检索结果-内蒙古大学图书馆

International Conference on Circuit, Power and computing Technologies (ICCPCT)

作者： R I Minu G Nagarajan Jabez J G. Yamini Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of English Veltech Institute of Science and Technology Tamil Nadu India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Utilizing discriminative deep belief networks (DDBNs) within the framework of semi-supervised learning, this technique leverages a combination of a limited set of labeled samples to enhance intrusion prevention efficacy while reducing recognition errors. By integrating both non-local and local regularization techniques, the method effectively tackles the inherent challenge of high error rates in cyber prevention models. Additionally, this approach is designed for implementation on Quantum computing platforms, harnessing advanced machine learning algorithms to significantly improve cybersecurity measures.

关键词： Privacy Quantum computing Machine learning algorithms Data analysis Error analysis Prevention and mitigation Distributed databases

来源：评论

学校读者我要写书评

暂无评论

Design of Nonlinear Delay Differential System for Analyzing Vulnerabilities in Nanoscale Hardware Implants: A Deep Dive into Intelligent computing Networks

引用

Nano 2025年第3期20卷

作者： Asif Raja, Muhammad Junaid Ali Masood, Zaheer Hussain, Ijaz Zameer, Aneela Mehmood, Ammara Raja, Muhammad Asif Zahoor Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Department of Electrical Engineering Capital University of Science and Technology Islamabad Pakistan Department of Computer and Information Sciences Pakistan Institute of Engineering & Applied Sciences Islamabad Pakistan School of Electrical and Data Engineering University of Technology Sydney Australia Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan

In the annals of contemporary innovation, the study of miniature marvels nanoscale hardware implants emerged as a pivotal instrument, scarcely perceptible to the naked eye, embodies a prowess of cutting-edge technologies and clandestine intrigue. The objective of this research is to introduce a time delay nonlinear system for nanoscale hardware implants vulnerabilities that portray the exploitation of a system by utilizing the stupendous knacks of advanced deep bidirectional long short-term memory (LSTM) networks for time series predictions. Firmware-level bugs present the potential for escalating privileges and executing of code remotely beneath the operating system, allowing for infiltration or complete intervention within a computer system. The designed deep bidirectional LSTM is configured to precisely predict and forecast the dynamic states of time delay differential system, offering a robust framework for mimicking delays that commonly manifest in the real cyber-physical systems. To orchestrate the system compromises in real scenarios through the activation of bugged hardware, time delay factors τ1 and τ2 are introduced to account the time delays necessary for exploiting the bugged and patched nodes, respectively. Synthetic data is generated to train the LSTM network for all scenarios of the model for the dynamics of bugged, compromised, patched nodes and these acquired information is used for training, testing and validation purposes regarding exploitation of the bugged hardware. Comparative analysis on exhaustive simulations revealed a minimal difference between the LSTM's predictions and those from the numerical outcomes with MSE in the range of 10-7, underscoring the network's effectiveness, robustness and stability in modeling complex system dynamics of hardware vulnerabilities. © 2025 World Scientific Publishing Company.

关键词： Firmware

来源：评论

学校读者我要写书评

暂无评论

Tele-Rheumatology-Advanced Neural Network Model for Remote Assessment and Management of Rheumatic Conditions 10

Tele-Rheumatology-Advanced Neural Network Model for Remote A...

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Aghalya, S. Sarasu, R. Mishra, Nilamadhab Arunachalam, G. Gobinath, D. Naganathan, Suresh Babu Thandullu St. Joseph's College of Engineering Omr Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Spatial Informatics Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The study presents an advanced neural network model for real-time remote evaluation and treatment of rheumatic diseases using sensor fusion. The model was developed for use in this investigation. The suggested system uses a wide variety of sensing modalities, such as image, motion, and biometric sensors. This is accomplished via artificial intelligence (AI) and the data gleaned from sensors. The neural network, which was trained on a dataset that was both varied and labeled, performs autonomous analyses of input data in order to accurately predict and categorize rheumatic diseases. The integration of the model into remote medical treatment is made easier by the fact that it is run on a Raspberry Pi inside a tele-rheumatology framework. This unique strategy not only improves diagnostic accuracy but also expands rheumatology services to geographically inaccessible or disadvantaged groups. As a result, the landscape of rheumatic healthcare delivery is completely transformed as a result of this new approach. For the purpose of ensuring the ethical use of this revolutionary technology, the procedures for data security and ethical concerns have been given priority. © 2024 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Optimizing Cervical Lesion Detection Using Deep Learning with Particle Swarm Optimization

Optimizing Cervical Lesion Detection Using Deep Learning wit...

引用

International Conference on Advancements in Computational sciences (ICACS)

作者： Zia Khan Saif Ur Rehman Khan Omair Bilal Asif Raza Ghazanfar Ali School of Computer Science and Engineering Central South University Lushan Road Changsha China Department of Computer Science Bahauddin Zakariya University Multan Pakistan Institute of Computing Muhammad Nawaz Sharif University Multan Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Cervical cancer ranks as the second most frequent malignancy in women worldwide. Cervigram is a type of cervical cancer screening tool that uses digital imaging technology. It captures images of the cervix to detect abnormalities or signs of cancerous growth. This method can assist in early detection and prompt treatment, potentially saving lives. Cervical is its reliance on human interpretation rather than automated analysis for detecting cervical abnormalities. In this study, we employed a metaheuristic approach PSO to enhance hierarchical feature learning in the Xception model for classifying cervigram images into cancerous and normal categories. By leveraging PSO, our approach optimizes the feature extraction process, while Xception’s deep learning capabilities facilitate intricate feature extraction, enabling precise differentiation between cancerous and normal cervigrams. We employ residual block with the Xception pre-trained model to facilitate faster convergence during training due to its ability to capture and refine complex hierarchical features. In this study, we utilized the benchmark Cervigram dataset, comprising 2779 Cancer and 1960 Normal images. The experiment demonstrates that our proposed model achieved an accuracy of 97.57%. Furthermore, our model outperforms existing studies and other pre-trained models.

关键词： Deep learning Training Analytical models Accuracy Metaheuristics Benchmark testing Feature extraction Robustness Particle swarm optimization Cervical cancer

来源：评论

学校读者我要写书评

暂无评论

PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PARTIAL OBSERVATIONS 11

PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PART...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Hu, Jiachen Zhong, Han Jin, Chi Wang, Liwei School of Computer Science Peking University China Center for Data Science Peking University China Department of Electrical and Computer Engineering Princeton University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Center for Data Science Peking University Beijing Institute of Big Data Research China

Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Raspberry Pi-Enabled Wearable Sensors for Personal Health Tracking and Analysis

Raspberry Pi-Enabled Wearable Sensors for Personal Health Tr...

引用

2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Karthika, K. Dhanalakshmi, S. Murthy, S. Murugeswari Mishra, Nilamadhab Sasikala, S. Murugan, S. Kcg College of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sri Muthukumaran Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Bharath Institute of Higher Education and Research Department of Electronics and Communication Tamil Nadu Chennai India School of Computing Science and Engineering Vit Bhopal University Madhya Pradesh India Department of Computer Science and Engineering Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350300857

Proactive healthcare management has made personal health tracking and analysis commonplace. Raspberry Pi-enabled wearable sensors assess health in this study. Wearable sensors with the inexpensive Raspberry Pi give a flexible platform for real-time physiological and environmental data collection and analysis. Raspberry Pi's computing, connectivity, and expandability enable personal health tracking. The Raspberry Pi platform readily integrates wearable sensors like heart rate monitor, accelerometer, temperature sensor, and GPS module to detect physical activity, environmental variables, and vital signs. Machine learning algorithms and statistical methods analyze data to reveal a person's health status, trends, and risks. Consumers can receive threshold-based alerts and feedback via the Raspberry Pi platform. This article covers Raspberry Pi-enabled wearable sensor system design, sensor selection, data collection, processing, and UI development. The article describes the system's fitness monitoring, chronic illness management, and early health anomaly detection purposes. Experiments show the system's personal health tracking and analysis. The findings demonstrate the system's data processing and actionable insights. This research study offers a Raspberry Pi-enabled wearable sensor system for health monitoring and analysis. Raspberry Pi with wearable sensors collects, processes, and analyzes real-time data. This study suggests that the low-cost and adaptable phases improve individual well-being monitoring. © 2023 IEEE.

关键词： Data handling

来源：评论

学校读者我要写书评

暂无评论

Integrating Deep Learning in Project Management: Improving Efficiency and Effectiveness in Project Planning and Execution 4

Integrating Deep Learning in Project Management: Improving E...

引用

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

作者： Shukla, Sunil Singh, Jagendra Sahu, Hemant Kanthalia, Shilpa Taufikin Elangovan, Muniyandy Graphic Era Hill University School of Computing Dehradun India Bennett University School of Computer Science Engineering & Technology Greater Noida India Department of Cs & It Udaipur India Faculty of Management Studies Udaipur India Kudus Department of Postgraduate Kudus Indonesia Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Biosciences Chennai India

ISBN: (纸本)9798350354218

The purpose of this research is to explore the use of deep learning technologies, namely the K-Nearest Neighbors model within the textile manufacturing process to test the improvement of the said process. To properly implement the model, we must use real-time data from the process and evaluate the improvement depending on a number of factors: how much does the maintenance cost decrease, how accurate is the inventory management, and how well the program forecasts demand, all of which should lead to an improvement of profit. First, the analysis of the results proved that the implementation of KNN model leads to a significant decrease of maintenance costs: while without the model the reduction was 15%, with it cost maintenance cost a 25% decrease. Second, the usage of a model improves inventory management accuracy: before the use of KNN, it was 85%, while after it rose to 95%. Third, demand forecasting becomes more accurate, as the difference was 75% to the positive without the model and 90% with it. The use of a model improved all aspects, and the most improved was profit: before the implementation of the KNN model, profit was 10%, and it was 40% after, which led to the conclusion that the implementation of deep learning models, namely the KNN model, improves the effectiveness of textile manufacturing process and positively affects the profitability and the production process as a whole. © 2024 IEEE.

关键词： Inventory control

来源：评论

学校读者我要写书评

暂无评论

Multi-dimensional Hybrid Bayesian Belief Network Based Approach for APT Malware Detection in Various Systems 1

引用

International Conference on Cyber Security, Privacy and Networking, ICSPN 2022

作者： Sharma, Amit Gupta, Brij B. Singh, Awadhesh Kumar Saraswat, V.K. Department of Computer Engineering National Institute of Technology Kurukshetra Haryana Kurukshetra136119 India International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Lebanese American University Beirut1102 Lebanon NITI Aayog New Delhi110001 India

ISBN: (数字)9783031220180

ISBN: (纸本)9783031220173

We are living in a digital world where information flows in the form of bits. The world is witnessing rapid transformation in easy of living by utilizing smart devices ranging from Inter of Things (IoT), personal computing to advanced super computers for solving complex problems in domains like medical, transportation, banking and day-to-day living etc. So protecting the digital assets is an important aspect of modern world, which is susceptible to highly advanced Cyber attacks which have wide range of ramifications like financial loss, intellectual property loss and privacy violations etc. The modern Cyber adversaries are evolving with new tactics, techniques and procedures (TTP) for longtime strategic information gathering by neutralizing the victim’s security defenses at the perimeter and system level. The sophistication and complexity of the Advanced Persistent Threats (APT) necessitate developing a new range of security solutions with unconventional defense technologies. The APT payloads are equipped with highly evasive manoeuvers to evade modern security solutions which rely on signature and rule-based methodologies. In these scenarios the malware behavioral analysis assists in detecting APT payloads. However it generates huge amount of behavioral logs which require domain experts review to decide the samples nature which is time consuming, resource intensive and less scalable. We need an automation process to mimic human intelligence by analyzing the internal dependencies of the behavioral patterns to solve the classification problem. So we are proposing an unconventional Bayesian Belief Network-based approach to address the APT malware detection problem by extracting unique features over the malware sample’s static, dynamic, and event analysis. The proposed Threat Detection Bayesian Belief Network Model (TDBBNM) is a combination of three Bayesian models named Static Analysis Bayesian Belief Network (SABBN), Dynamic Analysis Bayesian Belief Network (DABBN) an

关键词： Bayesian networks

来源：评论

学校读者我要写书评

暂无评论

A Case Study of AIOPs in Large Enterprises Using Predictive Analytics for IT Operations AIOPs using Predictive Analytics 5

A Case Study of AIOPs in Large Enterprises Using Predictive ...

引用

5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Sujatha, N. Narayanan Valliammal, Latha Joice Rathinam, E. Lokesh, V.S. Mech, Mukul PG and Research Department of Computer Science Sri Meenakshi Government Arts College for Women Tamil Nadu Madurai India Standard Chartered Global Business Services Sdn Bhd. Kuala Lumpur Malaysia Department of Computer Science Lady Doak College Tamil Nad Madurai India School of Engineering and Applied Sciences University at Buffalo The state University of New York United States School of Computer Science University of Birmingham Birmingham United Kingdom

ISBN: (纸本)9798400709418

Large enterprises and business processes are using Artificial Intelligence for IT Operations (AIOPs) to evaluate the use of predictive analytics and how major businesses are optimized and integrated. A survey will be conducted from a representative sample of big business organizations that have already integrated their IT operations using AIOPs with predictive analytics. This study aims to determine the types of predictive analytics techniques utilized in AIOPs, the degree to which these techniques successfully identify and resolve IT issues, and the advantages that result from their utilization. In addition, the research investigates the limitations that large businesses face when adopting and implementing AIOPs, such as the complexity of the technological aspects, the organizations' resistance, and data quality problems. The data obtained from the survey will be analysed using a research approach that includes both descriptive and inferential statistical methods, such as frequency distribution, correlation analysis, and regression analysis. The findings of this study will provide valuable insights into the use of AIOPs with predictive analytics in large enterprises to optimize IT operations and will identify challenges encountered during implementation and provide recommendations to overcome these challenges. © 2023 ACM.

关键词： Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

A Blackbox Model Is All You Need to Breach Privacy: Smart Grid Forecasting Models as a Use Case*

arXiv

引用

arXiv 2023年

作者： Aly, Hussein Al-Ali, Abdulaziz Al-Ali, Abdullah Malluhi, Qutaibah Qatar University Collage of Engineering Department of Computer Science and Engineering Doha Qatar KINDI Center for Computing Research Qatar

This paper investigates the potential privacy risks associated with forecasting models, with specific emphasis on their application in the context of smart grids. While machine learning and deep learning algorithms offer valuable utility, concerns arise regarding their exposure of sensitive information. Previous studies have focused on classification models, overlooking risks associated with forecasting models. Deep learning based forecasting models, such as Long Short Term Memory (LSTM), play a crucial role in several applications including optimizing smart grid systems but also introduce privacy risks. Our study analyzes the ability of forecasting models to leak global properties and privacy threats in smart grid systems. We demonstrate that a black box access to an LSTM model can reveal a significant amount of information equivalent to having access to the data itself (with the difference being as low as 1% in Area Under the ROC Curve). This highlights the importance of protecting forecasting models at the same level as the data. Copyright © 2023, The Authors. All rights reserved.

关键词： Smart power grids

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：