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Metaheuristics with Machine Learning Enabled Information Security on Cloud Environment

computers, Materials & Continua 2022年第10期73卷 1557-1570页

作者： Haya Mesfer Alshahrani Faisal S.Alsubaei Taiseer Abdalla Elfadil Eisa Mohamed K.Nour Manar Ahmed Hamza Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The increasing quantity of sensitive and personal data being gathered by data controllers has raised the security needs in the cloud *** computing(CC)is used for storing as well as processing ***,security becomes important as the CC handles massive quantity of outsourced,and unprotected sensitive data for public *** study introduces a novel chaotic chimp optimization with machine learning enabled information security(CCOML-IS)technique on cloud *** proposed CCOML-IS technique aims to accomplish maximum security in the CC environment by the identification of intrusions or anomalies in the *** proposed CCOML-IS technique primarily normalizes the networking data by the use of data conversion and min-max *** by,the CCOML-IS technique derives a feature selection technique using chaotic chimp optimization algorithm(CCOA).In addition,kernel ridge regression(KRR)classifier is used for the detection of security issues in the *** design of CCOA technique assists in choosing optimal features and thereby boost the classification performance.A wide set of experimentations were carried out on benchmark datasets and the results are assessed under several *** comparison study reported the enhanced outcomes of the CCOML-IS technique over the recent approaches interms of several measures.

关键词： Information security cloud computing intrusion anomalies data mining feature selection classification

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Intelligent Blocking and Prevention of SARS-CoV-2 Based on Evolutionary Reinforcement Learning

Intelligent Blocking and Prevention of SARS-CoV-2 Based on E...

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Congress on Evolutionary Computation

作者： Chengwei He Huaping Hong Lijia Ma Qingling Zhu Yuan Bai College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

In recent years, the global repercussions of SARS-CoV-2 and its variants have posed significant challenges to various areas, including the economic order, transportation, healthcare, and education, and the mitigation and prevention of SARS-CoV-2 and other infectious diseases have received much attention. This paper tries to develop a rational and effective intervention strategy in response to the dynamic evolution of the SARS-CoV-2 epidemic. To address this issue, we first use the SEIR model to analyze the population distribution under the SARS-CoV-2 epidemic, and then integrate a deep Q-neural network (DQN) to reflect an economic quantification model contextualized within the SARS-CoV-2 scenario. Next, we propose an evolutionary deep reinforcement learning to generate an optimal and adaptive intervention strategy, by optimizing the weight sequence of the DQN. The experimental results show the effectiveness of the proposed method in maximizing economic benefits, while ensuring the uninterrupted functionality of medical institutions. Importantly, the learned intervention strategy exhibits the applicability to other infectious diseases.

关键词： Economics Epidemics Infectious diseases Prevention and mitigation Heuristic algorithms Sociology Loss measurement

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YOLOv11-DEC: An Improved YOLOv11 Model for UAV Detection in Complex Contexts

YOLOv11-DEC: An Improved YOLOv11 Model for UAV Detection in ...

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International Conference on computer Supported Cooperative Work in Design

作者： Qiong Song Siwei Liu Kaiheng Dai Kun Bai School of Computer Science Northeast Electric Power University Jilin China College of Intelligent System Science and Engineering Harbin Engineering University Harbin China Xi'an Modern Control Technology Research Institute Xi'an China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

This work addresses the growing civilian applications of drone technology and the associated security risks, particularly with respect to public and air safety. To address these issues, we introduce an improved YOLOv11-DEC model for real-time detection of small-target unmanned aerial vehicles (UAVs) in complex environments. The model incorporates SBA, group normalisation, and detail-enhanced convolution, and integrates RepViT with EMA attention to increase feature representation and detection accuracy. Additionally, we developed a lightweight LSDECD detection head to maintain model size while preserving accuracy. Experiments on the DUT dataset show that YOLOv11-DEC outperforms baseline models such as YOLOv11, in terms of detection accuracy, offering an effective solution for UAV monitoring and defence systems.

关键词： Adaptation models Accuracy Convolution Computational modeling Atmospheric modeling Object detection Autonomous aerial vehicles Feature extraction Load modeling Context modeling

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Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

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computers, Materials & Continua 2023年第5期75卷 4255-4272页

作者： Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Khaled Tarmissi Amal S.Mehanna Abdelwahed Motwakel Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityMuhayil63763Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab *** false information is harmful to society and *** and detecting the spread of fake news in Arabic becomes *** artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake ***,fake news in Arabic is identified by utilizing AI *** article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic *** HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful ***,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and ***,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN *** performance validation of the HPOHDL-FND technique is tested using two Arabic *** outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.

关键词： Arabic corpus fake news detection deep learning hunter prey optimizer classification model

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Mixed Color Channels (MCC): A Universal Module for Mixed Sample Data Augmentation Methods

Mixed Color Channels (MCC): A Universal Module for Mixed Sam...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wei, Yang Ma, Jianfeng Jiang, Zhongyuan Xiao, Bin School of Cyber Engineering Xidian University China Shaanxi Key Laboratory of Network and System Security China School of Computer Science and Technology Chongqing University of Posts and Telecommunication China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Color invariance is critical for computer vision systems since it significantly increases the robustness and effectiveness of the system. MSDA approaches (e.g., FMix and CutMix) have attracted considerable attention in recent years since they are simple, effective, and do not require extra computation con-sumption. By mixing samples, these approaches extend the distribution of training samples. However, the color information of these mixed samples is not changed, which makes it still difficult for trained models to achieve color invariance. To address this issue, we propose a universal module called Mixed Color Channels (MCC) that implements color changes by mixing the sample and its color variants, which enables trained models to achieve color invariance. In the experimen-tal section, we insert MCC into four state-of-the-art MSDA approaches, evaluate its effectiveness, and embed MCC into a non-MSDA method to demonstrate its extensibility. © 2022 IEEE.

关键词： Color

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StraightLine: An End-to-End Resource-Aware Scheduler for Machine Learning Application Requests

StraightLine: An End-to-End Resource-Aware Scheduler for Mac...

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Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC), International Conference on

作者： Cheng-Wei Ching Boyuan Guan Hailu Xu Liting Hu Department of Computer Science and Engineering University of California Santa Cruz Santa Cruz CA USA Geographic Information System (GIS) Center of Florida International University Miami FL USA Department of Computer Engineering and Computer Science California State University Long Beach CA USA

ISBN: (数字)9798331530280

ISBN: (纸本)9798331530297

The life cycle of machine learning (ML) applications consists of two stages: model development and model deployment. However, traditional ML systems (e.g., training-specific or inference-specific systems) focus on one particular stage or phase of the life cycle of ML applications. These systems often aim at optimizing model training or accelerating model inference, and they frequently assume homogeneous infrastructure, which may not always reflect real-world scenarios that include cloud data centers, local servers, containers, and serverless platforms. We present StraightLine, an end-to-end resource-aware scheduler that schedules the optimal resources (e.g., container, virtual machine, or serverless) for different ML application requests in a hybrid infrastructure. The key innovation is an empirical dynamic placing algorithm that intelligently places requests based on their unique characteristics (e.g., request frequency, input data size, and data distribution). In contrast to existing ML systems, StraightLine offers end-to-end resource-aware placement, thereby it can significantly reduce response time and failure rate for model deployment when facing different computing resources in the hybrid infrastructure.

关键词： Training Cloud computing Technological innovation Schedules Heuristic algorithms Machine learning Containers Data models Time factors Servers

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Metric ideals and its Structures in AL-monoids

arXiv

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arXiv 2024年

作者： Regasa Ashale, Tekalign Aklilu Abebe, Girum Venkateswarlu, Kolluru Department of Mathematics College of Natural and Computational Science College Addis Ababa University Addis Ababa Ethiopia Department of Computer Science and System Engineering College of Engineering Andhra University AP Visakhapatanam India

This paper introduces the concept of metric ideals in AL-monoids. We also examine the structure of AL-monoids and describe some of the properties of homomorphism and fundamentalisomorphism *** we introduce and examine... 详细信息

关键词： Algebra

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Robust UAV Policy Learning for Urban Infrastructure Surface Screening*

Robust UAV Policy Learning for Urban Infrastructure Surface ...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Bingqing Du Uddin Md. Borhan Tianxing Chen Jianyong Chen Jianqiang Li Jie Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Unmanned Aerial Vehicles (UAVs) have emerged as pivotal tools for infrastructure inspection, mitigating risks to human life and enhancing operational efficiency. Despite advancements, achieving autonomous surface coverage inspection in GPS-denied environments remains a significant challenge. This paper presents a novel approach to enhance UAV policy learning for surface screening of urban infrastructure in environments lacking GPS. Utilizing Deep Reinforcement Learning (DRL), the methodology enables UAVs to autonomously conduct surface coverage inspections more robustly and efficiently. A key innovation is the Optimal Speed Decision (OSD) model, which allows UAVs to adjust flight speed dynamically based on scene perception, enhancing efficiency and safety. Additionally, an Off-Policy Critic-Based Reward Shaping (OPCBRS) method is introduced to improve learning efficiency and performance by addressing the issue of sparse rewards using offline data. Simulation results confirm the effectiveness of these methods, with the OSD model demonstrating significant improvements in navigating complex scenarios and maintaining a balance between efficiency and robustness over traditional uniform speed policies. This research offers a robust framework for UAV-based infrastructure inspection in GPS-denied environments, advancing autonomous UAV applications.

关键词： Technological innovation Navigation Simulation Inspection Autonomous aerial vehicles Deep reinforcement learning Robustness Real-time systems Safety Robots

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Modeling Method for DFIG-Based Wind Farm in High-Efficiency Real-Time Electromagnetic Transient (EMT) Simulations

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IEEE Transactions on Power Electronics 2025年第9期40卷 13632-13642页

作者： Liu, Yifan Xu, Jianzhong Zhu, Yiyang Tian, Zhaoxuan Zhao, Chengyong Li, Gen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources China Energy Technology and Computer Science Section Department of Engineering Technology and Didactics Ballerup2750 Denmark

With the increasing integration of renewable energy into power systems, electromagnetic transient (EMT) simulation has become indispensable for accurate system analysis. However, the complexity of wind turbine (WT) modeling, characterized by a large number of electrical nodes, poses significant challenges and necessitates substantial real-time simulation hardware. Existing methods for reducing circuit complexity improve simulation efficiency but are each associated with inherent limitations. Aggregation methods sacrifice considerable internal station information, while existing decoupling techniques are constrained by specific requirements. This paper proposes a real-time simulation model for a Doubly Fed Induction Generator (DFIG)-based wind farm (WF) using latency decoupling and a multi-level nested fast and simultaneous solution (M-NFSS) approach, effectively reducing node count while preserving internal details. A WF test model is implemented in RTDS for validation. Results demonstrate highly accurate impedance characteristics and time-domain waveforms of the proposed model, utilizing only 33.3% of the hardware resources compared to the traditional detailed model. © 1986-2012 IEEE.

关键词： Electromagnetic transients

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An In-Depth Study of Cybersecurity Threats and Intelligent Broadcasting Network Topology 1

An In-Depth Study of Cybersecurity Threats and Intelligent B...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Chauhan, Usha Gupta, Manali Galgotias University Department of Electrical Electronics and Communication Engineering Uttar Pradesh Greater Noida India Noida Institute of Engineering and Technology Department of Computer Science and Business System Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665456876

The smart grid has stacked ICT power equipment. Smart grids use modern electrical sensors. Future adoption of smart devices and their interaction with networks may offer cyberthreats. Several smart grid subsystems may be exposed if a component doesn't respond in real time, reducing efficiency and reliability. Superannuated networks create cyber security risks. This article explores a secure cyber infrastructure, related breakthroughs, and cyber security concerns in a complex system. Cybersecurity goals, objectives, and risk assessments are complete. These papers are summarized. Prioritize security. Then, examine smart grid hacks to uncover weaknesses and their repercussions. This research designed a cyber-security plan for breaches, counterattacks, and actions. Future research is discussed. © 2022 IEEE.

关键词： Cybersecurity

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