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

共 40755 条记录，以下是4361-4370 订阅

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Efficient Fetal Health Monitoring and Classification with Machine learning 7

Efficient Fetal Health Monitoring and Classification with Ma...

引用

7th international conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Singh, Kabir Shyry, Prayla Franklin, Ramya G Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

The goal of the research on fetal health categorization using machine learning is to create a model that can precisely predict the condition of a fetus during pregnancy. This is crucial because prompt action following early identification of fetal health issues might enhance the pregnancy's outcome. This research study suggests classifying fetal health status using machine learning approaches based on ultrasound images and other clinical parameters. To train and test our model, we will gather a sizable dataset of ultrasound images and clinical characteristics from pregnant women. To categorize the state of fetal health, the proposed model will employ machine learning algorithms and image processing techniques. Our approach should categorize fetal health status with a high degree of accuracy, and it will help obstetricians and gynecologists provide better treatment for pregnant patients. © 2023 IEEE.

关键词： Genetic programming

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Mitigating Bias in Image Classification - Pretraining on Diverse Data for Improved Generalization 7

Mitigating Bias in Image Classification - Pretraining on Div...

引用

7th international conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2023

作者： Shirvalkar, Rigved Geetha, M. Amrita School of Engineering Amritapuri Department of Electronics and Communication Engineering Kerala 690525 India

ISBN: (纸本)9798350305517

Deep learning models have demonstrated remarkable abilities to learn complex patterns and concepts from the data they are trained on. However, recent studies have also revealed that these models can inherit and amplify the biases that exist in the data, such as those related to gender or race. These biases can pose serious ethical issues and need to be addressed. One possible solution to reduce these biases is to pretrain the models on large and diverse datasets that cover a broad spectrum of scenarios and features. In the current study,, we investigate the effect of pretraining and transfer learning on a biased dataset that we created using a subset of the celebA dataset. The dataset is biased towards people with black hair smiling and people with blonde hair not smiling. We use the new YOLO V8 model to classify the images based on whether the person in the image is smiling or not. The paper conducts a comparative analysis between a model that underwent no prior pretraining and a model pretrained on the ImageNet dataset. We find that the pretrained model has a lower bias and also a higher accuracy than the non-pretrained model. Our findings support that pretraining has the potential to reduce the bias in computer vision tasks while enhancing the model's generalization capabilities. © 2023 IEEE.

关键词： Deep learning

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A Novel Survey on Medical Image Segmentation and Reconstruction Approaches based on Deep learning Methodologies 2

A Novel Survey on Medical Image Segmentation and Reconstruct...

引用

2nd international conference on Edge Computing and Applications, ICECAA 2023

作者： Jyothsna Devi, Koppagiri Sudha, S.V. School of Computer Science and Engineering VIT-AP University Andhra pradesh India

ISBN: (纸本)9798350347579

Medical image segmentation or image reconstruction targets to attain the finest quality of images for clinical examination in cost-efficient and reduced patient risk. With the advent of learning approaches, deep learning and its medical imaging applications have recently attracted huge interest. This comprehensive review shows extensive analysis of various imaging techniques adopted in medical applications. This review includes an analysis of deep learning-based image construction, image reconstruction, tools used for image reconstruction and various imaging techniques. This analysis shows that deep learning- based segmentation and reconstruction approaches enhance image quality quantitatively and qualitatively. Moreover, deep learning approaches are usually cost-effectual and handle many training samples with better robustness and generalization. The basic research challenge relies on the dataset training process, which has to be addressed or resolved using modern learning techniques. © 2023 IEEE.

关键词： Image reconstruction

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Pruning and Mixed Precision Techniques for Accelerating Neural Network 13

Pruning and Mixed Precision Techniques for Accelerating Neur...

引用

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

作者： Zahedi, Mahsa Abazari, Mohammad.S Savadi, Abdorreza Ferdowsi University of Mashhad Computer Engineering Dept. Mashhad Iran

ISBN: (纸本)9798350330151

This study investigates the use of pruning and mixed precision techniques to enhance neural network performance, focusing on the AlexNet model trained on the MNIST dataset. Pruning removes unnecessary components, while mixed precision optimizes memory and computation efficiency. The study applies structured pruning to create a pruned model, which achieves improved inference time compared to the baseline. Automatic mixed precision is also employed, further enhancing inference speed. Combining pruning and mixed precision in a single model yields superior performance, surpassing the individual approaches. The combined model achieves significantly faster inference time by leveraging both techniques. The research highlights the potential of combining pruning and mixed precision for faster and more efficient neural network computations, reducing network size, optimizing memory utilization, and accelerating computations. The findings provide valuable insights for integrating these techniques into the AlexNet model and lay the groundwork for future exploration in larger and more complex models. This work holds promise for developing faster and more efficient deep learning models to meet the demands of real-world applications, particularly in resource-constrained environments. © 2023 IEEE.

关键词： Computational efficiency

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Cluster Head Selection and Cluster Construction for Federated learning in Mobile Ad Hoc Networks 8

Cluster Head Selection and Cluster Construction for Federate...

引用

8th international conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Mei, Linsheng Meng, Xiaowei Yi, Yibo Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

ISBN: (纸本)9798350302455

Federated learning (FL), as a distributed learning framework, which can effectively protect user privacy and address data imbalance issues. In this paper, we investigate cluster head (CH) selection and client clustering issues based on hierarchical FL framework in mobile ad hoc networks (MANETs), wherein all the clients are randomly distributed without infrastructure support. Specifically, we formulate the joint client association and CH selection problem, with the target of minimizing the training latency while maximizing the system lifetime. Furthermore, we propose a heuristic algorithm for client association and an entropy weight Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method for CH selection. Simulation results demonstrate that our proposed algorithms can effectively shorten training latency and extend lifetime compared with other schemes. © 2023 IEEE.

关键词： Heuristic algorithms

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Power of kernel functions, its benefits, and limitations

Power of kernel functions, its benefits, and limitations

引用

2023 international conference on Intelligent Systems, Advanced Computing and Communication, ISACC 2023

作者： Indu, Richa Dimri, Sushil Chandra Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350399387

In today's realm of machine learning, the non-linearity of data is the problem often faced during data analysis. A well-known supervised learning algorithm that is known to efficiently handle this problem is the Support Vector Machine. The trick deployed in the background to overcome the problem of non-linearity is the transformation of data from the existing dimension to a higher dimension. To make this conversion of dimensions possible, kernel functions are utilized. These kernel functions are simply mathematical functions, which are available in the literature for a long ago. The present study briefly describes the mathematical background of the kernel functions with examples and a quick summarization of the recent applications of the renowned kernel functions across various domains. Further, the numerous benefits, a few limitations of kernel functions, and future directions are also highlighted in the work. © 2023 IEEE.

关键词： Support vector machines

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ML-based Java UI for Residence Predictor

ML-based Java UI for Residence Predictor

引用

2023 international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Saudagar, Saleha Kulkarni, Mukund Raghvani, Isha Hirkani, Haider Bassan, Inderdeep Hole, Pranav Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9781665474511

When people consider buying a home or property, they first tend to go online to try trends and other related research. The proposed system presents a console application in Java that can predict house prices using machine learning algorithms based on data mining concepts that extract relevant knowledge from raw data and perform practical analysis. The system proposed here analyzes related properties for predicting the prices. Using the wide varieties of libraries available in Python, the proposed system attempts to implement a way to create user interfaces in Java by interacting with Java and Python machine learning model. Random Forest Regression model has been used to predict the prices with an R-squared score of 86.72%. The proposed system implements a JAVA graphical user interface using Swing technology which is simple and lightweight. © 2023 IEEE.

关键词： Python

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An Adaptive Frame Selection and Deep learning-Based Dynamic Hand Gesture Recognition System for Sterile Environments

An Adaptive Frame Selection and Deep Learning-Based Dynamic ...

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2023 international conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Kaur, Arpneek Bansal, Sandhya Maharishi Markandeshwar Engineering College Maharishi Markandeshwar Deemed to Be University Department of Computer Science and Engineering Ambala Haryana Mullana133207 India

ISBN: (纸本)9798350313796

Medical professionals such as doctors, surgeons, and medical lab technicians working in sterile environments cannot use their fingers on touch screens or other interfaces. For such environments, touchless interfaces can be proposed by using dynamic hand gesture recognition, where the doctors can study the patient's images by making different hand gestures in front of a camera. This article proposes a smart hand gesture-controlled image gallery application for sterile environments such as operation rooms, medical laboratories, etc. A 3DCNN-based deep learning model has been thus developed and trained on four selective gestures from the IPN hand gesture dataset, which are compatible with our application. An adaptive frame selection methodology has been developed in order to address memory and time constraints. The system has reached an accuracy level of 86.03%, which shows that it is well suitable for such an application. © 2023 IEEE.

关键词： Medical imaging

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Exploration of OBE-Based Competence Development Model for Undergraduate computer Science Students 5th

Exploration of OBE-Based Competence Development Model for Un...

引用

5th international conference on computer Science and Educational Informatization, CSEI 2023

作者： Luo, Juan Qiao, Ying Xiao, Degui Zhao, Huan College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9789819994984

The inconsistency and lack of synchronization between computer professional competence education and talents' market demand are the bottleneck problems of undergraduate education. In this paper, we establish a "student-centered" output teaching model based on OBE concept, and construct a computer core competence model based on KSD. We propose a personalized teaching system based on the knowledge graph of teaching contents and students’ learning trajectories. It continuously improves and optimizes the training plan according to students’ learning outcomes, and improves and adjusts the course contents, which achieves the cultivation of undergraduate students’ ability to solve complex engineering problems. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Education computing

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Deep Neural Network Based Discrimination of Mental States During Real-Time Posture Yoga Using EEG

Deep Neural Network Based Discrimination of Mental States Du...

引用

2023 international conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Rashmi, C.R. Shantala, C.P. Channabasaveshwara Institute of Technology Department of Computer Science & Engineering Tumkur Karnataka Gubbi India

ISBN: (纸本)9798350313987

Practicing posture yoga is known to bring both physical and mental benefits. The impact of posture yoga on the brain can be analyzed using Electroencephalography (EEG), which records brain activity and helps distinguish between mental states during realtime yoga practice and resting states. This study presents a deep learning model for automatically discerning these mental states. The results of this discrimination could serve as a biomarker for identifying the effect of yoga on the brain. EEG signals were recorded using an Enobio-8 device during realtime yoga posture and resting states. The raw EEG signals were filtered with a 2-40Hz band pass filter to remove artifacts, and statistical features were extracted from each EEG band. A significant increase in Alpha relative band power was observed during yoga, indicating higher relaxation. The extracted features were used to train a deep neural network (DNN), which achieved a test accuracy of 100% in the gamma band, clearly demonstrating the difference between yoga and resting states. © 2023 IEEE.

关键词： Brain computer Interface Deep learning Electroencephalogram Yoga

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