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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40847 条记录，以下是4321-4330 订阅

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Implementing Deep learning Techniques into Healthcare System Improvement 2

Implementing Deep Learning Techniques into Healthcare System...

引用

2nd international conference on Electronics and Renewable Systems, ICEARS 2023

作者： Kochhar, Harmeet Kaur Kilaru, Mrudula Aljohani, Nasser Goyal, Himanshu Rai Singh, Devendra Saini, DilipKumar Jang Bahadur Chandigarh University University Institute of Media Studies India Velagapudi Ramakrishna Siddhartha Engineering College India Islamic University of Madinah Information Systems Department Madinah Saudi Arabia Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Uttaranchal University Uttaranchal Institute of Technology Uttarakhand Dehradun India Sawmi Rama Himalayan University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350346640

Artificial intelligence (AI) has the potential to transform clinical decision-making and assistance. In any case, the utilization of current AI methods remains only as a supportive solution to make clinical decisions. The existing methodologies fail to perform in-depth analysis to deliver accurate predictions. This study reviews the integration of advanced technologies AutoML in healthcare sector by considering the data privacy, heterogeneity and interpretability. This review will be a useful resource for medical practitioners interested in applying information science methods to their field of study. © 2023 IEEE.

关键词： Data privacy

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Research on Error Evaluation of Capacitive Voltage Transformer Based on EMD-LSTM 5

Research on Error Evaluation of Capacitive Voltage Transform...

引用

5th international conference on Communications, Information System and computer engineering, CISCE 2023

作者： Fang, Huanhuan School of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350326796

Capacitive voltage transformer (CVT) is a kind of electrical isolation and metering equipment. At present, the most widely used type of gateway voltage transformer in high-voltage and ultra-high voltage power grids is capacitive voltage transformer. The evaluation of its error determines the fairness and quality of the electric energy market. The safety of the power system. In this paper, by analyzing the error characteristics of the gateway voltage transformer, the error formation mechanism is analyzed and determined, and the deep learning algorithm is used to evaluate the error. Before the evaluation, the empirical mode decomposition method is used to decompose the characteristics of the monitoring data of the voltage transformer, and the components containing different time scale information are extracted. Each component is predicted using the long-short-term memory (LSTM) network, and finally the prediction result is obtained. At the same time, to avoid sudden changes caused by fluctuations and noise, multi-step averaging is used to reduce the possibility of misjudgment. The final false alarm rate is reduced to 0.1790%, improving the performance of the prediction. © 2023 IEEE.

关键词： Errors

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A Survey of Punjabi Language Translation using OCR and ML 10

A Survey of Punjabi Language Translation using OCR and ML

引用

17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Khepra, Shaveta Kumari, Priya Gupta, Raj Abhishek Bramhe, Vijendra Singh School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9789380544472

The use of Natural Language Processing (NLP) in machine translation has grown in significance as technologies as well as computers have become more prevalent in our daily lives. This essay's goal is to evaluate the work done in the field of natural language processing (NLP) to translate the Gurmukhi script, which is primarily used to represent the Punjabi language. Together with Hindi and Punjabi, the languages written throughout the Gurumukhi script has more than 1.5 billion native users, making it among the most used language worldwide. However, there is limited research available on the Gurmukhi script. The study presented in this paper focuses on using machine learning and AI techniques to translate written Gurmukhi text into Hindi and English languages. The input for the translation process will be in the form of machine-printed Gurmukhi text, while the output will be provided in both Hindi and English languages. The goal of this study is to enhance how well global languages absorb information and technology, making it easier and more accurate to translate from regional languages. In conclusion, the paper provides a comprehensive review of the work related to the translation of Gurmukhi script using NLP techniques, with a focus on the use of machine learning and AI. The findings of this study can contribute to the development of more advanced and accurate translation systems, making the exchange of information and knowledge more accessible for people who speak different languages. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Machine learning

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A large input-space-margin approach for adversarial training 13

A large input-space-margin approach for adversarial training

引用

13th international conference on computer and Knowledge engineering, ICCKE 2023

作者： Nikouei, Reihaneh Taheri, Mohammad Shiraz University Department of Computer Science and Engineering Shiraz Iran

ISBN: (纸本)9798350330151

It is shown that machine learning models are vulnerable to adversarial attacks. Therefore, different defense methods such as adversarial training have been proposed to improve models' robustness against these attacks. Some recent approaches proposed to structurally improve the robustness of the models. For example, large margin methods try to increase a margin, empty of instances, along decision boundaries that structurally increase necessary change to modify a training instance to an adversarial one. However, nonlinear large-margin models, maximize the margin in a high dimensional space although adversarial examples are generated with a little change in the original space. In this paper, a novel mixed approach is proposed, called LIM (Large Input Margin) to improve the robustness of the model by minimizing both structural and empirical risks. Specifically, both training and adversarial example generation are done based on a loss function to maximize the margin in the original feature space even in a non-linear model. The proposed method is evaluated with FGSM and PGD attacks on MNIST and CIFAR10 datasets. The experimental results show that LIM method outperforms the state-of-the-art defense methods significantly and improves adversarial robustness against FGSM and PGD attacks on both datasets. © 2023 IEEE.

关键词： Network security

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Deep learning Approach in Cotton Plant Disease Detection: A Survey 2

Deep Learning Approach in Cotton Plant Disease Detection: A ...

引用

2nd international conference for Innovation in Technology, INOCON 2023

作者： Bera, Soumyajit Kumar, Puneet Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350320923

The economy of our nation is rooted in agriculture,, which also has a significant impact on how we live our everyday lives. Nevertheless, cotton plant diseases anthracnose and crown gall led to by organisms that cause illness, including fungi as well as bacteria have cut back on agricultural production *** illnesses can be managed if identified and cured at an appropriate suitable ***, it seems to be quite challenging to manually detect these illnesses from the onset over a large area of *** most of the often when it is performed manually, these abnormalities really aren't discovered, and no suitable measures are made to remedy the *** today 's society, everything is intelligent and scientifically reliant. Machine learning as well as artificial intelligence (AI) are crucial to the development of intelligent *** consequence is the introduction of a quick and clever recommended method for diagnosing agricultural *** study emphasizes on the application of CNN modeling to identify and categorise illnesses affecting cotton plants. © 2023 IEEE.

关键词： Deep learning

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Predictive Maintenance by Machine learning Methods 11

Predictive Maintenance by Machine Learning Methods

引用

11th IEEE international conference on Intelligent Computing and Information Systems, ICICIS 2023

作者： Amer, Sara Mohamed, Hoda K. Badr Monir Mansour, Marvy Ain Shams University Department of Computer and Systems Engineering Cairo Egypt British University in Egypt Department of Electrical Engineering Cairo Egypt

ISBN: (纸本)9798350322101

Predictive maintenance techniques are designed to determine the state of equipment in action to help us know when we can intervene to perform maintenance on it. Predictive maintenance design applies artificial intelligence techniques such as machine learning to analyze data and monitor efficiency. In this paper, we predict the maintenance of any equipment before it stops to reduce unplanned equipment maintenance by means of machine learning algorithms. We adopted in our work the following supervised machine learning algorithms: Random Forest, Support Vector Machine, KNN, Decision Tree, Logistic Regression, Naïve Bayes, and XGBOOST. Simulations and results show that the Random Forest and XGBOOST have almost the same performance. The XGBOOST machine learning algorithm is preferred for bigger datasets compared to Random Forest and it works more effectively in the case of small datasets. Finally, the proposed predictive maintenance system is successful in identifying possible failure indicators and reducing some production stoppages as shown by our simulation results. © 2023 IEEE.

关键词： Decision trees

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Machine learning based Object Detection to Protect Marine Ecosystem 7

Machine Learning based Object Detection to Protect Marine Ec...

引用

7th international conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Prakash, Vallakati Bhanu Shirisha, Dhanavath Reddy, Gopu Suryaprakash Hariharan, Shanmugasundaram Kukreja, Vinay Prasad, Andraju Bhanu Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350340600

The majority of the earth's water bodies are currently contaminated. This occurs primarily as a result of massive amounts of waste being dumped into it. The majority of the garbage is made up of toxic materials, non-biodegradable materials, and e-waste, which severely harms the marine ecosystem. Additionally, it causes the loss of human life as a result of natural disasters. It ceases throwing this waste into the water bodies in order to solve these issues and keep them tidy and clean. If it can't be done, it would be better to start the cleaning process, which involves getting rid of these kinds of things. The study will be crucial in resolving this issue through the use of some machine learning techniques, including image classification and image recognition. Here, sizable collection of related e-waste image datasets is used to train the model in this procedure. Further, the machine will begin recognizing it so that it can be distinguished from the water bodies. Since the use of these kinds of procedures will make nature more environment friendly. In the end, it promotes economic expansion and sustainable development. © 2023 IEEE.

关键词： Object detection

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Leveraging Artificial Intelligence and Machine learning for Enhanced Crop Yield: A Crop Recommendation System for Indian Agriculture

Leveraging Artificial Intelligence and Machine Learning for ...

引用

2023 international conference on IoT, Communication and Automation Technology, ICICAT 2023

作者： Mishra, Saksham Gupta, Manish Buddha Institute of Technology Gida Department of Computer Science and Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350302820

This paper introduces a all-inclusive crop recommendation system for Indian agriculture, leveraging artificial intelligence and machine learning to increase crop yield and its productivity. Indian agriculture faces challenges from rapid population growth, urbanization, and climate change, necessitating innovative solutions to maintain food security. The proposed system combines AI and ML techniques with domain-specific knowledge to provide farmers with accurate, well timed and personalized recommendations on suitable crops, taking into account factors like soil type, climatic conditions, and market demand. By optimizing crop management through data-driven insights, this system aims to revolutionize traditional agricultural practices, ultimately leading to increased productivity, profitability, and viable farming in India. © 2023 IEEE.

关键词： Crops

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Plant Leaf Disease Detection: A Deep Hybrid learning Approach

Plant Leaf Disease Detection: A Deep Hybrid Learning Approac...

引用

2023 IEEE international conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Kumar, Santosh Singh, Shivam National Institute of Technology Department of Computer Engineering Kurukshetra136119 India

ISBN: (纸本)9798350393415

Recent breakthroughs in Deep learning have led to a shift in the classification methods for plant infections towards automated feature detection. In this work, we evaluate the performance of existing algorithms for identifying plant leaf diseases on a particular dataset. In addition, we aim to identify the most efficient methods for detecting and classifying plant leaf diseases. First, we employed state-of-the-art techniques such as VGG16, VGG19, and RESNET50. Due to the small number of members per class in the data set, these techniques could not produce satisfactory results. On the other hand, traditional machine learning procedures require an expert to identify and validate feature extraction approaches. Consequently, our goal has been to design a hybrid model combining both advantages. We envisioned developing a method for detecting plant leaf diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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An Improved learning Experience for English Language Using Big Data Analytics and AAC 2

An Improved Learning Experience for English Language Using B...

引用

2nd international conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Kumar, P.Vinoth Balamurugan, D. Suriya, K. Reshma, R. Faculty of Engineering Karpagam Academy of Higher Education Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9781665491990

Teachers and students in the 21st century are shifting their attention from the classroom to digital education, aiming for meaningful, high-quality, and dynamic learning. With the introduction of digital learning, the educational system has undergone significant transformations. Some teachers/instructors, on the other hand, are still experiencing difficulties. For this reason, this research finds a solution named AAC integrated Big Data Analytics Framework (AACBDAF) for people with neural disorders in the English language. AAC-BDAF gathers all the linguistic data and categorizes it based on how frequently it is used. To input data, the AAC system uses eyeball tracking to display symbols and characters on a screen. To help the listener follow along, the selected option/word is converted into a text-to-speech paradigm. The detection algorithms of machine learning and deep learning are evaluated for word detection and suggest the convolutional neural network (CNN) support AACBDAF. According to simulation findings, the proposed AAC-BDAF recognizes phrases and predicts the word from their eye-gazing with higher accuracy of 98.63%. © 2023 IEEE.

关键词： Students

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