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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33943 条记录，以下是701-710 订阅

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Quantum-Inspired Neural Network on Handwriting datasets 4th

Quantum-Inspired Neural Network on Handwriting Datasets

引用

4th international conference on Trends in Cognitive Computation engineering, TCCE 2022

作者： Shah, Manik Ratna Sarraf, Jay Pattnaik, Prasant Kumar Bandyopadhyay, Anjan School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India

ISBN: (纸本)9789811994821

With the advancement of machine learning for classifying and categorizing larger sets of data, there is a high need for greater computational power. Quantum computing in machine learning advances to solve this in a lesser time, consuming less energy and power. This paper includes the comparative analysis of Traditional Neural Networks with Quantum Neural Networks using handwritten digits from the simplified version of the MNIST dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Comparison of Affine and DCGAN-based data Augmentation Techniques for Chest X-Ray Classification

Comparison of Affine and DCGAN-based Data Augmentation Techn...

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Bali, Mayank Mahara, Tripti SBM Christ University Bangalore560029 India

data augmentation, also called implicit regularization, is one of the popular strategies to improve the generalization capability of deep neural networks. It is crucial in situations where there is a scarcity of high-quality ground-truth data. Also getting new samples is expensive and time consuming. This is a typical issue in the medical domain. Therefore, this study compares the performance of Affine and Generative Adversarial Networks (GAN)- based data augmentation techniques on the chest image X-Ray dataset. The Pneumonia dataset contains 5863 chest X-Ray images. The traditional Affine data augmentation technique is applied as a pre-processing technique to various deep learning-based CNN models like VGG16, Inception V3, InceptionResNetV2, DenseNet-169 and DenseNet-202 to compare their performance. On the other hand, DCGAN architecture is applied to the dataset for augmentation. Evaluation measures like accuracy, recall and AUC depict that DCGAN outperforms other traditional models. The most important advantage of DCGAN is that it is able to identify fake images with 100% accuracy. This is especially relevant for the medical domain as it deals with the life of individuals. Thus, it can be concluded that DCGAN has better performance as compared to affine transformations applied to traditional CNN models. © 2023 The Authors. Published by Elsevier B.V.

关键词： Generative adversarial networks

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Prediction of Heat Transfer Performance of Heat Pipe Using machine learning Approach 1

引用

1st international conference on Fluid, Thermal and Energy Systems, ICFTES 2022

作者： Das, Sudev Jha, Ashutosh K. Johnsan, R. Bestha, Chandra S. Teja Reddy, V. Department of Chemical Engineering NIT Kerala Calicut673601 India

ISBN: (数字)9789819959907

ISBN: (纸本)9789819959891

The heat pipe is widely used for electronic cooling. ln the simplest configuration of heat pipe, it consists of a cylindrical vessel having three sections (Evaporator, Adiabatic, Condenser). It has a wide range of applications, from smartphones to satellites. Different working fluids are used in the heat pipe based on the operating conditions. This paper discusses the machine learning approach along with some analytical methods to predict the performance of Heat Pipes. Regression, Random Forest, Support Vector machine (SVM), and Gaussian Matrix Model (GMM) have been tested to find an optimal model based on 2500 experimental data points collected from different research papers and to show the relationship between different parameters. Our objective is to predict the heat transfer performance of a heat pipe by using different machine learning algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Regression analysis

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Road Accident Prediction in Highways using machine learning Algorithms 5

Road Accident Prediction in Highways using Machine Learning ...

引用

5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Reddy, Jambula Snehith Rashmi, M.R. Pin, Lee Hoong Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Bengaluru560035 India Inti International University Faculty of Engineering and Qs Nilai Malaysia

ISBN: (纸本)9798331504403

Road accidents are increasing day by day. In India, every year nearly 33% of road accidents take place in national highways and 23% in state highways. This paper focuses on predicting the road accidents in national and state highways in India using machine learning algorithms. The data is curated using annual reports published on road accidents in India by ministry of road transport & highways also, the reports on the compensation to the victims by motor vehicle accident fund trust. Road accident details are captured from the year 1990-2022. This covers the information about monthwise, state-wise accidents due to all types of vehicles, road environment, type of traffic control, vehicle condition, weather condition, rural/urban areas and so on. The data contains information about total number vehicles registered, road length, number of accidents in a year, number of people killed and the number of people injured. The road accidents and the number of vehicles causing accidents are predicted using machine learning algorithms viz., Linear Regression, Ridge Regression, Decision Tree, K-Nearest Neighbour (KNN) and Multilayer Perceptron algorithms. Multilayer perceptron model outperformed all other prediction models with highest R2-Score of 0.93278 and Mean Squared Error (MSE) of 0.277503 and Root Mean Squared Error (RMSE) of 0.387880 for five-fold cross validation. Prediction helps in identifying the critical regions/roads where the number of accidents are more. This helps in implementing accident preventive measures like restricting speed limit in such highways. © 2024 IEEE.

关键词： Nearest neighbor search

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Predictive Analytics for University Student Admission: A Literature Review 16th

Predictive Analytics for University Student Admission: A Lit...

引用

16th international conference on Blended learning (ICBL)

作者： Li, Kam Cheong Wong, Billy Tak-Ming Chan, Hon Tung Hong Kong Metropolitan Univ Hong Kong Peoples R China

ISBN: (纸本)9783031357305;9783031357312

This paper presents a literature review on the use of learning analytics to support prediction in university student admission. The review covers four areas: types of research issues examined, types of data, analytical techniques, and performance metrics used. A total of 59 research articles published between 2013 and 2022 in relation to the use of predictive learning analytics for student admission were collected from Scopus for analysis. The findings show the major types of research issues including admission outcome, academic performance, admission yield, chance of admission, and suitable major/field of study. The types of data frequently used include academic performance, educational background, socio-demographic data, admission-related data, and application-related data. The findings also show that logistic regression, decision tree, random forest, support vector machine, and neural network are the most commonly adopted analytical techniques, whereas accuracy, recall, precision, F-measure, and R-squared are the most frequently used performance metrics. The results contribute to identifying the features and patterns of predictive learning analytics with respect to university student admission.

关键词： learning analytics predictive analytics machine learning student admission university education

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Speeding up machine learning Inference on Edge Devices by Improving Memory Access Patterns using Coroutines 25

Speeding up Machine Learning Inference on Edge Devices by Im...

引用

25th IEEE international conference on Computational Science and engineering (CSE)

作者： Belson, Bruce Philippa, Bronson James Cook Univ Coll Sci & Engn Cairns Australia

ISBN: (纸本)9798350396331

We demonstrate a novel method of speeding up large iterative tasks such as machine learning inference. Our approach is to improve the memory access pattern, taking advantage of coroutines as a programming language feature to minimise the developer effort and reduce code complexity. We evaluate our approach using a comprehensive set of benchmarks run on three hardware platforms (one ARM and two Intel CPUs). The best observed performance boosts were 65% for scanning the nodes in a B+ tree, 34% for support vector machine inference, 12% for image pixel normalisation, and 15.5% for two dimensional convolution. Performance varied with data size, numeric type, and other factors, but overall the method is practical and can lead to significant improvements for edge computing.

关键词： embedded software edge computing coroutines C plus Internet of Things neural networks machine learning

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Financial Warning of Manufacturing Listed Companies Based on Text Mining 24

Financial Warning of Manufacturing Listed Companies Based on...

引用

international conference on Modeling, Natural Language Processing and machine learning (CMNM)

作者： Chen, Ming Chu, Chiawei City Univ Macau Fac Data Sci Macau Peoples R China

ISBN: (纸本)9798400709760

The audit report, as disclosed in the annual reports of listed companies, represents a critical source of textual data for assessing enterprise risk and conducting financial analysis. Descriptions of key audit matters in the audit report offer detailed explanations and insights. This study aims to develop predictive models for assessing the financial distress status of Chinese manufacturing listed companies, enhancing early warning accuracy through the integration of financial indicators and textual data. Utilizing data from 2016 to 2022, which includes Z-Score, F-Score, and descriptions of key audit matters from the financial reports of manufacturing listed companies, we introduce text mining techniques such as TF-IDF, Word2Vec, Doc2Vec, and LDA to extract crucial information. Subsequently, the extracted textual features are dimensionally reduced via PCA and Truncated SVD techniques. We construct multiple deep learning models, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), using ACC and AUC-ROC as evaluation metrics. The findings indicate that deep learning models incorporating textual data surpass those relying solely on traditional financial indicators in predicting financial distress, achieving an AUC value of 0.9028, marking an improvement of 0.0483. This study not only furnishes a new perspective on financial distress prediction research but also provides effective tools for investors and regulatory agencies to identify potential financial crises.

关键词： Financial early warning Text mining Neural networks Deep learning

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Autonomous Piloting Algorithms for Drones in Underground Space with No GPS Navigation 30th

Autonomous Piloting Algorithms for Drones in Underground Spa...

引用

30th international conference on Computational and Experimental engineering and Sciences

作者： Zhong, Zicheng Lin, Zhiyan Jiang, Xiangzhou Wang, Chenhao Zhang, Chao Li, Zichang Tsinghua Univ Sichuan Energy Internet Res Inst Chengdu 610213 Peoples R China Beijing Univ Chem Technol Int Educ Coll Beijing 100013 Peoples R China Tsinghua Univ Dept Hydraul Engn Beijing 100084 Peoples R China

ISBN: (纸本)9783031774911;9783031774898;9783031774881

Developing intelligent robotic helpers for underground engineering presents significant hurdles, particularly with regard to autonomous navigation in areas devoid of GPS signals. Although drones using GPS navigation in open areas are sophisticated and widely used, when GPS signals are poor or in areas with subterranean space, drones become blind and lose their benefits. In this study, a thorough foundation for autonomous piloting algorithms was put forward in order to create an autonomous drone, which can pilot autonomously in surroundings without external referrals. The technological framework combines sensor fusion, machine learning, path planning, simultaneous localization and mapping (SLAM), and real-time communication. First, data from LiDAR, ultrasonic sensors, and inertial measurement units (IMUs) are first combined using sensor fusion techniques to generate a 3D virtual model of the surrounding area. Real-time-mapping and locating are facilitated by SLAM algorithms. Second, machine learning models use sensor data to inform piloting decisions, while computer vision algorithms facilitate obstacle detection and recognition. Third, using the virtual model's observed impediments and predetermined mission objectives, path planning algorithms create safe and effective flying routes. Hardware and software were created to accompany a drone for test and demonstration. Through a loop of testing, validation, redundancy, and fail-safes, the autonomous piloting algorithms were integrated and improved. The drone equipped with the autonomous piloting algorithms was able to navigate in subterranean space on its own with no GPS navigation after trial and error. The capability and dependability of autonomous drone systems for inspection support in such conditions are to be improved through ongoing research and development.

关键词： Autonomous piloting underground space LiDAR sensor fusion machine learning path planning

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Comparative Analysis of machine learning Techniques in Effort Estimation

Comparative Analysis of Machine Learning Techniques in Effor...

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2022 international conference on machine learning, Big data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Ritu Garg, Yashika Guru Nanak Dev Engineering College Punjab Ludhiana India APEX Institute of Technology Chandigarh University Department of Computer Science & Engineering Punjab Mohali India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

In Software engineering effort estimation provides an important role for software development and managing project cost, quality, and time. Since last decades, software estimation has been receiving significant attention from researchers and substantial research has been performed using various techniques and algorithms of machine learning. This paper suggests different machine learning techniques such as Naïve Bayes, Random Forests Logistic Regression, stochastic gradient boosting, decision tree, and story point for estimation to assess prediction more efficiently. Nowadays uses of these approaches by software development industries for software estimation aim to tackle deficiencies of parametric and traditional estimation techniques, rise project. A comparative study of the mentioned techniques is presented and examined in this paper to critically evaluate the performance of these techniques. © 2022 IEEE.

关键词： Decision trees

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Trompt: Towards a Better Deep Neural Network for Tabular data 40

Trompt: Towards a Better Deep Neural Network for Tabular Dat...

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40th international conference on machine learning

作者： Chen, Kuan-Yu Chiang, Ping-Han Chou, Hsin-Rung Chen, Ting-Wei Chang, Darby Tien-Hao SinoPac Holdings Taipei Taiwan Natl Cheng Kung Univ Dept Elect Engn Tainan Taiwan

Tabular data is arguably one of the most commonly used data structures in various practical domains, including finance, healthcare and e-commerce. However, based on a recently published tabular benchmark, we can see deep neural networks still fall behind tree-based models on tabular datasets (Grinsztajn et al., 2022). In this paper, we propose Trompt-which stands for Tabular Prompt-a novel architecture inspired by prompt learning of language models. The essence of prompt learning is to adjust a large pre-trained model through a set of prompts outside the model without directly modifying the model. Based on this idea, Trompt separates the learning strategy of tabular data into two parts for the intrinsic information of a table and the varied information among samples. Trompt is evaluated with the benchmark mentioned above. The experimental results demonstrate that Trompt outperforms state-of-the-art deep neural networks and is comparable to tree-based models (Figure 1).

关键词： Deep neural networks

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