LSTM-based Prediction of Short-term Water Level for Three Gorges and Gezhouba Cascade Powerplants by WANG Tao, XU Yang, CAO Hui, LIU Ya-xin, MA Hao-yu, ZHANG Zheng, XIE Shuai, CHANG Xin-yu

“…We utilized water-level, inflow, and output data to forecast the ultra-short-term water-level processes of the stations. …”
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"When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware attention framework for relationship extraction. by Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, Amit Sheth

“…We develop an end-to-end knowledge infused deep learning framework (Gated-K-BERT) that leverages the pre-trained BERT language representation model and domain-specific declarative knowledge source (Drug Abuse Ontology) to jointly extract entities and their relationship using gated fusion sharing mechanism. …”
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Enhancing Cybersecurity: Hybrid Deep Learning Approaches to Smishing Attack Detection by Tanjim Mahmud, Md. Alif Hossen Prince, Md. Hasan Ali, Mohammad Shahadat Hossain, Karl Andersson

“…The SMS Phishing Collection dataset was used, with a preparatory procedure involving the transformation of unstructured text data into numerical representations and the training of Word2Vec on preprocessed text. …”
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A Systematic Mapping Study on State Estimation Techniques for Lithium-Ion Batteries in Electric Vehicles by Carolina Tripp-Barba, José Alfonso Aguilar-Calderón, Luis Urquiza-Aguiar, Aníbal Zaldívar-Colado, Alan Ramírez-Noriega

“…For estimating SoH, prevalent data-driven techniques include support vector regression (SVR) and Gaussian process regression (GPR), alongside hybrid models merging machine learning with conventional estimation techniques to heighten predictive accuracy. …”
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Solution structure and phylogenetics of Prod1, a member of the three-finger protein superfamily implicated in salamander limb regeneration. by Acely Garza-Garcia, Richard Harris, Diego Esposito, Phillip B Gates, Paul C Driscoll

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Toward Sustainable Agriculture: DPA-UNet for Semantic Segmentation of Landscapes Using Remote Sensing Imagery by J. Kavipriya, G. Vadivu

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Research on channel estimation based on joint perception and deep enhancement learning in complex communication scenarios by Xin Liu, Shanghong Zhao, Yanxia Liang, Shahid Karim

“…The framework initially acquires the received signal by converting the guide-frequency symbols at the transmitter into time-domain sequences to be transmitted, and after propagating through the direct channel and the IRS reflection channel, processes the data at the receiver. Subsequently, the spatial and temporal features in the received signal are extracted using the CRPG-Net model, with the adaptive optimization capability of the model enhanced by deep reinforcement learning. …”
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Manipulating Photocatalytic Oxidation Reaction Selectivity and Pathway by Simultaneous Harvesting of Ag+, Hot Holes, and Reactive Oxygen Species by Shivangee Rai, Avijit Pramanik, Olorunsola Praise Kolawole, Kaelin Gates, Sanchita Kundu, Paresh Chandra Ray

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Flood Forecasting for Small Reservoirs Based on Neural Networks by PENG Wei, XIONG Jiayi, JIANG Xianqun, GAO Yueming

“…In flood forecasting,empirical prediction methods report low accuracy,and traditional hydrological models face the problems of large workloads and difficult promotion when they are applied to small reservoirs.Hence,an artificial neural network (ANN) method is introduced,which is equipped with powerful feature-learning capability.It is combined with the genetic algorithm (GA) to find the optimal parameters for flood forecasting of small reservoirs as GA can realize automatic optimization of the time step and hidden-layer neuron nodes in ANN.In this way,parameter search can be targeted,and personalized flood forecasting models can be constructed for each small reservoir.In addition,the flood forecasting models based on the back propagation (BP),long short-term memory (LSTM),and gated recurrent unit (GRU) neural networks are built,and comparisons between simulations and measured data are conducted for the flood process.The results show that the LSTM model has high prediction accuracy and good stability and can learn and simulate the water-level change pattern of the actual flood process,demonstrating better prediction performance than BP and GRU models.…”
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Advanced Temporal Convolutional Network Framework for Intrusion Detection in Electric Vehicle Charging Stations by Ikram Benfarhat, Vik Tor Goh, Chun Lim Siow, It Ee Lee, Muhammad Sheraz, Eng Eng Ngu, Teong Chee Chuah

“…The proposed model effectively processes multiple temporal scales, regulates the flow of information, adapts to varying time steps, and focuses on essential components of time-series data. …”
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Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid Convolutional Neural Network–Long Short-Ter... by Saif Ullah, Niamat Ullah, Muhammad Farooq Siddique, Zahoor Ahmad, Jong-Myon Kim

“…The performance of the proposed model was compared with two alternative approaches: a method that employs combined features from the time domain and LSTM and a bidirectional gated recurrent unit model. The proposed approach demonstrated superior performance, as evidenced by lower validation loss, higher validation accuracy, enhanced confusion matrices, and improved t-distributed stochastic neighbor embedding plots compared to the other models when tested on industrial data. …”
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Lessons Learned From the Implementation of an Integrated Health and Social Care Child and Family Hub – a Case Study by Sarah Loveday, Natalie White, Leanne Constable, Anthony Gates, Lena Sanci, Sharon Goldfeld, Harriet Hiscock

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Application and realization of key technologies in China railway e-ticketing system by Xinghua Shan, Zhiqiang Zhang, Fei Ning, Shida Li, Linlin Dai

“…The average time for passengers to pass through the automatic ticket gates has decreased from 3 seconds to 1.3 seconds, significantly improving the efficiency of passenger transport organization. …”
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Developing a framework for monitoring the stages towards achieving effective coverage and equity for maternal, newborn, child health and nutrition interventions by Jessica C Shearer, Savitha Subramanian, Megan E Parker, Patricia S Coffey, Katharine D Shelley, Sadaf Khan, Sali Ahmed, Elan Ebeling, Lydia Nguti, Sophia Knudson, Nesibu Agonafir, Kimberly Mansen, Cyril Engmann

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Molecular basis of TRPV3 channel blockade by intracellular polyamines by Jingying Zhang, Peng Yuan, Colin G. Nichols, Grigory Maksaev

“…A modified blocking model, in which spermine interacts with the cytoplasmic entrance to the channel, from which spermine may permeate, or cause closure of the channel, provides a unifying explanation for electrophysiological and structural data and furnishes the essential background for further exploitation of this regulatory process.…”
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Mapping residual malaria transmission in VietnamResearch in context by Michael A. McPhail, Yalemzewod Assefa Gelaw, Xuan Thang Nguyen, Win Han Oo, Freya J.I. Fowkes, Duc Thang Ngo, Thi Hong Phuc Nguyen, Tasmin L. Symons, Dan J. Weiss, Peter W. Gething

“…Funding: This work was supported, in whole or in part, by the Bill & Melinda Gates Foundation [INV-055192 and INV-009390/OPP1197730]. …”
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Multimodal Sentiment Analysis Based on Expert Mixing of Subtask Representations by Ling Lei, Wangjun He, Qiuyan Zheng, Bing Zhu

“…The module consisted of two parts: an expert network and a gating network. The expert network contains multiple identical experts, each trained to process a specific feature subspace and extract specific subtask representations from the multimodal fused representation to enhance the subtask performance. …”
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Metaparameter optimized hybrid deep learning model for next generation cybersecurity in software defined networking environment by C. Labesh Kumar, Suresh Betam, Denis Pustokhin, E. Laxmi Lydia, Kanchan Bala, Rajanikanth Aluvalu, Bhawani Sankar Panigrahi

“…For the DDoS attack classification process, the attention mechanism with convolutional neural network and bidirectional gated recurrent units (CNN-BiGRU-AM) is employed. …”
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Deep Learning-Based Predictive Control of Injection Velocity in Injection Molding Machines by Zhigang Ren, Yaodong Li, Zongze Wu, Shengli Xie

“…The proposed method utilizes the gated recurrent unit neural network to learn and predict the optimal time series control process data produced by the traditional model predictive controller. …”
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Protein-protein interaction prediction using bidirectional GRUs with explicit ensemble. by Qiuhong Lan, Zhongtuan Zheng, Zhen Tang, Xuehua Qiu, Zhixiang Yin

“…Protein sequence data serves as the primary source for computational protein prediction. …”
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