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11th International Conference on applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Wei, Yanxi Yao, Fenggang Kang, Yuru School of Artificial Intelligence Xi'an Aeronautical Polytechnic Institute Xi'an China

Among various data processing tools, neural networks stand out prominently in the fields of computer vision, intelligent control, and brain-like computing. They emulate the operational processes of the human brain, constituting artificial neural networks. In the ongoing technological evolution, spiking networks have progressively captivated the attention of scholars across diverse disciplines. Integrating principles from neuroscience and artificial intelligence, these networks employ impulse signals for event-driven processing and utilize communication signals within the network, exhibiting a closer resemblance to the operational mode of the biological nervous system when compared to traditional neural networks. Pulses manifest as discrete signals, diverging from the generally continuous nature of input signals. Simultaneously, there exists limited comprehension regarding the brain's coding stimulus and signal processing procedures, rendering the encoding of information into pulse signals a challenging task. Furthermore, while convolutional neural networks excel in image recognition, they suffer from the drawback of poor biological interpretability. To bolster the self-extraction capability of spiking neural networks, a pivotal focus lies in enhancing their sign extraction proficiency. Consequently, this paper proposes a hybrid structure for a spiking neural network, with experimental results demonstrating significant improvements in feature extraction compared to preceding neural network models. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Heart Signal processing Using Wavelet Analysis and Deep Learning Algorithms Based on artificial Intelligence

Heart Signal Processing Using Wavelet Analysis and Deep Lear...

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International Conference on applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Zilaie, Morteza Mohammadkhani, Zohreh Asrari, Keyvan Azimi Talebiyan, Seyed Reza Islamic Azad University Science and Research Branch Khorasan Razavi Mashhad Iran The University of Pavia Pavia Italy Imam Reza International University Khorasan Razavi Mashhad Iran

ISBN: (纸本)9783031840999

The electrocardiogram signal of the heart is used to monitor the health status and function of the human heart and to a doctor in diagnosing the type of disease. For this purpose, first, the scalogram of the different heart failures produces different types of this signal. In this research, the cardiac signals of the patients are analyzed by an artificial intelligence neural network, and the type of the person’s disease is diagnosed. The artificial intelligence in question can be learned and can be used as an assistant ECG signal is calculated using continuous wavelet transform and then it is subjected to learning and evaluation using a deep convolutional neural network. In the architecture of artificial intelligence, two well-known neural networks GoogLeNet and SqueezeNet are used, which have been sufficiently trained in similar applications such as image processing and machine vision. In the end, the type of heart failure is diagnosed and classified. The cardiac signals of the patients used in the simulation were extracted from the PhysioNet medical engineering standard dataset of MIT University. In the end, the simulation results of the research are given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Potato diseases detection and classification using deep learning methods

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MULTIMEDIA TOOLS AND applications 2023年第4期82卷 5725-5742页

作者： Arshaghi, Ali Ashourian, Mohsen Ghabeli, Leila Islamic Azad Univ Dept Elect Engn Cent Tehran Branch Tehran Iran Islamic Azad Univ Dept Elect Engn Majlesi Branch Esfahan Iran

Using machine vision and image processing methods has an important role in the identification of defects of agricultural products, especially potatoes. The applications of image processing and artificial intelligence in agriculture in identifying and classifying pests and diseases of plants and fruits have increased and research in this field is ongoing. In this paper, we use the convolution neural network (CNN) methods, also, we examined 5 classes of potato diseases with the names: Healthy, Black Scurf, Common Scab, Black Leg, Pink Rot. We used a database of 5000 potato images. We compared the results of potato defect classification our methods with other methods such as Alexnet, Googlenet, VGG, R-CNN, Transfer Learning. The results show that the accuracy of the deep learning proposed method is higher than other existing works. We get 100% and 99% accuracy in some of the classes, respectively.

关键词： Convolutional neural networks Deep learning Defect detection Potato diseases Potato classification

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Morlet wavelet transformation based deep similarity structured neural learning for image quality assessment

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Multimedia Tools and applications 2024年 1-19页

作者： Balakrishnan, N. Robinson, Y. Harold Department of MCA Sona College of Technology Salem India Department of Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India

Quality assessment is a key problem to be resolved in image processing. Few research works have been designed to analyze the quality of images using different techniques. However, the accuracy involved during the process of image quality assessment was not sufficient. A novel method Morlet Wavelet Transformation Based Deep Similarity Structured neural Learning (MWT-DSSNL) is proposed to enhance the performance of image quality assessment with minimal peak signal-to-noise ratio. The MWT-DSSNL Method is based on artificial neural networks with representation learning. The MWT-DSSNL Method initially gets the number of images from a given dataset as input. The MWT-DSSNL Method uses multiple layers to extract higher-level features from the input images. The MWT-DSSNL Method is a Feed-Forward network where each layer employs the output from the previous layer as input. The MWT-DSSNL Method is designed based on a biological neural network of the human brain. Contrary to the existing method, the MWT-DSSNL Method uses multiple hidden layers and Morlet wavelet transformation to deeply analyze input images and thereby extract features such as luminance, contrast, and structure with a minimal amount of time consumption. By considering the discovered features, finally MWT-DSSNL Method determines structural similarity and thereby exactly identifies the quality of input images with a lower time. From that, the MWT-DSSNL Method achieves enhanced image quality assessment performance when compared to existing works. The simulation of the MWT-DSSNL Method is conducted on factors such as PSNR, average processing time, quality detection accuracy, and false positive rate with different numbers of input images. The simulation result depicts that the MWT-DSSNL method increases the accuracy and also minimizes the time of image quality assessment when compared to conventional works. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024

关键词： Network layers

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Cancer Diagnosis With the Aid of artificial Intelligence Modeling Tools

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IEEE ACCESS 2022年 10卷 20816-20831页

作者： Danku, Alex Ede Dulf, Eva H. Banut, Raul Petru Silaghi, Horatiu Silaghi, Cristina Alina Tech Univ Cluj Napoca Dept Automat Cluj Napoca 400014 Romania Iuliu Hatieganu Univ Med & Pharm Cluj Napoca Dept Surg 5 Cluj Napoca 400012 Romania Iuliu Hatieganu Univ Med & Pharm Cluj Napoca Dept Endocrinol Cluj Napoca 400012 Romania

Cancer is one of the deadliest diseases in the present days. Its survivability is mostly corelated to early detection and treatment, which means that it is of utmost importance to successfully diagnose the patients. Unfortunately, even with years of experience human errors can happen which leads to the death of many individuals being misdiagnosed. Throughout the years there have been several applications created which could possibly aid doctors in the diagnosis. neural networks have always been a powerful tool which can be used in different applications that require an accurate model and the complexity of these models exceeds a human's computational capabilities. In image processing for example, a convolutional neural network can analyze each particular pixel and determine through the convolution function the common properties of different pictures. The objective of this study is to analyze different types of cancer diagnosing methods that have been developed and tested using image processing methods. The analyzed factors are training parameters, image processing technique and the obtained performances. This survey/review can be of significant value to researchers and professionals in medicine and computer science, highlighting areas where there are opportunities to make significant new contributions.

关键词： Cancer artificial intelligence Training neural networks Medical services image color analysis Ultrasonic imaging Cancer diagnosis image processing neural networks

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Deep Learning-based image Text processing Research 9

Deep Learning-based Image Text Processing Research

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9th IEEE International Conference on Big Data Security on Cloud (BigDataSecurity) / 9th IEEE International Conference on High Performance and Smart Computing (IEEE HPSC) / 9th IEEE International Conference on Intelligent Data and Security (IEEE IDS)

作者： Xiong, Huixuan Jin, Kai Liu, Jingnian Cai, Jiahong Xiao, Lijun Hunan Univ Sci & Technol Sch Comp Sci & Engn Hunan Key Lab Serv Comp & Novel Software Technol Xiangtan 411201 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410002 Peoples R China Guangzhou Coll Technol & Business Big data Dev & Res Ctr Guangzhou 510850 Peoples R China

ISBN: (纸本)9798350312935

Deep learning is a powerful multi-layer architecture that has important applications in image processing and text classification. This paper first introduces the development of deep learning and two important algorithms of deep learning: convolutional neural networks and recurrent neural networks. The paper then introduces three applications of deep learning for image recognition, image detection, and image forensics, as well as three text classification methods based on convolutional neural networks, recurrent neural networks, and other text classification methods. Finally, the development trend of deep learning in the field of text and image processing and the difficulties to be further researched are summarised and prospected.

关键词： Deep learning image processing text classification artificial Intelligence

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Vector-Valued Hopfield neural networks and Distributed Synapse Based Convolutional and Linear Time-Variant Associative Memories

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neural processing LETTERS 2023年第4期55卷 4163-4182页

作者： Garimella, Rama Murthy Valle, Marcos Eduardo Vieira, Guilherme Rayala, Anil Munugoti, Dileep Mahindra Univ Ecole Cent Sch Engn Dept Comp Sci Hyderabad India Univ Estadual Campinas Campinas SP Brazil Int Inst Informat Technol Hyderabad India Indian Inst Technol Gauhati India

The Hopfield network is an example of an artificial neural network used to implement associative memories. A binary digit represents the neuron's state of a traditional Hopfield neural network. Inspired by the human brain's ability to cope simultaneously with multiple sensorial inputs, this paper presents three multi-modal Hopfield-type neural networks that treat multi-dimensional data as a single entity. In the first model, called the vector-valued Hopfield neural network, the neuron's state is a vector of binary digits. Synaptic weights are modeled as finite impulse response (FIR) filters in the second model, yielding the so-called convolutional associative memory. Finally, the synaptic weights are modeled by linear time-varying (LTV) filters in the third model. Besides their potential applications for multi-modal intelligence, the new associative memories may also be used for signal and image processing and solve optimization and classification tasks.

关键词： Vector-valued Hopfield neural network Finite impulse response filter Convolution Sub-sampling matrix Linear time-varying filter Multimodal intelligence

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A survey of the vision transformers and their CNN-transformer based variants

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artificial INTELLIGENCE REVIEW 2023年第SUPPL3期56卷 S2917-S2970页

作者： Khan, Asifullah Raufu, Zunaira Sohail, Anabia Khan, Abdul Rehman Asif, Hifsa Asif, Aqsa Farooq, Umair Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Pattern Recognit Lab Islamabad 45650 Pakistan Pakistan Inst Engn & Appl Sci PIEAS Artificial Intelligence Ctr PAIC Islamabad 45650 Pakistan Pakistan Inst Engn & Appl Sci Ctr Math Sci Islamabad 45650 Pakistan Khalifa Univ Sci & Technol Dept Elect Engn & Comp Sci Abu Dhabi U Arab Emirates Air Univ E-9 Islamabad 44230 Pakistan

Vision transformers have become popular as a possible substitute to convolutional neural networks (CNNs) for a variety of computer vision applications. These transformers, with their ability to focus on global relationships in images, offer large learning capacity. However, they may suffer from limited generalization as they do not tend to model local correlation in images. Recently, in vision transformers hybridization of both the convolution operation and self-attention mechanism has emerged, to exploit both the local and global image representations. These hybrid vision transformers, also referred to as CNN-Transformer architectures, have demonstrated remarkable results in vision applications. Given the rapidly growing number of hybrid vision transformers, it has become necessary to provide a taxonomy and explanation of these hybrid architectures. This survey presents a taxonomy of the recent vision transformer architectures and more specifically that of the hybrid vision transformers. Additionally, the key features of these architectures such as the attention mechanisms, positional embeddings, multi-scale processing, and convolution are also discussed. In contrast to the previous survey papers that are primarily focused on individual vision transformer architectures or CNNs, this survey uniquely emphasizes the emerging trend of hybrid vision transformers. By showcasing the potential of hybrid vision transformers to deliver exceptional performance across a range of computer vision tasks, this survey sheds light on the future directions of this rapidly evolving architecture.

关键词： Auto encoder Channel boosting Computer vision Convolutional neural networks Deep learning Hybrid vision transformers image processing Self-attention Transformer

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Research on image and text in visual communication design under artificial intelligence

Research on image and text in visual communication design un...

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2024 International Conference on image, Signal processing, and Pattern Recognition, ISPP 2024

作者： Yuan, Yuanyi Wu, Longjun Zou, Yong Chengdu Technological University Chengdu China

ISBN: (纸本)9781510680425

The paper examines the integration of AI in visual communication design, particularly focusing on the enhanced impact of images when combined with text. It explores the evolution of visual communication design in the context of AI advancements, emphasizing the increasing significance of this discipline in today's technology-driven society. Key areas of discussion include the role of artificial intelligence, specifically deep learning, in image recognition and natural language processing, and its application in visual communication design. The paper details the development and training of an image recognition and text matching model, showcasing the effective application of convolutional neural networks (CNNs) and Word2Vec in visual design tasks. The research concludes by highlighting the successful translation of deep learning theoretical achievements into practical visual communication design applications, demonstrating the model's proficiency in image recognition and text matching within a business and engineering framework. © 2024 SPIE.

关键词： Convolutional neural networks

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Scale-preserving automatic concept extraction (SPACE)

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MACHINE LEARNING 2023年第11期112卷 4495-4525页

作者： Posada-Moreno, Andres Felipe Kreiskoether, Lukas Glander, Tassilo Trimpe, Sebastian Rhein Westfal TH Aachen Inst Data Sci Mech Engn DSME Aachen Germany Deevio GmbH Berlin Germany

Convolutional neural networks (CNN) have become a common choice for industrial quality control, as well as other critical applications in the Industry 4.0. When these CNNs behave in ways unexpected to human users or developers, severe consequences can arise, such as economic losses or an increased risk to human life. Concept extraction techniques can be applied to increase the reliability and transparency of CNNs through generating global explanations for trained neural network models. The decisive features of image datasets in quality control often depend on the feature's scale;for example, the size of a hole or an edge. However, existing concept extraction methods do not correctly represent scale, which leads to problems interpreting these models as we show herein. To address this issue, we introduce the Scale-Preserving Automatic Concept Extraction (SPACE) algorithm, as a state-of-the-art alternative concept extraction technique for CNNs, focused on industrial applications. SPACE is specifically designed to overcome the aforementioned problems by avoiding scale changes throughout the concept extraction process. SPACE proposes an approach based on square slices of input images, which are selected and then tiled before being clustered into concepts. Our method provides explanations of the models' decision-making process in the form of human-understandable concepts. We evaluate SPACE on three image classification datasets in the context of industrial quality control. Through experimental results, we illustrate how SPACE outperforms other methods and provides actionable insights on the decision mechanisms of CNNs. Finally, code for the implementation of SPACE is provided.

关键词： Explainable artificial intelligence image processing neural networks Industry

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