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2024 Conference on Spectral Technology and applications, CSTA 2024

作者： You, Zhichao Du, Yuheng Wang, Xi Li, Ziteng Liu, Huan Li, Duo Company Ltd. Shanghai200240 China Center for Precision Engineering Harbin Institute of Technology 92 West Dazhi St. Harbin150001 China Center of Ultra-Precision Optoelectronic Instrumentation Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781510683082

This research aims to develop a multi-threading method for rapid tool wear detection by integrating image classification and object detection techniques to address the challenge of tool wear detection. The research proposes a two-stage method that leverages a fast image classification model (VGG-16) and a high-accuracy object detection model (YOLOv5) to enable efficient multi-threading detection of tool wear regions across a large number of the flank wear images. The experiment results reveal that, when the wear images account for less than 70% of the total, this method can achieve detection speeds exceeding that of YOLOv5 while maintaining comparable detection accuracy. © 2024 SPIE.

关键词： Object detection image classification image processing Data modeling Sensors artificial neural networks Convolutional neural networks Education and training Engineering Signal detection

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Analysis of Impact of image Restoration and Segmentation on Classification Model 7

Analysis of Impact of Image Restoration and Segmentation on ...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Vispute, Sushma Rahul Rajeswari, K. Nema, Aryan Jagtap, Arya Kulkarni, Mrugendra Mohite, Pranav PCCOE Department of Computer Engineering Pune India

ISBN: (纸本)9798350304268

A widely studied problem in computer science is the restoration, segmentation, and classification of images, which involves image processing, computer vision, and machine learning techniques. Deep learning has made significant contributions to this field, bringing machine learning closer to artificial intelligence. Deep learning has a broad range of applications, including in surveillance, healthcare, medicine, and sports. Convolutional neural networks (CNN), a combination of artificial neural networks (ANN) and deep learning techniques, have made incredible advancements in deep learning. This survey compares methods for restoring noisy images, such as wiener filter, wavelet method, and wiener filtering with BM3D technique, using Gaussian blurring and noising methods. The survey also examines the RGB colour model and YCbCr colour model for image segmentation. image classification is studied using CNN, where the survey discusses various parameters of convolutional neural networks, including activation functions and pooling methods. © 2023 IEEE.

关键词： computer vision convolutional neural network face detection face recognition image classification image restoration noise reduction segmentation

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Training a neural Network to Predict House Rents Using Artifical Intelligence and Deep Learning

Training a Neural Network to Predict House Rents Using Artif...

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2023 IEEE International Conference on image processing and Computer applications, ICIPCA 2023

作者： Chao, Chung-Hsing Minth University of Science and Technology Department of Intelligent Vehicles and Energy Hsinchu Taiwan

ISBN: (纸本)9798350314670

We are training neural networks to predict house rents using artificial intelligence and deep learning, which is being used in the real estate and financial industries. Real estate agents, financial institutions, and real estate developers can benefit from rent forecasting, which is an important application scenario. Many real estate websites now use machine learning models to predict rents to help tenants and landlords find the right rental price. Machine learning models are also being used by some real estate companies and financial institutions to determine rents more accurately. Training neural networks to predict rent is still a relatively new field that needs more research and experimentation to improve its performance and accuracy. © 2023 IEEE.

关键词： Deep learning

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Meta-heuristic Techniques to Train artificial neural networks for Medical image Classification: A Review

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Recent Advances in Computer Science and Communications 2022年第4期15卷 513-530页

作者： Priyanka Kumar, Dharmender Hisar India

Medical imaging has been utilized in various forms in clinical applications for better diagnosis and treatment of diseases. These imaging technologies help in recognizing body's ailing region easily. In addition, it causes no pain to the patient as the interior part of the body can be examined without difficulty. Nowadays, various image processing techniques such as segmentation, registration, classification, restoration, contrast enhancement and many more exist to enhance image quality. Among all these techniques, classification plays an important role in computer-aided diagnosis for easy analysis and interpretation of these images. image classification not only classifies diseases with high accuracy but also analyses which part of the body is infected. The usage of neural networks classifier in medical imaging applications has opened new doors or opportunities to researchers stir-ring them to excel in this domain. Moreover, accuracy in clinical practices and the development of more sophisticated equipment are necessary in the medical field for more accurate and quicker decisions. Therefore, keeping this in mind, researchers started using meta-heuristic techniques to classify the methods. This paper provides a brief survey on the role of artificial neural networks in medical image classification, various types of meta-heuristic algorithms applied for optimization purposes, and their hybridization. A comparative analysis showing the effect of applying these algorithms on some classification parameters such as accuracy, sensitivity, and specificity is also provided. From the comparison, it can be observed that the usage of these methods significantly optimizes these parameters leading us to diagnose and treat a number of diseases in their early stage. © 2022 Bentham Science Publishers.

关键词： Optimization

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LRGAN: Learnable Weighted Recurrent Generative Adversarial Network for End-to-End Shadow Generation

LRGAN: Learnable Weighted Recurrent Generative Adversarial N...

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International Joint Conference on neural networks (IJCNN)

作者： Xue, Junsheng Huang, Hai Zhou, Zhong Xu, Shibiao Chen, Aoran Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350359329;9798350359312

In augmented reality(AR) applications, it is a challenging task to generate virtual object shadows while maintaining the precision and consistency of virtual and real areas. To achieve the above target, we propose a learnable weighted recurrent generative adversarial network(LRGAN) for end-to-end shadow generation. Without any additional computational overhead, LRGAN only needs to analyze the background context to create a bridge between the target shadows and the background. Our model incorporates multiple progressive steps to recurrently compute the precise reference masks, based on which a fine-grained shadow generation module generates the shadows. A learnable weighted fusion module, which can normalize pixel values to deal with pixel overflow, fuses the generated shadows with the original image. In addition, we adopt the combined method of module training and the whole model training. Experimental results show that our proposed LRGAN not only improves the plausibility of shadow location and shape but also achieves color harmony in the shadow areas. In the absence of other prior knowledge or post-processing, it outperforms the State-of-the-Art end-to-end methods.

关键词： virtual shadow generation augmented reality generative adversarial network weighted fusion recurrent structure

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IPM-Model: AI and metaheuristic-enabled face recognition using image partial matching for multimedia forensics investigation with genetic algorithm

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MULTIMEDIA TOOLS AND applications 2022年第17期81卷 23533-23549页

作者： Khan, Abdullah Ayub Shaikh, Aftab Ahmed Shaikh, Zaffar Ahmed Laghari, Asif Ali Karim, Shahid Sindh Madressatul Islam Univ Dept Comp Sci Karachi 74000 Sindh Pakistan Benazir Bhutto Shaheed Univ Fac Comp Sci & Informat Technol Karachi 75660 Pakistan Ilma Univ Dept Comp Sci Karachi Sindh Pakistan

The rapid enhancement in the development of information technology has driven the development of human facial image recognition. Recently, facial recognition has been successfully applied in several distinct domains with the help of computing and information technology. This kind of application plays a significant role in the process of digital forensics investigation, recognizing the patterns of a human face based on the partial matching of images that would be in 24-bit color image format, including the spacing of the eyes, the bridging of the nose, the contour of the lips, ears, and chin. In this paper, we have proposed and implemented an image recognition model based on principal component analysis, genetic algorithms, and neural networks, in which PCA reduces the dimension of the benchmark dataset, while genetic algorithms and neural nets optimize the searching patterns of image matching and provide highly efficient output with a minimal amount of time. Through the experiment results on the human facial images dataset of the Georgia Institute of Technology, the overall match showed that the proposed model can achieve the recognition of human face images with an accuracy rate of 93.7%. Moreover, this model helps to examine, analyze, and detect individuals by partial matching with reidentification in the procedure of forensics investigation. The experimental result shows the robustness of the proposed model in terms of efficiency compared to other state-of-the-art methods.

关键词： artificial Intelligence (AI) Metaheuristic image processing and Recognition Partial image matching Principal component analysis (PCA) artificial neural networks (ANN) Digital Forensics Investigation

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A grammar-based GP approach applied to the design of deep neural networks

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2022年第3期23卷 427-452页

作者： Lima, Ricardo H. R. Magalhaes, Dimmy Pozo, Aurora Mendiburu, Alexander Santana, Roberto Univ Fed Parana Dept Comp Sci Curitiba Parana Brazil Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence San Sebastian Spain

Deep Learning has been very successful in automating the feature engineering process, widely applied for various tasks, such as speech recognition, classification, segmentation of images, time-series forecasting, among others. Deep neural networks (DNNs) incorporate the power to learn patterns through data, following an end-to-end fashion and expand the applicability in real world problems, since less pre-processing is necessary. With the fast growth in both scale and complexity, a new challenge has emerged regarding the design and configuration of DNNs. In this work, we present a study on applying an evolutionary grammar-based genetic programming algorithm (GP) as a unified approach to the design of DNNs. Evolutionary approaches have been growing in popularity for this subject as Neuroevolution is studied more. We validate our approach in three different applications: the design of Convolutional neural networks for image classification, Graph neural networks for text classification, and U-Nets for image segmentation. The results show that evolutionary grammar-based GP can efficiently generate different DNN architectures, adapted to each problem, employing choices that differ from what is usually seen in networks designed by hand. This approach has shown a lot of promise regarding the design of architectures, reaching competitive results with their counterparts.

关键词： Evolutionary algorithms Genetic programming Automatic design Grammatical evolution Deep neural networks

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L-MFFN: Lightweight Multiscale Feature Fusion Network with Limited Samples for HSI Classification

L-MFFN: Lightweight Multiscale Feature Fusion Network with L...

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9th International Conference on Signal and image processing (ICSIP)

作者： Ali, Aamir Mu, Caihong Wang, Yafeng Liu, Yi Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (纸本)9798350350920

Hyperspectral image (HSI) classification is valuable in remote sensing due to its rich spectral and spatial information. In the last decade, deep learning methods, especially Convolutional neural networks (CNNs), have revolutionized HSI classification by extracting intangible semantic features and maintaining the spatial structure during feature extraction. However, the efficacy of these techniques can be constrained by the limited availability of labeled samples in HSI data. To address the issue of small-sample HSI classification, a Lightweight Multiscale Feature Fusion Network (L-MFFN) is introduced. The Multiscale Feature Extraction Module (MFEM) and the enhanced Spectral-Spatial Attention Module (SSAM) are designed and combined in L-MFFN, optimizing the use of deep and shallow features. This integration improves the extraction and fusion of multiscale spectral-spatial features, enhancing classification performance. The proposed model demonstrates state-of-the-art performance across two HSI datasets and stands out in situations with limited labeled samples, highlighting its capability to effectively tackle the challenge of small-sample HSI classification.

关键词： hyperspectral image classification small sample multiscale feature fusion spectral-spatial attention

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FPGA-Based Lightweight CNN Acceleration System for Real-Time Recognition 9

FPGA-Based Lightweight CNN Acceleration System for Real-Time...

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9th International Conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Ju, Junlei Liu, Cheng School of Microelectronics Shanghai University Shanghai China

ISBN: (纸本)9798331509453

In recent years, convolutional neural networks (CNNs) have become the core of many artificial intelligence applications, especially in fields such as image recognition and speech recognition. Deploying convolutional neural networks in hardware, as opposed to software, can increase speed and reduce power consumption. In this article, we propose an FPGA-based convolutional neural network acceleration system. This system optimizes LeNet-5 into a lightweight convolutional neural network model by replacing traditional convolution with depthwise separable convolutions and reducing the number of fully connected layers. After designing a parallel processing scheme for the computation process of the model, a CNN acceleration IP core is implemented using Verilog and applied to real-time handwritten digit recognition. This system can recognize one frame of image in 326.24μs, which is approximately 1s faster than CPU recognition. The total power consumption of the entire system is 1.947W, meeting the requirements of high real-time performance and low power consumption. © 2024 IEEE.

关键词： Convolutional neural networks

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Automatic Detection of Thyroid Nodules in Ultrasound images using Convolutional neural networks for Early Cancer Diagnosis

Automatic Detection of Thyroid Nodules in Ultrasound Images ...

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2024 International Conference on artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Veni, N. Harshavardhan, Mummaka Lakshmi, T.K. Sritha, P. Chitra, P. Yaseen, Haseeba SRM Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur Chennai India Dr. DY Patil Medical College & RI Department Critical Care Medicine Pimpri Pune India Malla Reddy University Department of CSE Telangana Hyderabad India Bannari Amman Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Sathyamangalam India Communication Engineering Agni College of Technology Department of Electronics Tamil Nadu Chennai India Vasavi College of Engineering Department of Information Technology Telangana Hyderabad India

ISBN: (纸本)9798331517953

Thyroid nodule detection in ultrasonic images mostly rely on conventional image processing techniques, which have major negative effects including high false positive and negative rates, limited generalizability over various patient populations, and reliance on hand feature extraction. By comparison, the proposed system greatly increases detection accuracy and dependability by using Convolutional neural networks (CNNs) for automatic feature extraction and classification. To improve model generalizing and fight overfitting, the system combines cutting-edge data augmentation techniques including rotation, scaling, and flips. It uses pre-trained architectures such as VGG16 and ResNet using transfer learning to enable fast adaptation to the thyroid nodule detection challenge even with small data. Before entering the CNN, extensive preprocessing methods include Gaussian filtering and adaptive histogram equalization improve image quality. The system outperforms existing methods and provides a strong tool for early thyroid cancer diagnosis since its results show accuracy of 90.7%, sensitivity of 88.3%, and specificity of 92.1%. © 2024 IEEE.

关键词： Convolutional neural networks

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