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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40714 条记录，以下是4771-4780 订阅

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AI-Enable Machine learning for Lung Cancer Prediction and Classification 1

AI-Enable Machine Learning for Lung Cancer Prediction and Cl...

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1st international conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Singh, Kamred Udham Kumar, Ankit Sinha, Anurag Kumar, Gaurav Singh, Teekam Pandey, Saroj Kumar School of Computing Graphic Era Hill University Dehradun India Gla University Department of Computer Engineering & Apllications UP Mathura India Amity University Department of Computer Science Ranchi India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330915

In comparison to other types of cancer, lung cancer has the highest fatality rate, making it one of the most dangerous illnesses in the world. In India, there are over 70,000 new cases recorded each year, which demonstrates a startlingly rapid growth in the frequency of the disease. Lung cancer's sneaky nature makes early detection difficult. Early lung cancer identification improves survival rates, making it a top priority. Technology is crucial for early detection since it is more reliable. Many scholars have proposed innovative concepts after extensive study. computer-Aided Diagnosis (CAD) systems, which use computers to diagnose, are a major advancement in this field. These cutting-edge methods employ machine learning, deep learning, and image processing to predict malignant lung cancer risk. This article evaluates and compares technologies for early lung cancer detection and diagnosis. This category includes image segmentation, feature extraction, and classification algorithms. This essay examines these current technological options to help fight this life-threatening sickness. The article's ultimate goal is to improve the outlook for those who are at risk of acquiring lung cancer due to the sickness. © 2023 IEEE.

关键词： CAD Classification CT Scan Lung Cancer MRI PET Pre-processing ReLU

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Research progress of part point cloud detection based on deep learning

Research progress of part point cloud detection based on dee...

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2023 IEEE international conference on Control, Electronics and computer Technology, ICCECT 2023

作者： Qi, Guanghao Lei, Baozhen Guan, Huaiguang Liu, Ziping Su, Yizhou Wang, Xunwei Beijing Union University Beijing Key Laboratory of Information Service Engineering Beijing China Beijing Union University Beijing Key Laboratory of Information Service Engineering Beijing Intelligent Machinery Innovation Design Service Engineering Technology Research Center Beijing China Automotive Engineering Research Institute Co. Ltd Tianjin China Beijing Union University Collage of Robotics Beijing China Beijing Union University Beijing Intelligent Machinery Innovation Design Service Engineering Technology Research Center Beijing China

ISBN: (纸本)9798350399028

As the demand for 3D vision technology in industry continues to rise, 3D point cloud part classification based on deep learning has received widespread attention. Classification methods based on 3D point cloud data outperform traditional 2D image classification in terms of inspection accuracy, and are advantageous in detecting parts with similar and poorly differentiated part shapes and parts with uncertainty in the type and extent of damage areas. The aim of this paper is to introduce some of the mainstream part inspection methods based on 3D point cloud data. Firstly, the advantages and background of 3D point clouds in industrial applications and the characteristics of their acquisition methods are introduced;secondly, the classification methods based on 3D point clouds of parts by classical and deep learning algorithms and the related industrial applications are introduced;the common datasets for 3D point cloud classification are summarised;Finally, the development trends and potential research directions of deep learning in the direction of part point cloud detection are foreseen. © 2023 IEEE.

关键词： Damage detection

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Modeling method of skylight polarization patterns based on distribution of neutral point 4

Modeling method of skylight polarization patterns based on d...

引用

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

作者： Gui, Ceding Wei, Kun School of Computer Science and Information Engineering Hefei University of Technology AnHui Hefei China

ISBN: (纸本)9798350326444

The skylight polarization patterns are essential in polarization navigation and remote sensing. The available models are unable to properly describe the distribution characteristics of skylight polarization patterns due to the complexity of actual atmospheric conditions. The neutral point is one of the important distribution properties of skylight polarization patterns, which represent the primary difference between measured patterns and theoretical skylight polarization patterns based on single Rayleigh scattering. In this paper, the actual spatial locations of the neutral points are calculated based on the crossover characteristics of the neutral lines and the projection model of the fisheye lens, and the distribution characteristic of the neutral point is analyzed. Based on the distribution of neutral points under actual atmospheric conditions and the equidistant projection mode of measurement equipment, a simulation model of skylight polarization patterns is constructed, taking into account the influence of skylight intensity and sky clarity. The simulation results reveal that the proposed model is more consistent with the measured skylight polarization patterns than the Rayleigh model. © 2023 IEEE.

关键词： Remote sensing

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Global Positioning System Based Dual Axis Tracker for Solar Car

Global Positioning System Based Dual Axis Tracker for Solar ...

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2023 international conference on Clean Electrical Power, ICCEP 2023

作者： Almajali, Ziyad S. Aldmour, Sana A. Mutah University Electrical Engineering Department Karak Jordan Mutah University Computer Engineering Department Karak Jordan

ISBN: (纸本)9798350348378

The development of solar cars faces a significant challenge in maximization power absorption and efficient energy conversion, primarily due to the limited surface area of flat mounted solar panels. In this paper, we propose a simulation for a dual-axis solar car tracker model using MATLAB/Simulink and study its benefits by simulating its performance over a hypothetical travel distance with multiple changes in the car's direction. We also conduct experiments to demonstrate that our design can potentially increase power generation, assess the benefits of changing the tilt angle, and present design features to overcome the harsh operating conditions of the vehicle, including airflow resistance. Our proposed tracker system design requires retrieving Global Positioning System information of the vehicle, and we provide an illustration of the system design in the paper. © 2023 IEEE.

关键词： Systems analysis

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A Network Security Situational Awareness Model Based on Multi-Source Heterogeneous Sensors 3

A Network Security Situational Awareness Model Based on Mult...

引用

3rd international conference on Machine learning and computer Application, ICMLCA 2022

作者： Tan, Xiaobo Zhang, Guangjie College of Information Science and Engineering Ligong University Liaoning Shenyang China Department of Computer Science and Technology Shenyang Ligong University Liaoning Shenyang China

ISBN: (纸本)9781510664814

In the research of network security situational awareness, an important problem that needs to be solved urgently is that in the face of a large number of noisy multi-source data, the accuracy of obtaining network security situational awareness indicators will be affected. This paper proposes a data fusion processing method based on deep neural network to solve the above problems, which first uses natural language processing technology to process high-noise data in the data, and does a good job of word reduction of the data through the WordNetLemmatizer module. The TF-IDF feature extraction algorithm is applied to extract features through the frequency of data occurrence and the weight of the data. The experimental results show that the accuracy of obtaining network security situational awareness indicators by using the data fusion method based on deep neural network reaches more than 85% compared with the data fusion methods of other machine learning algorithms, and can be applied to the network security situational awareness model. © 2023 SPIE.

关键词： Deep neural networks

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Chili Disease Detection and Classification using Various Machine learning Techniques 1

Chili Disease Detection and Classification using Various Mac...

引用

1st IEEE international conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Sambrani, Yashodha Rajashekarappa Bhairannawar, Satish Computer Science & Engineering SDM College of Engineering and Technology Dharwad India Information Science & Engineering SDM College of Engineering and Technology Dharwad India Electronics & Communication Engineering SDM College of Engineering and Technology Dharwad India

ISBN: (纸本)9798350323795

For many developing countries like India the role of the agricultural sector is very significant. Chili has a very high economic value. India is ranked 5th in the production and 1st in exporting chili. Chili is one of the important cash crops of our country but they are vulnerable to various fungal, bacterial, and viral diseases. Among these diseases, the leaves of chili plants are more susceptible to harm, it's one main reason we have considered chili leaves for our study. This paper presents a detailed overview of various chili diseases and the work done in this field till now. Manual detection of diseases is a time consuming and strenuous task for the farmers. In this paper we are introducing various image processing and machine learning techniques for early and efficient detection of diseases in chili leaves. The diagnosis of chili disease by capturing leaf images is a very efficient and affordable system, especially for helping farmers to keep an eye on large plantations. © 2023 IEEE.

关键词： Developing countries

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Levy Swallow Swarm Optimization with Machine learning-based Intrusion Detection in IIoT 4

Levy Swallow Swarm Optimization with Machine Learning-based ...

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

作者： Alzahrani, Abdulrahman Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Hafr Al Batin Saudi Arabia

ISBN: (数字)9781837240258

ISBN: (纸本)9781839539954

With the recent evolution ofIndustry 4.0, integrated devices and sensors started to generate dynamic, extensive, and heterogeneous in Industrial Internet of Things (IIoT) environments. The immense heterogeneous data probably raises the difficulties of data analysis procedures and security risks. As IIoT develops, cyber-attacks are more complex and diverse, making current anomaly detection methods’ operations less effective. Intrusion Detection System (IDS) had a major contribution to protecting IoT systems from cyberattacks by constructing complementing classical preventive security measures (e.g., encryption, authentication, and authorization). Therefore, in this paper, we present a Levy Swallow Swarm Optimization with Machine learning-based Intrusion Detection (LSSOML-ID) technique in IIoTenviornments. The presented LSSOML-ID technique focuses on the identification and classification of intrusions in IIoT environments. To achieve this, the LSSOML-ID technique uses a Z-score normalization algorithm to scale the input data into a uniform format. For feature selection purposes, the LSSO algorithm was used which reduces the number of features involved in the classification process. Finally, the Aquila optimizer (AO) algorithm with an extreme learning machine-based autoencoder (ELM-AE) model is proficiently employed for intrusion classification. To exemplify the significant outcomes of the LSSOML-ID technique, a thoroughset of experiments areconducted. The comprehensive outcomes highlighted the betterment of the LSSOML-ID approach over recent methods. © The Institution of engineering & Technology 2023.

关键词： Network intrusion

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AutoML - learning, Understanding and Applying Machine learning to Datasets 9

AutoML - Learning, Understanding and Applying Machine Learni...

引用

9th international conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Patankar, Shreya Prajapati, Hitesh Shah, Jeet Upadhyay, Ankit K J Somaiya Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350397376

Current scenario of the digital world is loaded with sample amounts of data and to smartly analyze this data requires researching an interesting machine learning model to suit a dataset which is a topic of research and is more focused upon. Various state of art machine learning models like supervised, unsupervised and reinforcement models along with deep learning models are available to analyze varied datasets and for a given machine learning task, selecting one model from a large pool of options is a surprisingly challenging problem, particularly if there is a lack of evaluation data to distinguish between models and this requires for a thorough understanding of the dataset which you are using to solve the task. Dataset plays an important role in the performance of any machine learning model and not always you will get a clean / well balanced dataset to directly apply the model on. The key contribution of this paper is focused on the challenge of picking a good model which can be reformulated by doing a proper analysis and cleaning of the dataset, with the objective of recommending a suitable model for a given task based on the performance of a small set of probe models under the domain of classification and regression. © 2023 IEEE.

关键词： Classification (of information)

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Air-Writing Segmentation using a single IMU-based system 19

Air-Writing Segmentation using a single IMU-based system

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19th international conference on Intelligent Environments, IE 2023

作者： Younas, Junaid Narayan, Shilpa Lukowicz, Paul Technical University of Kaiserslautern Computer Science Department Kaiserslautern Germany Kaiserslautern Germany School of Electrical Engineering and Computer Sciences H-12 NUST Islamabad Pakistan

ISBN: (纸本)9798350312225

This paper presents a novel and generic method to employ deep neural networks for segmenting in-air performed gestures to detect writing activity. We consider various factors such as temporal, geometric, and frequency constraints to define the parameters and fine-tune the deep-learning methods. The proposed method is benchmarked on air-gesture data from 50 participants, which included air-writing gestures followed and preceded by non-writing gestures. The reported results establish the potential of deep-learning methods to segment air-writing activity. The proposed novel approach provides a foundation to develop sophisticated systems for recognizing air gestures to enhance interaction in virtual and augmented reality environments. © 2023 IEEE.

关键词： Deep neural networks

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Research on image retrieval based on artificial intelligence technology 2

Research on image retrieval based on artificial intelligence...

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2nd international conference on Image Processing, computer Vision and Machine learning, ICICML 2023

作者： Yang, Zhiqiang Liu, Hengzhi Yuan, Yili Hou, Yingying Fu, Bin Wang, Rui Zhang, Li Jiang, Tengguang Shandong Electrical Engineering and Equipment Group Co. Ltd Jinan China

ISBN: (纸本)9798350331417

Image retrieval holds significant importance within the realm of computer vision. This paper introduces SCDNet, a novel network model, leveraging selected feature aggregation for enhanced image retrieval. Using Thangka images as an illustrative example, the proposed model aims to address issues such as inadequate semantic feature extraction and image feature distortions due to background or noise. SCDNet firstly uses VGG16 network to extract semantic features from different convolutional layers of Thangka images, then passes through feature screening module, feature aggregation module, and rearrangement module, thereby completing the retrieval. Our experimental validation on homemade Thangka image dataset and publicly available fine-grained image dataset, demonstrating the superiority of SCDNet compared with other retrieval algorithms. © 2023 IEEE.

关键词： component feature aggregation feature extractiont retrieval SCDNett

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