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

共 40714 条记录，以下是4851-4860 订阅

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Dynamic Heuristic Approach to Enhance the Performance of Few-Shot Meta-learning 16

Dynamic Heuristic Approach to Enhance the Performance of Few...

引用

16th international conference on Developments in eSystems engineering, DeSE 2023

作者： Mirkhan, Amer Çelebi, Numan Sakarya University Computer Engineering Department Sakarya Turkey Sakarya University Information Systems Engineering Department Sakarya Turkey

ISBN: (纸本)9798350381344

Meta-learning has recently become an interesting topic for researchers, particularly in supervised learning problems with a lack of training data. Meta-learning has shown effectiveness in generalization and adapting to solve new tasks with only a few data points. To train the meta-learning model, an optimizer should be used to update parameters during each training cycle. Most of the developed meta-learning models have used one of the standard gradient-based optimizers. However, due to the special nature of meta-learning models, we propose a custom optimizer to train the meta-learning model. Our proposal is a new optimizer based on combining a metaheuristic algorithm with traditional gradient-based techniques. The algorithm initiates the searching for the optimal values of the parameters of the model (weights vector) using some random initial candidate solutions. Iteratively, according to the performance of each individual, population members might either be reproduced or eliminated from the population. The learning process will then continue using classic gradient optimization, starting with the best solution found by the heuristic algorithm. Our experimental analysis shows that this strategy could enhance the performance of training meta-learning models and enable the efficient finding of optimal parameters due to the dynamic characteristics of our proposed strategy. © 2023 IEEE.

关键词： Heuristic algorithms

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Cow Estrus Status Identification and Individual Identification System Based on Voiceprint Data

Cow Estrus Status Identification and Individual Identificati...

引用

2023 IEEE international conference on Electrical, Automation and computer engineering, ICEACE 2023

作者： Wang, Yi Sun, Wei Cao, Shanshan Kong, Fantao Agricultural Information Institute Chinese Academy of Agricultural Sciences Beijing China College of Computer and Information Engineering Xinjiang Agricultural University Urumqi China

ISBN: (纸本)9798350309614

This paper proposes a cow estrus status identification and individual identity recognition system based on voiceprint data. By introducing deep learning and voiceprint recognition technology to replace the traditional manual monitoring of cattle, we can achieve accurate monitoring of the estrus status of cattle. It can effectively solve the problem of missed and wrong detection of estrus cows in large-scale beef cattle breeding scenarios. The method includes: building a data collection system through collars, pickups and high-definition network cameras, using the data collection system to build a cattle voiceprint database and obtaining a high-quality cattle estrus cry data set, and cascade training Conv-TasNet and EcapaTdnn two voiceprint field depths Learn the model to accurately and effectively identify the estrus state and identity of cattle. This research provides an efficient and accurate solution for cattle estrus identification and individual identification. It is expected to be widely used in the breeding industry, improve breeding efficiency, reduce labor costs, and make a positive contribution to the development of the livestock breeding industry. © 2023 IEEE.

关键词： Deep learning Estrus state Individual identity Voiceprint data Voiceprint recognition

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Exploration of Global Temperature Warming Based on Deep learning and Statistical Analysis 6

Exploration of Global Temperature Warming Based on Deep Lear...

引用

6th international conference on computer Information Science and Application Technology, CISAT 2023

作者： Li, Zhenning School of Computer Science China University of Geosciences Wuhan China

ISBN: (纸本)9781510668546

Global temperature change threatens our planet's ecosystems. This study introduces a new approach to address the problem of global temperature change. A global temperature prediction model was developed using LSTM and ARIMA techniques. The Pearson correlation model was used to investigate the relationship between temperature change and related factors, while an independent sample t-test model determined whether forest fires significantly affect temperature. The method has practical implications in climate monitoring and management, and high accuracy was achieved in the experiment. The findings provide valuable insights into the dynamics of global temperature change and lay the foundation for policymakers to implement evidence-based interventions to reduce the impacts of climate change on the Earth's ecosystems. © 2023 SPIE.

关键词： Ecosystems

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Brain Tumor Detection and Classification Using Hybrid VGG Network 26

Brain Tumor Detection and Classification Using Hybrid VGG Ne...

引用

26th international conference on computer and Information Technology, ICCIT 2023

作者： Sakib, Adnan Khan, Nisha Jaman Shahrier, Labiba Plabon, Kaushik Al Mahmud, Jubayer Mridha, M.F. Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798350359015

In their highest degree, brain tumors can be extremely deadly. Misdiagnosis can lead to the incorrect course of treatment and lower the likelihood of recovery for patients. To tackle these problems, a hybrid VGG network is extensively researched in the proposed framework to classify brain tumors, including meningioma, pituitary, glioma, and healthy brain MRI slices. In this work, we propose a hybrid model for brain tumor classification. We made use of a publicly available Kaggle brain tumor dataset that included MRI scans showing no brain tumors and three different types of brain tumors: meningiomas, pituitary tumors, and gliomas. We performed preprocessing on the dataset. As our base model, we employed VGG16 and added more CNN layers to it. We trained the additional CNN layers while using transfer learning to freeze the base model layers. The pre-trained VGG16 model is familiar with a variety of textures, forms, and features related to medical imaging. We also added more CNN layers that discovered more complex and distinct task-related brain tumor pattern features that might not have been present in the original ImageNet dataset. We obtained 96.52% test accuracy with our model. © 2023 IEEE.

关键词： Brain tumor CNN Data Augmentation Deep learning Empirical Evaluation Machine learning MRI

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Research on the Application of Deep learning-Based Product Spray Code Text Recognition Algorithm 5

Research on the Application of Deep Learning-Based Product S...

引用

5th international conference on Artificial Intelligence and computer engineering, ICAICE 2024

作者： Fan, Mingxing Lin, Kairong Wu, Longhui Feng, Hao Jimei University College of Marine Equipment and Mechanical Engineering Fujian Xiamen China

ISBN: (纸本)9798331528911

In the process of product manufacturing, the characters printed on the packaging may be incomplete due to reasons such as rapid mechanical vibrations, low ink quality, and poor operating environments. Therefore, manufacturers often perform defect detection of printed codes during the product quality inspection phase. To achieve efficient recognition of printed codes in complex environments, this paper adopts a sequence-based CRNN text recognition algorithm and designs a lightweight network structure. First, MobileNetV3_small is used as the feature extraction network, optimizing the sampling stride to better preserve the character features in the image. Then, a bidirectional Long Short-Term Memory network (Bi-LSTM) is used to predict the text sequence, combined with the CTC loss function to achieve end-to-end variable-length text sequence recognition. To verify the performance of the improved algorithm, experiments were conducted on two commonly used text recognition datasets,IIIT5K-Words and ICDAR2013. The experimental results show that the combination of CRNN and MobileNetV3_small not only effectively improves the speed of text recognition but also ensures the accuracy of printed code recognition, thereby enhancing the effectiveness of quality inspection for printed codes. © 2024 IEEE.

关键词： Deep learning

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An Overview on Breast Cancer Detection & Classification Methods Using Machine learning Techniques 3

An Overview on Breast Cancer Detection & Classification Meth...

引用

3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Philip, Rohan Mathew Raj, Rishabh K Shaji, Ashwin Althaf, Fathima Sudarsan, Dhanya Muthoot Institute of Technology and Science Dept. of Computer Science & Engineering Kerala India

ISBN: (纸本)9798350322842

Breast cancer is a prevalent and deadly disease that primarily affects women. Breast cancer has claimed the lives of many people all over the world. Early breast cancer detection, on the other hand, can significantly improve a woman's chances of survival. There are various approaches and procedures for detecting cancer in breast tissue. Breast cancer may be detected using Image Processing, Machine learning, and Deep learning methodologies and techniques. People who use trustworthy techniques and make better selections will be able to discover breast cancer in its early stages and save a woman's life. This research aims to contemplate and make inferences from diverse studies on the subject of classification using Machine learning approaches. The researchers and physicians who devote their time and expertise to enhancing breast cancer screening methods will definitely profit from this research study. © 2023 IEEE.

关键词： Convolutional neural networks

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Inspired by Social-Spider Behavior for Microwave Filter Optimization, Swarm Optimization Algorithm

Inspired by Social-Spider Behavior for Microwave Filter Opti...

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2023 international conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Karthick, R. Prabha, M. Sabapathy, S.Rathana Jiju, Divya Selvan, R.Senthamil K.L.N College of Engineering Department of Computer Science and Engineering Sivaganga India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Panimalar Engineering College Department of It Chennai India Mathematics Jain Deemed-to-be University Kochi India Annamacharya Institute of Technology and Sciences Department of Ece Tirupati India

ISBN: (纸本)9798350317060

In this research, our researchers suggest using a contemporary swarm optimization approach for microwave filter optimization dubbed Social Spider Optimization (SSO), which is based on modeling social spiders' cooperative actions. PSO (particle swarm optimization) and teaching-learning-based optimization (TLBO) are two evolutionary algorithms that demonstrate their efficacy and resilience compared to this optimization. The results demonstrate the effectiveness of the recommended approach. © 2023 IEEE.

关键词： Microwave filters

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Automated diabetes diagnosis and risk assessment using machine learning 2nd

Automated diabetes diagnosis and risk assessment using machi...

引用

2nd international conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Mathukiya, Deep Kumar, Yogesh Koul, Apeksha Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gandhinagar India Punjabi University Punjab Patiala India

ISBN: (纸本)9781032738659

Currently, Diabetes is a prevalent issue worldwide, affecting millions of people. The detection machinery for this disease is only available at healthcare centers, so individuals with diabetes are only aware of their condition when they visit and undergo a checkup. Prolonged diabetes can lead to various complications like heart disorders, kidney disease, nerve damage, and diabetic retinopathy. However, the risk can be reduced if the disease at its early stage is predicted. By utilizing a deep learning approach, this model can easily determine if an individual is diabetic or not. The aim of the research is to develop machine learning model that can identify diabetic people on the basis of their clinical data. The machine learning algorithms utilized in this study include Naive Bayes (NB), k-nearest neighbor (KNN), Stochastic Gradient Descent (SGD), Decision tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR). We have implemented efficient pre-processing techniques to detect as well as handle missing/ NAN and duplicate values and later trained our models with the cleaned dataset. During execution, we found that Random Forest as well as Decision Tree performed well for both diabetic and non-diabetic class by computing 100% and 99% accuracy respectively. © 2025 The Author(s).

关键词： Decision trees

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Supervised Machine learning: Discriminative learning Methods for Pattern Classification using HELM 5

Supervised Machine Learning: Discriminative Learning Methods...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Shirisha, N. Kiran, Ajmeera Prashanthi, A. Reddy, MrKamjula Lakshmi Kanth Mlr Institute of Technology Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Nalla Narasimha Reddy Education Society's Group of Institutions Department of Computer Science and Engineering India Anurag University Department of Artificial Intelligence Hyderabad India

ISBN: (纸本)9798350321425

The ability to forecast the data is significantly helped by machine learning's involvement. At the present time, data analysis is playing a very significant part in both the Information Technology (IT) industry and academic institutions. Numerous real-time applications, such as translation in real time, facial recognition, musical compositions, and so on, have been developed through the various initiatives that have been carried out up to this point. According to the standards that must be met by every data analyst, data must first be preprocessed before any predictions can be made on that data. Predictions must also be based exclusively on the preprocessed data. This work demonstrates the data pre-processing along with the prediction of a churn model. The model contains a set of records of various customers, including information regarding whether or not the client possesses credit cards and whether or not the consumer is an active member. This research works on the construction of a hybrid ensemble learning model (abbreviated HELM) to collect heterogeneous weak learners in order to anticipate such analyses. In order to work on the Churn Model classification, a variety of machine learning algorithms are integrated with one another. The findings revealed the efficiency of each machine learning algorithm that was used, in addition to HELM's operational effectiveness. © 2023 IEEE.

关键词： Forecasting

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Analysis of Irregular Menstrual Cycle and Its Consequences

Analysis of Irregular Menstrual Cycle and Its Consequences

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2023 international conference on computer Communication and Informatics, ICCCI 2023

作者： Sneha, M. Sai Sravani, K. Nithya, S. Kaladevi, R. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348217

Women in the 21st century is diagnosed with many kinds of health issues which are later found to be serious. One of the most worrying diseases today is Polycystic Ovarian Syndrome(PCOS) which is highly dangerous to an extent of affecting women's reproductive life to a huge extent. This causes various disorders like infertility, endometrial cancer, etc. The most prevalent symptoms of this disease are oily skin, acne, irregular menses, anxiety disorders, and hypertension. These women are also at increased risk for mood disorders, such as depression and anxiety, and non-alcoholic fatty liver. An estimation states that one in five women suffers from PCOS [1]. Many women wake up and notice the symptoms only at the time of conceiving. This is happening because of the lack of knowledge about this disorder. Our aim is to develop a medium, which helps the end user (Specially made for women) to know their Health level according to their period cycle. Data prediction provides the end user with Suggestions based on their input by classifying the data provided by the user using machine learning algorithms. In this way, the tremendous increase could be controlled and diagnosed on time. © 2023 IEEE.

关键词： Expert systems

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