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Vine Disease Detection UAV Multi Spectral image using Segnet and Mobilenet Method 2

Vine Disease Detection UAV Multi Spectral Image using Segnet...

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2nd International conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Aruna, M.G. Silvia, Ensteih Al-Fatlawy, Ramy Riad Rao, Hanumanthakari Kalyan Sowmya, M. Dayananda Sagar College of Engineering Department of Ai and Ml Karnataka India Dayanand Sagar College of Engineering Dept of Ai and Ml Bengaluru India The Islamic University Najaf Iraq Griet Dept of Cse Telangana Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India

ISBN: (纸本)9798350382952

Deep Learning-based vine disease detection has garnered significant attention from the community, particularly with the utilization of UAV multispectral images for grapevine disease detection. However, identifying vine diseases in various crop and horticultural conditions remains a complex challenge, especially under mobile and edge computing conditions. The vine disease detection process makes use of the PlantVillage dataset, which includes unlabelled data. data normalization is performed and UAVs are involved for data capture, while the SegNet architecture is utilized for segmentation. This enables the separation of healthy and unhealthy vines for detection. Subsequently, classification is performed using MobileNetV2, with layers split to detect diseases all combined with UAV spectral images and larger image sizes, greater than 32 × 32, resulting in better performance. The proposed method achieves high performance, with an accuracy achieve 99.50%, precision at 99.42%, recall at 99.39%, and mean average precision (MAP) at 99.20%. These metrics are compared to existing methods such as Deep Convolutional Neural Network (DCNN) and Inception V2. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Global Information Edge Augmentation Network for Remote Sensing image Continuous Super-Resolution 2

Global Information Edge Augmentation Network for Remote Sens...

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2nd IEEE International conference on Signal, Information and data Processing, ICSIDP 2024

作者： Gao, Shize Lei, Yong Zhang, Jingya Wang, Guoqing Liu, Wenchao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China

ISBN: (纸本)9798331515669

The continuous-scale super-resolution (SR) method for remote sensing (RS) images has the potential to achieve flexible scale SR with a single network, representing a significant research area in the field of RS. However, the majority of prevailing methods learn features from localization, thereby neglecting the global semantic coherence of RS images. This results in unbalanced SR outcomes. Furthermore, the optimization strategy based on the multilayer perceptron (MLP) utilized in the implicit neural representation (INR) method results in blurred reconstructed image edges. To address these issues, a novel continuous-scale SR method for RS images, a global information edge augmentation network (GIEAN), is proposed. Initially, the global state space model (GSSM) aggregates the global information of the image from diverse perspectives and learns the contextual interactions at disparate locations of the image globally. Subsequently, the dual edge enhancement module (DEEM) learns the image body and edges independently through the main branch and edge branch, respectively, with the objective of enhancing the edge component of the SR results. Extensive experimental evaluation on the UCMerced dataset demonstrates the superiority of GIEAN over existing continuous-scale SR techniques. © 2024 IEEE.

关键词： image enhancement

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Converting high resolution multi-lingual printed document images in to editable text using image processing and artificial intelligence 2

Converting high resolution multi-lingual printed document im...

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2nd International conference on image Processing and Robotics (ICIPRob)

作者： Premachandra, H. Waruna H. Jayakody, Anuradha Kawanaka, Hiroharu Wayamba Univ Sri Lanka Informat Commun Technol Ctr Makandura Sri Lanka Sri Lanka Inst Informat Technol Dept Informat Syst Engn Colombo Sri Lanka Mie Univ Dept Elect & Elect Engn Tsu Mie Japan

ISBN: (纸本)9781665407717

The optical character recognition technique is used to convert information, mainly printed or handwritten text in paper materials, into an electronic format that the computers can edit. According to the literature, there are few competent OCR systems for recognizing multilingual characters in the form of Sinhala and English characters together. The lack of an appropriate technology to recognize multilingual text still remains as a problem that the current research community must address, and it has been designated as the key problem for this study. The main goal of this research is to develop a multilingual character recognition system that uses character image geometry features and Artificial Neural Networks to recognize printed Sinhala and English scripts together. It is intended that the solution would be improved to cover three Sri Lanka's most commonly spoken languages, with the addition of Tamil as a later upgrade. The primary technologies for this study were character geometry features and Artificial Neural Networks. At the moment almost an 85% of success rate has been achieved with a database containing around 800 images, which are divided into 46 characters (20 Sinhala and 26 English), and each character is represented in 20 different forms of character images. Recognition of text from printed bi-lingual documents is experimented by extracting individual character data from such printed text documents and feeding them to the system.

关键词： Optical Character Recognition Artificial Intelligence Artificial Neural Network image Processing

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Mimic Deep Learning Technique for Retinal images Denoising in IoT based Medical Devices 2

Mimic Deep Learning Technique for Retinal Images Denoising i...

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2nd IEEE Mysore Sub Section International conference, MysuruCon 2022

作者： Rana, Arti Srivastava, Vineet Kishore Uttaranchal University Dehradun India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497916

Retinal images are careful, non-invasive, as well as non-radioactive imaging tool in the development of medical imaging. Yet, the large volume of image datasets and lengthy times of implementation of retinal images reconstruction reduce its effectiveness. In order to create a good recreated image, we thus developed a Sparse Aware Noise Removal Method (SANR CNN) which employs convolution neural network to remove noise and improves the standard of Retinal image restoration. The rapid CNN architecture utilized in the proposed de - noising method makes it easier to train huge amounts of data with better quality, and the SARN algorithm is being used to create a dictionary learning method for noise removal of huge image datasets. The borders and details of the image are likewise preserved during reconstruction using the suggested SANR CNN model. A test was run to assess the effectiveness of SANR CNN in relation to peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), as well as mean squared error within a few existing models (MSE). In comparison to existing noise reduction strategies, the suggested SANR CNN model outperformed them in terms of SSIM, MSE and PSNR performance. Additionally, the suggested architecture offers secure IoT infrastructure for the transfer of these denoised medical images. © 2022 IEEE.

关键词： image reconstruction

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Content Based image Retrieval Using data Mining Techniques 2

Content Based Image Retrieval Using Data Mining Techniques

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2nd International conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Divya Bhatnagar, Shaleen Kumari, Sushmita Dominic, Vinitha Presidency University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665437899

image processing is a method of removing information from photographs. The agreement covers picture compression, noise removal, image recognition, thankfulness delineation, image retrieval, and image variation. Probing and obtaining images from a database of photos is used in a variety of industries, including medicine, research, engineering, and many others. The two most prevalent approaches for locating the photographs needed to start a database are text-based image retrieval and content-based image retrieval. The text-based image retrieval system, TBIR, retrieves the image from the database using annotations. Traditional methods of information recovery are becoming obsolete as a result of Internet users and the expansion of multimedia technologies. CBIR extracts images from a large degree database by using the visual components of an input image, often known as low level features or image features. This required image must be recovered from the database by extracting visual features and comparing them to the input image. The histogram, colour moment, colour correlogram, gabor filter, and wavelet transform are all CBIR approaches. These techniques can be used separately or in tandem to achieve better outcomes. An image retrieval strategy based on the haar transform, K-means, and Euclidian distance is presented in this article. © 2022 IEEE.

关键词： Wavelet transforms

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Aerial image object detection based on improved YOLOv5 2

Aerial image object detection based on improved YOLOv5

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2nd International conference on Consumer Electronics and Computer Engineering, ICCECE 2022

作者： Wen, Qing Liu, Rong Wu, Xin Ao, Bin Li, Kuan Dongguan University of Technology DGUT Dongguan China

ISBN: (纸本)9781665408868

Object detection from aerial images has been a popular research field recently. The task, i.e., detecting objects from aerial images, is difficult due to the relatively large image dimensions and various directions of objects. In this paper, to improve the detection ability of small targets, we use mosaic for data enhancement during data preprocessing. A multi-scale test is performed on the test set to make the test result more accurate. In addition, an improved YOLOv5 algorithm, which introduces the circular smoothing label (CSL) angle processing method into YOLOv5, is proposed to detect rotating objects from aerial images. Experimental results on the DOTA-v2.0 dataset show that the proposed method is effective. © 2022 IEEE.

关键词： Object detection

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Breast Cancer image Classification: Leveraging Deep Learning and Large Language Models for Semantic Integration 2

Breast Cancer Image Classification: Leveraging Deep Learning...

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2nd IEEE International conference on Contemporary Computing and Communications, InC4 2024

作者： Harini, K.K. Nandhini, R. Rajeswari, A.M. Deepalakshmi, R. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350383652

Breast cancer, a significant global health problem that is mainly affecting women, demonstrates the importance of early detection to enhance survival rates. Medical image classification is a significant field that utilizes deep learning methods such as Convolutional Neural Networks (CNNs), Region-Based CNNs (R-CNNs), and Deep Neural Networks (DNNs). Despite this, challenges such as over fitting persist, particularly with limited data or complex models. Although these models learn hierarchical features, interpreting them in intricate image structures is still challenging and complicates decision rationale. To address this, we propose a comprehensive approach for breast cancer image classification. The initial process involves image segmentation using CNN to identify regions of interest, followed by precise object detection with OpenCV. data quality and diversity are ensured by preprocessing and augmentation with imagedataGenerator, while informative features are extracted using the InceptionV3model. Our analysis of textual descriptions and embeddings is done by integrating Sentence Transformers and CLIP from the Large Language Model (LLM) framework. Achieving robust decision-making is ensured by setting a threshold of 0.7 for image classification based on predicted malignancy probability. Finally, improving the computations of similarity scores can improve prediction accuracy for reliable diagnostic interpretations. An accuracy of 0.92 in breast cancer diagnosis is guaranteed by integrating LLM and CNN for classification. © 2024 IEEE.

关键词： Convolutional neural networks

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Neural Network Analysis Based on data Parallelism 2

Neural Network Analysis Based on Data Parallelism

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2022 2nd International conference on Detection Technology and Intelligence System, DTIS 2022

作者： Lu, Jiawei School of Pharmacy and Bioengineering Chongqing University of Technology Chongqing401300 China

With the development of Internet technology, many image data emerge on the Internet, and deep learning algorithms can extract abstract features from these data for image classification by using multi-layer nonlinear transformation. Therefore, deep learning algorithm has become a research hotspot in image processing. Aiming at the complex semantic features of images, an efficient deep learning algorithm has essential research significance for extracting and analyzing the semantic information about images quickly and effectively. This paper mainly explores the application of deep learning in image classification. Three neural models, LeNet, VGG, and ResNet are selected to test CIFAR10, and their training duration and accuracy are compared. Secondly, this paper further explores the impact of trying three neural network models on different graphics cards on their performance, and further researches and learns the application of these models. Finally, this paper tests the data parallelism solution of PyTorch, and the results and problems of the three models in this process are analyzed. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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Silent Speech Interface Using Lip-Reading Methods 1

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2nd International conference on Biomedical Engineering Science and Technology (ICBEST) - Roadway from Laboratory to Market

作者： Jothibalaji, Raghupathy Adithya, S. Siva Saravanan, N. V. Dhanalakshmi, M. Sri Sivasubramaniya Nadar Coll Engn Dept Biomed Engn Chennai 603110 India

ISBN: (数字)9783031545474

ISBN: (纸本)9783031545467;9783031545474

A silent speech interface (SSI) maps articulatory movement data to forecast the speech of mute people. SSIs have vast potential for improving oral communication in peoplewho have had laryngectomy surgery or thosewith severe voice abnormalities. In this paper, wepresent a lip-reading recognition algorithm to recognize Englishwords from the lipswhen speaking. The lip contour and English words were distinguished using a variety of lip metric metrics, including width, height, contour points, area, and ratio (width/height). In this test, fifteen talkers mouthed (silently articulated) sentences to finish a phrase-reading assignment in an experimental setting. Among the fifteen participants, 89% and 81% of the mouthed phrases were correctly recognized for two different classifier cases respectively. The automated ROI detection, elimination of lip shape effects, and operation without prior data training are strengths of the proposed method. The experimental outcomes showed the viability and potential of lip-reading-based silent speech interfaces for future clinical applications.

关键词： Silent Speech Interface Total Laryngectomy image processing

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Learning to Predict Short-Term Volatility with Order Flow image Representation 2

Learning to Predict Short-Term Volatility with Order Flow Im...

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2nd IEEE conference on Artificial Intelligence (CAI)

作者： Lensky, Artem Hao, Mingyu Univ New South Wales Sch Engn & Technol Canberra ACT Australia Australian Natl Univ Sch Comp Canberra ACT Australia

ISBN: (纸本)9798350354102;9798350354096

Introduction: The paper addresses the challenging problem of predicting the short-term realized volatility of the Bitcoin price using order flow information. The inherent stochastic nature and anti-persistence of price pose difficulties in accurate prediction. Methods: To address this, we propose a method that transforms order flow data over a fixed time interval (snapshots) into images. The order flow includes trade sizes, trade directions, and limit order book (LOB), and is mapped into image colour channels. These images are then used to train both a simple 3layer Convolutional Neural Network (CNN) and more advanced ResNet-18 as well as ConvMixer augmented with hand-crafted features. The models are evaluated against classical GARCH, Multilayer Perceptron trained on raw data, and a naive guess method that considers current volatility as a prediction. Results: The experiments are conducted using price data from January 2021 and evaluate model performance in terms of root mean square error (RMSPE). The results show that our order flow representation with a CNN as a predictive model achieves the best performance, with an RMSPE of 0.85 +/- 1.1 for the model with aggregated features and 1.0 +/- 1.4 for the model without feature supplementation. ConvMixer with feature supplementation follows closely. In comparison, the RMSPE for the naive guess method was 1.4 +/- 3.0.

关键词： Commerce

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