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Comparative Study of Various Machine Learning Algorithms for American Sign Language Recognition 1

6th International Conference on Soft Computing and Signal processing, ICSCSP 2023

作者： Malhan, Prabhat Gusain, Surbhi Raturi, Shashank Singh, Prabhdeep CS/IT Graphic Era University Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (数字)9789819986286

ISBN: (纸本)9789819986279

This research paper presents a comparative study on various machine learning algorithms for sign language detection. The objective of this study is to find the sign language identification method that is most accurate and effective for usage in real-time applications. The Residual Network (ResNet), artificial neural Network (ANN), Convolutional neural networks (CNNs), VGG16, and MobileNet are five well-known machine learning methods whose performance is compared. The dataset utilized in this study comprises camera-captured sign language motions made by diverse people. We assess each algorithm’s performance using a variety of parameters, including accuracy. According to our findings, VGG16 performs better than the other four algorithms, with an accuracy rate of 99%. Therefore, we conclude that VGG16 is the most suitable algorithm for sign language detection, which can be used in real-time applications for the deaf and hearing-impaired community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image classification

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Classification Using U-Net CN on Multi-Resolution CT Scan image 10

Classification Using U-Net CN on Multi-Resolution CT Scan Im...

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10th International Conference on Fuzzy Systems and Data Mining, FSDM 2024

作者： Surono, Sugiyarto Rivaldi, Muhammad Irsalinda, Nursyiva Department of Mathematics FAST UAD Yogyakarta Indonesia Indonesia

ISBN: (纸本)9781643685694

image processing has become a central topic in the era of big data, particularly within computer vision, due to the growing volume and diverse resolutions of images. Low-resolution images introduce uncertainty, underscoring the need for high-performance classification methods. Convolutional neural networks (CNN), especially the U-Net architecture, are widely applied for pixel-level segmentation due to their encoder-decoder structure. This study applied U-Net on a CT scan image dataset to segment lung images, followed by a CNN classifier to classify lung cancer stages (I, II, IIIa, IIIb). The U-Net model outperformed standard CNNs, achieving 99% in accuracy, precision, sensitivity, and F1 score, compared to the conventional CNN's 97%, 95%, 97%, and 96%, respectively. © 2024 The Authors.

关键词： Convolutional neural networks

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Reducing Texture Bias of Deep neural networks via Edge Enhancing Diffusion 27

Reducing Texture Bias of Deep Neural Networks via Edge Enhan...

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27th European Conference on artificial Intelligence, ECAI 2024

作者： Heinert, Edgar Rottmann, Matthias Maag, Kira Kahl, Karsten School of Mathematics and Natural Sciences University of Wuppertal Germany Faculty of Mathematics and Natural Sciences Technical University of Berlin Germany

ISBN: (纸本)9781643685489

Convolutional neural networks (CNNs) for image processing tend to focus on localized texture patterns, commonly referred to as texture bias. While most of the previous works in the literature focus on the task of image classification, we go beyond this and study the texture bias of CNNs in semantic segmentation. In this work, we propose to train CNNs on pre-processed images with less texture to reduce the texture bias. Therein, the challenge is to suppress image texture while preserving shape information. To this end, we utilize edge enhancing diffusion (EED), an anisotropic image diffusion method initially introduced for image compression, to create texture reduced duplicates of existing datasets. Extensive numerical studies are performed with both CNNs and vision transformer models trained on original data and EED-processed data from the Cityscapes dataset and the CARLA driving simulator. We observe strong texture-dependence of CNNs and moderate texture-dependence of transformers. Training CNNs on EED-processed images enables the models to become completely ignorant with respect to texture, demonstrating resilience with respect to texture reintroduction to any degree. Additionally we analyze the performance reduction in depth on a level of connected components in the semantic segmentation and study the influence of EED pre-processing on domain generalization as well as adversarial robustness. © 2024 The Authors.

关键词： Semantic Segmentation

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FMRNet: image Deraining via Frequency Mutual Revision 38

FMRNet: Image Deraining via Frequency Mutual Revision

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38th AAAI Conference on artificial Intelligence (AAAI) / 36th Conference on Innovative applications of artificial Intelligence / 14th Symposium on Educational Advances in artificial Intelligence

作者： Jiang, Kui Jiang, Junjun Liu, Xianming Xu, Xin Ma, Xianzheng Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China Univ Oxford Active Vis Lab Oxford England

ISBN: (纸本)1577358872

The wavelet transform has emerged as a powerful tool in deciphering structural information within images. And now, the latest research suggests that combining the prowess of wavelet transform with neural networks can lead to unparalleled image deraining results. By harnessing the strengths of both the spatial domain and frequency space, this innovative approach is poised to revolutionize the field of image processing. The fascinating challenge of developing a comprehensive framework that takes into account the intrinsic frequency property and the correlation between rain residue and background is yet to be fully explored. In this work, we propose to investigate the potential relationships among rainfree and residue components at the frequency domain, forming a frequency mutual revision network (FMRNet) for image deraining. Specifically, we explore the mutual representation of rain residue and background components at frequency domain, so as to better separate the rain layer from clean background while preserving structural textures of the degraded images. Meanwhile, the rain distribution prediction from the low-frequency coefficient, which can be seen as the degradation prior is used to refine the separation of rain residue and background components. Inversely, the updated rain residue is used to benefit the low-frequency rain distribution prediction, forming the multi-layer mutual learning. Extensive experiments demonstrate that our proposed FMRNet delivers significant performance gains for seven datasets on image deraining task, surpassing the state-of-the-art method ELFormer by 1.14 dB in PSNR on the Rain100L dataset, while with similar computation cost. Code and retrained models are available at https://***/kuijiang94/FMRNet.

关键词： Rain

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Deep learning for few-shot white blood cell image classification and feature learning

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2023年第6期11卷 2081-2091页

作者： Deng, Yixiang Li, He MIT & Harvard Ragon Inst Mass Gen Cambridge MA 02139 USA Univ Georgia Sch Chem Mat & Biomed Engn Lawrenceville Georgia

Differential counting of white blood cells (WBCs) in bone marrow using artificial intelligence (AI)-based models, such as convolutional neural network (CNN) and its various variants, can help physicians to efficiently diagnose many critical diseases such as leukaemia, AIDS and cancers. In this work, we implement a deep transfer learning to several CNN models to examine their effectiveness on automatically classifying WBCs into lymphocytes and non-lymphocytes groups. Our results show that transfer learning can enhance the training of the model and improve the model performance. We also discover that using image masking to remove irrelevant image pixels can further increase the accuracy of the model predictions. Moreover, we assess the impact of three data augmentation techniques to address the imbalance in the data set, which commonly occurs in many biological applications. Our results show that all the three examined data augmentation methods improve the classification results on both training and testing data sets. Altogether, we demonstrate that deep neural networks, when combined with transfer learning and imaging processing techniques, can serve as a powerful tool to conduct automatic differential counting of WBCs, and thus facilitate the diagnosis of the WBC-related disorders, monitor the disease progression and improve the effectiveness of therapeutics.

关键词： image Classification white blood cell few-shot learning data imbalance deep learning

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A Review of Research Progress and Application of Wavelet neural networks

A Review of Research Progress and Application of Wavelet Neu...

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International Conference on New Technologies, Development and Application

作者： Wang, Tonghao Guercio, Vincenzo Cattani, Piercarlo Villecco, Francesco China Agr Univ Coll Informat & Elect Engn 17 Tsinghua East Rd Beijing 100083 Peoples R China Deim Univ Tuscia Largo Univ Engn Sch I-01100 Viterbo Italy Univ Roma La Sapienza Dept Comp Control & Management Engn Via Ariosto 25 I-00185 Rome Italy Univ Salerno Dept Ind Engn Via Giovanni Paolo II 132 I-84084 Fisciano Italy

ISBN: (纸本)9783031310652;9783031310669

artificial neural Network (ANN) has been used extensively and constantly developed. The combination of wavelet transform theory and the neural network has become an important branch to explore the optimization of neural network structure, and Wavelet neural Network (WNN), a special network structure, was born. This paper reviews WNN's development and summarizes the system structure and algorithm implementation and presents derivative models and cutting-edge applications with obvious characteristics. The sorting and analysis of the above contents show that the combination of wavelet theory and neural network algorithm can make the network model have the advantages of fast convergence speed and high model accuracy, and has a rapid development trend in many fields such as audio signal and image processing. The work of this paper is intended to provide a reference for potential applications based on WNN and new network model design ideas.

关键词： Wavelet Transform Wavelet neural Network

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artificial intelligence 101 for veterinary diagnostic imaging

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VETERINARY RADIOLOGY & ULTRASOUND 2022年第Sup1期63卷 817-827页

作者： Hespel, Adrien-Maxence Zhang, Youshan Basran, Parminder S. S. Univ Tennessee Dept Small Anim Clin Sci Knoxville TN 37996 USA Cornell Univ Dept Clin Sci Ithaca NY USA

The prevalence and pervasiveness of artificial intelligence (AI) with medical images in veterinary and human medicine is rapidly increasing. This article provides essential definitions of AI with medical images with a focus on veterinary radiology. Machine learning methods common in medical image analysis are compared, and a detailed description of convolutional neural networks commonly used in deep learning classification and regression models is provided. A brief introduction to natural language processing (NLP) and its utility in machine learning is also provided. NLP can economize the creation of "truth-data" needed when training AI systems for both diagnostic radiology and radiation oncology applications. The goal of this publication is to provide veterinarians, veterinary radiologists, and radiation oncologists the necessary background needed to understand and comprehend AI-focused research projects and publications.

关键词： artificial intelligence convolutional neural network machine learning natural language processing veterinary radiologist

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Comparative Study of Plant Disease Detection Techniques: A Performance-Based Review 4

Comparative Study of Plant Disease Detection Techniques: A P...

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4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Singh, Sarita Goel, Noopur Veer Bahadur Singh Purvanchal University India Veer Bahadur Singh Purvanchal University Department of Computer Applications India

ISBN: (纸本)9798350364729

Agriculture is essential for human civilization, contributing to the economy and ensuring food supply. However, plant diseases can hinder growth and reduce crop yield. It is crucial to identify and categorize these diseases as soon as possible and accurately. Manual methods for disease prediction and classification may lead to inaccuracies and tedium. Therefore, implementing computerized image processing methods in agriculture can help to minimize losses and increase productivity. To detect and categorize plant illnesses on the basis of photos of sick leaves or crops, a variety of approaches, including DL and ML techniques like k-means clustering, NB and convolutional neural networks have been investigated. Even though there has been a lot of development in this area, talks and changing technologies call for ongoing improvements. The particular problem, data accessibility, and processing capacity all influence which of the conventional machine learning and deep learning approaches are the best. When a large amount of data and computational resources are available, deep learning, especially with CNNs, is generally the method of choice for picture identification and classification. This paper introduced a brief overview of the latest research on identifying and categorizes crop diseases and illness using image processing in the field of artificial intelligence. It covers the performance, assessment criteria, and findings of different approaches, aiming to keep future researchers informed about the progress in this area. © 2024 IEEE.

关键词： Convolutional neural networks

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Recommendation model combining RippleNet and KGCN

Recommendation model combining RippleNet and KGCN

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

作者： Lu Wanting Zhai Yuqing Southeast Univ Sch Cyber Sci & Engn Wuxi Jiangsu Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350920

Recommender systems are widely used artificial intelligence technologies that provide personalized recommendations to users from massive amounts of data. In the era of the Internet, recommender systems have become essential components in e-commerce, social media, news media, audio-video entertainment, and other fields. However, traditional recommender systems often rely on user's historical behavior or related attribute information for recommendations, which may lead to issues such as "over-recommendation" or "inefficient recommendation" due to their limited ability to uncover the latent connections between users and *** address these issues, many deep learning-based recommendation models have emerged in recent years, such as DeepFM, NCF, etc,which have achieved significant improvements in recommendation performance. RippleNet and KGCN are two popular recommendation models among them. The RippleNet model employs graph neural networks to explore the interaction relationships between users and items as the basis for recommendations. On the other hand, the KGCN model utilizes knowledge graphs to better understand the semantic relationships between ***, both models have their respective limitations. For instance, RippleNet only focuses on user representation while neglecting item representation, whereas KGCN overlooks the shortcomings in user representation. To further enhance recommendation performance, this paper proposes a new RNKN recommendation model that combines the strengths of RippleNet and KGCN, paying attention to both user and item representations to better uncover the latent connections between them. And apply the model to three datasets: MovieLens-1M (movies), Book Crossing (books) and *** (music). Compared with RippleNet and KGCN, the AUC index of RNKN on the MovieLens-1M data set has increased by 0.4% and 1.4% respectively;the ACC index has increased by 0.45% and 1.2%;compared with the AUC index of RNKN on the Book-Crossing data set Ri

关键词： Intelligent Recommendation Knowledge Graph artificial Intelligence

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STAFuse: A Feature Decomposition Network with Super Token Attention for Multi-modality image Fusion 20th

STAFuse: A Feature Decomposition Network with Super Token At...

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20th International Conference on Intelligent Computing (ICIC)

作者： Chen, Peng Chen, Aiguo Wang, Chuang Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Engn Res Ctr Intelligent Technol Healthcare Minist Educ Wuxi 214122 Jiangsu Peoples R China

ISBN: (纸本)9789819756773;9789819756780

The multimodal fusion of infrared-visible images in a high-quality way allows for the preservation of the respective advantages offered by each modality. However, existing methods encounter the challenge of high redundancy in local information within early neural networks. Specifically, excessive feature extraction of infrared information can cause the retention of excessive noise in the fused image, thereby obscuring its clarity. To solve this problem, we introduce the concept of super-token attention into an improved auto-encoder fusion network for better global modeling by reducing the number of tokens in the self-attention-mechanism. Specifically, we first use STA blocks as shared encoders to extract shallow features from different modalities. Next, we employ the CNN-Attention extractor to extract deeper features from various modalities using a two-branch approach. Extensive experiments have confirmed that the proposed network achieves state-of-the-art fusion performance across multiple metrics. Furthermore, our approach exhibits strong transferability to the field of medical image processing.

关键词： Multi-modality image fuse Auto encoder Super token attention Feature decomposition Self-attention mechanism

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