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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

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12th Ecuadorian conference on Information and Communication Technologies, TICEC 2024

12th Ecuadorian Conference on Information and Communication ...

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12th Ecuadorian conference on Information and Communication Technologies, TICEC 2024

ISBN: (纸本)9783031754302

The proceedings contain 24 papers. The special focus in this conference is on Information and Communication Technologies. The topics include: Pancreas Segmentation Using SRGAN Combined with U-Net neural Network;Using artificial Intelligence and X-ray images to Train and Predict COVID-19 and Pneumonia: Tool for Diagnosis and Treatment;Region of Interest Features and Classification of MRI Brain Lesions;deep Learning-Based Leukemia Diagnosis from Bone Marrow images;Breast Thermographic image Augmentation Using Generative Adversarial networks (GANs);Evaluating Histopathological Cancer Detection: A Comparative Analysis of CNN Architectures for Tumor Detection in Lymph Node Pathology;ViTSigat: Early Black Sigatoka Detection in Banana Plants Using Vision Transformer;automatic Parking Space Segmentation Using K-Means Clustering and image processing Techniques;neural Agents with Continual Learning Capacities;centinela: An Intelligent System Based on an Integrated Architecture for Supporting Scholars;Optimizing Predictive Models in Healthcare Using artificial Intelligence: A Comprehensive Approach with a COVID-19 Case Study;predictive Model Proposal in Telemetry Using Machine Learning Techniques to Anticipate Water Degradation in Aquaculture;Evaluation of the Use of artificial Intelligence Techniques in the Mitigation of the Broadcast Storm Problem in FANET networks;adaptation Dynamics of Galápagos Finches: Evolutionary Responses to Climate Variation Explored through Machine Learning;incident Alert Priority Levels Classification in Command and Control Centre Using Word Embedding Techniques;physics Informed neural networks and Gaussian Processes-Hamiltonian Monte Carlo to Solve Ordinary Differential Equations;Pre-processing of the Text of ECU 911 Emergency Calls;Secure BLE Communication Between Android Devices and Embedded Systems for IoT applications;modeling and Control of a Peltier Thermoelectric System Applying a Multi-objective Optimization Approach;wearable Device for Ac

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applications of artificial neural networks in image processing ii

Applications of Artificial Neural Networks in Image Processi...

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applications of artificial neural networks in image processing ii 1997

The proceedings contain 17 papers. The topics discussed include: entropy-constrained learning vector quantization algorithms and their application in image compression;predictive vector quantization using neural networks;design of an adaptive genetic learning neural network system for image compression;binary image compression using identity mapping backpropagation neural network;block-predictive image coder of neural network in multiresolution domain;and comparison of ML parameter estimation and neural network classifier for texture classification.

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28th International conference on image processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd International conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

28th International Conference on Image Processing, Computer ...

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28th International conference on image processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd International conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031859328

The proceedings contain 28 papers. The special focus in this conference is on image processing, Computer Vision, and Pattern Recognition. The topics include: image-Based Seal Recognition: Approaches and Challenges in Current Automated Systems;Deep Learning Techniques for Lunar Impact Crater Identification Based on CCD and DEM Data;silicon Wafer Map Defect Classification Using artificial Intelligence Models;low Light image Enhancement Using Autoencoder-Based Deep neural networks;towards Elephants Intelligent Monitoring in Zakouma National Park, Chad;a Review of Multi-modal and Multi-view applications in Hand-Drawn Sketch images;residential Real Estate image Classification for Property Valuation;Novel Method to Investigate Decay in Rotting Bananas Using RGB Color images;lalitha: A Hand Gesture-Based Computer Control System;dishari: A Novel Gesture-Based Educational Application for Specially Challenged People;mobility Anomaly Detection with Intelligent Video Surveillance;Weakly-Supervised Video Anomaly Detection Using Modified Anomaly Score Module and Modified BERT;Contour Detection of Seeds Based on Traditional and Convolutional neural Network (CNN) Based Algorithms;Early Detection of Lameness in Dairy Cattle Using Activity Data, image Analysis, AI and ML - An Approach for Improved Animal Welfare and Economic Impact;implications for Designing Hawks Detection with Data Augmentation and Network Optimizations;a Review of Detecting and Quantification of Cracks Using Convolutional neural networks and image processing Techniques;using Linkage Context for Automated Correction in Unsupervised Entity Resolution;the Rising Threat Against Modern Technology in Cybersecurity;an Online Bookstore Design and Implementation;cybersecurity: Sight and Foresight;the Application of Blockchain Technology in the Transmission of Semiconductor Process Recipes;the Use of Social Media and the Internet for the Facilitation of Human Trafficking.

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Deep Learning applications for Malaria Detection and Diagnosis: A Review 1st

Deep Learning Applications for Malaria Detection and Diagnos...

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1st International conference on artificial Intelligence and its applications, ICAIA 2023

作者： Vandana Khan, Shakir Gupta, Gaurav Mahajan, Shubham School of Bioengineering and Food Technology Shoolini University Himachal Pradesh 173229 India Riyadh11564 Saudi Arabia Computers and Data Science Yogananda School of AI Shoolini University Himachal Pradesh 173229 India Ajeenkya D Y Patil University Maharashtra Pune412105 India

ISBN: (纸本)9783031843969

Malaria is a life-threatening disease that affects millions of people worldwide, particularly in developing countries. Early and accurate detection of malaria is crucial for effective treatment and control of the disease. In recent years, deep learning techniques have shown promising results in various medical imaging tasks, including malaria detection and diagnosis. This paper presents a comprehensive review of deep learning applications for malaria detection and diagnosis. It covers the different stages of the malaria diagnosis pipeline, including image acquisition, pre-processing, parasite detection, and classification. The review discusses various deep learning architectures employed in malaria detection, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. It also highlights the challenges and limitations of existing approaches and identifies potential areas for future research. The findings of this review demonstrate the potential of deep learning techniques in improving the accuracy and efficiency of malaria detection and diagnosis, ultimately contributing to the efforts in eradicating this global health burden. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Reviving Poor Object Segmentations in OOD Medical images using Variational-Deep-PCA Modeling on Segmentation Maps with Sampling-Free Learning

Reviving Poor Object Segmentations in OOD Medical Images usi...

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IEEE Workshop on applications of Computer Vision (WACV)

作者： Jimut B. Pal Shantanu Welling Himali Saini Suyash P. Awate Centre for Machine Intelligence and Data Science Indian Institute of Technology (IIT) Bombay Computer Science and Engineering Department Indian Institute of Technology (IIT) Bombay

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

For object segmentation in medical images, deep neural networks (DNNs) typically perform poorly on out-of-distribution (OOD) images stemming from the large variability in image-acquisition equipment and protocols across sites. However, we observe that the variability in the underlying object-segmentation maps is far lower. Thus, we propose a novel DNN framework to model this variability in segmentation maps, and leverage it to revive poor segmentations produced by existing DNNs on OOD images. Our DNN framework (i) learns the principal modes of variation in a class of segmentation maps, (ii) models each segmentation map using a low-dimensional mixture-of-modes latent representation on a simplex, (iii) enables sampling-free variational learning and uncertainty estimation, and (iv) trains using small in-distribution image sets. In special cases when OOD-image segmentations are extremely poor, we propose a human-in-the-loop method needing minuscule human intervention. Results using 6 publicly-available datasets and 8 existing DNN segmenters show the benefits of our framework in OOD-image object segmentation.

关键词： Geometry image segmentation Uncertainty Protocols Computational modeling Estimation Object segmentation artificial neural networks Human in the loop Biomedical imaging

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Automated Detection of Tomato Leaf Diseases Using Convolutional neural networks

Automated Detection of Tomato Leaf Diseases Using Convolutio...

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Automation and Computation (AUTOCOM), International conference on

作者： Hemant Kumar Yadav Varsha Srivastava Sreenu Banoth Rohini Daksh Rawat Priyansha Rawat Artificial Intelligence & Data Science IIMT College of Engineering Greater Noida Uttar Pradesh India School of Computer Science and Engineering IILM University Greater Noida Uttar Pradesh Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

In this study, an automated system to detect tomato leaf diseases with Convolutional neural networks (CNNs) is introduced to aid farmers in early disease identification to avert crop losses and promote yield. The dataset is multiple classes of tomato leaf diseases, and the model was trained and tuned extensively for its performance. In particular, a high training accuracy of 95.73%, and validation accuracy of 89.85% was achieved by CNN. Furthermore, the system's precision, recall, and F1 metric were around 90%, indicating the balanced system performance. The proposed CNN model is shown to be more reliable and faster than the traditional image processing techniques and hence is an appropriate choice for real time agricultural applications. The integration of modern varieties technologies in AI based approaches in agriculture which will feed productivity while maintaining Sustainability. The results indicate that the proposed system could provide an effective means for disease detection for farmers and may alleviate some of the need for chemical treatments.

关键词： Training Productivity Plant diseases Accuracy Crops Predictive models Agriculture Convolutional neural networks Monitoring Diseases

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An image processing-Based Tomato Leaf Disease Prediction Using Deep CNN 5th

An Image Processing-Based Tomato Leaf Disease Prediction Usi...

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5th International conference on artificial Intelligence and Speech Technology, AIST 2023

作者： Vanisri, Kesana Raju, K. Srujan Laxmaiah, Bagam Department of CSE CMR Technical Campus Hyderabad India

ISBN: (纸本)9783031751660

Pest outbreaks have a significant impact on plants and crops, important to a reduction in national agricultural productivity. Conventionally, farmers and specialists rely on physical observation, and it can be timeconsuming, more expensive, and prone to errors, to classify diseases in plants. Though, by employing leading-edge image processing techniques, an instinctive detection system can be established, providing rapid and precise results. This paper aims to produce a Tomato Leaf Disease identification model with different categorizing leaf images and utilizing deep neural networks. The development of specific plant protection has the potential for substantial growth and improvement, with computer vision applications in the precision agriculture market also showing promising growth. This discussion about outlines all the critical stages for implementing the disease recognition model, initial from the image database group, assessment by agricultural specialists, and applying a deep learning framework to conduct the training of deep CNNs. By using this CNN, we will apply Max pooling layer, Multiple implementations are applied and finally we will get more accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dataset Deep CNN Diseases neural network

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CNN-Based Plant Leaf Disease Identification and Classification for Sustainable Agriculture

CNN-Based Plant Leaf Disease Identification and Classificati...

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Mobile Computing and Sustainable Informatics (ICMCSI), International conference on

作者： A Srilatha P Praveen School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The rapid spread of leaf-diseases among the large-scale crops or plants will pose a great risk to the agricultural output and crop yield. Earlier detection and proper analysis of foliar disorders are essential for minimizing yield losses as well as forwarding continuous farming trials. This research presents a novel way to find and analyze leaf diseases using various deep learning algorithms. The proposed approach integrates Convolutional neural networks (CNN) in feature extraction for automatically identifying disease patterns from leaf-based images with high accuracy to exploit manual inspection of plant leaves. To achieve higher accuracy, this model is train and tested on a wide variety from diseased & healthy leaf images. This approach surpasses traditional image processing methods, exhibiting high precision and recall in identifying various disease types across multiple crops. The deep learning-based solution offers a scalable, efficient, and reliable tool for farmers and agricultural experts, potentially revolutionizing plant disease management strategies. In addition, it can expand the dataset and enhance real-time detection ability which probably could be applied into user-friendly mobile applications in future work

关键词： Deep learning Plant diseases Accuracy Plants (biology) User interfaces Feature extraction Real-time systems Convolutional neural networks Reliability Diseases

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Imagined Speech and Visual imagery as Intuitive Paradigms for Brain-Computer Interfaces

Imagined Speech and Visual Imagery as Intuitive Paradigms fo...

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International Winter Workshop on Brain-Computer Interface (BCI)

作者： Seo-Hyun Lee Ji-Ha Park Deok-Seon Kim Dept. of Brain and Cognitive Engineering Korea University Seoul Republic of Korea Dept. of Artificial Intelligence Korea University Seoul Republic of Korea

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Brain-computer interfaces (BCIs) have shown promise in supporting communication for individuals with motor or speech impairments. Recent advancements such as brain-to-speech or brain-to-image technology aim to reconstruct speech from neural activity. However, robust decoding of communication-related paradigms, such as imagined speech and visual imagery, using non-invasive techniques still remains challenging. This study analyzes brain dynamics in these two paradigms by examining neural synchronization and functional connectivity through phase-locking values (PLV) in noninvasively collected EEG data. Results show that visual imagery produces higher PLV values in visual cortex, engaging spatial networks, while imagined speech demonstrates consistent synchronization, primarily engaging language-related regions. These findings suggest that imagined speech is suitable for language-driven BCI applications, while visual imagery can complement BCI systems for users with speech impairments. Furthermore, the brain connectivity results implies that personalized calibration is crucial for optimizing BCI performance.

关键词： Visualization Communication systems neural activity Signal processing Motors Brain-computer interfaces Decoding Calibration Synchronization Speech processing

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Enhancing Industrial Automation Flexibility Through neural Network-Empowered Machine Vision applications 1st

Enhancing Industrial Automation Flexibility Through Neural N...

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1st International conference on artificial Intelligence and its applications, ICAIA 2023

作者： George, Navin M. Rosero, Evelyn Mayakannan, S. Shameem, A. Divya, S. Arul, N. Electronics and Communication Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore641114 India Riobamba060155 Ecuador Department of Mechanical Engineering Vidya Vikas College of Engineering and Technology Namakkal Tamilnadu Tiruchengode India AMET Business School AMET University Tamilnadu Chennai India Erode Tamil Nadu Perundurai638057 India Department of Mechatronics Engineering Sona College of Technology Tamilnadu Salem India

ISBN: (纸本)9783031843969

Vision-based systems provide non-contact sensory input for processing and feedback, expanding the capabilities of industrial automation applications. Prediction using ANNs aids such conformities in navigating nonlinear computational landscapes. They map the plethora of potential outcomes or zones of uncertainty introduced by the system’s constituents onto a set of feasible options for action. A measure of intelligence is transferred to robotic systems through the use of trained networks. Two uses of machine vision are presented here. The 3 DOF robotic assembly enables precision cutting of fragile materials with the help of visual guiding and pixel elimination. The 6-degrees-of-freedom robot uses a combination of visual feedback from an onboard camera and supervision from an external camera to achieve its goals. Pick-and-place operations are executed with a switching control method. Both systems improved in terms of computing time and convergence once ANN was used to make the strategies intelligent. The retrieved scene image features are put to use by the networks in a variety of ways. The simulation and experimental results that back up the suggested approaches demonstrates the value of artificial neural network in machine vision applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robotic assembly

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