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检索条件"任意字段=1st International Conference on Image Processing Machine Learning and Pattern Recognition"

共 1528 条记录，以下是121-130 订阅

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Internal Defect Detection in Mango X-ray images using image processing and machine learning Techniques 1

Internal Defect Detection in Mango X-ray Images using Image ...

引用

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

作者： Singh, Archana Dhiraj Pilani333031 India Ghaziabad201002 India

ISBN: (纸本)9798350364590

The mango seed weevil is found in the world's major mango-producing regions and causes significant economic losses in mango fruit production. Many nations have identified the mango seed weevil as a dangerous quarantine pest, and its presence in exported mangoes can lead to the rejection of entire cargoes to stop the pest from spreading into mango-producing nations. It can also cause decreased seed germination, fruit pulp damage, and early fruit drop in mangoes. To prevent this menace, non-destructive evaluation methods need to be developed to detect the presence of seed weevils without cutting the mango fruit. A non-destructive method makes it possible to identify infested mangoes before they reach the market or processing facilities. This early detection helps prevent the spread of infestation, reducing overall fruit loss. For this experiment, 55 healthy mangoes and 5 defective mango samples were used in this work. A pipeline has been developed using image processing and machine learning-based techniques to process the X-ray scans of mangoes, automatically detect the presence of seed weevils inside the fruit, and display the final result on the screen. It also generates an imprint of the inner seed profile, depicting the presence of the infection inside it. In identifying the defective and normal samples, a 94% accuracy rate was achieved. At the end of the scan of mango samples, the generated report includes internal defect details regarding the seed weevil defect presence and its location in the mango sample. © 2024 IEEE.

关键词： Fruits

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A Model for Content-Based image Retrieval Using machine learning 1

A Model for Content-Based Image Retrieval Using Machine Lear...

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1st international conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Mahajan, Yash Batta, Priya Sharma, Muskan Saxena, Devansh Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350315769

With the value of digital data increasing in recent years, substantial improvements have been made in image processing. It is now simpler to produce large image datasets. picture processing is carrying out a variety of processes to improve a picture or extract informative data from it. With several theories and models relating to the retrieval of images from the collection of images called datasets, it has developed into a crucial study subject within the disciplines of engineering and computer science. A well-liked paradigm for the retrieval of images, image retrieval based on content is quicker and more advantageous than textual indexation. The authors of this study provide a paradigm for feature-based picture retrieval. Based on a user query, the model searches a database for the most relevant photographs and presents them with the least amount of vector distance. The suggested model can greatly enhance image retrieval systems and the applications of such systems in numerous sectors. Overall, this study makes a positive contribution to attempts to advance image processing technologies and their usefulness in the contemporary world. © 2023 IEEE.

关键词： machine learning

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machine learning Approach for Classification of Herbal Plant Leaves 1

Machine Learning Approach for Classification of Herbal Plant...

引用

1st international conference on Trends in Engineering Systems and Technologies, ICTEst 2024

作者： Naik, Nora Bhogan, Snehal Agnel Institute of Technology and Design Department of Computer Engineering Goa Assagao India

ISBN: (纸本)9798350307795

In the proposed system, an innovative Android application is designed to identify various herbal plant species by simply scanning their leaves. The app provides valuable information, like the plant's name and medicinal status, making it a practical tool for daily and medical needs. Using Indian herbal plant species dataset, the application combines image processing techniques for accurate leaf detection, feature extraction, training the model and then finally apply classification techniques to match the leaf accurately to its appropriate species. The grayscale conversion and noise reduction during preprocessing ensure optimal image quality, while segmentation and boundary extraction enhances accuracy. Extracting key leaf features such as area, length, and width contributes to effective model training. Finally, a classifier is applied to match the leaf to the species it belongs to and displays useful information about the herbal plant on the application. © 2024 IEEE.

关键词： Feature extraction

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Deep learning Character recognition of Handwritten Devanagari Script: A Complete Survey 1

Deep Learning Character Recognition of Handwritten Devanagar...

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1st IEEE international conference on Contemporary Computing and Communications, InC4 2023

作者： Sharma, Arpit Mithun, B.N. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

recognition of handwritten characters is a concept in which the single characters are classified, it is a facility of an electronic device to scan and decipher the handwritten input from a variety of sources, including written texts, images, and other digital touch-screen devices. This concept is being used in distinctive sectors such as the processing of bank checks, form data entry, and parcel posting and nowadays it is becoming a very important issue in the pattern recognition domain and a very challenging task to resolve it. Since deep learning is a crucial strategy in solving detection and pattern recognition problems, several algorithms are available to classify the characters with better prediction rates on different datasets, and ultimately, whichever algorithm gives the optimized results will be considered the best solution for the character recognition problem. As a result, various solutions proposed by the existing researchers are discussed using deep learning algorithms in this survey article. © 2023 IEEE.

关键词： Character recognition

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Semi-supervised polarimetric SAR images classification based on FixMatch 12

Semi-supervised polarimetric SAR images classification based...

引用

SPIE 12th international Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Lv, Qiwen Wang, Lei Hong, Hanyu Zhao, Shuwei Peng, Lingmu Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology 206 Guanggu 1st Road East Lake New Technology Development ZoneHubei Province Wuhan City430000 China

ISBN: (数字)9781510674929

ISBN: (纸本)9781510674912

Polarimetric synthetic aperture radar (PolSAR) images are widely used in the fields of disaster detection and military reconnaissance. PolSAR image classification is one of the most important applications in PolSAR image interpretation. Existing image classification methods based on deep learning usually rely on a large number of labeled training data. Labeling large-scale data sets is expensive and time-consuming Therefore, semi-supervised learning is an important research direction in the field of image classification. Fixmath has achieved excellent semi-supervised classification performance for nature image, so it is introduced into the field of PolSAR image interpretation, and to improve the performance of semi-supervised PolSAR image classification. Fixmatch has the following advantages: (1) It extends the training data, using generated, labeled and unlabeled data to train the network. (2) By utilizing the information of unlabeled data, FixMatch can improve the generalization ability of the classification model, thus allowing the model to better predict on unseen data. Because of these two advantages, FixMatch has shown excellent results in semi-supervised PolSAR image classification. Experiments on two real PolSAR datasets show that the overall accuracy reaches 89.9 % when the number of labeled samples is set to 110, and reaches 98.6 % when the number of labeled samples is set to 1000. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image classification

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Speech-enabled machine learning-based automated attendance monitoring system through face recognition 1

Speech-enabled machine learning-based automated attendance m...

引用

1st IEEE international conference on Contemporary Computing and Communications, InC4 2023

作者： Dhanvina, N. Mahesh, N.D. Sen, Snigdha Global Academy of Technology Dept. CSE GAT Bengaluru India

ISBN: (纸本)9798350335774

Verification, and validation are important issues in computerized systems for security in almost every domain. Amongst the many authentication methods, identification via Face ID is an important task. Face recognition plays a huge role in authenticity and is majorly used in the applications of theft prevention, door control systems (DCS), video surveillance systems, and aerial drones. Most universities still rely on traditional methods such as manual name-calling or paper-based record-keeping, which are both inefficient and prone to errors while taking attendance. To make this attendance monitoring system more hassle-free, in this paper, we have proposed and implemented an automated speech-recognized attendance monitoring system primarily based on image processing, speech recognition, and the machine learning algorithm LBPH. The method proposed in this paper aims to take attendance by the teacher in a full-fledged Speech automated system. The system automatically accumulates the required information in the classroom environment and records their attendance with the teacher's commands by taking live images through a camera. Our proposed system has been tested as robust and efficient in automating the monitoring task. © 2023 IEEE.

关键词： machine learning

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Sentiment Analysis Using a Hybrid Model Using machine learning 1

Sentiment Analysis Using a Hybrid Model Using Machine Learni...

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1st IEEE international conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Keswani, Arul Vashisht, Ansh Bhardwaj, Aditya Jain, Tarun Manipal University Jaipur Department of Computer Science and Engineering India Bennett University Greater Noida India

ISBN: (纸本)9798350377002

In the contemporary landscape, user feedback on social media platforms has become ubiquitous, driving the exploration of sentiment mining and analysis in the realm of machine learning research. Recent studies have shown a profound reliance on unimodal and multimodal approaches within machine learning algorithms, particularly in the context of sentiment analysis. Moreover, the evolution of deep learning models, particularly in image and text processing, has significantly advanced the field of sentiment analysis and classification. This paper introduces a hybrid deep learning framework comprising Convolutional Neural Network (CNN), K-Nearest Neighbours (KNN), stacked over Long Short-Term Memory (LstM). Additionally, it proposes a hybrid CNN + LstM model utilizing pre-trained vectors for enhanced performance. This study explores different hyperparameters and optimization strategies to address overfitting in the model, aiming to attain peak performance while mitigating the impact of overfitting it has been validated on stanford Sentiment Treebank2 Dataset (Sst2). © 2024 IEEE.

关键词： Adversarial machine learning

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1st international conference on Artificial Intelligence: Theories and Applications, ICAITA 2022

1st International Conference on Artificial Intelligence: The...

引用

1st international conference on Artificial Intelligence: Theories and Applications, ICAITA 2022

ISBN: (纸本)9783031285394

The proceedings contain 23 papers. The special focus in this conference is on Artificial Intelligence: Theories and Applications. The topics include: Convolution Neural Network Based Approach for Glaucoma Disease Detection;Deep Multi-Scale Hashing for image Retrieval (DMSH);genetic Programming for Screen Content image Quality Assessment;A Progressive Deep Transfer learning for the Diagnosis of Alzheimer’s Disease on Brain MRI images;An Optimized MSER Using Bat Algorithm for Skin Lesion Detection;accurate Detection of Brain Tumor Using Compound Filter and Deep Neural Network;Bi-ESRGAN: A New Approach of Document image Super-Resolution Based on Dual Deep Transfer learning;preface;expanding Convolutional Neural Network Kernel for Facial Expression recognition;a Novel Classification Method for Group Decision-Making Dimensions;intelligent Cache Placement in Multi-cache and One-Tenant Networks;enhanced Grey Wolf Optimizer for Data Clustering;artificial Orca Algorithm for Solving University Course Timetabling Issue;global Automatic Tuning of Fuzzy Sliding Mode Controller for an Inverted Pendulum: A Genetic Solution;Intelligent ITSC Fault Detection in PMSG Using the machine learning Technique;Series Elastic Actuator Cascade PID Controller Design Using Genetic Algorithm Method;Quantum Natural Language processing: A New and Promising Way to Solve NLP Problems;learning More with Less Data: Reaching the Power-Law Limits in steganalysis Using Larger Batch Sizes;offline Text-Independent Writer Identification Using Local Black pattern Histograms;Robust Method for Breast Cancer Classification Based on Feature Selection Using RGWO Algorithm;digital Modulation Classification Based on Automatic K-Nearest Neighbors Classifier.

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Optimal Feature Subset Selection for pattern Classification and recognition Using Spectral Entropy as a Complexity Measure from Alcohol and Sleep EEG Data 1

Optimal Feature Subset Selection for Pattern Classification ...

引用

1st IEEE international conference on Innovations in High Speed Communication and Signal processing, IHCSP 2023

作者： Singh, Takveer Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350345957

The quantity of unprocessed data in multi-channel EEG analysis is enormous. Most datasets have some level of noise or redundancy, which could lower the accuracy of any relevant features that are uncovered. This result in a rise in computational complexity and a corresponding rise in the importance of concerns related to memory consumption. Consequently, feature selection has become the matter of significant research in the arenas of machine learning and statistics since it is crucial for efficient analysis of data while needing less memory and time. Based on the findings of this study, selecting features with high class separability from multichannel EEG recordings is an efficient method for performing supervised learning tasks. With this study, we intend to test the following theory: The best feature subset will have features that are substantially linked with the class labels but not connected with each other. Using the same alcoholic EEG database as these other investigations, we created and compared three feature selection algorithms. The purpose here is to use class knowledge in a feature selection technique to enhance the accuracy with which classes may be detected. © 2023 IEEE.

关键词： Feature Selection

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Unlocking The Power Of Deep learning: A Guide To Automatic License Number recognition 1

Unlocking The Power Of Deep Learning: A Guide To Automatic L...

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1st international conference on Optimization Techniques for learning, ICOTL 2023

作者： Hemalatha, D. Begum, Almas Alex David, S. Rishitha, Gaddam Praharsha, Pothanaboyina Dimple Nandini, Chundi Vel Tech Rangarajan Dr Sagunathala R & D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350328042

An image processing technique called Automatic Number Plate recognition (ANPR) utilizes the vehicle's number plate to identify it. The goal is to create a successful automatic authorized car identification system that makes use of the license plate. In the majority of applications involving the movement of vehicles, the detection and recognition of a vehicle's license plate (LP) is a crucial method. Additionally, it is a hot research subject in the area of image processing. To find and identify LPs, numerous techniques, methods, and algorithms have been created. Prior to taking a picture of the car, the developed system first recognizes it. The region of an image that contains the car number plate is retrieved using image segmentation. An optical character recognition technique is used to recognize characters. However, due to the LP's characteristics which include the numbering system, colors, character languages, fonts, and size varying from one nation to the next. To make the detection and recognition procedure very effective, more research in this area is still required. Despite the fact that many scholars have studied this field, many current systems function in defined and regulated environments. For instance, some frameworks need sophisticated hardware in order to produce high-quality images or record video from cars moving slowly. Because of this, it is still challenging to successfully discover and identify LPs under various environmental circumstances and climatic variations. This article presents a deep learning-based automatic system for LP detection and recognition that is divided into three sections: character recognition, segmentation and detection. © 2023 IEEE.

关键词： Optical character recognition

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