检索结果-内蒙古大学图书馆

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Varun Somanna, M.N. Yadav, Rakesh Kumar Inzimam Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

This study aimed to quantify the temporal capabilities of radiotherapy facts from sufferers with Hodgkin lymphoma (HL) and correlate those functions with time collection evaluation (TSA) measures to perceive potential temporal relationships with early analysis of HL. Excessive-decision 3-dimensional computed tomography (CT) scans of HL sufferers have been analyzed using batch-clever TSA methods to derive the temporal collection that characterizes the radiotherapy facts. In the end, a set of temporal features from the measured temporal series have been extracted and correlated with HL's medical and laboratory analysis. The outcomes showed that the derived temporal capabilities had undoubtedly correlated with the analysis, indicating a capability for using radiotherapy information to diagnose HL early. The mean and general deviation of the temporal collection in the radiotherapy curves from HL sufferers has been notably one-of-a-kind from wholesome patients. Furthermore, the temporal series of radiotherapy statistics from HL sufferers showed notably better peaks in the temporal capabilities than healthful patients, indicating an elevated stage of radiotherapy for HL sufferers. The effects of this observation recommend that TSA may be a viable tool for early analysis of HL, presenting a more comprehensive view of the temporal traits of radiotherapy data for HL sufferers. © 2024 IEEE.

关键词： capabilities evaluation radiotherapy relationships tomography

来源：评论

学校读者我要写书评

暂无评论

Developing Multi-Scale Patch Representations for Low Energy Data Aggregation Using Unit Block Building Model

Developing Multi-Scale Patch Representations for Low Energy ...

引用

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Bhuvana, J. Kumar, Ritesh Kharbas, Vikas Kumar Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

Developing Multi-Scale Patch Representations using Low energy information Aggregation is a paper examining the development of multi-scale representations from massive aggregated datasets for pc vision duties. The paper presents the low-strength records aggregation (LEDA) framework, an aggregate of electricity-green processes to extract multi-scale representations from big datasets. This paper provides a unique method for developing multi-scale patch representations for image analysis and using low-energy data aggregation. The technique utilizes records aggregation to create texture capabilities from which neighbourhood patch representations are constructed. These representations are then used to assemble more than one scale of features for analysis in pc vision structures. The proposed approach is evaluated for texture segmentation, and the consequences endorse that the patch representations received from the proposed approach outperform those acquired from conventional strategies. The paper additionally discusses the consequences of using different levels of information aggregation for generating patch representations. Subsequently, the paper explores the want for further studies to assess the overall performance gains acquired from the proposed method. © 2024 IEEE.

关键词： aggregation datasets minimizing performance processes

来源：评论

学校读者我要写书评

暂无评论

Deep learning for deepfake detection: Scientific landscape and future directions 4th

Deep learning for deepfake detection: Scientific landscape a...

引用

4th International Conference on Computational Methods in Science and technology, ICCMST 2024

作者： Dhiman, Pummy Kaur, Amandeep Sharma, Dilip Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Engineering and Application GLA University Mathura India

ISBN: (纸本)9781032911571

In today’s digital age, technology has advanced to the point where it is difficult to distinguish be-tween genuine and forged media content. It was intended to be used for entertainment, but it is now being used to deceive people and create biasness, which can lead to disastrous situations. The goal of this bibliometric study is to provide scientific mapping on the role of deep learning in deepfake detection published online. Scopus and VOSviewer are used to create a concept map of 128 articles. Additionally, future directions for research are suggested. © 2025 the Author(s).

关键词： Fake detection

来源：评论

学校读者我要写书评

暂无评论

Selection of Digital Watermarking Techniques for Medical Image Security by Using the Fuzzy Analytical Hierarchy Process

引用

Recent Advances in computer Science and Communications 2023年第7期16卷 74-80页

作者： Ahmad, Masood Nadeem, Mohd Islam, Mohd Ali, Saquib Agrawal, Alka Khan, Raees Ahmad Department of Information Technology Babasaheb Bhimrao Ambedkar University Uttar Pradesh Lucknow226025 India Department of Computer Science & Application Era University Lucknow India

Background: The watermarking technique is a security algorithm for medical images and the patient's information. Watermarking is used for maintaining the robustness, integrity, confidentiality, authentication, and complexity of medical images. Objective: The selection of medical image watermarking technique is multi-criteria decision-making and an automatic way of algorithm selection for security and privacy. However, it is diffi-cult to select a better watermarking technique through traditional selection techniques. Methods: To deal with this problem, a multicriteria-based fuzzy analytic hierarchy process (FAHP) was proposed. This method is applied for algorithm selection for the security of medical images in healthcare. In this method, we first determined the list of criteria and alternatives, which directly affect the decision of the medical image security algorithm. Then, the proposed method was applied to the criteria and alternatives. Results: We provided the rank according to the obtained weights of the algorithm. Furthermore, the overall results and ranking of the algorithms were also presented in this article. Conclusion: Integrity was found to have the highest weight (0.509) compared to the other crite-ria. The weight of the other criteria, namely authentication, was 0.165, robustness was 0.151, con-fidentiality was 0.135, and complexity was 0.038. Thus, in terms of ranking, integrity was report-ed to be of the highest priority among the five criteria attributes. © 2023 Bentham Science Publishers.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

An AI-integrated Enhanced Anatomical Brain Tumor Segmentation in Substantia Nigra for Early Disease Identification using Deep Resnet50-CNN

An AI-integrated Enhanced Anatomical Brain Tumor Segmentatio...

引用

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Ganeshkumar, P.V. Prasanna, S. Vels Institute of Science Technology and Advanced Studies Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Application Chennai India

ISBN: (纸本)9798350375442

The early identification of brain tumours is crucial for improving patient prognosis and treatment planning. Recent advancements in neuroimaging techniques have paved the way for the enhanced detection and characterization of these lesions through CT or MRI image scans. During medical image analysis, the feature analysis is important factor to know the object relations, entity resolution information and disease relational properties, from the prior analysis, machine learning models are mostly covered to segment the disease region by retaining non scaling factors. Due to lower precision class, poor segmentation leads low accuracy to identifying the disease regions to produce higher false rates. In this research explores a novel approach to brain tumour identification utilizing Neuromelanin-Sensitive Magnetic Resonance Imaging (NMS-MRI). We propose a novel preprocessing methodology leveraging the Weigner filter to enhance image normalization and overall quality, facilitating more accurate subsequent analyses. Following preprocessing, we employ Cascading Slice Window Volumetry Segmentation (CSWVM) to achieve refined tumor delineation in three-dimensional MRI scans. The segmentation results are further refined through Cross Modality Particle Swarm Optimization (CMPSO)-based feature selection, which effectively identifies the most relevant features across various imaging modalities to optimize the classification process. To classify the detected tumors, further to integrate a ResNet 50-Convolutional Neural Network (ResNet50-CNN), leveraging its deep learning capabilities for robust pattern recognition in neuroimaging data. The proposed system proves higher performance in precision, recall, f1 score by attaining feature selection, and deep learning algorithms, ultimately contributing to enhanced accuracy and effectiveness in brain tumor identification. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

Optimized Sensor Network Design for Real-Time Environmental Monitoring

Optimized Sensor Network Design for Real-Time Environmental ...

引用

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Harron, Syed Shukla, Gaurav Kaushik, Harshita Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

A sensor community is a set of interconnected sensors that can degree, monitor, and report phenomena within the environment. Sensor networks have many packages consisting of actual-time environmental tracking. A good way to effectively use these networks for monitoring, an optimized network layout needs to be employed to ensure that the network can meet the necessities of its operational demands. This paper proposes a fashionable-motive sensor network design framework for real-time environmental tracking. The proposed framework gives a step-by-step technique to optimize a sensor network design for the project at hand. It begins with a version of the surroundings that the sensor community will screen, which is used to generate sensor node configurations and topologies. These configurations and topologies are then used to lay out the sensor community such that its deployment is optimized for the surroundings. The design framework makes use of a community optimization set of rules to attain a configuration that maximizes the network's performance and is capable of meeting its operational requirements. The set of rules operates with the aid of looking through the distance of all feasible sensor community topologies, looking for those that have the first-rate value-benefit ratio. The algorithm can be further progressed by way of the usage of the device, gaining knowledge of strategies, which makes it capable of managing extra complex eventualities. © 2024 IEEE.

关键词： Algorithm Community Eventualities Interconnected Requirements Sensor Strategies Value-Benefit

来源：评论

学校读者我要写书评

暂无评论

Exploiting Convolutional Neural Networks for Automated Pathology Image Classification

Exploiting Convolutional Neural Networks for Automated Patho...

引用

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sharma, Sunil Menaka, C. Singh, Akhilendra Pratap Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Convolutional neural networks (CNNs) are a deep mastering method for the computerized pathology photograph category. With the improvement of superior imaging techniques, the quantity of virtual and representable pathology pix has increased notably. Automatic picture evaluation has become increasingly crucial to exploit those photographs for clinical selections. CNNs have attracted huge interest and were established to be very powerful for automated photograph classification and object detection. The improvement of deep getting-to-know based on Convolutional Neural Networks (CNNs) for computerized pathology photo classes has been a focal point of research in current years. This paper critiques the recent advances in CNNs in automatic pathology image type. The key architectures and algorithms for CNNs are summarized, and their capability applicability in pathology picture classification is discussed. This overview also introduces the maximum hit CNN methods that have been proposed and have proven promising consequences in pathology photograph category duties. The paper concludes with a dialogue of future research directions in this area. Convolutional neural networks (CNNs) are a deep mastering method for the computerized pathology photograph category. With the improvement of superior imaging techniques, the quantity of virtual and representable pathology pix has increased notably. To exploit those photographs for clinical selections, automatic picture evaluation has grown to be an increasingly number of crucial. CNNs have attracted huge interest and were established to be very powerful for automated photograph classification and object detection. The improvement of deep getting-to-know based on Convolutional Neural Networks (CNNs) for computerized pathology photo classes has been a focal point of research in current years. This paper critiques the recent advances in CNNs in automatic pathology image type. The key architectures and algorithms for CNNs are summarized,

关键词： classification Convolutional development discussion pathology

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Representation Learning for Improved Diagnosis of Prostate Cancer on MRI Scans

Hierarchical Representation Learning for Improved Diagnosis ...

引用

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Prabhu, A. Pandeya, Megha Kaushik, Harshita Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

It focuses on using hierarchical illustration mastering (HRL) for the progressed prognosis of most prostate cancers on MRI scans. HRL is a gadget getting-to-know technique using a hierarchy of function vectors to encode record sets, allowing extra complicated non-linear styles to be recognized and applied. In this examination, HRL is compared to a fashionable convolution neural network (CNN) classifier for the challenge of prostate cancer analysis on MRI scans. The effects show that HRL outperforms the CNN classifier, providing a higher accuracy fee and universal predictive performance. Furthermore, HRL results in fewer fake positives and extra correct type accuracy. Its improved overall performance suggests that utilizing HRL in medical decision-making can offer a more correct, practical analysis of most prostate cancers. © 2024 IEEE.

关键词： convolutional improved performance predictive representation

来源：评论

学校读者我要写书评

暂无评论

Internet of Everything: A Review on Smart Healthcare Taxonomy, Research Challenges and Methodologies 14

Internet of Everything: A Review on Smart Healthcare Taxonom...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Raj, Anu Prakash, Shiva Srivastava, Jyoti Madan Mohan Malaviya University of Technolgy Information Technology & Computer Application Department Gorakhpur India

ISBN: (纸本)9798350335095

The Internet of Everything (IoE) has the potential to revolutionize many aspects of our daily lives, including healthcare, business and industrial world etc. Smart healthcare systems that utilize IoE technologies could greatly improve patient outcomes, increase efficiency, and reduce costs. However, there are several challenges that must be overcome in order to develop effective and reliable power supply systems for IoE-enabled healthcare devices. IoE healthcare devices may be deployed in a variety of settings, and the power supply system must be able to monitor and manage these devices remotely. The development of high-performance smart healthcare systems for the IoE requires the development of reliable and scalable power supply systems that can overcome the challenges of distribution, connection, charging technologies, and management. This paper mainly emphasizes on the IoE and their components that are used in the smart healthcare system and rising of the research challenges. In this paper, by addressing the taxonomy of healthcare and research challenges, researchers and businesses can develop innovative solutions that will transform healthcare delivery and improve patient outcomes. © 2023 IEEE.

关键词： Internet of Everything

来源：评论

学校读者我要写书评

暂无评论

Research on Cloud Computing Security technology under computer Big Data Network 3

Research on Cloud Computing Security Technology under Comput...

引用

3rd IEEE International Conference on Electronic technology, Communication and Information, ICETCI 2023

作者： Zhou, Qiang Ou, Wenjisi Zheng, Wei Jiangxi Vocational and Technical College of Information Application Department of Computer Technology Nanchang330043 China

ISBN: (纸本)9798350398410

In this paper, a parameter estimation-based security access integration method for computer network smart data is designed. The scale of data clustering is determined according to the distribution and evolution characteristics in the clustering process. Finally, this paper proposes a new security access control scheme. The system uses local abnormal factors to judge non-safe data and eliminates abnormal data according to the judgment results. The system sorts and outputs the factors that may have abnormal data points in the current time window. The Java object-oriented programming language is used. The network attack will be divided into fuzzy measures of DoS attack and non-DoS attack, and finally, the results of various attacks will be displayed. Simulation results show that this paper's entire access control scheme is highly feasible. The proposed algorithm has a high recognition rate. © 2023 IEEE.

关键词： Access control

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：