Applications of Machine Learning and Remote Sensing in Soil and Water Conservation by Ye Inn Kim, Woo Hyeon Park, Yongchul Shin, Jin-Woo Park, Bernie Engel, Young-Jo Yun, Won Seok Jang

“…As analytical tools continue to advance, the variety of ML algorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. …”
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An automated network-based tool to search for metabolic vulnerabilities in cancer by Luis V. Valcárcel, Edurne San José-Enériz, Raquel Ordoñez, Iñigo Apaolaza, Danel Olaverri-Mendizabal, Naroa Barrena, Ana Valcárcel, Leire Garate, Jesús San Miguel, Antonio Pineda-Lucena, Xabier Agirre, Felipe Prósper, Francisco J. Planes

“…Here, we present gmctool, a freely accessible online tool that allows us to accomplish this task in a simple, efficient and intuitive environment. gmctool exploits the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to synthetic lethality based on genome-scale metabolic networks, including a unique database of synthetic lethals computed from Human1, the most recent metabolic reconstruction of human cells. gmctool introduces qualitative and quantitative improvements over our previously developed algorithms to predict, visualize and analyze metabolic vulnerabilities in cancer, demonstrating a superior performance than competing algorithms. …”
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Analysis of Blockchain-Technology by Danylo Dvorchuk, Iryna Shpinareva

“…While Layer 1 and Layer 2 solutions provide significant improvements in throughput and efficiency, their integration remains a key challenge. …”
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Development and validation of a nomogram for predicting in-hospital mortality in older adult hip fracture patients with atrial fibrillation: a retrospective study by Zhenli Li, Jing He, Tiezhu Yao, Guang Liu, Jing Liu, Ling Guo, Mengjia Li, Mengjia Li, Zhengkun Guan, Zhengkun Guan, Ruolian Gao, Jingtao Ma

“…The area under curves (AUC), calibration curves, Delong test, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were performed to evaluate the prediction performance. …”
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Enhancing Security in Industrial IoT Networks: Machine Learning Solutions for Feature Selection and Reduction by Ahmad Houkan, Ashwin Kumar Sahoo, Sarada Prasad Gochhayat, Prabodh Kumar Sahoo, Haipeng Liu, Syed Ghufran Khalid, Prince Jain

“…This research focuses on improving anomaly detection in industrial Internet of Things networks through feature reduction and selection. …”
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Comparative analysis of machine learning techniques in metabolomic-based preterm birth prediction by Ying-Chieh Han, Jane Shearer, Chunlong Mu, Donna M. Slater, Suzanne C. Tough, Gavin E. Duggan

“…Background: Machine learning (ML), with advancements in algorithms and computations, is seeing an increased presence in life science research. …”
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CLUSTERING ON DISSIMILARITY REPRESENTATIONS FOR DETECTING MISLABELLED SEISMIC SIGNALS AT NEVADO DEL RUIZ VOLCANO by Castellanos-Domínguez César Germán, Alzate Mauricio Orozco

“…Our experiments,<br />performed on re-analyzed seismic signals, show a significant improvement respect to recognition accuracies for the original data sets.…”
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AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review by Lea Marie Pehrson, Jens Petersen, Nathalie Sarup Panduro, Carsten Ammitzbøl Lauridsen, Jonathan Frederik Carlsen, Sune Darkner, Michael Bachmann Nielsen, Silvia Ingala

“…The algorithms with the highest DSC deployed an encoder–decoder architecture. …”
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Enabling High-Level Worker-Centric Semantic Understanding of Onsite Images Using Visual Language Models with Attention Mechanism and Beam Search Strategy by Hui Deng, Kejie Fu, Binglin Yu, Huimin Li, Rui Duan, Yichuan Deng, Jia-rui Lin

“…However, a significant portion of this information remains underutilized by construction managers due to the limitations of existing image processing algorithms. These algorithms primarily rely on low-level visual features and struggle to capture high-order semantic information, leading to a gap between computer-generated image semantics and human interpretation. …”
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PREDICTION OF SOFTWARE ANOMALIES METHODS BASED ON ENSEMBLE LEARNING METHODS by Raghda Azad Hasan, Ibrahim Ahmed Saleh

“…The model applies the basic algorithms (Random Forest (RF), Decision Tree (DT), Extra Tree) and the learning model ensemble (Adaboost, xgboost ,Stack, Voting, bagging) and metrics (accuracy, recall, F1 score, accuracy) to measure the prediction performance of the models and a comparison was made between the proposed model algorithms. …”
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A Model for Automated Business Writing Assessment by Daniil Dmitrievich Zafievsky, Nadezhda Stanislavona Lagutina, Oksana Andreyevna Melnikova, Anatoliy Yurievich Poletaev

“…The algorithms are based on a syntactic analysis with the use of dictionaries. …”
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Artificial Intelligence in STEM Education: Interactive Handson Environment using Open Source Electronic Platforms by Danny Xie-Li, Esteban Arias Mendez

“…The main purpose is to motivate the participants to explode its creativity, improving their innovation skills to provide solutions for XXI century problems, better quality of life, health, among others. …”
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The Role of Time in Facial Dynamics and Challenges in Automatic Emotion Recognition (2019–2024) by Williams Contreras-Higuera, Lucrezia Crescenzi-Lanna

“…Based on a comprehensive literature review, this study highlights the critical role of the temporal dimension of facial dynamics in understanding facial expressions and improving the accuracy and robustness of automatic emotion recognition systems (machine-FER). …”
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A Novel Approach to Efficiently Verify Sequential Consistency in Concurrent Programs by Mohammed H. Abdulwahhab, Parosh Aziz Abdulla, Karwan Jacksi

“…The final variant exhibited superior performance, achieving substantial improvements in execution time—ranging from 31.919% to 99.992%—compared to the optimal existing practical SC verification algorithms. …”
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Applications of Machine Learning for Wine Recognition Based on ¹H-NMR Spectroscopy by Ariana Raluca Hategan, Adrian Pirnau, Dana Alina Magdas

“…The present study aims to explore the possibility of applying machine learning (ML) algorithms for the development of reliable wine authentication instruments able to encompass the exhaustive performances offered by learning-based methods, and, at the same time, to propose reference practices for improving the recognition ability of the developed models. …”
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A Dual Grid Collaborative Navigation Method and Computer Simulation Based on Data Link by Li Yongfeng, Hao Xiuqiang, Wu Zhenguo, Wu Gang, Guo Chubing

“…Firstly, by adopting geographic navigation filtering algorithms and relative navigation filtering algorithms that closely combine ranging information with inertial navigation, precise relative positioning of mobile formations can be achieved without fixed ground station and GNSS. …”
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Securing smart cities through machine learning: A honeypot‐driven approach to attack detection in Internet of Things ecosystems by Yussuf Ahmed, Kehinde Beyioku, Mehdi Yousefi

“…Real‐world cyber‐attack datasets from diverse honeypots simulating IoT devices are analysed using various machine learning and neural network algorithms. Results demonstrate significant improvement in cyber‐attack detection and mitigation when integrating honeypot data into IoT security frameworks. …”
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Hybrid Filtering Technique for Accurate GNSS State Estimation by Jahnvi Verma, Nischal Bhattarai, Thejesh N. Bandi

“…We demonstrate that this hybrid method outperforms classical algorithms both in terms of accuracy and flexibility for easier implementation, showing better than 30% improvement in the variance of the horizontal position for the simulated as well as the real-world dynamic receiver dataset. …”
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New Strategies Based on Hierarchical Matrices for Matrix Polynomial Evaluation in Exascale Computing Era by Luisa Carracciuolo, Valeria Mele

“…Advancements in computing platform deployment have acted as both push and pull elements for the advancement of engineering design and scientific knowledge. Historically, improvements in computing platforms were mostly dependent on simultaneous developments in hardware, software, architecture, and algorithms (a process known as co-design), which raised the performance of computational models. …”
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Robust vector-weighted and matrix-weighted multi-view hard c-means clustering by Zhe Liu, Sarah Aljohani, Sijia Zhu, Tapan Senapati, Gözde Ulutagay, Salma Haque, Nabil Mlaiki

“…With the rapid advancement of information technology, multi-view data has become ubiquitous, prompting extensive attention towards multi-view clustering algorithms. Despite significant strides, several challenges persist: (1) the prevalence of noise and outliers in real-world multi-view data often compromises the efficacy of clustering; (2) most existing multi-view clustering algorithms predominantly assess the overall contribution of each view, while neglecting the intra-view contributions. …”
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