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检索条件"任意字段=7th International Conference on the Art, Science, and Engineering of Programming, Programming 2023"

共 1075 条记录，以下是361-370 订阅

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Rain Streak Removal via Spatio-Channel Based Spectral Graph CNN for Image Deraining 1

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7th international conference on Computer Vision and Image Processing, CVIP 2022

作者： Ragini, thatikonda Prakash, Kodali Department of Electronics and Communication Engineering National Institute of Technology Telangana Warangal506004 India

ISBN: (数字)9783031314179

ISBN: (纸本)9783031314162

Removing rain streaks from the captured single rainy images plays a dominant role in high-level Computer Vision (CV) applications. Since, many existing deraining methods ignores long range contextual information and utilize only local spatial information. To address this issue, a Spatio-channel based Spectral Graph Convolutional Neural Network (SCSGCNet) for image deraining was proposed and two new modules were introduced to extract representations along spatial and channel wise dimensions. therefore, we integrate deep Convolutional neural network (CNN) with spatial based spectral graph convolutional neural network (SSGCNN) and channel based spectral graph convolutional network (CSGCNN) modules into a single end-to-end network. therefore, our network was able to model feature representations from local, global spatial patterns and channel correlations. Experimental results on five synthetic and real-world datasets shows that the proposed network achieves state-of-the-art (SOTA) results. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Convolutional neural networks

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Predictive Analysis of Cardiovascular Disease using Machine Learning Techniques 7

Predictive Analysis of Cardiovascular Disease using Machine ...

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7th international conference on Image Information Processing, ICIIP 2023

作者： Sreekruti Vijh, Surbhi Kumar, Sumit Gupta, Neetu Roy, Nihar Ranjan Amity University Department of Computer Science & Engineering Uttar Pradesh Noida India Sharda University Department of Computer Science & Engineering Uttar Pradesh Greater Noida India Amity University Department of Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9798350371406

Cardiovascular or Heart diseases encompass a range of medical condition that impact the heart and blood vessels. the prediction of heart disease pose considerable complexity and present a formidable challenge within the medical field. According to the World Health Organization (WHO), cardiovascular diseases are the leading cause of death worldwide, claiming approximately 17.9 million lives each year, accounting for around 31% of all global deaths. In the United States, cardiovascular disease is responsible for an estimated 655,000 deaths annually, making it the leading cause of death, accounting for approximately 1 in every 4 deaths. To address the need for accurate prediction of heart disease, extensive research has employed advanced machine learning models. this study conducted a comprehensive evaluation of seven distinct classification models, combining various physiological factors with well-known machine learning algorithms. the model employed in this study includes Naïve Bayes, Logistic Regression, Decision Trees, Random Forest, XGBoost, CatBoost, and Voting Classifiers. through the utilization of these models, a robust heart disease prediction system is designed, facilitating precise evaluation of an individual's risk. the observation shows that through meticulous evaluation and comparison, the Random Forest algorithm, which is a bagging technique, outperforms the existing state-of-the-art methods. It exhibited remarkable accuracy, yielding prediction results of approximately 90.16%. this exceptional accuracy establishes the random forest algorithm as the pre-eminent model for precise and reliable heart disease prediction within the scope of this study. © 2023 IEEE.

关键词： Predictive analytics

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Development of a Cognitive Question Answering System to Learn Concepts for Placement Assistance 10th

Development of a Cognitive Question Answering System to Lear...

引用

10th international conference on Innovations in Computer science and engineering, ICICSE 2022

作者： Lakshmi, R. Dhana Murugappan, Abirami Srivani, M. Department of Information Science and Technology College of Engineering Gunidy Anna University Chennai India

ISBN: (纸本)9789811974540

Cognitive assistants help humans and enhance their capability to solve a large range of complex tasks. A cognitive assistant has developed a pedagogical assistant. this work aims to improve learning capabilities and helps to identify learning preferences. A cognitive assistant can hold conversations with users in natural language to help the user to solve a complex problem. the proposed system has been implemented to assist as a personal agent for students to learn Python programming language. the steps are user capability level identification, construction of assertion graph, QA analyzer, question analyzer, and primary search analysis, hypothesis generation, evidence identification and evidence scorers, final evidence identification user answer validation, and resource generation. the cognitive assistant facilitates natural interactions with the students, and it applies human reasoning skills to judge the student’s ability and train them further. Normal conversational is employed in question-answering systems. this increases users’ satisfaction and easily engages them with the system. A social dialogue and question-answering system has achieved significantly higher learning gains than a non-interactive online course. the result of the system is evaluated using the confidence-weighted score and expert judgments. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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A Data science Approach to Precision Medicine: Allostatic Load as a Predictor of Cardiovascular Disease 7

A Data Science Approach to Precision Medicine: Allostatic Lo...

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7th international Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Ikome, Otto M. Melapu, Venkata K. Kwembe, Tor A. Watson, Ella Jackson State University Department of Computer Science Engineering and Technology Jackson United States Jackson State University Department of Mathematics and Statistical Sciences Jackson United States Jackson State University Jackson Public School System Jackson United States

ISBN: (纸本)9798350342154

Introduction: Precision medicine, tailored to individual genetic and environmental attributes, has emerged as a practical approach in healthcare. Allostatic load (AL), reflecting the body's response to stressors, holds promise in predicting susceptibility to chronic illnesses like cardiovascular disease (CVD). this study explored the relationship between CVD and AL, employing big data analytics and machine learning to enhance prediction-accuracy. Objectives: 1. Develop a predictive model using AL measures for CVD prediction. 2. Create a clinical AL tool for personalized medicine. 3. Demonstrate the relationship between AL scores and CVD. Methods: Data from the National Center for Health Statistics encompassed demographic, health, and clinical variables. AL was computed using a count-based method, considering factors like blood pressure, body mass index, glucose, lipids, etc. Machine learning (ML) algorithms, including random forest and logistic regression, were employed to predict CVD based on AL factors. Missing data were managed using imputation methods tailored to variable distributions. Results: the count-based AL method correlated more with age than the weighted method. Both methods showed significant correlations with age, emphasizing the association between AL and aging. AL was positively correlated with CVD, with an 82% accuracy rate in predicting CVD using the random forest algorithm. Black/African American populations displayed the highest correlation between AL and CVD. Conclusions: this study highlights the potential of AL as a predictor for CVD and supports its integration into clinical practice. AL provides a comprehensive view of physiological health and empowers healthcare providers to make informed decisions, particularly in vulnerable populations. Big data analytics and ML enhance prediction accuracy and offer insights into the complex factors contributing to CVD risk. the convergence of healthcare, statistics, and computer programming is essential

关键词： Machine learning

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An Improved System for Brain Pathology Classification using Hybrid Deep Learning Algorithm

An Improved System for Brain Pathology Classification using ...

引用

international conference on Computing Methodologies and Communication (ICCMC)

作者： V. Sathya S. Dhanabal Department of Computer Science and Engineering Kongunadu College of Engineering and Technology (Autonomous) Trichy India

Speech, recognizing, learning, programming, and problem-solving are all meant to be included in artificial intelligence computing activities. artificial intelligence (AI), a subfield of computer engineering, is concerned with developing sophisticated software or technologies that act and function like human. Deep learning, a category of algorithms used in deep learning, is based on learning data representations and is one of many machine learning techniques. Deep learning is used to develop brain tumour screening and classification models for rapid and uncomplicated tumour identification using Magnetic Resonance Images (MRI) imaging. this paper shows how the pretrained model Alexnet with Transfer Learning (TL) may be trained on tiny and innovative fresh data for classification problems using the stochastic-gradient descent with momentum-SGDM optimizer. Numerous hybrid models were presented with the aim of classifying a brain tumour MR image as a benign as well as malignant tumour. the accuracy, error rate, and confusion matrix parameter of the suggested models are evaluated in order to show how the works have improve.

关键词： Deep learning Machine learning algorithms Error analysis Magnetic resonance imaging Computational modeling Transfer learning Speech recognition

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Deep Learning-Based Lung Cancer Detection and Classification 1

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7th international conference on Information and Communication Technology for Competitive Strategies , ICTCS 2022

作者： Venkatesh, P. Kiran, K. Ravi Bharath, S.Y. Kalyani, G. Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9789811996382

ISBN: (纸本)9789811996375

Nowadays, people are busy with their day-to-day works, and they cannot observe changes in their body except when it appears in external parts. Lung cancer is an interior disease which people cannot observe quickly and take any precautions in initial stage. If it appears in our body, we cannot identify it as a lung cancer. We are taking it as a little thing and moving on. So, it is becoming a major disease in many countries. It can lead to lung failure, and people may die in initial stage without knowing any idea on them. According to some researches, the death rate of lung cancer patients was increased. If lung cancer is detected at stage 1, then it can be curable. the traditional computer programming is not possible to do such things. Using machine learning, there are some problems which many had faced like lack of quality data and implementation. So, deep learning models are our problematic solution. the main objectives of the work are feature extraction, image segmentation, contrast enhancement, and classification using deep learning models. We used deep learning as base model for three types of classifier for CT scan image data set. the methodology uses the EfficientNet-B7 as the model and ResNet50 as the backbone. the experimental results indicate EfficientNet is having good results in detecting the lung cancer. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

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Enhancing Arabic Information Retrieval for Question Answering 7

Enhancing Arabic Information Retrieval for Question Answerin...

引用

7th international conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Alghamdi, Muath Abushawarib, Mohammed Ellouh, Mahmoud Ghaleb, Mustafa Felemban, Muhamad Information & Computer Science King Fahd University Of Petroleum And Minerals Dhahran Saudi Arabia Center For Intelligent Secure Systems King Fahd University Of Petroleum And Minerals Dhahran Saudi Arabia Computer Engineering Information & Computer Science Center For Intelligent Secure Systems King Fahd University Of Petroleum And Minerals Dhahran Saudi Arabia

ISBN: (纸本)9798400709036

In the modern landscape of Natural Language Processing (NLP), intelligent chatbots like ChatGPT 3.5 and Google's Bard have shown remarkable competence in generic question-answering (QA) tasks. However, their performance falters when navigating domain-specific QA, particularly in the Arabic language, which is celebrated for its complex morphology and syntax. this paper presents a comprehensive approach to address these issues. the aim of this research is to build a chatbot tailored for a university community. We first create an extensive Arabic Q&A dataset by extracting data from academic documents, employing state-of-the-art Optical Character Recognition (OCR) tools. then, we evaluate multiple text similarity measures like Pooled FastText Word embedding, BM25 ranking functions, and various semantic sentence embedding models. A thorough performance assessment reveals that the domain-specific model excels at both sentence-level similarity and context-relevance tasks. the developed web application chatbot, leveraging LangChain library and Retrieval Augmented Generation (RAG) methods, outperforms existing chatbots in domain-specific, Arabic language QA scenarios. © 2023 ACM.

关键词： Information retrieval

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Extended Abstract of Candidate Test Set Reduction for Adaptive Random Testing: An Overheads Reduction Technique

Extended Abstract of Candidate Test Set Reduction for Adapti...

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IEEE international conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Rubing Huang Haibo Chen Weifeng Sun Dave Towey Faculty of Information Technology Macau University of Science and Technology Taipa Macau China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang Jiangsu China School of Big Data and Software Engineering Chongqing University Chongqing China School of Computer Science University of Nottingham Ningbo China Ningbo Zhejiang China

this document 1 is an extended abstract of a science of Computer programming paper, "Candidate Test Set Reduction for Adaptive Random Testing: An Overheads Reduction Technique," presented as a J1C2 (Journal publication first, conference presentation following) at the 30th IEEE international conference on Software Analysis, Evolution and Reengineering (Saner 2023).the paper presents a candidate set reduction strategy to enhance the Fixed-Sized-Candidate-Set version of Adaptive Random Testing (FSCS-art). the proposed method reduces the number of randomly-generated candidate test cases by retaining valuable, unused candidates from previous iterations. As the computational costs associated with a stored/retained candidate are less than the costs associated with a randomly-generating one, the overall computational overheads of FSCS-art are reduced. the reported experimental studies show that the proposed method has a comparable failure-detection effectiveness to FSCS-art, but less computational overheads.

关键词： Costs programming Software Power capacitors Computational efficiency Testing

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Cross-Temperature Reliabilities in TLC 3D NAND Flash Memory: Characterization and Solution

Cross-Temperature Reliabilities in TLC 3D NAND Flash Memory:...

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IEEE Electron Devices Technology and Manufacturing conference (EDTM)

作者： Yifan Guo Kenie Xie Xiaotong Fang Xuepeng Zhan Jixuan Wu Jiezhi Chen School of Information Science and Engineering Shandong University Qingdao P. R. China

Temperature impacts on the reliabilities of TLC 3D NAND flash memory are investigated through cross-temperature measurements, and a simple method is proposed to estimate the optimal read voltage $(\mathrm{V}_{\text{opt}})$ to lower the error bits. With real chip characterizations, it is shown that $\mathrm{V}_{\text{opt}}$ is strongly correlated to the temperature difference between data reading and data programming, rather than the temperature itself. This indicates that $\mathrm{V}_{\text{opt}}$ can be obtained by using $\mathrm{V}_{\text{th}}$ offset between operation temperatures. The effects of the proposed scheme are also evaluated in real chips, showing that ~60% error bits can be well suppressed.

关键词： Temperature measurement Temperature distribution Semiconductor device measurement three-dimensional displays Voltage measurement Measurement uncertainty programming

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Video Colorization Using Modified Autoencoder Generative Adversarial Networks 7th

Video Colorization Using Modified Autoencoder Generative Adv...

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7th international conference on Computer Vision and Image Processing, CVIP 2022

作者： Agarwal, Rishesh Das, Manisha Gupta, Deep Radeva, Petia Department of Electronics and Communication Engineering VNIT Nagpur Nagpur India Universitat de Barcelona and Computer Vision Center Barcelona Spain

ISBN: (纸本)9783031314063

Colorization is a visual tool to convey ideas and emotions, especially when it comes to entertainment or film making. It is easy to perceive information from colored images rather than black and white or grayscale images. In the modern digitization era, colorization is even more important, because it helps in-person tracking, objects detection and provides an edge to the new state-of-the-art technologies. this paper presents a method for colorizing grayscale videos using Modified Autoencoder Gen-erative Adversarial Network. the proposed generative model uses modified autoencoders by embedding a DenseNet based architecture. Its property of concatenation of all previous layers helps to increase the color consistency with the increase in the selection of color for an object. Subjective and quantitative results analysis is done using several performance measures. Experimental results show that the proposed model provides an improved and consistent mapping of colors on the video dataset. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Color

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