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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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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Multiobjective Optimization for Topology and Coverage Control in Wireless Sensor Networks by Seyed Mahdi Jameii, Karim Faez, Mehdi Dehghan

“…However, transmission range reduction and smaller number of active nodes can affect the network topology and may cause the network to be disconnected. …”
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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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Refined Assessment Method of Offshore Wind Resources Based on Interpolation Method by Wenchuan Meng, Zaimin Yang, Zhi Rao, Shuang Li, Xin Lin, Jingkang Peng, Yuwei Cao, Yingquan Chen

“…To enhance the prediction accuracy of offshore wind speed, this study employs an interpolation algorithm to improve spatial resolution based on the ERA5 reanalysis dataset. …”
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Multi-Objective Optimization of the Forming Process Parameters of Disc Forgings Based on Grey Correlation Analysis and the Response Surface Method by Shizhong Wei, Yuna Liang, Hao Li, Guizhong Xie, Feng Mao, Ji Zhang

“…Then, a quadratic polynomial prediction model based on the RSM was constructed, and its accuracy was ensured through model validation. …”
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Optimization of Injection-Molding Process Parameters for Weight Control: Converting Optimization Problem to Classification Problem by Peng Zhao, Zhengyang Dong, Jianfeng Zhang, Yi Zhang, Mingyi Cao, Zhou Zhu, Hongwei Zhou, Jianzhong Fu

“…A particle swarm optimization (PSO) algorithm contributes to the tuning of the hyperparameters of the SVC model in order to minimize the error between the SVC prediction results and the experimental results. …”
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A balanced sensor scheduling for multitarget localization in a distributed multiple-input multiple-output radar network by Chenggang Wang, Zengfu Wang, Xiong Xu, Yuhang Hao

“…The effectiveness and the efficiency of the proposed algorithm are demonstrated in simulated examples.…”
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