Classification of Biological Data using Deep Learning Technique by Azha Javed, Muhammad Javed Iqbal

“…In our work, we have proposed 1D-convolution neural network which classifies the protein sequences to 10 top common classes. …”
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Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network by Stefano A. Bini, MD, Nicholas Gillian, PhD, Thomas A. Peterson, PhD, Richard B. Souza, PhD, PT, Brooke Schultz, MS, ACE-CPT, Wojciech Mormul, MS, Marek K. Cichoń, MS, Agnieszka Barbara Szczotka, MS, Ivan Poupyrev, PhD

“…Model performance was assessed using mean absolute error. Results: The convolutional neural network models exhibited high accuracy in replicating motion capture-derived kinematic variables. …”
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Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder by Shubham Chaudhry, Azzedine Abdedou, Azzeddine Soulaïmani

“…The compressed latent space is then projected onto the input variables using a multilayer perceptron (MLP) regression model. …”
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DHS-YOLO: Enhanced Detection of Slender Wheat Seedlings Under Dynamic Illumination Conditions by Xuhua Dong, Jingbang Pan

“…This detection task presents significant technical challenges due to two inherent complexities: (1) environmental interference from variable illumination conditions and (2) morphological characteristics of wheat seedlings characterized by slender leaf structures and flexible posture variations. …”
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Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals. by Reihaneh Hassanzadeh, Rogers F Silva, Anees Abrol, Mustafa Salman, Anna Bonkhoff, Yuhui Du, Zening Fu, Thomas DeRamus, Eswar Damaraju, Bradley Baker, Vince D Calhoun

“…Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. …”
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AG-MS3D-CNN multiscale attention guided 3D convolutional neural network for robust brain tumor segmentation across MRI protocols by Umesh Kumar Lilhore, R. Sunder, Sarita Simaiya, Majed Alsafyani, M. D. Monish Khan, Roobaea Alroobaea, Hamed Alsufyani, Abdullah M. Baqasah

“…Traditional methods of tumor segmentation, often manual and labour-intensive, are prone to inconsistencies and inter-observer variability. Recently, deep learning models, particularly Convolutional Neural Networks (CNNs), have shown great promise in automating this process. …”
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In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module by Junwei Zhu, Xupeng Ouyang, Zongkang Jiang, Yanlong Xu, Hongtao Xue, Huiyu Yue, Huayuan Feng

“…To address the challenge of fault diagnosis for in-wheel motors in four-wheel independent driving systems under variable driving conditions and harsh environments, this paper proposes a novel method based on two-stream 2DCNNs (two-dimensional convolutional neural networks) with a DCBA (depthwise convolution block attention) module. …”
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Advanced Defect Detection in Wrap Film Products: A Hybrid Approach with Convolutional Neural Networks and One-Class Support Vector Machines with Variational Autoencoder-Derived Cov... by Tatsuki Shimizu, Fusaomi Nagata, Maki K. Habib, Koki Arima, Akimasa Otsuka, Keigo Watanabe

“…We introduce a pioneering methodology centered on covariance vectors extracted from latent variables, a product of a Variational Autoencoder (VAE). …”
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Active Acoustics in Concert Halls – A New Approach by Wieslaw WOSZCZYK

“…One critical benefit of active architecture is the controlled variability of acoustics. Although many improvements have been made over the last 60 years in the quality and usability of active acoustics, some problems still persist and the acceptance of this technology is advancing cautiously. …”
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A Deep Learning Framework for the Classification of Brazilian Coins by Debabrata Swain, Viral Rupapara, Amro Nour, Santosh Satapathy, Biswaranjan Acharya, Shakti Mishra, Ali Bostani

“…Our proposed deep learning framework leverages state-of-the-art convolutional neural networks (CNNs) to address these challenges. …”
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Research on Unsupervised Domain Adaptive Bearing Fault Diagnosis Method Based on Migration Learning Using MSACNN-IJMMD-DANN by Xiaoxu Li, Jiahao Wang, Jianqiang Wang, Jixuan Wang, Qinghua Li, Xuelian Yu, Jiaming Chen

“…To address the problems of feature extraction, cost of obtaining labeled samples, and large differences in domain distribution in bearing fault diagnosis on variable operating conditions, an unsupervised domain-adaptive bearing fault diagnosis method based on migration learning using MSACNN-IJMMD-DANN (multi-scale and attention-based convolutional neural network, MSACNN, improved joint maximum mean discrepancy, IJMMD, domain adversarial neural network, DANN) is proposed. …”
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Estimating PM_2.5 Exposures and Cardiovascular Disease Risks in the Yangtze River Delta Region Using a Spatiotemporal Convolutional Approach to Fill Gaps in Satellite Dat... by Muhammad Jawad Hussain, Myeongsu Seong, Behjat Shahid, Heming Bai

“…This study introduced a spatiotemporal convolutional approach to fill sampling gaps in TOAR and AOD data from the Himawari-8 geostationary satellite over the Yangtze River Delta (YRD) in 2016. …”
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Abnormal traffic detection method based on LSTM and improved residual neural network optimization by Wengang MA, Yadong ZHANG, Jin GUO

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Bayesian optimization of hybrid quantum LSTM in a mixed model for precipitation forecasting by Yumin Dong, Huanxin Ding

“…However, the factors affecting precipitation are complex and nonlinear, and have spatiotemporal variability, making rainfall forecasting extremely challenging. …”
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Deep Learning for Cardiovascular Disease Detection by Shivan H. Hussein, Najdavan A. Kako

“…A review of publicly available datasets underlines challenges in data variability and generalizability and points to the need for standardized models and explainable AI approaches. …”
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Online Pre-Diagnosis of Multiple Faults in Proton Exchange Membrane Fuel Cells by Convolutional Neural Network Based Bi-Directional Long Short-Term Memory Parallel Model with Atten... by Junyi Chen, Huijun Ran, Ziyang Chen, Trevor Hocksun Kwan, Qinghe Yao

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Spectra of algebras of block-symmetric analytic functions of bounded type by A. Zagorodnyuk, V. V. Kravtsiv

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Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features by Aiguo Lu, Zican Li, Yanwei Liu, Pandi Liu, Ke Wang

“…The Omni-Dimensional Dynamic Convolution module and Bi-Level Routing Attention mechanism are introduced to enhance the model’s adaptability to complex scenes and variable features, thereby improving its capability to capture details of small targets. …”
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Approach for identifying crop seeds with similar appearances using hyperspectral images and improved ResNet 18 based on cloud platform by Hui Li, Xuliang Duan

“…Hyperspectral images are preprocessed by moving average method (MA) and standard normal variable transformation (SNV) to reduce spectral data interference. …”
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PGHDR: Dynamic HDR reconstruction with progressive feature alignment and quality-guided fusion by Ying Qi, Qiushi Li, Zhaoyuan Huang, Jian Li, Chenyang Wang, Teng Wan, Qiang Zhang

“…Existing methods typically adopt an align-then-fuse strategy, often overlooking the spatial variability of alignment quality, which makes it difficult to balance ghosting suppression and detail preservation when handling complex motion. …”
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