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1st international conference on Artificial Intelligence and Data Science, ICAIDS 2021

1st International Conference on Artificial Intelligence and ...

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1st international conference on Artificial Intelligence and Data Science, ICAIDS 2021

ISBN: (纸本)9783031213847

The proceedings contain 43 papers. The special focus in this conference is on Artificial Intelligence and Data Science. The topics include: Challenges in Crop Selection Using machine learning;Handcrafted Features for Human Gait recognition: CASIA-A Dataset;local Binary pattern Symmetric Centre Feature Extraction Method for Detection of image Forgery;a Copy and Move image Forged Classification by Using Hybrid Neural Networks;preface;comparative Analysis of Advanced machine learning Based Techniques to Identify the Lung Cancer: A Review;cardiac Surveillance System Using by the Modified Kalman Filter;an Extensive study on Parkinson’s Disease Using Different Approaches of Supervised learning Algorithms;adaptive Convolution Neural Networks for Facial Emotion recognition;A Gravitational Search Algorithm study on Text Summarization Using NLP;analysis of Deep learning Methods for Prediction of Plant Diseases;ACHM: An Efficient Scheme for Vehicle Routing Using ACO and Hidden Markov Model;detection of Leaf Disease Using Artificial Intelligence;Deep CNN Based Whale Optimization for Predicting the Rice Plant Disease in Real Time;DCBC_DeepL: Detection and Counting of Blood Cells Employing Deep learning and YOLOv5 Model;ML_SPS: stroke Prediction System Employing machine learning Approach;Classification and Identification of Objects in images Using CNN;a System for Network Based Intrusion Avoidance Using Dedicated machine learning and Artificial Intelligence-Based Model for Application and Data Safety;AI-Based COVID Alert System on Embedded Platform;an Efficient Detection of Brain stroke Using machine learning Robust Classification;apple Leaf Diseases Detection System: A Review of the Different Segmentation and Deep learning Methods;underwater image Restoration and Enhancement Based on machine and Deep learning Algorithms.

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Odia Braille: Text Transcription via image processing 1

Odia Braille: Text Transcription via Image Processing

引用

international conference On Futuristic Trends in Computational Analysis and Knowledge Management

作者： Parvathi, K. Samal, Bijet Maynoher Das, Jitendra Kumar KIIT Univ Sch Elect Engn Bhubaneswar Odisha India

ISBN: (纸本)9781479984336

Braille-a model introduced to reduce the illiteracy rate among the visually challenged people. There has been a lot of scope for the conversion of English language to Braille but, the problem arises when the masses are unable to understand the communication by the visually challenged people. This paper focuses on the conversion of the Braille code representing Odia language(a language widely spoken in East India) into Odia word as text. For this, image processing using MATLAB technique provides a suitable platform to perform the segmentation of Braille cell for pattern selection and hence, Odia letter and word recognition. Braille Data Base creation acts as a storage system for the process and its accuracy is also tested which is explained in this paper.

关键词： visually challenged image processing image segmentation Braille Data Base Odia braille pattern recognition Histogram

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A survey on deep learning: Convolution neural network (cnn) 1st

A survey on deep learning: Convolution neural network (cnn)

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1st international conference on Intelligent and Cloud Computing, ICICC 2019

作者： Sahu, Madhusmita Dash, Rasmita BhubaneswarOdisha751030 India BhubaneswarOdisha751030 India

ISBN: (纸本)9789811562013

Deep learning is a subfield of machine learning and plays a vital role in the area of image processing, natural language processing, computer vision, etc. As compared to traditional machine learning methods, it has a strong ability of self-learning and self-debugging. Convolution neural network (CNN) is the most widely used technique of deep learning for better feature extraction from large datasets. Many researchers adopted CNN for object classification, face recognition, automatic handwritten, etc. In this paper, the detailed concepts behind CNN are discussed with their broad applications. © Springer Nature Singapore Pte Ltd 2021.

关键词： learning algorithms

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Unlocking Textual Content from Historical Maps - Potentials and Applications, Trends, and Outlooks 1st

Unlocking Textual Content from Historical Maps - Potentials ...

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1st international conference on Recent Trends in image processing and pattern recognition (RTIP2R)

作者： Chiang, Yao-Yi Univ Southern Calif Spatial Sci Inst Los Angeles CA 90089 USA

ISBN: (纸本)9789811048593;9789811048586

Digital map processing has been an interest in the image processing and pattern recognition community since the early 80s. With the exponential growth of available map scans in the archives and on the internet, a variety of disciplines in the natural and social sciences grow interests in using historical maps as a primary source of geographical and political information in their studies. Today, many organizations such as the United states Geological Survey, David Rumsey Map Collection, ***, and National Library of Scotland, store numerous historical maps in either paper or scanned format. Only a small portion of these historical maps is georeferenced, and even fewer of them have machine-readable content or comprehensive metadata. The lack of a searchable textual content including the spatial and temporal information prevents researchers from efficiently finding relevant maps for their research and using the map content in their studies. These challenges present a tremendous collaboration opportunity for the image processing and pattern recognition community to build advance map processing technologies for transforming the natural and social science studies that use historical maps. This paper presents the potentials of using historical maps in scientific research, describes the current trends and challenges in extracting and recognizing text content from historical maps, and discusses the future outlook.

关键词： Digital map processing Text recognition Optical character recognition Historical maps Geographic information system Natural science Social science Biology Spatial humanity

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The Sequential learning Ahead Masking (SLAM) model of neural networks for pattern classification

The Sequential Learning Ahead Masking (SLAM) model of neural...

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4th international conference on Computer Science and Informatics, JCIS 1998, 1st international Workshop on High Performance, 1st international Workshop on Computer Vision, pattern recognition and image processing Volume 4

作者： Shoujue, Wang Yuanmao, Yin Xiangdong, Chen Semiconductor Institute Chinese Academy of Sciences P.O.BOX 912 Beijing 100083 China

On the basis of discussing General Feed-Forward Networks (GFFN), a Sequential learning Ahead Masking (SLAM) model and its relevant algorithm for pattern classification are proposed. By adapting this model to the pattern classifier, the computer simulation results show that not only the convergence speed and performance of the network are much better than the existing modified BP algorithms, but also the required network scale is greatly reduced. Moreover, Double- Threshold Neuron (DTN) has been applied to SLAM network for pattern classification. The SLAM pattern classifier has been implemented on the domestic micro-neurocomputer CASSANDRA-I and the results are provided as below: For two-class pattern classification problem with 1024 patterns generated randomly in 256-dimensional pattern space, the training time is about 3 hours 20 minutes, and the running time for pattern classification is 0.007 seconds.

关键词： Neural networks

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machine learning approaches for Microscopic image analysis and Microbial Object Detection(MOD) as a decision support system 1

Machine Learning approaches for Microscopic Image analysis a...

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1st international conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

作者： Chaudhuri, Rapti Deb, Suman National Institute of Technology Cse Department Agartala India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Microbes are tiny living organisms beyond the scope to be seen by the naked eye that are coexisting all around the biosphere along with other animals. Significant identification of the microbes from the elementary forms of cell structure to deadly pandemic causing elements are vital for health and hygiene support systems. Manual identification of such creatures consumes infinite amount of time resulting in frequent contamination. In this work, the context is primarily focused on automated microscopic image analysis on visual features by incorporating systematic pattern matching approaches for rapid identification of the microbes. The microbes are classified and recognized using the state of art of real time object detection strategy. Experimental result analysis is done with bacterial images to confirm the precision and accuracy of the utilized technique. Visual and graphical representation of the result obtained confers the validity and correctness of the concerned procedure. Further, the challenges and the difficulties faced during the microbe identification and the techniques to tackle have also been discussed subsequently. The proposed solution is proved to be quick and swift analyzing technique of pathogenic microbes in an organized way and has potential to be used as a source of field-level pathology decision support system. © 2022 IEEE.

关键词： Object detection

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Smart Diet Management Through Food image and Cooking Recipe Analysis 21st

Smart Diet Management Through Food Image and Cooking Recipe ...

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21st international conference on image Analysis and processing (ICIAP)

作者： He, Yinchao Hakguder, Zeynep Shi, Xu Univ Calif San Diego La Jolla CA 92093 USA Prorize LLC Marietta GA 30062 USA Univ Nebraska Lincoln NE 68508 USA

ISBN: (纸本)9783031133213;9783031133206

Food monitoring has become an indispensable practice for personal health management in increasingly growing populations. To facilitate this process, advanced image processing and AI technology have empowered automated recognition of food items and nutrients using food images taken by smart mobile devices. However, precision is often compromised for convenience, which is also applicable in food logging. In this study, we have explored new solutions that can help improve food recognition accuracy with a particular focus on domestic cooking, by leveraging advanced machine learning and natural language processing techniques, in conjunction with comprehensive food nutrient profiles in the knowledge base, as well as contextual ingredient information parsed from publicly available recipes. The optimized models were proved to be effective and have been integrated into an Android app named "FoodInsight" .

关键词： image recognition Recipe analysis Transfer deep learning Natural language processing Recipe recommendation Food intake monitoring

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Semantic learning in Interactive image Retrieval

引用

1st international conference on Digital image processing and pattern recognition (DPPR 2011)

作者： Patil, Pushpa B. Kokare, Manesh BLDEAs VPDr PG Halakatti CET Dept Comp Sci & Engg Bijapur India SGGS Inst Engn & Technol Dept Elect & Telecommun Nanded India

ISBN: (纸本)9783642240546;9783642240553

This paper presents content-based image retrieval frameworks with relevance feedback based on AdaBoost learning method. As we know relevance feedback (RF) is online process. so we have optimized the learning process by considering the most positive image selection on each feedback iteration. To learn the system we have used AdaBoost. The main significances of our system are to address the small training sample and to reduce retrieval time. Experiments are conducted on 1856 texture images to demonstrate the effectiveness of the proposed framework. These experiments employed large image databases and combined RCWFs and DT-CWT texture descriptors to represent content of the images.

关键词： Content-Based image Retrieval (CBIR) Relevance Feedback (RF) Rotated Complex Wavelet Filters (RCWFs) Dual Tree Complex Wavelet (DT-CWT) and image retrieval

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Multi-valued and universal binary neurons: learning algorithms, application to image processing and recognition 1st

Multi-valued and universal binary neurons: Learning algorith...

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1st international Workshop on machine learning and Data Mining in pattern recognition, MLDM 1999

作者： Aizenberg, Igor N. Aizenberg, Naum N. Krivosheev, Georgy A. Minaiskaya 28 kv. 49 Uzhgorod294015 Ukraine Paustovskogo 3 kv. 430 Moscow Russia

ISBN: (纸本)3540665994

Multi-valued and universal binary neurons (MVN and UBN) are the neural processing elements with complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partial-defined multiple-valued function on the single MVN and an arbitrary mapping described by partial-defined or fully-defined Boolean function (which can be not threshold) on the single UBN. The fast-converged learning algorithms are existing for both types of neurons. Such features of the MVN and UBN may be used for solution of the different kinds of problems. One of the most successful applications of the MVN and UBN is their usage as basic neurons in the Cellular Neural Networks (CNN) for solution of the image processing and image analysis problems. Another effective application of the MVN is their use as the basic neurons in the neural networks oriented to the image recognition. © Springer-Verlag Berlin Heidelberg 1999.

关键词： Neurons

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Two efficient edge detecting operators derived from 3 x 3 magic squares

Two efficient edge detecting operators derived from 3 x 3 ma...

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5th international conference on Wavelet Analysis and pattern recognition

作者： Chen, Zao-Xue Nie, Sheng-Dong Shanghai Univ Sci & Technol Coll Med Instrumentat & Foodstuff Shanghai 200093 Peoples R China

ISBN: (纸本)9781424410651

Two efficient edge-detecting operators are introduced. They are both based on templates derived from a 3 x 3 magic square, one operator can obtain edge grodient map with more continuous histogram, and the other can detect different edge directions efficiently. Experiments implemented based on the fainous standard image Lena have shown the validity of the two approaches. This paper not only presents two efficient approaches for edge detection, but also enlightens new methodology for image processing research based on magic squares.

关键词： image processing image enhancement edge detection magic square template gradient map

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