Machine Learning for Prediction of Relapses in Multiple Drug Resistant Tuberculosis Patients by A. S. Аlliluev, O. V. Filinyuk, E. E. Shnаyder, S. V. Аksenov

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Heuristic XAI: AIME and HMM Integration Improved-Feature-Importance Assessment Through Heuristics by Takafumi Nakanishi, Ponlawat Chophuk, Krisana Chinnasarn

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Features of single treasury account management in the context of budget funds liquidity management by N. S. Sergienko

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Interpretable machine learning for predicting optimal surgical timing in polytrauma patients with TBI and fractures to reduce postoperative infection risk by Xing Han, Jia-Hui Zhang, Xin Zhao, Xi-Guang Sang

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Towards Economic Sustainability: A Comprehensive Review of Artificial Intelligence and Machine Learning Techniques in Improving the Accuracy of Stock Market Movements by Atoosa Rezaei, Iheb Abdellatif, Amjad Umar

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Optimizing coverage in wireless sensor networks using deep reinforcement learning with graph neural networks by G. Pushpa, R. Anand Babu, S. Subashree, S. Senthilkumar

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Role of green infrastructure planning in achieving sustainable development goals through an environmental efficiency lens: An integrated literature review by Zih-Hong Lin, Shawn W. Laffan, Graciela Metternicht

“…The key findings highlight that a) GI can contribute directly to SDG 2, 3, 6, 10, 11, 13, 14, and 15; b) GI has the potential to benefit 32 SDG targets; c) the Data Envelopment Analysis (DEA) method may be suitable for assessing environmental efficiency; and d) integrating machine learning algorithms with DEA can enhance the reliability of environmental efficiency measurement. …”
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BoneCLIP-XGBoost: A Multimodal Approach for Bone Fracture Diagnosis by Zhihao Su, Yuhuai Zhou, Jianqun Zhou, Hanhua Cao, Huanping Zhang

“…The integration of visual and textual data in medical diagnostics holds significant potential for improving the accuracy and reliability of clinical decision-making. Classical algorithms like CLIP, ConVIRT, and MedCLIP have made strides in this direction by leveraging multimodal data, but they face several challenges. …”
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Integrating Machine Learning and IoT for Effective Plant Disease Management by Bhoi Manjulata, Dubey Ahilya

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Presepsin in Hepatic Pathology: Bridging the Gap in Early Sepsis Detection by Dana-Maria Bilous, Mihai Ciocîrlan, Cătălina Vlăduț, Carmen-Georgeta Fierbințeanu-Braticevici

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Approach or avoidance? Relationship between perceived AI explainability and employee job crafting by Weiwei Huo, Jiaying Xie, Jiaqi Yan, Tianyi Long, Bingqian Liang

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Advances in Breast Cancer Care: The Role of Artificial Intelligence and Digital Pathology in Precision Medicine by Ayşe Hümeyra Dur Karasayar, İbrahim Kulaç, Nilgün Kapucuoğlu

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Prediction of tea leaf characteristics using spectral data and machine learning techniques by Sum Tateh, Suyog Balasaheb Khose, Damodhara Rao Mailapalli, Chandranath Chatterjee, Narendra Singh Raghuwanshi

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Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinoma by Yu Zhang, Long Liu, Mengyu Han, Linrui Li, Qibing Wu, Xin Wang

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Analyzing Data Incompleteness for MRI Data for Quality Enhancement by Sanjay Shanbhag, Supreetha Raju, Varadraj P. Gurupur, S. Sowmya Kamath, Rajesh N. V. P. S. Kandala, Elizabeth A. Trader, Shyam Lal

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Development and validation of a modified SOFA score for mortality prediction in candidemia patients by Xiaofei Liu, Ranran Ding, Guangming Yang, Yuling Qiao, Zhen Ma, Yaping Feng, Feng Qu, Qiang Meng

“…These findings highlight the mSOFA_3 as a robust and clinically relevant tool for early risk stratification, offering potential for improved decision-making and therapeutic management in critically ill patients with candidemia.…”
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Radiomics in pediatric brain tumors: from images to insights by Pranjal Rai, Sabha Ahmed, Abhishek Mahajan

“…By integrating radiomics with machine learning algorithms, studies have demonstrated strong performance in classifying tumor types such as medulloblastoma, ependymoma, and gliomas, and predicting molecular subgroups and mutations such as H3K27M and BRAF. …”
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MetaStackD A robust meta learning based deep ensemble model for prediction of sensors battery life in IoE environment by D. Gayathri, S. P. Shantharajah

“…The data gathered helps for monitoring, decision-making, and automation of smart systems in multi-disciplinary domains. …”
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Jet substructure probe on scalar leptoquark models via top polarization by Anupam Ghosh, Partha Konar, Tousik Samui, Ritesh K. Singh

“…The analysis has been performed using both fixed radius and dynamic radius jet clustering algorithms. A multivariate analysis using a boosted decision tree (BDT) is performed to isolate signal from the Standard Model background. …”
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Ensemble learning guided survival prediction and chemotherapy benefit analysis in high-grade chondrosarcoma: A study based on the surveillance, epidemiology, and end results (SEER)... by Xu Zheng, Longqiang Shu, Shanyi Lin, Hanqiang Jin, Xiaoyu Wang, Ting Yuan

“…Ensemble learning has better overall performance than single computational approaches for clinical decision. The primary objective was to select prognostic variables and develop optimal ensemble learning algorithms for survival prediction and analyzing chemotherapy benefit in high-grade chondrosarcoma. …”
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