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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3232 条记录，以下是471-480 订阅

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Multiband Antenna Design for Wlan and Wimax Applications 2

Multiband Antenna Design for Wlan and Wimax Applications

引用

2nd international conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

作者： Allin, Joe D Gopika, S. Sobiya, Selsiya M Manusa, K.R. Department of Electronics and Communication Engineering Kumaraguru College of Technology India

ISBN: (纸本)9798350306811

Antenna is a basic and a component of importance in all communication modules. Different techniques are involved in the design of an antenna for various applications. Handpicked machine learning algorithms like Random Forest, Decision Tree, and Artificial Neural Network (ANN) are utilized to optimize the parameters of an antenna that is proposed using the High Frequency Structure Simulator (HFSS) software. The objective is to find the optimal antenna configuration that maximizes its performance which shall be validated using antenna parameters. The performance parameters considered for the design of the antenna include the gain, bandwidth, and directivity. The non-optimized antenna parameters can negatively affect its performance. Therefore, it is essential to optimize the parameters involved in the design of antenna for optimal performance of the communication devices. machine learning algorithms are trained by information obtained from the simulation solutions that arises through the HFSS. The generated antenna dataset includes various antenna configurations and their corresponding performance parameters. The trained models are then used to predict the optimal antenna parameters for a given set of performance requirements. The outcomes acquired from the machine learning models remain assessed with the performance of the antenna designed using HFSS without optimization. The comparison shows that the optimized antenna design yields better performance than the non-optimized design. This paper demonstrates the effectiveness of using machine learning algorithms to optimize antenna parameters, which can lead to improved performance and reduced design time in antenna engineering. © 2023 IEEE.

关键词： Neural networks

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IoT Security: A Deep learning-Based Approach for Intrusion Detection and Prevention

IoT Security: A Deep Learning-Based Approach for Intrusion D...

引用

2023 international conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kauhsik, Bhanu Nandanwar, Himanshu Katarya, Rahul Delhi Technological University Dept. of Software Engineering Delhi India Delhi Technological University Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350313413

The size and market worth of the Internet of Things (IoT) have expanded, but unfortunately, the likelihood of user data being compromised has also risen. This presents a notable danger that has the potential to create disorder. Consequently, the main aim of this paper is to recognize any weaknesses within existing security solutions and suggest an innovative approach to address them. In order to accomplish this, the paper puts forth four research inquiries concerning IoT security that leverage machine learning and Deep learning techniques. Additionally, it conducts an extensive review of the latest publications and concludes that a combination of specific techniques can provide a potential solution to the security problems in IoT. Overall, the findings of this study suggest that this systematic literature review could help in significantly enhancing the security of IoT devices and applications, safeguarding user data from unauthorized access and potential misuse. © 2023 IEEE.

关键词： Cyberattack Deep learning Internet of Things (IOT) Intrusion Detection System (IDS) Network Security

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Visualization Analysis and XGBoost-Based Prediction of COVID-19 Mortality

Visualization Analysis and XGBoost-Based Prediction of COVID...

引用

2023 IEEE international conference on Image Processing and Computer Applications, ICIPCA 2023

作者： Chen, Zixin Zhang, Chujing Chun, Ngating School of Biomedical Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China School of International Pharmaceutical Business China Pharmaceutical University Jiangsu Nanjing210009 China School of Software Engineering Shandong University Shandong Jinan250101 China

ISBN: (纸本)9798350314670

For the best predictive results of novel coronavirus infection and COVID-19 mortality, this research bases on the XGBoost machine learning algorithm. Through the research of data on related diseases, it not only helps to prevent the infection of COVID-19 effectively, but also gives advice to specific patients who need treatment without delay. The existing machine learning model uses the logistic regression algorithm to train the uneven data but gets an unsatisfying precision. This research improves the result by combining undersampling with XGBoost, with random forest, logistic regression, decision tree, and other models as comparisons. The prediction of COVID-19 mortality has the same accuracy of 91% before and after using the undersampling method, however, the AUC rises about 13% and finally reaches 92%• This research is available for the prediction of prevalence rate and death rate of COVID-19 in people who have basic diseases. © 2023 IEEE.

关键词： COVID-19

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Kun Khmer Posture Recognition Using Body Landmarks: A Comparative Experiment 15

Kun Khmer Posture Recognition Using Body Landmarks: A Compar...

引用

15th international conference on software, Knowledge, Information Management and Applications, SKIMA 2023

作者： Prum, Sophea Kosal, Cholsa Saroeun, Bunnarath Cambodia University of Technology and Science Faculty of Engineering Phnom Penh Cambodia

ISBN: (纸本)9798350316551

In the context of Kun Khmer, a traditional combat technique in Cambodia, the challenge of correcting trainees' postures and movements is significant due to its impact on performance and outcomes. Moreover, incorrect movements during training can lead to physical injuries. This paper presents a machine learning-based system that aims to recognize Kun Khmer postures and potentially facilitate posture correction. The proposed solution utilizes anatomical landmarks and explores multiple classifiers, including Logistic Regression, Random Forest, Gradient Boosting, and Support Vector machine. Experimental results demonstrate a recognition rate of 96.32% using the Support Vector machine classifier, with an average recall and precision of 96.22% and 96.32% respectively. © 2023 IEEE.

关键词： Logistic regression

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PRINTABILITY PREDICTION IN PROJECTION TWO-PHOTON LITHOGRAPHY VIA machine learning BASED SURROGATE MODELING OF PHOTOPOLYMERIZATION 18

PRINTABILITY PREDICTION IN PROJECTION TWO-PHOTON LITHOGRAPHY...

引用

ASME 18th Int Mfg Sci and Engn conf held jointly with the 51st N Amer Res conf / Japan-Soc-of-Mech-Engineers Int conf on Leading Edge Mfg/Mat and Proc

作者： Pingali, Rushil Saha, Sourabh K. Georgia Inst Technol GW Woodruff Sch Mech Engn Atlanta GA 30332 USA

ISBN: (纸本)9780791887240

The two-photon lithography (TPL) direct laser writing process enables the fabrication of complex three-dimensional features with sub- micrometer resolution. While traditional TPL techniques have suffered from low throughput due to their serial writing scheme, the process has been recently parallelized through the development of the projection two photon lithography (P-TPL) process that allows printing of entire layers at once. In this work, we use machine learning-based surrogate modeling to predict the outcomes of P-TPL to >97% of the accuracy of a physics-based reaction-diffusion finite element simulation. Training a classification neural network on data points generated from physics- based simulations allows for computationally efficient and accurate prediction of whether a set of printing conditions will result in precise and controllable polymerization and the desired printing, as opposed to no printing or runaway polymerization. We interrogate this surrogate model to investigate the parameter regimes that are promising for successful printing. We predict combinations of rate constants necessary to print for a given set of material properties, thereby generating the first printability map for the P-TPL process and allowing informed selection of photoresists and printing parameters in the future.

关键词： two-photon polymerization additive manufacturing nanomanufacturing surrogate modeling

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machine learning at Work? The Issue of Data Quality When Developing New Insight in Occupational Accidents

Machine Learning at Work? The Issue of Data Quality When Dev...

引用

ASCE international conference on Computing in Civil engineering (I3CE)

作者： Shayboun, May Koch, Christian Kifokeris, Dimosthenis Halmstad Univ Dept Construct & Energy Engn Halmstad Sweden Chalmers Univ Technol Dept Architecture & Civil Engn Gothenburg Sweden

ISBN: (数字)9780784485248

ISBN: (纸本)9780784485248

Occupational accidents are an urgent problem in construction. machine learning (ML) methods for analyzing large amounts of data and the availability of accident report data have generated aspirations for novel learnings. Yet the quality of data in terms of input, inner availability, and output occurs as an issue in many ML development projects. This paper aims at investigating strategies to define, understand, and tackle poor data quality in a contracting company's accident reports. A selective literature review within software system data quality and ML shows different foci on external or internal data. A set of records of occupational accidents are then analyzed. There are many missing entries on causality, as well as shallow descriptions, which hinder the discovery of new risks-possibly due to the data collection format and procedures. The low number of full entries calls for new repair strategies-both externally and internally.

关键词： Accidents

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A Novel machine learning Approach to Delay Efficient Offloading Strategy for Mobile Edge Computing 1st

A Novel Machine Learning Approach to Delay Efficient Offload...

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1st international conference on Microelectronics, Communication Systems, machine learning, and Internet of Things, MCMI 2020

作者： Bharti, Piyush Chaudhary, Srishti Snigdh, Itu Department of Computer Science and Engineering Birla Institute of Technology Mesra India Software Development Microland Limited Bengaluru India

ISBN: (纸本)9789811919053

There have been studies on the problems experienced in offloading. But, how to leverage machine learning for resource-efficient computation offloading is not completely explored yet. We present this paper with the motivation to leverage machine learning and backward induction to suggest a method for the resource-efficient computation offloading in IoT devices. In particular, we use machine learning to predict the most efficient device to which a task should be offloaded through the use of a two-model machine learning system deployed in the edge cloud itself, since most high power IoT devices are fixed in their positions and display a certain pattern in their CPU utilization data. This is followed by using backward induction to determine the optimal execution location for each task component. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computation offloading

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software Fault Prediction Using Deep Neural Networks

引用

international conference on machine Vision and Augmented Intelligence, MAI 2022

作者： Mohana Ramya, Y. Deepthi, K. Vamsai, A. Juhi Sai, A. Sharma, Neeraj Ramachandra Reddy, B. Department of Computer Science and Engineering SRM University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819901883

software failure prediction is the process of building models that software interpreters can use to detect faulty constructs early in the software development life cycle. Faults are the main source of time consumption and cost less over the life cycle of applications. Early failure prediction increases device consistency and reliability and decreases the expense of software development. However, machine learning techniques are also valuable in detecting software bugs. There are various machine learning techniques for finding bugs, ambiguities, and faulty software. In this paper, we direct an exploratory review to assess the performance of popular techniques including logistical regression, decision tree, random forest algorithm, SVM algorithms, and DNN. Our experiment is performed on various types of datasets (jedit, Tomcat, Tomcat-1, Xalan, Xerces, and prop-6). The experimental results show that DNN produces a better accuracy among all techniques used above. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Weather Forecasting using machine learning 6

Weather Forecasting using Machine Learning

引用

6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Saketh, Podicheti Sriram Rohit, Relangi Suneetha, B. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Greenfields Vaddeswaram Guntur522302 India

ISBN: (纸本)9798350398496

Weather forecasting is a software algorithm, which is used to predict the future weather by analyzing the historic data. In this paper we tried to address the problem of inconsistency in the present-day technologies used for predicting the weather from historic data. This study has used the most optimal algorithms from machine learning to address the issue of multiple collinearity, which often leads to outliers that are difficult to monitor. By using machine learning, a model has been built to make historical predictions i.e., for predicting the future weather by analyzing the already available historic data (csv file). While the weights are found out as correctly as viable, a version can expect the suitable output or the target price will be given as a test data report. © 2023 IEEE.

关键词： Weather forecasting

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Research on Performance Evaluation of Industrial Economic Management Based on Improved machine learning 5th

Research on Performance Evaluation of Industrial Economic Ma...

引用

5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

作者： Gao, Jie Wang, Aiqing Department of Building Management Chongqing College of Architecture and Technology Chongqing400030 China

ISBN: (纸本)9783031505768

Aiming at the problem of poor self-protection ability caused by too many iterations in the performance evaluation system of industrial economic management, a study on performance evaluation of industrial economic management based on improved machine learning is proposed. In terms of hardware design, design and deploy the system storage architecture and network environment for system operation;In software design, the improved machine learning algorithm is used to screen out appropriate evaluation indicators from the set of factors that affect the performance evaluation of industrial economic management. After the evaluation indicators are determined, the rating threshold is divided, and the total score of performance evaluation is calculated according to the score and weight of each indicator, which is compared with the rating table one by one to achieve the performance evaluation of industrial economic management. So far, the overall design of the system has been completed. The test results show that the industrial economic management performance evaluation system designed based on improved machine learning has low sensitivity, high fluency and high self-protection ability. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： software design

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