Student Engagement Recognition: Comprehensive Analysis Through EEG and Verification by Image Traits Using Deep Learning Techniques by Ajitha Sukumaran, Arun Manoharan

“…In this paper, we propose an engagement recognition system that detects student engagement using EEG signals by integrating levels of valence and arousal with the Russel 2D circumplex model using deep learning algorithm. The public DEAP dataset was used for training the model to predict valence and arousal values. …”
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DNFE: Directed network flow entropy for detecting tipping points during biological processes. by Xueqing Peng, Rui Qiao, Peiluan Li, Luonan Chen

“…Numerical simulation results demonstrate that the DNFE algorithm is robust across various noise levels and outperforms existing methods in detecting tipping points. …”
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Synergistic Framework for Fuel Cell Mass Transport Optimization: Coupling Reduced-Order Models with Machine Learning Surrogates by Shixin Li, Qingshan Liu, Yisong Chen

“…Subsequently, a neural network surrogate model and genetic algorithm are combined to optimize the mass transfer property parameters globally. …”
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X-FuseRLSTM: A Cross-Domain Explainable Intrusion Detection Framework in IoT Using the Attention-Guided Dual-Path Feature Fusion and Residual LSTM by Adel Alabbadi, Fuad Bajaber

“…For cross-domain intrusion detection, this paper proposes a novel algorithm, X-FuseRLSTM, a dual-path feature fusion framework that is attention guided and coupled with a residual LSTM architecture. …”
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Migration and mutation (MeTa) hybrid trained ANN for dynamic spectrum access in wireless body area network by Geoffrey Eappen, Shankar T, Rajesh A

“…The novel MeTa hybrid optimization algorithm achieves a balance between global and local search abilities by optimizing the weights of the ANN. …”
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Hyperspectral Imaging Combined with Deep Learning for the Early Detection of Strawberry Leaf Gray Mold Disease by Yunmeng Ou, Jingyi Yan, Zhiyan Liang, Baohua Zhang

“…Overall, the fused feature-based model can reduce the dimensionality of the classification data and effectively improve the predicting accuracy and precision of the classification algorithm.…”
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In-silico and in-vitro morphometric analysis of intestinal organoids. by Sandra Montes-Olivas, Danny Legge, Abbie Lund, Alexander G Fletcher, Ann C Williams, Lucia Marucci, Martin Homer

“…Here, we developed an algorithm to automate crypt-like structure counting on intestinal organoids in both in-vitro and in-silico images. …”
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Variables Selection in the Ultraviolet, Visible and Near Infrared Range for Calibration of a Mixture of Vegetable Oils by Absorbance Spectra by М. A. Khodasevich, D. A. Borisevich

“…Calibration uses three methods for spectral variables selection: the successive projections algorithm, the method of searching combination moving window, and method for ranking variables by correlation coefficient.The application of the successive projections algorithm, ranking variables by correlation coefficient and searching combination moving window makes it possible to reduce the value of the root mean square error of prediction from 0.63 % for wideband projection to latent structures to 0.46 %, 0.50 %, and 0.03 %, respectively.The developed method of multivariate calibration by projection to latent structures of absorbance spectra in UV, visible and near IR ranges using the spectral variables selection by searching combination moving window is a simple and effective method of detecting adulteration of flaxseed oil.…”
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AIMP-Based Power Allocation for Radar Network Tracking Under Countermeasures Environment by Xiaoyou Xing, Longxiao Xu, Lvwan Nie, Xueting Li

“…Next, the target RCS prediction algorithm is introduced, and the AIMP power allocation method is proposed jointly considering the electronic jamming and the impact of the target RCS. …”
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Optimization for Metro Operation Scheme of Suburban Lines: A New Method for Dealing with the Imbalanced Passenger Flow by Xiaobing Ding, Gan Shi, Jiangang Jin, Lixing Yang, Zhigang Liu

“…Finally, a set of numerical experiments with operation data from Shanghai Metro Line 16 are conducted to verify the performance and effectiveness of the proposed model and algorithm. The experimental results show that the proposed approach can effectively realize the collaborative optimization of passenger OD prediction, train proportion, stop scheme, and travel time, so as to provide decision-making support and method guidance for the optimization of metro organizations in megacities.…”
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Artificial Intelligence in Identifying Patients With Undiagnosed Nonalcoholic Steatohepatitis by Onur Baser, Gabriela Samayoa, Nehir Yapar, Erdem Baser

“…We performed a claims data analysis using a machine learning algorithm. To build our model, the study population was randomly divided into an 80% training subset and a 20% testing subset and tested and trained using a cross-validation technique. …”
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Experimental Investigation on Impact of EGR Configuration on Exhaust Emissions in Optimized PCCI-DI Diesel Engine by Getachew Alemayehu, Ramesh Babu Nallamothu, Deresse Firew, Rajendiran Gopal

“…Methanol port injection, dieseline direct injection, advanced injection timing, and different EGR rates were adapted and optimized on the baseline engine. A hybrid algorithm of grey relational analysis with the Taguchi method was implemented for optimization. …”
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Another look at the nb.t in the Moscow Mathematical Papyrus by George M. Hollenback

“…The area is found by configuring the two given dimensions as the sides of a rectangle and then reducing the long side of the rectangle by two successive reductions of    : 9 – = 8, and 8 – =7   , the product of 7   and 4½ yielding the area of the figure, 32. …”
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Optimal Scheduling Strategy of Newly-Built Microgrid in Small Sample Data-Driven Mode by CHEN Shi, YANG Linsen, LIU Yihong, LUO Huan, ZANG Tianlei, ZHOU Buxiang

“…Newly built microgrids lack historical operation data, making it challenging to predict renewable power output accurately using conventional data-driven methods, which in turn affects the accuracy of scheduling plans. …”
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Research on Soft-Sensing Method Based on Adam-FCNN Inversion in <i>Pichia pastoris</i> Fermentation by Bo Wang, Wenyu Ma, Hui Jiang, Shaowen Huang

“…Finally, a composite pseudo-linear system is formed by cascading the inverse model with the original system, achieving decoupling and the high-accuracy prediction of key parameters. Experimental results demonstrate that the proposed method significantly reduces prediction errors and enhances generalization capabilities compared to traditional models, validating the effectiveness of the proposed method in complex bioprocesses.…”
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Study of radioactive particle tracking using MCNP-X code and artificial neural network by Roos Sophia de Freitas Dam, César Marques Salgado

“…Counts obtained by an array of detectors properly positioned around the unit will be correlated to predict the instantaneous positions occupied by the radioactive particle by means of an appropriate mathematical search location algorithm. …”
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Optimal Task Offloading Strategy for Vehicular Networks in Mixed Coverage Scenarios by Xuewen He, Yuhao Cen, Yinsheng Liao, Xin Chen, Chao Yang

“…This study employs long short-term memory networks to predict the loading status of base stations. Then, based on the prediction results, we propose an optimized task offloading strategy using the proximal policy optimization algorithm. …”
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Design and Deployment of ML in CRM to Identify Leads by Alonso Yocupicio-Zazueta, Agustin Brau-Avila, Federico Cirett-Galán, Margarita Valenzuela-Galván

“…In Jupyter Notebooks, logistic regression was utilized to design and to train a model to accurately predict whether a lead will convert into a client or not. …”
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An Efficient Nonlinear Filter for Spacecraft Attitude Estimation by Bing Liu, Zhen Chen, Xiangdong Liu, Fan Yang

“…Then the local error attitude estimator is designed with constant coefficients based on the robust H2 filtering algorithm. Subsequently, the attitude predictions and the local error attitude estimations are calculated by a gyro based model and the local error attitude estimator. …”
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From Tweets to Trades: A Bibliometric and Systematic Review of Social Media’s Influence on Cryptocurrency by Sheela Sundarasen, Farida Saleem

“…This study finds that social media sentiment plays a crucial role in cryptocurrency price forecasting, with machine learning and natural language processing (NLP) techniques enhancing prediction accuracy. Thematic analysis reveals four primary areas of focus: sentiment analysis and market prediction, machine learning-driven algorithmic trading, blockchain investment risks, and influencer-driven market behavior. …”
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