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Template-Based Thinning Method for Handwritten Gujarati Character’s Strokes and its Classification for Writer-Dependent Gujarati Font Synthesis 1

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3rd international conference on Machine Intelligence and signal processing, MISP 2021

作者： Bhatt, Preeti P. Nasriwala, Jitendra V. Savant, Rakesh R. Faculty of Computer Science Babu Madhav Institute of Information Technology Uka Tarsadia University Bardoli India

ISBN: (数字)9789811908408

ISBN: (纸本)9789811908392

Handwriting synthesis simulates the human writing style by learning the structural features of handwritten characters. There are two approaches to handwriting synthesis: movement simulation and shape simulation. In movement simulation, the hand's neuromuscular movement is used to synthesis the writing. In shape simulation, glyph or structural features are used to synthesis the writing. Our main objective is to generate handwritten Gujarati font using a shape simulation approach. Therefore, to excerpt the structural features and shape of characters, the detailed strokes analysis from Gujarati handwritten characters is required. Earlier, we have proposed an algorithm for stroke extraction from handwritten Gujarati characters. So here, statistical analysis of stroke extraction has been carried out to verify extraction methodology. The image should be one pixel wide to work with the structural features, so an improved thinning algorithm has also been proposed. Likewise proposed an elegant way of extracting and generating features based on structural features. The classification is carried out on extracted strokes from handwritten Gujarati consonants using k-nearest neighbor (KNN) and neural network. Besides, results compared by tuning different parameters, where our extracted features on the proposed thin image, give an average 98% accuracy in both classifiers. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Character recognition

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Extracting and Classifying Salient Fields of View from Microscopy Slides of Tuberculosis Bacteria 3rd

Extracting and Classifying Salient Fields of View from Micro...

引用

3rd international conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Zachariou, Marios Arandjelović, Ognjen Dombay, Evelin Sabiiti, Wilber Mtafya, Bariki Sloan, Derek School of Computer Science St AndrewsKY16 9SX United Kingdom School of Medicine St AndrewsKY16 9TF United Kingdom Mbeya Medical Research Center Mbeya Tanzania United Republic of

ISBN: (纸本)9783031090363

Tuberculosis is one of the most serious infectious diseases, and its treatment is highly dependent on early detection. Microscopy-based analysis of sputum images for bacilli identification is a common technique used for both diagnosis and treatment monitoring. However, it a challenging process since sputum analysis requires time and highly trained experts to avoid potentially fatal mistakes. Capturing fields of view (FOVs) from high resolution whole slide images is a laborious procedure, since they are manually localized and then examined to determine the presence of bacteria. In the present paper we propose a method that automates the process, thus greatly reducing the amount of human labour. In particular, we (i) describe an image processing based method for the extraction of a FOV representation which emphasises salient, bacterial content, while suppressing confounding visual information, and (ii) introduce a novel deep learning based architecture which learns from coarsely labelled FOV images and the corresponding binary masks, and then classifies novel FOV images as salient (bacteria containing) or not. Using a real-world data corpus, the proposed method is shown to outperform 12 state of the art methods in the literature, achieving (i) an approximately 10% lower overall error rate than the next best model and (ii) perfect sensitivity (7% higher than the next best model). © 2022, Springer Nature Switzerland AG.

关键词： Fluorescence microscopy

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Research on Individual Recognition and Prediction of Cocaine Addiction Based on Convolutional Neural Networks 3

Research on Individual Recognition and Prediction of Cocaine...

引用

3rd international conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Xiao, Hongbo Xiao, Jianhua Zhang, Lebing Tang, Bo Cao, Zhuyou Luo, Tianwang School of Computer Science and Engineering Huaihua University Hunan Huaihua China Key Lab. of Wuling-Mt. Hlth. Big Data Intelligent Proc. and Applic. in Hunan Province Universities Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9798400716775

In response to the current difficulty in identifying and predicting cocaine addicted individuals based on brain MRI images, this paper designs a deep learning recognition and prediction model based on convolutional neural networks. 29 cocaine addicted individuals and 24 healthy controls were selected from brain MRI images. After per-forming a series of data preprocessing operations such as skull dissection and data augmentation on brain MRI images, a deep learning model based on convolutional neural networks is constructed to process the processed brain MRI images of cocaine addicted individuals and healthy controls to identify and predict cocaine addicted individuals. The experimental results show that the recognition and prediction accuracy of deep learning models based on convolutional neural networks is 89%. Compared with machine learning models such as SVM and support vector machines, it greatly improves the accuracy of model prediction and can accurately, quickly, and effectively identify and predict cocaine addicted individuals. © 2023 ACM.

关键词： Forecasting

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Identification of Biomarker Genes for Human Immunodeficiency Virus Using Ensemble Approach 3rd

Identification of Biomarker Genes for Human Immunodeficiency...

引用

3rd international conference on Machine Intelligence and signal processing, MISP 2021

作者： Baruah, Bikash Ayus, Ishan Dutta, Manash P. Department of Computer Science and Engineering NIT Arunachal Pradesh Yupia India

ISBN: (纸本)9789811908392

The human immunodeficiency virus (HIV) has a strong affinity towards CD4 + T cell, dendritic cells and macrophages and is present in white blood cells of the human body, and gradually, it leads to severe health problems. This manuscript has proposed a mapping-based ensemble approach to identify the biomarker genes associated with HIV. We carried out our experiment on the microarray dataset through co-expression analysis using Pearson correlation. We implement four prominent biclustering algorithms, viz. Plaid, Laplace Prior, Post-Projection, iBBiG, and ensemble them to capture the best set of biclusters. Next, union and intersection operations are performed on all four sets of biclusters and give two sets of ensemble biclusters. A well-known cluster quality indexing method, i.e. Dunn indexing, shows that the intersecting set of biclusters maintains the best quality among all sets of biclusters. Out of the set of biclusters, obtained from the intersection, we found three biclusters containing early detected biomarker genes, reported in NCBI. Co-expression analysis is performed on these three crucial biclusters using Pearson correlation, and it detects two new hub genes—T cell receptor beta constant 1 (TRBC1) and GNAS complex locus (GNAS). Hub genes always play an essential role in disease development. The genes TRBC1 and GNAS and the previously reported genes can be served as target genes in HIV-AIDS treatment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genes

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An Empirical Study on Chinese Student Translators’ Ability in Identifying In-text Terminology : With a Specific Focus on Terms of computer science

An Empirical Study on Chinese Student Translators’ Ability ...

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Education, Knowledge and Information Management (ICEKIM), international conference on

作者： Xiangmei Chen College of Foreign Languages Minjiang University Fuzhou China

ISBN: (纸本)9781665416672

This empirical study is designed to investigate how well Chinese student translators identify terms in specialized Chinese texts. Through contrastive comparisons on the findings based on the data processed in SPSS 19.00, it attempts to uncover how translation education, student translators’ terminology ability and texts of different degree of technicality relate, and accordingly offer some insights to current translation education in China’s mainland, automatic term extraction and terminology access strategy for online e-dictionaries.

关键词： computer science Terminology Education Software Natural language processing Information management Data mining

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Research on Image processing and Generative Teaching in the Context of AIGC

Research on Image Processing and Generative Teaching in the ...

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Information science and Education (ICISE-IE), international conference on

作者： Jian Rao Chuqi Qiu Mengzhen Xiong Art and Design College Hubei University of Technology (HBUT) Wuhan China

AIGC, meaning AI-generated content, is discussed in this paper, mainly in the form of painting, without discussing the automation of music composition and text-writing poetry, etc. While generative art has aesthetic characteristics, more content needs to be studied in the context of computer information science, with a particular emphasis on computer vision and computer graphics. The article focuses on a comparative analysis of two models, diffusion algorithms and generative adversarial networks, and their application to tools. The practical part of image processing uses a combination of case studies and questionnaires to demonstrate the lack of methodology, teaching experience, and introductory learning materials for non-computer professionals in the emerging field, and to explain “filter mapping” through processing, a visual programming software. The author’s reflections on the generated content combine the self-similarity of the “Uncanny Valley effect” and the “Dunning Kruger effect” lineage, comparing the “self-organizing” (machine) simulation personification and the “life form” (human) simulation personification. The process of cognitive assimilation of a “living organism” (human) is used to understand the new human-machine associative relationship.

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DNN Based Short Term Load Forecasting of Individual Household with Real and Synthetic Data-Set 3rd

DNN Based Short Term Load Forecasting of Individual Househol...

引用

3rd international conference on Machine Intelligence and signal processing, MISP 2021

作者： Tiwari, Praveen Mahanta, Pinakeswar Trivedi, Gaurav School of Energy Science and Engineering Indian Institute of Technology Guwahati Guwahati India Department of Mechanical Engineering Indian Institute of Technology Guwahati Guwahati India Department of Electronics and Electrical Engineering Indian Institute of Technology Guwahati Guwahati India

ISBN: (纸本)9789811908392

Short term load forecasting for household load patterns has primarily appeared to be a challenging task worldwide because of their usual stochastic nature that is originated from unpredictable customer behavior, weather actuations and noise patterns. And so, in order to study and design the advanced operation and control mechanism for an individual household, the localized load data-set is preferred. The customized household load data specific to a location is not available easily. Therefore, a simulated data-set of the household load pattern can also be helpful. In this paper, we have used a strategy to forecast short term load for households by pooling together load profiles from 20 households and achieved considerable improvement upon the primary results by analyzing one model parameter: the time-lag provided in the look-back window. Further, the model is validated with an additional data-set of pooled household load patterns, generated using the Smart Residential Load Simulator, which is a MATLAB/Simulink-based free tool-kit for the modeling of load patterns for various household appliances. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Domestic appliances

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Implementation of a Method Using Image Sequentialization, Patch Embedding and ViT Encoder to Detect the Breast Cancer on RGBA Images and Binary Masks 3rd

Implementation of a Method Using Image Sequentialization, Pa...

引用

3rd international conference on Artificial Intelligence and Speech Technology, AIST 2021

作者： Dixit, Tanishka Singh, Namrata Srivastava, Geetika Srivastava, Meenakshi Department of Computer Science and Engineering NarainaVidhyapeeth Engineering and Management Institute Kanpur Panki India Department of Physics and Electronics Dr. Ram Manohar Lohia Avadh University Ayodhya India Amity Institute of Information Technology Amity University Lucknow India

ISBN: (纸本)9783030957100

This paper uses an approach where we will be training the unlabelled and labelled datasets into the system and these datasets comprises of: RGBA images and Binary masks. This will be a benefit in order to get a much better result and more accurate also as pre-training method always helps in getting excellent output. We will use a Transformer Model in this paper where we will apply an image sequentialization technique having 51 million parameters which will help us in attaining the smoother images without any noise. At times, this approach could be time consuming due to used datasets and its sizes, however it shows more accurate and efficient results. Further, we will try to figure out the pixel wise label map using patch embedding technique. Once these techniques are applied then CNN-Transformer Hybrid will come into the role which will encode and decode the images to high-level feature extractions and full spatial resolution respectively. This way of doing encoding and decoding is also known as forward pass and back propagation. Also, it will involve the cascaded upsampler where we will try to use self-attention processes into the design of encoder using the transformers. This entire mechanism will involve few of the best evaluation metrics and those are: Pixel accuracy, IoU, Mean-IoU and Recall/Precision/F1 Score giving the effective and best results. © 2022, Springer Nature Switzerland AG.

关键词： signal encoding

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An Optimal Steering Vector Generation Using Chaotic Binary Crow Search Algorithm for MIMO System 3rd

An Optimal Steering Vector Generation Using Chaotic Binary C...

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3rd international conference on Recent Trends in Advanced Computing - Artificial Intelligence and Technologies, ICRTAC-AIT 2020

作者： Babu, P. Sekhar Naganjaneyulu, P.V. Prasad, K. Satya ECE Department UCEK JNTUK Andhra Pradesh Kakinada India Sri Mittapalli College of Engineering Andhra Pradesh Tummalapalem N16 Guntur India Rector of Vignan’s Foundation for Science Technology & Research Andhra Pradesh Guntur India

ISBN: (纸本)9789811664472

Beam forming is a signal processing technique used for directional signal transmission or reception, and it requires the antenna array to change the directionality during transmission. The beam forming improves the throughput for a great extent by matching the parameters of transistors to the time-invariant channels. The multiple-input multiple-output (MIMO) systems are well known in providing gains for spectral efficiency and reliability during beam forming. The MIMO is set up based on the knowledge of transmitter and receiver to obtain steering vectors and the weight of the channels. The formation of channel setup is important for improving the quality of signal transmission. However, the existing models failed to determine the accurate channel due to time-variant distortions that resulted optimization problem. This research overcomes such problems by proposing chaotic binary crow search algorithm (CBCSA) which is an efficient algorithm for handling both time-variant distortions and optimization problems. In this research work, artificial neural network (ANN) is tested and trained for the generation of steering vectors to select the particular channel for beam forming. The proposed CBCSA effectively predicts various channels for beam forming. The proposed CBCSA achieves better values of steering vectors, and these vectors are trained into ANN to achieve better signal-to-noise ratio (SNR) compared to other existing methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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Enhancement for Low-Contrast Images with Dynamical Saturating Nonlinearity and Adaptive Stochastic Resonance 1st

Enhancement for Low-Contrast Images with Dynamical Saturatin...

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1st international conference on Cognitive Computation and Systems, ICCCS 2022

作者： Wang, Guodong Wang, Xi Ma, Yumei Pan, Zhenkuan Zhang, Xuqun Yu, Jinpeng College of Computer Science Technology Qingdao University No. 308 Ningxia Road Qingdao266071 China Communication Dispatching Section Qingdao Emergency Center No. 120 Jinsong 3rd Road Qingdao266035 China College of Automation Qingdao University No. 308 Ningxia Road Qingdao266071 China

ISBN: (纸本)9789819927883

In recent years, image processing based on stochastic resonance has received more and more attention. In this paper, a dynamic saturated nonlinear stochastic pooling network model based on adaptive stochastic resonance is proposed. And it combines the dynamical saturating nonlinearity with the stochastic resonance, which can realize adaptive iteration and enhance low-contrast images. At the same time, a new method to solve the optimal parameters of nonlinear systems is proposed, which is used to solve the optimal parameter values of dynamical saturating nonlinearity in this paper. This new image enhancement method is tested on low-contrast images in the LOL dataset. By comparing the new model and some other image enhancement algorithms, it demonstrates that the results not only have good visual perception, but also obtain more excellent evaluation indicators value. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Adaptive algorithms

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