检索结果-内蒙古大学图书馆

Semantic Tree Based PPDP Technique for Multiple Sensitive Attributes in Inter Cloud

SN computer science 2024年第6期5卷 722页

作者： Gadad, Veena Sowmyarani, C.N. Dayananda, P. Department of Computer Science and Engineering R V College of Engineering Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education

Digital devices and information systems have made data privacy essential. The collected data contains sensitive attributes such as salary, marital status and health history that need to be protected. Such data is exchanged or published to a third party using cloud infrastructure to perform various analyses, conduct research, and make critical decisions. Unauthorized users of the published data may violate privacy, notwithstanding the benefits. Data anonymization is one of the technique for achieving data privacy. Existing techniques consider single sensitive attribute and data is anonymized using generalization or suppression approaches. On observation, it is found that these techniques are less efficient since the collected data contains multiple sensitive attributes when anonymized using the same approaches leads to higher information loss and residue records. In this paper, multiple sensitive attributes are considered and the dataset is anonymized by constructing a semantic hierarchical tree it is further partitioned using the anatomy approach. Later, the partitions are stored in interclouds to achieve better privacy protection. Experiments are conducted to observe and analyze the computational performance, residue records and diversity percentage. The results obtained prove that the proposed technique is efficient when compared to the existing ones. © The Author(s) 2024.

关键词： Anonymization Data privacy Information loss Multiple sensitive attributes Privacy threats

来源：评论

学校读者我要写书评

暂无评论

Ultra-low-defect homoepitaxial micro-LEDs with enhanced efficiency and monochromaticity for high-PPI AR/MR displays

引用

PhotoniX 2024年第1期5卷 335-356页

作者： Yibo Liu Guobin Wang Feng Feng Mengyuan Zhanghu Zhengnan Yuan Zichun Li Ke Xu Hoi Sing Kwok Zhaojun Liu Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong SARChina Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhenChina Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of SciencesJiangsu Institute of Advanced SemiconductorsSuzhouChina

The issue of brightness in strong ambient light conditions is one of the critical obstacles restricting the application of augmented reality(AR)and mixed reality(MR).Gallium nitride(GaN)-based micro-LEDs,renowned for their exceptional brightness and stability,are considered the foremost contenders for AR ***,conventional heteroepitaxial growth micro-LED devices confront formidable challenges,including substantial wavelength shifts and efficiency *** this paper,we firstly demonstrated the high-quality homoepitaxial GaN-on-GaN micro-LEDs microdisplay,and thoroughly analyzed the possible benefits for free-standing GaN substrate from the material-level characterization to device optoelectronic properties and microdisplay application compared with sapphire *** GaN-on-GaN structure exhibits a superior crystal quality with ultra-low threading dislocation densities(TDDs)of~105 cm^(-2),which is three orders of magnitude lower than that of *** an in-depth size-dependent optoelectronic analysis of blue/green emission GaN-on-GaN/Sapphire micro-LEDs from 100×100 shrink to 3×3μm^2),real that a lower forward voltage and series resistance,a consistent emission wavelength(1.21 nm for blue and 4.79 nm for green@500 A/cm2),coupled with a notable reduction in efficiency droop ratios(15.6%for blue and 28.5%for green@500 A/cm^(2))and expanded color gamut(103.57%over Rec.2020)within GaN-on-GaN 10μm *** but not least,the GaN-on-GaN micro-display with 3000 pixels per inch(PPI)showcased enhanced display uniformity and higher luminance in comparison to its GaN-on-Sapphire counterpart,demonstrating significant potentials for high-brightness AR/MR applications under strong ambient light.

关键词： optoelectronic ambient epitaxial

来源：评论

学校读者我要写书评

暂无评论

Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

引用

IEEE Internet of Things Journal 2025年第12期12卷 21784-21792页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

Secure deep learning-based energy efficient routing with intrusion detection system for wireless sensor networks

引用

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8587-8603页

作者： Sakthimohan, M. Deny, J. Elizabeth Rani, G. Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu India Department of Computer Science and Engineering Kalaignar Karunanidhi Institute of Technology Tamil Nadu Coimbatore India

In general, wireless sensor networks are used in various industries, including environmental monitoring, military applications, and queue tracking. To support vital applications, it is crucial to ensure effectiveness and security. To prolong the network lifetime, most current works either introduce energy-preserving and dynamic clustering strategies to maintain the optimal energy level or attempt to address intrusion detection to fix attacks. In addition, some strategies use routing algorithms to secure the network from one or two attacks to meet this requirement, but many fewer solutions can withstand multiple types of attacks. So, this paper proposes a secure deep learning-based energy-efficient routing (SDLEER) mechanism for WSNs that comes with an intrusion detection system for detecting attacks in the network. The proposed system overcomes the existing solutions’ drawbacks by including energy-efficient intrusion detection and prevention mechanisms in a single network. The system transfers the network’s data in an energy-aware manner and detects various kinds of network attacks in WSNs. The proposed system mainly comprises two phases, such as optimal cluster-based energy-aware routing and deep learning-based intrusion detection system. Initially, the cluster of sensor nodes is formed using the density peak k-mean clustering algorithm. After that, the proposed system applies an improved pelican optimization approach to select the cluster heads optimally. The data are transmitted to the base station via the chosen optimal cluster heads. Next, in the attack detection phase, the preprocessing operations, such as missing value imputation and normalization, are done on the gathered dataset. Next, the proposed system applies principal component analysis to reduce the dimensionality of the dataset. Finally, intrusion classification is performed by Smish activation included recurrent neural networks. The proposed system uses the NSL-KDD dataset to train and test it. The

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Timelog: Genetic AI Enabled Timetable Generation 2

Timelog: Genetic AI Enabled Timetable Generation

引用

2nd IEEE International Conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Singh, Divanshu Khandelwal, Avish Kumar, Aniket Bawaskar, Harsh Ugale, Prajakta Mit Academy of Engineering Information Technology Pune Alandi India Mit Academy of Engineering Computer Science Pune Alandi India Mit Academy of Engineering School of Computer Engineering Pune Alandi India

ISBN: (纸本)9798350383287

Efficient timetable creation is paramount for organizational productivity, yet often a labor-intensive task. In this paper, we present 'Timelog', a groundbreaking innovation that harnesses the power of Genetic Algorithms to generate bespoke, conflict-free timetables tailored to user-defined requirements. Timelog empowers users with the ability to customize key parameters such as density, duration, and compatibility, ensuring schedules that perfectly align with their unique *** evaluate the effectiveness of Timelog, we introduce a set of performance metrics, including schedule efficiency and resource utilization, providing a comprehensive assessment of its capabilities. Remarkably, Timelog consistently achieves an astounding accuracy rate of 99% when used in educational institutions compliant with UGC (University Grants Commission) norms, making it an ideal solution for *** seamlessly integrating genetic algorithms, Timelog represents a transformative leap in the scheduling domain, offering organizations a streamlined, efficient, and highly customizable scheduling solution. This innovation promises to revolutionize the scheduling process, significantly enhancing organizational efficiency and productivity. © 2024 IEEE.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Enhancing Early Detection of Pancreatic Cancer: A Machine Learning Approach with Explainable AI Insights

Enhancing Early Detection of Pancreatic Cancer: A Machine Le...

引用

2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Pattanashetti, Neeraj V Mayur, R. Prakash, Adarsh Adiga, Tryambaka B Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bangalore India Global Academy of Technology Dept. of Information Science & Engineering Karnataka Bangalore India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798350376876

Among the most deadly and complex illnesses in the world, pancreatic cancer is typified by its early asymptomatic stage and subsequent discovery of the disease when it has progressed to an advanced stage. Getting the patients in a correct treatment path at the earliest point is important for suitable intervention and patient outcomes in the long run. We utilized advanced algorithms and model prediction techniques includes, feature extraction, hyperparameter tuning and explainability methods which aims to enhance both the comprehension and predictive accuracy of models employed. The goal is to assess patient health status and diagnose pancreatic diseases (cancer vs. non-cancer) by developing a learning algorithm. We obtained a dataset containing demographic information and urine biomarkers to start the investigation. First stage of our analysis employs a single classifier and later we apply bagging and boosting algorithms to achieve ensemble models. Hyperparameter fine-tuning has got a positive outcome, which has Gradient Boosting and Cat Boosting as its top performing models. The ensemble model produced excellent results (92.02 %), demonstrating the usefulness of the suggested approach. LIME and SHAP techniques play the key role in model outcome interpretability providing an insight of algorithmic decision-making process. Such a wide range of combinations of biomarkers have a potential of pancreatic cancer detection in early stages and thus will help shape diagnostics strategies in near future. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

To Study Traffic Behaviors for Detecting Unusual Activities by Analyzing HTTP Packets

To Study Traffic Behaviors for Detecting Unusual Activities ...

引用

2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Deswal, Pooja Lal, Niranjan Singh, Mayank Srm Institute of Science and Technology Computer Science & Engineering Delhi NCR India Jss Academy of Technical Education Computer Science & Engineering Noida India

ISBN: (纸本)9798350344387

Cloud computing refers to the on-demand resource utilization, storage capability, and networking solutions over the Internet. Although, cloud computing is one of the critical mechanisms that provide the online storage solution with a subscription to the cloud environment. However, there are several security, privacy, and data breach issues with cloud computing. Network intrusion detection systems (NIDS) play a vital role in identifying and mitigating potential security threats. Traditional NIDS are not sufficient for complex and dynamic cloud problems, particularly with the widespread use of Hypertext Transfer Protocol (HTTP) as a primary communication protocol. This article proposes a behavioral intrusion detection system based on machine learning techniques to identify attacks by analyzing the HTTP traffic. The system is designed to monitor and analyze network traffic, specifically focusing on HTTP-based communications, to detect and prevent various types of network intrusions. The proposed NIDS employs a combination of supervised and unsupervised machine learning algorithms to handle the diverse nature of cloud-based network traffic. To enhance the accuracy and efficiency of the NIDS, feature engineering techniques are applied to extract relevant features from HTTP network traffic, including packet payload analysis, header inspection, and traffic flow characteristics. The results demonstrate that the system achieves high detection accuracy while maintaining low false-positive rates, showcasing its potential as a reliable and efficient solution for securing cloud-based networks. © 2023 IEEE.

关键词： Decision tree Intrusion detection NIDS Random Forest

来源：评论

学校读者我要写书评

暂无评论

Parkinson's Disease Detection & Self-Stabilizing Spoon with Health Analysis 1

Parkinson's Disease Detection & Self-Stabilizing Spoon with ...

引用

1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Pattankar, Veena V Sikhara Sai, Bodela Yuktha Muzammil, Daniya Ahamed S, Hassain Global Academy of Technology Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350339727

The degeneration of nerve cells, which causes Parkinson's disease, a prevalent neurodegenerative ailment that typically affects elderly people, inhibits everyday function, and hinders mobility. Unintentional shivering and hand tremors are early symptoms that make it difficult to do daily tasks like eating. Caretakers find it difficult to supervise patients remotely. Using IoT and sensor network concepts, including ML prediction, our study intends to develop a stabilising spoon and a health monitoring system. The stabilising spoon maintains the bowl's stability by adjusting to the user's shakes or shivers by aligning its head with these forces. With a gyroscope to monitor motion angles and an accelerometer to measure motion speed, we created a prototype device to improve the patient's feeding process. Our project also entails keeping track of the patient's temperature, Spo2 levels, and heart rate while sending the data to a mobile app for status updates. Early on, there were numerous patients, few physicians were available, and significant medical equipment was used in ICUs and other special care units, necessitating the attendance of one nurse or doctor for every patient in various wards. As a result, the patient could not be continually watched, leading to the development of the following issue formulation. The conventional, large-sized medical test equipment. The patient couldn't be located and assisted in time. Extended patient monitoring. Every patient needs human interaction. A lack of readily available medical equipment. It was difficult to continually verify everything. Due to a dearth of specialists and equipment, the majority of patients perished. This study aimed to identify the requirements of medical physicians for wireless patient monitoring. The system uses plug-in analytic components to handle data, and these components may be quickly combined into plans using a graphical programming environment. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Analysis of Prediction of Pneumonia from Chest X-ray images using CNN and Transfer Learning 5

Analysis of Prediction of Pneumonia from Chest X-ray images ...

引用

5th IEEE International Conference for Emerging technology, INCET 2024

作者： Vinay, K. Shashanka Kodipalli, Ashwini Swetha, P. Kumaraswamy, S. Dept. of Computer Science and Engineering Global Academy of Technology Karnataka Bengaluru India Dept of Artificial Intelligence and Data Science Global Academy of Technology Karnataka Bengaluru India

ISBN: (纸本)9798350361155

A good design for image classification problems is the CNN architecture you presented, which consists of 5 convolution blocks followed by 4 fully connected layers. From the input X-ray images, the convolutional blocks extract pertinent features, and the fully connected layers assist in determining the final classification based on those learned features. You have integrated various approaches to improve the performance of your model. The inputs to each layer are normalized through batch normalization, which can speed up training and enhance generalization. By removing certain neurons at random during training, dynamic dropout helps avoid overfitting. L2 regularization weight decay and learning rate decay are two efficient strategies for preventing overfitting and enhancing the model's capacity to expand to new data. Popular optimization algorithm Adam optimizer effectively neural network training. For binary classification problems like the diagnosis of pneumonia, the loss function for binary Cross-Entropy is the best option. To determine your model's efficacy, you must validate it using benchmark datasets that are available to the general public. You can evaluate your model's effectiveness by comparing its performance to that of current methods by conducting experimental investigations on these datasets. Your model performs well as evidenced by accuracy scores of 90.93%, 89.17% for multi-class classification and binary classification. tasks. Automated methods, such as the one you suggested, might help medical practitioners recognize pneumonia and spot diseased spots in chest X-ray pictures. However, it's crucial to remember that automated systems shouldn't take the place of professional radiologists' and doctors' skills and judgment;rather, they should be used as supportive tools. Medical To ensure accurate diagnosis and suitable patient care, specialists should always review and interpret the system's data. It's also crucial to take into account potential drawback

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Detection of Polycystic Overy Syndrome (PCOS) Using Machine Learning Techniques 1

Detection of Polycystic Overy Syndrome (PCOS) Using Machine ...

引用

1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Jyothi, R. Shivani, H.C. Yashaswi, R. vidyashree Sumanth, R. Global Academy of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350339727

Premature early terminations, infertility, an ovulation, and other factors commonly have an influence on the global female population. Polycystic ovarian disorder (PCOS), which is prevalent in women of reproductive age, has been identified as a significant contributor to the aetiology of infertility. Numerous women of reproductive age are impacted by PCOS. An aberrant generation of masculine chemicals and variations in the levels of female hormones describe this endocrine disorder. This condition raises the chance of infertility and unsuccessful pregnancies by causing ovarian insufficiency. PCOS is distinguished by weight gain, irregular menstruation, abnormal male hormone production, itchy skin, and hirsuteness. Due to the wide range of potential side effects and the presence of other related gynecological conditions, analyzing PCOS may be extremely difficult. Due to the time and money needed for several clinical testing and ovary inspections, patients with PCOS struggle. This study proposes a method for the early diagnosis and prediction of PCOS based on ideal and modest but promising clinical and metabolic markers that may be utilized as early indicators of this disorder. Using a modified version of the LGBM (Light Gradient Boosting Machine) Classifier, PCOS is categorized. © 2023 IEEE.

关键词： Hormones

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：