Digital Transition as a Driver for Sustainable Tailor-Made Farm Management: An Up-to-Date Overview on Precision Livestock Farming by Caterina Losacco, Gianluca Pugliese, Lucrezia Forte, Vincenzo Tufarelli, Aristide Maggiolino, Pasquale De Palo

“…The increasing integration of sensing devices with smart technologies, deep learning algorithms, and robotics is profoundly transforming the agricultural sector in the context of Farming 4.0. …”
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Analyzing the Impact of Geoenvironmental Factors on the Spatiotemporal Dynamics of Forest Cover via Random Forest by Hendaf N. Habeeb, Yaseen T. Mustafa

“…The Random Forest model demonstrated high predictive accuracy, achieving an R² value of 0.918 (RMSE of 0.016 and MAE of 0.013) for 2013 and 0.916 (RMSE of 0.018 and MAE of 0.014) for 2023, underscoring the model’s robustness in handling nonlinear ecological processes. …”
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Application of machine learning in forensic geochemistry using presalt oil samples from the Santos basin by Gil Marcio Avelino Silva, Fernando Pellon de Miranda, Jarbas Vicente Poley Guzzo, Wagner Leonel Bastos, Ygor Rocha, Igor Viegas Alves Fernandes de Souza, Italo Oliveira Matias, Sarah Barron Torres, Francisco Fabio de Araujo Ponte

“…A dataset comprising 2200 presalt oil samples and 75 attributes from the Santos Basin underwent preprocessing and exploratory analysis, resulting in 2137 samples and 62 predictive attributes. Seven machine learning algorithms were evaluated, with the random forest model achieving the highest classification accuracy of 91%. …”
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An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization by Jing Xiao, Jing-Jing Li, Xi-Xi Hong, Min-Mei Huang, Xiao-Min Hu, Yong Tang, Chang-Qin Huang

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Explainable Machine Learning for Mapping Minerals From CRISM Hyperspectral Data by Sandeepan Dhoundiyal, Moni Shankar Dey, Shashikant Singh, Pattathal V. Arun, Guneshwar Thangjam, Alok Porwal

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Learning from Octopuses: Cutting-Edge Developments and Future Directions by Jinjie Duan, Yuning Lei, Jie Fang, Qi Qi, Zhiming Zhan, Yuxiang Wu

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Application of Machine Learning Technologies for Managing Multifactor Threats in an Integrated Model of Cognitive Security Center at Defense Industry Enterprise by Pavel Panilov, Tatiana Tsibizova

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A Study on Distributed Multi-Sensor Fusion for Nonlinear Systems Under Non-Overlapping Fields of View by Liu Wang, Yang Zhou, Wenjia Li, Lijuan Shi, Jian Zhao, Haiyan Wang

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Decentralized multi-agent federated and reinforcement learning for smart water management and disaster response by H. Mancy, Naglaa E. Ghannam, Amr Abozeid, Ahmed I. Taloba

“…The new feature is a Decentralized Cooperation environment in which intelligent and self-managing agents learn utilizing Reinforcement Learning (RL) and Federated Learning (FL) algorithms for enhancing smart water management and real-time disaster relief. …”
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Swarm intelligence for energy-efficient heating, ventilation, and air conditioning (HVAC) systems: A case study in smart buildings by Vinoth Kanna I, Raja Subramani, Maher Ali Rusho, Shubham Sharma, Ramachandran T, Abinash Mahapatro, Deepak Gupta, Jasmina Lozanovic

“…Thermal comfort analysis through Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) metrics showed better indoor conditions, with the hybrid model keeping room temperatures within ±0.8 °C of the setpoint and bringing the PPD index down to 8.5 %. …”
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Identification of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis by Hang Chen, Biao Wu, Biao Wu, Kunyu Guan, Liang Chen, Kangjie Chai, Maoji Ying, Dazhi Li, Weicheng Zhao

“…This research provides a valuable approach for the predictive diagnosis and targeted therapy of atherosclerosis.…”
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Determination of Sequential Well Placements Using a Multi-Modal Convolutional Neural Network Integrated with Evolutionary Optimization by Seoyoon Kwon, Minsoo Ji, Min Kim, Juliana Y. Leung, Baehyun Min

“…This complex multi-million-dollar problem involves optimizing multiple parameters using computationally intensive reservoir simulations, often employing advanced algorithms such as optimization algorithms and machine/deep learning techniques to find near-optimal solutions efficiently while accounting for uncertainties and risks. …”
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G-OnRamp: Generating genome browsers to facilitate undergraduate-driven collaborative genome annotation. by Luke Sargent, Yating Liu, Wilson Leung, Nathan T Mortimer, David Lopatto, Jeremy Goecks, Sarah C R Elgin

“…Despite advances in computational gene prediction algorithms, most eukaryotic genomes still benefit from manual gene annotation. …”
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Study on the Processing Technology of Calamine Calcination by Near-Infrared Spectroscopy by Xiaodong Zhang, Long Chen, Yu Bai, Keli Chen

“…Then, matching the near-infrared spectroscopy data with the T value and establishing the T value analysis model using the PLS algorithm were performed. Through cross and independent validation and evaluation, it was proved that the two models were very effective and had strong predictive abilities. …”
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Transformation of Management Technologies in the Digital Economy by P. A. Levchaev, B. Khezazna

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Digital Eversion of a Hollow Structure: An Application in Virtual Colonography by Jun Zhao, Liji Cao, Tiange Zhuang, Ge Wang

“…Together with other techniques, digital eversion may help improve screening, diagnosis, surgical planning, and medical education. Two eversion algorithms are proposed and evaluated in numerical simulation to demonstrate the feasibility of the approach.…”
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Optimal Mobile Sensor Scheduling for a Guaranteed Coverage Ratio in Hybrid Wireless Sensor Networks by Zhixin Fu, Keyou You

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Monthly Rainfall Estimation Using Data-Mining Process by Özlem Terzi

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AI-aided short-term decision making of rockburst damage scale in underground engineering by Chukwuemeka Daniel, Shouye Cheng, Xin Yin, Zakaria Mohamed Barrie, Yucong Pan, Quansheng Liu, Feng Gao, Minsheng Li, Xing Huang

“…This study investigates the effectiveness of ensemble machine learning models optimized through Bayesian optimization (BO) in predicting rockburst damage scales. Nine classifier algorithms, including random forest (RF), were evaluated using a dataset of 254 samples. …”
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Comparison of clinical nasal endoscopy, optical biopsy, and artificial intelligence in early diagnosis and treatment planning in laryngeal cancer: a prospective observational study by Ruifang Hu, Xianping Liu, Yong Zhang, Clement Arthur, Dongguang Qin

“…Diagnostic performance was calculated using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).ResultsThe study revealed superior sensitivity (95.2%) and specificity (96.5%) with AI-enhanced endoscopy compared to conventional endoscopy (89.6%, 92.4%), respectively. …”
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