Impact of the STFT Window Size on Classification of Grain-Oriented Electrical Steels from Barkhausen Noise Time–Frequency Spectrograms via Deep CNNs by Michal Maciusowicz, Grzegorz Psuj

“…This paper investigates the influence of the STFT computational window size on the material state evaluation results obtained using convolutional neural network (CNN). …”
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Deep learning for rice leaf disease detection: A systematic literature review on emerging trends, methodologies and techniques by Chinna Gopi Simhadri, Hari Kishan Kondaveeti, Valli Kumari Vatsavayi, Alakananda Mitra, Preethi Ananthachari

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Continuous Glucose Monitoring (CGM) use in patients with diabetes mellitus type 1 is associated with higher healthcare costs, increased hospitalizations, and social disparities in... by Ameer Khowaja, Lina Adil, Teigan Dwyer, Tyson Barrett, Jamil Alkhaddo

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Driving Behavior Classification Using a ConvLSTM by Alberto Pingo, João Castro, Paulo Loureiro, Sílvio Mendes, Anabela Bernardino, Rolando Miragaia, Iryna Husyeva

“…This work explores the classification of driving behaviors using a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks (ConvLSTM). …”
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Development of a Tool for Comprehensive Balance Assessment Based on Artificial Intelligence and Anomaly Detection by Márcio Fagundes Goethel, Klaus Magno Becker, Franciele Carvalho Santos Parolini, Ulysses Fernandes Ervilha, João Paulo Vilas-Boas

“…Data analysis, employing an artificial neural network with 19 socio-anthropometric and postural variables, showed the tool’s exceptional accuracy (R = 0.99998) in differentiating among balance profiles. …”
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Research on Multi-Step Prediction of Pipeline Corrosion Rate Based on Adaptive MTGNN Spatio-Temporal Correlation Analysis by Mingyang Sun, Shiwei Qin

“…In order to comprehensively investigate the spatio-temporal dynamics of corrosion evolution under complex pipeline environments and improve the corrosion rate prediction accuracy, a novel framework for corrosion rate prediction based on adaptive multivariate time series graph neural network (MTGNN) multi-feature spatio-temporal correlation analysis is proposed. …”
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Enhancing Magnetotelluric Data Quality Using Deep Learning-Based Denoising Models: A Study of CNN and LSTM by Widya Utama, Maman Hermana, Dwa D. Warnana, Wien Lestari, Muhammad N. A. Zakariah, Sherly A. Garini, Rista F. Indriani, Dhea P. Novian Putra, M Ulin Nuha Abduh, Alif N. F. Insani, Dandi Syahtia Pratama, Khairul Arifin Mohd Noh, Abdul Halim Abdul Latiff

“…To address this critical issue, this study develops denoising models based on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to enhance the quality of MT signals while preserving their original structure. …”
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EEG Microstate Dynamics during Different Physiological Developmental Stages and the Effects of Medication in Schizophrenia by Shihai Ling, Lingyan Du, Xi Tan, Guozhi Tang, Yue Che, Shirui Song

“…Conclusions: Alterations in microstate dynamics were observed among SCZ patients across developmental stages, suggesting potential changes in brain activity patterns. Changes in microstates A and C may serve as potential biomarkers for evaluating treatment efficacy, establishing a foundation for personalized therapeutic approaches.…”
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Comparative analysis of data transformation methods for detecting non-technical losses in electricity grids by Maria Gabriel Chuwa, Daniel Ngondya, Rukia Mwifunyi

“…Convolutional neural networks (CNN) have emerged as effective tools for automatically extracting features from raw data, but raw data often lacks the structure needed for optimal feature extraction. …”
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Evolution of microbial carbon sequestration potential in farmland soil driven by natural restoration in coal mine subsidence area by Chunming HAO, Yantang WANG, Sihai YI, Shuo LIU

“…These findings provide new insights into the comprehensive evaluation of ecological benefits associated with natural recovery processes. …”
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Histopathological Image Analysis Using Deep Learning Framework by Sudha Rani Vupulluri, Jogendra Kumar Munagala

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EEG-based epilepsy detection using CNN-SVM and DNN-SVM with feature dimensionality reduction by PCA by Yousra Berrich, Zouhair Guennoun

“…Abstract This study focuses on epilepsy detection using hybrid CNN-SVM and DNN-SVM models, combined with feature dimensionality reduction through PCA. The goal is to evaluate the effectiveness and performance of these models in accurately identifying epileptic patterns. …”
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Filamentary Convolution for SLI: A Brain-Inspired Approach with High Efficiency by Boyuan Zhang, Xibang Yang, Tong Xie, Shuyuan Zhu, Bing Zeng

“…While the short-time Fourier transform (STFT) generates time–frequency acoustic features (TFAF) for deep learning networks (DLNs), rectangular convolution kernels cause frequency mixing and aliasing, degrading feature extraction. …”
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Geomagnetic Field Based Indoor Landmark Classification Using Deep Learning by Bimal Bhattarai, Rohan Kumar Yadav, Hui-Seon Gang, Jae-Young Pyun

“…We present long short-term memory DRNNs for spatial/temporal sequence learning of magnetic patterns and evaluate their positioning performance on our testbed datasets. …”
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Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews by Bosen Li, Rui Li, Junhao Wang, Aihong Song

“…Leveraging multidimensional attribute perceptions derived from tourist reviews, this study proposes a Spatial–Semantic Integrated Model for Tourist Attraction Representation (SSIM-TAR), which holistically encodes the composite attributes and multifaceted evaluations of attractions. Integrating these multidimensional features with inter-attraction relationships, three relational metrics are defined and fused: spatial proximity, resonance correlation, and thematic-sentiment similarity, forming a Tourist Attraction Multidimensional Association Network (MAN-SRT). …”
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Navigating the road ahead: using concept mapping to assess Clinical and Translational Science Award (CTSA) program goals by Cathleen Kane, William Trochim, Haim Bar, Andie Vaught, Heather Baker, Munziba Khan, Robin Wagner, Kristi Holmes, Keith Herzog, Jamie Mihoko Doyle

“…The results also revealed a pattern where long-term impacts were ranked among the highest in importance but among the lowest in feasibility, particularly for measures tied to the Translational Science Benefits Model (TSBM), a new evaluation framework gaining popularity across the CTSA. …”
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TFTformer: A novel transformer based model for short-term load forecasting by Ahmad Ahmad, Xun Xiao, Huadong Mo, Daoyi Dong

“…A linear transformation layer post embedding improves feature representation, aligning and standardising features across sequences for improved pattern recognition. Additionally, a Temporal Convolutional Network is integrated within the Transformer’s encoder, employing causal convolutions and dilation to adapt to the sequential nature of data with an expanded receptive field. …”
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Predicting changes of incisor and facial profile following orthodontic treatment: a machine learning approach by Jing Peng, Yan Zhang, Mengyu Zheng, Yanyan Wu, Guizhen Deng, Jun Lyu, Jianming Chen

“…MSE/MAE/R2 values for L1-MP were 0.0062/0.063/0.84, L1-MP, ANB and extraction pattern were identified as the top three influential predictors. …”
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Carbon Emissions From Low‐Order Streams in a Tropical, High‐Elevation, Peatland Ecosystem Are Mediated by Catchment Morphology by Keridwen M. Whitmore, Amanda G. DelVecchia, Elizabeth Farquhar, Gerard Rocher‐Ros, Esteban Suárez, Diego A. Riveros‐Iregui

“…However, few studies have examined the spatial variability of CO2 concentrations and fluxes occurring within these systems, particularly as a function of catchment morphology. Here we evaluated spatial patterns of CO2 in three tropical, headwater catchments in relation to the river network and stream geomorphology. …”
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Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neura... by Mizuho Nishio, Osamu Sugiyama, Masahiro Yakami, Syoko Ueno, Takeshi Kubo, Tomohiro Kuroda, Kaori Togashi

“…For the DCNN method, CADx was evaluated using the VGG-16 convolutional neural network with and without transfer learning, and hyperparameter optimization of the DCNN method was performed by random search. …”
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