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共 1328 条记录，以下是121-130 订阅

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Multi-Modal Framework for Skin Lesion Classification Using Deep learning and machine learning Techniques 2

Multi-Modal Framework for Skin Lesion Classification Using D...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Mehta, Shiva Singh, Amanveer Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Punjab Rajpura140401 India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India

ISBN: (纸本)9798350366570

Skin cancer, especially melanoma, is one of the most threatening diseases of the modern world. Therefore, accurate and timely diagnosis is critical. This work proposes a methodology based on CNNs and SVM for the dermoscopic and clinical picture classification of skin cancer. The foundation aims to enhance diagnostic consistency using the complementary properties of various imaging techniques. Continuing the tradition of the previous years, HAM10000 and the ISIC 2020 datasets became available to the public. The outcome suggests that the proposed multimodal method CNN-SVM is far better than simple CNN models. The recommended technique achieved a measure of accuracy of 92.5%, precision of91.8%, recall of 92.0%, F1 score of91.9%, and ROC-AUC of 0.95. The CNN model trained on dermoscopic pictures achieved an accuracy of 89.3% and ROC-AUC of 0.92, while the CNN model with clinical images numbered 84.7% and ROC AUC of 0.88%. The improvement of the multimodal method is attributed to the incorporation of complex lesion-specific features from dermoscopic photos and contextual information from clinical images. Thus, the current study focuses on the ability of the SVM to comprehend the features of high-dimensional data and the classification of instances that are often near the decision boundary and closely or equally related to any class. The offered methodology has potential, though small participant numbers and high photo quality limit its applicability. Future work should focus on data extension and management of real-world uncertainty. As such, this study shows that the proposed CNN-SVM multimodal model is a reliable and accurate tool for skin cancer diagnosis in clinical settings. © 2024 IEEE.

关键词： Oncology

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AIOIML: Automatic Integration of Ontologies for IoT Domain Using Hybridized machine learning Techniques 2

AIOIML: Automatic Integration of Ontologies for IoT Domain U...

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2nd international conference on Paradigm Shifts in Communications Embedded Systems, machine learning and signal processing, PCEMS 2023

作者： Shrivastava, Rishi Rakesh Deepak, Gerard Birla Institute of Technology and Science Department of CSE Pilani India Manipal Institute of Technology Bengaluru Department of CSE Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9798350310719

There is a need for Ontology modelling and automatic generation of Ontologies in order to assimilate knowledge World Wide Web knowledge as a strategic model. Ontologies are the best knowledge descriptor model as they have some amount of human cognition associated with them because either humans are major contributors when they are generated manually or are indirect contributors when they are semi automatically generated. Internet of Things is a domain which has strategically evolved in the last few years, and there is a need for integrating several facets of Internet of Things Ontology. In this paper a strategic scheme for Internet of Things Ontology integration for Internet of Things domain with different perspective are proposed wherein the dataset are subjected to tag generation which is further classified using the AdaBoost classifier which are aligned with the random core classes of the existing variational Ontologies in the Internet of Things domain using Shannon's entropy and the pointwise mutual information measure with differential step deviation measure. Which yields average precision and recall of 96.83 and 97.95 respectively. © 2023 IEEE.

关键词： Internet of things

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Advanced Cardiac signal processing with Federated learning and IoT for Remote Health Monitoring 2

Advanced Cardiac Signal Processing with Federated Learning a...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Mehta, Shiva Kundra, Danish Centre for Research Impact & Outcome Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India

ISBN: (纸本)9798350366570

Cardiovascular diseases are now ranked among the leading causes of death;medical care requires efficient, immediate cardiac monitoring devices. This study provides an original approach incorporating Federated learning (FL) with IoT-funded wearable devices for raw cardiac signal processing in real-time, aiming to avoid privacy problems and reduce time delay associated with centralized approaches. In this study, the performance of the developed system was evaluated using the two ECG databases that are freely available in the public domain, the MIT-BIH Arrhythmia Database and the PhysioNet Challenge Database. This proposed model achieved a performance of 97.8% in detecting cardiac disorders such as arrhythmias and atrial fibrillations. However, measuring by accuracy, it scored 96.5% overall accuracy while concerning the recall, it scored 98.2%, which accounted for an F1 measure of 97.3% overall score. By minimizing latency, the architecture offered a 35% improvement over the traditional centralized CM systems, which makes it very suitable for real-time monitoring. By applying differential privacy methods, the privacy loss parameter was set to ϵ = 1.5, thus ensuring the privacy of sensitive patient data. The study demonstrates how the proposed system provides a privacy-preserving, real-time, and scalable approach to monitoring heart health, making it a valuable contribution to biomedical signal processing. Current work shall focus on expanding the above model to other biological signals and improving its performance for more widespread use in clinical medicine. © 2024 IEEE.

关键词： Differential privacy

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Robust Assessment of Dysarthrophonic Voice with RASTA-PLP Features: A Nonlinear Spectral Measures 2

Robust Assessment of Dysarthrophonic Voice with RASTA-PLP Fe...

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2nd international conference on Mechatronics and Electrical Engineering (MEEE)

作者： Islam, Rumana Tarique, Mohammed Univ Windsor Dept Elect & Comp Engg Windsor ON Canada Univ Sci & Tech Fujairah USTF Dept Elect & Comp Engg Fujairah U Arab Emirates

ISBN: (纸本)9781665474450

This paper presents an artificial intelligence based speech signal processing technique to identify dysarthrophonic voice with relative spectral-perceptual linear prediction (RASTAPLP) features. Dysarthria is a neural motor speech disorder caused by muscular weakness. Voice analysis of dysarthrophonic patients is challenging as this disease has multidimensional effects on the human voice generation system. Conventional spectral analysis is unable to accurately characterize the pathology associated with nonlinear dynamicity of human voice. This work investigates the suitability of RASTA-PLP features excerpted from speech signals to identify dysarthrophonic patients. The speech samples of healthy and dysarthrophonic patients are collected from the Saarbrucken Voice Database (SVD). Several machine learning and Artificial neural network (ANN) based algorithms are developed to evaluate the classification performance of the proposed system. The designed system can achieve excellent performance in terms of accuracy (100%) considering female and male subjects separately.

关键词： Accuracy ANN classifier dysarthria dysarthophonia deep learning machine learning pathology PLP RASTA-PLP speech vocal disorder

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machine learning based Low-Scale Dipole Antenna Optimization using Bootstrap Aggregation 2

Machine Learning based Low-Scale Dipole Antenna Optimization...

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2nd international conference on Paradigm Shifts in Communications Embedded Systems, machine learning and signal processing, PCEMS 2023

作者： Neelamraju, Pavan Mohan Pothapragada, Pranav Rana, Goutam Chaturvedi, Divya Kumar, Rupesh SRM University AP Department of Electronics and Communication Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350310719

Dipole antennae are commonly used radio frequency devices. They gained good prominence as a result of their efficiency, consistent performance and flexibility. Different optimization strategies such as particle swarm optimization, differential evolution and machine learning algorithms have been utilized in the past to design dipole antennae. This helps in creating a complete device profile and increases its efficacy. Due to the complexity of modern antennas in terms of topology and performance requirements, standard antenna design approaches are tedious and cannot be guaranteed to produce effective results. Out of the strategies that are widely being utilized, machine learning (ML) algorithms evolved rapidly due to their capabilities in extrapolating the dimensional and material profiles of the device. Antenna design optimization still faces several difficulties, even though machine learning-based design optimization complements traditional antenna design methodologies. The effectiveness and optimization capabilities of available ML approaches to address a wide range of antenna design problems, considering the increasingly strict specifications of current antennas, are the fundamental difficulties in antenna design optimization which need to be focused on. In our current work, the capability of ML algorithms in elucidating minor trends in device profiles is tested. A bootstrap aggregation model is proposed, concatenating Linear Regression, Support Vector Regression and Decision Tree Regression algorithms. The concatenated model was used to optimize the parameters of reflection coefficient, directivity, efficiency and operating frequency, depending on the feed length, dipole radius and dipole length of the antenna. © 2023 IEEE.

关键词： Dipole antennas

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Gamification: An Emerging Educational Technology for Student learning 2

Gamification: An Emerging Educational Technology for Student...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Banerjee, Subeer Banerjee, Richa Gulati, Chanda Chauhan, Abhijeet Singh Sriram Institute of Information Technology M.P. Gwalior India Narsee Monjee Institute of Management Studies Maharashtra Mumbai India Prestige Institute of Management and Research Department of Management Gwalior India

ISBN: (纸本)9798350366570

Gamification in education is an excellent technique to make students more engaged and motivated in their studies. By incorporating games into the learning process, students can have fun while they learn and develop new skills. There are many ways to gamify education, and each classroom can find the right approach for them. Gamification has become a potential strategy to improve educational environments across the board. This study looks into how motivation and engagement affect learning outcomes when gamification is incorporated into the teaching and learning process. The present study focused on students of higher education engaged in learning language from a game. It analyzed the relationship between student engagement, Motivation for learning, skills for learning, and learning through gamification with the help of data that was collected on a standardized questionnaire from 256 students. The study evaluates how different gamification components, such as points, badges, and leader-boards, affect student motivation and engagement levels. The impact of motivation and engagement was evident on the learning of students. The results suggested that all independent variables have a strong affirmative relationship with learning through gamification as the dependent variable. By highlighting the crucial roles motivation and engagement play in improving learning outcomes, the study report enhances the increasing body of familiarity with the incorporation of gamification in education. For educators, instructional designers, and policymakers interested in using the potential of gamification to develop more efficient and interesting learning environments in the twenty-first century, the findings have practical ramifications. © 2024 IEEE.

关键词： Federated learning

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Implementing a Deep learning Based Bridge Defect Detection System Using Jetson Nano 2

Implementing a Deep Learning Based Bridge Defect Detection S...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Senapati, Ranjan K. Kumar, K. Akhil Prasad, P.M.K. VNR VJIET Dept. of ECE Hyderabad India GVPCE for Women Dept. of ECE Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9798350366570

This work describes the creation and application of an NVIDIA Jetson Nano platform-based deep learning bridge fault detection system. Bridges constitute crucial pieces of infrastructure, and maintaining safety and averting catastrophic failures depend on quick detection of flaws like corrosion, cracks, or structural damage. Conventional inspection techniques can be time-consuming, labor-intensive, and prone to mistakes. Here, we offer a computer vision-based methodology that uses deep learning techniques to automatically identify and categorize bridge structure faults from photos collected with fixed cameras or by drones. Real-time defect detection uses the NVIDIA Jetson Nano as an edge computing device because of its great computational capacity and low power consumption. The three primary phases of the suggested system include preprocessing images, employing convolutional neural networks (CNNs) for fault identification, and post-processing for analysis and visualization. Using the Jetson Nano's GPU acceleration for quick inference, we gathered a dataset of bridge photos with different kinds of flaws and carried out comprehensive tests to assess the system's performance. The suggested method shows efficient covering defects with an accuracy of 96%, while preserving the Jetson Nano platform's real-time processing capabilities. © 2024 IEEE.

关键词： Image segmentation

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Identification of Cardiovascular Disease using ECG Images based on Deep learning Procedure 2

Identification of Cardiovascular Disease using ECG Images ba...

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2nd IEEE international conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Esther T, Hannah Rose Periyasamy, P.N. Rajesh Kumar, K. Gururamasenthilvel, P. Duraimutharasan, N. Velammal Engineering College Department of Artificial Intelligence and Data Science India Hindusthan Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore641032 India Chendu College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Madurantakam603311 India Saveetha School of Engineering Department of Computer Science and Engineering Chennai602 105 India Amet University Department of Information Technology Tamil Nadu Kanathur603112 India

ISBN: (纸本)9798350315905

When it comes to finding out what's wrong with your heart, cardiac imaging is crucial (CVD). Its previous purpose was restricted to qualitative and quantitative evaluations of the heart. The advent of big data and machine learning, however, has opened up new possibilities for developing AI tools that can aid the physician in the diagnosis of CVDs. Our study offers a unique approach to ECG illness prediction by combining cardiology, signal processing techniques, and a deep learning model. Then, using wavelet transformation and cardiology, we convert the ECG data into a time-frequency representation. Next, a deep convolutional network is used to categories the results of the time-frequency analysis. We find that by combining the benefits of signal processing with those of deep learning, we can significantly enhance the precision of categorization using our approach. The suggested methodology has the potential to enhance performance and reduces staffing costs for major general hospitals, all while lowering the rates at which primary care facilities make diagnostic errors. © 2023 IEEE.

关键词： Electrocardiograms

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Automatic License Plate Detection Using Information processing System Vision and Deep learning 2

Automatic License Plate Detection Using Information Processi...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Sharma, Avinash Dwivedi, Ishita Dawar, Divya Saxena, Rini Barua, Tarkcshwar Goyel, Parul Chandigarh Engineering Chandigarh Group of Colleges Department of information processing system Science and Engineering Mohali140307 India Mohali140307 India Roorkee Institute of Technology Computer Science & Engineering Department Roorkee India M. M. Engineering College Maharishi Markandeshwar University CSE Department Mullana Haryana Ambala133207 India

ISBN: (纸本)9798350366570

The content of this probe is to overcome the current cognitive content with ALPR playacting, with a special accent on developing a dead and impelling playacting for distinguishing License plates from video film. The study attempts to ameliorate the presentation norms of ALPR technology by utilizing the powerful accumulation of optical character identification (OCR)[20] and deep learning based object detection algorithmic program. The core of this occupation is the use of the cutting-edge YOLOv8. object distinguishing hypothesis, which has been pains taking and disciplined on an unequaled dataset premeditated with License plate detection in mind. Focusing on quality and efficiency, the YOLOv8 model accomplish an amazing 99.8 quality rate when it comes to accurately localization and categorization License plates[18] inside video frames. This occupation aims to formalize the willingness and dependability of the advisable ALPR playacting through extensive scientific research. The project aims to intensify ALPR technology and determinant the forthcoming of transport and police investigation playactings[18] by breaking through current obstruction and establishing new presentation norms. © 2024 IEEE.

关键词： Deep learning

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Prediction and Data Analysis of Price Based on PSO with Extreme learning machine Algorithm and Particle Swarm Optimization 2

Prediction and Data Analysis of Price Based on PSO with Extr...

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2nd international signal processing, Communications and Engineering Management conference, ISPCEM 2022

作者： Chen, Huanyun Zhao, Weiming Private Hualian College Guangzhou China

ISBN: (纸本)9781665492713

The changes in the trading market are affected by many factors, and traditional forecasting methods are more and more difficult to meet people's needs. In order to improve the accuracy of prediction, this paper proposes an extreme learning machine algorithm based on particle swarm optimization for prediction and analysis. First, the fluctuation data of previous years are collected, and the closing price of the stock is determined as an experiment and simulated to establish a learning sample, and then the learning sample is input. Based on the online extreme learning machine model optimized by particle swarm optimization, the prediction model is established. The results show that the optimized extreme learning machine algorithm, compared with models such as ELM, PSO-BP and DE-ELM, has better surface prediction accuracy and fitting effect, and can more accurately describe the change trend, which is more efficient than other methods. © 2022 IEEE.

关键词： Forecasting

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