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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Exploration and Practice of Blended Teaching Model Based on Deep learning: the Example of a High School Information Technology Course 23

Exploration and Practice of Blended Teaching Model Based on ...

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4th international conference on Education Development and Studies (ICEDS)

作者： Yang, Hairu Chen, Yipan Ma, Mingyue Bai, Xinye Xiang, Qianchen Wang, Guomin China West Normal Univ Nanchong Peoples R China Lingnan Normal Univ Sch Teacher Educ Zhanjiang Peoples R China

ISBN: (纸本)9798400707506

With the rapid development of education informatization, blended teaching combines the advantages of both traditional offline teaching and emerging online teaching, becoming one of the key technologies to facilitate *** present, high school students generally have the problem of shallow and in depth learning of information technology learning. In order to promote students' professional knowledge learning and ability improvement at a deeper level, this study constructs a blended teaching model based on deep *** model is divided into two phases, online and offline, to gradually achieve the transition from the learning of shallow knowledge to deep learning for learners. the model was applied to an IT course to collect data and validate the effectiveness of the teaching model with a deep learning *** results showed that students in the experimental class using this teaching model had significantly higher behavioral, cognitive, and mindset dimensions of deep learning compared to the control class with traditional offline teaching. the model can effectively promote learners' deep learning, and has some implications for front-line teachers to carry out blended teaching and improve the effectiveness of deep learning.

关键词： Deep learning blended learning information technology teaching models

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Radar radiation identification based on online transfer learning 4

Radar radiation identification based on online transfer lear...

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4th international conference on Signal Processing and computer Science, SPCS 2023

作者： Lan, Chunyan Wang, Ying Guo, Richeng Zhao, Chenqian Zhang, Kaina China State Shipbuilding System Engineering Research Institute 1 Fengxian East Road Haidian District Beijing China

ISBN: (纸本)9781510672505

Radar radiation identification suffer from the problem that the existing identification method can not process radar radiation data streams which does not in the database. In this paper, in order to solve the problem, we first introduce Online Transfer learning (OTL) into radar radiation identification field. OTL is a machine learning framework, which aims to attack an online learning task by transferring knowledge from some source domain to a target domain. By extending binary classification online transfer learning to multiple classification tasks, we then consider the problem of training multiple classification online transfer learning using radar radiation data streams. Finally, the simulation results validate the efficacy of radar radiation identification based on online transfer learning techniques. © 2023 SPIE.

关键词： learning systems

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An Efficient Model for Plant Disease Detection in Agriculture Using Deep learning Approaches 4

An Efficient Model for Plant Disease Detection in Agricultur...

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4th IEEE Global conference for Advancement in Technology, GCAT 2023

作者： thotad, Puneeth N. Kallur, Shanta Nandeppanavar, Anupama Dept. of Master of Computer Applications K.L.E. Institute of Technology Hubballi580 027 India

ISBN: (纸本)9798350305258

Detecting plant diseases is crucial as they can significantly impact plant growth. At the same time, several machine-learning methodologies have been employed to distinguish and categorize plant diseases. Deep learning, a subset of machine learning, has shown promising results in terms of accuracy. Using CNN architectures and visualization methods, scientists can develop and implement effective strategies for detecting and classifying plant disease symptoms. these methods primarily rely on analyzing leaf characteristics such as color and damage. A plant-village dataset of 54, 306 images representing 14 different crop classes and 26 types of infections was used to train the models. the convolutional neural networks (CNNs) and additional machine learning models were trained on this dataset and achieved an impressive accuracy rate of 95% in identifying disease symptoms on plant leaves. Additionally, an efficient web-based application for crop disease detection was developed as part of this approach. © 2023 IEEE.

关键词： Convolutional neural networks Deep learning machine learning Plant disease

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2023 4th IEEE Global conference for Advancement in Technology, GCAT 2023

2023 4th IEEE Global Conference for Advancement in Technolog...

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4th IEEE Global conference for Advancement in Technology, GCAT 2023

ISBN: (纸本)9798350305258

the proceedings contain 307 papers. the topics discussed include: optimizing resource allocation using proactive predictive analytics and ML-driven dynamic VM placement;enhancing the performance of facial recognition technology;reduction in error-rate using BIOFDM technique and power comparison for M-QAM system under various fading channel environments;cryptography, code, consensus and currencies exploring the technological evolution of blockchain networks;dual-band microstrip fork shaped terahertz antenna for short range wireless application;improving tumor diagnosis accuracy with CNN based image segmentation and Arduino decision support;design and deployment of computer vision based smart patrolling robot using up squared board;implementation of a modified SHA-3 hash function on FPGA;enhancing diagnostic accuracy for lung disease in medical images: a transfer-learning approach;and enhancing real estate market insights through machine learning: predicting property prices with advanced data analytics.

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4th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

4th EAI International Conference on Cognitive Computing and ...

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4th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

ISBN: (纸本)9783031488900

the proceedings contain 39 papers. the special focus in this conference is on Cognitive Computing and Cyber Physical Systems. the topics include: Drug Recommendations Using a Reviews and Sentiment Analysis by RNN;optimizing Real Estate Prediction - A Comparative Analysis of Ensemble and Regression Models;estimation of Power Consumption Prediction of Electricity Using machine learning;medical Plants Identification Using Leaves Based on Convolutional Neural Networks;An Efficient Real-Time NIDS Using machine learning Methods;Maixdock Based Driver Drowsiness Detection System Using CNN;A Novel Approach to Visualize Arrhythmia Classification Using 1D CNN;exploring machine learning Models for Solar Energy Output Forecasting;the Survival Analysis of Mental Fatigue Utilizing the Estimator of Kaplan-Meier and Nelson-Aalen;an Optimized Ensemble machine learning Framework for Multi-class Classification of Date Fruits by Integrating Feature Selection Techniques;EEMS - Examining the Environment of the Job Metaverse Scheduling for Data Security;text Analysis Based Human Resource Productivity Profiling;a Novel Technique for Analyzing the Sentiment of Social Media Posts Using Deep learning Techniques;comparative Analysis of Pretrained Models for Speech Enhancement in Noisy Environments;Use of Improved Generative Adversarial Network (GAN) Under Insufficient Data;unraveling the Techniques for Speaker Diarization;LUT-Based Area-Optimized Accurate Multiplier Design for Signal Processing applications;speaker Recognition Using Convolutional Autoencoder in Mismatch Condition with Small Dataset in Noisy Background;face Emotion Recognition Based on Images Using the Haar-Cascade Front End Approach;textRank – Based Keyword Extraction for Constructing a Domain-Specific Dictionary;harmonizing Insights: Python-Based Data Analysis of Spotify's Musical Tapestry;Enlighten GAN for Super-Resolution Images from Surveillance Car.

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A machine learning Approach for Detection of Heart Diseases Using Wearable Devices 4

A Machine Learning Approach for Detection of Heart Diseases ...

引用

4th IEEE international conference on Smart Information Systems and Technologies, SIST 2024

作者： Abutalip, Spandiyar Baikuvekov, Meirzhan Zanggar, Duisen Department of Information Systems Al-Farabi Kazakh National University Almaty Kazakhstan Kazakh National Agrarian Research University Almaty Kazakhstan

ISBN: (纸本)9798350374865

this research paper delves into the integration of wearable devices and deep learning techniques to enhance heart disease detection and monitoring. the study highlights the importance of continuous cardiac health assessment and the transformative potential of wearable technology in the healthcare landscape. Our investigation showcases their efficacy in processing intricate physiological data for early heart disease recognition. Key variables such as age, maximum heart rate, blood pressure, cholesterol levels, and various electrocardiography (ECG) metrics are explored as pivotal factors in predicting and comprehending heart disease risk factors. Furthermore, we emphasize the significance of data normalization methodologies and secure data storage solutions to ensure data reliability and accessibility. Correlation analyses elucidate the intricate interplay among these variables, providing valuable insights into their collective influence on cardiac health. As the realm of machine learning and healthcare technology continues to evolve, this research lays a solid foundation for advancing heart disease detection and management, ultimately aiming to enhance patient outcomes and elevate the quality of healthcare delivery. © 2024 IEEE.

关键词： Blood pressure

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Research on sentiment recognition in online shopping reviews using an integrated BERT and BiLSTM approach 4

Research on sentiment recognition in online shopping reviews...

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4th international conference on Internet of things and Smart City, IoTSC 2024

作者： Huang, Jianrong Li, Guohua Li, Guowei Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges Universities Key Laboratory of Intelligent Industry Software Wuzhou University Wuzhou China

ISBN: (纸本)9781510681767

this study explores a method integrating BERT and BiLSTM technologies aimed at enhancing the accuracy of sentiment recognition in online shopping reviews. through detailed experimental validation, the fused model significantly surpasses traditional approaches that solely rely on either BERT or BiLSTM across various key performance metrics. this work not only confirms the potential of deep learning techniques in understanding complex textual emotional expressions but also provides new research directions and practical application possibilities for sentiment analysis of social media data. © 2024 SPIE.

关键词： Deep learning

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Adiabatic Quantum Support Vector machines 4

Adiabatic Quantum Support Vector Machines

引用

4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

作者： Woun, Dong Jun Date, Prasanna University of Tennessee KnoxvilleTN37996 United States Oak Ridge National Laboratory Oak RidgeTN37830 United States

ISBN: (纸本)9798350343236

machine learning's ability to analyze and predict complex data enables many scientific discoveries and industrial advances. However, state-of-the-art machine learning requires extensive computational resources for training. In machine learning, the training phase is crucial because it enables the model to acquire knowledge from the provided data. the duration of the training process varies significantly, ranging from a few hours to several months. As a result, the computational cost and time constraints hinder researchers from using machine learning. To address this issue, we investigated the possibility of using quantum computers for machine-learning tasks. We compared Date's [1] quantum support vector machine (SVM) approach on a D-Wave Advantage to a classical implementation on AMD Ryzen and Intel Xeon processors. We evaluated both the time-to-solution and the machine-learning performance of the quantum and classical systems. the results of our study demonstrated that SVMs scaled O(N2) on the quantum computer and O(N2d) on the classical computer. Here, N represents the number of samples, and d represents the number of features. So, when trained with a data set with eight million features, we found that the quantum annealer outperformed the classical computer by 3.5-4.5x. the quantum approach maintained a comparable accuracy to the classical approach as well. In conclusion, quantum computers offer a computational advantage over classical computers for machine learning. © 2023 IEEE.

关键词： learning systems

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Deep learning in Image Classification: An Overview 4

Deep Learning in Image Classification: An Overview

引用

4th international conference on computer Vision, Image and Deep learning, CVIDL 2023

作者： Xu, Kejian Chen, Jinlong Ning, Yi Tang, Wei Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Guilin University of Electronic Technology School of Continuing Education Guilin China

ISBN: (纸本)9798350326444

Deep learning has been one of the main trends in machine learning, and one of the most popular scientific research trends in recent years, which has played a revolutionary role in the development of computer vision. As a vital part of computer vision, many researchers have been attracted to participate in the research of image classification. With the emergence and development of deep neural networks, combined with the emerging various training and optimization methods, it has achieved great success in this field. In recent years, Transformer-based models has achieved the best experimental results in image classification and many other fields. this article will compactly introduce the development process of deep learning in image classification in the past few years from three aspects of training and optimization methods, deep neural networks and Transformers. © 2023 IEEE.

关键词： Deep neural networks

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Zeroth Order GreedyLR: An Adaptive learning Rate Scheduler for Deep Neural Network Training 4

Zeroth Order GreedyLR: An Adaptive Learning Rate Scheduler f...

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4th IEEE international conference on Pattern Recognition and machine learning, PRML 2023

作者： Subramanian, Shreyas Ganapathiraman, Vignesh Amazon Web Services Seattle United States

ISBN: (纸本)9798350324303

Deep neural networks are a powerful tool for a wide range of applications, including natural language processing (NLP) and computer vision (CV). However, training these networks can be a challenging task, as it requires careful selection of hyperparameters such as learning rates and scheduling strategies. Despite significant advances in designing dynamic (and adaptive) learning rate schedulers, choosing the right learning rate / schedule for a machine learning task is still more art than science. In this paper, we introduce Zeroth order GreedyLR, a novel scheduler that adaptively adjusts the learning rate during training based on the current loss and gradient information. To validate the effectiveness of our proposed method, we conduct experiments on several NLP and CV tasks. the results show that our approach outperforms several state-of-the-art schedulers in terms of accuracy, speed, and convergence. Furthermore, our method is easy to implement, computationally efficient, and requires minimal hyperparameter tuning. Overall, our study provides a useful tool for researchers and practitioners in the field of deep learning. © 2023 IEEE.

关键词： Deep neural networks

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