VSA-GCNN: Attention Guided Graph Neural Networks for Brain Tumor Segmentation and Classification by Kambham Pratap Joshi, Vishruth Boraiah Gowda, Parameshachari Bidare Divakarachari, Paramesh Siddappa Parameshwarappa, Raj Kumar Patra

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Pulse‐level work state recognition of multifunction radar based on MC‐RSG by Zijun Qin, Wenjuan Ren, Zhanpeng Yang, Xian Sun

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A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification by A. M. Mutawa, Sai Sruthi

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Comparative Analysis of LSTM and GRU Models for Ethereum (ETH) Price Prediction by Moch Panji Agung Saputra, Riza Andrian Ibrahim, Renda Sandi Saputra

“…The research methodology includes data preprocessing using Min-Max scaling, model development with various layer configurations, and comprehensive evaluation using several performance metrics. …”
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Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification by Mouna Bouchane, Wei Guo, Shuojin Yang

“…Efficient feature extraction from raw EEG signals is essential to improve classification accuracy while minimizing reliance on extensive preprocessing. In this study, we introduce new hybrid architectures to enhance MI classification using data augmentation and a limited number of EEG channels. …”
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Electrical Characterization of TiO2 Insulator Based Pd / TiO2 / Si MIS Structure Deposited by Sol-Gel Process by Kumar Shubham, R.U. Khan

“…The TiO2 layer has been deposited on n-Si substrate by spin coating sol-gel process using Titanium Tetraisopropoxide [Ti(OC3H7)4]. …”
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RUL Prediction Based on MBGD-WGAN-GRU for Lithium-Ion Batteries by Zhiguo Zhao, Ke Li, Yibo Dai, Biao Chen, Yeqin Wang, Qian Zhao

“…Subsequently, the mini-batch stochastic gradient descent algorithm is employed to optimize a Wasserstein generative adversarial network, thereby augmenting the training set and enhancing data diversity. The extracted IHF and expanded training set are then used to train a gated recurrent unit (GRU) model, leveraging GRU’s strengths in sequential data processing to improve the characterization of battery aging trends. …”
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A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range by Hao Chen, Zheng Dang

“…GCSG transforms time-series device load data into image representations using Gramian Angular Field (GAF) encoding, extracts spatial features via a Convolutional Neural Network (CNN) enhanced with a Squeeze-and-Excitation network (SENet), and captures temporal dependencies using a Gated Recurrent Unit (GRU). …”
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A modernized approach to sentiment analysis of product reviews using BiGRU and RNN based LSTM deep learning models by L. Godlin Atlas, Daniel Arockiam, Arvindhan Muthusamy, Balamurugan Balusamy, Shitharth Selvarajan, Taher Al-Shehari, Nasser A. Alsadhan

“…The input reviews are preprocessed using natural language processing techniques like tokenization, lemmatization, stop word removal, named entity recognition and part of speech tagging. …”
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Energy Consumption Optimization of Multi-Dimensional U-Nets on CGLA by Duong Thi Sang, Ren Imamura, Tomoya Akabe, Yasuhiko Nakashima

“…To address these challenges, we propose the Interposed Memory Accelerator eXtension series 3 (IMAX3), a novel chiplet-based, non-von Neumann architecture with coarse-grained linear accelerator (CGLA) to efficiently support both 2D and 3D U-Net models, delivering superior energy efficiency and computational performance. IMAX3 optimizes data processing by employing advanced data preloading mechanisms and double-buffered cache memory, reducing redundant memory retrievals and minimizing inter-chip communication. …”
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Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network by Wuyi Chen, Huafeng Cai, Qiu Sun

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Charging pile fault prediction method combining whale optimization algorithm and long short-term memory network by Yansheng Huang, Atthapol Ngaopitakkul, Suntiti Yoomak

“…., the model optimization process stays in the non-optimal regional minimum) in complex parameter space, the study innovatively proposes a hybrid prediction model that combines the whale optimization algorithm with the gated recurrent unit-long short-term memory neural network. …”
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Hybrid Graph Representation and Learning Framework for High-Level Synthesis Design Space Exploration by Pouya Taghipour, Eric Granger, Yves Blaquiere

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Enhanced twitter sentiment analysis with dual joint classifier integrating RoBERTa and BERT architectures by Luoyao He

“…Sentiment analysis, a crucial aspect of Natural Language Processing (NLP), aims to extract subjective information from textual data. …”
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MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition by Kowovi Comivi Alowonou, Ji-Hyeong Han

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Leveraging assistive technology for visually impaired people through optimal deep transfer learning based object detection model by Mahir Mohammed Sharif Adam, Nojood O. Aljehane, Mohammed Yahya Alzahrani, Samah Al Zanin

“…Additionally, the bidirectional gated recurrent unit (Bi-GRU) method is employed for the classification process. …”
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Life cycle assessment for calcination process of flue gas desulfurization gypsum and transformation into β-CaSO4·0.5H2O by Payal Bakshi, Asokan Pappu, Dhiraj Kumar Bharti

“…Present study will provide referential data set for FGD gypsum without chemical treatment and life cycle data of its calcination process. …”
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Process-Dependent Evolution of Channel Stress and Stress-Induced Mobility Gain in FinFET, Normal GAAFET, and Si/SiGe Hybrid Channel GAAFET by Chiang Zhu, Xiaona Zhu, Shaofeng Yu, David Wei Zhang

“…The stress evolution indicates that Fin recess, S/D epi growth and Gate removal are three crucial process steps that influence channel stress. …”
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A Novel Framework for Financial Cybersecurity and Fraud Detection Using XAI-RNN-SGRU by Smarajit Ghosh

“…Then, it combines the Ridgelet Neural Network with Soft Gated Recurrent Unit for accurate predictions and uses the Improved Homomorphic Encryption (IHE) process to reinforce data protection during computations. …”
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Forecasting Temperature Time Series Data Using Combined Statistical and Deep Learning Methods: A Case Study of Nairobi County Daily Temperature by John Kamwele Mutinda, Amos Kipkorir Langat, Samuel Musili Mwalili

“…Overall, this study underscores the importance of VMD in preprocessing data to enhance feature representation and forecasting accuracy. …”
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