Deep Learning based Models for Drug-Target Interactions by Ali K. Abdul Raheem, Ban N. Dhannoon

“…Incorporating machine learning technologies into the drug development process can aid in automating repetitive data processing and analysis processes. …”
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Research on Shale Oil Well Productivity Prediction Model Based on CNN-BiGRU Algorithm by Yuan Pan, Xuewei Liu, Fuchun Tian, Liyong Yang, Xiaoting Gou, Yunpeng Jia, Quan Wang, Yingxi Zhang

“…Nevertheless, conventional data-driven architectures suffer from structural simplicity, limited capacity for processing low-dimensional feature spaces, and exclusive applicability to intra-sequence learning paradigms (e.g., production-to-production sequence mapping). …”
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PS-GAN: A Novel Pseudo-Siamese Generative Adversarial Network for Multimodal Remote Sensing Image Change Detection by Zhifu Zhu, Xiping Yuan, Shu Gan, Raobo Li, Rui Bi, Weidong Luo, Cheng Chen

“…In recent years, research on image translation for multimodal remote sensing imagery in change detection (CD) has demonstrated that converting images from different sensors into a common image domain can effectively address the incomparability issues arising from imaging differences, thereby enabling traditional CD models to extract change information from diverse data sources. However, most existing studies generally overlook global contextual information during the image conversion process, resulting in an inability to fully capture the overall semantic structure of the image. …”
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Blockchain-Based Smart Monitoring Framework for Defense Industry by Abdullah Alqahtani, Shtwai Alsubai, Abed Alanazi, Munish Bhatia

“…The effectiveness of the suggested solution is evaluated using statistical metrics including vulnerable activity recognition (Precision 95.24%), model training and testing (Precision 95.24%, Recall (95.00%), and F-Measure 94.11%), latency rate (7.45 seconds), and data processing cost(<inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>((n-1) logn)).…”
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Fault detection and classification in overhead transmission lines through comprehensive feature extraction using temporal convolution neural network by Nadeem Ahmed Tunio, Ashfaque Ahmed Hashmani, Suhail Khokhar, Mohsin Ali Tunio, Muhammad Faheem

“…Furthermore, the simulation results of the TCN model compared to bidirectional long short‐term memory (BiLSTM) and Gated Recurrent Unit (GRU) and it has been found that TCN model is capable of classifying faults in 500 kV transmission line with high accuracy due to its ability to handle long receptive field size, less memory requirement and parallel processing due to dilated causal convolutions. …”
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Toward AI-Augmented Formal Verification: A Preliminary Investigation of ENGRU and Its Challenges by Chanon Dechsupa, Teerapong Panboonyuen, Wiwat Vatanawood, Praisan Padungweang, Chakchai So-In

“…Recurrent neural networks (RNNs) and language models are deeply intertwined, as RNNS provide the foundational architecture that enables language models to process sequential data, capture contextual dependencies, and improve natural language processing tasks. …”
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Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind Turbines by Ruifeng Chen, Ke Zhang, Min Luo, Ye An, Lixiang Guo

“…The comprehensive framework, which encompasses feature selection, data processing, deep learning model construction, and interpretation, demonstrates significant potential for addressing a broad range of engineering problems through deep learning methodologies.…”
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Psychomedical named entity recognition method based on multi-level feature extraction and multi-granularity embedding fusion by Zixuan Liu, Guofang Zhang, Yanguang Shen

“…Finally, feature vectors at three granularities are integrated using a gated feed-forward neural network attention mechanism (GA-FNNAtention). …”
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Aircraft Multi-stage Altitude Prediction Under Satellite Signal Loss by Mengchan HUANG, Qiang MIAO

“…Traditional clustering algorithms often struggle to capture transitional states in time-series data. Therefore, a fuzzy logic approach is adopted to map ambiguous inputs to explicit output states, enabling the extraction of climb, cruise, and descent phases from the aircraft’s entire flight process. …”
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Embedded Hardware-Efficient FPGA Architecture for SVM Learning and Inference by B. B. Shabarinath, Muralidhar Pullakandam

“…In several benchmarking data sets, the scheme reduces clock cycles per iteration consistently and improves throughput (up to 2427 iterations per second). …”
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Tumor ViT-GRU-XAI: Advanced Brain Tumor Diagnosis Framework: Vision Transformer and GRU Integration for Improved MRI Analysis: A Case Study of Egypt by Mohammed Aly, Abdullatif Ghallab, Islam S. Fathi

“…Our framework was applied to primary MRI data sourced from the National Cancer Institute in Cairo, Egypt, and Brast2013/2015. …”
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State of health prediction of lithium-ion batteries in charging chambers based on multi-modal deep learning by ZHAO Yinghua, CHEN Anbi, ZHANG Zengyu, LI Wenzhong, HAN Yu

“…In underground environments characterized by dust, humidity, and explosion risks, the degradation process of lithium-ion batteries (LIBs) often exhibits nonlinear and multistage characteristics, making it difficult for traditional single models to comprehensively capture their dynamic changes. …”
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Entity-level cross-modal fusion for multimodal chinese agricultural diseases and pests named entity recognition by Jingzhong Huang, Xia Hao, Yu Wang, Ruizhi Song, Zenan Mu, Wen Chu, Georgios Papadakis, Sijie Niu, Xuchao Guo

“…Secondly, we introduce a Dynamic Cross-modal Gated Attention (DCGA) mechanism that adaptively adjusts visual feature contributions through gating weights. …”
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Advancing arabic dialect detection with hybrid stacked transformer models by Hager Saleh, Hager Saleh, Hager Saleh, Abdulaziz AlMohimeed, Rasha Hassan, Mandour M. Ibrahim, Saeed Hamood Alsamhi, Moatamad Refaat Hassan, Sherif Mostafa

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Prediction of dam deformation using adaptive noise CEEMDAN and BiGRU time series modeling by WANG Zixuan, OU Bin, CHEN Dehui, YANG Shiyong, ZHAO Dingzhu, FU Shuyan

“…During monitoring, system noise and observation errors frequently interfere with data quality, posing additional challenges for analysis. …”
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Cardiotoxicity of Chemotherapeutic Drugs (Literature Review and Clinical Case Description) by Avagimyan Ashot A., Mkrtchyan Lusine G.

“…We used content analysis, a method of system and comparative analysis, a bibliosemantic method for studying relevant scientific research on the topic of cardiotoxicity of chemotherapeutic drugs. The data was searched in scientometric medical information databases PubMed, NCBI, Medline, ResearchGate for the key words: cardiotoxicity, cardiooncology, anthracycline cardiomyopathy, atrial fibrillation, cardiac lipomatosis, as well as on the basis of the State Medical Library of the Virmen Academy of Medical Sciences. …”
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Solar Wind Speed Prediction With Two‐Dimensional Attention Mechanism by Yanru Sun, Zongxia Xie, Yanhong Chen, Xin Huang, Qinghua Hu

“…In this study, we first analyze and preprocess data from 2011 to 2017. Second, considering the characteristics of time series data, we adopt the gated recurrent units (GRU) model which can deal with long‐term dependence as the prediction part of our model. …”
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Security anomaly detection for enterprise management network based on attention mechanism by Zhaohan You, Yucai Zheng

“…This method combines spatial convolutional network and gating mechanism, which are used to extract spatial features from enterprise management network security data and learn non-local interaction relationships between features. …”
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Operationalising an integrated mortality transition framework for programmatic priority setting across maternal and newborn health by Allisyn C Moran, Ties Boerma, Gloria Ikilezi, Ryan Fitzgerald, Uzma Syed

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YOLO-SBA: A Multi-Scale and Complex Background Aware Framework for Remote Sensing Target Detection by Yifei Yuan, Yingmei Wei, Xiaoyan Zhou, Yanming Guo, Jiangming Chen, Tingshuai Jiang

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