Evaluating the feasibility of 12-lead electrocardiogram reconstruction from limited leads using deep learning by Oriana Presacan, Alexandru Dorobanţiu, Jonas L. Isaksen, Tobias Willi, Claus Graff, Michael A. Riegler, Arun R. Sridhar, Jørgen K. Kanters, Vajira Thambawita

“…Original and recreated leads were measured with a commercially available algorithm. Differences in means and variances were assessed with Student’s t-tests and F-tests, respectively. …”
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Enhanced data-driven shear strength predictive modeling framework for RCDBs using explainable boosting-based ensemble learning algorithms coupled with Bayesian optimization by Imad Shakir Abbood, Noorhazlinda Abd Rahman, B.H. Abu Bakar

“…This research aims to present a novel data-driven predictive framework for the SS of RCDBs using an explainable ML approach and incorporating a large database compilation of 950 experimental specimens with different web reinforcement. To achieve this goal, four ensemble boosting ML algorithms, namely Gradient Boosting, HistGBoost, XGBoost, and LightGBM, were adopted for implementation. …”
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A Case Series of Different Cutaneous Adverse Drug Reactions Attending the Dermatology Clinic of a Tertiary Care Center by Debalina Kanjilal, Suhena Sarkar, Abanti Saha, Birupaksha Biswas, Soutrik Roy

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Who benefits from adjuvant chemotherapy? Identification of early recurrence in intrahepatic cholangiocarcinoma patients after curative-intent resection using machine learning algor... by Qi Li, Hengchao Liu, Yubo Ma, Zhenqi Tang, Chen Chen, Dong Zhang, Zhimin Geng

“…This study aimed to evaluate the effectiveness of machine learning algorithms in detecting early recurrence in ICC patients and select those who would benefit from ACT to improve prognosis.MethodsThe study analyzed 254 intrahepatic cholangiocarcinoma (ICC) patients who underwent curative-intent resection to identify early recurrence predictors. …”
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Relationships Between Oat Phenotypes and UAV Multispectral Imagery Under Different Water Deficit Conditions by Structural Equation Modelling by Yayang Feng, Guoshuai Wang, Jun Wang, Hexiang Zheng, Xiangyang Miao, Xiulu Sun, Peng Li, Yan Li, Yanhui Jia

“…Nonlinear machine learning algorithms (RF and ANN) significantly outperform conventional linear regression in estimating SWC from spectral vegetation indices.…”
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Evaluation of reparative capabilities in a chronic wound defect in patients with complicated forms of varicose veins by S.I. Savoliuk, A.R. Dembitskyi

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Quinary Classification of Human Gait Phases Using Machine Learning: Investigating the Potential of Different Training Methods and Scaling Techniques by Amal Mekni, Jyotindra Narayan, Hassène Gritli

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Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River by Manqi Wang, Caili Zhou, Jiaqi Shi, Fei Lin, Yucheng Li, Yimin Hu, Xuesheng Zhang

“…To intuitively evaluate the performance of the hybrid optimization algorithm, its prediction accuracy is compared with that of conventional machine learning algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN and PSO–BPNN). …”
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Comparative analysis of principal modulation techniques for modular multilevel converter and a modified reduced switching frequency algorithm for nearest level pulse width modulati... by M. Benboukous, H. Bahri, M. Talea, M. Bour, K. Abdouni

“…For the first time, a comparative analysis of 3 modulation techniques for the MMC, LS-PWM, NLC, and NL-PWM has been conducted, highlighting their performance under different operating conditions. The study also proposes a modified RSF capacitor voltage balancing algorithm specifically for NL-PWM, which has not been previously explored in the literature. …”
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Real-Time Performance Evaluation for Flooding and Recursive Time Synchronization Protocols over Arduino and XBee by Tarek R. Sheltami, Danish Sattar, Elhadi M. Shakshuki, Ashraf S. Mahmoud

“…Wireless sensor networks have three major goals: time synchronization, low bandwidth operation, and energy efficiency. Different time synchronization algorithms are aimed at achieving these objectives using various methods. …”
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Development of Anthro-Fitness Model for Evaluating Firefighter Recruits’ Performance Readiness Using Machine Learning by Mohamed Borhanudin Mohd Yusof, Musa Rabiu Muazu, Nazarudin Mohamad Nizam, Abdul Majeed Anwar P. P., Raj Naresh Bhaskar, Razmaan Mohd Azraai Mohd

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Results of parallel independent visual evaluation of projective cover of the bottom during macrophyte assesment survey by A. A. Dulenin

“…The method is based on visual evaluation of SAV projective cover. Such subjective data should be verified. …”
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Advancing Arabic Word Embeddings: A Multi-Corpora Approach with Optimized Hyperparameters and Custom Evaluation by Azzah Allahim, Asma Cherif

“…This paper addresses these gaps by developing and evaluating Arabic word embedding models trained on diverse Arabic corpora, investigating how varying hyperparameter values impact model performance across different NLP tasks. …”
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Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability by Danish Raza, Hong Shu, Muhsan Ehsan, Hong Fan, Kamal Abdelrahman, Hasnat Aslam, Abdul Quddoos, Rana Waqar Aslam, Majid Nazeer, Mohammed S. Fnais, Azeem Sardar

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New non-primitive codes formed from primitive BCH and Hamming codes and their norm evaluation by V. K. Konopelko, V. A. Lipnitski

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Defining Rural Types Nearby Large Cities from the Perspective of Urban–Rural Integration: A Case Study of Xi’an Metropolitan Area, China by Xiji Jiang, Jiaxin Sun, Tianzi Zhang, Qian Li, Yan Ma, Wen Qu, Dan Ye, Zhendong Lei

“…A clustering algorithm enhanced by the random forest (RF)–principal component analysis (PCA)–partitioning around medoids (PAM) method is applied to evaluate rural integration comprehensively. …”
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Evaluation of reference genes for qPCR in human liver and kidney tissue from individuals with obesity by Aleksandra Romaniuk-Drapała, Katarzyna Kosicka-Noworzyń, Yi-Hua Sheng, Christine Yohn, Luigi Brunetti, Leonid Kagan

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Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China by Ao Zhang, Xin-wen Zhao, Xing-yuezi Zhao, Xiao-zhan Zheng, Min Zeng, Xuan Huang, Pan Wu, Tuo Jiang, Shi-chang Wang, Jun He, Yi-yong Li

“…Machine learning is currently one of the research hotspots in the field of landslide prediction. To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models, Conghua District, which is the most prone to landslide disasters in Guangzhou, was selected for landslide susceptibility evaluation. …”
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Enhancing Privacy in IoT-Enabled Digital Infrastructure: Evaluating Federated Learning for Intrusion and Fraud Detection by Amogh Deshmukh, Peplluis Esteva de la Rosa, Raul Villamarin Rodriguez, Sandeep Dasari

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Comprehensive evaluation of machine learning models for predicting the cognitive status of Alzheimer's disease subjects and susceptible by Lucien Gnegne Meteumba, Vaghawan Prasad Ojha, Shantia Yarahmadian

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