Flu-CNN: identifying host specificity of Influenza A virus using convolutional networks by Mingda Hu, Nan Luo, Boqian Wang, Renjie Meng, Yunxiang Zhao, Zili Chai, Yuan Jin, Junjie Yue, Xin Wang, Wei Chen, Hongguang Ren

“…To address this, we proposed Flu-level Convolutional Neural Networks (Flu-CNN), a model designed to analyze genomic segments and identify IAV host specificity, with a particular focus on avian influenza viruses that could potentially infect humans. …”
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Design of Low-Cost and Highly Energy-Efficient Convolutional Neural Networks Based on Deterministic Encoding by Tiance Tong, Qiang He, Xiaofei Nie, Yudi Zhao

“…To realize high-energy-efficiency and low-cost hardware neural networks at the near-sensor end, a hardware optimization design of convolutional neural networks based on the hybrid encoding of deterministic encoding and binary encoding is proposed. …”
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DEVELOPMENT OF A METHOD TO REDUCE COMPUTATIONAL COSTS IN COMMUNICATION SYSTEMS WITH UFMC THROUGH THE USE OF NUMBER-THEORETIC TRANSFORMATIONS by M. I. Kalmykov, D. V. Yurdanov, I. A. Kalmykov, I. D. Efremenkov, E. A. Voloshin

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Neuroevolutionary Convolutional Neural Network Design for Low-Resolution Face Recognition by Jhon I. Pilataxi, Juan P. Perez, Claudio A. Perez, Kevin W. Bowyer

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Research on Arc Fault Detection Based on Conditional Batch Normalization Convolutional Neural Network with Cost-Sensitive Multi-Feature Extraction by Xin Ning, Tianli Ding, Hongwei Zhu

“…Although existing studies have made progress in improving the accuracy of their detection, most methods have not proposed effective solutions that address the cost-sensitive problem of feature selection. Thus, a multi-feature method is proposed by combining time-domain, frequency-domain, energy, and spatial features, which are integrated into a CBN (conditional batch normalization) convolutional neural network for detection. …”
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Pilot study: a low-cost, point-of-care microfluidic chip for smartphone-integrated colorimetric detection using convolutional neural networks by Mithun Kanchan, Pedapudi Anantha Hari Arun, Siddhant Rakesh Chutke

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Retracted: Detecting Distance between Surfaces of Large Transparent Material Based on Low-Cost TOF Sensor and Deep Convolutional Neural Network by Advances in Materials Science and Engineering

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Detection of Damage on Inner and Outer Races of Ball Bearings Using a Low-Cost Monitoring System and Deep Convolution Neural Networks by Handeul You, Dongyeon Kim, Juchan Kim, Keunu Park, Sangjin Maeng

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A Low-Cost AI-Powered System for Early Detection of Diabetic Retinopathy and Ocular Diseases in Resource Limited Settings by Harpreet Vohra, Mohammad Kamrul Hasan, Siti Norul Huda Sheikh Abdullah, Shayla Islam, Abdul Hadi Abd Rahman, Shreya N. Bhojake, Dhruv Bobal, Awal Kaur Gandhi, Muskan, Chandramohan Dhasarathan

“…The system features a cost-effective imaging setup utilizing a 28D lens and a camera to capture high-quality fundus images. …”
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LM-CNN-based Automatic Cost Calculation Model for Power Transmission and Transformation Projects by Xiaolin WU, Ling LUAN, Lianwu PAN, Hailong LI

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A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection by Noppadol Maneerat, Athasart Narkthewan, Kazuhiko Hamamoto

“…Early diagnosis and prompt treatment can cut off the rising number of TB deaths, and analysis of chest X-rays is a cost-effective method. We describe a deep learning-based cascade algorithm for detecting TB in chest X-rays. …”
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UGC-Net: Uncertainty-Guided Cost Volume Optimization with Contextual Features for Satellite Stereo Matching by Wonje Jeong, Soon-Yong Park

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Dense skip-attention for convolutional networks by Wenjie Liu, Guoqing Wu, Han Wang, Fuji Ren

“…Notably, it achieves these improvements without significantly increasing model parameters or computational cost, maintaining minimal impact on both aspects.…”
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Design of a hybrid AI network circuit for epilepsy detection with 97.5% accuracy and low cost-latency by Liufang Sheng, Xuanxu Chen, Yuejun Zhang, Ke Yan, Junping Chen, Zhikang Chen, Hanyu Shi, Yi Gong

“…Then, the design of the multiply-accumulate circuit was completed using a parallel-style row computation method, and a pipelined convolutional computation circuit was used to accelerate the convolutional computation and reduce the computational overhead and delay. …”
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Perspective of Creating Low-Cost Medical Assistant Robot Based on Waffle PI4 Platform with Palm Vein Pattern Scanner by V. V. Anufriiev, O. O. Levchenko, Ye. V. Levchenko, V. A. Chekubasheva, O. V. Glukhov, O. B. Galat

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Lightweight Convolutional Network for Bearing Fault Diagnosis by LIU Hui, LI Yang, HOU Yimin

“…In the field of bearing fault diagnosis, many convolutional models with excellent performance face challenges in industrial applications due to deployment cost constraints. …”
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Multithreaded Convolution Implementation Based on Block Methods by A. V. Sharamet

“…The analysis showed that one of the possible ways to reduce time costs is a multithreaded implementation of convolution based on block algorithms. …”
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Smart material selection strategies for sustainable and cost-effective high-performance concrete production using deep learning by T. Seethalakshmi, M. Murugan, P. Maria Antony Sebastin Vimalan

“…To overcome these limitations, we developed a Multi-Objective Ant Colony Adaptive Dense Convolutional Neural Network (MOAC-ADenseNet) with 5-K-Fold cross validation, a dependable and precise forecasting model for the cost-effective selection of HPC material. …”
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Convolutional neural networks and vision transformers for Plankton Classification by Loris Nanni, Alessandra Lumini, Leonardo Barcellona, Stefano Ghidoni

“…The study considers the creation of ensembles combining different Convolutional Neural Network (CNN) models and transformer architectures to understand whether different optimization algorithms can result in more robust and efficient classification across various plankton datasets. …”
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Tailoring convolutional neural networks for custom botanical data by Jamie R. Sykes, Katherine J. Denby, Daniel W. Franks

“…By contrast, PhytNet displayed excellent attention to relevant features, almost no overfitting, and an exceptionally low computation cost of 1.19 GFLOPS. Conclusions We show that PhytNet is a promising candidate for rapid disease or plant classification and for precise localisation of disease symptoms for autonomous systems. …”
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