检索结果-内蒙古大学图书馆

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Singh, Prabhdeep Singh, Pawan Agarwal, Abhay Kumar Kumar, Anil Amity School of Engineering and Technology Department of Computer Science and Engineering Lucknow India Kamla Nehru Institute of Technology Department of Computer Science and Engineering Sultanpur India Amity University Amity School of Engineering and Technology Lucknow India

ISBN: (纸本)9798331529246

Cloud Computing (CC) offers a diverse range of services along with huge data storage across a network. CC has collaborated with varied emerging technologies like IoT because of its numerous advantages. Despite CC's benefits, cloud-based servers are not always trustworthy in real-world scenarios. Users' privacy will be compromised if they send their data straight to cloud platforms since crucial information will be exposed to cloud servers. Thereby, this work develops a data security model in the cloud with two stages, proposed secured data storage and proxy re-encryption. Initially, the data is stored securely in the cloud based on improved AES-based encryption as well as obfuscation-based encryption. Based on the request from the data user, proxy re-encryption is done using Kronecker product-based encryption. The enhanced AES achieved the lowest value of 0.025 for the CCA attack with a key size of 128 bits. Thus, the final encoded key is obtained. © 2025 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

Small Object Detection & Classification In Cardiac Images For the Improvised Early Diagnosis 4

Small Object Detection & Classification In Cardiac Images Fo...

引用

4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Geethavani, B. Elankavi, R. Latha, M. Swarna Yugandhar, J. Varma, S. Vinay Pavithra, K. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andra Pradesh India

ISBN: (纸本)9798350354218

Early diagnosis of cardiac abnormalities plays a crucial role in preventing severe cardiovascular diseases. This paper presents a novel approach for detecting and classifying small objects, such as anomalies, in cardiac images to facilitate early diagnosis and intervention. The proposed methodology integrates various image processing and machine learning techniques, including input image preprocessing, edge detection, boundary extraction, KAZE feature extraction, region mapping, morphological analysis, ensemble learning, and convolutional neural network (CNN) classification, followed by decision-making mechanisms. Initially, the input cardiac images undergo preprocessing to enhance quality and reduce noise, followed by edge detection to identify potential regions of interest. Subsequently, boundary extraction techniques are applied to delineate object boundaries for more accurate analysis. KAZE feature extraction is then employed to capture discriminative features from the identified regions. Next, a region mapping approach is utilized to segment and classify small objects within the cardiac images. Morphological analysis is applied to refine the detected regions and improve classification accuracy. An ensemble learning method is then employed to integrate diverse classifiers for enhanced performance. Furthermore, a CNN classifier is trained on the extracted features to classify the detected objects into relevant categories, facilitating automated diagnosis. Finally, a decision-making mechanism is employed to interpret the classification results and provide actionable insights for healthcare professionals. The proposed approach offers a robust solution for early diagnosis of cardiac abnormalities by effectively detecting and classifying small objects in cardiac images. Experimental results demonstrate the efficacy of the proposed methodology in improving diagnostic accuracy and efficiency, thereby contributing to enhanced patient care and prognosis in cardiovascular

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Spatial and temporal analysis of urban heat island effect over Tiruchirappalli city using geospatial techniques

引用

Geodesy and Geodynamics 2023年第3期14卷 275-291页

作者： Ajay Badugu K.S.Arunab Aneesh Mathew P.Sarwesh Department of Civil Engineering National Institute of TechnologyTiruchirappalli620015Tamil NaduIndia School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014Tamil NaduIndia

Alterations made to the natural ground surface and the anthropogenic activity elevate the surface and air temperature in the urban areas compared with the surrounding rural areas,known as urban heat island *** remote sensors measure the radiation emitted by ground objects,which can be used to estimate the land surface temperature and are beneficial for studying urban heat island *** present study investigates the spatial and temporal variations in the effects of urban heat island over Tiruchirappalli city in India during the summer and winter *** study also identifies hot spots and cold spots within the study *** this study,a significant land surface temperature difference was observed between the urban and rural areas,predominantly at night,indicating the presence of urban heat island at *** diurnal land surface temperature fluctuations are also detected seasonally,with a relatively higher temperature intensity during the *** trend line analysis shows that the mean land surface temperature of the study area is increasing at a rate of 0.166 K/decade with p less than *** using the spatial autocorrelation method with the urban heat island index as the key parameter,hot spots with a 99 percent confidence level and a 95 percent confidence level were found within the urban area.A hot spot with 95 and 90 percent confidence level was identified outside the urban *** spike in temperature for a particular region in the rural area is due to industry and the associated built-up *** study also identified cold spots with a 90 percent confidence level within the rural ***,cold spots with a 95 and 99 percent confidence level were not identified within the study area.

关键词： Urbanization Land surface temperature Urban heat island effect Hot spots Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Deep Learning Approaches for Harmful Brain Activity Detection Using EEG 8

Comparative Analysis of Deep Learning Approaches for Harmful...

引用

8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Bhatti, Shivraj Singh Yadav, Aryan Monga, Mitali Kumar, Neeraj Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Punjab Patiala India

ISBN: (纸本)9798331531539

The classification of harmful brain activities, such as seizures and periodic discharges, play a vital role in neurocritical care, enabling timely diagnosis and intervention. Electroencephalography (EEG) provides a non-invasive method for monitoring brain activity, but the manual interpretation of EEG signals are time-consuming and rely heavily on expert judgment. This study presents a comparative analysis of deep learning architectures, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and EEGNet, applied to the classification of harmful brain activities using both raw EEG data and time-frequency representations generated through Continuous Wavelet Transform (CWT). We evaluate the performance of these models use multimodal data representations, including high-resolution spectrograms and waveform data, and introduce a multi-stage training strategy to improve model robustness. Our results show that training strategies, data preprocessing, and augmentation techniques are as critical to model success as architecture choice, with multi-stage TinyViT and EfficientNet demonstrating superior performance. The findings underscore the importance of robust training regimes in achieving accurate and efficient EEG classification, providing valuable insights for deploying AI models in clinical practice. © 2024 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Analysis of Human Action Recognition for Noisy Videos Using CNN and LSTM

A Comprehensive Analysis of Human Action Recognition for Noi...

引用

2024 IEEE International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Suma, S. Patel, Punyaban Tiwari, Rajesh Gundu, Lavanya Devi, Guntoju Kalpana Raju, K. Srujan Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering & Technology Hyderabad India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of Computer Science and Engineering CMR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798331505134

Human action identification has advanced significantly as a result of the development of deep learning algorithms. Convolutional Neural Networks (CNNs) are known for their adeptness at extracting crucial information within video frames, especially shapes and postures. However, understanding movements necessitates not just individual frames as well as the temporal flow of the movement;this is wherein LSTM (long short-term memory) variants of Recurrent Neural Networks (RNNs) come into play. By analyzing frame sequences, LSTMs are particularly effective in gathering temporal connections, recalling prior motions, and overcoming the gap amongst static features as well as dynamic actions. Even with CNNs and LSTMs' remarkable accuracy in human action recognition (HAR), challenges remain. © 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

Deep Learning-Based Maize Crop Disease Classification Model in Telangana Region of South India

IEEE Transactions on AgriFood Electronics

引用

IEEE Transactions on AgriFood Electronics 2024年第2期2卷 627-637页

作者： Nagaraju, M. Chawla, Priyanka Hyderabad500043 India National Institute of Technology Department of Computer Science and Engineering Warangal506004 India

One of India's main crops, maize, accounts for 2-3% of global production. Disease detection in maize fields has become increasingly difficult due to a lack of knowledge about disease symptoms. Furthermore, manual disease detection methods take a lot of time and are not effective. Recent developments in convolutional neural networks (CNNs) have exhibited remarkable performance in disease recognition and classification. A CNN is a deep learning technique that extracts the features from an image and performs the disease classification effectively. The optimization of hyperparameters is a tedious problem that impacts the performance of a model. The main purpose of the present research is to support future research to configure suitable hyperparameters to a model. In the present work, a deep CNN is proposed for the classification of seven different diseases of maize crop. Several hyperparameters, such as image size, batch size, number of epochs, optimizers, learning rate, kernel size, and number of hidden layers, were tested with various values in the experimental approach. The obtained results show that running the model for 200 epochs improved the classification accuracy with 87.44%. It also states that choosing input image sizes of 168 × 168 and 224 × 224 resulted in a good classification accuracy of 84.66% and 85.23%, respectively. The proposed deep CNN model has attained 85.83% classification accuracy with the Adam optimizer and a learning rate of 0.001. However, the results achieved by other optimizers, such as root-mean-square propagation (81.95%) and stochastic gradient descent (79.66%), are not better when compared with the Adam optimizer. Finally, the results have provided a better knowledge in selecting appropriate hyperparameters to the application of plant disease classification. © 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

A novel brain tumor segmentation and classification model using deep neural network over MRI-flair images

引用

Multimedia Tools and Applications 2025年第17期84卷 17645-17676页

作者： Rajendirane, Rajmohan Ananth Kumar, T. Sandhya, S.G. Hu, Yu-Chen Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus Tamil Nadu Kattankulathur India Department of Computer Science and Engineering IFET College of Engineering Tamil Nadu Villupuram India Department of Computer Science and Engineering Annamalai University Chidambaram India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung City407224 Taiwan

Brain tumors pose a significant health concern globally, with their detection and diagnosis being crucial for timely intervention and treatment planning. These abnormal growths can develop within the brain or originate from other parts of the body, spreading to the brain. They can be benign or malignant, and their impact on cognitive and physical function can vary widely depending on their location, size, and type. Detecting brain tumors involves a combination of imaging techniques and clinical assessment. Magnetic Resonance Imaging (MRI) is one of the primary imaging modalities used for this purpose due to its ability to provide detailed images of the brain's anatomy and pathology. Advanced image processing techniques and machine learning algorithms play an increasingly important role in enhancing the accuracy and efficiency of brain tumor detection. This study presents a comprehensive approach for enhancing brain tumor detection in MRI images using a combination of advanced techniques. Firstly, an improved K-means clustering algorithm is introduced to segment tumor regions effectively. Following this, Median Filtering is applied to refine image quality and reduce noise interference. Subsequently, a deep neural network architecture is employed for precise tumor classification utilizing the BRATS dataset. The proposed methodology aims to improve the accuracy and efficiency of brain tumor detection, offering potential advancements in medical image analysis and diagnosis. Experimental results demonstrate promising outcomes, indicating the effectiveness of the proposed approach in accurately identifying tumor regions in MRI scans. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Tightening QC Relaxations of AC Optimal Power Flow Through Improved Linear Convex Envelopes

引用

IEEE Transactions on Power Systems 2025年第2期40卷 1465-1480页

作者： Narimani, Mohammad Rasoul Molzahn, Daniel K. Davis, Katherine R. Crow, Mariesa L. Department of Electrical and Computer Engineering NorthridgeCA91330 United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Texas A&M University Electrical and Computer Engineering Department College StationTX77840 United States Missouri University of Science and Technology Electrical and Computer Engineering Department RollaMO65409 United States

AC optimal power flow (AC OPF) is a fundamental problem in power system operations. Accurately modeling the network physics via the AC power flow equations makes AC OPF a challenging nonconvex problem. To search for global optima, recent research has developed various convex relaxations that bound the optimal objective values of AC OPF problems. The QC relaxation convexifies the AC OPF problem by enclosing the non-convex terms within convex envelopes. The QC relaxation's accuracy strongly depends on the tightness of these envelopes. This paper proposes two improvements for tightening QC relaxations of OPF problems. We first consider a particular nonlinear function whose projections are the nonlinear expressions appearing in the polar representation of the power flow equations. We construct a polytope-shaped convex envelope around this nonlinear function and derive convex expressions for the nonlinear terms using its projections. Second, we use sine and cosine expression properties, along with changes in their curvature, to tighten this convex envelope. We also propose a coordinate transformation to tighten the envelope by rotating power flow equations based on individual bus-specific angles. We compare these enhancements to a state-of-the-art QC relaxation method using PGLib-OPF test cases, revealing improved optimality gaps in 68% of the cases. © 1969-2012 IEEE.

关键词： Load flow optimization

来源：评论

学校读者我要写书评

暂无评论

Adaptive DDoS Detection with DNN and FUZZY

Adaptive DDoS Detection with DNN and FUZZY

引用

2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Maheswari, K. Elankavi, R. Lahari, Koneti Navitha, Jootoori Manoj, Peta Maheshwar Reddy, Bala Obanna Gari Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350361537

Elastic, on-demand, and completely managed computer system resources and services are some of the highly alluring aspects of cloud computing. Several challenges include not being able to access data entering or leaving cloud services, data theft and misuse, lack of control over access to sensitive data, and advanced threats including distributed denial of service attacks (DDoS) and virtual machine escape. DDoS assaults are among of the most popular. This title demonstrates an innovative architecture that combines the strengths of neural networks and fuzzy neural networks with a well-posed stacking sparse AutoEncoder (AE) for feature learning. The result is an incredibly powerful hybrid that can distinguish between DDoS attack and regular network traffic. This AEF proposal's enhancements result in a smaller network that prevents overfitting by employing a fuzzy expert approach, reduced reconstruction error, and prevented exploding and vanishing gradients. Using performance criteria like detection precision, accuracy, recall, and F1-Score, a comparison between the suggested method and ten cutting-edge techniques will be carried out. To validate the results, experiments will be run on regular NSL-KDD datasets. © 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

An Intelligent System to Predict Diabetic Retinal Diseases Based on Diabetic Attributes

引用

SN computer science 2024年第6期5卷 755页

作者： Eswari, M. Shanmuga Balamurali, S. Ramkumar, K. Department of Computer Applications Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Rajalakshmi Institute of Technology

The most common illness among individuals and the general population in the medical field is diabetes. This is coupled with a careful diabetic retinal that has no signal. The historical record offers unrecoverable insight. To avoid a hallucinatory dispute at the time, early detection of retinal illness is necessary. Positively, the results of diabetic patients have been able to foretell the threat of diabetic-related eyeailments such as Diabetic Retinopathy, Glaucoma, and Diabetic Macular Edema.A diabetic retinal disease prediction exemplary have been created by via system learning rules, such as narrow neural network, bilayered neural network, wide neural network and trilayer neural network, in close proximity to the onset of the disease. The trilayer neural network with various activation functions accuracy based results servedas the foundation for the final model that evaluates real time data to predict the likelihood of *** learner representation accuracy of 0.95and the evaluation phase accuracy of 0.94 are *** ideal diabetic features-based diabetic retinal system outcome occupies a crucial place in the medical decision support system to make prompt assessments in the field of E-healthcare system to connect ophthalmology field to eradicate insulin based eye problems at the time of regular assessment. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Boosting computer-aided Design Diabetic Mellitus Ensemble Machine Learner Retinal eye Disease

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：