A comprehensive evaluation of deep learning approaches for ground-level ozone prediction across different regions by Guanjun Lin, Hang Zhao, Yufeng Chi

“…This lack of uniformity in evaluations creates a gap in our understanding of the influence of different neural network architectures and depths on ozone concentration predictions. …”
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Enhancing Model Robustness in Noisy Environments: Unlocking Advanced Mono-Channel Speech Enhancement With Cooperative Learning and Transformer Networks by Wei Hu, Yan Wu

“…Through collaborative learning across network layers, the UDU facilitates the extraction of richer feature spaces from speech data, thereby enhancing the network’s ability to capture pertinent information. …”
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Aggregated approach for interstitial lung diseases classification using attention based CNN and radial basis function neural network by S. Kumarganesh, K.V.M. Shree, P. Rishabavarthani, C. Ganesh, S. Anthoniraj, B. Thiyaneswaran, Lam Dang, K. Martin Sagayam, Linh Dinh, Hien Dang

“…The reduced features were then trained and tested using a radial basis function neural network (RBFNN). …”
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A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology by Yanni Wang, Wisit Cheungpasitporn, Hatem Ali, Jianbo Qing, Charat Thongprayoon, Wisit Kaewput, Karim M. Soliman, Zhengxing Huang, Min Yang, Zhongheng Zhang

“…To address these issues, this article offers a structured framework for nephrologist peer reviewers, integrating the TRIPOD-AI (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis–AI Extension) checklist. Key evaluation criteria include dataset integrity, feature selection, model validation, reporting transparency, ethics, and real-world applicability. …”
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Multi-Head Graph Attention Adversarial Autoencoder Network for Unsupervised Change Detection Using Heterogeneous Remote Sensing Images by Meng Jia, Xiangyu Lou, Zhiqiang Zhao, Xiaofeng Lu, Zhenghao Shi

“…The MHGAN employs a bidirectional adversarial convolutional autoencoder network to reconstruct and perform style transformation of heterogeneous images. …”
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3D Reconstruction of Asphalt Pavement Macro-Texture Based on Convolutional Neural Network and Monocular Image Depth Estimation by Xinliang Liu, Chao Yin

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An integrated deep convolutional neural networks framework for the automatic segmentation and grading of glioma tumors using multimodal MRI scans by Otung John Peter Odong, Mohammed Abo-Zahhad, Moataz Abdelwahab

“…Based on 3D U-Net architecture, the first CNN performs tumor segmentation from multimodal MRI volumes. The segmentation network utilizes the encoder for feature extraction and dimensionality reduction, while the decoder reconstructs the output to the original input size. …”
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Label-Free Evaluation of Lung and Heart Transplant Biopsies Using Tissue Autofluorescence-Based Virtual Staining by Yuzhu Li, Nir Pillar, Tairan Liu, Guangdong Ma, Yuxuan Qi, Kevin Haan, Yijie Zhang, Xilin Yang, Adrian J. Correa, Guangqian Xiao, Kuang-Yu Jen, Kenneth A. Iczkowski, Yulun Wu, William Dean Wallace, Aydogan Ozcan

“…Specifically, we virtually generated hematoxylin and eosin (H&E), Masson’s Trichrome (MT), and elastic Verhoeff-Van Gieson stains for label-free transplant lung tissue, along with H&E and MT stains for label-free transplant heart tissue. Results: Blind evaluations conducted by 3 board-certified pathologists confirmed that the virtual staining networks consistently produce high-quality histology images with high color uniformity, closely resembling their well-stained histochemical counterparts across various tissue features. …”
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Demystifying User Needs in Wardrobe Furniture Design: A Network Analysis via Text Mining and DEMATEL-ANP Integration by Xinlian Li, Peijun Jia, Jinyang Xu

“…This study integrated web text mining, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, and the Analytic Network Process (ANP) to construct a causal network model for wardrobe design, and further optimized design proposals through the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). …”
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Dual-Stream Enhanced Deep Network for Transmission Near-Infrared Dorsal Hand Vein Age Estimation with Attention Mechanisms by Zhenghua Shu, Zhihua Xie, Xiaowei Zou

“…To this end, this paper proposes an efficient dorsal hand vein age estimation model using a deep neural network with attention mechanisms. Specifically, a convolutional neural network (CNN) is developed to extract the expressive features for age estimation. …”
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Supply-Demand Matching Evaluation and Coupling Coordination of Cultural Ecosystem Services in Urban Park Green Spaces by Yang YU, Xian YANG, Xiang LI, Liyuan QIAN, Sixiang ZHOU

“…Overall, the coupling coordination degree is suboptimal, with the zones featuring the highest coordination degree only reaching a barely coordinated level. …”
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MACA-Net: Mamba-Driven Adaptive Cross-Layer Attention Network for Multi-Behavior Recognition in Group-Housed Pigs by Zhixiong Zeng, Zaoming Wu, Runtao Xie, Kai Lin, Shenwen Tan, Xinyuan He, Yizhi Luo

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Taking a look at your speech: identifying diagnostic status and negative symptoms of psychosis using convolutional neural networks by Gleb Melshin, Anthony DiMaggio, Nadia Zeramdini, Michael MacKinley, Lena Palaniyappan, Alban Voppel

“…Abstract Speech-based indices are promising objective biomarkers for identifying schizophrenia and monitoring symptom burden. Static acoustic features show potential but often overlook time-varying acoustic cues that clinicians naturally evaluate—such as negative symptoms—during clinical interviews. …”
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Deep vision-based real-time hand gesture recognition: a review by Cui Cui, Mohd Shahrizal Sunar, Goh Eg Su

“…The choice of evaluation metrics and dataset is critical since different tasks require different evaluation parameters, and the model learns more patterns and features from diverse data. …”
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Functional and Structural Network Alterations in HIV-Associated Asymptomatic Neurocognitive Disorders: Evidence for Functional Disruptions Preceding Structural Changes by Zhou Z, Gong W, Hu H, Wang F, Li H, Xu F, Li H, Wang W

“…Furthermore, machine learning classifiers were used to construct and evaluate classification models based on imaging features from both groups. …”
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METAHEURISTIC-AI ENHANCED CUSTOM DEEP LEARNING NETWORK OPTIMIZED WITH SAND CAT SWARM ALGORITHM FOR ORAL CANCER DIAGNOSIS by Vinod Kumar Venkatesan, V Sujatha, Audithan Sivaraman, S Durga Devi, Praveen SR Konduri

“…To address this, a novel hybrid deep learning framework is proposed that combines a Convolutional Neural Network (CNN) with Sand Cat Swarm Optimization (SCSO) for the effective classification of oral squamous cell carcinoma (OSCC) from histopathological images. …”
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Multi-spatial urban function modeling: A multi-modal deep network approach for transfer and multi-task learning by Zhaoya Gong, Chenglong Wang, Bin Liu, Binbo Li, Wei Tu, Yuting Chen, Zhicheng Deng, Pengjun Zhao

“…Understanding dynamics of urban land-use is crucial for comprehending urban spaces and evaluating planning strategies. A range of data-driven models based on the representation learning of multiple data sources have focused on extracting spatially explicit characteristics at the feature level for urban function inference. …”
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Integration of LSTM networks with gradient boosting machines (GBM) for assessing heating and cooling load requirements in building energy efficiency by Reenu Batra, Shakti Arora, Mayank Mohan Sharma, Sonu Rana, Kanishka Raheja, Abeer Saber, Mohd Asif Shah

“…Due to rising demand for energy-efficient buildings, advanced predictive models are needed to evaluate heating and cooling load requirements. This research presents a unified strategy that blends LSTM networks and GBM to improve building energy load estimates’ precision and reliability. …”
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Learning physics and temporal dependencies: real-time modeling of water distribution systems via Kolmogorov–Arnold attention networks by Zekun Zou, Zhihong Long, Gang Xu, Raziyeh Farmani, Tingchao Yu, Shipeng Chu

“…To address these challenges, this study proposes the Kolmogorov–Arnold Attention Network for the real-time modeling of WDSs (KANSA), which combines Kolmogorov–Arnold Networks with attention mechanisms to extract temporal dependency features through bidirectional spatiotemporal processing. …”
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Papayafreshnet: a hybrid deep learning framework for non-destructive freshness classification of papayas using convolutional and transformer networks by Ayan Sar, Tanupriya Choudhury, Tanmay Sarkar, Ketan Kotecha

“…The architecture integrates the advantages of Convolutional Neural Networks (CNNs) with sophisticated Transformer-based feature extraction and attention methods. …”
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