Forecasting basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas by P. Casas-Gómez, J.F. Torres, J.C. Linares, A. Troncoso, F. Martínez-Álvarez

“…Traditional forecasting techniques, such as Linear Mixed Models, Random Forest and standard Artificial Neural Networks, often fail to account for the time-dependent nature of tree growth and utilize simple architectures. …”
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Multi-Scale Building Load Forecasting Without Relying on Weather Forecast Data: A Temporal Convolutional Network, Long Short-Term Memory Network, and Self-Attention Mechanism Appro... by Lanqian Yang, Jinmin Guo, Huili Tian, Min Liu, Chang Huang, Yang Cai

“…The reconstructed features are then input into the long short-term memory (LSTM) neural network to achieve the extraction of load time features. …”
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Robust Forest Sound Classification Using Pareto-Mordukhovich Optimized MFCC in Environmental Monitoring by Ahmad Qurthobi, Robertas Damasevicius, Vytautas Barzdaitis, Rytis Maskeliunas

“…To improve classification capabilities, the study introduces a hybrid model that combines neural network (CNN) with a Bidirectional Long-Short-Term Memory (BiLSTM) layer, designed to capture both spatial and temporal features of the sound data. …”
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AtOMICS: a deep learning-based automated optomechanical intelligent coupling system for testing and characterization of silicon photonics chiplets by Jaime Gonzalo Flor Flores, Jim Solomon, Connor Nasseraddin, Talha Yerebakan, Andrey B Matsko, Chee Wei Wong

“…This paper presents a neural network-based automated system designed for in-plane fiber-chip-fiber testing, characterization, and active alignment of silicon photonic devices that use process-design-kit library edge couplers. …”
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Heuristic Forest Fire Detection Using the Deep Learning Model with Optimized Cluster Head Selection Technique by Sengottaiyan N., Ananthi J., Rajesh Sharma R., Hamsanandhini S., Sungheetha Akey, Chinnaiyan R., Ketema Adare Gemeda

“…These parameters are processed through a sophisticated neural network architecture designed to identify patterns and correlations that signify the likelihood of a forest fire. …”
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A Tuning Method for the Supplementary Voltage Controller of Dual-Side Grid Forming Converters in Distributed Storage Systems by Angel Perez-Basante, Asier Gildemuro, Ander Ordono, Salvador Ceballos, Eneko Unamuno, Jon Andoni Barrena

“…Real-time estimation of the optimum controller gains by making use of an artificial neural network is proposed. Simulation and experimental results are presented to validate the method.…”
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Automated String Art Creation: Integrated Advanced Computational Techniques and Precision Art Designing by Spoorthi Singh, Navya T. Hegde, Mohammad Zuber, Yuvraj Singh Nain, Vishnu G. Nair

“…The project faced challenges such as material selection, CAD design, and hardware-software interfacing, all of which were addressed through iterative design and validation processes. A convolutional neural network (CNN) was employed to process grayscale images, extracting and reconstructing features using pooling and deconvolution techniques, with the model achieving stable performance over multiple epochs. …”
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Breast mass classification based on supervised contrastive learning and multi‐view consistency penalty on mammography by Lilei Sun, Jie Wen, Junqian Wang, Zheng Zhang, Yong Zhao, Guiying Zhang, Yong Xu

“…In this paper, A novel classification algorithm based on Convolutional Neural Network (CNN) is proposed to improve the diagnostic performance for breast cancer on mammography. …”
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Acoustic Trauma Changes the Parvalbumin-Positive Neurons in Rat Auditory Cortex by Congli Liu, Tao Xu, Xiaopeng Liu, Yina Huang, Haitao Wang, Bin Luo, Jingwu Sun

“…Parvalbumin-containing neurons (PV neurons), a subset of GABAergic neurons, greatly shape and synchronize neural network activities. However, the change of PV neurons following acoustic trauma remains to be elucidated. …”
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A Free-Space-Based Model for Predicting Peanut Moisture Content during Natural Drying by Xin Xu, Ying Sun, Yuanyuan Yin, Yiwei Xue, Fangyan Ma, Chao Song, Hang Yin, Liqing Zhao

“…According to the findings, the ELM neural network model, which is based on the optimization of the SSA, has an improved prediction accuracy. …”
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Enhancing paddy leaf disease diagnosis -a hybrid CNN model using simulated thermal imaging by Jagamohan Padhi, Kunal Mishra, Ashoka Kumar Ratha, Santi Kumari Behera, Prabira Kumar Sethy, Aziz Nanthaamornphong

“…Eighteen Convolutional Neural Network (CNN) models were evaluated using transfer learning, with statistical analysis via Duncan's multiple range test (DMRT) identifying Darknet53 as the best-performing model, achieving an accuracy of 95.79 %, sensitivity of 95.79 %, specificity of 95.93 %, and an F1 score of 0.96. …”
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Early Diagnosis of Alzheimer’s Disease Using Adaptive Neuro K-Means Clustering Technique by Karan Kumar, Shweta Agrawal, Isha Suwalka, Celestine Iwendi, Cresantus N. Biamba

“…The approach integrates the Adaptive Moving Self-Organizing Map (AMSOM), a neural network technique for unsupervised training and tissue segmentation, with K-means clustering and Principal Component Analysis (PCA) for feature selection. …”
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MambaShadowDet: A High-Speed and High-Accuracy Moving Target Shadow Detection Network for Video SAR by Xiaowo Xu, Tianwen Zhang, Xiaoling Zhang, Wensi Zhang, Xiao Ke, Tianjiao Zeng

“…Existing convolution neural network (CNN)-based video synthetic aperture radar (SAR) moving target shadow detectors are difficult to model long-range dependencies, while transformer-based ones often suffer from greater complexity. …”
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Image Quality Assessment Based on Multi-Scale Representation and Shifting Transformer by Geng Fu, Ziyu Wang, Cuijuan Zhang, Zerong Qi, Mingzheng Hu, Shujun Fu, Yunfeng Zhang

“…Recently, transformer-based algorithms have excelled in computer vision, particularly in image classification, surpassing convolutional neural network (CNN) methods. To enhance IQA using transformers, we propose Swin-MIQT, a multi-scale spatial pooling transformer with shifted windows. …”
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An Automatic Recognition Method of Microseismic Signals Based on S Transformation and Improved Gaussian Mixture Model by Kaikai Wang, Chun’an Tang, Ke Ma, Xintang Wang, Qiang Li

“…The identification accuracy is as high as 94%, and its recognition effect is superior to other recognition models (such as traditional Gaussian Mixture Model based on Expectation-Maximum (EM-GMM), Backpropagation (BP) neural network, Random Forests (RF), Bayes (Bayes) methods, and Logistic Regression (LR) method). …”
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Low-threshold dual-polarization electro-optic nonlinear activation functions by Tao Jin, Jian Lin, Pengjun Wang, Qiang Fu, Yuhan Sun, Yi Zou, Shixun Dai, Weiwei Chen, Jun Li, Tingge Dai, Jianyi Yang

“…If the above-mentioned two nonlinear activation functions are introduced into the convolutional neural network to perform the modified National Institute of Standards and Technology handwritten digit classification task, validation accuracies of 97.3% and 96.85% will be achieved.…”
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End-to-end neural automatic speech recognition system for low resource languages by Sami Dhahbi, Nasir Saleem, Sami Bourouis, Mouhebeddine Berrima, Elena Verdú

“…An on-the-fly data augmentation method is applied to these mel-spectrograms, treating them as images from which features are extracted to train a convolutional neural network (CNN) and a bidirectional long short-term memory (BLSTM)-based ASR. …”
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Multitask Learning for Estimation of Magnetic Parameters Using Pattern Recognition by Anubha Sehgal, Shipra Saini, Hemkant Nehete, Kunal Kranti Das, Sourajeet Roy, Brajesh Kumar Kaushik

“…Using these techniques, we introduce a multi-task convolutional neural network (CNN) model and support vector regression (SVR) model that is intended to precisely estimate two important parameters of magnetic systems such as the Dzyaloshinskii-Moriya interaction (DMI) constant and the exchange constant (A_ex). …”
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Enhancing Particulate Matter Estimation in Livestock-Farming Areas with a Spatiotemporal Deep Learning Model by Dohyeong Kim, Heeseok Kim, Minseon Hwang, Yongchan Lee, Choongki Min, Sungwon Yoon, Sungchul Seo

“…Using a 200 m × 200 m prediction grid, forecasts were generated for both 1 h and 24 h intervals using the Graz Lagrangian model (GRAL) and a one-dimensional convolutional neural network combined with the long short-term memory algorithm (1DCNN-LSTM). …”
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Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Op... by Wei Xie, Jie-sheng Wang, Cheng Xing, Sha-Sha Guo, Meng-wei Guo, Ling-feng Zhu

“…Compared with the previous MW-LSSVM, MW-neural network trained with extended Kalman filter(MW-KNN), and MW-RELM, the prediction accuracy of the hybrid model is further improved. …”
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