Early Prediction of Sepsis in the Intensive Care Unit Using the GRU-D-MGP-TCN Model by Seunghee Lee, Geonchul Shin, Jeongseok Hwang, Yunjeong Hwang, Hyunwoo Jang, Ju Han Park, Sunmi Han, Kyeongmin Ryu, Jong-Yeup Kim

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Evidence classification method of chat text based on DSR and BGRU model by Yu ZHANG, Binglong LI, Xuejuan LI, Heyu ZHANG

“…It is always unlikely to efficiently identify and extract chat text evidence related to criminal events, due to the complex semantics such as “slang” in the chat content and the huge amount of chat text data generated by social software such as instant messaging.Based on this motivation, a chat text evidence classification model (DSR-BGRU) based on the DSR (dynamic semantic representation) model and the BGRU (bidirectional gated recurrent unit) model was proposed.The chat text data was pre-processed to preserve the characteristics of the criminal field.Then a multi-layer chat text feature extraction and classification model using the Keras framework was proposed.With the text matrix composed of vector representation of words in the DSR model as the input vector, the input layer of the DSR model featured the chat text from the semantic level.Then the hidden layer of the BGRU model extracted the context characteristics of the text composed of the word vectors.The softmax classification layer recognized and extracted the chat text evidence.The experimental results show that the proposed DSR-BGRU can more accurately identify and extract chat records compared with other models and methods for text classification, and it can also effectively extract the criminal text information from the chat information with the accuracy rate 92.06% and the F1 score 91.00%.…”
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A Study on Hyperspectral Soil Moisture Content Prediction by Incorporating a Hybrid Neural Network into Stacking Ensemble Learning by Yuzhu Yang, Hongda Li, Miao Sun, Xingyu Liu, Liying Cao

“…First, raw hyperspectral data are processed by removing edge noise and standardization. …”
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Short-Term Daily Univariate Streamflow Forecasting Using Deep Learning Models by Eyob Betru Wegayehu, Fiseha Behulu Muluneh

“…All data sets passed through rigorous quality control processes, and null values were filled using linear interpolation. …”
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Low-Power and High-Performance Double-Node-Upset-Tolerant Latch Using Input-Splitting C-Element by Qi Chen, Binyu He, Renjie Kong, Pengjia Qi, Yanyun Dai

“…Data accuracy is critical for sensor systems. As essential components of digital circuits within sensor systems, nanoscale CMOS latches are particularly susceptible to single-node upsets (SNUs) and double-node upsets (DNUs), which can lead to data errors. …”
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The development of CC-TF-BiGRU model for enhancing accuracy in photovoltaic power forecasting by Guomin Xie, Zijian Zhang, Zhongbao Lin, Sen Xie

“…Besides, a potent fusion of gradient boosting decision trees (GBDT) and BiGRU is leveraged to adeptly process time series data. Moreover, teacher forcing is seamlessly integrated into the model to bolster forecasting accuracy and stability. …”
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ADVANCED NEURAL NETWORKS AND DEEP LEARNING TECHNIQUES IN FINANCIAL MARKET PREDICTION by ENE CEZAR CATALIN

“…Mimicking the computational structure of the human brain, ANN processes interconnected data points enabling efficient analysis and forecasting. …”
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Experience of establishing and coordinating a nationwide network for bidirectional intussusception surveillance in India: lessons for multisite research studies by Rakesh Kumar, Ashish Wakhlu, Manoja Kumar Das, Narendra Kumar Arora, Atul Gupta, John Mathai, Bhadresh R Vyas, Pavai Arunachalam, Cenita J Sam, Arindam Ray, Javeed Iqbal Bhat, Jayanta K Goswami, K Kameswari, Lalit Bharadia, Lalit Sankhe, Neelam Mohan, Pradeep K Jena, Rachita Sarangi, Rashmi Shad, Sanjib K Debbarma, J Shyamala, Simmi K Ratan, Suman Sarkar, Vijayendra Kumar, Anand P Dubey, Gowhar Nazir Mufti, Harsh Trivedi, Jimmy Shad, Kaushik Lahiri, Meera Luthra, P Padmalatha, Ruchirendu Sarkar, A Santosh Kumar, Subrat Kumar Sahoo, Sunil K Ghosh, Sushant Mane, Bashir Ahmad Charoo, Bikasha Bihary Tripathy, G Rajendra Prasad, S Harish Kumar, K Jothilakshmi, Nihar Ranjan Sarkar, Saurabh Garge, MK Ajaya Kumar, Satya SG Mohapatra

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A novel end-to-end privacy preserving deep Aquila feed forward networks on healthcare 4.0 environment by Ponugoti Kalpana, Sunitha Tappari, L. Smitha, Dasari Madhavi, K. Naresh, Maddala Vijayalakshmi

“…These Internet-enabled devices compile human data and deliver it to remote clinical centers for efficient diagnosis and treatment processes. …”
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SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals by Davide Lillini, Carlo Aironi, Lucia Migliorelli, Leonardo Gabrielli, Stefano Squartini

“…Our proposed SiCRNN processes Mel spectrograms using a Siamese approach, integrating a convolutional neural network (CNN) backbone and a bidirectional gated recurrent unit (GRU). …”
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Fault detection for Li-ion batteries of electric vehicles with segmented regression method by Muaaz Bin Kaleem, Yun Zhou, Fu Jiang, Zhijun Liu, Heng Li

“…Thirdly, an optimized gated recurrent unit network is developed and integrated with the segmented regression to enable accurate cell voltage estimation. …”
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Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning by ZHANG Hongrui, CAO Xin, JIANG Chao, ZU Anjun, XU Mingxiang

“…Subsequently, the BiLSTM network established temporal dependencies of deformation data from both forward and backward directions, effectively capturing long-term dependencies in dam deformation processes through its dual-directional temporal modeling capability. …”
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DPRM: DeBERTa-based potential relationship multi-headed self-attention joint extraction model. by Songjiang Li, Jinming Cao, Jiao Yang, Yunjiangcan He, Peng Wang

“…The relationship extraction and entity recognition module is responsible for identifying potential relationships within the sentences and integrating a relational gated mechanism to minimize the interference of irrelevant information during the entity recognition process. …”
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MLA-Machine Learning Approach for Dependable Battery Condition Monitoring in Electric Vehicles by Tirgar Pravin, Priya R Karpaga, Sampath Kumar Vankadara, Lakhanpal Sorabh, Raj R Gowtham, N K Rayaguru

“…By focusing on relevant data at each moment, the monitoring process enhances the model’s ability to track long- term changes in battery life. …”
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Using Deep Learning in Forecasting the Production of Electricity from Photovoltaic and Wind Farms by Michał Pikus, Jarosław Wąs, Agata Kozina

“…These data contain daily records of all the recorded parameters from individual solar and wind farms over the past three years. …”
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Synergistic Artificial Intelligence framework for robust multivariate medium-term wind power prediction with uncertainty envelopes by Bo Wu, Xiuli Wang, Bangyan Wang, Yaohong Xie, Shixiong Qi, Wenduo Sun, Qihang Huang, Xiang Ma

“…The vertical–horizontal process integrates the Polar Lights Optimizer with Joint Opposite Selection and a regression model based on the bidirectional long short-term memory and the gated recurrent unit, enabling efficient hyperparameter optimization and yielding a determination coefficient above 0.9996 for training data and a normalized root mean square error of 0.2448 for test data. …”
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Deep Transfer Learning for Lip Reading Based on NASNetMobile Pretrained Model in Wild Dataset by Ashwaq Waleed Abdul Ameer, Pedram Salehpour, Mohammad Asadpour

“…The proposed framework involves a process that extracts features from video frames in a time sequence, employing methods such as Convolutional Neural Networks (CNN), CNN-Gated Recurrent Units (CNN-GRU), Temporal CNN, and Temporal PoinWise. …”
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Methods of security situation prediction for industrial internet fused attention mechanism and BSRU by Xiangdong HU, Zhengguo TIAN

“…The security situation prediction plays an important role in balanced and reliable work for industrial internet.In the face of massive, high-dimensional and time-series data generated in the industrial production process, traditional prediction models are difficult to accurately and efficiently predict the network security situation.Therefore, the methods of security situation prediction for industrial internet fused attention mechanism and bi-directional simple recurrent unit (BSRU) were proposed to meet the real-time and accuracy requirements of industrial production.Each security element was analyzed and processed, so that it could reflect the current network state and facilitate the calculation of the situation value.One-dimensional convolutional network was used to extract the spatial dimension features between each security element and preserve the temporal correlation between features.The BSRU network was used to extract the time dimension features between the data information and reduced the loss of historical information.Meanwhile, with the powerful parallel capability of SRU network, the training time of model was reduced.Attention mechanism was introduced to optimize the correlation weight of BSRU hidden state to highlight strong correlation factors, reduced the influence of weak correlation factors, and realized the prediction of industrial internet security situation combining attention mechanism and BSRU.The comparative experimental results show that the model reduces the training time and training error by 13.1% and 28.5% than the model using bidirectional long short-term memory network and bidirectional gated recurrent unit.Compared with the convolutional and BSRU network fusion model without attention mechanism, the prediction error is reduced by 28.8% despite the training time increased by 2%.The prediction effect under different prediction time is better than other models.Compared with other prediction network models, this model achieves the optimization of time performance and uses the attention mechanism to improve the prediction accuracy of the model under the premise of increasing a small amount of time cost.The proposed model can well fit the trend of network security situation, meanwhile, it has some advantages in multistep prediction.…”
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A modular entanglement-based quantum computer architecture by Ferran Riera-Sàbat, Wolfgang Dür

“…These states are stored in memory qubits where they can be further processed so they can eventually be used to deterministically perform certain classes of gates or circuits between modules on demand, including parallel controlled- Z gates with arbitrary interaction patterns, multi-qubit gates or whole Clifford circuits, depending on their entanglement structure. …”
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Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators by Hoa Thi Nguyen, Roland Olsson, Oystein Haugen

“…Analyzing time series data in industrial settings demands domain knowledge and computer science expertise to develop effective algorithms. …”
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