Potentialities and development of groundwater resources applying machine learning models in the extended section of Manbhum-Singhbhum Plateau, India by Arijit Ghosh, Biswajit Bera

“…In this study, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) algorithms have been applied. The accuracy of each model has been estimated using the receiver operating characteristics (ROC) curve. …”
Get full text

AI-Driven Future Farming: Achieving Climate-Smart and Sustainable Agriculture by Karishma Kumari, Ali Mirzakhani Nafchi, Salman Mirzaee, Ahmed Abdalla

“…Using sophisticated algorithms to predict soil conditions, improving agricultural yield projections, diagnosing water stress from sensor data, and identifying plant diseases and weeds through image recognition, crop mapping, and AI-guided crop selection are some of the main applications investigated. …”
Get full text

Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning by Abimbola Efunogbon, Enjie Liu, Renxie Qiu, Taiwo Efunogbon

“…The framework leverages the LazyPredict module to automatically select optimal supervised learning algorithms based on real-time network conditions and historical data. …”
Get full text

Hybrid Cat-Transmon Architecture for Scalable, Hardware-Efficient Quantum Error Correction by Connor T. Hann, Kyungjoo Noh, Harald Putterman, Matthew H. Matheny, Joseph K. Iverson, Michael T. Fang, Christopher Chamberland, Oskar Painter, Fernando G.S.L. Brandão

“…We numerically estimate logical memory performance, finding significant overhead reductions in comparison to architectures without biased noise. …”
Get full text

Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI by Dayeong An, El-Sayed Ibrahim

“…Cardiac MRI was performed 8 and 10 weeks post-treatment to assess global and regional cardiac function. ML algorithms were applied to differentiate sham-treated and irradiated rats based on early changes in myocardial function. …”
Get full text

(Meta)verse as the next escaper from competition public enforcement by Luca Megale

“…Moving on, it is asserted as tacit collusion might be a crucial issue due to algorithms and blockchain. Metaverse might also be characterized by repeating concerns. …”
Get full text

Unravelling NK cell subset dynamics and specific gene signatures post-ibrutinib therapy in chronic lymphocytic leukaemia via single-cell transcriptomics by Chunlan Liu, Tianjian Ding, Rong Zou, Aili Zhang, Zhengzhuo Zhi, Sili Wang

“…A cell subset-specific novel index (CNI) was constructed based on these core genes and was shown to be capable of predicting responses to immunotherapy. Oncopredictive algorithms and molecular docking screenings further identified semaxanib and ulixertinib as potential therapeutic candidates for CLL. …”
Get full text

Machine learning-based prognostic modeling integrating PANoptosis in head and neck squamous cell carcinoma by Chen Li, Jiajing Lu, Jialin Zhu, Tao Zhou, Qijie Shen, Bikun Huang, Qingsong Li

“…A prognostic model was constructed using 101 machine learning algorithms and their combinations, with TCGA-HNSCC as the training set and GSE41613 and GSE65858 as validation sets. …”
Get full text

AI-assisted ophthalmic imaging for early detection of neurodegenerative diseases by Hajar Nasir Tukur, Olivier Uwishema, Hatice Akbay, Dalal Sheikhah, Inês Filipa Silva Correia

“…Future research should focus on refining AI algorithms, integrating multimodal imaging techniques, and developing predictive biomarkers to optimize early intervention strategies for neurodegenerative diseases. …”
Get full text

Integrative multi-omics analysis reveals the role of toll-like receptor signaling in pancreatic cancer by Jie Peng, Jiaao Sun, Youfeng Yu, Qihang Yuan, Yong Zhang

“…The model showed strong performance in predicting the survival state of pancreatic cancer samples. …”
Get full text

Effective Machine Learning Techniques for Dealing with Poor Credit Data by Dumisani Selby Nkambule, Bhekisipho Twala, Jan Harm Christiaan Pretorius

“…The differences in performance of all seven machine learning algorithms are significant at the 95% level.…”
Get full text

Мethods of Machine Learning in Ophthalmology: Review by D. D. Garri, S. V. Saakyan, I. P. Khoroshilova-Maslova, A. Yu. Tsygankov, O. I. Nikitin, G. Yu. Tarasov

“…Machine learning includes models and algorithms that mimic the architecture of biological neural networks. …”
Get full text

Identification and experimental validation of BMX as a crucial PANoptosis‑related gene for immune response in Spinal Cord Injury. by Tianbao Feng, Jiating Hu, Mi Xie, Guodong Shi, Qi Wang, Jingyuan Yao, Xiaoqin Liu

“…Additionally, a protein-protein interaction (PPI) network was established to identify hub genes, and 8 machine learning algorithms were used to narrowed down hub genes. BMX and CASP5 were consistently identified across all algorithms. …”
Get full text

Delivering data: A real-world dataset for last-mile delivery optimizationZenodo by Anna Vrani, Savvas D. Apostolidis, Athanasios Ch. Kapoutsis, Elias B. Kosmatopoulos

“…The collected matrices were processed and structured for direct use in VRP algorithms.The dataset offers substantial reuse potential by serving as a benchmark for evaluating VRP algorithms, enabling the comparison of optimization methods based on real-world logistics problems. …”
Get full text

Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach by Li X, Xiao Y, Yang M, Zhang X, Yuan Z, Zhang Z, Zhang H, Liu L, Zhao M

“…Functional enrichment analyses were conducted to explore the biological roles of these genes. LASSO and SVM-RFE algorithms identified shared diagnostic genes, which were evaluated using receiver operating characteristic (ROC) curves. …”
Get full text

Integrating machine learning with mendelian randomization for unveiling causal gene networks in glioblastoma multiforme by Lixin Du, Pan Wang, Xiaoting Qiu, Zhigang Li, Jianlan Ma, Pengfei Chen

“…Methods This study employed a comprehensive analysis approach integrating 113 machine learning algorithms with Mendelian Randomization (MR) analysis to investigate the molecular underpinnings of GBM. …”
Get full text

A multi-model approach for estimation of ash yield in coal using Fourier transform infrared spectroscopy by Sameeksha Mishra, Anup K. Prasad, Arya Vinod, Anubhav Shukla, Shailayee Mukherjee, Bitan Purkait, Atul K. Varma, Bhabesh C. Sarkar

“…Multiple algorithms, including piecewise linear regression (PLR), artificial neural networks (ANN), partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF), were utilized to predict the ash yield in coal. …”
Get full text

An artificial intelligence model integrating culprit lesion diagnosis and risk assessment for acute coronary syndromeResearch in context by Peng Peng Xu, Bin Hu, Fan Zhou, Zhi Han Xu, Qian Chen, Tong Yuan Liu, Bang Jun Guo, Chang Sheng Zhou, Xin Wei Tao, Hong Yan Qiao, Jia Ni Zou, Xiang Ming Fang, Wen Cai Huang, Long Jiang Zhang

“…The diagnostic performance of five commonly used ML algorithms for identifying ACS culprit lesions was developed and compared within this cohort. …”
Get full text

Committee Machine Learning for Electrofacies-Guided Well Placement and Oil Recovery Optimization by Adewale Amosu, Dung Bui, Oluwapelumi Oke, Abdul-Muaizz Koray, Emmanuel Appiah Kubi, Najmudeen Sibaweihi, William Ampomah

“…In this study, we develop a workflow that mitigates the variability in results produced by different clustering algorithms using a committee machine. Using several unsupervised machine learning methods, including k-means, k-median, hierarchical clustering, spectral clustering, and the Gaussian mixture model, we predict electrofacies from wireline well log data and generate their 3D vertical and lateral distributions and inferred geological properties. …”
Get full text

Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets by Catarina Lopes, Andreia Brandão, Manuel R. Teixeira, Mário Dinis-Ribeiro, Carina Pereira

“…Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. …”
Get full text