Artificial neural networking for computational assessment of ternary hybrid nanofluid flow caused by a stretching sheet: implications of machine-learning approach by Imad Khan, M. Waleed Ahmed Khan

“…Researchers are mainly interested in using soft computing artificial intelligence (AI) methods due to their broad applications in analysis, modelling and simulations. Backpropagation neural networks, one of the supervised learning algorithms, is commonly used to train data networks by optimizing the error between actual and predicted values. …”
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Machine learning-based evaluation of performance of silicon nitride waveguide fabrication: Gradient-boosted forests for predicting propagation and bend excess losses by Hinum-Wagner Jakob Wilhelm, Hoermann Samuel Marko, Feigl Gandolf, Schmidt Christoph, Kraft Jochen, Bergmann Alexander

“…The impact of waveguide geometry and layer properties on loss was examined using a full factorial design of experiment. These machine learning models’ predictive accuracy and ability to capture complex relationships between fabrication parameters and different loss mechanisms were assessed. …”
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Optimization method for main parameters of vibration protection system in motor grader seat with quasi-zero static characteristic by M. S. Korytov, I. E. Kashapova, V. S. Shcherbakov

“…Obtaining each individual value of the target function in the local optimization was performed by processing the results of discrete values of seat acceleration obtained by simulating the movement of the machine on a simulation mathematical model. …”
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Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma by Adane Adugna, Gashaw Azanaw Amare, Mohammed Jemal

“…Genomic signatures play important roles in addressing this issue. Recently, machine learning (ML) models and bioinformatics analysis have become very important in discovering novel genomic signatures for the early diagnosis, treatment, and prognosis of HBV-induced hepatic cell remodeling and HCC. …”
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A Three-Level Meta-Frontier Framework with Machine Learning Projections for Carbon Emission Efficiency Analysis: Heterogeneity Decomposition and Policy Implications by Xiaoxia Zhu, Tongyue Feng, Yuhe Shen, Ning Zhang, Xu Guo

“…Methodologically, we introduce two novel projection combinations—“exogenous-exogenous-accumulation (E-E-A) and exogenous-exogenous-consistent (E-E-C)”—to resolve the inconsistency of technology gap ratios (TGRs > 1) in traditional nonradial directional distance function (DDF) models. Reinforcement learning (RL) optimizes dynamic direction vectors, whereas graph neural networks (GNNs) encode spatial interdependencies to constrain the TGR within [0, 1]. …”
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Using machine learning to reveal seasonal nutrient dynamics and their impact on chlorophyll-a levels in lake ecosystems: A focus on nitrogen and phosphorus by Yong Fang, Ruting Huang, Xianyang Shi

“…Furthermore, we focus on the application of three machine-learning models (i.e., eXtreme Gradient Boosting, Support Vector Machines, and Naive Bayes Classifier) to analyze the seasonal nutrient dynamics in lake ecosystems. …”
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Determination of future gully erosion risk and its spatially quantitative interpretability of driving factors in regional scale using machine learning algorithms by Xin Liu, Dichen Wang, Mingming Guo, Xingyi Zhang, Zhuoxin Chen, Zhaokai Wan, Jielin Liu

“…This study proposed a new gully erosion risk modeling (GERM) method by combining GESM reflecting gully erosion potential and gully density modeling (GDM) reflecting current gully erosion situation in the whole rolling hilly region of northeast China with an area of 177,584 km2. …”
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Comparing sparse inertial sensor setups for sagittal-plane walking and running reconstructions by Eva Dorschky, Marlies Nitschke, Matthias Mayer, Ive Weygers, Heiko Gassner, Heiko Gassner, Thomas Seel, Bjoern M. Eskofier, Bjoern M. Eskofier, Anne D. Koelewijn, Anne D. Koelewijn

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Predicting Surface Roughness and Grinding Forces in UNS S34700 Steel Grinding: A Machine Learning and Genetic Algorithm Approach to Coolant Effects by Mohsen Dehghanpour Abyaneh, Parviz Narimani, Mohammad Sadegh Javadi, Marzieh Golabchi, Samareh Attarsharghi, Mohammadjafar Hadad

“…Learning from machine models like the Gaussian process regression exhibited stability, with an R² value of 0.98 and a mean accuracy of 93 percent. …”
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Advancements and gaps in natural language processing and machine learning applications in healthcare: a comprehensive review of electronic medical records and medical imaging by Priyanka Khalate, Shilpa Gite, Shilpa Gite, Biswajeet Pradhan, Chang-Wook Lee

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Role of oxidative balance score in staging and mortality risk of cardiovascular-kidney-metabolic syndrome: Insights from traditional and machine learning approaches by Yang Chen, Shuang Wu, Hongyu Liu, Ziyi Zhong, Tommaso Bucci, Yimeng Wang, Manlin Zhao, Yang Liu, Zhengkun Yang, Ying Gue, Garry McDowell, Bi Huang, Gregory Y.H. Lip

“…Additionally, mediation analyses were performed to explore whether OBS mediated the relationships between specific predictors (Life's Simple 7 score [LS7], systemic immune-inflammation index [SII], frailty score) and mortality outcomes. Then, machine learning models were developed to classify CKM stages 3/4 and predict all-cause mortality, with SHapley Additive exPlanations values used to interpret the contribution of OBS components. …”
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Stochastic Optimal Power Flow of Wind Turbine through Flexible Grid Connected System Considering Converter Loss by Jiahui DU, Changjiang WANG, Ling LI, Chenguang YANG

“…Finally, taking the modified WECC 2 machine 5 node and IEEE-118 node system as examples, the calculation example is analyzed to verify the rationality and validity of the proposed optimal model.…”
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An optimal representation to Random Maximum k Satisfiability on the Hopfield Neural Network for High order logic(k ≤ 3) by Hamza Abubakar

“…The energy function of a Hopfield neural network has been considered as a programming language for dynamics minimization mechanism. Several optimization problems associated with machine learning (ML) and artificial intelligence (AI) have been expressed on the Hopfield neural network(HNN) optimally by modelling the problem into variables to minimize the objective function that corresponds to Lyapunov energy function of the Hopfield neural network(HNN).  …”
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Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and Q-Learning techniques by Soraya Khanmohmmadi, Toktam Khatibi, Golnaz Tajeddin, Elham Akhondzadeh, Amir Shojaee

“…The outcomes demonstrated that the multi-task learning model using these two optimization methods, attained 98% accuracy on the test data for predicting partial sleep deprivation. …”
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Leveraging ensemble learning-based stock preselection with multiobjective investment optimization for stepwise decision-supported portfolio management by Jui-Sheng Chou, Tran-Bao-Quyen Pham

“…The AID-MOFBI-XGB model combines the MOFBI optimization algorithm, XGB machine learning technique, and an expanded mean–variance strategy. …”
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A novel fault diagnosis method for gearbox based on RVMD and TELM with composite chaotic grey wolf optimizer by Xuebin Huang, Anfeng Xu, Hongbing Liu, Bingcheng Ye

“…The proposed twin extreme learning machine with composite chaotic grey wolf optimizer (CCGTELM) model can extract higher-level features and has higher classification accuracy than traditional ELM. …”
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Coupling Artificial Intelligence with Proper Mathematical Algorithms to Gain Deeper Insights into the Biology of Birds’ Eggs by Valeriy G. Narushin, Natalia A. Volkova, Alan Yu. Dzhagaev, Darren K. Griffin, Michael N. Romanov, Natalia A. Zinovieva

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A novel approach for breast cancer detection using a Nesterov accelerated adam optimizer with an attention mechanism by Abeer Saber, Tamer Emara, Samar Elbedwehy, Esraa Hassan

“…The proposed model refines features extracted by MobileNet-V2 using the Nesterov-accelerated Adaptive Moment Estimation (Nadam) optimizer. …”
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Multi-Class Skin Cancer Classification Using a Hybrid Dynamic Salp Swarm Algorithm and Weighted Extreme Learning Machines with Transfer Learning by Ramya Panneerselvam, Sathiyabhama Balasubramaniam

“…GoogleNet is a pre-trained network model used with the hybrid model, which helps converge faster with the optimization process. …”
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Optimizing the neural network and iterated function system parameters for fractal approximation using a modified evolutionary algorithm by Sana Abdulla, K. Mahipal Reddy

“…To validate the effectiveness of our method, we present a detailed numerical example showcasing the impact of optimized parameters on RFC spline interpolation. Furthermore, as a practical application, we develop a predictive model by approximating the FDE-optimized RFC spline using an Artificial Neural Network (ANN). …”
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