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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 769 条记录，以下是381-390 订阅

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Image Enhancement by Various Segmentation Techniques Using machine learning 6th

Image Enhancement by Various Segmentation Techniques Using M...

引用

6th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Chanchlani, Akshita thakare, Vilas M. Wadhai, Vijay M. Department of Computer Science and Engineering SGBA University Amravati India Department of Electronics and Telecommunications Engineering D. Y. Patil College of Engineering Akurdi Pune India

ISBN: (纸本)9789811935701

Brain Cancer is a disease where abnormal cells grow without control and can invade other tissues, if it is identified at the early stage and treated appropriately then it can save human life. In developing countries cause of disease like brain cancer is effecting many people. Brain Cancer Medical image processing is most important and challenging field now a days. Image preprocessing and its segmentation on brain cancer Magnetic Resonance Imaging (MRI) and its analysis is process of finding the objects and understanding their significance. Image segmentation have become most popular due to its usage into various applications. the objective of this paper is exploring and implementation of various algorithms of image segmentation on medical image specifically on brain cancer MRI. Image segmentation is very important step of understanding the images for further processing and analysis. this paper represents implementation of various image segmentation techniques that includes Image thresholding, Applying Various Morphological Operations, edge and region-based segmentation for enhancement of brain cancer images and detection of contours from brain cancer MRI. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image analysis

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Facial Feature Analysis for Anxiety Detection in People with Drug Addiction: Establishment and Validation of machine learning Models

Facial Feature Analysis for Anxiety Detection in People with...

引用

6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Xiaoming Zhong Ang Li Xiangyang Zhang Tingshao Zhu Institute of Psychology Chinese Academy of Sciences Beijing China Department of Psychology University of Chinese Academy of Sciences Beijing China Department of Psychology Beijing Forestry University Beijing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Dynamic monitoring of anxiety levels of drug addicts can help improve the effectiveness of drug addiction treatment. However, traditional psychological measurement methods have limitations and cannot effectively meet these needs. there is an urgent need to explore novel measurement methods. In view of this, this study collected and analyzed facial movements and anxiety level data of 279 male drug addicts using image sensor devices and psychological questionnaires, respectively. the aim was to establish and validate prediction models of anxiety levels in drug addicts based on facial feature analysis using machine learning methods. Results showed that prediction models had good predictive effects (the highest split-half reliability level: r=0.976; the highest criterion-related validity level: r=0.390). the results of this study can provide novel and reliable research methods and measurement tools for dynamic monitoring of the mental health of drug addicts.

关键词： Drugs Analytical models Computational modeling Anxiety disorders Mental health machine learning Predictive models Reliability Monitoring Facial features

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Development of a Mobile App to Detect Counterfeit Currency using machine learning

Development of a Mobile App to Detect Counterfeit Currency u...

引用

international conference on Recent Trends in Advance Computing (ICRTAC)

作者： Rakesh K Kadu Praneet Gupta Pranay Jain Saurabh Nakade Information Technology Dept. Shri Ramdeobaba College of Engineering and Management Nagpur India

Counterfeit poses a serious threat to financial institutions and the global economy. this work introduces machine learning techniques that use image processing, pattern recognition, and classification algorithms to accurately identify false positives. these studies are based on analysis of real banknotes, their security features and counterfeiting techniques. Images of real and fake invoices are carefully recorded to create robust data for training and testing learning models such as neural networks (CNN). Image processing techniques such as edge detection and feature extraction improve the model's detection capabilities. the development model, evaluated in terms of efficiency, is integrated into a user-friendly interface and accepts cash register images as input. this solution aims to contribute to continuous efforts to combat fraud, providing a clear and effective way to protect financial institutions and increase business security.

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A Statistical Study of Lewy Body and Other Types of Dementia

A Statistical Study of Lewy Body and Other Types of Dementia

引用

Intelligent signal processing and Effective Communication Technologies (INSPECT), IEEE international conference on

作者： Rajnish K Ranjan Divyarth Rai Department of Computer Science LNCT University Bhopal India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

Dementia is a global health concern, affecting over 55 million people worldwide, as reported in the Alzheimer Report 2022. It ranks 6th among chronic diseases in terms of mortality, emphasizing the need for accurate diagnosis tools. Alzheimer's disease (AD) is the most common type, accounting for 60-70% of cases, followed by Vascular Dementia, Lewy Body Dementia, Frontotemporal Dementia, and Parkinson's disease. Forgetfulness and loss of items are the most prevalent symptoms, occurring in 80-90% and 60-80% of cases, respectively. machine learning techniques, such as Support Vector machine, Gradient Boosting, and ensemble learning, have shown promise in identifying dementia stages. Deep learning techniques, including Convolutional Neural Network, Recurrent Neural Network, and Long Short-Term Memory Networks, have also been successful. Notably, Recent novel research ADCPBOBM achieved 96.64% and 98.39% accuracy for different dataset size MRI type images in the OASIS dataset and 90% accuracy for the GENFI dataset in identifying Frontotemporal Dementia. To provide more precise and useful methods for handling this expanding global health issue, more research is necessary.

关键词： Deep learning Training Support vector machines Accuracy Recurrent neural networks Reviews signal processing algorithms signal processing Prediction algorithms Alzheimer's disease

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Research on Gene Identification Algorithms Based on signal processing Techniques

Research on Gene Identification Algorithms Based on Signal P...

引用

IEEE international conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Chongwei Shi Oral and Maxillofacial Surgery University of Michigan Michigan USA

ISBN: (数字)9798350377255

ISBN: (纸本)9798350377262

With the rapid advancements in genomics and bioinformatics, gene identification has assumed a pivotal role in contemporary biological research. Gene identification involves pinpointing regions within DNA sequences that encode proteins or functional RNAs, which is of profound significance for understanding the fundamental mechanisms of life, exploring the genetic underpinnings of diseases, and drug development. While traditional experimental biology methods offer reliable tools, they are often time-consuming and costly. Consequently, the application of signal processing techniques for gene identification has emerged as a focal point of current research. signal processing, with its formidable analytical capabilities when dealing with vast amounts of data, is particularly well-suited for high-dimensional and complex data such as DNA sequences. By converting DNA sequences into numerical signals and employing spectral analysis methods like Fourier and wavelet transforms, intrinsic features of gene structures can be unveiled. Moreover, integrating machine learning algorithms such as Support Vector machines (SVM) further enhances the accuracy of gene identification. signal processing methods provide robust support for the efficient and automated processing of complex genomic data from both prokaryotic and eukaryotic organisms.

关键词： Support vector machines Wavelet transforms machine learning algorithms signal processing algorithms Genomics DNA signal processing Wavelet analysis Bioinformatics Spectral analysis

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A Survey on Automated Disease Diagnosis and Classification of Herbal Plants Using Digital Image processing

A Survey on Automated Disease Diagnosis and Classification o...

引用

international conference on Information Systems and Computer Networks (ISCON)

作者： Ankit Khandelwal Aasheesh Shukla Mangal Sain Electronics & Communication Dept. GLA University Mathura India Information Engineering Dept. Dongseo University Busan Republic of Korea

this paper presents a review on automated disease detection processing. the primary issue in herbal plants is the diagnosis and stratification of its diseases. the conventional process is unpredictable and inconsistent, therefore techniques for classifying plants diseases using digital method are necessary as they speed up the identification process. this literature discusses various digital methods for classifying plant diseases. the first step is image processing of herbal plants, which make use of traditional image processing techniques and algorithms to categories diseases in herbal plants based on attributes like texture, color, and shape. the second step is plant categorization based on deep learning and machine learning, such as CNN, KNN, logistic regression etc. Deep learning applications mainly lessen the reliance on designs and preprocessing techniques by joining the entire process. this review provides a basic understanding and knowledge of the current state-of-the-art researches, as well as some dynamic trends for future work.

关键词： Deep learning Plant diseases machine learning algorithms Shape Image color analysis Digital images Neurons

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the Sentiment Attitude of Weibo Users towards Annual Individual Income Tax Return: Based on Natural Language processing and machine learning Methods

The Sentiment Attitude of Weibo Users towards Annual Individ...

引用

international conference on Big Data and Artificial Intelligence (BDAI)

作者： Yuanyan Chen Wei Liu School of Public Finance and Taxation Capital University of Economics and Business BeiJing China

this passage discusses a study that collected Weibo posts from different users related to China’s Annual Individual Income Tax Return(AIITR), and compared the performance of six machine learning models (support vector machine, random forest, naive Bayes, K-nearest neighbor, decision tree, and logistic regression), as well as a deep learning LSTM model, to choose the more accurate LSTM model for sentiment analysis. the study aims to explore the compliance status of individuals in tax-paying and uses the Latent Direct Allocation (LDA) model to display the topics discussed in the Weibo posts, in order to gain a deeper understanding of the issues that Weibo users are concerned about related to AIITR, and provide policy recommendations.

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Application and Development of Oil Pipeline Magnetic Flux Leakage Detection Based on machine learning Algorithm

Application and Development of Oil Pipeline Magnetic Flux Le...

引用

Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), international conference on

作者： Zheng Wang Yi Tang Jianbin Yu Bing Liu Yaxin Wu Digital Intelligent Industry College Hebei University of Engineering Science Shijiazhuang China Aviation College of Hunan Automotive Engineering Vocational University Zhuzhou China Hebei X-Into Technology Co. Ltd Shijiazhuang China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

In order to evaluate the condition of oil pipeline, the main nondestructive testing method currently used is magnetic flux leakage testing technology. the magnetic flux leakage data of pipeline are collected by magnetic flux leakage detector, and the magnetic flux leakage signal curve is drawn according to the data, and then the variation characteristics of the curve are judged manually to judge the defects on pipeline components. this method has great error and low efficiency. With the rapid development of artificial intelligence technology, many magnetic flux leakage detection technologies based on machine learning algorithms have been proposed, which can realize more efficient and accurate intelligent detection. In this paper, the intelligent methods of pipeline magnetic flux leakage detection are summarized. At first, the basic principle of magnetic flux leakage detection is briefly introduced, and then the detection methods combining multiple physical fields and the methods using machine learning in pipeline magnetic flux leakage detection are emphasized in detail. Finally, the summary is made, and the problems that the intelligent methods still face are discussed.

关键词： Performance evaluation Magnetic flux leakage machine learning algorithms Oils Pipelines Noise signal processing algorithms Robustness Real-time systems Faces

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Design of a Smart Camera Vulnerability Detection System Based on Homology Analysis

Design of a Smart Camera Vulnerability Detection System Base...

引用

Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), international conference on

作者： Mingyang Li Yan Yu Yixin Zou Yuanyuan Luo China Telecom Research Institute Shanghai China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

In recent years, the number of smart cameras has grown exponentially, and the number of attacks targeting them has also increased. Many of these devices lack adequate security mechanisms and connect to networks with vulnerabilities, creating significant pressure on the overall network environment. Traditional vulnerability scanning tools typically rely on simple feature matching methods for vulnerability detection, and they often perform redundant scans on devices with similar characteristics. this not only results in low efficiency but also leads to a higher false positive rate. this paper proposes a smart camera vulnerability scanning system based on device homogeneity analysis. By quickly identifying security vulnerabilities in devices with the same architecture and components through homogeneity analysis, and leveraging machine learning algorithms to enhance the accuracy of vulnerability detection, the system significantly improves scanning efficiency and reduces false positives.

关键词： Performance evaluation Accuracy machine learning algorithms Smart cameras signal processing Feature extraction Security Object recognition Testing Intelligent control

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Enhancing Marine Species Detection: A Comparative Study of Backbone Networks in fastRCNN with Limited Dataset Constraints

Enhancing Marine Species Detection: A Comparative Study of B...

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), international conference on

作者： Shuangye Yang Haoyang Li Biao Guo Bintao Yan Hongwei Zhang Benyu Wang Department Of Information Center Beijing Petroleum Machinery Co. Ltd Beijing China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

this study aims to harness a camera system for collecting marine ecological data and potentially uncovering novel species. Although numerous research teams have successfully employed machine learning models to analyze visual data, these models frequently encounter challenges in adapting to the dynamic marine environment. In response to this issue, our research is centered on refining the application of the Fast RCNN machine learning model through the acquisition of extensive visual data from marine habitats. A comparative analysis of various backbone feature extraction networks, including VGG16-BN, ResNet50-FPN, and MobileNetV3-FPN, revealed that VGG16 exhibits the highest effectiveness in accurately identifying, quantifying, and classifying marine species.

关键词： Training Visualization Adaptation models Analytical models Accuracy Biological system modeling signal processing Feature extraction Data models Stability analysis

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