Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach by Illia Mushta, Sulev Koks, Anton Popov, Oleksandr Lysenko

“…To ensure interpretability, we applied the local interpretable model-agnostic explainer (LIME), identifying contralateral putamen SBR as the most predictive feature for distinguishing PD from healthy controls. …”
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High-dimensional outlier detection based on deep belief network and linear one-class SVM by Haoqi LI, Na YING, Chunsheng GUO, Jinhua WANG

“…Aiming at the difficulties in high-dimensional outlier detection at present,an algorithm of high-dimensional outlier detection based on deep belief network and linear one-class SVM was proposed.The algorithm firstly used the deep belief network which had a good performance in the feature extraction to realize the dimensionality reduction of high-dimensional data,and then the outlier detection was achieved based on a one-class SVM with the linear kernel function.High-dimensional data sets in UCI machine learning repository were selected to experiment,result shows that the algorithm has obvious advantages in detection accuracy and computational complexity.Compared with the PCA-SVDD algorithm,the detection accuracy is improved by 4.65%.Compared with the automatic encoder algorithm,its training time and testing time decrease significantly.…”
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Machinability Assessment and Multi-Objective Optimization of Graphene Nanoplatelets-Reinforced Aluminum Matrix Composite in Dry CNC Turning by Nikolaos A. Fountas, Dimitrios E. Manolakos, Nikolaos M. Vaxevanidis

“…The results indicated that the feed rate was the dominant parameter affecting both objectives, namely the main cutting force and surface roughness, while the NSGA-II algorithm was capable of delivering advantageous solutions for enhancing machinability with less than 10% error predictions when comparing simulated and actual machining results.…”
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China’s county-level monthly CO2 emissions during 2013–2021 by Ming Gao, Chaofan Tu, Miaomiao Liu, Jiandong Chen, Xingyu Chen, Hong Zou, Thomas Shiu Tong, Long Chen, Shuke Fu

“…After the main feature variables were identified, a hybrid regression algorithm combining deep neural networks and CatBoost was constructed to generate instrumental variable for predicting CO2 emissions. …”
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Estimating Economic Insights: A Machine Learning Method for Estimating the Shanghai Stock Exchange by Reza Seifi Majdar, Seyed Hadi Seyed Hatami

“…This work aims to create an accurate hybrid model for predicting stock prices which includes Adaptive Boosting, Slime mould algorithm, and Empirical mode decomposition (EMD) to forecast the stock market values. …”
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Assessing the generalization capabilities of TCR binding predictors via peptide distance analysis. by Leonardo V Castorina, Filippo Grazioli, Pierre Machart, Anja Mösch, Federico Errica

“…This could then be used to estimate a confidence score on predictions on novel and unseen peptides, based on how different they are from the training ones. …”
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Fine monitoring method for mangrove wetland ecosystem based on sentinel data fusion and RF optimization by Shuwen Wang, Taisi Chen, Zimin Li, Haitao Sang

“…The accuracy rate in biomass prediction and environmental data classification was as high as 98%, highlighting its potential to completely change mangrove monitoring through precise land feature identification and ecological protection applications. …”
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Glucagon-like peptide-1 receptor agonist use prior to spinal surgery results in reduced postoperative length of stay: A propensity-score matched analysis by Samuel N. Goldman, BS, Kyle Mani, BE, Thomas Scharfenberger, BS, Emily Kleinbart, BA, Aaron T. Hui, BS, Rafael De la Garza Ramos, MD, Mitchell S. Fourman, MD, MPhil, Ananth S. Eleswarapu, MD

“…GLP-1 RA use was associated with a significant reduction in median postoperative LOS (3 days vs. 4 days; p = 0.036), particularly among patients undergoing lumbar fusion. …”
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From Tweets to Threats: A Survey of Cybersecurity Threat Detection Challenges, AI-Based Solutions and Potential Opportunities in X by Omar Alsodi, Xujuan Zhou, Raj Gururajan, Anup Shrestha, Eyad Btoush

“…The findings indicate that current studies often lack comprehensive evaluations of critical aspects such as prediction scope, types of cybersecurity threats, feature extraction techniques, algorithm complexity, information summarization levels, scalability over time, and performance measurements. …”
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Network intrusion detection model using wrapper based feature selection and multi head attention transformers by Muhammad Umer, Muhammad Tahir, Muhammad Sardaraz, Muhammad Sharif, Hela Elmannai, Abeer D. Algarni

“…The model uses a wrapper-based feature selection technique using machine learning algorithms to select the best features, which are then combined and fed into a Multi-Head Attention-based transformer for getting the predictions. …”
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Honeybee Colony Growth Period Recognition Based on Multivariate Temperature Feature Extraction and Machine Learning by Chuanqi Lu, Lin Li, Denghua Li, Qiuying Huang, Wei Hong

“…The results demonstrate that the proposed features can effectively characterize the growth period of bee colonies, and the BP method performs best in predicting growth period categories, with an MAE of only 1.45%. …”
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Open-Loop Control System for High Precision Extrusion-Based Bioprinting Through Machine Learning Modeling by Javier Arduengo, Nicolas Hascoet, Francisco Chinesta, Jean-Yves Hascoet

“…Thus, the control system offers predictability and adaptability capabilities to ensure the consistent production of high-quality bioprinted structures. …”
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Dim and Small Target Detection Based on Improved Bilateral Filtering and Gaussian Motion Probability Estimation by Fan Xiangsuo, Qin Wenlin, Feng Gaoshan, Huang Qingnan, Min Lei

“…Six scenes and eleven comparison algorithms are selected for experiments, experimental results show the effectiveness and robustness of the proposed algorithm.…”
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Symmetry-Based Data Augmentation Method for Deep Learning-Based Structural Damage Identification by Long Li, Xiaoming Tao, Hui Song, Xiaolong Li, Zhilong Ye, Yao Jin, Qiuyu He, Shiyin Wei, Wenli Chen

“…Finally, a probabilistic generative model based on a deep belief network (DBN) is developed to predict damage locations and degrees. The proposed methods are validated using vibration data from a numerical three-span continuous bridge subjected to random vehicle excitations. …”
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Hypothesis: the generation of T cells directed against neoepitopes employing immune-mediating agents other than neoepitope vaccines by Renee N Donahue, Jeffrey Schlom, James L Gulley, James W Hodge, Claudia Palena, Duane H Hamilton, Sofia R Gameiro

“…As with all cancer therapy modalities, neoepitope vaccine development and delivery also has some drawbacks, including the level of effort to develop a patient-specific product, accuracy of algorithms to predict neoepitopes, and with the exception of melanoma and some other tumor types, biopsies of metastatic lesions of solid tumors are often not available. …”
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Novel Data-Driven PDF Modeling in FGM Method Based on Sparse Turbulent Flame Data by Guihua Zhang, Jiayue Liu, Yuxin Wu, Guangxi Yue

“…The results demonstrate that the random forest algorithm represents the optimal choice when both training complexity and predictive performance are comprehensively considered. …”
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Improved Electrochemical–Mechanical Parameter Estimation Technique for Lithium-Ion Battery Models by Salvatore Scalzo, Davide Clerici, Francesca Pistorio, Aurelio Somà

“…Accurate and predictive models of lithium-ion batteries are essential for optimizing performance, extending lifespan, and ensuring safety. …”
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Design and Assessment of an Austenitic Stainless Alloy for Laser Powder Bed Additive Manufacturing by Mariam Assi, Julien Favre, Marcin Brykala, Franck Tancret, Anna Fraczkiewicz

“…This method integrates a suite of predictive tools, including machine learning, calculation of phase diagrams (CALPHAD) and physical models, all piloted by a multi-objective genetic algorithm. …”
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Machine Learning in Public Governance: A Systematic Review of Applications, Trends and Challenges by Y. Nuruly, G. N. Sansyzbayeva, L. Z. Ashirbekova, S. K. Tazhiyeva

“…Despite significant progress in the models’ technical implementation and predictive accuracy, in many cases, mechanisms for equity, transparency, and citizen participation have been poorly implemented. …”
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Advancing Agricultural Machinery Maintenance: Deep Learning-Enabled Motor Fault Diagnosis by Xusong Bai, Qian Chen, Xiangjin Song, Weihang Hong

“…These technologies offer reliable and efficient solutions for predictive maintenance in agricultural machinery, enabling proactive intervention in the early stages of catastrophic failures. …”
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