Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids by Xin-Ru Wen, Jia-Wei Tang, Jie Chen, Hui-Min Chen, Muhammad Usman, Quan Yuan, Yu-Rong Tang, Yu-Dong Zhang, Hui-Jin Chen, Liang Wang

“…Multiple ML models were constructed and optimized, with the convolutional neural network (CNN) model achieving the highest prediction accuracy at 99%. …”
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An efficient hybrid model of CNNs and different kernels of SVM for brain tumor classification by Mohammed Bourennane, Hilal Naimi

“…A convolutional neural network (CNN)-based model was developed in this study to classify brain tumor. …”
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Integrating Data Mining, Deep Learning, and Gene Ontology Analysis for Gene Expression-Based Disease Diagnosis Systems by Sergii Babichev, Igor Liakh, Jiri Skvor

“…The manuscript details the outcomes of a comprehensive study on the application of cluster-bicluster analysis, gene ontology analysis, and convolutional neural network (CNN) for diagnosing cancer and Alzheimer’s disease using gene expression data derived from both DNA microarray experiments and mRNA sequencing. …”
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Vision Transformer untuk Klasifikasi Kematangan Pisang by Arya Pangestu, Bedy Purnama, Risnandar Risnandar

“…Model ViT menunjukkan kemampuan generalisasi yang lebih baik, sementara CNN memiliki ukuran model dan waktu pelatihan yang lebih efisien. …”
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Artificial intelligence in degenerative cervical disease: A systematic review of MRI-based diagnostic models by Qian Du, Xinxin Shao, Minbo Zhang, Guangru Cao

“…Convolutional neural networks (CNN) were the most frequently used models (four studies), followed by support vector machines (three studies). …”
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Fixed-Time Sliding Mode Control for Vehicle Platoon With Input Dead-Zone and Prescribed Performance by Yongqiang Jiang, Yiguang Wang, Xiaojie Li, Xiaoyan Zhan, Xubing Tang

“…Furthermore, Chebyshev neural network (CNN) is adopted to approximate unknown nonlinearities, and new adaptive mechanisms are designed to estimate IDZ slope and external disturbances. …”
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Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy by Fabio Pisano, Giuliana Sias, Alessandra Fanni, Barbara Cannas, António Dourado, Barbara Pisano, Cesar A. Teixeira

“…In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. …”
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Enhanced Hyperspectral Forest Soil Organic Matter Prediction Using a Black-Winged Kite Algorithm-Optimized Convolutional Neural Network and Support Vector Machine by Yun Deng, Lifan Xiao, Yuanyuan Shi

“…This model leverages the powerful parameter tuning capabilities of BKA, uses CNN for feature extraction, and uses SVM for classification and regression, further improving the accuracy of SOM prediction. …”
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WGAN-DL-IDS: An Efficient Framework for Intrusion Detection System Using WGAN, Random Forest, and Deep Learning Approaches by Shehla Gul, Sobia Arshad, Sanay Muhammad Umar Saeed, Adeel Akram, Muhammad Awais Azam

“…Experimental results show that our proposed framework outperforms state-of-the-art approaches, vastly improving DL model detection accuracy by 98% using CNN.…”
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Minirhizotron measurements can supplement deep soil coring to evaluate root growth of winter wheat when certain pitfalls are avoided by Jessica Arnhold, Facundo R. Ispizua Yamati, Henning Kage, Anne-Katrin Mahlein, Heinz-Josef Koch, Dennis Grunwald

“…Results The previously existing CNN could successfully be adapted for wheat root images. …”
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Machine Learning-Based Lithium Battery State of Health Prediction Research by Kun Li, Xinling Chen

“…To address the problem of predicting the state of health (SOH) of lithium-ion batteries, this study develops three models optimized using the particle swarm optimization (PSO) algorithm, including the long short-term memory (LSTM) network, convolutional neural network (CNN), and support vector regression (SVR), for accurate SOH estimation. …”
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Towards Transparent AI in Medicine: ECG-Based Arrhythmia Detection with Explainable Deep Learning by Oleksii Kovalchuk, Oleksandr Barmak, Pavlo Radiuk, Liliana Klymenko, Iurii Krak

“…Third, we implemented an interpretation method that explains CNN’s decisions using clinically relevant features, making the results understandable to clinicians. …”
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A proximal policy optimization based deep reinforcement learning framework for tracking control of a flexible robotic manipulator by Joshi Kumar V, Vinodh Kumar Elumalai

“…Identifying the optimal hyper-parameters of DRL using the grid search method, we exploit the capability of CNN in actor-critic architecture to extract the spatial dependencies in the state sequences of the dynamical system and boost the DRL performance. …”
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Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory–Attention Mechanism by Geunseok Yang, Jinfeng Ji, Jaehee Kim

“…Compared to baseline models such as Naïve Bayes, Random Forest, Decision Tree, SVM, CNN, LSTM, and CNN-LSTM, the proposed model achieved a superior performance. …”
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Anomaly Detection in Spatiotemporal Data from Fiber Optic Distributed Temperature Sensing for Outdoor Fire Monitoring by Haitao Bian, Xiaohan Luo, Zhichao Zhu, Xiaowei Zang, Yu Tian

“…Results showed that, compared to AE and VAE models handling spatial or temporal data, the CNN-AE demonstrated superior anomaly detection performance and strong robustness when applied to spatiotemporal data. …”
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Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers by Mohammed Achite, Okan Mert Katipoğlu, Veysi Kartal, Metin Sarıgöl, Muhammad Jehanzaib, Enes Gül

“…In this study, advanced soft computing techniques, including long short-term memory (LSTM), convolutional neural network–recurrent neural network (CNN-RNN), and group method of data handling (GMDH) algorithms, were employed to forecast monthly streamflow time series at two different stations in the Wadi Mina basin. …”
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Assessing nighttime artificial light pollution from the perspective of an unmanned aerial vehicle tilt by Jiejie Wu, Liang Zhou, Deping Li, Daoquan Zhang, Tingting Jiang, Chengzhi Zong

“…Key findings include: (1) With the drone camera set to f2.8, the best correlation between image RGB color channel brightness and measured illuminance occurs at ISO 200 and 1/4s, achieving R2 reaches 0.83 and MAE reaches 7.89 lx. (2) Mask R-CNN and Deeplabv3 achieve over 0.91 in extraction accuracy, with Mask R-CNN excelling in window, street, and neon lights, while Deeplabv3 better handles building sides and road surfaces. (3) Vertical light pollution from typical residential buildings shows a gradual attenuation trend with increasing height. …”
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A Deep Learning-Based Approach to Enable Action Recognition for Construction Equipment by Jinyue Zhang, Lijun Zi, Yuexian Hou, Mingen Wang, Wenting Jiang, Da Deng

“…The contributions of this research are as follows: (1) the development of a comprehensive video dataset of 2,064 clips with five action types for excavators and dump trucks; (2) a new deep learning-based CEAR approach (known as a simplified temporal convolutional network or STCN) that combines a convolutional neural network (CNN) with long short-term memory (LSTM, an artificial recurrent neural network), where CNN is used to extract image features and LSTM is used to extract temporal features from video frame sequences; and (3) the comparison between this proposed new approach and a similar CEAR method and two of the best-performing HAR approaches, namely, three-dimensional (3D) convolutional networks (ConvNets) and two-stream ConvNets, to evaluate the performance of STCN and investigate the possibility of directly transferring HAR approaches to the field of CEAR.…”
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Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait Perception by Changjie Wang, Zhihua Li, Benjamin Sarpong

“…First, in combination with the collected gait information of individuals from triaxial accelerometers on smartphones, the collected information is preprocessed, and multimodal fusion is used with the existing standard datasets to yield a multimodal synthetic dataset; then, with the multimodal characteristics of the collected biological gait information, a Convolutional Neural Network based Gait Recognition (CNN-GR) model and the related scheme for the multimodal features are developed; at last, regarding the proposed CNN-GR model and scheme, a unimodal gait feature identity single-gait feature identification algorithm and a multimodal gait feature fusion identity multimodal gait information algorithm are proposed. …”
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ECG heartbeat classification using progressive moving average transform by Rabah Mokhtari, Samir Brahim Belhouari, Khelil Kassoul, Abderraouf Hocini

“…Our approach integrates PMAT with a 2D-Convolutional Neural Network (CNN) model for the classification of ECG heartbeat signals. …”
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