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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4221-4230 订阅

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An Enhanced Machine learning Security Algorithm for the Anonymous user Detection in Ultra Dense 5G Cloud Networks

An Enhanced Machine Learning Security Algorithm for the Anon...

引用

2023 international conference on Disruptive Technologies, ICDT 2023

作者： Ramesh Babu, P. Birhanu, Tariku Kiran Kumar, K.R.N. Gadiparthi, Manjunath College of Engineering and Technology Wollega University Department of Computer Science Nekemte Ethiopia Malla Reddy Institute of Technology Department of Cse Hyderabad India School of Information Technology and Engineering AAiT Aau Addis Ababa Ethiopia

ISBN: (纸本)9798350323887

In general, high-density network services have a large number of users. This is seen as the main problem of that network. As users increase, so does the amount of service provided to them. Thus, have to pay separate attention to serving and serving them. It is imperative to ensure their maximum security if they are the primary user. Thus, security management is much less on high density 5G networks. A security algorithm has been proposed to improve these issues. This algorithm, designed for machine learning, first detects the primary user. Their security is prioritized by calculating their input and output times. It is also designed to detect secondary users and anonymous user. These anonymous users were creating the resource utilization and security vulnerabilities in the network. So, the primary user protection and anonymous user identification getting more priority in the ultra dense cloud networks. © 2023 IEEE.

关键词： Machine learning

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Channel Attention-Guided Lightweight CNN Meets Extreme learning Machine: Multi-Class Crop Leaf Disease Classification with Explainable AI 26

Channel Attention-Guided Lightweight CNN Meets Extreme Learn...

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

作者： Islam, Md. Minhazul Hasan, S. M. Mahedy Hossain, Md. Rakib Srizon, Azmain Yakin Akter, Farzana Faruk, Md. Farukuzzaman Shraboni Akter, Sheikh Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

Crop leaf diseases pose a significant challenge to global agriculture, leading to substantial economic losses and reduced yields. These ailments, caused by various pathogens, manifest as symptoms such as leaf discoloration and impaired photosynthesis, severely impacting crop health and productivity. Existing classification methods face challenges related to accuracy, computational efficiency, and interpretability. This study introduces an innovative solution that combines a meticulously designed lightweight convolutional neural network (CNN) architecture with a channel attention (CA) mechanism for extracting crucial features and leverages the discriminative power of an extreme learning machine (ELM) for classification. The lightweight CNN developed demonstrates significantly reduced training times. Additionally, the CA mechanism within the model effectively prioritizes channel-wise critical features while eliminating irrelevant and redundant ones. An empirical evaluation is conducted using a publicly available dataset and the proposed model achieves a flawless 100% accuracy in distinguishing between 39 distinct crop leaf diseases. Furthermore, the study employs gradient class activation maps (Grad-CAM) visualization to delve into the model's decision-making process, shedding light on influential factors and regions. It holds the promise of transforming crop health and agricultural yields, instilling hope for farmers and researchers dedicated to achieving agricultural prosperity and sustainability. © 2023 IEEE.

关键词： Channel Attention Convolutional Neural Network Crop Leaf Disease Extreme learning Machine Grad-CAM

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A Futuristic Approach to Synaptic Fusion of INN and CNN Architectures for Tissue Classification

A Futuristic Approach to Synaptic Fusion of INN and CNN Arch...

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2023 IEEE international conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Verma, Harsh Deep Gourisaria, Mahendra Kumar Ghosh, Soumili Dewangan, Bhupesh Kumar School of Computer Engineering Kiit Deemed to Be University Odisha Bhubaneswar751024 India Op Jindal University Dept. of Computer Science and Engineering Raigarh496109 India

ISBN: (纸本)9798350300826

Radiology refers to a branch of medical specialization that focuses on image processing for diagnostics. It has seen immense advances in predictive methodologies in the past few decades, with its predictive capacities being mostly accounted for by machine learning and deep learning approaches. However, classical machine learning has shown comparatively poor performance with regard to non-linear, irrelevant, and highly correlated features in data, and has also shown high reliance on proper presentation of data. This paper aims to improve that notion and aid radiological diagnostics using advanced deep learning and image processing techniques, which will eventually result in improved diagnostic accuracy and productivity of radiologists. This experiment efficiently fuses hybrid neural networks by using CNN (Convolutional Neural Network Architecture) and INN (Involutional neural networks) architectures, which was then compared with other pre-trained models, trained and tested on multiple color models such as RGB (Red, Green, Blue), HSV (Hue, Saturation, Value), and YCbCr (Luminance, Chrominance), to get optimal efficiency. Our proposed Convolutional Neural Network architectures, which include two fused hybrid models of INN and CNN, when trained with carefully handcrafted features, outperform other pre-trained models in terms of micro accuracy. © 2023 IEEE.

关键词： Convolutional neural networks

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Building Annotated Image Dataset for Myanmar Text to Image Synthesis

Building Annotated Image Dataset for Myanmar Text to Image S...

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2023 IEEE conference on computer Applications, ICCA 2023

作者： Htwe, Nan Kham Pa, Win Pa University of Computer Studies Natural Language Processing Lab Yangon Yangon Myanmar

ISBN: (纸本)9781665435994

Text to image synthesis is the translation of images from the input language text. The learning process can become easier when the spoken words can visualize with the images. It is one of the popular research field in combination of NLP and computer vision. Generative Adversarial Networks (GAN) have growth in the generation of images from text descriptions. We build the baseline system of Myanmar text to image synthesis and a type of annotated images dataset because there is not efficient annotated image dataset to be used in this implementation. It was created by using partial part of Oxford-102 flowers dataset. Word2Vec algorithms is used to convert word to vectors for the input sentence to GAN. GAN is applied for generation of images from Myanmar language text. This is the first text to image generation using GAN in Myanmar. The two-evaluation metrics are used to measure the quality of images. The quality of the generated images is evaluated using Inception score. The Frechet Inception distance (FID) is used to measure the distance between the real images (images from original dataset) and the generated images from the model. © 2023 IEEE.

关键词： Generative adversarial networks

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25th international conference on Cross-Cultural Design, CCD 2023, held as part of the 25th international conference on Human-computer Interaction, HCII 2023

25th International Conference on Cross-Cultural Design, CCD ...

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25th international conference on Cross-Cultural Design, CCD 2023, held as part of the 25th international conference on Human-computer Interaction, HCII 2023

ISBN: (纸本)9783031359453

The proceedings contain 125 papers. The special focus in this conference is on Cross-Cultural Design. The topics include: Kansei Marketing Strategies for Cultural and Creative Products;a Study on the Model of Experience Activities in Wineries;a Study on the Differences of Consumers’ Emotional Experience of Cultural Creative Products in the Palace Museum of Beijing;immersive Museum: Design and Develop an Interactive Virtual Museum Experience;a New Style of Meta-Cosmic Cultural Interaction: "Light Up" National Digital Museum;the Aesthetic Education of Traditional Chinese Mending Embroidery Technology in the Spread of Costume Culture;immersion and Intersectionality - Virtual Reality in Cross Cultural Art Exhibition Courses;Preliminary Research on the Design of the Evaluation Ruler for the Judge of the Higher Education CIE (Creativity, Innovation and Entrepreneurship) Competition;Impact of Synchronous and Asynchronous learning Approach: A Comparative Study of Graphic Design Students in a Developing Country (Botswana) Verses a Developed Country (UK);Design of an AR Application to Support Students with CVD in learning Chemistry;Viewer’s Perceptual Difference in NFT Aesthetics – A Case Study of Popular NFT Avatars (PFPs) at the Opensea;a Preliminary Study on Primary School Teachers’ User Experiences in distance learning;how Scientific Illustration and Photography Aid Learners’ Reading – Evidence from Eye Movements;Effects of VR Technology in the Cross-Cultural Theatre Design Course;social Innovation in Design Education: The Emergence and Evolution of Design Academic Programs;Analysis of the Effect of Applying VR Technology in Cross-Cultural Art Course;exploring Design Factors of Teaching Aids in Design Based learning.

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A DDPG Algorithm Based Reinforcement learning Controller for Three-Phase DC-AC Inverters

A DDPG Algorithm Based Reinforcement Learning Controller for...

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2023 international conference on Power Energy Systems and Applications, ICoPESA 2023

作者： Ye, Jian Mei, Sen Guo, Huanyu Hu, Yingjie Zhang, Xinan Harbin Institute of Technology Shenzhen School of Mechanical Engineering and Automation Shenzhen China University of Western Australia School of Electrical Electronic and Computer Engineering Perth Australia

ISBN: (纸本)9798350345605

This paper proposes a DC-AC inverter controller based on reinforcement learning (RL) algorithm. Compared with the traditional PID control method, the structure of the RL algorithm based controller is simpler. The deep deterministic policy gradient (DDPG) algorithm in RL algorithm is used to realize model-free control of inverters, so that the control algorithm has adaptive ability to different types of DC-AC inverters. It can avoid the dependence of the control strategy on the system model. Through simulation, the control strategy of three-phase two-level DC-AC inverter based on DDPG algorithm is compared with the traditional PID control. The simulation results show that the total harmonic distortion (THD) is reduced by 12% and the current tracking performance is improved by at least 75%. © 2023 IEEE.

关键词： Reinforcement learning

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Analysis of Machine learning algorithms used in Face Recognition Attendance System

Analysis of Machine Learning algorithms used in Face Recogni...

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2022 OPJU international Technology conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Goel, Gourisha Bansal, Vinay Bura, Deepa Manav Rachna International Institute of Research & Studies Computer Science & Engineering Faridabad India

ISBN: (纸本)9781665492942

Biometric is learning of human features and behavior. The Face Recognition system is used for security. Its purpose is to identify someone's face with his 2D image, which involves the extraction of his facial features and recognize it regardless of aging, beard, expression or pose which is very difficult. Image Analysis and computer Vision are the challenges of face recognition for which many techniques have been built which we are going to study in this paper. So how different algorithm work and their comparison is discussed in this paper. © 2023 IEEE.

关键词： Local binary pattern

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Credit Card Fraud Detection using Machine learning 14

Credit Card Fraud Detection using Machine Learning

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14th international conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Sushma, V. Neelamma, Shriya Machaiah, Yathin Fathima, Suroor ATME College of Engineering Computer Science Department Mysuru India

The amount of online transactions is growing these days to a large number. A big portion of these transactions contains credit card transactions. The growth of online fraud, on the other hand, is notable, which is generally a result of ease of access to edge technology for everyone. There has been research done on many models and methods for credit card fraud prevention and detection. Artificial Immune Systems is one of them. However, organizations need accuracy along with speed in the fraud detection systems, which is not completely gained yet. In this process we mined the transaction of each data and it finally predict the fraud detection of data. © Grenze Scientific Society, 2023.

关键词： Machine learning

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HEAT SINK DESIGN OPTIMIZATION VIA GAN-CNN COMBINED DEEP-learning

HEAT SINK DESIGN OPTIMIZATION VIA GAN-CNN COMBINED DEEP-LEAR...

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ASME international Design engineering Technical conferences / computers and Information in engineering conference (IDETC-CIE) / 49th Design Automation conference (DAC)

作者： Flynn, Nathan Qian, Xiaoping Univ Wisconsin Dept Mech Engn Madison WI 53706 USA

ISBN: (纸本)9780791887301

This work proposes a combined deep learning based approach to improve thermal component heat sinks involving turbulent fluid flow. A Generative Adversarial Network (GAN) is trained to learn and recreate the new ellipse based heat sinks. Simulation data for new designs is efficiently generated using OpenFOAM 7 (Open Source Computational Fluid Dynamics software) along with high throughput computing. To improve the speed of design evaluation, a Convolutional Neural Network (CNN) is trained to predict the entire temperature field for a given design. The trained CNN is able to predict the entire temperature field for the design with a mean average error of 1.140 degrees kelvin in 0.04 seconds (22,500 times faster than the simulation). A combined model is formed using the trained CNN and GAN networks to create and simulate new designs. The combined model optimizes the latent representation of 64 random designs on a Graphical Processing Unit (GPU) in ten minutes. The optimized designs perform fourteen degrees kelvin better on average than the non-optimized designs. The highest preforming design outperforms any design in the training data by 1.83 degrees kelvin.

关键词： Simulation Optimization Heat sinks Errors Design Fluid dynamics Graphics processing units Computational fluid dynamics computer software Temperature Turbulence Artificial neural networks

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Turbulence Modeling Through Deep learning: An In-Depth Study of Wasserstein GANs 4

Turbulence Modeling Through Deep Learning: An In-Depth Study...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Alghamdi, Wajdi Mayakannan, S. Sivasankar, G.A. Singh, Jagendra Ravi Naik, B. Venkata Krishna Reddy, Ch. King Abdulaziz University Faculty of Computing and Information Technology Department of Information Technology Jeddah21589 Saudi Arabia Vidyaa Vikas College of Engineering and Technology Tiruchengode Mechanical Engineering Tamilnadu Namakkal India Kit - Kalaignar Karunanidhi Institute of Technology Department of Aeronautical Engineering Coimbatore641402 India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Great Noida India St. Martin's Engineering College Department of Mechanical Engineering Telangana Secunderabad India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350300888

This research study compares the accuracy of different techniques based on deep learning (DL) for predicting turbulent flows. Different types of Generative Adversarial Networks (GANs) are examined in terms of their applicability to the study and simulation of turbulence. Next, we select Wasserstein Gans (WGANs) to produce localized disturbances. Network features including the learning rate and loss function are examined as they pertain to the performance of the WGANs during training on turbulent data gleaned from high-resolution Direct Numerical Simulations (DNS). DNS input data and the generated turbulent structures are proven to agree qualitatively well. The projected turbulent fields are evaluated quantitatively and statistically. © 2023 IEEE.

关键词： Turbulence

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