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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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application of Audio Processing Based on Image Semantic Segmentation in Applied Mathematics Online Course 4

Application of Audio Processing Based on Image Semantic Segm...

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4th international conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Yao, Haiyan Liaocheng University Dongchang College Shandong Liaocheng252000 China

ISBN: (纸本)9781665401180

this paper uses multimedia, network technology and computer-aided software as the core information technology, and audio processing technology based on image semantic segmentation. It analyzes the application of applied mathematics network courses, explores the connotation, characteristics, strategies and information resources of the construction of engineering mathematics network courses, and builds the online course teaching platform for students understands the core value of mathematics education and teaching, develops inquiry-based collaborative learning, and forms a personalized learning atmosphere. It has a unique function. It is a good environment for students to learn independently and lifelong learning, and improve the effect of after-class reviewto 6.5%. © 2022 IEEE

关键词： Semantics

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Study on the Correlation of Trainable Parameters and Hyperparameters with the Performance of Deep learning Models 4

Study on the Correlation of Trainable Parameters and Hyperpa...

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4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Ong, Song-Quan Isawasan, Pradeep Nair, Gomesh Salleh, Khairulliza Ahmad Yusof, Umi Kalsom Institute for Tropical Biology and Conservation Universiti Malaysia Sabah Sabah Malaysia College of Computing Informatics and Mathematics Universiti Teknologi Mara Perak Branch Malaysia School of Electrical and Electronics Universiti Sains Malaysia Penang Malaysia School of Computer Sciences Universiti Sains Malaysia Penang Malaysia

ISBN: (纸本)9798350318432

Trainable parameters and hyperparameters are critical to the development of a deep learning model. However, the components have typically been studied individually, and most studies have found it difficult to investigate the effects of their combination on model performance. We are interested in examining the correlation between the number of trainable parameters in a deep learning model and its performance metrics under different hyperparameters. Specifically, we want to study the effects of using either the Adam or SGD optimizers at three varying learning rates. We use six pre-trained models whose trainable parameters have been quantitatively defined using two strategies: (1) freezing the convolutional basis with partially trainable weights and (2) training the whole model with most trainable weights to obtain a set of trainable parameters. Our experimental result shows a positive correlation between the trainable parameters and the test accuracy regardless of the level of the learning rate. However, for the generalization of the model, it was not guaranteed that a higher number of trainable parameters would lead to higher accuracy and F1 score. We have shown that the correlation between trainable parameters and model generalization becomes positive by using Adam with the smallest learning rate. © 2023 IEEE.

关键词： Convolutional neural networks

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Smartphone-Based Object Recognition application for Educational Engagement 4

Smartphone-Based Object Recognition Application for Educatio...

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4th international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Fadhil, Heba M. Muayad, Abdullah Majid, Summer Munthder, Muhammed Husain, Jalal Department of Information and Communication Al-Khwarizmi College of Engineering University of Baghdad Baghdad Iraq

ISBN: (纸本)9781839539954

computers and mobile phones are common today and play an important role in society for children and adults, providing many opportunities and advantages. this new application is intended for everyone, including children, and offers an enjoyable learning process. It takes advantage of children’s natural ability to identify individuals, objects, scenery, and other visual features in still photos and moving clips. this paper aims to create an educative but engaging environment that stimulates children to learn about and understand various visuals. In addition, the incorporation of deep NN models into the software demonstrates the need to modify software engineering methodologies and practices to cope with evolving technologies. the program can recognize an array of objects, such as animals, cars, common housewares, and natural scenes. the software uses state-of-the-art deep neural network architectures that are readily plugged into modern software stacks for accurate and reliable object detection. Aside from merely identifying objects, it also engages users in the learning process, where they are prompted to recognize and label different objects and parts of the visual content. this method provides a stimulus or motivation for children to learn object names in English because it facilitates the swift and faster growth of their own vocabularies. therefore, this project is at the crossroads of computer vision, deep learning, and education, as such tools contribute to the enhancement of children’s language knowledge and object identification abilities in mobile application development. © the Institution of Engineering & Technology 2023.

关键词： Mobile applications

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Novel Approach for Software Reliability Analysis Controlled with Multifunctional machine learning Approach 4

Novel Approach for Software Reliability Analysis Controlled ...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： William, P. Gupta, Manish Chinthamu, Narendar Shrivastava, Anurag Kumar, Indradeep Kakoli Rao, A. Sanjivani College of Engineering Savitribai Phule Pune University Department of Information Technology Pune India Lovely Professional University Division of Research and Development Phagwara India Cto Candidate DallasTX United States Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Aeronautical Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Department of CSE Greater Noida India

ISBN: (纸本)9798350300093

Reliability engineering is distinguished from other fields by its focus on software. Models that forecast when things will go wrong are used to evaluate the reliability of a piece of software. Real-world issues might arise when it comes to reliability. Many computational algorithms have been developed in order to provide simple, trustworthy, and fast answers. Even though it doesn't work, 'Quality' is an important part of any programme that is often missing from software solutions. Using defect prediction models and object-oriented metrics, problem classes in software are found and used to measure its quality. Modeling software failure data and incorporating class-specific metrics peculiar to defective classes are used in this work to conduct an experimental investigation of defective classes. machine learning and object-oriented metrics are used to do this. It is known how likely the classes of Marian Jureczko (MJ) Data sets are to go wrong. After looking at the Marian Jureczko Data set, it has been shown that RF gives the best accuracy and ROCAUC values. the RF model that was built is great. © 2023 IEEE.

关键词： Software reliability

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Detection and Analysis of Drowsiness using machine learning 4

Detection and Analysis of Drowsiness using Machine Learning

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4th international conference for Emerging Technology, INCET 2023

作者： Varhadpande, Prathamesh R. Dhakare, Tejas M. Patmase, Shantanu M. Kulkarni, Kaustubh C. Shah, Saurabh A. Bhagat, Amol P. Prof Ram Meghe College of Engineering & Management Department of Computer Science and Engineering Badnera Amravati India

ISBN: (纸本)9798350335750

Drowsiness is a major source of fatigue, which can endanger the lives of drivers and other passengers. Teachers are confronted every day with students not paying attention in class because they can be seen falling asleep, yawning and wandering off, which ultimately reduces their attention span and becomes less engaged in class. With workplaces online due to the current COVID situation and employees preferring to work from home during their free time, it's clear they can feel sluggish. Our real-time drowsiness detection system is designed to detect human drowsiness based on body changes, i.e. Open/close eye and mouth status, then alert that person with an on-screen alert. In addition, the sleepy behavior of the person is analyzed using data visualization to understand the sleepy behavior of the person, and the sleepiness report of the person is emailed to the people. the labeled dataset is prepared from real-time facial features obtained from the detection step. A supervised machine learning algorithm was then implemented on the dataset to predict sleepiness. Logistic regression and Naive Bayes achieved 77% and 79% accuracy in predicting sleepiness, but failed to accurately classify sleepiness based on labels. the KNN algorithm was found to achieve the highest accuracy for sleepiness prediction with an accuracy of 97.5%. It ranked all three labels with higher accuracy and was found to be the best algorithm in the dataset for sleepiness prediction. SVM is second only to KNN, achieving an accuracy of 87%. K-Nearest Neighbors and Support Vector machines have proven to be the best supervised machine learning algorithms to more accurately classify sleepiness and predict sleepiness. Our approach benefits taxi owners, drivers, students, teachers, staff and organizations who need to monitor their concerns, analyze and review their behavior, which can help them take constructive action to prevent sleepiness, increase the productivity. © 2023 IEEE.

关键词： Forecasting

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DC Optimization in Adversarial Sparse Support Vector machine 4th

DC Optimization in Adversarial Sparse Support Vector Machin...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Astorino, Annabella Di Francesco, Massimo Gaudioso, Manlio Gorgone, Enrico Manca, Benedetto Dipartimento di Ingegneria Informatica Modellistica Elettronica e Sistemistica Universitá della Calabria Rende87036 Italy Dipartimento di Matematica e Informatica Universitá degli Studi di Cagliari Cagliari09124 Italy

ISBN: (纸本)9783031812408

In supervised classification models, such as Support Vector machine, the main purpose is to predict the class membership of the incoming samples. In some real applications malicious inputs are inserted to mislead a vulnerable classifier, leading to a wrong prediction. In our work we focus first on the problem of introducing the smallest perturbation of a sample to induce incorrect classification and then on how to produce a significant downgrading of the classifier acting on a subset of the input samples. the novelty of the proposed approach is in the attempt of calculating sparse perturbations by minimizing the relative 0-pseudo-norm, which gives rise to a Difference of Convex (DC) optimization model. We present the results of some preliminary experiments. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Drivers' Drowsiness Detection System Enhancement Using Deep learning: CNN-based Approach 26

Drivers' Drowsiness Detection System Enhancement Using Deep ...

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

作者： Ahmed, Tamal Jyoti, Oishi Mou, Tamanna Hossain Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

Driver drowsiness detection is a critical safety technology aimed at reducing accidents caused by driver fatigue. this study presents the creation of a real-time system for detecting driver drowsiness through the application of machine learning techniques. the system utilizes data on eye and mouth states, recognized as reliable indicators of drowsiness. the model, developed using Convolutional Neural Network (CNN), was trained on two factors and four features, providing a comprehensive approach to drowsiness detection. the resulting driver drowsiness detection system achieved an average accuracy of 97.23%. this research contributes significantly to the progress of road safety technology and accident prevention. Additionally, the proposed model demonstrates superior accuracy and speed compared to previous work, with a detailed comparison of outcomes from various models employed. © 2023 IEEE.

关键词： 4D CNN Convolutional Neural Network(CNN) Driver Drowsiness Detection

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An Effective machine learning Technique for Rice Leaf Disease Detection 4

An Effective Machine Learning Technique for Rice Leaf Diseas...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Chikati, Divya Manyam, Rajasekhar Reddy Kancharla, Ramkumar Amrita School of Computing Amrita Vishwa Vidyapeetham Amaravati Campus Andhra Pradesh Amaravati India Environment and Computing Coventry University Faculty of Engineering Coventry United Kingdom

ISBN: (纸本)9798350306118

Plant diseases, especially those that attack rice leaves, cause serious problems for farmers since they make it more challenging to provide enough food to satisfy the world's growing population. Farmers' livelihoods that rely on agriculture have been threatened by the economic losses brought on by rice illnesses. As a tragic outcome of the hardships they encounter, several farmers have taken their own lives. It is crucial to detect and identify disease-infected plants in order to execute efficient disease management strategies. the Inception V3 model is utilized for disease classification, and the Convolutional Neural Network (CNN) autoencoder is employed to reduce noise in images of rice leaves. the dataset is then expanded through data augmentation approaches. the primary objective of the initial stage is to gather photographs of rice leaves from the UCI machine learning Repository. Once a collection of photographs has been gathered, the images are subjected to processing using a Convolutional Neural Network (CNN) autoencoder model. this process aims to enhance the quality of the images and eliminate any unwantednoise present in them. Subsequently, data augmentation techniques are employed to increase the volume of the dataset and standardize the dimensions of the images, both of which play a role in training a robust and adaptable model. Diseases are categorized using the popular inception v3 model, which can process vast and diverse picture datasets. this method allows for the precise categorization and detection of a wide variety of rice leaf diseases with the accuracy of 98.8%. © 2023 IEEE.

关键词： Diseases

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Currency Recognition Based on Deep learning 4

Currency Recognition Based on Deep Learning

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Wang, Yihao Wenzhou Kean University Computer Science Wenzhou China

ISBN: (纸本)9798350342710

Currency recognition is a critical task with diverse applications. Recent advancements in deep learning and neural networks have shown significance in improving its accuracy and robustness. this paper presents an approach that utilizes the convolutional neural network (CNN) for accurate and reliable currency recognition. the paper begins with an introduction to the background of the neural network, including its architecture and types, focusing on CNN and its significance in image-processing tasks. then, the methodology section describes the data collection process, dataset preparation, and the construction of the CNN model. Next, the results and discussion section present the performance of the developed model, discusses the accuracy of the model, and figure out the model's strengths and areas for improvement. Last, the conclusion summarizes the research findings and evaluates the effectiveness of the proposed neural network-based approach, while also suggesting directions for future research. Overall, the study demonstrates the potential of CNNs in currency recognition and provides insights for further development in this field. © 2023 IEEE.

关键词： Convolutional neural networks

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Temperature Prediction Based on EAL Deep learning Model 4

Temperature Prediction Based on EAL Deep Learning Model

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Xiao, Ziyan Shanxi University Department of Computer and Information Technology Taiyuan China

ISBN: (纸本)9798350342710

Temperature prediction has certain reference significance for future warming and related extreme weather. the existing traditional models cannot capture the features of temperature time series. In order to solve the problem of low accuracy of temperature time series prediction by traditional models, this paper proposes an EAL-based deep learning model to capture its features. In this paper, firstly, ARIMA and LSTM models were constructed and the temperature series were analyzed, and it was found that the prediction results were poor. therefore, in this paper, with the help of EEMD decomposition, the fused temperature prediction model EAL is proposed, and finally a good fitting and prediction result is obtained. the three models of LSTM, ARIMA and EAL were compared, where the RMSE was 0.2912, 0.1064 and 0.0546, MAE was 0.1818, 0.0863 and 0.0277, and MAPE was 0.0131, 0.0061 and 0.002, respectively. therefore, it was found that the EAL model in all indicators was outperformed other models and had better applicability for this field of temperature prediction. © 2023 IEEE.

关键词： Global warming

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