Design and implementation of quantum hippo inspired convolutional neural networks using parametric quantum circuits for an efficient lung cancer classification by S. Radhika, G. Sharada

“…Abstract With the advancement of Artificial Intelligence in medical and engineering fields, the most unique solutions were deployed in any individual’s life for prolonging their life span against the odds of growing disorders. …”
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Partial renal deletion of Klotho is not sufficient to impact renal electrolyte handling in distal convoluted tubule specific knock‐out mice by Teodora V. Grigore, Quinty M. Leusink, Malou Zuidscherwoude, Caro Bos, Hannes Olauson, Joost Hoenderop

“…Considering that the distal convoluted tubule is the most important site for Ca2+ regulation in the kidney, Ksp‐KL−/− mice were challenged with a Ca2+‐deficient diet for determining the Ca2+ handling and pinpointing the Klotho levels needed for controlling renal Ca2+ handling. …”
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A convolutional neural network-based deep learning approach for predicting surface chloride concentration of concrete in marine tidal zones by Mohamed Abdellatief, Mahmoud E. Abd-Elmaboud, Mohamed Mortagi, Ahmed M. Saqr

“…Shapley additive explanation (SHAP) analysis revealed exposure time, water content, and fine aggregate as the most critical factors influencing Cs predictions. …”
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Application of deep learning based on convolutional neural network model in multimodal ultrasound diagnosis of unexplained cervical lymph node enlargement by Shanshan Jiang, Naiqian Zhang, Chen Li, Lingxia Tong, Xiuhua Yang

“…First, we pre-trained four convolutional neural networks using a public medical image dataset. …”
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A topology-guided high-quality solution learning framework for security-constraint unit commitment based on graph convolutional network by Liqian Gao, Lishen Wei, Shichang Cui, Jiakun Fang, Xiaomeng Ai, Wei Yao, Jinyu Wen

“…In the end, it can be concluded that the feasibility and high-quality of the solution can be guaranteed while reducing most of the computation time.…”
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Skin-lesion segmentation using boundary-aware segmentation network and classification based on a mixture of convolutional and transformer neural networks by Javaria Amin, Marium Azhar, Habiba Arshad, Amad Zafar, Seong-Han Kim

“…We further propose a compact convolutional transformer model (CCTM) based on convolution and transformers for classification. …”
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Enhancing Mild Cognitive Impairment Auxiliary Identification Through Multimodal Cognitive Assessment with Eye Tracking and Convolutional Neural Network Analysis by Na Li, Ziming Wang, Wen Ren, Hong Zheng, Shuai Liu, Yi Zhou, Kang Ju, Zhongting Chen

“…Eye movement features only, behavioral features only, and combined features models were developed and tested respectively, to find out the most effective approach for MCI auxiliary identification. …”
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A hybrid convolutional neural network model for dental age estimation using buccal alveolar bone level for Saudi children by Seyed Matin Mazloom Nezhad, Erma Rahayu Mohd Faizal Abdullah, Norliza Ibrahim, Heba H. Bakhsh, Uzair Ishtiaq, Rabiah Al Adawiyah Rahmat, Sarah AlMugairin, Sara M. ElKhateeb

“…Dental Age Estimation (DAE) is one of the most common methods for age estimation. Teeth are commonly used for age estimation because the schedules of tooth development and eruption are barely affected by the environment, nutrition, and socio-economic factors. …”
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Auto Machine Learning and Convolutional Neural Network in Diabetes Mellitus Research—The Role of Histopathological Images in Designing and Exploring Experimental Models by Iulian Tătaru, Simona Moldovanu, Oana-Maria Dragostin, Carmen Lidia Chiţescu, Alexandra-Simona Zamfir, Ionut Dragostin, Liliana Strat, Carmen Lăcrămioara Zamfir

“…The second comprises image classification with a custom-built convolutional neural network (CB-CNN), the extraction of textural features (contrast, entropy, energy, and homogeneity), and their classification with PyCaret Auto Machine Learning (AutoML). …”
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Tumor detection in breast cancer pathology patches using a Multi-scale Multi-head Self-attention Ensemble Network on Whole Slide Images by Ruigang Ge, Guoyue Chen, Kazuki Saruta, Yuki Terata

“…Breast cancer (BC) is the most common type of cancer among women globally and is one of the leading causes of cancer-related deaths among women. …”
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Optimized ReXNet variants with spatial pyramid pooling, CoordAttention, and convolutional block attention module for money plant disease detection by Parul Nasra, Jaibir Singh, Suman Rani, Gunjan Shandilya, Salil Bharany, Sandeep Sood, Ateeq Ur Rehman, Seada Hussen

“…Abstract Money plants, widely appreciated for their ornamental appeal and air-purifying capabilities, are susceptible to several diseases most notably Bacterial Wilt Disease and Manganese Toxicity—that can significantly impact their vitality and aesthetic value. …”
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DeepRice6mA: A convolutional neural network approach for 6mA site prediction in the rice Genome. by Hussam Alsharif

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Classification of Known and Unknown Study Items in a Memory Task Using Single-Trial Event-Related Potentials and Convolutional Neural Networks by Jorge Delgado-Munoz, Reiko Matsunaka, Kazuo Hiraki

“…The EEGNET-based model achieved the most reliable performance in terms of precision, recall, and specificity compared with the shallow and deep convolutional approaches. …”
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Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs) by Robert A. Sowah, Kwaku Apeadu, Francis Gatsi, Kwame O. Ampadu, Baffour S. Mensah

“…Until recently, most consumer-grade fire detection systems relied solely on smoke detectors. …”
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HiGMA-DADCN: Hirudinaria granulosa multitropic algorithm optimised double attention enabled deep convolutional neural network for psoriasis classification by Soumya C S, Jayanna H S

“…The HiGMA algorithm plays a crucial role in identifying and extracting the most relevant regions of affected skin through optimal segmentation. …”
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Evaluating pedestrian crossing safety: Implementing and evaluating a convolutional neural network model trained on paired aerial and subjective perspective images by Dylan Russon, Antoine Guennec, Juan Naredo-Turrado, Binbin Xu, Cédric Boussuge, Valérie Battaglia, Benoit Hiron, Emmanuel Lagarde

“…The analysis reveals that the ConvNextV2 model, in particular, demonstrates superior performance across most tasks, despite challenges such as data imbalance and the complex nature of variables like visibility and parking proximity.The findings highlight the potential of convolutional neural networks in improving pedestrian safety by enabling scalable and objective evaluations of crossings. …”
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Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification by Umesh Kumar Lilhore, Yogesh Kumar Sharma, Brajesh Kumar Shukla, Muniraju Naidu Vadlamudi, Sarita Simaiya, Roobaea Alroobaea, Majed Alsafyani, Abdullah M. Baqasah

“…Abstract Breast cancer detection remains one of the most challenging problems in medical imaging. We propose a novel hybrid model that integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM) networks, and EfficientNet-B0, a pre-trained model. …”
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A multi-graph convolutional network method for Alzheimer’s disease diagnosis based on multi-frequency EEG data with dual-mode connectivity by Qingjie Xu, Qingjie Xu, Libing An, Haiqiang Yang, Haiqiang Yang, Keum-Shik Hong, Keum-Shik Hong

“…Currently, electroencephalography (EEG) is widely used in the study of neurodegenerative diseases. However, most existing research relies solely on functional connectivity methods to infer inter-regional brain connectivity, overlooking the importance of spatial connections. …”
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An Adaptive Graph Convolutional Network with Spatial Autocorrelation for Enhancing 3D Soil Pollutant Mapping Precision from Sparse Borehole Data by Huan Tao, Ziyang Li, Shengdong Nie, Hengkai Li, Dan Zhao

“…We propose an adaptive graph convolutional network with spatial autocorrelation (ASI-GCN) model to overcome this challenge. …”
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Space-Frequency Fusion Dual-Branch Convolutional Neural Networks for Significant Wave Height Retrieval From GF-3 SAR Data by Xuan Jin, Yawei Zhao, Xin Zhang, Yanlei Du, Jinsong Chong

“…By employing the space-frequency feature cross layer (SFFCL) and the gated feature fusion layer (GFFL), it enhances and fuses space-frequency features, thereby achieving more accurate SAR SWH retrieval. Most retrieval models based on GF-3 data primarily focus on wave mode data, with limited utilization of data from other imaging modes. …”
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