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ACM 2022 - Proceedings of the algorithms, Computing and Mathematics conference 2022

ACM 2022 - Proceedings of the Algorithms, Computing and Math...

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2022 algorithms, Computing and Mathematics conference, ACM 2022

The proceedings contain 11 papers. The topics discussed include: multiple ensembling techniques for monitoring the physical activities and predicting the performance of the students;a comprehensive review of influence node identification in complex networks;improving lifestyle of visually impaired people using virtual reality;algorithm for optimization in medical image processing applied in heterogeneous architecture;application of real-time operating systems in the design of medical devices;smart farming using artificial intelligence;optimized cluster centroids for segmentation of skin cancer using triangular intuitionistic fuzzy sets;analog and digital RoF spatial Mux MIMO-LTE system based A2 (arithmetic Aquila) optimization model for 5G network;comparison of state - of - art deep learning algorithms for detecting cyberbullying in twitter;and stock market technical analysis using Japanese candlesticks and machine learning.

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Machine Learning Approach for Detection of Diabetic Retinopathy with Improved Pre-processing

Machine Learning Approach for Detection of Diabetic Retinopa...

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International conference on Computing, Communication, and Intelligent systems (ICCCIS)

作者： Sharma, Ayushi Shinde, Swapnil Shaikh, Imran Ismail Vyas, Madhav Rani, Soumya Ramrao Adik Inst Technol Dept Informat Technol Nerul India

ISBN: (纸本)9781728185293

Diabetic Retinopathy has been one of the common reason why diabetic patients lose sight. It is caused by the damaging of the blood vessel of the light sensitive tissue at the back of the eye or the retina. The traditional methods for the detection of the disease requires lots of time and efforts hence the proposed system aims to detect diabetic retinopathy using image processing and Machine Learning. The input provided to the proposed system are the retinal images from the standard databases DIARETDB0 and DIARETDB1. The system aims to automate the process of detection using a combination of basic image processing steps with more focus on pre-processing to obtain clear image for feature extraction, further Machine Learning algorithms are applied for classification. The statistical features such as area and perimeter are obtained from blood vessels and exudates after pre-processing stage, different pre-processing techniques such as grayscale conversion, binarization, canny edge detection and some morphological operations including image dilation and erosion are applied to obtain clear image. The Machine Learning algorithms that are used for result analysis are Weighted KNN, Cubic SVM and Simple Tree which provides an accuracy of 85.8%, 87.2% and 88.6% respectively. The sensitivity and specificity parameters are also obtained, Simple tree yields better results for all the three result parameters.

关键词： Blood Vessels Diabetic Retinopathy Exudates Machine Learning Weighted KNN

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Enhancing Efficiency and Quality of image Caption Generation with CARU 17th

Enhancing Efficiency and Quality of Image Caption Generation...

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17th International conference on Wireless algorithms, systems, and Applications (WASA)

作者： Huang, Xuefei Ke, Wei Sheng, Hao Macao Polytechn Univ Fac Sci Appl Macau Peoples R China Macao Polytechn Univ Engn Res Ctr Appl Technol Machine Translat & Arti Minist Educ Macau Peoples R China Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Hangzhou Innovat Inst Yuhang Hangzhou 310023 Peoples R China

ISBN: (纸本)9783031192135;9783031192142

image caption is textual explanation automatically generated by a computer according to the content in an image. It involves both image and natural language processing, and thus becomes an important research topic in pattern recognition. Deep learning has been successful in accomplishing this task, and the quality of captions generated by existing methods is already high. However, due to the broadness and variety of image caption applications, the current generated captions are still not sufficiently detailed, and the training efficiency can also be improved. Therefore, under the encoder-decoder framework of deep learning, how to use fewer parameters to improve the training efficiency and retain the quality of the generated image descriptions is a huge challenge. In this work, we introduce an improved method based on the encoder-decoder structure, adding an attention mechanism, and applying the content adaptive recurrent unit (CARU), as the decoder, to generate image captions. Inspired by GRU, CARU is designed to have comparable performance with fewer parameters, and is sensitive to the features in hidden layers. The experimental results show, based on MsCOCO dataset, the proposed method achieved better performance than that using GRU as the decoder, and took less training time, effectively improves the training efficiency.

关键词： image caption generation Content adaptive recurrent unit Feature extraction Deep learning Training efficiency

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Extended field of vision by using two synchronized camera acquisition off-axis holograms

Extended field of vision by using two synchronized camera ac...

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International conference on image and Signal processing and their Applications (ISPA)

作者： Omar Chaab Fatan Bougheziou Malika Mimi Serge Simoëns Department of Sciences and Technology Signals and Systems Laboratory University Abdelhamid Ibn Badis Mostaganem Algeria Applied Optics Laboratory Institute of Optics and Fine Mechanics University Ferhat Abbas Setif Algeria Department of Electrical Engineering Signals and Systems Laboratory University Abdelhamid Ibn Badis Mostaganem Algeria Acoustic and Fluid Mechanics Laboratory UMR CNRS 5509 UCB Lyon I INSA Central School of Lyon Ecully Lyon France

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Digital holography (DH) is an imaging technique that allows the recording and the reconstruction of the complex optical field (complete information) in both amplitude and phase. Its main advantage is the possibility to modify the recorded wavefront to get free-aberration version. This technique represents the best contender for metrological applications especially in mechanical fluids where it gives an additional dimension for the determination of velocity field of different kind of fluid flow. In order to extend the fluid field of investigation, two synchronized cameras CCD and CMOS with different characteristic (pitch and size) are used for recording two views holograms; this task is implemented under Labview interface which also permits to record successive several holograms with a predefined time interval. The holograms are obtained with the help of holographic setup in off-axis configuration of two views (direct and orthogonal). In order to obtain the total field of view, the two reconstructed holograms must have the same output resolution.

关键词： image resolution Heuristic algorithms Signal processing algorithms Transforms Holography Cameras Synchronization

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Research on Motion image Recognition Based on Deep Learning algorithms

Research on Motion Image Recognition Based on Deep Learning ...

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Applied Intelligence and Computing (AIC), IEEE World conference on

作者： Jixia He Hui Tian Weifang Engineering Vocational College Weifang China

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

The paper aims to explore the application effects of deep learning algorithms in sports image recognition and analysis. Faced with the challenges of insufficient feature extraction ability in the field of sports image recognition. This article proposes a fusion algorithm based on CNN-GCN (Convolutional Neural Network Graph Convolutional Network). Through in-depth analysis of previous research achievements and challenges encountered, this article not only points out the limitations of traditional methods, but also demonstrates how deep learning technology effectively solves these problems. In the experimental section, it evaluated the performance of the CNN-GCN fusion algorithm and Support Vector Machine (SVM), Random Forest (RF) algorithm, and Multi-Layer Perceptron (MLP) algorithm in sports image recognition and analysis tasks through four different experiments. In the benchmark performance comparison experiment, the CNN-GCN fusion algorithm achieved an accuracy of 93%, and achieved recall, precision, and F1 scores of 91%, 92%, and 91.5%, respectively, all higher than other reference algorithms. In the robustness testing experiment, in the face of degraded image quality, the accuracy of CNN-GCN can also be maintained at 85%, significantly higher than the performance of SVM, RF, and MLP algorithms under the same conditions. In the experiment of dynamic scene analysis capability, the CNN-GCN fusion algorithm achieved an accuracy of 85% when dealing with high complexity dynamic scenes. In the final real-time processing capability evaluation, the CNN-GCN fusion algorithm also took the lead with a processing time of 25 milliseconds. In the data conclusion of the above experiment, the CNN-GCN fusion algorithm has shown excellent performance in the field of sports image recognition and analysis, and also reflects the enormous potential of the CNN-GCN fusion algorithm in real-time application scenarios.

关键词： Support vector machines Deep learning Radio frequency image recognition Accuracy Heuristic algorithms Real-time systems Robustness Convolutional neural networks Sports

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Iterative approach based on the FDTD method for the design of metal-dielectric photonic crystal devices 8

Iterative approach based on the FDTD method for the design o...

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8th International conference on Information Technology and Nanotechnology, ITNT 2022

作者： Mokshin, Pavel Golovashkin, Dimitry Pavelyev, Vladimir Yablokova, Liudmila Samara National Research University Samara Russia Image Processing Systems Institute Branch of the Federal Scientific Research Centre 'Crystallography Photonics' of Russian Academy of Science Samara National Research University Samara Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

Present article deals with a noble approach for designing the photonic crystal devices by using information about diffraction patterns at different frequencies;it is useful for optimizing an element designed to operate at one selected wavelength. Proposed approach differs from the known general-purpose optimization methods (for example, a genetic algorithm or gradient procedures). © 2022 IEEE.

关键词： Genetic algorithms

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Research on the Application of Improved Genetic Algorithm in the Design of Physical Education Teaching systems

Research on the Application of Improved Genetic Algorithm in...

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image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Xue Zhang Fushun Vocational Technical Institute (Fushun Teachers College) Fushun Liaoning China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In colleges and universities in China, physical education is a crucial public fundamental subject. Traditional instruction often takes place during the designated class period in the training program. The instructor imparts to the pupils the principles of physical education so that they may become proficient in sports and ultimately raise their physical and athletic abilities. To lessen the issue of the basic genetic algorithm for intelligent grouping systems' delayed convergence and partial convergence, an enhanced genetic algorithm is suggested. By consistently rejecting comparable candidates, the algorithm may quickly expand the search field and guarantee the stability and diversity of the group. As a result, this work suggests a novel approach to intelligent grouping that is based on an enhanced genetic algorithm. Compared to the conventional algorithm, the novel approach is more effective and straightforward in resolving the issue of the algorithm's early convergence. Numerous tests have demonstrated how effectively the suggested algorithm satisfies every need for physical education. Test papers with a moderate level of difficulty and a sensible structure can be automatically generated by the genetic algorithm.

关键词： Training image processing Education Production Pupils Intelligent systems Thermal stability Genetic algorithms Convergence Sports

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Research on Intelligent Data processing Support Model of Government Information System Based on Deep Learning

Research on Intelligent Data Processing Support Model of Gov...

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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe conference on

作者： Dapeng Lu Haojie Liu School of Tourism and Public Administration Zhuhai College of Science and Technology Guangdong Zhuhai China Computer Application Technology Henan University of Technology Zhengzhou China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

This study constructs a data processing model that supports government information system. By designing a deep learning algorithm suitable for government scenarios, intelligent processing functions such as text analysis and image recognition are integrated into the system, aiming to achieve accurate processing of multi-source government data. The model design includes data preprocessing, feature extraction, algorithm optimization and adaptive learning modules to improve the recognition and analysis capabilities of complex government data. Through system simulation, this paper verifies the effectiveness of the proposed model. The simulation data shows that the model is significantly superior to traditional methods in data processing speed, accuracy and resource utilization. Precise experimental results show that the accuracy of the model in text data classification, image data recognition and other aspects reaches more than 96%. At the same time, it can effectively shorten the processing time in large-scale data processing tasks, providing technical support for the practical application of intelligent government information system. This study provides an efficient and scalable solution for intelligent data processing in government information system.

关键词： Deep learning Adaptation models Accuracy image recognition Heuristic algorithms Government Data processing Data models Optimization Information systems

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Design of a College Student Mental Health Assessment System Based on Improved Apriori Algorithm

Design of a College Student Mental Health Assessment System ...

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image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Lanfang Mao College of Surveying Mapping and Geographic Information Lanzhou University of Resources and Environment Lanzhou Gansu China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This paper emphasizes the need to design an enhanced Apriori algorithm-based mental health assessment system to address the mental health issues college students face. The article aims to assess the current mental health evaluation systems of college students and develop a framework for a mental health evaluation. It proposes using data mining algorithms, precisely the Apriori algorithm, to analyze and categorize the data. This process ultimately yields intelligent mental health evaluation results. Finally, the mental health astute assessment system's practicality and superiority are examined by targeted simulation tests. The findings demonstrate that the system described in the article effectively addresses the limitations of the existing mental health intelligence evaluation system. It enhances the precision and efficiency of mental health intelligence evaluation while ensuring system stability. Consequently, it satisfactorily fulfills college students' current mental health evaluation needs.

关键词： Databases image processing Mental health Medical services Computer applications Stability analysis Data systems Data mining Faces

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Improving Sparse Dictionary Learning with Rejection of Fully Exposed Patches

Improving Sparse Dictionary Learning with Rejection of Fully...

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European Signal processing conference (EUSIPCO)

作者： Matthieu Chancel Hacheme Ayasso Michel Desvignes Jean-Michel Vignolle Eric Lespessailles GIPSA-Lab Univ. Grenoble Alpes CNRS Grenoble INP Grenoble France Image Group TRIXELL Moirans France Department of Rheumatology Centre Hospitalier Universitaire Régional d'Orléans Orléans France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Sparse dictionary learning is widely used in image processing tasks such as denoising, classification and image enhancement. However, learning features from X-ray images can be challenging since some parts of the image contains no or very little clinical information which makes them totally exposed to X-rays. These areas are irrelevant for medical content reconstruction and waste the space in the dictionary. Moreover, they slow down the convergence of the learning methods. In this paper, we present a prior knowledge about these fully exposed areas based on their physical distribution and propose a specialized dictionary learning algorithm to improve convergence speed and the quality of the learned dictionary while keeping the same quality of reconstruction. We compare our solution to a variance based approach and show that our solution is robust to the X-ray dose and then more efficient than the variance-based approach.

关键词： Learning systems Dictionaries Noise reduction Signal processing algorithms Machine learning Signal processing X-ray imaging image reconstruction image enhancement Convergence

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