A Bi‐level stacked LSTM‐DNN‐based decoder network for AGC dispatch under regulation market framework in presence of VPP and EV aggregators by Kingshuk Roy, Sanjoy Debbarma, Siddhartha Deb Roy, Liza Debbarma

“…In this context, a bi‐level AGC dispatch approach based on a stacked long short‐term memory (LSTM)‐deep neural network (DNN)‐based decoder framework is proposed for a power system comprising diverse CIGs forming a virtual power plant and electric vehicle aggregators. …”
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Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm by Raji Krishna, Hemamalini S

“…The LSTM outperforms the artificial neural network (ANN) model in terms of mean square error (MSE) and prediction accuracy (R2) for both training and testing datasets. …”
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Abnormal neural circuits and altered brain network topological properties in patients with persistent postural-perceptual dizziness by Kangzhi Li, Xia Ling, Jing Zhao, Zhiqun Wang, Xu Yang

“…Network-based statistic results reveal an abnormal neural network in PPPD patients with key nodes in the occipital visual cortex, precuneus, sensorimotor cortex, multisensory vestibular cortex and cerebellum. …”
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A Pervasive Approach to EEG-Based Depression Detection by Hanshu Cai, Jiashuo Han, Yunfei Chen, Xiaocong Sha, Ziyang Wang, Bin Hu, Jing Yang, Lei Feng, Zhijie Ding, Yiqiang Chen, Jürg Gutknecht

“…Four classification methods (Support Vector Machine, K-Nearest Neighbor, Classification Trees, and Artificial Neural Network) distinguished the depressed participants from normal controls. …”
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Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production Prediction by Chao Wang, Ruogu Wang, Yuhan Lin, Jiafei Zhang, Xiaofei Xie, Zidan Zhao, Yunlin Xu

“…Compared with traditional support vector machine, BP neural network, KNN and naive Bayes, the improved support vector machine has a higher prediction accuracy, and the average error is only 2.7%. …”
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A Correlation Analysis-Based Structural Load Estimation Method for RC Beams Using Machine Vision and Numerical Simulation by Chun Zhang, Yinjie Zhao, Guangyu Wu, Han Wu, Hongli Ding, Jian Yu, Ruoqing Wan

“…Subsequently, a deep neural network (DNN) is trained as a FEM surrogate model to quickly predict the structural strain response by considering material uncertainties. …”
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Systematic Research on the Application of Steel Slag Resources under the Background of Big Data by Le Kang, Hui Ling Du, Hao Zhang, Wan Li Ma

“…Secondly, the steel slag prediction model based on the convolution neural network (CNN) is established. The material data of steelmaking, the operation data of steelmaking process, and the data of steel slag composition are put into the model from the Hadoop platform, and the prediction of the slag composition is further realized. …”
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Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators by Shenqiong Jiang, Xiangju Cheng, Baoshan Shi, Dantong Zhu, Jun Xie, Zhihong Zhou

“…The evaluation results showed that the generalized regression neural network (GRNN) model optimized by particle swarm optimization (PSO) had the best prediction among all the models under the conditions of six input variables, namely COD, NH4+-N, DO, WT, TN, and pH. …”
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Vehicle Detection and Tracking Based on Improved YOLOv8 by Yunxiang Liu, Shujun Shen

“…Then we replaced the convolutional kernel with a dual convolutional kernel to construct a lightweight deep neural network. Subsequently, the Focaler-EIoU loss function is introduced to improve the accuracy. …”
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Improving the accuracy of soil texture determination using pH and electro conductivity values with ultrasound penetration-based digital soil texture analyzer by Emre Kilinc, Umut Orhan

“…Using the Ultrasound Penetration-based Digital Soil Texture Analyzer (USTA), this research combined ultrasound time series data with pH and EC measurements to predict sand, silt, and clay ratios through machine learning methods—support vector regression (SVR), Random Forest (RF), and multi-layer perceptron neural network (MLPNN). Simulations showed that RF yielded the best results, improving R2 values to 0.52, 0.33, and 0.31 for sand, silt, and clay, respectively. …”
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Dynamic Response of a Casting Crane Rigid-Flexible Coupling System to High Temperature by Yunsheng Xin, Qing Dong, Qisong Qi, Qinglu Shi

“…The constitutive equation for the elastic modulus of Q355 alloy steel at different temperatures was predicted using test data and a neural network algorithm. Based on crane structural characteristics and the principle of system dynamics, a coupling vibration model was established that included the crane flexible girder, cabin, trolley, crane, and temperature. …”
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Important Citation Identification by Exploding the Sentiment Analysis and Section-Wise In-Text Citation Weights by Shahzad Nazir, Muhammad Asif, Shahbaz Ahmad, Hanan Aljuaid, Rimsha Iftikhar, Zubair Nawaz, Yazeed Yasin Ghadi

“…The first technique is based on extracting the different sections of the research articles and performing citation count. We applied Neural Network and Multiple Regression on section-wise citations for automatic weight assignment. …”
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A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering by Yu Liu, Shuai Wang, M. Shahrukh Khan, Jieyu He

“…To tackle these problems, some authors have considered the integration of a deep neural network to learn user and item features with traditional collaborative filtering. …”
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Winter Wheat Yield Prediction Using Satellite Remote Sensing Data and Deep Learning Models by Hongkun Fu, Jian Lu, Jian Li, Wenlong Zou, Xuhui Tang, Xiangyu Ning, Yue Sun

“…By adjusting the key parameters of the Convolutional Neural Network (CNN) with IGWO, the prediction accuracy is significantly enhanced. …”
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Chaotic gradient based optimization with fuzzy temporal optimized CNN for heart failure prediction by G. Kajeeth Kumar, S. Muthurajkumar

“…Additionally, we introduce the Fuzzy Temporal Optimized Convolutional Neural Network (FTOCNN) classifier that incorporates CGBO and fuzzy temporal rules to enhance detection accuracy. …”
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Deep Recurrent Model for Server Load and Performance Prediction in Data Center by Zheng Huang, Jiajun Peng, Huijuan Lian, Jie Guo, Weidong Qiu

“…Recurrent neural network (RNN) has been widely applied to many sequential tagging tasks such as natural language process (NLP) and time series analysis, and it has been proved that RNN works well in those areas. …”
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Automated Measurements of Tooth Size and Arch Widths on Cone-Beam Computerized Tomography and Scan Images of Plaster Dental Models by Thong Phi Nguyen, Jang-Hoon Ahn, Hyun-Kyo Lim, Ami Kim, Jonghun Yoon

“…The third step uses a decentralized convolutional neural network to calculate key points representing the parameters. …”
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Machine learning to identify environmental drivers of phytoplankton blooms in the Southern Baltic Sea by Maximilian Berthold, Pascal Nieters, Rahel Vortmeyer-Kley

“…We employed generalized additive mixed models to characterize similar blooming patterns and trained an artificial neural network within the Universal Differential Equation framework to learn a differential equation representation of these pattern. …”
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Short-Term Traffic Flow Prediction with Weather Conditions: Based on Deep Learning Algorithms and Data Fusion by Yue Hou, Zhiyuan Deng, Hanke Cui

“…This paper proposes a combined framework of stacked autoencoder (SAE) and radial basis function (RBF) neural network to predict traffic flow, which can effectively capture the temporal correlation and periodicity of traffic flow data and disturbance of weather factors. …”
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Сognitive Сomplaints with Unilateral Temporal Lobe Compression by M.U. Kaverina, U.V. Strunina, O.A. Krotkova

“…The phenomenology of neural network compression makes it possible to register hemispheric specificity in spontaneously generated thoughts and memories.…”
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