Machine learning based method for forecasting short-term passenger flow in urban rail stations by HU Mingwei, SHI Xiaolong, WU Wenlin, HE Guoqing

“…However, the single model has poor adaptability to different types of subway stations. Compared with a single model, LightGBM-LSTM model equipped with merits of multiple models, fulfills a better function in forecasting and a better adaptability to different types of urban rail transit stations.…”
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SegMatch: semi-supervised surgical instrument segmentation by Meng Wei, Charlie Budd, Luis C. Garcia-Peraza-Herrera, Reuben Dorent, Miaojing Shi, Tom Vercauteren

“…For binary segmentation tasks, our algorithm was evaluated on the MICCAI Instrument Segmentation Challenge datasets, Robust-MIS 2019 and EndoVis 2017. …”
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Naive Bayes Analysis for Nutritional Fulfillment Prediction in Children by Satrio Agung Wicaksono, Satrio Hadi Wijoyo, Fatmawati Fatmawati, Tri Afirianto, Diva Kurnianingtyas, Mochammad Chandra Saputra

“…Six test scenarios were conducted using different percentages of training data (90%, 80%, 70%, 60%, 50%, and 40%) to evaluate the reliability of the Naïve Bayes method. …”
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“Decision Tree” as a Tool for Analyzing the Psychodiagnostics Data Latent Connections by Elena V. Slavutskaya, Leonid A. Slavutsky

“…The study demonstrated the possibilities of the proposed data processing algorithm for evaluating the latent connections between psychological characteristics and for building the hierarchical structures corresponding to vertical system data analysis. …”
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Energy Management of a 1 MW Photovoltaic Power-to-Electricity and Power-to-Gas for Green Hydrogen Storage Station by Dalila Hidouri, Ines Ben Omrane, Kassmi Khalil, Adnen Cherif

“…Dynamic performance assessments were conducted under different conditions of solar radiation, temperature, and load, demonstrating the system’s adaptability. …”
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Improving Scheduling Efficiency: A Mathematical Approach to Multi-Operation Optimization in MSMEs by Reyner Pérez-Campdesuñer, Alexander Sánchez-Rodríguez, Margarita De Miguel-Guzmán, Gelmar García-Vidal, Rodobaldo Martínez-Vivar

“…A multi-criteria optimization method with weighted aggregation was used, employing either an exhaustive search or a heuristic algorithm with nested loops, in which multiple possible combinations of operational sequences were evaluated, considering several key indicators and their respective weights. …”
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Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models by Junchen Pan, Zhen Yue, Jing Ji, Yongping You, Liqing Bi, Yun Liu, Xinglin Xiong, Genying Gu, Ming Chen, Shen Zhang

“…Despite differences in populations and algorithms, the models we constructed demonstrated reliable predictive performance.…”
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Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction by Zhengbiao Hu, Shuangshan Bu, Kai Wang, Qianqian Cao, Huanhuan Zheng, Jie Yang, Shanshan Chen, Yuemeng Wu, Wenkai Ren, Chenlei He

“…In this study, multiple machine learning algorithms were used to develop a non-invasive and improved model for the diagnosis of IgAN. …”
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AI-Driven Drought Monitoring: Advanced Machine Learning Techniques for Early Prediction by Vij Priya, Tiwari Ankita

“…Moreover, the research conducts a comparative evaluation of various machine learning models to identify the most effective algorithms for different climatic zones and crop types. …”
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Forest cover restoration analysis using remote sensing and machine learning in central Malawi by Jabulani Nyengere, Precious Masuku, Sylvester Chikabvumbwa, Weston Mwase, Msaiwale Kathewera, Allena Laura Njala, Wilson Tchongwe, Isaac Tchuwa, Tiwonge I Mzumara, Chikondi Chisenga, Wilfred Kadewa, Emmanuel Chinkaka, Harineck Tholo

“…Utilizing a Support Vector Machine (SVM) classification algorithm applied to time-series Landsat and high-resolution imagery (2003–2023), we quantify land cover changes, while Normalized Difference Vegetation Index (NDVI) trends serve as indicators of ecological recovery. …”
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Detection and Analysis of Malicious Software Using Machine Learning Models by Selman Hızal, Ahmet Öztürk

“…Utilizing a real-world dataset of prevalent malware types such as spyware, ransomware, and trojan horses, our study addresses the evolving challenges of cybersecurity. In this study, we evaluate the performance of ML algorithms for obfuscated malware detection using the CIC-MalMem-2022 dataset. …”
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Recent Advancements in Neuroimaging‐Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e‐Health: A Systematic Review by Zia‐Ur‐Rehman, Mohd Khalid Awang, Ghulam Ali, Muhammad Faheem

“…This comprehensive review intends to examine current developments in deep learning (DL) approaches with neuroimaging for AD diagnosis, where popular imaging types, reviews well‐known online accessible data sets, and describes different algorithms used in DL for the correct initial evaluation of AD are presented. …”
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Data-Driven Approaches for Estimation of EV Battery SoC and SoH: A Review by Shahid Gulzar Padder, Jayesh Ambulkar, Atul Banotra, Sudhakar Modem, Sidharth Maheshwari, Kolleboyina Jayaramulu, Chinmoy Kundu

“…Data-driven estimation using Machine learning algorithms demonstrates superior accuracy and adaptability in sophisticated battery management systems. …”
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Implementation Of Deep Learning Using Convolutional Neural Network Method In A Rupiah Banknote Detection System For Those With Low Vision by Dinul Akhiyar, Tukino Tukino, Sarjon Defit

“…For validation, 140 test images were utilized, which yielded an impressive mAP value of 97.5%. To further evaluate the system's reliability, tests were conducted under varying conditions, such as banknotes with creases, folds, or different lighting scenarios. …”
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Artificial Intelligence in Healthcare: Innovation and Impact in Medical Practice by Amelia-Maria ISAC, Corina VERNIC

“…One limitation of this study is the variability in dataset sizes and the potential lack of standardization in feature selection across different AI models. …”
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Hybrid Attention-Enhanced Xception and Dynamic Chaotic Whale Optimization for Brain Tumor Diagnosis by Aliyu Tetengi Ibrahim, Ibrahim Hayatu Hassan, Mohammed Abdullahi, Armand Florentin Donfack Kana, Amina Hassan Abubakar, Mohammed Tukur Mohammed, Lubna A. Gabralla, Mohamad Khoiru Rusydi, Haruna Chiroma

“…Our proposed method was evaluated on benchmark datasets achieving remarkable accuracies of 99.67%, 99.09%, and 99.67% compared to the classical algorithms.…”
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Methylated SEPTIN9 plasma test for colorectal cancer detection may be applicable to Lynch syndrome by Maria Christensen, Andrew E Hendifar, Rachel Pearlman, Sigurdis Haraldsdottir, Albert de la Chapelle, Heather Hampel, Megan P Hitchins, Ingrid P Vogelaar, Kevin Brennan, Nianmin Zhou, Brock Martin, Rocio Alvarez, Xiaopu Yuan, Sungjin Kim, Maha Guindi, Matthew F Kalady, Jennifer DeVecchio, James M Church, Carrie Snyder, Stephen J Lanspa, Robert W Haile, Henry T Lynch

“…Secondly, to detect mSEPT9 as circulating tumor DNA, the plasma-based mSEPT9 test was retrospectively evaluated in LS subjects using the Epi proColon 2.0 CE assay adapted for 1mL plasma using the “1/1 algorithm”. …”
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Pure data correction enhancing remote sensing image classification with a lightweight ensemble model by Huaxiang Song, Hanglu Xie, Yingying Duan, Xinyi Xie, Fang Gan, Wei Wang, Jinling Liu

“…Abstract The classification of remote sensing images is inherently challenging due to the complexity, diversity, and sparsity of the data across different image samples. Existing advanced methods often require substantial modifications to model architectures to achieve optimal performance, resulting in complex frameworks that are difficult to adapt. …”
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Development of <i>OptiCon</i>: A Mathematical Model with a Graphical User Interface for Designing Sustainable Portland Cement Concrete Mixes with Budget Constraint by Angie Pineda, Rita Peñabaena-Niebles, Gilberto Martínez-Arguelles, Rodrigo Polo-Mendoza

“…The proposed model, denominated <i>OptiCon</i>, employs the Life-Cycle Assessment and Life-Cycle Costs Analysis methodologies to evaluate the incorporation of three different SCMs (i.e., fly ash, silica fume, and steel slag) and RCA into PCC mixes. …”
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A Hybrid Method Combining Variational Mode Decomposition and Deep Neural Networks for Predicting PM2.5 Concentration in China by Senlin Li, Bo Tang, Xiaowu Deng

“…To demonstrate the effectiveness of VDPS, we conducted comparative evaluations of different models&#x2019; performance on many experimental datasets of PM2.5 concentrations in four cities: Beijing, Shanghai, Guangzhou, and Chengdu. …”
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