检索结果-内蒙古大学图书馆

7th International Conference on electronics, communication and Aerospace Technology, ICECA 2023

作者： Sudhakar, K. Mahaveerakannan, R. Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350340600

A heart attack occurs when the veins and arteries that provide blood to muscle get blocked. The hazards and obligations of modern life are shared equally by people of both sexes. When people have a heart attack, they often don't know what to do, so they put off getting help, which can make the injury worse and, in most cases, proves fatal. Medical diagnosis using data mining techniques has been explored by a number of researchers with promising outcomes. Different approaches can either predict a single disease or a large range of diseases. Improved health prediction is possible as well. This article discussed in depth the many methods of data categorization that are currently in use. In the field of IT, data categorization is common yet computationally demanding. In order to solve optimisation problems, metaheuristics are widely used. Metaheuristic methods can be used to increase a model's precision in computation. Artificial intelligence is widely used for early illness detection, severity appraisal, and prediction. To address this issue, our IoT platform employs an Optimised Self-Adaptive Bayesian algorithm (OSABA) to deliver more accurate diagnoses of HD. The MSABA is applied to the collected sensor data in order to ascertain its normalcy or abnormality. Patients, health care budgets, and disease progression can all benefit from this strategy. This research makes an effort to categorise and forecast human cardiac disease by using machine learning and metaheuristics. We may summarise the usefulness of the proposed OSABA by comparing it to already-existing models. Results metrics demonstrate the superiority of the proposed OSABA-based prediction system. Following the recommended procedure, we show that the OSABA outperforms state-of-the-art classifiers on the fullest datasets. © 2023 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Innovative Approach for Early Heart Disease Prediction: Gaussian Mixture Model and K-Nearest Neighbour 3

Innovative Approach for Early Heart Disease Prediction: Gaus...

引用

3rd International Conference on Intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Surendran, R. Anandaraj, B. Deepa, P. Mageshwari, M. Balamurugan, K.S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Madnapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Electronics and communication engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

An Electrocardiogram (ECG) contributes significantly to early diagnosis and classification of heart diseases, arrhythmia which means irregular heart rate. Regrettably, the process became difficult due to asymmetric and minimal amplitude from recordings of ECG and cannot do the extraction from multiple signals. By using machine learning technology, it can classify and extract multiple samples, but cannot extract the data features from complex data structures. To overcome the limitations, previously introduced metaheuristic optimization (MHO) to improve the classification and extraction. Proposed a method which supports and K-Nearest Neighbour (KNN) Gaussian Mixture Model (GMM) algorithms for determine the abnormalities, predicting cardiac events, and improve diagnostic accuracy. As a result, this can help us to get results more quickly and accurately with sensitivity 99.7% and accuracy 99.2%, for helping healthcare professionals in diagnosing arrythmias, myocardial infarction and any other heart conditions. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Flexible Colon Polyp Detection: A Dual Mode Approach for Detection and Segmentation of Colon Polyps with Optional Inpainting for Specular Highlight Mitigation

引用

SN computer science 2024年第5期5卷 641页

作者： Sushama, Geetha Menon, Gopakumar Chandrasekhara Department of Computer Science and Engineering College of Engineering Chengannur Kerala Alappuzha India Department of Electronics and Communication Engineering College of Engineering Karunagappally Kerala Kollam India APJ Abdul Kalam Technological University Kerala Thiruvananthapuram India

Colorectal cancer (CRC) is one of the top ten cancers in terms of both incidence and mortality rates in India, and it ranks among the top three cancers in the world. Early detection and removal of malicious colorectal polyp regions, which are precursors of this cancer condition, can enormously lessen the deaths due to colorectal cancer. Machine learning, particularly deep learning based on convolutional neural networks (CNNs), is actively employed in the medical domain for the detection and segmentation of malignant regions, but the generalizability of the model remains an open challenge. In this paper, we address the challenge of improving polyp detection, localization, and segmentation in endoscopic images by mitigating selection bias and facilitating on-demand removal of specular effects as needed by medical professionals. Our proposed method incorporates both polyp and non-polyp images for model development and selectively eliminates specular effects to enhance image quality. Additionally we introduce a deep CNN for region suggestion, enabling simultaneous detection, localization, and segmentation of polyp regions. The model is trained on multi center polyp detection and segmentation dataset, PolypGen. To assess generalizability, we evaluate the model on recently published datasets such as the Gastrointestinal atlas-Colon Polyp dataset and Gastrolab-Polyp test dataset, as well as the widely used ETIS-Larib dataset after initially training on different datasets. The evaluation results demonstrate that our model, trained on a multicenter polyp detection and segmentation dataset containing both positive and negative samples, produces promising results. The model consistently exhibits performance metrics, including precision, recall, F1, and F2 scores, exceeding 75% across all evaluated datasets. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Colorectal polyps Detection and instance segmentation Large mask inpainting Region suggestion based deep neural networks Specular reflection removal

来源：评论

学校读者我要写书评

暂无评论

E-waste management in the digital era: A sustainable computing approach

引用

IEEE Potentials 2024年第2期43卷 30-36页

作者： Ramasamy, Rajkumar Gopi, Rahul Sanmugam Palanisamy, Rohini Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai600062 India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India

In the digital era, the proliferation of electronic devices has led to a surge in electronic waste (e-waste), posing significant environmental and health challenges. This article proposes a sustainable computing approach to e-waste management. The article explores the complexities of e-waste management and recycling frameworks within the context of sustainable computing and applications. It delves into the challenges posed by the rapid advancement of technology and the resultant surge in electronic waste. Strategies to mitigate e-waste are discussed, including approaches in product design. The article emphasizes collaborative efforts to address these challenges effectively. By adopting sustainable computing practices and leveraging innovative solutions, the aim is to create a more sustainable approach to managing electronic waste and its environmental impacts. © 1988-2012 IEEE.

关键词： Electronic waste Recycling Green computing Metals Maintenance engineering Soil Regulation Water pollution Energy efficiency Resource management

来源：评论

学校读者我要写书评

暂无评论

A Novel Method for Lung Nodule Segmentation and Lung Cancer Severity Categorization using Deep Learning Models

A Novel Method for Lung Nodule Segmentation and Lung Cancer ...

引用

2023 International Conference on Sustainable communication Networks and Application, ICSCNA 2023

作者： Priscilla, A. Anto Sagaya Balamanigandan, R. Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350313987

The fluctuating nature of lung cancer, which is impacted by recurrent radiation exposure and Computed Tomography (CT) pictures, makes the early detection of the disease challenging. Even seasoned professionals find manually inspecting the provided photos for lung nodules to be tiresome. In this research, a novel hybrid model that combines U-net and Dense Convolutional Network (DenseNet) - 121 is presented to enhance the diagnostic ability and accuracy in classifying the lung cancer disease. In comparison to the current methods, the suggested model for classifying lung nodules and lung cancer is assessed using metrics including accuracy, precision, sensitivity, and F1-Score. It is evident from the comparison results that the suggested model outperforms the current models. © 2023 IEEE.

关键词： CT image DenseNet-121 Lung cancer U-net

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Home Connectivity with IoT-Enabled Smart Mirrors for Internet Browsing and Smart Home Integration 5

Revolutionizing Home Connectivity with IoT-Enabled Smart Mir...

引用

5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Prabha, M. Manicka Jegadeesan, S. Jayavathi, S.D. Rajkumar, G. Vinoth Jothi, J. Nirmal Krishnan, R. Santhana SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Information Technology Madurai India JP College of Engineering Department of Electronics and Communication Engineering Ayikudi Tenkasi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350395235

This research study explores the transformative potential of IoT-Enabled Smart Mirrors to redefine home connectivity by enabling seamless internet browsing and smart home integration. It introduces a novel model algorithm, IoT-ViSense (IVS), which leverages IoT sensor data fusion with vision-based Convolutional Neural Networks (CNN) for real-time environmental monitoring and security. A comprehensive simulation analysis of IoT-ViSense is conducted and compared against existing algorithms using pertinent simulation metrics. IoT-Enabled Smart Mirrors serve as multifunctional interfaces, integrating personalized weather updates, news, calendar schedules, and real-time compliments. They also incorporate features such as a built-in camera for capturing high-quality selfies, on-demand workout sessions facilitated by fitness experts, and the ability to browse the internet, make calls, and interact with other connected devices. The proposed IoT-ViSense (IVS) algorithm combines IoT sensor data with Vision-based CNN analysis to enhance environmental monitoring and security. It continuously processes data from IoT sensors and camera feeds, detecting anomalies and patterns in real-time. By fusing these data streams, IVS enhances security by identifying unauthorized access and suspicious activities while concurrently optimizing resource usage and ensuring comfortable living and working environments. The simulation analysis of IoT-ViSense (IVS) is compared with suitable existing algorithms, assessing its efficiency in terms of accuracy, response time, and resource optimization. Results reveal that IoT-ViSense (IVS) outperforms other algorithms, demonstrating its prowess in achieving a harmonious blend of smart home integration and real-time environmental monitoring. © 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

Optimizing Early Detection of Lung Disease Through Data-Driven Approaches 8

Optimizing Early Detection of Lung Disease Through Data-Driv...

引用

8th International Conference on electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Balapriya, S. Anandaraj, B. Sathish Kumar, P.J. Surendran, R. Balamurugan, K.S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Madnapalle Institute of Technology & Science Department of Computer Science and Engineering Andhra Pradesh Madanapalle India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331520762

Lung illness is any condition that impairs the lungs' capacity to function normally;diabetes, in particular, can have a variety of effects on the lungs and increases the risk of respiratory infections such as pneumonia and TB in those who have it. According to recent studies, 384 million individuals have chronic obstructive pulmonary disease (COPD), and 3 million of them pass away each year as a result of delayed diagnosis. Early detection of any disease can lessen its severity, which will lower the death rate and number of lung disease sufferers. Used deep learning methods for lung detection diagnostics, such as improved fish bee (IFB) and chaotic bird swam optimization (CBSO), despite the prediction rate's high false rate and lack of accuracy. Introduced a technique using machine learning methods, such as the Principal Component Analysis (PCA) algorithm for extraction of features, the Decision Tree approach for sample categorization, and the Support Vector Machine (SVM) for very accurate early illness diagnosis. The results of the suggested approach for early lung tumor identification with a low incorrect rate showed an accuracy of 98.07. © 2025 IEEE.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Regularized Hybrid Deep Learning for DDoS Attack Prediction in Software Defined Internet of Things (SD-IoT) 3

Regularized Hybrid Deep Learning for DDoS Attack Prediction ...

引用

3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Deepthy, J. Radhika, N. Denny, Alosh Department of Electronics & Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India School of Engineering Ernakulam Cochin University of Science and Technology Division of Computer Science and Engineering India

ISBN: (纸本)9798350349207

The availability of online resource is seriously threatened by the fatal DDoS (Distributed Denial of Service) attacks, which can earnestly break up a targeted network or website by flooding it with traffic. DDoS attacks can use a variety of tactics and can be launched from different points around the world, making it difficult to predict when they will occur. In order to improve accuracy by minimizing overfitting of data in CNN, this work proposes an efficient method by combining CNN with Bi-LSTM for predicting DDoS attacks based on a modified loss function with regularization. We test our method on the CICDDOS2019 dataset of network traffic and demonstrate that it can accurately and successfully predict DDoS attacks with an accuracy of 99.8% and an F-score of 97.29%. © 2024 IEEE.

关键词： Denial-of-service attack

来源：评论

学校读者我要写书评

暂无评论

A Multi-input Deep Neural Network Framework for Non-invasive Detection of Anemia using Finger Nail Images 10

A Multi-input Deep Neural Network Framework for Non-invasive...

引用

10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： Krithika, S. Reddy Poreddy, Ajay Kumar Balakrishna, Thunakala Kokil, Priyanka Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai601103 India Lab Department of Electronics and Communication Engineering Chennai600 127 India

ISBN: (纸本)9798350350951

Anemia, characterized by a deficiency in red blood corpuscles or hemoglobin, poses a significant global health challenge, particularly affecting vulnerable populations. Traditional diagnostic methods often involve invasive procedures, posing challenges in resource-limited settings. This study aims to explore non-invasive anemia detection using fingernail images and convolutional neural networks (CNNs) as a promising alternative to conventional diagnostic approaches. The study utilizes a dataset of fingernail images collected from hospitals in Ghana, comprising both anemic and non-anemic cases. The dataset undergoes preprocessing, including selective enhancement of red components, conversion to the CIELAB color space, and feature extraction. A multi-input Deep Neural Network (DNN) framework employing pre-trained CNNs is proposed for anemia classification. The pre-trained CNN architectures include EfficientNet B1, EfficientNet B4, and MobileNet V3. The framework's performance was assessed using two methodologies: The first involved random shuffling of the dataset, followed by division into training, testing, and validation sets, with evaluation metrics including Accuracy, Precision, F1 scores, and a Confusion Matrix. The second employed five-fold cross-validation, measured using accuracy. The evaluation of the proposed DNN framework using both of the methodologies indicates that EfficientNet B4 achieved the highest testing accuracy (97.87%), precision (97.88%), recall (97.87%), and F1 score (97.88%) and a cross-validation accuracy of 97.37% for the first and second methodologies respectively making it best fit for the proposed DNN framework. The findings demonstrate that the proposed framework yields promising results, especially under the second approach, and opens avenues for further exploration in transfer learning, fine-tuning of deep neural networks for multi-input feature integration, and cross-validation. © 2024 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Malware image classification: comparative analysis of a fine-tuned CNN and pre-trained models

引用

International Journal of computers and Applications 2023年第11期45卷 709-721页

作者： Majhi, Santosh Kumar Panda, Abhipsa Srichandan, Suresh Kumar Desai, Usha Acharya, Biswaranjan Department of Computer Science and Engineering Veer Surendra Sai University of Technology Burla India Department of Information Technology Veer Surendra Sai University of Technology Burla India Department of Electronics and Communication Engineering SR University Telangana Warangal India Department of Computer Engineering-AI & BDA Marwadi University Gujarat Rajkot India Department of Electronics and Communication Engineering S.E.A. College of Engineering & Technology Bengaluru India

A crucial part is played by malware detection and classification in ensuring the safety and security of computer systems. In this work, a comprehensive study has been presented for the classification of harmful or malware images that uses a Convolutional Neural Network (CNN) which has been finely tuned and its performance has been compared with five pre-trained models: ResNet50, InceptionResNetV2, VGG16, Xception and InceptionV3. The suggested CNN framework has been trained using the dataset MalImg_9010, consisting of 9,376 grayscale images resized to 128 × 128 pixels. The models have been evaluated based on their F1 score, recall, precision, and accuracy. The experiments that were conducted demonstrate that the fine-tuned CNN model achieves an impressive 0.965 as the F1 score and a 95.57% accuracy. Furthermore, the comparison with pre-trained models reveals the dominance of the presented framework concerning the F1 score and accuracy. The output of the conducted simulation suggests that the fine-tuned CNN approach shows promise for accurate malware image classification. Additionally, the paper discusses potential improvements, such as increasing the number of training epochs and incorporating larger and more diverse malware datasets, including RGB images and a broader range of malware families. The current research article gives valuable observations on various models’ effectiveness for classifying malware images and highlights the future scopes for research incorporating this domain. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image classification

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：