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Model-Free H∞Optimal Control of Multi-Agent systems with Intermittent Exploration Noise via Adaptive Dynamic Programming

Model-Free H∞Optimal Control of Multi-Agent Systems with In...

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2024 international Annual conference on Complex systems and intelligent Science, CSIS-IAC 2024

作者： Hu, Shunwei Luo, Yanhong Zhang, Juan Tang, Fanghua College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798331504755

In this paper, the H∞optimal control problem for linear multi-agent systems is studied, and a hierarchical control scheme is designed to reduce the computational complexity. By introducing an additional compensating input indicator, the expected performance indicators are met. The designed output feedback control protocol introduces relaxation parameters, which relax the design requirements of the performance indicators. In addition, by using the idea of adaptive dynamic programming (ADP) at the local level and designing the data set at the collaborative level, the model-free control of the multi-agent system is realized. In the data acquisition phase, intermittent noise detection is used to suppress the system oscillation caused by the introduced external noise. Finally, the effectiveness of the proposed control protocol and algorithm is verified by simulation on the F-16 aircraft model. © 2024 ieee.

关键词： Multi agent systems

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Privacy Preservation on the Cloud data Multikeyword Search Using Deep Learning

Privacy Preservation on the Cloud Data Multikeyword Search U...

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2023 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Antony Samy, I. Arockia Mary, M. Safish Manonmaniam Sundaranar University Abishekapatti St. Xavier's College Department of Computer Science Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350319200

The technology was vulnerable to online hackers and assaults on the open and dispersed nature of the cloud. To recognize and stop both internal and external attacks in a cloud ecosystem with high identification accuracy and minimal false alerts, it should be essential to create an anomalous network platform. In this study, researchers proposed an intelligent method of instantaneously creating an effective and hybrid classification method called Network Privacy Preservation based K-Means Recurrent Neural Network (NPK-RNN) predicted using both of normalized K-means segmentation method to RNN & Privacy Preservation that results would be known as NPK. The researchers use three freely accessible data sets to detect negative/offensive comments on social media in multilingual languages to respond to such an in-depth study. The proposed approach is to improve the privacy preservation on searching the data on cloud using multikeyword. As demonstrated empirically by comparison with other classification techniques developed successful invasions. © 2023 ieee.

关键词： and Privacy Preservation Artificial Intelligence Classification Model Cloud computing Clustering Algorithm Recurrent Neural Network

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advanced Techniques in data Ingestion and Pipelining for Scalable Big data Platforms: A Comprehensive Review 4

Advanced Techniques in Data Ingestion and Pipelining for Sca...

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4th ieee international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Murarka, Sachin Jain, Anshuj Singh, Laxmi Rabindranath Tagore University Department of Electronics & Communication Engineering Bhopal India Scope Global University Department of Electronics & Communication Engineering Bhopal India

ISBN: (纸本)9798331518981

In the era of big data, effective data ingestion and pipelining are critical for organizations to harness the power of vast and diverse data sources, such as IoT devices, social media, and transactional systems. data ingestion refers to the process of systematically collecting and importing data for processing, while data pipelining ensures the continuous flow of this data through various transformation stages, preparing it for analysis. As data volumes grow, platforms like Hadoop, with its HDFS (Hadoop Distributed File System) and MapReduce, have become essential in managing large-scale datasets by providing distributed storage and parallel processing capabilities. Additionally, tools such as Apache Kafka and Apache Flume facilitate both batch and real-time data ingestion, ensuring flexibility in handling different types of data streams. However, challenges remain, especially with the increasing complexity of datasets and the growing need for real-time processing. Emerging technologies such as cloud-native ingestion frameworks, edge computing, and AI-enhanced pipelines offer solutions to these challenges by improving scalability, performance, and cost efficiency. Machine learning is also playing an evolving role in optimizing pipeline workflows, enabling more intelligent and adaptive data processing. This paper reviews key technologies for data ingestion and pipelining, discusses their limitations, and explores cutting-edge trends aimed at enhancing the efficiency of big data platforms. These advancements promise to reshape how organizations handle big data, offering improved agility and operational effectiveness. In addition to established technologies, the rise of cloud-native architectures and AI-driven solutions is revolutionizing data ingestion and pipelining. Cloud platforms offer scalable, on-demand resources, while AI and machine learning enhance automation, anomaly detection, and predictive analytics within pipelines. © 2024 ieee.

关键词： Cloud platforms

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Prediction of Suicidal Behaviour among the users on Social Media using NLP and ML 4

Prediction of Suicidal Behaviour among the users on Social M...

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4th international conference on Emerging systems and intelligent computing, ESIC 2024

作者： Mishra, Tapas Kumar Sucharitha, G. Siddhartha, Narala Raju, Bommena Mohanty, Sachi Nandan Srm University-Ap Dept of Computer Science and Engineering Andhra Pradesh 522240 India Institute of Aeronautical Engineering Dept of Electronics and Communication Engineering Telangana India Telangana India VIT-AP University School of Computer Science and Engineering Andhra Pradesh Amaravati India

ISBN: (纸本)9798350349856

suicide attempts. Our findings contribute to the growing field of suicide prevention through cutting-edge machine learning techniques applied to natural language data. According to our experimental results, the PM model outperformed other machine learning algorithms with the highest classification score. It achieved an impressive accuracy of 92% and an F1 score of 85%. These findings highlight the effectiveness and potential of the PM model for the task at hand. Suicides are happening more often these days. Social media messages and chats are the preferred means of expression for those who are committing suicide. Several studies have shown that it is possible to spot suicide suspects from their online conversations and posts on social media. Consequently, it is imperative to create a machine learning system for automatic early identification of suicide ideation or any abrupt changes in a user's behaviour by examining his or her posts and chats on social media such as Twitter, Instagram, WhatsApp, and Facebook. Our research is focused on significantly improving the performance of our model to accurately recognize early indications of suicide attempts with high precision, thus contributing to the prevention of such tragic events. To achieve this, we utilized advanced text pre-processing techniques and feature extraction approaches, including CountVectorizer and word embedding. Our study involved training both XGBoost and NLP models, which were rigorously evaluated using a substantial dataset of 34,100 samples. Furthermore, to assess real-world applicability, we conducted live tests of our model using tweets collected via the Stream lit python-based web interface tool. The results of our experiments are promising, demonstrating the potential of our approach in detecting early signs of distress and aiding in the timely intervention to prevent. © 2024 ieee.

关键词： Natural language processing systems

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Convolutional Neural Network-based image tamper detection with Error Level Analysis 2

Convolutional Neural Network-based image tamper detection wi...

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2nd ieee international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Manjunatha, S. Swetha, M.D. Rashmi, S. Subramanian, Ananda Kumar Vinoth Kumar, V. Mallikarjuna Swamy, S. Global Academy of Technology Department of Information Science and Engineering Bengaluru India BNM Institute of Technology Department of Computer Science and Engineering Bengaluru India Bengaluru India Vit - University School of Computer Science Engineering Dept of Computational Intelligence Vellore India Vit - University School of Computer Science Engineering and Information Systems Vellore India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350306415

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution. © 2024 ieee.

关键词： Convolutional Neural Network Deep Learning Efficiency Error Level Analysis Image Forgery Detection

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Reinforcement Learning Pathfinding Algorithm Based on Fusion Information

Reinforcement Learning Pathfinding Algorithm Based on Fusion...

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2024 international conference on intelligent computing and Robotics, ICICR 2024

作者： Zhang, Kun Dai, Yao Ma, Ye Department of Information Systems Dalian Naval Academy Dalian China Missile and Naval Gun Department Dalian Naval Academy Dalian China

ISBN: (纸本)9798350372274

This article proposes an improved Near End Policy Optimization (PPO) routing algorithm that integrates global and local information, and verifies its effectiveness with the routing task in self avoidance walking problems. The paper includes content such as scenario modeling, reward design, and algorithm comparison. Firstly, based on the self avoidance walking subject motion scene modeling, an algorithm input that integrates global and local information is constructed. Convolutional and fully connected networks are concatenated as the network model. Then, a three-stage reward project is designed that includes immediate rewards, stage rewards, and final rewards. Finally, it is compared with Monte Carlo method, BFS, DFS, longest path method, and PPO algorithm that uses global or local information alone. The experimental data results show that compared to traditional algorithms, our algorithm has improved the number of arrivals per round by 42.14% and the score by 40.38%. ©2024 ieee.

关键词： Adversarial machine learning

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Improving Access Control in Heterogeneous Industrial Big data systems

Improving Access Control in Heterogeneous Industrial Big Dat...

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2024 international Russian Automation conference, RusAutoCon 2024

作者： Poltavtseva, Maria Aleksandrova, Elena Institute of Computer Science and Cybersecurity Peter the Great St. Petersburg Polytechnic University St. Petersburg Russia

Digital systems integration technologies, digital twins, intelligent control technologies, data-driven methodologies are already widely used in modern industry. Big data is becoming the basis for advanced manufacturing solutions. Distributed heterogeneous (multi-modal) industrial big data systems are appearing in large enterprises. However, the number of attacks on such systems is also increasing day by day. Multi-modal big data systems have special vulnerabilities due to their heterogeneous nature. The most vulnerable is the access control system. Heterogeneous components have different data structuring and security models, which makes it difficult to manage access and develop a unified security policy. In this paper, a new method is advanced to improve the access control system and solve this problem. It is based on a new conceptual data model and distributed audit technology, which were applied earlier. The new method involves finding the optimal unified security policy using multivariate optimization. The final architecture of the access control subsystem includes components of distributed auditing;analysis, security policy design and implementation. © 2024 ieee.

关键词： Smart manufacturing

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The Allocation of Channels Within Cognitive Radio Based Smart Grid Using RHGO with Deep Q Probabilistic Procedure

The Allocation of Channels Within Cognitive Radio Based Smar...

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2024 ieee international conference on Information Technology, Electronics and intelligent Communication systems, ICITEICS 2024

作者： Hosen, Md Sabbir Shamim, Md. Mehadi Hasan Silmee, Sidratul Montaha School of Electronic Engineering Beijing University of Posts & Telecommunications Beijing China Indian Institute of Technology Madras Department of Energy Systems Chennai India Daffodil International University Department of Eee Dhaka Bangladesh

ISBN: (纸本)9798350382693

Compared to conventional electricity grids, Smart Grids have advanced features more as a result of smart communications. The cognitive radio sensor networks (CRSNs) are being used to make up the increased demands for quality of services in Smart Grids. The majority of currently applied techniques are not enough to fulfil the spectrum demand of users, which results in data transmission delays. Therefore, this rock hyraxes group optimization (RHGO) built on deep Q-probabilistic procedure is suggested in this study to enhance the allocation of channels in CRSN Smart Grids. The Q-table is located within the sink nodes that store the users' IDs. The channels send request to user IDs. The RHGO selects the highly prioritized user from the Q-table after searching for the initial demanded users. According to the users' requests, deep probabilistic neural networks (DPNNs) find the unoccupied channel. According to the active and sleeping period, the DPNNs search for unoccupied channels. By utilizing DPNNs, the suggested technique enhances the allocation of channels while taking less time. This study applies the MATLAB program to accomplish the suggested approach. © 2024 ieee.

关键词： Smart power grids

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PowerSched - managing power consumption in overprovisioned systems

PowerSched - managing power consumption in overprovisioned s...

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2024 international conference on Cluster computing

作者： Simmendinger, Christian Marquardt, Marcel Maeder, Jan Schneider, Ralf Hewlett Packard Enterprise Ratingen Germany High Performance Comp Ctr Stuttgart Stuttgart Germany

ISBN: (纸本)9798350383461;9798350383454

Over the last decade both capital costs for the acquisition of modern HPC systems as well as costs to power and cool these systems have increased significantly. In addition to direct operational costs, the investment costs for the infrastructure required to provide the necessary energy and cooling capacity have also risen substantially. In order to shift the ratio between infrastructure costs and system costs in favor of the latter, the High Performance computing Center Stuttgart (HLRS) has decided to size the system Hawk based on energy requirements of typical user workloads. To not overload the data centre's infrastructure the power consumption of the Hawk system has to be constrained. The Hawk system hence is operated as an over-provisioned system where nameplate thermal design power exceeds provisioned power. In this work we explain how the dynamic power management solution called PowerSched which is implemented by Hewlett Packard Enterprise (HPE) at HLRS, is able to balance compute jobs with moderate demands for CPU-power against CPU-power hungry jobs and thus keeps the overall power consumption of the system and its components within the required limits. We elaborate how the solution minimizes power consumption while maximizing the overall performance of all applications executed in the system at any given time. We further demonstrate the effectiveness of the chosen approach and the effects of PowerSched on power consumption at system and component level. Finally, the performance impact at application level is compared against executions with static power capping per CPU and against executions with unconstrained power.

关键词： Power Management Overprovisioned systems Energy Efficiency Dynamic Golden Section Search

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Impact of dataset Balancing on Machine Learning-Based Intrusion Detection systems 10

Impact of Dataset Balancing on Machine Learning-Based Intrus...

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10th ieee international conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Yusri, Muhammad Iqbal Habaebi, Mohamed Hadi Gunawan, Teddy Surya Mansor, Hasmah Kartiwi, Mira Nur, Levy Olivia International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Telkom University School of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9798350365283

Intrusion Detection systems (IDS) are indispensable for cybersecurity, as they safeguard networks from increasingly sophisticated and sophisticated cyberattacks. This paper assesses the influence of dataset balancing on the performance of machine learning-based IDS, thereby addressing the challenge of imbalanced data in detecting network intrusions. We concentrate on three IDS implementations: Tree-based intelligent IDS, Multi-Tiered Hybrid IDS (MTH-IDS), and Leader Class and Confidence Decision Ensemble (LCCDE). We utilized the Synthetic Minority Over-Sampling Technique (SMOTE) to balance data and implemented feature selection and hyperparameter optimization to improve the model's performance using the CICIDS 2017 dataset. The LCCDE model exhibits the highest performance, as our comparative analysis demonstrates that the combination of SMOTE and feature selection enhances the F1 scores. The results underscore the significance of advanced ensemble techniques and data preprocessing in developing resilient IDS. This research emphasizes the necessity of ongoing optimization and evaluation of IDS models to guarantee effective protection against the development of cyber threats. © 2024 ieee.

关键词： Network intrusion

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