Enhancing accuracy through ensemble based machine learning for intrusion detection and privacy preservation over the network of smart cities by Mudita Uppal, Yonis Gulzar, Deepali Gupta, Jayant Uppal, Mukesh Kumar, Shilpa Saini

“…The dataset utilized for anomaly-based detection techniques is KDDCup99 dataset, on which the different algorithms have been applied. The goal is to gain knowledge about data integrity and improve the predictive power of data. …”
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Hydrogen Enhancement in Syngas Through Biomass Steam Gasification: Assessment with Machine Learning Models by Yunye Shi, Diego Mauricio Yepes Maya, Electo Silva Lora, Albert Ratner

“…Artificial intelligence (AI), particularly supervised machine learning, has revolutionized the biofuel industry by enhancing feedstock selection, predicting fluid compositions, optimizing operations, and streamlining decision-making. …”
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Advancing lakes algal chlorophyll estimation in the contiguous USA: A comparative study of machine learning models and satellite data by Md Mamun, Xiao Yang

“…We assess the performance of four ML algorithms (random forest, extra tree regressor, bagging regressor, and xgboost model), discern the most influential spectral bands and indices, and compare these methods to established remote sensing techniques for CHL-a prediction. …”
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Method of terminal control in ascent segment of unmanned aerial vehicle with ballistic phase by N. Y. Polovinchuk, S. V. Ivanov, M. Y. Zhukova, D. G. Belonozhko

“…Such algorithms have good convergence and injection accuracy due to the prediction of parameters during the flight at a shorter time interval.Discussion and Conclusions. …”
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Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing by Katarzyna Antosz, Lucia Knapčíková, Jozef Husár

“…A variety of classification algorithms, including neural networks (NNs), bagged trees (BT), and support vector machines (SVMs), were employed to efficiently analyse and predict defects. …”
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Pedotransfer functions for estimating the van Genuchten model parameters in the Cerrado biome by Mariana F. Veloso, Lineu N. Rodrigues, Elpídio I. Fernandes Filho, Carolina F. Veloso, Bruna N. Rezende

“…As for θr (residual water content), the models showed a moderate predictive capacity. For the other parameters, the models did not perform satisfactorily for α and n (fit parameters).…”
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A Review of Numerical Techniques for Frictional Contact Analysis by Govind Vashishtha, Sumika Chauhan, Riya Singh, Manpreet Singh, Ghanshyam G. Tejani

“…These advancements will ultimately improve the predictive power of simulations in diverse fields.…”
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Sensor-Based Rock Hardness Characterization in a Gold Mine Using Hyperspectral Imaging and Portable X-Ray Fluorescence Technologies by Saleh Ghadernejad, Kamran Esmaeili, Mariano P. Consens

“…Three ML algorithms, including Random Forest Regressor (RFR), Adaptive Boosting (AdaBoost), and Multivariate Linear Regression (MLR), were applied to develop predictive hardness models considering three scenarios: using chemical features, using refined spectral features, and their combination. …”
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Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques by Abbas Jafar, Myungho Lee

“…However, the performance of previous automated approaches has often been hindered by suboptimal feature selection and algorithms’ “black-box” nature, which adversely affect their interpretability and clinical applicability. …”
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Investigation of the impact of service parameters on the degree of passenger satisfaction based on the application of the apparatus of neural networks by S. S. Pastukhov, K. V. Stel’Mashenko

“…These models could tages of various methodological approaches to the search for ways be used to guide management to implement several areas of service to increase the quality of service existing in the marketing practice of improvement at the same time, and also to make efficient decisions in the passenger complex, the need for further development of such the context of possible synergies between the multiple aspects of the robust nonparametric mechanisms for constructing predictive va-service being investigated in their impact on customer satisfaction. …”
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Teaching a neural network modeling socio-economic development of the region by S. V. Romanchukov, O. G. Berestneva, L. A. Petrova

“…Under these conditions, the expansion of the mathematical apparatus and the more active introduction of information technologies (including in the area of Big Data analysis and the construction of predictive models based on artificial neural networks) can be viable. …”
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Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review by Suli Li, Yihang Chu, Ying Wang, Yantong Wang, Shipeng Hu, Xiangye Wu, Xinwei Qi

“…Four databases (Web of Science, PubMed, Embase, and the Cochrane library) were searched to retrieve the relevant studies published until March 26, 2022. The Predictive model Deviation Risk Assessment tool (PROBAST) was used to assess the deviation risk of opposing law. …”
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Accelerated discovery of high-density pyrazole-based energetic materials using machine learning and density functional theory by Muhammad Tukur Ibrahim, Muktar Musa Ibrahim, Adamu Uzairu, Gideon Adamu Shallangwa, Sani Uba

“…Using genetic function approximation algorithm, pertinent molecular descriptors were identified and used to build robust Quantitative Structure Property Relationship (QSPR) models for predicting crystalline density of energetic materials. …”
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Gas permeability, diffusivity, and solubility in polymers: Simulation-experiment data fusion and multi-task machine learning by Brandon K. Phan, Kuan-Hsuan Shen, Rishi Gurnani, Huan Tran, Ryan Lively, Rampi Ramprasad

“…To address this challenge, we present a multi-tiered multi-task learning framework empowered with advanced machine-crafted polymer fingerprinting algorithms and data fusion techniques. This framework combines scarce “high-fidelity” experimental data with abundant diverse “low-fidelity” simulation or synthetic data, resulting in predictive models that display a high level of generalizability across novel chemical spaces. …”
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Spontaneous emergence of metacognition in neuronal computation by Hengyuan Ma, Wenlian Lu, Jianfeng Feng

“…Metacognition, a hallmark of human intelligence, enables individuals to assess prediction uncertainty, providing an advantage over artificial intelligence in anticipating risks and performing tasks that demand trustworthiness and reliability. …”
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Regional Economy Using Hybrid Sequence-to-Sequence-Based Deep Learning Approach by Bo Peng

“…Hybrid sequence to sequence (seq2seq) algorithms of deep learning fed with previous information from past years and run the system to compare the predicted result data with current information to evaluate the method to be certified for the coming years.…”
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Foreign Trade Export Forecast Based on Fuzzy Neural Network by Yang Liu

“…Subsequently, the related concepts and principles of artificial neural network and fuzzy theory are explained, the types and training algorithms of the fuzzy neural network are introduced, and the neural network and fuzzy theory are combined to establish the prediction model. …”
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Adaptive Integral Second-Order Sliding Mode Control Design for Load Frequency Control of Large-Scale Power System with Communication Delays by Anh-Tuan Tran, Bui Le Ngoc Minh, Phong Thanh Tran, Van Van Huynh, Van-Duc Phan, Viet-Thanh Pham, Tam Minh Nguyen

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Optimizing travel time reliability with XAI: A Virginia interstate network case using machine learning and meta-heuristics by Navid Khorshidi, Shahriar Afandizadeh Zargari, Soheil Rezashoar, Hamid Mirzahossein

“…This paper applies machine learning models to predict travel time reliability in transportation networks, using XGBoost, LightGBM, and CatBoost optimized with seven metaheuristic algorithms. …”
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Mathematical Modeling of Optimal Drone Flight Trajectories for Enhanced Object Detection in Video Streams Using Kolmogorov–Arnold Networks by Aida Issembayeva, Oleksandr Kuznetsov, Anargul Shaushenova, Ardak Nurpeisova, Gabit Shuitenov, Maral Ongarbayeva

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