检索结果-内蒙古大学图书馆

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Shankar, G. Kumar, V.R. Abhishek Srikanth, Akkaru Reddy, Thummala Jathin Sahil, Shaik Rmd Engineering College Department of Computer Science and Engineering India Rmd Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798331510022

To enhance the precision of diagnosis, this research provides a new structure for identifying brain tumors that integrates an Improved Fast Mask Region based Convolutional Neural Network (IFMRCNN) with complex image processing algorithms. Identification of brain tumors is still a significant difficulty in medical imaging since successful therapy depends on early and accurate identification. By precisely identifying tumor boundaries in brain MRI scans, the proposed model makes use of an enhanced version of FMRCNN is intended to enhance separating and categorizing performance. Sophisticated image processing methods are used to improve the extraction of features, allowing the model to more accurately differentiate between tumor and non-tumor areas. By lowering false positives and true negatives, the structure seeks to maintain computing power while greatly increasing detection rates. The proposed strategy outperforms conventional techniques in terms of reliability, specificity, and sensitivity, according to the results of experiments, which makes it a viable tool for support with clinical choices as well as initial diagnosis in neuroimaging. IFRCNN was applied using the Open CV package in conjunction with an SVM algorithm. Using 218 photos as an initial training set, this study's approach demonstrated a 100% success rate in classifying meningiomas and 87.5% accuracy in classifying gliomas, with an average conviction score of 94.6% in the segments of meningioma tumors. A neurologist does a manual examination and ground truth assessment to verify the divided tumor areas. © 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

DenseRSE-ASPPNet: An Enhanced DenseNet169 with Residual Dense Blocks and CE-HSOA-Based Optimization for IoT Botnet Detection

引用

International Journal of Advanced computer science and Applications 2025年第4期16卷 688-699页

作者： Khan, Mohd Abdul Rahim Department of Electrical Engineering and Computer Science A’sharqiyah University IBRA-400 Oman

The growing prevalence of Internet of Things (IoT) devices has heightened vulnerabilities to botnet-based cyberattacks, necessitating robust detection mechanisms. This paper proposes DenseRSE-ASPPNet, an advanced deep learning framework for botnet detection, incorporating comprehensive preprocessing, feature extraction, and optimization. The preprocessing pipeline includes data cleaning and Min-Max normalization to ensure high-quality input data. The DenseNet169 backbone is enhanced with Residual Squeeze-and-Excitation (RSE) blocks for channel-wise attention recalibration and Atrous Spatial Pyramid Pooling (ASPP) for capturing multi-scale spatial patterns, enabling effective feature extraction. Hyperparameter optimization is performed using the Cyclone-Enhanced Humboldt Squid Optimization Algorithm (CE-HSOA), which balances global exploration and local exploitation, ensuring faster convergence and enhanced robustness. Experimental results demonstrate the superior performance of the proposed framework, achieving 99.00 per cent accuracy, 96.40 per cent sensitivity, and 99.95 per cent specificity, significantly minimizing false positives and false negatives. The proposed DenseRSE-ASPPNet provides an efficient, scalable, and effective solution for mitigating botnet threats in IoT environments. © (2025), (science and Information Organization). All rights reserved.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Enhanced segmentation of optic disc and cup using attention-based U-Net with dense dilated series convolutions

引用

Neural Computing and Applications 2025年第9期37卷 6831-6847页

作者： Kumar, G. Bharadwaja Kumar, Soham Department of Computer Science and Engineering VIT Chennai TamilNadu Chennai600127 India

Delineating the boundaries of the optic disc and cup regions is a critical pre-requisite for glaucoma screening because it allows for precise measurement of key parameters, such as cup-to-disc ratio, which is a critical indicator of optic nerve head damage, a hallmark of glaucoma progression. Accurate segmentation enables early detection and monitoring of the disease, aiding in timely intervention to prevent vision loss. The main contribution of this research work is to develop an automated process to isolate and demarcate the optic disc and cup from retinal fundus images. To prevent the blood vessels from interfering with the segmentation process, a novel method is used for vessel mask generation and vessel inpainting. Most of the research works have used based encoder–decoder models like U-Net architecture or handcrafted feature extraction techniques such as hough transform, fuzzy clustering, etc. The proposed model has made significant modifications to the U-Net model. (1) Dual attention mechanism at every layer of decoder and (2) dense dilated series convolutions as skip connections to generate higher level feature map. The proposed model achieved benchmark accuracies - Dice score of 95.95% and IoU score of 92.22% for optic disc segmentation averaged over fivefold. For the task of outlining the optic cup region, it attained a Dice score of 88.7% and IoU of 79.72%. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

A novel cluster based reliable security enhancement in FANET directed by game theory

引用

Soft Computing 2025年第2期29卷 1091-1106页

作者： Gupta, Shikha Sharma, Neetu Department of Computer Science Engineering College Ajmer Rajasthan Ajmer305001 India

The emergence of interconnected UAVs has given rise to the creation of flying ad hoc networks (FANETs) aimed at efficiently facilitating network-dependent services. However, FANET encountered considerable challenges in achieving reliability due to security issues influenced by the existence of malicious nodes. These nodes continuously engage in communication and present a significant threat. These issues are precisely addressed in this article by introducing a novel methodology enhancing network security through a game theory-driven decision tree approach. The proposed strategy involves the implementation of a cluster mechanism based on path similarity techniques. Additionally, an optimized strategy put forth to minimize cluster overhead. Comparing with state-of-the-art protocols in identifying malicious nodes within the network, the results indicate a notable enhancement averaging at 52.84%. Moreover, in aspects such as delay reduction, precision rate increase, and message drop minimization, the experimental outcomes exhibit average improvements of 42.17%, 65.24%, and 46.63%, respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

BEFF-SIGS: blockchain-enhanced fog framework- securing IoT data integrity and green sustainability through scalable authentication-authorization

引用

International Journal of computers and Applications 2025年第3期47卷 293-311页

作者： Khan, Neelam Saleem Mir, Roohie Naaz Chishti, Mohammad Ahsan Department of Computer Science & Engineering NIT Srinagar J&K India

This paper introduces a comprehensive framework designed to address security concerns in the Internet of Things (IoT) environment while contributing to the global Green Environment Initiative. The proposed mechanism utilizes a secure and scalable Authentication-Authorization system, employing JSON Web Token (JWT) and Casbin to ensure that only authorized entities access critical data. Data processing is strategically offloaded to the fog layer to enhance scalability and reduce latency, optimizing IoT data handling. The framework actively supports environmental sustainability by promoting eco-friendly practices within industrial setups. Industries are incentivized to embrace environmentally responsible approaches through tax incentives, fostering a greener future. Our model is a robust mechanism to guarantee the consistency and authenticity of the data stored in the cloud environment, offering assurance against potential tampering attempts by securing data integrity. This holistic approach demonstrates how governments can leverage cutting-edge technology to incentivize industries and promote a sustainable and eco-friendly future. Utilizing blockchain technology ensures unparalleled transparency, robust security, and tamper-resistant record-keeping. We evaluate the impact on network consumption, latency, and energy consumption for analyzing the performance of the proposed system. The proposed framework offers better latency, network efficiency, and distributed computational power as major benefits over depending exclusively on cloud resources in IoT deployments. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

L3Buddy: a location-aware academic content-recommendation system through machine learning based cache techniques

引用

Multimedia Tools and Applications 2025年第5期84卷 2633-2649页

作者： Guddadmani, Ashwini Chougale, Supriya Gokanvi, Megha Tapale, Manisha Pattar, Santosh Department of Computer Science and Engineering KLE Technological University Belagavi Campus India

At present, recommendation systems have become pivotal in personalized education learning management systems, where there is a growing need for location-based suggestions. Our problem addresses the inefficiency of current systems in integrating real-time location data, leading to latency and irrelevant content delivery. We propose L3Buddy, a context-aware system leveraging machine learning for location-specific video recommendations. This approach enhances accuracy and reduces latency, improving the user experience by delivering timely, relevant content. Our results demonstrate significant improvements in both recommendation precision and system responsiveness. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Electrocardiogram-based heart disease prediction using hybrid deep feature engineering with sequential deep classifier

引用

Multimedia Tools and Applications 2025年第10期84卷 7443-7475页

作者： Golande, Avinash L. Pavankumar, T. Department of Computer Science & Engineering KLEF A. P. Vijayawada India

Several approaches can detect early heart problems. Electrocardiograms (ECGs) are better and more affordable for early heart disease prediction. ECG data can better predict heart diseases and abnormalities. Standard methods are slow and limited to a narrow ECG signal pattern range. Newer deep learning-based algorithms require tedious and unimportant human feature extraction from raw ECG data. This study presents a new electrocardiogram-based cardiac disease detection system using an ensemble method for feature extraction and deep learning for classification. The approach includes pre-processing of input ECG signals, hybrid feature engineering, and disease categorization. Pre-processing the input signal preserved its integrity by reducing the artifact and noise using a robust filtering technique. ECG feature extraction should use generic automation and beat-specific features. We designed the hybrid feature engineering approach using a Convolutional Neural Network (CNN) and Stationary Wavelet Transform (SWT). The SWT method employs dynamic thresholding to segment ECG data into individual heartbeats, from which the beat-specific feature vector is derived. To automate the process of classifying cardiac diseases, a powerful 3-layer CNN has been trained to do so and now extracts those attributes automatically. Manifold learning of feature selection is then used to normalize the combined features from the two methods. Long Term Short Memory (LSTM), a deep learning classifier, has been given the ensemble feature vector. For LSTM, the sequential characteristics vector transmits information to the LSTM layer and then calculates the probabilities for each class to provide precise disease classification in the classification phase. The suggested model surpasses cutting-edge deep learning-driven techniques when used with a publicly available multi-disease ECG dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

Comprehensive overview of Alzheimer's disease utilizing Machine Learning approaches

引用

Multimedia Tools and Applications 2024年第37期83卷 85277-85329页

作者： Kumar, Rahul Azad, Chandrashekhar Department of Computer Science and Engineering National Institute of Technology Jamshedpur India

Alzheimer's disease is a common and complex brain disorder that primarily affects the elderly. Because it is progressing and has few effective therapies, it requires a thorough understanding of the condition;our study offers a comprehensive analysis of AD with a dual approach that combines both bibliometric and experimental analyses. The bibliometric analysis applies statistical and mathematical techniques to figure out the states of AD research, including publishing trends, prestigious journals, and collaborative networks. Concurrently, the experimental examination explores current advancements, focusing on Machine Learning, Deep Learning, and Metaheuristic approaches, tackling complex issues resulting from varied datasets. The experimental work is fascinating because it uses twenty classifiers and two datasets, initially without feature selection and then with seven feature selection techniques. This thorough investigation focuses on developments in disease processes, therapeutic approaches, and diagnostic tool development. This research offers a multidimensional overview of Alzheimer's disease by combining bibliometric and experimental methods, addressing problems and highlighting the shortcomings of earlier studies. By helping academics, policymakers, and healthcare professionals navigate the complexities of AD, this novel methodology advances a more thorough understanding of the Alzheimer's disease domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Speech processing

来源：评论

学校读者我要写书评

暂无评论

Content Caching Algorithms in Drone-Aided Ad Hoc Networks

引用

computers, Materials & Continua 2024年第12期81卷 4727-4742页

作者： Yong Beom Park Jian Kim Beom Kyu Suh Ismatov Akobir Ki-Il Kim Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Republic of Korea

Content delivery networks(CDNs)lead to fast content distribution through content caching at specific CDN servers near end ***,existing CDNs based on infrastructure cannot be employed in special cases,such as military ***,a temporary CDN without an existing infrastructure is *** achieve this goal,we introduce a new CDN for drone-aided ad hoc networks,whereby multiple drones form ad hoc networks and quickly store specific content according to new caching *** the typical CDN server,the content-caching algorithm in the proposed architecture considers the limited storage capacity of the *** present three content distribution algorithms that consider the constraints and mobility of *** main contribution of content caching for drone-aided ad hoc networks is to keep partial segments rather than whole content as well as move the drone near to area with a high volume of *** proposed scheme is evaluated to demonstrate its feasibility in terms of content acquisition time and utilization in several practical scenarios through ***,acquisition time in CDN to support drone movement is improved by approximately 50%and 40%rather than one in the proposed naive greedy approach as a function of content request interval and size,respectively.

关键词： Content delivery networks content caching unmanned aerial networks Ad hoc networks partial segment duplicated contents drone

来源：评论

学校读者我要写书评

暂无评论

Editor-in-Chief's foreword: understanding internet of energy (IoE)

引用

International Journal of computers and Applications 2025年第4期47卷 339-340页

作者： Pathan, Al-Sakib Khan Department of Computer Science and Engineering United International University Dhaka Bangladesh

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：