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Deep Learning Empowered Weather image Classification for Accurate Analysis 2

Deep Learning Empowered Weather Image Classification for Acc...

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2nd IEEE International conference on Industrial Electronics: Developments and Applications, ICIDeA 2023

作者： Gupta, Akanksha Goel, Silky University of Petroleum and Energy Studies School of Computer Science Dehradun India

ISBN: (纸本)9798350381979

Accurate weather classification is essential in numerous real-world applications, significantly impacting areas such as solar energy systems, outdoor events, and visual systems. For minimizing the adverse effects stemming from untimely weather fluctuations, its prediction is of utmost importance. The present work reflects an approach for multi-class weather classification by harnessing the power of deep learning techniques and transfer learning. The research utilizes the Multi-class Weather dataset (MWD), comprising four weather conditions: sunrise, shine, rain, and cloudy. Using pre-trained deep learning architectures such as ResNet50, Densenet-161, InceptionV3, VGG19, and MobileNet, the present research extracts intricate features from the dataset images. Also, Transfer learning is employed to fine-tune these models for weather classification, effectively addressing the constraints posed by a scarcity of labeled data. The role of a logistic regression model is pivotal as the multi-class classifier. In-depth evaluation metrics encompassing accuracy, precision, recall, and F1 score are employed, accompanied by a meticulous analysis utilizing confusion matrix techniques. The results show that Densenet-161 demonstrates superior performance while other models exhibit competitive results presenting viable alternatives for specific use cases. Overall, the study contributes insights into the effectiveness of pre-trained models and transfer learning in multi-class weather classification tasks. © 2023 IEEE.

关键词： image classification

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Pick-and-Place Application Based on 2D Camera System Usage 2nd

Pick-and-Place Application Based on 2D Camera System Usage

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2nd EAI International conference on Automation and Control in Theory and Practice, EAI ARTEP 2024

作者： Vagas, Marek Galajdova, Alena Sarga, Patrik Rakay, Robert Romancik, Jaroslav Majercak, Ondrej Department of Industrial Automation and Mechatronics Faculty of Mechanical Engineering Technical University of Košice Košice Slovakia

ISBN: (纸本)9783031592379

The authors describe an Ethernet communication link between a superior PLC system and a selected (collaborative) robotic system. Part of the presented approach is the hardware and software implementation of the 2D camera system in an experimental automated workplace. Its emphasis is on experimental verification based on pick-and-place principles. It describes the design, development, and testing of selected communication. Another purpose is to point out the possibilities of image data processing from the obtained image using suitable software modules. The common principle of our approach is a unified solution that includes as many components as possible from one provider, which is often impossible. Moreover, the image processing process requires three specifically focused software modules (Threshold HSV, Pattern Match, and Spreadsheet). The task of these modules is to gradually find the desired object from the obtained image scene (based on the comparison of black and white pixels). Subsequently, it compares it with the database of objects and selects the one with the highest percentage match. Part of this process is also finding the center of gravity and orientation of the object. Finally, with appropriate mathematical operations, it transforms the obtained coordination coordinates into a suitable length in such a way that the control system of the robotic arm can read it and implement the required application. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Robotic arms

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Early Detection of Brain Tumor from MRI images Using Different Machine Learning Techniques 2

Early Detection of Brain Tumor from MRI Images Using Differe...

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2nd International conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Raghuwanshi, Sumit Sukhad, Ambuj Rasool, Akhtar Meena, Vikas Kumar Jadhav, Abhishek Shivakarthik, Katravath

Effective treatment of brain tumours depends on early identification of tumour tissues. Categorizing tumors is essential for its early identification. In order to split and categorise brain tumours using MRI images, various machine learning algorithms are examined. Preprocessing methods including image augmentation, rotation flipping, and adaptive deformation are used on the BRATs dataset to improve the data quality. Convolutional neural network (CNN) feature extraction techniques like VGG19 and InceptionV3 are applied. K-Nearest Neighbours (KNN) and logistic regression techniques are used to categorise the features. The proposed models' accuracy for Logistic Regression, KNN, VGG19, Inception V3 and VGG19 with augmentation is 84.66%, 90.68%, 92.17%, 91.56% and 95.43% respectively. These findings imply that CNN-based feature extraction and picture augmentation techniques greatly improves the accuracy of brain tumour identification, Overall thecInception V3 with augmentation produces the best results. © 2024 Elsevier B.V.. All rights reserved.

关键词： image enhancement

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Imbalanced data Classification using Optimized Support Vector Machine for IoMT Application 2

Imbalanced Data Classification using Optimized Support Vecto...

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2nd International conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Pattanayak, Binod Kumar Mohanty, Mihir Narayan Satpathy, Smitarani Arjun Das, Jay Bijay Dept. of CSE ITER Odisha Bhubaneswar751030 India Advanced Signal Processing Lab ITER Odisha Bhubaneswar751030 India

ISBN: (纸本)9798350344882

In this digital age, most of the applications need to be Internet of Things based for easy access. Biomedical research has made significant growth related to it. The visual pathological information provides comparatively better information as compared to signals that help physicians with further analysis and suggestions. Medical data often exhibit class imbalance, where the number of samples in different classes is not proportional. This imbalance can pose significant challenges to traditional classification algorithms. This report explores the application of Support Vector Machines (SVM) to imbalanced medical data classification, discussing the inherent issues, techniques for addressing class imbalance, and experimental results showcasing the effectiveness of SVM in handling imbalanced medical datasets. As a result, the retrieval process over the internet will be faster. In this paper, authors have considered three different cancerous image data of breast cancer, brain tumor, and liver cancer for analysis and detection using a machine learning approach. The data is collected from the UCI machine learning repository. The statistical features like class, radius, and area are considered and optimized using the Brain Storm Optimization algorithm for good accuracy. The optimized features are applied to the support vector machine classifier. Further, use for different kernels such as radial basis function (RBF), linear, polynomial, and sigmoid are used with SVM and verified for comparison. It is found that the RBF kernel provides SVM better accuracy as compared to other kernels. © 2023 IEEE.

关键词： Brain

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Artificial Neural Network using image Processing for Digital Forensics Crime Scene Object Detection 2

Artificial Neural Network using Image Processing for Digital...

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2nd International conference on Edge Computing and Applications, ICECAA 2023

作者： Devasenapathy, Deepa Raja, M. Dwibedi, Rajat Kumar Vinoth, N. Jayasudha, T. Ganesh, V. Dilli Computing & Software Engineering U.A. Whitaker College of Engineering Florida Gulf Coast University United States Cmr Institute of Technology Department of Computer Science and Engineeting Bengaluru India Aarupadai Veedu Institute of Technology Vmrf Chennai India Chennai Institute of Technology Department of Mechatronics Engineering Chennai India Kpr Institute of Engineering and Technology Department of English Coimbatore India Chennai India

ISBN: (纸本)9798350347579

Digital forensics science places a significant emphasis on the detection of objects as one of the most vital areas of study. Several industries and institutions may benefit from the object detection method, including those concerned with medical diagnostic scanning, traffic monitoring, airport security, law enforcement, and data rescue on a local and global scale. This study aims to detect weapons in video surveillance images by using various enhancement, segmentation, feature extraction, and classification methods by Artificial Neural Network to improve the detection accuracy. Yet, several mathematical and algorithmic models are computed to provide the appropriate approaches. © 2023 IEEE.

关键词： Neural networks

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2nd International conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2023

2nd International Conference on Advances in Data-driven Comp...

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2nd International conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2023

ISBN: (纸本)9789819995301

The proceedings contain 40 papers. The special focus in this conference is on Advances in data-driven Computing and Intelligent Systems. The topics include: The eXtreme Gradient Boosting Method Optimized by Hybridized Sine Cosine Metaheuristics for Ship Vessel Classification;does the Resilience Learning Game Foster Workforce Open Innovation and Sustainability Attributes? Empirical Evidence from Greek Food Industry;Seizure Detection by Analyzing EEG Signals Using Deep Learning Networks;Parts-of-Speech Tagger in Assamese Using LSTM and Bi-LSTM;Dynamic Retransmission Count Prediction (DRCP) Algorithm for FANET Using Machine Learning Techniques;video-Based Action Recognition of Spatial and Temporal Deep Learning Models;aeroDetect: Real-Time Small Object Detection in Aerial images;integrated Whale Swarm and Neuro-Evolutionary Computing for Large-Scale Sparse Optimization Problems;deep Reinforcement Learning for Credit Card Fraud Detection;influences of Specimen and Fiber Sizes on the Direct Tensile Resistance of Ultra-High-Performance Fiber-Reinforced Concretes;segmentation-Based Transformer Network for Automated Skin Disease Detection;multi-sensor data Fusion and Deep Machine Learning Models-Based Mental Stress Detection System;anti-forensic Analysis for image Splicing Detection Through Advanced Filters;classification of Organic and Recyclable Waste Using a Deep Learning Approach;prediction of Soil Properties for Agriculture Using Ensemble Learning Techniques;enhancing Intelligent Video Surveillance: Deep Learning Approaches for Human Anomalous Behavior Recognition;detection of Explicit Lyrics in Hindi Music Using Different Machine Learning Algorithms;artificial Intelligence Empowered Language Models: A Review;mass Production Lab;Automatic Multiple Sounds Detection with Recurrent Neural Networks (LSTM);energy Efficiency Techniques in 5G/6G Networks: Green Communication Solutions;assessing the Feasibility and Scalability of Using Spark for Identifying Tip Burn Diseases i

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DisControlFace: Adding Disentangled Control to Diffusion Autoencoder for One-shot Explicit Facial image Editing 24

DisControlFace: Adding Disentangled Control to Diffusion Aut...

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32nd ACM International conference on Multimedia, MM 2024

作者： Jia, Haozhe Li, Yan Cui, Hengfei Xu, Di Wang, Yuwang Yu, Tao Huawei Technologies Co. Ltd Xi'an China Northwestern Polytechnical University Xi'an China Tsinghua University Beijing China

ISBN: (纸本)9798400706868

In this work, we focus on exploring explicit fine-grained control of generative facial image editing, all while generating faithful facial appearances and consistent semantic details, which however, is quite challenging and has not been extensively explored, especially under an one-shot scenario. We identify the key challenge as the exploration of disentangled conditional control between high-level semantics and explicit parameters ( e.g., 3DMM) in the generation process, and accordingly propose a novel diffusion-based editing framework, named DisControlFace. Specifically, we leverage a Diffusion Autoencoder (Diff-AE) as the semantic reconstruction backbone. To enable explicit face editing, we construct an Exp-FaceNet that is compatible with Diff-AE to generate spatial-wise explicit control conditions based on estimated 3DMM parameters. Different from current diffusion-based editing methods that train the whole conditional generative model from scratch, we freeze the pre-trained weights of the Diff-AE to maintain its semantically deterministic conditioning capability and accordingly propose a random semantic masking (RSM) strategy to effectively achieve an independent training of Exp-FaceNet. This setting endows the model with disentangled face control meanwhile reducing semantic information shift in editing. Our model can be trained using 2D in-the-wild portrait images without requiring 3D or video data and perform robust editing on any new facial image through a simple one-shot fine-tuning. Comprehensive experiments demonstrate that DisControlFace can generate realistic facial images with better editing accuracy and identity preservation over SOTA methods. © 2024 ACM.

关键词： image denoising

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image Intelligence in Cyber Security using Sensing System towards the Future Generation Intelligence 2

Image Intelligence in Cyber Security using Sensing System to...

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

作者： Kumar, Rajeev Sharma, Neha Kumar, Sandeep Chandigarh University Department of Computer Science and Engineering Punjab Mohali India Rightzone Technologies Private Limited

ISBN: (纸本)9781665437899

The fundamental target of tone mapping is to duplicate the given scene or an image close to the 64000 world brilliance coordinating the human read inside the show gadgets. Therapeutic imaging utilizes that is procedures to downsize commotion and sharpness subtleties to upgrade the visual delineation of the picture. Because details play such an important role in determining proof and treating disease, it's critical to concentrate on the most important options when displaying medical images. It could be a method for reducing the unpredictability of high-dimensional data. You'll be able to use essential part analysis to rough out high-dimensional data with fewer measurements. Each measurement is regarded as the most important component and refers to a direct blend of the underlying components, as well as the amount of data. This data can be used to solve a wide range of problems that happen on a regular basis. It also highlighted how Big data may be used to analyse Internet and image data sources effectively. concerns of privacy, methods for securing the components of pattern environments and systems, Edges, on the other hand, which nearly always square measure fascinating options of associate degree image) are also characterized by sharp transitions in grey levels, therefore averaging filters have the undesirable facet result that they blur edges. Another application of this kind of method includes the smoothing of false contours that result from victimization associate degree meagerly range of grey levels. © 2022 IEEE.

关键词： image analysis

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2nd International conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2023

2nd International Conference on Advances in Data-driven Comp...

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2nd International conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2023

ISBN: (纸本)9789819995202

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2nd International conference on Computer System Information Technology and Electrical Engineering, CFP23AP1-ART 2023

2nd International Conference on Computer System Information ...

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2nd International conference on Computer System, Information Technology, and Electrical Engineering, COSITE 2023

ISBN: (纸本)9798350343069

The proceedings contain 46 papers. The special focus in this conference is on Computer System Information Technology and Electrical Engineering. The topics include: The Effect of Photovoltaic Plant Integration on Power Flow Based on The Newton Raphson Method on The Krueng Raya Feeder;designing a Microcontroller-Based Sumo Robot with Analysis of Thrust Parameters;blockchain Research in Indonesia: A Bibliometric Analysis;improvement of User Satisfaction in Educational Core data Transactions using A Service Quality Model Analysis;harmonics Step Filter Control Model In Household Electricity;development of Intraoral Clinical image dataset for Deep Learning Caries Detection;content based image Retrieval using Fine-tuned Deep Features with Transfer Learning;forensic Analysis of Car Accident Using MobileNet and Optical Flow;Pseudo-LiDAR Meets Agriculture: Leveraging 3D Monocular Point Cloud Processing for Coffee Beans;impact of image Enhancement for Osteoporosis Detection Based on Deep Learning Algorithm;Implementation of Faster R-CNN with Colour and Blur Augmentation For Differentiate Cloves from Debris;a Real-Time Application for Rail Surface Defect Inspection Utilizing Rectangular-Shaped Labels;Real-Time Object Detection Performance of YOLOv8 Models for Self-Driving Cars in a Mixed Traffic Environment;Hybridization Solution of Electrical Energy Demand Response and Forecasting Program by Using PSO-LSSVM Technique;IP-to-ndN Integration with Socket Translation Gateway;SASSD: A Smart Assessment System For Sector Damage Post-Natural Disaster Using Artificial Neural Networks;Comparative Analysis of Lightweight Pre-Trained CNN Models for Coffee Bean Roasting Level Identification;utilizing Different Predictive Methods to Optimize Hyperledger Fabric Instance;Evaluation of CyPACH: A Cyber Privacy Advisor Chatbot.

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