INFORMATION IMAGE MODEL by Evgeniy V. Yurkevich

“…A model of a convolution of information in the form of image and symbol. …”
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Construction and application of a TCN-LSTM-SVM-based time series prediction model for water inflow in coal seam roofs by Xuan LIU, Yadong JI, Kaipeng ZHU, Chunhu ZHAO, Kai LI, Chaofeng LI, Chenhan YUAN, Panpan LI, Pengzhen YAN

“…This model exhibited more accurate prediction results compared to the commonly used prediction models like backpropagation neural network (BPNN), random forest (RF), and Transformer while avoiding excessive errors produced by most of these models on the validation and test sets. …”
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A Comparative Study of Image Processing and Machine Learning Methods for Classification of Rail Welding Defects by Mohale Emmanuel Molefe, Jules Raymond Tapamo, Siboniso Sithembiso Vilakazi

“…Defects formed during the thermite welding process of two sections of rails require the welded joints to be inspected for quality, and the most used non-destructive method for inspection is radiography testing. …”
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Field-grown tomato yield estimation using point cloud segmentation with 3D shaping and RGB pictures from a field robot and digital single lens reflex cameras by B. Ambrus, G. Teschner, A.J. Kovács, M. Neményi, L. Helyes, Z. Pék, S. Takács, T. Alahmad, A. Nyéki

“…The best fitting sphere model was generated using the 3D model. The most optimal model was the 3D model, which gave the best representation and provided the weight of the tomatoes with a relative error of 21.90 % and a standard deviation of 17.9665 %. …”
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Adaptive deep SVM for detecting early heart disease among cardiac patients by S. N. Netra, N. N. Srinidhi, E. Naresh

“…Abstract Heart attack is one of the most common heart diseases, which causes more deaths worldwide. …”
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HIDS-RPL: A Hybrid Deep Learning-Based Intrusion Detection System for RPL in Internet of Medical Things Network by Abdelwahed Berguiga, Ahlem Harchay, Ayman Massaoudi

“…We evaluated our novel methodology against five DDoS attacks: DNS, UDP, UDP-Lag, NTP, and SYN. In comparison to the most recent methods, our suggested model achieves an accuracy of 99.87%, a precision of 98.5%, a recall rate of 98.64%, and an F1-score of 98.54%.…”
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Semantic Segmentation of Archaeological Features on Public Lands: Case Study of Historical Cotton Terraces within the Piedmont National Wildlife Refuge, Georgia, USA by Claudine Gravel-Miguel, Grant Snitker, Jayde N. Hirniak, Katherine Peck, Alex Fetterhoff

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MACHINE LEARNING TECHNIQUES FOR RETINOPATHY DETECTION IN DIABETIC PATIENTS by Ajay Kushwaha, Ahankari Sachin Suresh, Chennoju Phanindra, Anil Kumar Sahu, Devanand Bhonsle, Yamini Chouhan

“…The suggested method analyzes high-resolution retinal pictures using deep learning methods, most especially Convolutional Neural Networks (CNNs). …”
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Virtual Reality Video Image Classification Based on Texture Features by Guofang Qin, Guoliang Qin

“…As one of the most widely used methods in deep learning technology, convolutional neural networks have powerful feature extraction capabilities and nonlinear data fitting capabilities. …”
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Academic Emotion Classification Using FER: A Systematic Review by Jeniffer Xin-Ying Lek, Jason Teo

“…Moreover, support vector machine (SVM) is the conventional learning emotion classifier that is widely used in the FER systems, while convolutional neural network (CNN) is the most frequently used deep learning classifier. …”
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BCDnet: Parallel heterogeneous eight-class classification model of breast pathology. by Qingfang He, Guang Cheng, Huimin Ju

“…The model uses the VGG16 convolution base and Resnet50 convolution base as the parallel convolution base of the model. …”
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Neural network analysis of small samples using a large number of statistical criteria to test the sequence of hypotheses about the value of mathematical expectations of correlation... by A.I. Ivanov, A.I. Godunov, E.A. Malygina, N.A. Papusha, A.I. Ermakova

“…The correlation coefficient is one of the most significant second-order statistical points. …”

Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data by Bas Peters, Eldad Haber, Keegan Lensink

“…While reversibility saves the major amount of memory used in deep networks by the data, the convolutional kernels can take up most memory if fully invertible networks contain multiple invertible pooling/coarsening layers. …”
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Novel feature extraction method for signal analysis based on independent component analysis and wavelet transform. by Mariusz Topolski, Jędrzej Kozal

“…Compared to other literature methods, our approach was better than most feature extraction methods except for convolutional neural networks. …”
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Vessel Trajectory Prediction Method Based on the Time Series Data Fusion Model by Xinyun WU, Jiafei CHEN, Caiquan XIONG, Donghua LIU, Xiang WAN, Zexi CHEN

“…To address this issue, this study introduces a method consisting of temporal convolutional network (TCN), convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM) to predict vessel trajectories, called TCC. …”
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An analytical examination of the performance assessment of CNN-LSTM architectures for state-of-health evaluation of lithium-ion batteries by Arun Jose, Sonam Shrivastava

“…In this study, an assessment was conducted on various Convolutional Neural Network-Long Short-Term Memory architectures to explore their complete potential, with the generic architecture yielding the most favorable outcomes. …”
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Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation by J. M. Torres, R. M. Aguilar

“…We also conduct a sensitivity analysis to determine which estimator type is most robust to perturbations. An analysis of our findings shows that the most accurate and robust estimators are those based on feedforward neural networks with a SELU activation function and convolutional neural networks.…”
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A Novel High Performance Object Identification Approach in Care Homes Using Gaussian Preprocessing by Sara Auweiler, Melina Mueller, Diana Puhla, Pascal Penava, Ricardo Buettner

“…Therefore we compare the performance of four well-known Convolutional Neural Network architectures — VGG16, VGG19, InceptionV3, and ResNet50 — to identify the most effective model for this application. …”
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MSAmix-Net: Diabetic Retinopathy Classification by Jianyun Gao, Shu Li, Yiwen Chen, Rongwu Xiang

“…With the development of deep learning, various automatic diagnosis models for DR have been proposed. Most models are based on convolutional neural networks, but due to the small size of convolution kernels in shallow networks, the receptive field is limited, preventing the capture of global information. …”
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A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI by Sayyed Shahid Hussain, Pir Masoom Shah, Hussain Dawood, Xu Degang, Ahmad Alshamayleh, Muhammad Adnan Khan, Taher M. Ghazal

“…Abstract Parkinson’s disease ranks as the second most prevalent neurological disorder after Alzheimer’s disease. …”
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