To address the problems of low recognition accuracy, slow convergence speed and weak generalization ability of traditional LeNet-5 network used in rolling-element bearing fault diagnosis, a rolling-element bearing fault diagnosis method using improve...
Human sitting posture recognition is critical for smart seating, ergonomic monitoring, and healthcare systems. However, existing deep learning approaches typically rely on highly complex network architectures that are computationally expensive, hinde...
Pneumonia remains a critical health concern worldwide, necessitating efficient diagnostic tools to enhance patient care. This research proposes a concatenated modified LeNet classifier to classify pneumonia images accurately. The model leverages deep...
Convolutional neural networks (CNNs) have been extensively utilized in medical image processing to automatically extract meaningful features and classify various medical conditions, enabling faster and more accurate diagnoses. In this paper, LeNet, a...
Accurate power prediction is essential for assessing wind turbine performance under real-world operating conditions and for supporting condition monitoring and maintenance planning using SCADA data. Most existing approaches rely directly on raw SCADA...
Specific emitter identification involves extracting the fingerprint features that represent the individual differences of the emitter through processing the received signals. By identifying the extracted fingerprint features, one can also identify th...
AI systems have an increasing sprawling impact in many application areas. Embedded systems built on AI have strong conflictual implementation constraints, including high computation speed, low power consumption, high energy efficiency, strong robustn...
A new LeNet-5 gas identification convolutional neural network structure for electronic noses is proposed and developed in this paper. Inspired by the tremendous achievements made by convolutional neural networks in the field of computer vision, the L...
In this paper, research was conducted on anomaly detection of wheel flats. In the railway sector, conducting tests with actual railway vehicles is challenging due to safety concerns for passengers and maintenance issues as it is a public industry. Th...
Current research on computer-aided diagnosis (CAD) of liver cancer is based on traditional feature engineering methods, which have several drawbacks including redundant features and high computational cost. Recent deep learning models overcome these...
Traditional Chinese medicine (TCM) has relied on pulse diagnosis as a cornerstone of healthcare assessment for thousands of years. Despite its long history and widespread use, TCM pulse diagnosis has faced challenges in terms of diagnostic accuracy a...
Pashto is one of the most ancient and historical languages in the world and is spoken in Pakistan and Afghanistan. Various languages like Urdu, English, Chinese, and Japanese have OCR applications, but very little work has been conducted on the Pasht...
The ELVD (Ensemble-based Lenet VGGNet and DropoutNet) model is used in this paper to examine hypothetical principles and theoretical identification of a real-time image classification and object, tracking, and recognition device running on board a ve...
This tutorial takes the reader on a historical and technical journey from the simple Perceptron (1958) to modern Convolutional Neural Networks (CNNs) that dominate spatial data processing (images and video). We start with the Perceptron’s linea...
Image recognition has been applied to many fields, but it is relatively rarely applied to medical images. Recent significant deep learning progress for image recognition has raised strong research interest in medical image recognition. First of all,...
To distinguish debris flow infrasound from other infrasound sources, previous works have used one-dimensional infrasound shapes and parameters. In this study, we converted infrasound signals into two-dimensional signal time–frequency graphs and...
Forecasting stock markets is challenging due to the influence of various internal and external factors compounded by the effects of globalization. This study introduces a data-driven approach to forecast S&P 500 returns by incorporating macroecon...
Cooperative spectrum sensing (CSS) is an important topic due to its capacity to solve the issue of the hidden terminal. However, the sensing performance of CSS is still poor, especially in low signal-to-noise ratio (SNR) situations. In this paper, co...
The prevailing method for handling municipal solid waste (MSW) is incineration, a critical process that demands safe, stable, and eco-conscious operation. While grate-typed furnaces offer operational flexibility, they often generate pollution during...
The convolutional neural network (CNN) has become a powerful tool in machine learning (ML) that is used to solve complex problems such as image recognition, natural language processing, and video analysis. Notably, the idea of exploring convolutional...
To solve the problem of recognizing artificial tire-side pressure printing characters with low efficiency and high labor intensity, we propose a CNN-based method for tire surface character recognition. In the image pre-processing, the SSR algorithm i...
Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging is critical for successful treatment and patient survival. However, manual diagnosis by radiologist experts is time-...
Depression is a common illness worldwide with doubtless severe implications. Due to the absence of early identification and treatment for depression, millions of individuals worldwide suffer from mental illnesses. It might be difficult to identify th...
This work improves a LeNet model algorithm based on a signal’s bispectral features to recognize the communication behaviors of a non-collaborative short-wave radio station. At first, the mapping relationships between the burst waveforms and the...
Lane-level road information is especially crucial now that high-precision navigation maps are in more demand. Road information may be obtained rapidly and affordably by mining floating vehicle data (FCD). A method is proposed to extract the number of...
This paper proposes an intelligent power adaptation framework for Free-Space Optics (FSO) communication systems operating under different weather conditions exploiting a deep learning (DL) analysis of received eye diagram images. The system incorpora...
Neural networks (NNs) are increasingly used in embedded systems driven by artificial intelligence (AI) features. Such systems are now developed for many critical applications with strong dependability constraints and trustworthiness requirements. The...
This paper presents an innovative approach that utilizes infused images from vibration signals and visual inspections to enhance the efficiency and accuracy of structure health monitoring through GoogLeNet. Scrutiny of the structure of GoogLeNet iden...
Rice, as one of the significant grain products across the world, features a wide range of varieties in terms of usability and efficiency. It may be known with various varieties and regional names depending on the specific locations. To specify a part...
Quick and accurate structural damage detection is essential for maintaining the safety and integrity of infrastructure, especially following natural disasters. Traditional methods of damage assessment, which rely on manual inspections, can be labor-i...
Regular screening and timely treatment play a crucial role in addressing the progression and visual impairment caused by cataracts, the leading cause of blindness in Thailand and many other countries. Despite the potential for prevention and successf...
Deep learning has emerged as a prominent focus in both academia and industry, with a wide range of models being applied across diverse domains. Fast and efficient model inference is essential for the practical deployment of deep learning models. Unde...
To solve the problems of backward means of coal mine gas and coal dust explosion monitoring, late reporting, and low leakage rate, a sound recognition method of coal mine gas and coal dust explosion based on GoogLeNet was proposed. After installing m...
Aiming at the influence of the fundamental frequency and its harmonics in transformer vibration signals on fault signals, which cause a low fault identification rate and degradation of classification model performance, a new strategy is proposed for...
In modern networks, a Network Intrusion Detection System (NIDS) is a critical security device for detecting unauthorized activity. The categorization effectiveness for minority classes is limited by the imbalanced class issues connected with the data...
To address the challenges of weak fault features and strong non-stationarity in early-stage vibration signals, this study proposes a novel fault diagnosis method combining enhanced variational mode decomposition (VMD) with a structurally improved Goo...
Aiming at the model overfitting problem caused by limited datasets and visual complexity in succulent plant classification tasks, this study proposes a GoogLeNet classification method based on lightweighting and improving the Convolutional Block atte...
Due to the strong volatility of the electrical load and the defect of a time-consuming problem, in addition to overfitting existing in published forecasting methods, short-term electrical demand is difficult to forecast accurately and robustly. Given...
Adaptive optics (AO) is an effective method to compensate the wavefront distortion caused by atmospheric turbulence and system distortion. The accuracy and speed of aberration restoration are important factors affecting the performance of adaptive op...
Because a cardiac function signal cannot reflect cardiac health in all directions, we propose a classification method using ECG and PCG signals based on BiLSTM-GoogLeNet-DS. The electrocardiogram (ECG) and phonocardiogram (PCG) signals used as resear...
Machine learning is a science of computer algorithms that enable systems to automatically learn actions and adjust them without explicit programming and improve from experience using pattern recognition. This work offers a practical introduction to t...
The process of recognizing manufacturing parts in real time requires fast, accurate, small, and low-power-consumption sensors. Here, we describe a method to extract descriptors from several objects observed from a wide range of angles in a three-dime...
The sneaker industry is continuing to expand at a fast rate and will be worth over USD 120 billion in the next few years. This is, in part due to social media and online retailers building hype around releases of limited-edition sneakers, which are u...
Texture analysis is key to better understanding of the relationships between the microstructures of the materials and their properties, as well as the use of models in process systems using raw signals or images as input. Recently, new methods based...
Diabetic foot ulcer (DFU) is one of the major complications of diabetes and results in the amputation of lower limb if not treated timely and properly. Despite the traditional clinical approaches used in DFU classification, automatic methods based on...
Urdu is a complex language as it is an amalgam of many South Asian and East Asian languages; hence, its character recognition is a huge and difficult task. It is a bidirectional language with its numerals written from left to right while script is wr...
Deep learning is playing a major role in identifying complicated structure, and it outperforms in term of training and classification tasks in comparison to traditional algorithms. In this work, a local cloud-based solution is developed for classific...
The objective of this study was to verify the accuracy of tree species identification using deep learning with leaf images of broadleaf and coniferous trees in outdoor photographs. For each of 12 broadleaf and eight coniferous tree species, we acquir...
Lung and colon cancer are among humanity’s most common and deadly cancers. In 2020, there were 4.19 million people diagnosed with lung and colon cancer, and more than 2.7 million died worldwide. Some people develop lung and colon cancer simulta...
The objective of this study was to develop a deep learning-based tree species identification model using pollen grain images taken with a camera mounted on an optical microscope. From five focal points, we took photographs of pollen collected from tr...
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