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Reinforcement Learning Pathfinding Algorithm Based on Fusion Information

Reinforcement Learning Pathfinding Algorithm Based on Fusion...

引用

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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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...

引用

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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Privacy Preservation on the Cloud data Multikeyword Search Using Deep Learning

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

引用

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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Improving Access Control in Heterogeneous Industrial Big data systems

Improving Access Control in Heterogeneous Industrial Big Dat...

引用

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

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

引用

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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Optimizing Microgrid Energy Management: intelligent Techniques 28

Optimizing Microgrid Energy Management: Intelligent Techniqu...

引用

28th international Electrical Power Distribution conference, EPDC 2024

作者： Meydani, Amir Shahinzadeh, Hossein Nafisi, Hamed Gharehpetian, Gevork B. Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350385502

advanced methodologies like Artificial Intelligence (AI), Consensus Algorithms (CA), and Model Predictive Control (MPC) significantly enhance Microgrid Energy Management (MG EMS). This study highlights how these technologies boost the effectiveness, durability, and eco-friendliness of decentralized energy systems. AI is used for predictive maintenance, demand forecasting, and anomaly detection. CAs facilitate decentralized decision-making among distributed energy resources, as shown through case studies. MPC optimizes energy generation, storage, and consumption using real-time data and predictive models, improving grid stability and economic efficiency. These intelligent methods help MG operators manage modern energy systems, promoting sustainable energy integration and cost optimization. The evolving global energy landscape is expected to benefit greatly from these advancements in MG technology. © 2024 ieee.

关键词： Model predictive control

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Centering on Humans - Intersectionality in Vision systems for Human Order Picking 43rd

Centering on Humans - Intersectionality in Vision Systems fo...

引用

43rd IFIP WG 5.7 international conference on Advances in Production Management systems (APMS)

作者： Flores-Garcia, Erik Jeong, Yongkuk Zuniga, Enrique Ruiz Wiktorsson, Magnus KTH Royal Inst Technol Dept Prod Engn S-15136 Sodertalje Sweden Univ Skovde Sch Engn Sci Dept Intelligent Automat S-54136 Skovde Sweden

ISBN: (纸本)9783031716355;9783031716331;9783031716324

This study applies an intersectional approach to address concerns about diversity of data acquisition when applying computer vision systems in human order picking. The study draws empirical data from a single case study conducted at an automotive manufacturer. It identifies critical factors of intersectionality for the use of vision systems to enrich data collection in human order picking at four levels including form and function, experience and services, systems and infrastructure, and paradigm and purpose. These findings are helpful for mitigating bias and ensuring accurate representation of the target population in training datasets. The results of our study are indispensable for enhancing human-centricity when applying computer vision systems, and facilitating the acquisition of unstructured data in human order picking. The study contributes to enhancing diversity in human order picking, a situation that is highly relevant because of the variations in age, gender, cultural background, and language of staff. The study discusses theoretical and managerial implications of findings, alongside suggestions for future research.

关键词： vision systems diversity human-centricity

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The data Structure of an intelligent System for Developing Educational Programs 10

The Data Structure of an Intelligent System for Developing E...

引用

10th international conference on systems and Informatics, ICSAI 2024

作者： Kaibassova, Dinara Seipisheva, Elmira Mukhametzhanova, Bigul Assanova, Baktygul Sagatbekova, Dinara Astana IT University Department of Computing Engeneering Astana Kazakhstan Abylkas Saginov Karaganda Technical University Department of Information Computing System Karaganda Kazakhstan Kh. Dosmukhamedov Atyrau University Faculty of Physics Mathematics and Information Technology Atyrau Kazakhstan

ISBN: (纸本)9798331510138

The results of the research on supporting the process of formation of educational programs by processing large volumes of poorly structured texts without re-processing and adapting professional content are presented. In connection with this, the design of educational programs involves the procedure of creating a database of professional competencies based on professional standards, which, in turn, will serve as the foundation for the process of developing and updating educational programs. The subject of the study is the content of professional standards, defined as a set of data characterizing learning outcomes and qualification skills. As a result of the work, the authors have created a formal-structural model of representing elements of professional content in the form of entities connected by structural relations. The obtained base of professional competencies based on professional standards will be the basis for the process of formation and updating of educational programs. © 2024 ieee.

关键词： data assimilation

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Software and Hardware Processing Method for Blockchain Light Node SPV Verification 21

Software and Hardware Processing Method for Blockchain Light...

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21st ieee international Symposium on Parallel and Distributed Processing with Applications, 13th ieee international conference on Big data and Cloud computing, 16th ieee international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Ma, Zhan'Gang Yan, Jiandong Zou, Feng Cao, Xixin Peking University School of Software and Microelectronics Dept. Integrated Circuits and Intelligent Systems Beijing China Ruiyuan Venture Capital Co. Ltd. Changzhou Zhonglou Jinlong Holding Group Dept. Development Planning and Strategic Inverstment Changzhou China

ISBN: (纸本)9798350329223

With the application of blockchain light nodes in embedded devices, how to alleviate computing pressure brought by complex operations such as transaction's SPV Verification for CPU of embedded devices and improve the performance of devices in these aspects has gradually become a research topic in industry and academia. This paper proposes a series of methods to improve the performance of blockchain SPV Verification from the perspectives of system architecture and hash computing unit: (1) According to the computational characteristics of SPV Verification, this paper customizes macro instructions and microinstructions for the coprocessor to meet the requirements of flexibility;The built-in dedicated cache holds transaction data fetched from external memory and intermediate data generated by internal Hash computing Unit, which not only prepares transaction data for hash computation, but also avoids frequent access to the bus and external memory. (2) Techniques like two-round unfolded computing, timing-balanced pipeline architecture and optimized adders are adopted to improve the performance of SHA256 computation. (3) When double hash computing is required for transactions, Hash computing Unit can directly perform the second hash computation based on the first hash computation, reducing the frequency of accessing to external memory, thereby improving the performance to a certain extent. Through these methods, the performance of the hardware coprocessor for SVP verification of transactions is more than double that of traditional solutions. © 2023 ieee.

关键词： Blockchain

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Pediatric Vascular Anomalies Recognition with a Swin Transformer Framework 9

Pediatric Vascular Anomalies Recognition with a Swin Transfo...

引用

9th international conference on Image, Vision and computing, ICIVC 2024

作者： Zhang, Xieyang Zhang, Zhao Chen, Siyu Lang, Xun He, Bingbing Zhang, Yufeng School of Information Science and Engineering Yunnan University Kunming China Department of Dermatology Kunming Children’s Hospital Kunming Children’s Hospital Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9798350385991

Vascular anomalies primarily manifest in the head and neck area, impacting roughly one in every 22 children. Early and accurate diagnosis of these anomalies holds the potential to significantly improve physical functioning impairments in children. While computer-aided diagnosis (CAD) system can enhance diagnostic accuracy and address the scarcity of medical resources, existing systems for vascular anomalies identification are limited and mainly focus on binary classification tasks like infantile hemangiomas. Motivated by the above problem, this work develops a classification framework using clinical imaging data from Kunming Children’s Hospital, targeting six common vascular anomalies. Specifically, Swin Transformer was chosen as the backbone network for classification due to its ability to capture long-range dependencies of lesions. In addition, Modified RandAugment was employed to enrich the diversity of lesion data and enhance the robustness of the constructed Swin Transformer. Our experimental results demonstrate that the proposed framework can achieve good overall performance and excel in certain disease recognition tasks. This achievement may promote the development of high-quality CAD systems for diagnosing vascular anomalies in children. © 2024 ieee.

关键词： Pediatrics

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