检索结果-内蒙古大学图书馆

2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Bharathi, S. Prasanna Subin, P. Glaret Hariharan, R. Balaji, T.S. Balaji, S. Vellore Institute of Technology School of Electronics Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Ece Tamilnadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electrical and Electronics Engineering Tamilnadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350364699

Due to adoption of IoT in healthcare systems, patients' care has been improved by monitoring and constant data acquiring in real time. Nevertheless, some issues like data privacy and security and data accessibility impede the usage of IoT data. To this end, this research outlines a robust framework for the generation of synthetic data with the help of generative AI methods, namely GANs and VAEs appropriate for the context of IoT-based healthcare applications. The framework also tackles the privacy issue since the methods used, namely differential privacy, protects information of an individual while maintaining usefulness. Some of the activities include data collection and preparation, model training and development, synthetic data generation, plus testing, and validation techniques. The generated synthetic data captures similar characteristics as the original data as well as important patterns that can be used to improve model building exercising and validating. Altogether the experimental results show how the synthetic data with the same characteristics can be useful in different healthcare situations while keeping high fidelities and utility. This paper demonstrates ways in which synthetic data can revolutionalize healthcare through secure opening of data for innovation and quality improvement of patients' care. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

An Efficient Prototype for PV based Water Pumping Solutions 3

An Efficient Prototype for PV based Water Pumping Solutions

引用

3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Kalaiarasi, N. Ravichandran, Akshaya Sahoo, Swathipurna Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Tamilnadu Kattankulathur Chennai India

ISBN: (纸本)9781665462167

This paper discusses the Cuk and Zeta converter-fed BLDC motors based on power factor correction. The comparative study is based on parameters like torque, stator current, power factor, and rotor speed of both converter-fed BLDC drives. The DC link voltage of the Voltage Source Inverter (VSI) is maintained constant in traditional PFC converter designs. This PFC converter's primary drawback was complicated control, which necessitates a significant number of sensors. The proposed DC-DC converter follows the voltage follower approach in continuous conduction mode (CCM) to improve the power factor. Also, it helps to reduce switching losses and the stress on power devices. Low-frequency switching is utilized in the VSI to control the speed of the motor by changing the dc bus voltage. The switching loss of VSI is reduced by adopting fundamental frequency switching. This improves the efficiency of the system. Electronic commutation of the BLDC motor helps to eliminate the sensors required for the measurement of DC link voltage and phase currents. The proposed system with proportional-integral (PI) control provides a profitable solution for low-power applications. The performance of converters fed by BLDC motors is studied using MATLAB simulation. The results from Simulink show that the Cuk converter's performance is better than the Zeta converter. So, the prototype hardware of the suggested model, i.e., the Cuk converter-fed BLDC motor, is implemented. © 2023 IEEE.

关键词： Simulink

来源：评论

学校读者我要写书评

暂无评论

Enhancing Medical Imaging Trust with an Efficient Deep Learning System for Forgery Detection 2

Enhancing Medical Imaging Trust with an Efficient Deep Learn...

引用

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Saini, Dimple Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media India Sri Balaji University Pune India Parul Institute of Technology Parul University Computer Science and Engineering Engineering and Technology India Pune India Chennai Institute of Technology Deparment of Computer Science and Engineering India

ISBN: (纸本)9798350373110

The integrity of medical images is critical to accurate diagnosis and treatment, but the proliferation of sophisticated editing tools has heightened the risk of forgery, compromising the trustworthiness of medical imaging. This paper introduces an efficient deep learning system designed to safeguard medical imaging by identifying alterations such as splicing, copy-move, and retouching forgeries. Our proposed solution utilizes a convolutional neural network (CNN) architecture enhanced with attention mechanisms, optimized to detect subtle changes in medical images. The development process involved meticulous data preparation, augmentation, and the implementation of advanced mathematical models to refine the system's ability to differentiate between authentic and manipulated images. Evaluation was conducted on a diverse dataset encompassing various imaging modalities like MRI, CT scans, and X-rays, with both genuine and tampered instances. The proposed system delivered superior performance, achieving an accuracy of 97.5%, a precision of 96.8%, a recall of 98.2%, and an F1 score of 97.5%, significantly outperforming existing methods. These results highlight the system's robustness in distinguishing real images from forged ones, thanks to its attention mechanism that allows the model to focus on key areas within the images. Ultimately, this system offers a highly accurate and efficient tool for enhancing the security of medical imaging, reinforcing the potential of deep learning in tackling healthcare challenges and ensuring reliable diagnostics and treatment planning. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Particle-based Data-driven Nonlinear State Estimation of Model-free Process from Nonlinear Measurements

Particle-based Data-driven Nonlinear State Estimation of Mod...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ghosh, Anubhab Eldar, Yonina C. Chatterjee, Saikat Digital Futures Centre School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Faculty of Mathematics and Computer Science The Weizmann Institute of Science Israel

ISBN: (纸本)9798350368741

We consider the problem of causal filtering of a model-free process from (noisy) nonlinear measurements. The 'model-free process' means that we do not have a state-space model (SSM) of the process dynamics, limiting the use of traditional model-driven filters, such as unscented Kalman filter (UKF) and particle filter (PF). To address the problem we propose a particle-based data-driven nonlinear state estimation (pDANSE) method. In pDANSE, a recurrent neural network (RNN) provides the statistical parameters of a Gaussian prior of the underlying state, and particles are then drawn from the prior to compute the posterior moments. pDANSE is typically trained in a semi-supervised fashion. For our experiments we study the use of half-wave rectification as a nonlinear transformation of measurements. We first show that an unsupervised learning-based method under-performs, and subsequently the semi-supervised learning-based pDANSE performs satisfactorily. Using Lorenz-63 system as benchmark, pDANSE is found to be competitive against a model-driven PF that knows the exact SSM. © 2025 IEEE.

关键词： deep neural network Kalman filter particle filter semi-supervised learning State estimation

来源：评论

学校读者我要写书评

暂无评论

A deep learning-based early alzheimer’s disease detection using magnetic resonance images

引用

Multimedia Tools and Applications 2025年第16期84卷 16561-16582页

作者： Suchitra, S. Krishnasamy, Lalitha Poovaraghan, R.J. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R &D Institute of Science and Technology Avadi Tamil Nadu Chennai India Department of Information Technology Nandha Engineering College Tamil Nadu Erode India Department of Information Technology Jaya Engineering College Tamil Nadu Thiruninravur Chennai India

Alzheimer’s Disease (AD) is a degenerative, chronic condition of the brain for which there is now no effective treatment. However, there are medications that can slow its development. In order to stop and control the development of AD, earlier diagnosis of the disease is quintessential. Our proposed method’s primary objective is to establish a comprehensive model for the prior detection of Alzheimer’s disease and the categorization of distinct AD stages. This work employs a deep learning methodology, especially CNN. The proposed approach makes use of well-known models that have already been trained to classify medical images, like the EfficentNetB7 model, by applying the transfer learning principle. In order to achieve greater accuracy, convolutional neural networks (CNNs) are frequently scaled up as new resources become available at a fixed cost throughout the construction phase. A compound coefficient is used by the CNN architecture and scaling approach, which is the foundation of the pre-trained EfficientNetB7 model, to scale the dimensions equally. This proposed EfficientNetB7 model is quicker, easier, and more effective than other pre-trained models like VGG19 and InceptionV3. The proposed model includes simple structures that have memory requirements, provide manageable time, overfitting, and low computational complexity as well as training and inference speeds. The Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset was employed for a comprehensive assessment of the method proposed, utilizing well-known performance metrics including sensitivity, specificity, and accuracy. The findings revealed that the improvised results achieved the accuracy metric when compared to existing methods. The EfficientNetB7 model has been enhanced, and this model achieves a sensitivity of 98.08%, specificity-98%, accuracy-98.2%, and F-score-98.95%, for multi-class AD stage classifications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, par

关键词： Magnetic resonance

来源：评论

学校读者我要写书评

暂无评论

Performance enhancement Of GaAs and InP Based Multi-Junction Silicon Solar Cells using thickness and doping profile optimization

Performance enhancement Of GaAs and InP Based Multi-Junction...

引用

2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2023

作者： Roy, Arijit Bardhan Mounika, Mandla Kathi, Roshika Vishal, N Sai Srm Institute of Science and Technology University of Madras Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350337426

Among all the non-conventional sources of energy, solar energy has become increasingly popular in recent years. While there are several types of solar energy technologies available, including solar thermal and concentrated solar power but solar photovoltaic (PV) is the most widely used and preferred form. Due to this reason, increasing the efficiency of the solar cell is one of the key factors for establishing the PV technology as a primal source of energy. So multi-junction solar cell with proper bandgap matching is one of the best ways to achieve high efficiency through improving open circuit voltage and unified photon electron harvesting in a broad area of the spectrum. This paper is about the design and analysis of GaAs-Si and InP-Si based two types of multi-junction solar cells with the efficiency of 18 and 20 % respectively by optimizing doping profile and materials thickness through PC1D software. By utilizing PC1D software, we can design and analyze the performance of the solar cells in a virtual environment before producing physical prototypes, which can help to save time and resources. © 2023 IEEE.

关键词： Indium phosphide

来源：评论

学校读者我要写书评

暂无评论

Existing Techniques For Fundamental, Technical And Sentimental Analysis For Stock Investement

Existing Techniques For Fundamental, Technical And Sentiment...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Muzaffar, Mohammad Shaazan Niranjan, Shivam Joel, Kadugula Kumar, Anuj Baniya, Pashupati Agrawal, Atul Sharda University Department of Computer Science and Engineering Greater Noida India Greater Noida Institute of Technology Department of Computer Science and Engineering Greater Noida India ITS Engineering College Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350378092

In today's complex financial world, where things like global economics, company strategies, and how people feel about things can all affect the stock market, buying and selling stocks has become really popular. People are attracted to it because they think they can make a lot of money quickly. But there's a big problem: while making money is exciting, there's also a big chance of losing money because the stock market can be very unpredictable. Trying to figure out what the stock market will do next is super important, but also really hard. There are lots of different things that can affect it, like what's happening in the world, how well companies are doing, and even how people are feeling about investing. So, our paper is all about using existing Techniques (like AI and machine learning) to try to predict what the stock market might do. This paper explores three main ways of doing this.i.e, Fundamental, Technical and Sentimental Analysis: looking at basic stuff about companies, studying the patterns of stock prices, and analyzing how people feel about investing. This paper will take in account what other researchers have already found out about these things, our main goal is to help other researchers to understand more about predicting the stock market. The aim of this paper is to initiate a comprehensive discourse on this subject, inviting widespread participation to equip individuals with the knowledge and confidence needed to make informed investment decisions, even among the unpredictable fluctuations of the stock market. © 2024 IEEE.

关键词： Marketplaces

来源：评论

学校读者我要写书评

暂无评论

Supervised Learning Approaches on the Prediction of Diabetic Disease in Healthcare 3rd

Supervised Learning Approaches on the Prediction of Diabetic...

引用

3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Patel, Riyam Sivaiah, Borra Patel, Punyaban Sahoo, Bibhudatta Kattankulathur Chennai India Department of Computer Science and Engineering CMR College of Engineering and Technology Kandlakoya Hyderabad India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad India Department of Computer Science and Engineering National Institute of Technology Odisha Rourkela India

ISBN: (纸本)9789819939312

There are many chronic diseases out of which Diabetes is one;that increases sugar level in the blood and is one of the most fatal that effect different organs in the human body. Diabetes can cause a variety of slow bad consequences if not detected and left without given medical care. The emergence of machine learning approaches, on the other hand, solves this crucial issue. The purpose and objectives of this work is to build a prototypical model that can properly forecast diabetes whether or not a person will suffer from it. To detect diabetes at an early stage, our work employs three classification algorithms based on supervised learning: Random Forest, Naïve Bayes Classifier and Multilayer Perceptron Network. The PIDD Database has been used in the experiments. The Precision, Accuracy, Recall, F-Measure, and ROC Area are all used to calculate the efficiency of the above three algorithms. The correctness and accuracy of a classification system is measured by the number of occurrences that are correctly classified and those that are mistakenly classified. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multilayer neural networks

来源：评论

学校读者我要写书评

暂无评论

Noise Removal of EMG Signal for Healthy Person Using Wavelet Decomposition Method with Three Different Wavelet Families to Identify the Best Wavelet for Noise Removal

Noise Removal of EMG Signal for Healthy Person Using Wavelet...

引用

2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Dubey, Alok Singh, Rupali Rai, Amrita Department of Electronics and Communication Engineering SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad India ECE Department Lloyd Institute of Engineering and Technology Greater Noida India

ISBN: (纸本)9798350355338

Electromyography (EMG) is a study of muscle function and finds application in biomedicine and clinical settings. During myoelectrical simulation, EMG signal records useful muscle responses and noise. Noise is originated normally from technical and biological sources which interferes with muscle activity analysis and thus, requires careful study. Removal of this noise from the EMG signal is crucial which can be carried out using different techniques. This paper explores the wavelet decomposition technique for the removal of noise from EMG signal with different types of mother wavelets. Further, the analysis is performed to identify the best mother wavelet to produce processed biomedical signal. Moreover, the Signal-to-Noise Ratio (SNR) for the noise free EMG signal has been estimated to determine the best wavelet for removing the noise from the biomedical signal. © 2024 IEEE.

关键词： Wavelet decomposition

来源：评论

学校读者我要写书评

暂无评论

Benchmarking Semantic Segmentation Approaches for Polyp and Lesion Detection in Medical Imaging 2

Benchmarking Semantic Segmentation Approaches for Polyp and ...

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Pandiyaraju, V. Sunil, Meghana Shravya, V. Venkatraman, Shravan Kannan, A. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Anna University Department of Information Science and Technology Chennai India

ISBN: (纸本)9798331534950

In medical imaging, semantic segmentation is essential because it can precisely locate and isolate regions of interest, such lesions or tumours, from intricate anatomical systems. Deep learning (DL) has led to a substantial evolution in segmentation approaches, which have improved medical diagnostics' accuracy and efficiency. The performance of six cutting-edge models is assessed in this paper: LinkNet, U-Net with ResNet50, LinkNet with ResNet50, FPN Net, Simple U-Net, and SegNet-Transformer across four segmentation tasks - polyp detection, lung lesion segmentation from Computed Tomography (CT) scans, breast lesion detection from ultrasound images, and Segmenting brain tumours with magnetic resonance imaging (MRI). With results like 0.9618 for brain tumour segmentation and 0.9483 for lung lesion segmentation, SegNet-Transformer consistently obtained the highest Dice Coefficient across all tests, demonstrating superior ability in capturing intricate boundaries. Other models, such as U-Net with ResNet50, performed competitively with Dice scores of 0.9375 for lung lesion segmentation and 0.9472 for brain tumors but required slightly more memory and longer training times. This study underscores that model performance varies across different tasks, revealing specific strengths and weaknesses. Ultimately, while SegNet-Transformer demonstrates robustness, the choice of model should be informed by the unique requirements of each segmentation task, balancing accuracy, efficiency, and computational demands. © 2024 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：