Towards a Holistic Approach for UAV-Based Large-Scale Photovoltaic Inspection: A Review on Deep Learning and Image Processing Techniques by Zoubir Barraz, Imane Sebari, Kenza Ait El Kadi, Ibtihal Ait Abdelmoula

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Breast Lesion Detection Using Weakly Dependent Customized Features and Machine Learning Models with Explainable Artificial Intelligence by Simona Moldovanu, Dan Munteanu, Keka C. Biswas, Luminita Moraru

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Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES by Ningyi Cheng, Yukun Chen, Lei Jin, Liangwan Chen

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Attention-driven multi-model architecture for unbalanced network traffic intrusion detection via extreme gradient boosting by Oluwadamilare Harazeem Abdulganiyu, Taha Ait Tchakoucht, Ahmed El Hilali Alaoui, Yakub Kayode Saheed

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Advanced biomedical imaging for identifying blood cell type: Integrating segmentation, feature extraction, and GraphSAGE model by Nur Mohammad Fahad, Mohaimenul Azam Khan Raiaan, Arefin Ittesafun Abian, Ripon Kumar Debnath, Sidratul Montaha, Mirjam Jonkman, Sami Azam

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Space-scale exploration of the poor reliability of deep learning models: the case of the remote sensing of rooftop photovoltaic systems by Gabriel Kasmi, Laurent Dubus, Yves-Marie Saint-Drenan, Philippe Blanc

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Predicting Financial Failure: Empirical Evidence from Publicly – Quoted Firms in Developed and Developing Countries by Serpil Altınırmak, Yavuz Gül

“…This paper analyzes the data of 570 firms from developed and developing countries between 2010 and 2019 in an attempt to create high–accuracy financial failure prediction models. In this sense, we utilize three different methods, namely logistic regression (LR), artificial neural networks (ANN), and decision trees (DT), and compare the classification accuracy performances of these techniques. …”
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Deep learning-based tumor segmentation and radiogenomic model for predicting EGFR amplification and assessing intratumoural heterogeneity in glioblastoma by Jianpeng Liu, Chuyun Shen, Shufan Jiang, Yanfei Wu, Jiaqi Tu, Yifang Bao, Haiqing Li, Na Wang, Ying Liu, Ji Xiong, Xueling Liu, Yuxin Li

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XGBoost-based machine learning model combining clinical and ultrasound data for personalized prediction of thyroid nodule malignancy by Wenhan Li, Wenhan Li, Yajing Zhou, Ziyu Luo, Ziyu Luo, Miao Tan, Miao Tan, Rui Yin, Jianhui Li, Jianhui Li

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Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models by Youzhi Lian, Yinyu Shi, Haibin Shang, Hongsheng Zhan

“…From these genes, 45 machine learning models were constructed using different combinations of feature selection methods and classification algorithms. …”
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Automated interpretation of PD-L1 CPS based on multi-AI models integration strategy in gastric cancer by Ting Han, Meng Zhuo, Ziyu Song, Peilin Chen, Shiting Chen, Wei Zhang, Yuanyuan Zhou, Hong Li, Dadong Zhang, Xiaolin Lin, Zebing Liu, Xiuying Xiao

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Predictive modeling and interpretative analysis of risks of instability in patients with Myasthenia Gravis requiring intensive care unit admission by Chao-Yang Kuo, Emily Chia-Yu Su, Hsu-Ling Yeh, Jiann-Horng Yeh, Hou-Chang Chiu, Chen-Chih Chung

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Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model by Fuyu Guo, Shiwei Sun, Xiaoqian Deng, Yue Wang, Wei Yao, Peng Yue, Shaoduo Wu, Junrong Yan, Junrong Yan, Junrong Yan, Xiaojun Zhang, Xiaojun Zhang, Xiaojun Zhang, Yangang Zhang, Yangang Zhang, Yangang Zhang

“…The samples were randomly divided into training and test sets in a 7:3 ratio. Dimensionality reduction and feature selection were performed using the least absolute shrinkage and selection operator (LASSO) regression model, and other methods. …”
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An Integrative Machine Learning Model for Predicting Early Safety Outcomes in Patients Undergoing Transcatheter Aortic Valve Implantation by Abilkhair Kurmanaliyev, Kristina Sutiene, Rima Braukylienė, Ali Aldujeli, Martynas Jurenas, Rugile Kregzdyte, Laurynas Braukyla, Rassul Zhumagaliyev, Serik Aitaliyev, Nurlan Zhanabayev, Rauan Botabayeva, Yerlan Orazymbetov, Ramunas Unikas

“…A fined-tuned random forest (RF) machine learning model was developed to predict early safety outcomes, defined as all-cause mortality, stroke, life-threatening bleeding, acute kidney injury (stage 2 or 3), coronary artery obstruction requiring intervention, major vascular complications, and valve-related dysfunction requiring repeat procedures. …”
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Modelling the Trend of Zagros Forest Degradation using Logistic Regression (Case study: Chardavol Forest of Ilam province) by Ali Mahdavi, Somayeh Rangin, Hossein Mehdizadeh, Vahid Mirzaei Zadeh

“…Then, forest cover changes map and physiographic (slope, aspect, altitude) and human (distance to road and distance to residential areas) variables were integrated into regression logistic model. 3-Results and Discussion The results of the supervised classification in the studied area were compared and statistically analyzed for classification accuracy using general and Kappa reliability coefficients, as the images of years 1997 and 2014 had a total accuracy of 86.11 and 86.39%, respectively. …”
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Providing a model of consumer behavior in creating brand attachment with an emphasis on the packaging component of food industry companies by maziar Ghasemzadeh Sangroudi, karim hamdi, SHADAN VAHABZADEH MUNSHI

“…The results of the quantitative part of the research showed that the proposed model has good fit and validity. Conclusion Based on the results of qualitative analysis, three main dimensions including packaging, brand attachment and consumer behavior were identified. …”
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Risk stratification system and visualized dynamic nomogram constructed for predicting postoperative pneumonia in geriatric patients with hip fracture by Wanyun Tang, Yudong Hou, Chenglin Bai, Yaohua Shang

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Multilayer Network Modeling for Brand Knowledge Discovery: Integrating TF-IDF and TextRank in Heterogeneous Semantic Space by Peng Xu, Rixu Zang, Zongshui Wang, Zhuo Sun

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Improved landslide susceptibility assessment: A new negative sample collection strategy and a comparative analysis of zoning methods by Jiani Wang, Yunqi Wang, Manyi Li, Zihan Qi, Cheng Li, Haimei Qi, Xiaoming Zhang

“…Taking Fengjie County, Chongqing City, China as the study area, this study proposes three negative sample collection strategies based on slope unit, buffer zone, and information value, and combines them with C5.0 decision tree (DT) model respectively to construct an LSA model. …”
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Development of a predictive model for risk factors of multidrug-resistant bacterial pneumonia in critically ill post-neurosurgical patients by Aixiang Hu, Dayan Ma, Yanni Lei, Fangqiang Li, Xi Wang, Yuewei Zhang

“…However, existing prediction frameworks exhibit limitations in elucidating the relative importance of risk factors, thereby impeding precise clinical decision-making and individualized patient management.ObjectiveTo evaluate the performance of six ensemble classification algorithms and three single classification algorithms in predicting MDR-BP risk factors among neurosurgical postoperative critically ill patients, identify the optimal predictive model, and determine key influential factors.MethodsWe conducted a retrospective study involving 750 neurosurgical patients admitted to a neurosurgery center at a tertiary hospital in Beijing between January 2020 and December 2023. …”
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