Prediction of 1p/19q state in glioma by integrated deep learning method based on MRI radiomics by Fengda Li, Zeyi Li, Hong Xu, Gang Kong, Ze Zhang, Kaiyuan Cheng, Longyuan Gu, Lei Hua

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Toward value-based care using cost mining: cost aggregation and visualization across the entire colorectal cancer patient pathway by Maura Leusder, Sven Relijveld, Derya Demirtas, Jon Emery, Michelle Tew, Peter Gibbs, Jeremy Millar, Victoria White, Michael Jefford, Fanny Franchini, Maarten IJzerman

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Machine learning, clinical-radiomics approach with HIM for hemorrhagic transformation prediction after thrombectomy and treatment by Sheng Hu, Junyu Liu, Jiayi Hong, Yuting Chen, Ziwen Wang, Jibo Hu, Shiying Gai, Xiaochao Yu, Jingjing Fu

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Evaluation of the DendrisKIT^®DP for the Diagnosis of Superficial Fungal Infections by Pauline Tirard-Collet, François Durupt, Marion Hérault, Charline Miossec, Jean-Philippe Lemoine, Martine Wallon, Damien Dupont, Florence Persat, Jean Menotti

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Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and laboratory pa... by Jing Zhang, Cheng Zhang, Xi Zhang, Wuchen Yang, Jingya Liu, Yang Gou, Xingqin Huang, Maoshan Chen, Dezhi Huang, Shengwang Wu, Shuiqing Liu, Xiangui Peng

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Developing a novel diagnostic model for identifying high-risk plaques in new onset unstable angina pectoris using coronary CT angiography by Hui Li, Hui Li, Yao Li, Zhuoya Yao, Bin Chen, Shaohuan Qian, Miaonan Li, Miaonan Li, Hongju Wang, Hongju Wang

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Screening Model for Bladder Cancer Early Detection With Serum miRNAs Based on Machine Learning: A Mixed‐Cohort Study Based on 16,189 Participants by Cong Lai, Zhensheng Hu, Jintao Hu, Zhuohang Li, Lin Li, Mimi Liu, Zhikai Wu, Yi Zhou, Cheng Liu, Kewei Xu

“…Furthermore, the selected model's screening performance was evaluated using both the validation set and external set. Results The BCaS3miR model, utilizing only three serum miRNAs (miR‐6087, miR‐1343‐3p, and miR‐5100) and based on the KNN algorithm, is the superior screening model chosen for BCa. …”
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Spatial analysis of air pollutant exposure and its association with metabolic diseases using machine learning by Jingjing Liu, Chang Liu, Zhangdaihong Liu, Yibin Zhou, Xiaoguang Li, Yang Yang

“…(ii) dimensionality reduction and spatial similarities identification between APs and MDs clusters using Principal Component Analysis (PCA), k-means clustering, and Jaccard index calculations, further validated through spatial maps. (iii) AP exposure is adjusted by demographic and lifestyle confounders to predict MDs using machine learning models (e.g., eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), LightGBM, and Multi-Layer Perceptron (MLP)). …”
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Tool wear prediction based on XGBoost feature selection combined with PSO-BP network by Zhangwen Lin, Yankun Fan, Jinling Tan, Zhen Li, Peng Yang, Hua Wang, Weiwei Duan

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Hyperspectral imagery quality assessment and band reconstruction using the prophet model by Ping Ma, Jinchang Ren, Zhi Gao, Yinhe Li, Rongjun Chen

“…In a head‐to‐head comparison, the framework against six state‐of‐the‐art band reconstruction algorithms including three spectral methods, two spatial‐spectral methods and one deep learning method is benchmarked. …”
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Assessment of flight fatigue using heart rate variability and machine learning approaches by Dalong Guo, Cong Wang, Yufei Qin, Lamei Shang, Aijing Gao, Baosen Tan, Yubin Zhou, Guangyun Wang

“…A subset of HRV features and the respiratory metric were used as input variables for four machine learning algorithms: decision tree, support vector machine, K-nearest neighbor, and light gradient-boosting machine (LightGBM). …”
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Development and testing of an open source mobile application for audiometry test result analysis and diagnosis support by Michał Kassjański, Marcin Kulawiak, Tomasz Przewoźny, Dmitry Tretiakow, Andrzej Molisz

“…The application workflow is divided into three main stages: scanning, digitalization and classification of the audiogram. …”
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Enhanced Feature Selection via Hierarchical Concept Modeling by Jarunee Saelee, Patsita Wetchapram, Apirat Wanichsombat, Arthit Intarasit, Jirapond Muangprathub, Laor Boongasame, Boonyarit Choopradit

“…The presented methods are evaluated based on all learned attributes with 10 datasets from the UCI Machine Learning Repository by using three classification algorithms, namely decision trees, support vector machines (SVM), and artificial neural networks (ANN). …”
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Oversampling and undersampling for intrusion detection system in the supervisory control and data acquisition IEC 60870‐5‐104 by M. Agus Syamsul Arifin, Deris Stiawan, Bhakti Yudho Suprapto, Susanto Susanto, Tasmi Salim, Mohd Yazid Idris, Rahmat Budiarto

“…Gradient boosting, decision tree, and random forest algorithms are used as classifiers for the intrusion detection system models. …”
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A Blending Ensemble Approach to Predicting Student Dropout in Massive Open Online Courses (MOOCs) by Muhammad Ricky Perdana Putra, Ema Utami

“…This research builds a BEL model consisting of two layers including base model with KNN, Decision Tree, and Naïve Bayes algorithms, then meta model with XGBoost. …”
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Propensity to childhood anxiety and depression due to exposure to adversity: A multidimensional construct. by Angélica C G Santos, Ariane Silva, Matheus Libório, Cristiane Neri Nobre, Hasheem Mannan, Marcos Flávio S V D'Angelo

“…The literature does not present a consensus on the composition of indicators that make predictions, prognostic algorithms, or management strategies in mental health promotion and prevention. …”
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The value of radiomics features of white matter hyperintensities in diagnosing cognitive frailty: a study based on T2-FLAIR imaging by Qinmei Liao, Xihao Hu, Zhiqiong Jiang, Xiaoyun Huang, Jiacheng Guo, Yuanzhong Zhu, Wenjing He

“…Optimal radiomic features were extracted and selected from T2-FLAIR images, and multiple logistic regression analysis was utilized to identify independent risk factors. Three machine learning algorithms—K-Nearest Neighbors (KNN), Logistic Regression (LR), and Support Vector Machine (SVM)—were used to construct radiomic models, clinical models, and combined models. …”
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Developing and validating an artificial intelligence-based application for predicting some pregnancy outcomes: a multi-phase study protocol by Fatemeh Shabani, Ata Jodeiri, Sakineh Mohammad‑Alizadeh‑Charandabi, Fatemeh Abbasalizadeh, Jafar Tanha, Mojgan Mirghafourvand

“…If successful, this application could support personalized obstetric decision-making.…”
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Dynamic Spectrum Coexistence of NR-V2X and Wi-Fi 6E Using Deep Reinforcement Learning by Kashish D. Shah, Dhaval K. Patel, Brijesh Soni, Siddhartan Govindasamy, Mehul S. Raval, Mukesh Zaveri

“…The proposed algorithm is implemented through extensive simulations using the Network Simulator-3 (ns-3), integrated with a custom Deep Reinforcement Learning (DRL) framework developed using OpenAIGym. …”
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A systematic review of data privacy in Mobility as a Service (MaaS) by Zineb Garroussi, Antoine Legrain, Sébastien Gambs, Vincent Gautrais, Brunilde Sansò

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