A Non-Invasive and Highly Accurate Multi-Wavelength Light Near-Infrared Glucose Sensor Using A Multilevel Metric Learning–Back Propagation Network by Yuwei Chen, Chenxi Li, Bo Gao, Huangrong Xu, Weixing Yu

“…Finally, the optimized data were utilized as the BP network input to predict blood glucose concentrations. The predicted results showed that the factor analysis algorithm had the best performance in our HMML-BP network and that all the predicted glucose values fell into region A, with a mean absolute relative difference of 9.98%, meeting the requirements of daily glucose monitoring. …”
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Development of an alkaliptosis-related lncRNA risk model and immunotherapy target analysis in lung adenocarcinoma by Xiang Xiong, Wen Liu, Chuan Yao

“…Immune cell infiltration and Tumor Mutational Burden (TMB) analyses were carried out using the CIBERSORT and maftools algorithms. Finally, the “oncoPredict” package was employed to predict immunotherapy sensitivity and to further forecast potential anti-tumor immune drugs. qPCR was used for experimental verification.ResultsWe identified 155 alkaliptosis-related lncRNAs and determined that 5 of these lncRNAs serve as independent prognostic factors. …”
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Impact of climate change on the potential global prevalence of Macrophomina phaseolina (Tassi) Goid. under several climatological scenarios by Peter F. Farag, Dalal Hussien M. Alkhalifah, Shimaa K. Ali, Aya I. Tagyan, Wael N. Hozzein

“…Maximum Entropy (MaxEnt) model was used to predict the spatial distribution of this fungus throughout the world while algorithms of DIVA-GIS were chosen to confirm the predicted model.ResultsBased on the Jackknife test, minimum temperature of coldest month (bio_6) represented the most effective bioclimatological parameter to fungus distribution with a 52.5% contribution. …”
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Efficiently Learning an Encoder That Classifies Token Replacements and Masked Permuted Network-Based BIGRU Attention Classifier for Enhancing Sentiment Classification of Scientific... by Muhammad Inaam Ul Haq, Khalid Mahmood, Qianmu Li, Ashok Kumar Das, Sachin Shetty, Majid Hussain

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Use of artificial intelligence in the diagnosis, treatment and surveillance of patients with kidney cancer by E. Yu. Timofeeva, С. R. Azilgareeva, A. O. Morozov, M. S. Taratkin, D. V. Enikeev

“…AI shows great prospects in the diagnosis urinary system lesions, in the ability to differentiate benign and malignant neoplasm (due to machine learning systems), as well as in predicting the histological subtype of the tumor. AI can be used at the intraoperative stage (thanks to the integration of virtual 3D models during surgical interventions), which reduces the frequency of thermal ischemia and damage to the kidney cavity system. …”
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Engineering Synthetic Microbial Communities: Diversity and Applications in Soil for Plant Resilience by Arneeb Tariq, Shengzhi Guo, Fozia Farhat, Xihui Shen

“…Artificial intelligence-driven models can predict complex microbial interactions, while machine learning algorithms can analyze vast datasets to identify key microbial taxa and their functions. …”
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Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability Analysis by Renhai Feng, Khan Wajid, Muhammad Faheem, Jiang Wang, Fazal E. Subhan, Muhammad Shoaib Bhutta

“…Moreover, its emphasis the importance of computed and estimated indices obtained through UPINN for predicting voltage collapse occurrences within the system.…”
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Real-time congestion control using cascaded LSTM deep neural networks for deregulated power markets by G. Madhu Mohan, T. Anil Kumar, A. Srujana, Yasser Fouad, Alexey Mikhaylov, Nora Baranyai, Kitmo, Ch. Rami Reddy

“…The proposed CM system consists of three cascaded long short-term memory (LSTM) DNNs that operate sequentially. These LSTM modules predict congestion status, violated power, and adjusted active power for rescheduling generation. …”
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Dynamic control of a 10 MW solar-autothermal hybrid biomass gasifier for round-the-clock processing with stable syngas production by Axel Curcio, Sylvain Rodat, Valéry Vuillerme, Stéphane Abanades

“…Insufficient solar power is dynamically counterbalanced by in situ oxy-combustion, to maintain the reaction temperature at 1200 K and the total H2 + CO flowrate production at 1000 NL/s. A Model Predictive Control (MPC) algorithm is thus implemented, and the feasibility of hybridized operation is demonstrated on a second-per-second basis. …”
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Defect intelligent recognition of membrane product based on deep learning by Maonian Wu, Ling Li, Wei Peng, Tao Wu, Jinwei Yu, Bo Zheng, Shaojun Zhu

“…This module is designed to efficiently capture tiny defects in images, prioritize defect information features, and ultimately enhance the model’s predictive performance and accuracy in defect recognition tasks. …”
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Robust Framework for PMU Placement and Voltage Estimation of Power Distribution Network by Nida Khanam, Mohd. Rihan, Salman Hameed

“…Future research will look into real-time adaptive state estimation utilizing machine learning-based predictive modelling, as well as expanding the framework to account for communication constraints and dynamic grid topology changes to improve its practical usefulness.…”
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Implementation of laser-light backscattering imaging for authentication of the geographic origin of Indonesia region citrus by Muhammad Achirul Nanda, S. Rosalinda, Rahmat Budiarto, Inna Novianty, Taufik Ibnu Salim, Pradeka Brilyan Purwandoko, Dimas Firmanda Al Riza

“…The proposed LLBI technique applied a laser wavelength of 450 nm and a polynomial kernel function as the best combination to produce reliable predictive power. This study held valuable implications for advancing sensing technology devices to authenticate geographic origin, specifically citrus fruit.…”
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Estimation of performance measures in a novel M/M/1 queueing model with reverse balking: A simulation-based approach by Asmita Tamuli, Dhruba Das, V. Deepthi, Amit Choudhury, Dibyajyoti Bora, Bhushita Patowari, Supahi Mahanta

“…Moreover, Bayesian estimates yield lower RMSE compared to ML estimates, highlighting their superior accuracy and robustness. Additionally, predictive probabilities for the number of customers in the system are obtained. …”
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The future of precision orthopaedics: personalized data-driven practice by Deiary F. Kader, Andrew Coppola, Aditya Vijay, Andreas Fontalis, Fares S. Haddad

“…AI technologies, such as machine learning and Bayesian networks, enable predictive analytics, treatment personalization, and real-time data integration, fostering a shift from reactive to proactive care. …”
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Bio-Inspired Compliant Joints and Economic MPC Co-Design for Energy-Efficient, High-Speed Locomotion in Snake-like Robots by Shuai Zhou, Gengbiao Chen, Mingyu Gong, Jing Liu, Peng Xu, Binshuo Liu, Nian Yin

“…The proposed predictive control strategy optimizes locomotion speed while minimizing energy consumption. …”
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Pioneering CPMI framework for accurate state-of-health assessment in Lithium ion battery power management using FBG sensors by Taher M. Ghazal, Ali Q. Saeed, Mosleh M. Abualhaj, Taj-Aldeen Naser Abdali, Munir Ahmad

“…Integrating FBG sensors with the CPMI framework provides a robust Li-ion battery SOH monitoring solution, enabling predictive maintenance strategies and enhanced power management capabilities. …”
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Unveiling diagnostic biomarkers and therapeutic targets in lung adenocarcinoma using bioinformatics and experimental validation by Sixuan Wu, Yuanbin Tang, Qihong Pan, Yaqin Zheng, Yeru Tan, Junfan Pan, Yuehua Li

“…In addition, a machine learning model constructed based on Stepglm[backward] with the random forest algorithm achieved the highest C-index (0.999) and screened eight core genes, among which ST14 was noted for its excellent predictive ability. …”
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Automated Class Imbalance Learning via Few-Shot Multi-Objective Bayesian Optimization With Deep Kernel Gaussian Processes by Zhaoyang Wang, Shuo Wang, Damien Ernst, Chenguang Xiao

“…However, real-world applications often involve multiple, conflicting objectives—such as predictive performance and computational cost—that must be jointly optimized. …”
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Snow Depth Retrieval Using Sentinel-1 Radar Data: A Comparative Analysis of Random Forest and Support Vector Machine Models with Simulated Annealing Optimization by Yurong Cui, Sixuan Chen, Guiquan Mo, Dabin Ji, Lansong Lv, Juan Fu

“…Overall, the SVM model exhibits deficiencies in both predictive accuracy and spatial stability. This study provides a valuable reference for snow depth retrieval research based on active microwave remote sensing techniques.…”
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PhyIndBC: Development of a machine learning tool for screening of potential breast cancer inhibitors from phytochemicalsGitHub by Agneesh Pratim Das, Subhash M. Agarwal

“…., k-nearest neighbor (KNN), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) were combined with various molecular fingerprints (MACCS and Morgan2) to develop multiple predictive models. Among these models, the RF algorithm coupled with the MACCS fingerprint emerged as the best performing model. …”
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