Applied Sciences

Research

Jump to: Review, Other

24 pages, 2450 KiB

Open AccessArticle

Progressive Pruning of Light Dehaze Networks for Static Scenes

by Byeongseon Park, Heekwon Lee, Yong-Kab Kim and Sungkwan Youm

Appl. Sci. 2024, 14(23), 10820; https://doi.org/10.3390/app142310820 - 22 Nov 2024

Viewed by 229

Abstract

This paper introduces an progressive pruning method for Light DeHaze Networks, focusing on a static scene captured by a fixed camera environments. We develop a progressive pruning algorithm that aims to reduce computational complexity while maintaining dehazing quality within a specified threshold. Our [...] Read more.

This paper introduces an progressive pruning method for Light DeHaze Networks, focusing on a static scene captured by a fixed camera environments. We develop a progressive pruning algorithm that aims to reduce computational complexity while maintaining dehazing quality within a specified threshold. Our key contributions include a fine-tuning strategy for specific scenes, channel importance analysis, and an progressive pruning approach considering layer-wise sensitivity. Our experiments demonstrate the effectiveness of our progressive pruning method. Our progressive pruning algorithm, targeting a specific PSNR(Peak Signal-to-Noise Ratio) threshold, achieved optimal results at a certain pruning ratio, significantly reducing the number of channels in the target layer while maintaining PSNR above the threshold and preserving good structural similarity, before automatically stopping when performance dropped below the target. This demonstrates the algorithm’s ability to find an optimal balance between model compression and performance maintenance. This research enables efficient deployment of high-quality dehazing algorithms in resource-constrained environments, applicable to traffic monitoring and outdoor surveillance. Our method paves the way for more accessible image dehazing systems, enhancing visibility in various real-world hazy conditions while optimizing computational resources for fixed camera setups. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

► Show Figures

Figure 1

16 pages, 1788 KiB

Open AccessArticle

A Stock Prediction Method Based on Heterogeneous Bidirectional LSTM

by Shuai Sang and Lu Li

Appl. Sci. 2024, 14(20), 9158; https://doi.org/10.3390/app14209158 - 10 Oct 2024

Viewed by 795

Abstract

LSTM (long short-term memory) networks have been proven effective in processing stock data. However, the stability of LSTM is poor, it is greatly affected by data fluctuations, and it is weak in capturing long-term dependencies in sequential data. BiLSTM (bidirectional LSTM) has alleviated [...] Read more.

LSTM (long short-term memory) networks have been proven effective in processing stock data. However, the stability of LSTM is poor, it is greatly affected by data fluctuations, and it is weak in capturing long-term dependencies in sequential data. BiLSTM (bidirectional LSTM) has alleviated this issue to some extent; however, due to the inefficiency of information transmission within the LSTM units themselves, the generalization performance and accuracy of BiLSTM is still not very satisfactory. To address this problem, this paper improves LSTM units on the basis of traditional BiLSTM and proposes a He-BiLSTM (heterogeneous bidirectional LSTM) with a corresponding backpropagation algorithm. The parameters in He-BiLSTM are updated using the Adam gradient descent method. Experimental results show that compared to BiLSTM, He-BiLSTM has further improved in terms of accuracy, robustness, and generalization performance. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

► Show Figures

Figure 1

20 pages, 16267 KiB

Open AccessArticle

Multi-Scale Detail–Noise Complementary Learning for Image Denoising

by Yan Cui, Mingyue Shi and Jielin Jiang

Appl. Sci. 2024, 14(16), 7044; https://doi.org/10.3390/app14167044 - 11 Aug 2024

Viewed by 1326

Abstract

Deep convolutional neural networks (CNNs) have demonstrated significant potential in enhancing image denoising performance. However, most denoising methods fuse different levels of features through long and short skip connections, easily generating a lot of redundant information, thereby weakening the complementarity of different levels [...] Read more.

Deep convolutional neural networks (CNNs) have demonstrated significant potential in enhancing image denoising performance. However, most denoising methods fuse different levels of features through long and short skip connections, easily generating a lot of redundant information, thereby weakening the complementarity of different levels of features, resulting in the loss of image details. In this paper, we propose a multi-scale detail–noise complementary learning (MDNCL) network for additive white Gaussian noise removal and real-world noise removal. The MDNCL network comprises two branches, namely the Detail Feature Learning Branch (DLB) and the Noise Learning Branch (NLB). Specifically, a loss function is applied to guide the complementary learning of image detail features and noisy mappings in these two branches. This learning approach effectively balances noise reduction and detail restoration, especially when dealing with high ratios of noise. To enhance the complementarity of features between different network layers and avoid redundant information, we designed a Feature Subtraction Unit (FSU) to capture the differences in features across the DLB network layers. Our extensive experimental evaluations demonstrate that the MDNCL approach achieves impressive denoising performance and outperforms other popular denoising methods. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

► Show Figures

Figure 1

15 pages, 2587 KiB

Open AccessArticle

Unsupervised Scene Image Text Segmentation Based on Improved CycleGAN

by Xian Liu, Fang Yang and Wei Guo

Appl. Sci. 2024, 14(11), 4420; https://doi.org/10.3390/app14114420 - 23 May 2024

Viewed by 822

Abstract

Scene image text segmentation is an important task in computer vision, but the complexity and diversity of backgrounds make it challenging. All supervised image segmentation tasks require paired semantic label data to ensure the accuracy of segmentation, but semantic labels are often difficult [...] Read more.

Scene image text segmentation is an important task in computer vision, but the complexity and diversity of backgrounds make it challenging. All supervised image segmentation tasks require paired semantic label data to ensure the accuracy of segmentation, but semantic labels are often difficult to obtain. To solve this problem, we propose an unsupervised scene image text segmentation model based on the image style transfer model cyclic uniform Generation Adversarial network (CycleGAN), which is trained by partial unpaired label data. Text segmentation is achieved by converting a complex background to a simple background. Since the images generated by CycleGAN cannot retain the details of the text content, we also introduced the Atrous spatial Pyramid pool module (ASPP) to obtain the features of the text from multiple scales. The resulting image quality is improved. The proposed method is verified by experiments on a synthetic data set, the IIIT 5k word data set and the MACT data set, which effectively segments the text and preserves the details of the text content. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

► Show Figures

Figure 1

21 pages, 8442 KiB

Open AccessArticle

Multi-Step Multidimensional Statistical Arbitrage Prediction Using PSO Deep-ConvLSTM: An Enhanced Approach for Forecasting Price Spreads

by Sensen Tu, Panke Qin, Mingfu Zhu, Zeliang Zeng, Shenjie Cheng and Bo Ye

Appl. Sci. 2024, 14(9), 3798; https://doi.org/10.3390/app14093798 - 29 Apr 2024

Viewed by 971

Abstract

Due to its effectiveness as a risk-hedging trading strategy in financial markets, futures arbitrage is highly sought after by investors in turbulent market conditions. The essence of futures arbitrage lies in formulating strategies based on predictions of future futures price differentials. However, contemporary [...] Read more.

Due to its effectiveness as a risk-hedging trading strategy in financial markets, futures arbitrage is highly sought after by investors in turbulent market conditions. The essence of futures arbitrage lies in formulating strategies based on predictions of future futures price differentials. However, contemporary research predominantly focuses on projections of single indicators for the subsequent temporal juncture, and devising efficacious arbitrage strategies often necessitates the examination of multiple indicators across timeframes. To tackle the aforementioned challenge, our methodology leverages a PSO Deep-ConvLSTM network, which, through particle swarm optimization (PSO), refines hyperparameters, including layer architectures and learning rates, culminating in superior predictive performance. By analyzing temporal-spatial data within financial markets through ConvLSTM, the model captures intricate market patterns, performing better in forecasting than traditional models. Multistep forward simulation experiments and extensive ablation studies using future data from the Shanghai Futures Exchange in China validate the effectiveness of the integrated model. Compared with the gate recurrent unit (GRU), long short-term memory (LSTM), Transformer, and FEDformer, this model exhibits an average reduction of 39.8% in root mean squared error (RMSE), 42.5% in mean absolute error (MAE), 45.6% in mean absolute percentage error (MAPE), and an average increase of 1.96% in coefficient of determination (R2) values. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

► Show Figures

Figure 1

25 pages, 3314 KiB

Open AccessArticle

Novel GA-Based DNN Architecture for Identifying the Failure Mode with High Accuracy and Analyzing Its Effects on the System

by Naeim Rezaeian, Regina Gurina, Olga A. Saltykova, Lokmane Hezla, Mammetnazar Nohurov and Kazem Reza Kashyzadeh

Appl. Sci. 2024, 14(8), 3354; https://doi.org/10.3390/app14083354 - 16 Apr 2024

Cited by 9 | Viewed by 1074

Abstract

Symmetric data play an effective role in the risk assessment process, and, therefore, integrating symmetrical information using Failure Mode and Effects Analysis (FMEA) is essential in implementing projects with big data. This proactive approach helps to quickly identify risks and take measures to [...] Read more.

Symmetric data play an effective role in the risk assessment process, and, therefore, integrating symmetrical information using Failure Mode and Effects Analysis (FMEA) is essential in implementing projects with big data. This proactive approach helps to quickly identify risks and take measures to address them. However, this task is always time-consuming and costly. On the other hand, there is an essential need for an expert in this field to carry out this process manually. Therefore, in the present study, the authors propose a new methodology to automatically manage this task through a deep-learning technique. Moreover, due to the different nature of the risk data, it is not possible to consider a single neural network architecture for all of them. To overcome this problem, a Genetic Algorithm (GA) was employed to find the best architecture and hyperparameters. Finally, the risks were processed and predicted using the new proposed methodology without sending data to other servers, i.e., external servers. The results of the analysis for the first risk, i.e., latency and real-time processing, showed that using the proposed methodology can improve the detection accuracy of the failure mode by 71.52%, 54.72%, 72.47%, and 75.73% compared to the unique algorithm with the activation function of Relu and number of neurons 32, respectively, related to the one, two, three, and four hidden layers. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

► Show Figures

Figure 1

22 pages, 5750 KiB

Open AccessArticle

Deep Q-Learning-Based Smart Scheduling of EVs for Demand Response in Smart Grids

by Viorica Rozina Chifu, Tudor Cioara, Cristina Bianca Pop, Horia Gabriel Rusu and Ionut Anghel

Appl. Sci. 2024, 14(4), 1421; https://doi.org/10.3390/app14041421 - 8 Feb 2024

Cited by 4 | Viewed by 1227

Abstract

Economic and policy factors are driving the continuous increase in the adoption and usage of electrical vehicles (EVs). However, despite being a cleaner alternative to combustion engine vehicles, EVs have negative impacts on the lifespan of microgrid equipment and energy balance due to [...] Read more.

Economic and policy factors are driving the continuous increase in the adoption and usage of electrical vehicles (EVs). However, despite being a cleaner alternative to combustion engine vehicles, EVs have negative impacts on the lifespan of microgrid equipment and energy balance due to increased power demands and the timing of their usage. In our view, grid management should leverage on EV scheduling flexibility to support local network balancing through active participation in demand response programs. In this paper, we propose a model-free solution, leveraging deep Q-learning to schedule the charging and discharging activities of EVs within a microgrid to align with a target energy profile provided by the distribution system operator. We adapted the Bellman equation to assess the value of a state based on specific rewards for EV scheduling actions and used a neural network to estimate Q-values for available actions and the epsilon-greedy algorithm to balance exploitation and exploration to meet the target energy profile. The results are promising, showing the effectiveness of the proposed solution in scheduling the charging and discharging actions for a fleet of 30 EVs to align with the target energy profile in demand response programs, achieving a Pearson coefficient of 0.99. This solution also demonstrates a high degree of adaptability in effectively managing scheduling situations for EVs that involve dynamicity, influenced by various state-of-charge distributions and e-mobility features. Adaptability is achieved solely through learning from data without requiring prior knowledge, configurations, or fine-tuning. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

► Show Figures

Figure 1

17 pages, 16054 KiB

Open AccessArticle

Simulation of Spinal Cord Reflexes

by Mihai Popescu and Cristian Ravariu

Appl. Sci. 2024, 14(1), 310; https://doi.org/10.3390/app14010310 - 29 Dec 2023

Cited by 1 | Viewed by 977

Abstract

The importance of spinal reflexes is connected to the rehabilitation processes in neural prostheses and to the neuromuscular junction. In order to model neuron networks as electronic circuits, a simulation environment like LTSpice XVII or PSpice can be used to create a complete [...] Read more.

The importance of spinal reflexes is connected to the rehabilitation processes in neural prostheses and to the neuromuscular junction. In order to model neuron networks as electronic circuits, a simulation environment like LTSpice XVII or PSpice can be used to create a complete electronic description. There are four types of neurons employed in spinal reflexes: α-motoneurons, sensitive neurons, excitatory interneurons, and inhibitory interneurons. Many proposals have been made regarding methods that can be used for assimilating neurons using electronic circuits. In this paper, only a single internal model of a neuron is considered enough to simulate all four types of neurons implicated in the control loops. The main contribution of this paper is to propose the modeling of neurons using some electronic circuits designed either with a bipolar transistor or with CMOS transistors for the input and output of circuits stages. In this way, it is possible to mimic the neural pulses’ circulation along the loops of the spinal reflexes and to prove the accuracy of the simulation results with respect to the biological signals collected from the bibliographic materials. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

► Show Figures

Figure 1

22 pages, 5601 KiB

Open AccessArticle

Dynamic Depth Learning in Stacked AutoEncoders

by Sarah Alfayez, Ouiem Bchir and Mohamed Maher Ben Ismail

Appl. Sci. 2023, 13(19), 10994; https://doi.org/10.3390/app131910994 - 5 Oct 2023

Viewed by 1462

Abstract

The effectiveness of deep learning models depends on their architecture and topology. Thus, it is essential to determine the optimal depth of the network. In this paper, we propose a novel approach to learn the optimal depth of a stacked AutoEncoder, called Dynamic [...] Read more.

The effectiveness of deep learning models depends on their architecture and topology. Thus, it is essential to determine the optimal depth of the network. In this paper, we propose a novel approach to learn the optimal depth of a stacked AutoEncoder, called Dynamic Depth for Stacked AutoEncoders (DDSAE). DDSAE learns in an unsupervised manner the depth of a stacked AutoEncoder while training the network model. Specifically, we propose a novel objective function, aside from the AutoEncoder’s loss function to optimize the network depth: The optimization of the objective function determines the layers’ relevance weights. Additionally, we propose an algorithm that iteratively prunes the irrelevant layers based on the learned relevance weights. The performance of DDSAE was assessed using benchmark and real datasets. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

► Show Figures

Figure 1

14 pages, 1831 KiB

Open AccessArticle

Patch-Level Consistency Regularization in Self-Supervised Transfer Learning for Fine-Grained Image Recognition

by Yejin Lee, Suho Lee and Sangheum Hwang

Appl. Sci. 2023, 13(18), 10493; https://doi.org/10.3390/app131810493 - 20 Sep 2023

Viewed by 1293

Abstract

Fine-grained image recognition aims to classify fine subcategories belonging to the same parent category, such as vehicle model or bird species classification. This is an inherently challenging task because a classifier must capture subtle interclass differences under large intraclass variances. Most previous approaches [...] Read more.

Fine-grained image recognition aims to classify fine subcategories belonging to the same parent category, such as vehicle model or bird species classification. This is an inherently challenging task because a classifier must capture subtle interclass differences under large intraclass variances. Most previous approaches are based on supervised learning, which requires a large-scale labeled dataset. However, such large-scale annotated datasets for fine-grained image recognition are difficult to collect because they generally require domain expertise during the labeling process. In this study, we propose a self-supervised transfer learning method based on Vision Transformer (ViT) to learn finer representations without human annotations. Interestingly, it is observed that existing self-supervised learning methods using ViT (e.g., DINO) show poor patch-level semantic consistency, which may be detrimental to learning finer representations. Motivated by this observation, we propose a consistency loss function that encourages patch embeddings of the overlapping area between two augmented views to be similar to each other during self-supervised learning on fine-grained datasets. In addition, we explore effective transfer learning strategies to fully leverage existing self-supervised models trained on large-scale labeled datasets. Contrary to the previous literature, our findings indicate that training only the last block of ViT is effective for self-supervised transfer learning. We demonstrate the effectiveness of our proposed approach through extensive experiments using six fine-grained image classification benchmark datasets, including FGVC Aircraft, CUB-200-2011, Food-101, Oxford 102 Flowers, Stanford Cars, and Stanford Dogs. Under the linear evaluation protocol, our method achieves an average accuracy of

78.5 %

, outperforming the existing transfer learning method, which yields

77.2 %

. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

► Show Figures

Figure 1

18 pages, 6770 KiB

Open AccessArticle

Research on Improved GRU-Based Stock Price Prediction Method

by Chi Chen, Lei Xue and Wanqi Xing

Appl. Sci. 2023, 13(15), 8813; https://doi.org/10.3390/app13158813 - 30 Jul 2023

Cited by 6 | Viewed by 5844

Abstract

The prediction of stock prices holds significant implications for researchers and investors evaluating stock value and risk. In recent years, researchers have increasingly replaced traditional machine learning methods with deep learning approaches in this domain. However, the application of deep learning in forecasting [...] Read more.

The prediction of stock prices holds significant implications for researchers and investors evaluating stock value and risk. In recent years, researchers have increasingly replaced traditional machine learning methods with deep learning approaches in this domain. However, the application of deep learning in forecasting stock prices is confronted with the challenge of overfitting. To address the issue of overfitting and enhance predictive accuracy, this study proposes a stock prediction model based on a gated recurrent unit (GRU) with reconstructed datasets. This model integrates data from other stocks within the same industry, thereby enriching the extracted features and mitigating the risk of overfitting. Additionally, an auxiliary module is employed to augment the volume of data through dataset reconstruction, thereby enhancing the model’s training comprehensiveness and generalization capabilities. Experimental results demonstrate a substantial improvement in prediction accuracy across various industries. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

► Show Figures

Figure 1

19 pages, 3494 KiB

Open AccessArticle

A Multi-Layer Feature Fusion Model Based on Convolution and Attention Mechanisms for Text Classification

by Hua Yang, Shuxiang Zhang, Hao Shen, Gexiang Zhang, Xingquan Deng, Jianglin Xiong, Li Feng, Junxiong Wang, Haifeng Zhang and Shenyang Sheng

Appl. Sci. 2023, 13(14), 8550; https://doi.org/10.3390/app13148550 - 24 Jul 2023

Cited by 5 | Viewed by 2878

Abstract

Text classification is one of the fundamental tasks in natural language processing and is widely applied in various domains. CNN effectively utilizes local features, while the Attention mechanism performs well in capturing content-based global interactions. In this paper, we propose a multi-layer feature [...] Read more.

Text classification is one of the fundamental tasks in natural language processing and is widely applied in various domains. CNN effectively utilizes local features, while the Attention mechanism performs well in capturing content-based global interactions. In this paper, we propose a multi-layer feature fusion text classification model called CAC, based on the Combination of CNN and Attention. The model adopts the idea of first extracting local features and then calculating global attention, while drawing inspiration from the interaction process between membranes in membrane computing to improve the performance of text classification. Specifically, the CAC model utilizes the local feature extraction capability of CNN to transform the original semantics into a multi-dimensional feature space. Then, global attention is computed in each respective feature space to capture global contextual information within the text. Finally, the locally extracted features and globally extracted features are fused for classification. Experimental results on various public datasets demonstrate that the CAC model, which combines CNN and Attention, outperforms models that solely rely on the Attention mechanism. In terms of accuracy and performance, the CAC model also exhibits significant improvements over other models based on CNN, RNN, and Attention. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

► Show Figures

Figure 1

17 pages, 8822 KiB

Open AccessArticle

Removing Rain Streaks from Visual Image Using a Combination of Bilateral Filter and Generative Adversarial Network

by Yue Yang, Minglong Xu, Chuang Chen and Fan Xue

Appl. Sci. 2023, 13(11), 6387; https://doi.org/10.3390/app13116387 - 23 May 2023

Viewed by 1513

Abstract

Images acquired using vision sensors are easily affected by environmental limitations, especially rain streaks. These streaks will seriously reduce image quality, which, in turn, reduces the accuracy of the algorithms that use the resulting images in vision sensor systems. In this paper, we [...] Read more.

Images acquired using vision sensors are easily affected by environmental limitations, especially rain streaks. These streaks will seriously reduce image quality, which, in turn, reduces the accuracy of the algorithms that use the resulting images in vision sensor systems. In this paper, we proposed a method that combined the bilateral filter with the generative adversarial network to eliminate the interference of rain streaks. Unlike other methods that use all the information in an image as the input to the generative adversarial network, we used a bilateral filter to preprocess and separate the high frequency part of the original image. The generator for the high-frequency layer of the image was designed to generate an image with no rain streaks. The high-frequency information of the image was used in a high-frequency global discriminator designed to measure the authenticity of the generated image from multiple perspectives. We also designed a loss function based on the structural similarity index to further improve the effect of removal of the rain streaks. An ablation experiment proved the validity of the method. We also compared images in synthetic and real-world datasets. Our method could retain more image information, and the generated image was clearer. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

► Show Figures

Figure 1

Review

Jump to: Research, Other

27 pages, 3698 KiB

Open AccessReview

A Historical Survey of Advances in Transformer Architectures

by Ali Reza Sajun, Imran Zualkernan and Donthi Sankalpa

Appl. Sci. 2024, 14(10), 4316; https://doi.org/10.3390/app14104316 - 20 May 2024

Cited by 1 | Viewed by 3719

Abstract

In recent times, transformer-based deep learning models have risen in prominence in the field of machine learning for a variety of tasks such as computer vision and text generation. Given this increased interest, a historical outlook at the development and rapid progression of [...] Read more.

In recent times, transformer-based deep learning models have risen in prominence in the field of machine learning for a variety of tasks such as computer vision and text generation. Given this increased interest, a historical outlook at the development and rapid progression of transformer-based models becomes imperative in order to gain an understanding of the rise of this key architecture. This paper presents a survey of key works related to the early development and implementation of transformer models in various domains such as generative deep learning and as backbones of large language models. Previous works are classified based on their historical approaches, followed by key works in the domain of text-based applications, image-based applications, and miscellaneous applications. A quantitative and qualitative analysis of the various approaches is presented. Additionally, recent directions of transformer-related research such as those in the biomedical and timeseries domains are discussed. Finally, future research opportunities, especially regarding the multi-modality and optimization of the transformer training process, are identified. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

► Show Figures

Figure 1

Other

Jump to: Research, Review

26 pages, 767 KiB

Open AccessTutorial

Hands-On Fundamentals of 1D Convolutional Neural Networks—A Tutorial for Beginner Users

by Ilaria Cacciari and Anedio Ranfagni

Appl. Sci. 2024, 14(18), 8500; https://doi.org/10.3390/app14188500 - 20 Sep 2024

Viewed by 1767

Abstract

In recent years, deep learning (DL) has garnered significant attention for its successful applications across various domains in solving complex problems. This interest has spurred the development of numerous neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial [...] Read more.

In recent years, deep learning (DL) has garnered significant attention for its successful applications across various domains in solving complex problems. This interest has spurred the development of numerous neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and the more recently introduced Transformers. The choice of architecture depends on the data characteristics and the specific task at hand. In the 1D domain, one-dimensional CNNs (1D CNNs) are widely used, particularly for tasks involving the classification and recognition of 1D signals. While there are many applications of 1D CNNs in the literature, the technical details of their training are often not thoroughly explained, posing challenges for those developing new libraries in languages other than those supported by available open-source solutions. This paper offers a comprehensive, step-by-step tutorial on deriving feedforward and backpropagation equations for 1D CNNs, applicable to both regression and classification tasks. By linking neural networks with linear algebra, statistics, and optimization, this tutorial aims to clarify concepts related to 1D CNNs, making it a valuable resource for those interested in developing new libraries beyond existing ones. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Advances in Neural Networks and Deep Learning

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (15 papers)

Research

Review

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI