Hierarchical quantitative prediction of photovoltaic power generation depreciation expense based on matrix task prioritization considering uncertainty risk by Yinming Liu, Wengang Wang, Xiangyue Meng, Yuchen Zhang, Zhuyu Chen

“…According to the calculated risk value, a double-layer photovoltaic power generation cost planning model is constructed, the upper and lower objective functions of the model are determined, and the constraint conditions are designed; Obtain a cost planning objective function solution base on a matrix task prioritization method, and generating a prioritization table; Prediction of photovoltaic power generation depreciation expense based on long-short memory neural network for each solution in the sorting table. …”
Get full text

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. …”
Get full text

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. …”
Get full text

Prediction of the RFID Identification Rate Based on the Neighborhood Rough Set and Random Forest for Robot Application Scenarios by Hong-Gang Wang, Shan-Shan Wang, Ruo-Yu Pan, Sheng-Li Pang, Xiao-Song Liu, Zhi-Yong Luo, Sheng-Pei Zhou

“…Compared with BP neural network (BPNN) and other prediction models, NRS-RF has shorter prediction time and faster calculation speed. …”
Get full text

Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data by Haytam Elyoussfi, Abdelghani Boudhar, Salwa Belaqziz, Mostafa Bousbaa, Karima Nifa, Bouchra Bargam, Abdelghani Chehbouni

“…The models evaluated include two ML approaches: Support Vector Regression (SVR) and eXtreme Gradient Boosting (XGBoost) and four DL models: 1-Dimensional Convolutional Neural Network (1D-CNN), Long Short-Term Memory Networks (LSTM), Gated Recurrent Unit (GRU), and Bi-directional Long Short-Term Memory Network (Bi-LSTM). …”
Get full text

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. …”
Get full text

Analysis of tensile properties in tempered martensite steels with different cementite particle size distributions by Kengo Sawai, Keiya Sugiura, Toshio Ogawa, Ta-Te Chen, Fei Sun, Yoshitaka Adachi

“…We succeeded in developing image-based regression models with high accuracy using a convolutional neural network (CNN). Moreover, gradient-weighted class activation mapping (Grad-CAM) suggested that fine cementite particles and coarse and spheroidal cementite particles are the dominant factors for tensile strength and total elongation, respectively.…”
Get full text

Learning to Boost the Performance of Stable Nonlinear Systems by Luca Furieri, Clara Lucia Galimberti, Giancarlo Ferrari-Trecate

“…Our methods enable learning over specific classes of deep neural network performance-boosting controllers for stable nonlinear systems; crucially, we guarantee <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_{p}$</tex-math></inline-formula> closed-loop stability even if optimization is halted prematurely. …”
Get full text

QoE-Driven Big Data Management in Pervasive Edge Computing Environment by Qianyu Meng, Kun Wang, Xiaoming He, Minyi Guo

“…Then, with respect to accuracy, we propose a Tensor-Fast Convolutional Neural Network (TF-CNN) algorithm based on deep learning, which is suitable for high-dimensional big data analysis in the pervasive edge computing environment. …”
Get full text

Urdu Handwritten Characters Data Visualization and Recognition Using Distributed Stochastic Neighborhood Embedding and Deep Network by Mujtaba Husnain, Malik Muhammad Saad Missen, Shahzad Mumtaz, Dost Muhammad Khan, Mickäel Coustaty, Muhammad Muzzamil Luqman, Jean-Marc Ogier, Hizbullah Khattak, Sikandar Ali, Ali Samad

“…We performed three tasks in a disciplined order; namely, (i) we generated a state-of-the-art dataset of both the Urdu handwritten characters and numerals by inviting a number of native Urdu participants from different social and academic groups, since there is no publicly available dataset of such type till date, then (ii) applied classical approaches of dimensionality reduction and data visualization like Principal Component Analysis (PCA), Autoencoders (AE) in comparison with t-Stochastic Neighborhood Embedding (t-SNE), and (iii) used the reduced dimensions obtained through PCA, AE, and t-SNE for recognition of Urdu handwritten characters and numerals using a deep network like Convolution Neural Network (CNN). The accuracy achieved in recognition of Urdu characters and numerals among the approaches for the same task is found to be much better. …”
Get full text

Forward model emulator for atmospheric radiative transfer using Gaussian processes and cross validation by O. Lamminpää, J. Susiluoto, J. Hobbs, J. McDuffie, A. Braverman, H. Owhadi

“…In contrast with artificial neural network (ANN)-based methods, it is interpretable, and its efficiency is based on learning a kernel in an engineered and expressive family of kernels.…”
Get full text

A CNN-RF Hybrid Approach for Rice Paddy Fields Mapping in Indramayu Using Sentinel-1 and Sentinel-2 Data by Dodi Sudiana, Mia Rizkinia, Rahmat Arief, Tiara De Arifani, Anugrah Indah Lestari, Dony Kushardono, Anton Satria Prabuwono, Josaphat Tetuko Sri Sumantyo

“…This study proposes the CNN-RF method, which combines a convolutional neural network (CNN) as a feature extractor and a random forest (RF) as a classifier. …”
Get full text

Vibration Images-Driven Fault Diagnosis Based on CNN and Transfer Learning of Rolling Bearing under Strong Noise by Hongwei Fan, Ceyi Xue, Xuhui Zhang, Xiangang Cao, Shuoqi Gao, Sijie Shao

“…In this paper, aiming at the vibration image samples of rolling bearing affected by strong noise, the convolutional neural network- (CNN-) and transfer learning- (TL-) based fault diagnosis method is proposed. …”
Get full text

Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases by Mosleh Hmoud Al-Adhaileh, Ebrahim Mohammed Senan, Waselallah Alsaade, Theyazn H. H Aldhyani, Nizar Alsharif, Ahmed Abdullah Alqarni, M. Irfan Uddin, Mohammed Y. Alzahrani, Elham D. Alzain, Mukti E. Jadhav

“…In the classification stage, pretrained convolutional neural network (CNN) models are tuned by transferring learning to perform new tasks. …”
Get full text

Predicting the Botanical Origin of Honeys with Chemometric Analysis According to Their Antioxidant and Physicochemical Properties by Anna Maria Kaczmarek, Małgorzata Muzolf-Panek, Jolanta Tomaszewska-Gras, Piotr Konieczny

“…The aim of this study was to develop models based on Linear Discriminant Analysis (LDA), Classification and Regression Trees (C&RT), and Artificial Neural Network (ANN) for the prediction of the botanical origin of honeys using their physicochemical parameters as well as their antioxidative and thermal properties. …”
Get full text

Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease by Wei Zheng, Xiaoyan Qin, Ronghua Mu, Peng Yang, Bingqin Huang, Bingqin Huang, Zhixuan Song, Xiqi Zhu, Xiqi Zhu

“…An Unet-based deep learning neural network model was developed to automate the segmentation of the hippocampus. …”
Get full text

A novel data augmentation tool for enhancing machine learning classification: A new application of the higher order dynamic mode decomposition for improved cardiac disease identifi... by Nourelhouda Groun, María Villalba-Orero, Lucía Casado-Martín, Enrique Lara-Pezzi, Eusebio Valero, Jesús Garicano-Mena, Soledad Le Clainche

“…In this work, a data-driven, modal decomposition method, the higher order dynamic mode decomposition (HODMD), is combined with a convolutional neural network (CNN) in order to improve the classification accuracy of several cardiac diseases using echocardiography images. …”
Get full text

Tongue-LiteSAM: A Lightweight Model for Tongue Image Segmentation With Zero-Shot by Daiqing Tan, Hao Zang, Xinyue Zhang, Han Gao, Ji Wang, Zaijian Wang, Xing Zhai, Huixia Li, Yan Tang, Aiqing Han

“…Results: Experiments conducted on six distinct tongue image datasets demonstrated that the Tongue-LiteSAM model outperformed traditional convolutional neural network-based models and transformers, the original SAM model, and other related improved models in tongue image segmentation tasks. …”
Get full text

Adaptive algorithms for change point detection in financial time series by Alexander Musaev, Dmitry Grigoriev, Maxim Kolosov

“…The work highlights the potential for future advancements in neural network applications and multi-expert decision systems, further enhancing predictive accuracy in volatile environments.…”
Get full text

An Improved Deep Learning Network Structure for Multitask Text Implication Translation Character Recognition by Xiaoli Ma, Hongyan Xu, Xiaoqian Zhang, Haoyong Wang

“…The coarse filter is based on some simple morphological features and stroke width features, and the fine filter is trained by a two-recognition convolutional neural network. The remaining character candidate regions are merged into horizontal or multidirectional character strings through the graph model. …”
Get full text