Optimizing XGBoost Hyperparameters for Credit Scoring Classification Using Weighted Cognitive Avoidance Particle Swarm by Atul Vikas Lakra, Sudarson Jena, Kaushik Mishra

“…The proposed WCAPSO-XGB model tunes the hyperparameters of XGBoost and classifies the credit scoring, and the experimental results are compared with various classifier such as Random Forest (RF), K-neighbors (KNN), Gaussian Naive Bayes (NB), AdaBoost, Gradient Boosting, Logistic Regression (LR), Neural Network (NN), Decision Tree (DT) and Linear Discriminant Analysis (LDA), and hyperparameter optimization methods, such as Grid Search (GS), Random Search (RS), Bays Optimization, Optuna Optimization, Hybrid Snake Optimizer Algorithm (HSOA), Exploratory Cuckoo Search, island Cuckoo Search (iCSPM and iCSPM2), and Improved SSA (ISSA) with HDPM, on four different datasets with a varying number of instances from small to large. …”
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Risk factors and an interpretability tool of in-hospital mortality in critically ill patients with acute myocardial infarction by Rui Yang, Tao Huang, Renqi Yao, Di Wang, Yang Hu, Longbing Ren, Shaojie Li, Yali Zhao, Zhijun Dai

“…Conclusion: We built the BRF model and the web-based tool by the model algorithm. The model effect has been verified externally. …”
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Comparing and Combining Artificial Intelligence and Spectral/Statistical Approaches for Elevating Prostate Cancer Assessment in a Biparametric MRI: A Pilot Study by Rulon Mayer, Yuan Yuan, Jayaram Udupa, Baris Turkbey, Peter Choyke, Dong Han, Haibo Lin, Charles B. Simone

“…Recently, a different spectral/statistical approach has been used to successfully evaluate spatially registered biparametric MRIs for prostate cancer. …”
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Initial experience of parenchyma-sparing liver resection with systematic selective hepatic vein reconstruction for colorectal metastases by Yevhenii Trehub, Åsmund Avdem Fretland, Artem Zelinskyi, Dzmitrii Kharkov, Oleksii Babashev, Dmytro Chieverdiuk, Artem Shchebetun, Kyrylo Khyzhniak, Maksym Pavlovskii, Andrii Strokan, Sergii Zemskov

“…Objectives This study aims to assess the feasibility and short-term and intermediate-term technical success rate of the concept of systematic selective hepatic vein (HV) reconstruction for parenchyma-sparing hepatectomies (PSHs) in patients with colorectal liver metastases (CRLM) in accordance with stage 2a of the IDEAL framework.Design The prospective case series of patients deemed eligible and operated on according to the concept.Setting All patients were treated by a single surgical team in three hospitals in Ukraine from June 2022 to November 2023.Participants The study included nine cases of resectable CRLM with at least one lesion located in the hepatocaval confluence with HV(s) invasion, for whom reconstruction of the HV(s) allowed for additional parenchyma preservation, being an alternative to major or extended hepatectomy.Interventions Liver resections with different types of HVs reconstruction (primary closure, patching, end-to-end anastomosis with or without grafting) were performed after a thorough evaluation of the future liver remnant volume, volume of potentially additionally preserved parenchyma and possibility of future repeat hepatectomies.Main outcome measures Postoperative morbidity, short-term and long-term patency of the reconstructed vessels, and the volume of additionally preserved parenchyma were the focus.Results Segmental resection was performed in four cases, two with graft interposition. …”
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Sustainable analysis of COVID-19 Co-packaged paxlovid: exploring advanced sampling techniques and multivariate processing tools by Shymaa S. Soliman, Nisreen F Abo- Talib, Mohamed R. Elghobashy, Mona A. Abdel Rahman

“…During the current study, a pioneering statistical technique namely, Latin Hypercube Sampling (LHS) was integrated with different multivariate chemometric models namely; Partial Least Squares (PLS), Genetic Algorithm‑Partial Least Squares (GA-PLS), Artificial Neural Networks (ANN), and Multivariate Curve Resolution‑Alternating Least Squares (MCR-ALS). …”
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The application of artificial intelligence models in predicting the risk of diabetic foot: a multicenter study by Yao Li, Siyuan Zhou, Bichen Ren, Shuai Ju, Xiaoyan Li, Wenqiang Li, Bingzhe Li, Yunmin Cai, Chunlei Chang, Lihong Huang, Zhihui Dong

“…Future work will focus on algorithm optimization, expanded datasets, and real-time monitoring integration to enable more precise, dynamic risk evaluation for improved DF prevention and early intervention.…”
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Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin by Pei Zhang, Qiong Chen, Jiahui Lao, Juan Shi, Jia Cao, Xiao Li, Xin Huang

“…The machine learning models were developed using five different algorithms, including logistic regression (LR), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting model (XGBoost), and light gradient boosting machine (Light GBM).ResultsThe XGBoost model exhibited the most superior performance in predicting amikacin-associated AKI among the developed machine learning models. …”
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Partial-linear single-index Cox regression models with multiple time-dependent covariates by Myeonggyun Lee, Andrea B. Troxel, Sophia Kwon, George Crowley, Theresa Schwartz, Rachel Zeig-Owens, David J. Prezant, Anna Nolan, Mengling Liu

“…We developed an iterative estimation algorithm using spline techniques to model the nonparametric single-index component for potential nonlinear effects, followed by maximum partial likelihood estimation of the parameters. …”
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Results of the surgical treatment of diffuse pigmented villonodular synovitis (diffuse-type tenosynovial giant-cell tumor) of the knee by S. I. Herasymenko, O. A. Kostogryz, Yu. O. Kostogryz, A. M. Babko, V. M. Mayko

“…Using the method of mathematical statistics to evaluate the results obtained, we see that scores obtained through the Lysholm Scoring Scale were statistically significantly different in stages before a surgery (P = 0.000782), 3 months (P = 0.00005) and 6 months after the surgery (P = 0.04); but over time, these differences diminished and became actually insignificant 12 months after the surgery (P = 0.89). …”
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Combining machine learning with UAV derived multispectral aerial images for wheat yield prediction, in southern Brazil by Henrique dos Santos Felipetto, Erivelto Mercante, Octavio Viana, Adão Robson Elias, Giovani Benin, Lucas Scolari, Arthur Armadori, Diandra Ganascini Donato

“…This research aims to evaluate the performance of machine learning algorithms and multispectral aerial images in estimating wheat grain yield, contributing to the eradication of hunger and food security. …”
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An Adaptive Framework for Collective Anomaly Detection in Key Performance Indicators From Mobile Networks by Madalena Cilinio, Thaina Saraiva, Marco Sousa, Pedro Vieira, Antonio Rodrigues

“…Then, representative KPIs from each cluster are selected to evaluate the anomaly detection performance using two algorithms: the Smart Trouble Ticket Management (STTM) and STUMPY. …”
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Closing the multichannel gap through computational reconstruction of interaction in super-resolution microscopy by Ben Cardoen, Hanene Ben Yedder, Ivan Robert Nabi, Ghassan Hamarneh

“…To evaluate these and future approaches, we introduce a mathematical framework, simplifying evaluation of existing and future algorithms. …”
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Enhancing acute leukemia classification through hybrid fuzzy C means and random forest methods by K. Lakshmi Narayanan, R. Santhana Krishnan, Y. Harold Robinson, S. Vimal, Tarik A. Rashid, Chetna Kausha, Md. Mehedi Hassan

“…In this proposed method the classification is tested with two Machine Learning algorithms which are Hybrid Fuzzy C Means (FCM) and Random Forest algorithm (RF) and Support Vector Machine for the detection and classification of Acute Leukemia disease and their performance was evaluated. …”
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FPGA-oriented lightweight multi-modal free-space detection network by Feiyi Fang, Junzhu Mao, Wei Yu, Jianfeng Lu

“…A real-time application of scene segmentation on KITTI-Road is used to evaluate our algorithm, and the model achieves a $ 94.39\% $ MaxF score and minimum 14 ms runtime on 20W FPGA devices.…”
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Enhancing stochastic optimal power flow with modified cheetah optimizer for integrating renewable energy sources by Majid Saeidi, Taher Niknam, Mohsen Zare, Zulfiqar Ali Memon

“…The MCO methodology was applied to various objective functions such as overall operating cost, voltage deviation, pollutant emissions, and power loss, which were evaluated under different cases. Regarding the valve point effect observed in case 1, the optimal response provided by MCO amounts to $781.9862. …”
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Digital Low-Altitude Airspace Unmanned Aerial Vehicle Path Planning and Operational Capacity Assessment in Urban Risk Environments by Ouge Feng, Honghai Zhang, Weibin Tang, Fei Wang, Dikun Feng, Gang Zhong

“…In the implementation of path planning, compared with the A* and Weight-A* algorithms, the Parallel-A* algorithm demonstrates clear advantages in terms of lower average comprehensive risk and fewer turning points. …”
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Power Management Controller for Microgrid Integration of Hybrid PV/Fuel Cell System Based on Artificial Deep Neural Network by Abdulbaset Abdulhamed Mohamed Nureddin, Javad Rahebi, Adel Ab-BelKhair

“…After developing and simulating the proposed system, we performed the analysis in different possible operating conditions. Finally, we evaluated the simulation outcomes based on IEEE 1547 and 519 standards to prove the system’s effectiveness.…”
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Effects of PET image reconstruction parameters and tumor-to-background uptake ratio on quantification of PET images from PET/MRI and PET/CT systems by Amena Ali Hussain, Eva Forssell-Aronsson, Tobias Rosholm, Esmaeil Mehrara

“…This study aims to evaluate how reconstruction algorithms and lesion radioactivity levels affect PET image quality and quantitative accuracy across three different PET systems. …”
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Optimized exposer region-based modified adaptive histogram equalization method for contrast enhancement in CXR imaging by Shivam Gangwar, Reeta Devi, Nor Ashidi Mat Isa

“…Each region undergoes adaptive contrast enhancement via a novel weighted probability density function (PDF) and power-law transformation to ensure balanced enhancement across different exposure levels. The PSO algorithm is then employed to optimize power-law parameters, further refining contrast enhancement and illumination uniformity while maintaining the natural appearance of medical images. …”
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Symbiotic Learning Grey Wolf Optimizer for Engineering and Power Flow Optimization Problems by Aala Kalananda Vamsi Krishna Reddy, Komanapalli Venkata Lakshmi Narayana

“…SL-GWO is tested and validated through a series of benchmarking, engineering and real-world optimization problems and compared against the standard version of GWO, eight of its latest and state-of-the-art variants and five modern meta-heuristics. Different testing scenarios are considered to analyze and evaluate the performance of the proposed method such as the effect of dimensionality (CEC2018 benchmarking suite), convergence speeds, avoidance of local entrapment (CEC2019 benchmarking suite) and constrained optimization problems (four standard engineering problems). …”
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