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Multimodal learning for enhanced SPECT/CT imaging in sports injury diagnosis by Zhengzheng Jiang, YaWen Shen

“…However, traditional SPECT/CT techniques often suffer from poor image quality, low spatial resolution, and limited capacity for integrating multiple data sources, which can hinder accurate diagnosis and intervention.MethodsTo address these limitations, this study proposes a novel multimodal learning framework that enhances SPECT/CT imaging through biomechanical data integration and deep learning. …”
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Novel Hybrid Deep Learning Model for Forecasting FOWT Power Output by Mohammad Barooni, Deniz Velioglu Sogut, Parviz Sedigh, Masoumeh Bahrami

“…This study presents a novel approach in the field of renewable energy, focusing on the power generation capabilities of floating offshore wind turbines (FOWTs). …”
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Performance evaluation of machine learning techniques for heart disease prediction: An overview by Dhanashri Shankar Karande, Shailendrakumar Mahadeo Mukane

“…ML techniques, including Random Forest, ANN, Linear Regression (LR), Logistic Regression (LR), K-Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), Gradient Boosting, and Decision Tree (DT), are utilized to create the machine learning model. This paper presents an overview of the Heart Disease Prediction system using Machine Learning techniques. …”
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The technology of formation of teamwork skills in terms of project-based learning students by Svetlana D. Lipatova, Evgeniya A. Khokholeva

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UNIFIED MULTIMODAL BIOMETRICS FUSION USING DEEP LEARNING FOR SECURING IOT by Prabhjot Kaur, Chander Kaur

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Improved bio-inspired with machine learning computing approach for thyroid prediction by Divya Kesavulu, Kannadasan R

“…This study investigates the use of several machine learning (ML) and deep learning (DL) methods, such as random forest (RF), decision tree, SVM, and KNN, to improve the precision of predicting thyroid illnesses. …”
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A Comparative Analysis of Machine Learning Algorithms in Energy Poverty Prediction by Elpida Kalfountzou, Lefkothea Papada, Christos Tourkolias, Sevastianos Mirasgedis, Dimitris Kaliampakos, Dimitris Damigos

“…Given the limited potential of conventional statistical models, machine learning (ML) techniques in the field of energy poverty have attracted growing interest, especially during the last five years. …”
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Online Machine Learning for Intrusion Detection in Electric Vehicle Charging Systems by Fazliddin Makhmudov, Dusmurod Kilichev, Ulugbek Giyosov, Farkhod Akhmedov

“…These results confirm that online machine learning methods can effectively secure EV charging infrastructures. …”
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Weather Classification in West Java using Ensemble Learning on Meteorological Data by Cynthia Nur Azzahra, Yulison Herry Chrisnanto, Gunawan Abdillah

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From Misinformation to Insight: Machine Learning Strategies for Fake News Detection by Despoina Mouratidis, Andreas Kanavos, Katia Kermanidis

“…In the digital age, the rapid proliferation of misinformation and disinformation poses a critical challenge to societal trust and the integrity of public discourse. This study presents a comprehensive machine learning framework for fake news detection, integrating advanced natural language processing techniques and deep learning architectures. …”
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Predictive Analytics in Agriculture: Machine Learning Models for Coconut Tree Health by Goswami Anjali, Kirit Dhablia Dharmesh

“…Active machine learning (ML) models have started replacing the conventional methods of crops health management and farm productivity with predictive analytics. …”
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LAGOS: learning health systems and how they can integrate with patient care by Norman Fenton, Scott McLachlan, Kudakwashe Dube, Evangelia Kyrimi

“…Problem Learning health systems (LHS) are an underexplored concept. …”
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An investigation of far and near transfer in a gamified visual learning paradigm. by Stefanie Duyck, Hans Op de Beeck

“…After training, visual perceptual learning improvements are mostly constrained to the trained stimulus feature and retinal location. …”
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