Review on the Innovation of Investment Banks’ Credit Risk Assessment System in a Highly Volatile Market by Wei Yakun

“…Challenges such as model interpretability and data quality persist, necessitating future research on explainable AI and ESG integration. …”
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Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool by Hatem Ali, Arun Shroff, Tibor Fülöp, Miklos Z. Molnar, Adnan Sharif, Bernard Burke, Sunil Shroff, David Briggs, Nithya Krishnan

“…However, the discriminative or calibration capacity of the currently employed models are limited. We set out to apply artificial intelligence (AI) algorithms to create a highly predictive risk stratification indicator, applicable to the UK’s transplant selection process.Methodology: Pre-transplant characteristics from 12,661 live-donor kidney transplants (performed between 2007 and 2022) from the United Kingdom Transplant Registry database were analyzed. …”
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The vessel movement optimisation with excessive control by С. Зинченко, А. Бень, П. Носов, И. Попович, В. Матейчук, О. Грошева

“…It is concluded that the development of such systems is relevant. Mathematical, algorithmic, and software have been developed for an onboard controller simulator of a vessel’s motion control system with excessive control, the operability and efficiency of which has been verified by numerical simulation in a closed circuit with a mathematical model of the control object for various types of vessels, navigation areas and weather conditions. …”
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PD-L1 expression predicts the efficacy of PD-1 blockade plus chemotherapy versus chemotherapy alone in treatment-naïve advanced or metastatic gastric cancer: a pooled analysis of r... by Wei Zhou, Zeng-Zhi Cai, Zhuolin Fan, Xu Zheng, Yu-Tong Chen

“…Treatment effects in PD-L1-high and PD-L1-low subgroups were evaluated using Cox proportional hazards models with shared frailty to account for between-study heterogeneity. …”
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Streamlining Thoracic Radiotherapy Quality assurance: One-Class Classification for Automated OAR Contour Assessment by Yihao Zhao BSc, Cuiyun Yuan MSc, Ying Liang PhD, Yang Li MSc, Chunxia Li MSc, Man Zhao MSc, Jun Hu PhD, Ningze Zhong Bsc, Wei Liu PhD, Chenbin Liu PhD

“…Purpose Automating quality assurance (QA) for contours generated by automatic algorithms is critical in radiotherapy treatment planning. …”
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Attention mechanism and mixup data augmentation for classification of COVID-19 Computed Tomography images by Özgür Özdemir, Elena Battini Sönmez

“…Latest achievements of deep learning algorithms suggest the use of deep Convolutional Neural Network to implement a computer-aided diagnostic system for automatic classification of COVID-19 CT images. …”
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The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma by Atsuto Katano

“…Clinical studies have demonstrated notable benefits, such as a reduction in contouring time and improved dosimetric quality using machine learning–based optimization algorithms. …”
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Automated differentiation of wide QRS complex tachycardia using QRS complex polarity by Adam M. May, Bhavesh B. Katbamna, Preet A. Shaikh, Sarah LoCoco, Elena Deych, Ruiwen Zhou, Lei Liu, Krasimira M. Mikhova, Rugheed Ghadban, Phillip S. Cuculich, Daniel H. Cooper, Thomas M. Maddox, Peter A. Noseworthy, Anthony Kashou

“…Abstract Background Wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) remains challenging despite numerous 12-lead electrocardiogram (ECG) criteria and algorithms. Automated solutions leveraging computerized ECG interpretation (CEI) measurements and engineered features offer practical ways to improve diagnostic accuracy. …”
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Parametric Optimization and Assessment of Modern Heritage Shading Screen for a Mid-Rise Building in Arid Climate: Modernizing Traditional Designs by Anwar Ahmad, Lindita Bande, Waleed Ahmed, Kheira Tabet Aoul, Mukesh Jha

“…This research explores how parametric design and optimization based on genetic algorithms (GAs) can improve shading structures to reduce solar radiation and lower cooling energy consumption. …”
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Advancement of artificial intelligence based treatment strategy in type 2 diabetes: A critical update by Aniruddha Sen, Palani Selvam Mohanraj, Vijaya Laxmi, Sumel Ashique, Rajalakshimi Vasudevan, Afaf Aldahish, Anupriya Velu, Arani Das, Iman Ehsan, Anas Islam, Sabina Yasmin, Mohammad Yousuf Ansari

“…At the same time, the rapidly increasing role of AI in diabetes care is woven into the story, mainly targeting how insulin therapy can be modified and personalized through algorithms and predictive modelling. It leaves a deep review of their pre-existing synergies, which helps understand how collaborative opportunities will unlock the future of T2DM care. …”
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CoMSeC: A Comparative Analysis of Various Service Classification Techniques by Malabika Das, Ansh Sarkar, Sujata Swain

“…This paper aims to formulate a novel classification model and conduct a comparative study to analyze the performance of base models with various classical and modern clustering algorithms. …”
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Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies by Rahul Kumar, Ethan Waisberg, Joshua Ong, Phani Paladugu, Dylan Amiri, Jeremy Saintyl, Jahnavi Yelamanchi, Robert Nahouraii, Ram Jagadeesan, Alireza Tavakkoli

“…Additionally, next-generation PET tracers targeting misfolded proteins, such as tau and alpha-synuclein, along with inflammatory markers, enhance the visualization and quantification of pathological processes in vivo. Deep learning models, including convolutional neural networks and multimodal transformers, further improve diagnostic accuracy by integrating multimodal imaging data and predicting disease progression. …”
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Predicting cognitive decline in cognitively impaired patients with ischemic stroke with high risk of cerebral hemorrhage: a machine learning approach by Eun Namgung, Young Sun Kim, Sun U. Kwon, Dong-Wha Kang, Dong-Wha Kang

“…Four machine learning algorithms were trained, Categorical Boosting (CatBoost), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), and logistic regression, to predict cognitive decliners, defined as a decline of ≥3 K-MMSE points over 9 months, and ranked variable importance using the SHapley Additive exPlanations methodology.ResultsCatBoost outperformed the other models in classifying cognitive decliners within 9 months. …”
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Conformal prediction for uncertainty quantification in dynamic biological systems. by Alberto Portela, Julio R Banga, Marcos Matabuena

“…These approaches can provide non-asymptotic guarantees, improving robustness and scalability across various applications, even when the predictive models are misspecified. …”
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Temporal Attention-Enhanced Stacking Networks: Revolutionizing Multi-Step Bitcoin Forecasting by Phumudzo Lloyd Seabe, Edson Pindza, Claude Rodrigue Bambe Moutsinga, Maggie Aphane

“…The proposed Temporal Attention-Enhanced Stacking Network (TAESN) integrates the complementary strengths of diverse machine learning algorithms while emphasizing critical temporal features, leading to substantial improvements in forecasting accuracy over traditional methods. …”
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Optimized hybrid SVM-RF multi-biometric framework for enhanced authentication using fingerprint, iris, and face recognition by Sonal, Ajit Singh, Chander Kant

“…The integration of support vector machine (SVM) and random forest (RF) classifiers, along with optimization techniques like bacterial foraging optimization (BFO) and genetic algorithms (GA), improves efficiency and robustness. …”
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Contributions to the Development of Tetrahedral Mobile Robots with Omnidirectional Locomotion Units by Anca-Corina Simerean, Mihai Olimpiu Tătar

“…The second prototype is presented as an advanced and improved version of the first model, integrating significant modifications in both the structural design and the robot’s functionality. …”
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Understanding discrepancies in soil moisture from SMAP and AMSR2: insights into performance and dry-down behavior by Zhiqing Peng, Tianjie Zhao, Jiancheng Shi, Lu Hu, Thomas J. Jackson, Michael H. Cosh, Hui Lu, Xiaodong Gao, Jingyao Zheng, Panpan Yao, Qian Cui, Peng Guo, Peilin Song, Zushuai Wei, Mengjia Wang, Anmin Fu

“…These results highlight the importance of considering differences in payload configurations when using remote sensing SM products for studies of land–atmosphere interactions in hydrometeorology and for improving land surface models (LSMs).…”
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Estimating Winter Canola Aboveground Biomass from Hyperspectral Images Using Narrowband Spectra-Texture Features and Machine Learning by Xia Liu, Ruiqi Du, Youzhen Xiang, Junying Chen, Fucang Zhang, Hongzhao Shi, Zijun Tang, Xin Wang

“…Subsequently, machine learning algorithms were applied to develop estimation models for winter canola biomass. …”
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Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence by Marco Zeppieri, Lorenzo Gardini, Carola Culiersi, Luigi Fontana, Mutali Musa, Fabiana D’Esposito, Pier Luigi Surico, Caterina Gagliano, Francesco Saverio Sorrentino

“…Results: Convolutional neural networks (CNNs) and other deep learning algorithms are among the AI models included in this paper that have been shown to have excellent sensitivity and specificity in identifying glaucomatous alterations in fundus photos, OCT scans, and visual field tests. …”
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