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2024 international conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Wang, Wei Ruan, Yanchun Lin, Mei Bu, Ruichen College of Cuisine Science and Technology Jiangsu Vocational College of Tourism Jiangsu Yangzhou225100 China College of Food Science and Engineering Yangzhou University Jiangsu Yangzhou225100 China

ISBN: (纸本)9798350369212

Microbial contamination of food is one of the important factors affecting food safety, and it is of great significance to detect microbial contamination of food quickly and accurately for ensuring public health. The purpose of this study is to develop an automatic detection model of food microbial contamination combining image processing and pattern recognition, so as to improve the detection efficiency and accuracy of food microbial contamination. First of all, the paper constructs an image data set containing different categories of food microorganisms, and preprocesses the image data, including denoising, enhancement and other operations to improve the image quality. Then, using deep learning technology and pre-trained convolutional neural network (CNN) to extract features, the high-level abstract features of the image are obtained. Then, a new deep feature fusion classifier (DFFC) is constructed by combining the traditional machine learning method support vector machine (SVM) with deep learning features, which is used for automatic detection of food microbial contamination. In the experiment, the DFFC model is evaluated in detail and compared with the model using CNN features or traditional machine learning methods alone. The experimental results show that DFFC model is superior to other models in Accuracy, Recall, Precision and F1 Score, and achieves 92% Accuracy, 89% Recall, 94% Precision and 91 % F1 score. This shows that DFFC model can effectively identify food microbial contamination, and has high classification accuracy and robustness. The DFFC model proposed in this study provides a reliable solution for the automatic detection of food microbial contamination, and is expected to play an important role in food safety monitoring and management. © 2024 IEEE.

关键词： Automatic detection food microbial contamination image processing pattern recognition

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Gesture recognition of the Kazakh alphabet based on machine and deep learning models 19

Gesture recognition of the Kazakh alphabet based on machine ...

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19th international conference on Future Networks and Communications, FNC 2024 / 21st international conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th international conference on Sustainable Energy Information Technology, SEIT 2024

作者： Mukhanov, Samat Uskenbayeva, Raissa Rakhim, Abd A. Akim, Akbota Mamanova, Symbat International Information Technology University Manas St. 34/1 Almaty050043 Kazakhstan Satpayev University Satpayev St. 22 Almaty050000 Kazakhstan Universiti Tenaga Nasional Kajang50000 Malaysia

Currently, a growing body of research focuses on addressing problems using computer vision libraries and artificial intelligence tools. The predominant approaches involve employing machine and deep learning models of artificial neural networks to recognize gestures in the Kazakh Sign Alphabet (KSA) via supervised and deep learning techniques for sequential data processing. pattern recognition in this context involves identifying an object within an image, where the object can be abstract and vary in shape. We have chosen to investigate the field of gesture recognition, specifically. For recognizing Kazakh Sign Language (KSL), the initial step involves mastering the KSA. Training a neural network to recognize KSL necessitates the collection of datasets in the form of images depicting hand gestures. In this research, prominent hand gesture recognition models such as the Convolutional Neural Network (CNN), Long Short-Term Memory (LstM), and Support Vector machine (SVM) were analyzed. These models differ in their methodologies, processing times, and training data requirements. A significant aspect of this study is the application of unsupervised and supervised learning techniques including CNN, LstM, and SVM. The experiments yielded diverse results when training neural networks for recognizing gestures in Kazakh sign language based on the dactyl alphabet. This article provides a comprehensive overview of each method, their specific purposes, and their effectiveness in terms of performance and training. Numerous experimental outcomes were documented in a table, showcasing the accuracy of recognizing each gesture. Additionally, specific hand gestures were tested in front of a camera to identify the gesture and display the result on the screen. A notable feature was the use of mathematical formulas and functions to elucidate the operating principles of the machine learning methods, as well as the logical structure and design of the LstM model. © 2024 The Authors. Published

关键词： Convolutional neural networks

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Automated Near-Duplicate image Detection using Sparrow Search Algorithm with Deep learning Model 1

Automated Near-Duplicate Image Detection using Sparrow Searc...

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1st IEEE international conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Sundaram, Srinidhi Jayaraman, Sasikala Somasundaram, Kamalakkannan Annamalai University Department of Information Technology Annamalai Nagar608002 India School of Computing Sciences Department of Information Technology Tamil Nadu Chennai600 117 India

ISBN: (纸本)9798350372748

Recently, automatic Near-Duplicate (ND) imaging pair recognition by utilizing computer vision and pattern detection machinery has attracted extensive interest, but it has significant possible values within the application of image copyright violation recognition, management of device hardware stored, fake image recognition, and automated automobile driving. A common human-crafted local factor-based method like extremely implemented Histograms of Oriented Gradients (HOG) methods, achieves the image-level factors by integrating manners, like Vectors of Locally Aggregated Descriptors, fisher vector, Scale-Invariant Feature Transform, and so on. These approaches are affected by the difficulty of complex extraction steps and restricted representation capabilities. Most recently, because of the excellent feature learning skill of Convolutional Neural Network (CNN), scientists have employed CNN to manage the ND image pair recognition problem. In this aspect, this study presents an automated ND image Detection by utilizing a Sparrow Search Algorithm with Deep learning (NDID-SSADL) model. The objective of the NDID-SSADL model is to accomplish accurate and automated recognition of ND images. To achieve this, the AVPR-SSADL methodology employs a Gaussian Filtering approach for image pre-processing. For learning complex and intricate features, the Inception v3 feature extractor is used with an SSA-based hyperparameter optimizer. Finally, Manhattan distance-based similarity measurement can be used for the recognition of ND images. The experimental analysis of the NDID-SSADL methodology on the benchmark dataset exhibits a maximum achievement of 94.50% over the compared methods. © 2024 IEEE.

关键词： image recognition

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Enhancing Anatomical Landmark Detection in Brain images through Multiagent Deep Reinforcement learning 1

Enhancing Anatomical Landmark Detection in Brain Images thro...

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1st international conference on Electronics, Communication and Signal processing, ICECSP 2024

作者： Sharvani, Burra Shree Jayalakshmi, G. Bhargavi, Kanugula Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798350364590

Identifying anatomical landmarks in medical imaging is critical for accurate diagnosis and treatment planning. However, manual annotation varies widely among observers and demands specialized expertise. We propose an innovative approach using multi-agent reinforcement learning, based on the understanding that anatomical landmarks are interconnected and exhibit non-random configurations. Our framework employs a Deep Q-Network architecture with interconnected agents that collaborate by sharing knowledge during training, improving accuracy. It supports multiple agents, each tasked with identifying specific landmarks, and achieves a 50% reduction in detection errors compared to existing methods, while requiring fewer computational resources and less training time. This technology holds promise for advancing anatomical landmark recognition in brain imaging, facilitating more precise medical image interpretation. © 2024 IEEE.

关键词： Deep reinforcement learning

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Exploring the Potential of Convolution Neural Network Based image Classification 1

Exploring the Potential of Convolution Neural Network Based ...

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2023 1st DMIHER international conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Sharma, Ritika Bashir, Saimul Tiwary, Vinay Nath Kumar, Sanjeev Chandigarh University Computer Science and Engineering Punjab India

ISBN: (纸本)9798350338423

This research examines the utilization of Convolutional Neural Networks (CNNs) in image classification, shedding light on recent progress in machine learning algorithms. Recognized as a unique component of deep learning, CNNs have earned significant acclaim in a myriad of visual tasks, attributed to their capacity for hierarchical pattern recognition and extraction of stable features. In this investigation, an enhanced CNN model is introduced, which employs cutting-edge preprocessing and augmentation strategies to elevate image readability, thereby streamlining the learning mechanism. key machine learning principles were employed, equipping the CNN model to identify and distinguish intricate structures within images with amplified precision. The model's efficiency is assessed using multiple benchmark datasets, achieving optimistic outcomes concerning precision, recall, and F1-score. The paper also touches upon inherent obstacles such as overfitting and computational expenses linked with CNNs, suggesting remedies to boost scalability and efficiency. The observations suggest that with meticulous planning and execution, CNNs can serve as a potent instrument for automated image classification, paving the way for a wide array of applications from medical imaging to self-driving vehicles. This research augments the expanding literature in machine learning, offering perspectives that could inform the design of more effective and efficient image classification systems. © 2023 IEEE.

关键词： Classification (of information)

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Neuromorphic devices assisted by machine learning algorithms

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international JOURNAL OF EXTREME MANUFACTURING 2025年第4期7卷 042007-042007页

作者： Huo, Ziwei Sun, Qijun Yu, Jinran Wei, Yichen Wang, Yifei Cho, Jeong Ho Wang, Zhong Lin Chinese Acad Sci Beijing Inst Nanoenergy & Nanosyst Beijing 101400 Peoples R China Univ Chinese Acad Sci Sch Nanosci & Engn Beijing 100049 Peoples R China Guangxi Univ Ctr Nanoenergy Res Sch Phys Sci & Technol Nanning 530004 Peoples R China Shandong Zhongke Naneng Energy Technol Co Ltd Dongying Peoples R China Yonsei Univ Dept Chem & Biomol Engn Seoul 03722 South Korea Georgia Inst Technol Atlanta GA 30332 USA

Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks, e.g., pattern processing, image recognition, and decision making. It features parallel interconnected neural networks, high fault tolerance, robustness, autonomous learning capability, and ultralow energy dissipation. The algorithms of artificial neural network (ANN) have also been widely used because of their facile self-organization and self-learning capabilities, which mimic those of the human brain. To some extent, ANN reflects several basic functions of the human brain and can be efficiently integrated into neuromorphic devices to perform neuromorphic computations. This review highlights recent advances in neuromorphic devices assisted by machine learning algorithms. First, the basic structure of simple neuron models inspired by biological neurons and the information processing in simple neural networks are particularly discussed. Second, the fabrication and research progress of neuromorphic devices are presented regarding to materials and structures. Furthermore, the fabrication of neuromorphic devices, including stand-alone neuromorphic devices, neuromorphic device arrays, and integrated neuromorphic systems, is discussed and demonstrated with reference to some respective studies. The applications of neuromorphic devices assisted by machine learning algorithms in different fields are categorized and investigated. Finally, perspectives, suggestions, and potential solutions to the current challenges of neuromorphic devices are provided. The review discusses the basic structure of simple neuron models inspired by biological neurons and how they process information in simple neural networks, laying the foundation for neuromorphic device *** progress in the fabrication of neuromorphic devices is highlighted, focusing on advancements in materials, structures, and the development of st

关键词： neuromorphic devices machine learning algorithms artificial synapses memristors field-effect transistors

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Harnessing Deep learning for Accurate Potato Leaf Disease Identification 1

Harnessing Deep Learning for Accurate Potato Leaf Disease Id...

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1st international conference on Software, Systems and Information Technology, SSITCON 2024

作者： Mamatha, A.S. Kushalatha, M.R. Karunakara Rai, B. Nmam Institute of Technology Department of Electronics and Communication Engineering Karnataka India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798350352931

With a long history of cultivation spanning several decades, potatoes are one of the mainstay crops in agriculture. Despite its continued appeal, the spread of illnesses poses serious challenges to potato production, increasing expenses and upsetting farmers. To boost potato yields and usher in a new era of digitalized agriculture techniques, it is becoming more and more clear that quick and automated ease detection processes are desperately needed. Utilizing state-of-the-art machine learning technology to analyse leaf photos and diagnose potato illnesses is the main goal of this research. The suggested remedy manifests as an advanced automated system that seamlessly combines machine learning and image processing technologies to achieve the results. Training accuracy of 99.48 %and a Validation accuracy of 99.22 %were obtained using Convolution Neural Networks (CNN). © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Melanoma Diagnostic Accuracy through Deep learning and Attention Mechanisms 1

Optimizing Melanoma Diagnostic Accuracy through Deep Learnin...

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1st international conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Naik, Anushka Bakliwal, Aagam Deshmukh, Vaibhav COEP Technological University Department of CSE Pune India Cornell University Department of CS New YorkNY United States BJ Government Medical College Pune India

ISBN: (纸本)9798350365016

This paper explores the application of multiple deep learning architectures for the detection of melanoma, a severe and potentially deadly form of skin cancer. Utilizing a sophisticated ensemble approach that integrates VGG-16, ResNet-50, and Vision Transformers, each model leverages unique strengths in image processing and feature recognition to enhance diagnostic accuracy. The study employs the ISIC dataset, enhanced through extensive data augmentation techniques to address class imbalance and increase model robustness. Our findings reveal that the ensemble model outperforms individual architectures, achieving an impressive accuracy of 93.54%, demonstrating the effectiveness of combining diverse computational strategies. Furthermore, the incorporation of the Convolutional Block Attention Module into VGG-16 significantly refines its capability to focus on critical dermatological features, suggesting that attention mechanisms can substantially improve the performance of convolutional networks in medical image analysis. The promising results signify the potential of advanced machine learning models to transform melanoma diagnostics, supporting their integration into clinical settings for better patient outcomes. © 2024 IEEE.

关键词： Dermatology

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Proceedings - 1st international conference on Electronics, Communication and Signal processing, ICECSP 2024

Proceedings - 1st International Conference on Electronics, C...

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1st international conference on Electronics, Communication and Signal processing, ICECSP 2024

ISBN: (纸本)9798350364590

The proceedings contain 152 papers. The topics discussed include: FPGA implementation of area efficient adaptive filter for low computational complexity;GAN-based image augmentation and comparative analysis of various CNN models for monkeypox detection;a ladybug beetle coati optimized joint energy and spectral efficiency maximization in cooperative cognitive radio networks;intelligent control of distribution system FACTS devices using deep learning models: a comparative study;automatic number plate recognition in hazy conditions;design and analysis of 38GHz mmWave modified MIMO antenna on low permittivity substrate for 5G Ka band applications;and implementation of subject specific model and general model using Bayesian algorithm and compare their performance.

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1st Analytics Global conference, AGC 2023

1st Analytics Global Conference, AGC 2023

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1st Analytics Global conference, AGC 2023

ISBN: (纸本)9783031508141

The proceedings contain 11 papers. The special focus in this conference is on Analytics Global. The topics include: Detecting Emotional Impact on Young Minds Based on Web Page Text Classification Using Data Analytics and machine learning;a machine learning Framework Developed by Leveraging the Topological pattern Analysis of Bitcoin Addresses in Blockchain Network for Ransomware Identification;A Taxonomy and Survey of Software Bill of Materials (SBOM) Generation Approaches;Find Your Donor (FYD): An Algorithmic Approach Towards Empowering Lives and Innovating Healthcare;deep learning-Based Multiple Detection Techniques of Covid-19 Disease From Chest X-Ray images Using Advanced image processing Methods and Transfer learning;Classification Model to Predict the Outcome of an IPL Match;Knowledge Graph-Based Evaluation Metric for Conversational AI Systems: A step Towards Quantifying Semantic Textual Similarity;Are We Nearing Singularity? A study of Language Capabilities of ChatGPT;thresholding Techniques Comparsion on Grayscale image Segmentation.

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