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Key applications and Challenges of Virtual Software Simulation Technology in Data Resource Searching Schemes 4

Key Applications and Challenges of Virtual Software Simulati...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Yueyue, Jiao Shandong Institute of Commerce and Technology Shandong Jinan250103 China

ISBN: (纸本)9798331523923

With the continuous development of big data and complex software systems, virtual software simulation technology plays an increasingly important role in the fields of data resource search and security prevention applications. Inspired by this, this study first analyzes the basic principles of virtual software simulation technology, and proposes a new virtual simulation platform model by combining resource allocation strategy, search algorithm optimization and real-time interactivity, and conducts systematic extensibility research from the perspective of DDos detection. At the practical level, this study introduces a virtual simulation model based on the VERICUT platform to analyze the complex data search process and improve the modeling. The core methods of this research framework include the following three aspects: 1) Proposing a new collaborative management model for automated testing based on data flow;2) Performing coverage testing of multi-dimensional data input and intermediate state parameterization at the practical level;3) Proposing a new DDoS attack analysis and detection algorithm as an extension study. From the experimental results, it can be seen that in 50 sets of independent repeated tests, the DDos detection accuracy of the proposed algorithm is stable between 98% and 100%, with a small fluctuation rang. This shows the stability of the proposed model. © 2025 ieee.

关键词： Virtual environments

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Actor-Critic RL Approach to Stabilize Voltage in DC Microgrids for Linear, CPL, and PPL Load Profiles 14

Actor-Critic RL Approach to Stabilize Voltage in DC Microgri...

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14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Maurya, Rinki Singh, Ashutosh Kumar Electronics & Communication Department IIIT Allahabad Prayagraj India

ISBN: (纸本)9798331531935

Voltage regulation is essential for stable and efficient operation in DC microgrids, especially under varying load conditions, including pulsed power loads (PPL) and constant power loads (CPL). Nonlinear and dynamic load profiles cause significant voltage fluctuations, challenging the performance of conventional control techniques. This paper introduces an Actor-Critic reinforcement learning (RL)-based approach combined with a Proportional-Integral (PI) controller to enhance voltage regulation in DC microgrids. The proposed method dynamically adjusts the duty cycle of DC-DC converters, minimizing voltage variations and ensuring smooth transitions. Simulation results, validated through MATLAB Simulink, demonstrate the superiority of the proposed method over traditional PI controllers. For PPL conditions, the approach achieves a 60% reduction in voltage dip, 50% faster recovery time, and improved damping. Under CPL scenarios, voltage variation is reduced by 57%, steady-state error decreases by 57%, and power quality is enhanced with stable performance. During additional PPL events, the voltage spike is reduced by 55%, settling time improves by 52%, and overall system stability is significantly enhanced. These findings highlight the robustness and adaptability of the proposed hybrid Actor-Critic RL framework in addressing the challenges posed by nonlinear and dynamic load variations in DC microgrids, offering a practical solution for real-world applications. © 2025 ieee.

关键词： Proportional control systems

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NoSQL Database Selection Process: An Integrated Proof of Concept and Comparison for Targeted Document Store Databases 4

NoSQL Database Selection Process: An Integrated Proof of Con...

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4th international conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Sakib, Fahim Faysal Roy, Shuva Dev Rahman, Zahid Saha, Shoumik Salam, Abdus American International University-Bangladesh Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798331530938

The study offers an analysis of NoSQL document store databases focusing on MongoDB, Couchbase, and CouchDB and includes an integrated Proof of Concept (POC) along with a research-based comparison. It begins with an overview of the evolving landscape of Database Management systems (DBMS) highlighting the role of NoSQL databases in overcoming the limitations of traditional relational databases. The research includes a contextual evaluation and qualitative comparison of the three databases incorporating performance metrics to gauge their suitability for various applications. MongoDB is identified as the best in query performance (1.5 ms), transaction support, and integration with cloud platforms. Couchbase shows competitive scalability and good query performance (2.6ms) while CouchDB lacks ACID transactions, optimization, and support barriers and shows query time (6 ms), This analysis highlights each database's strengths and weaknesses providing key insights for real-world deployment. © 2025 ieee.

关键词： Scalability

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Enhanced Power Quality with PV-Driven Active Power Filter in Two-Area applications 3

Enhanced Power Quality with PV-Driven Active Power Filter in...

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3rd ieee international conference on Industrial Electronics: Developments and applications, ICIDeA 2025

作者： Sahoo, Buddhadeva SR University Department of Electrical and Electronics Engineering Telengana Warangal506371 India

ISBN: (纸本)9798331533205

The proposed study presents an advanced PV driven active power filter (PV_APF) based adaptive coordinated control (ADCA) controller aimed at optimizing power flow and improve the power quality (PQ) in large-scale power systems. To offer excellent transient support and provide faster action, a transient control unit (TCU) is also associated with the proposed ADCA. In addition to that, a transient detection unit (TDU) has developed to isolate the PV generation from the main grid within 8-10sec and uses the APF full capacity to provide the desired reactive power, which effectively eliminates the power instability. Moreover, for smooth power transition, a ramp based improved real power recovery strategy (IRPRS) is proposed to restore the PV power output incrementally after the minimization of transient, which improves the settling time as per the standard grid code requirement. During night, the full capacity of APF is applicable for transient mitigation, where as partial shading condition, the APF is applicable for maximum power extraction and stabilize the power fluctuations. The overall system performance is validated through simulation platform at a wide range of possible test cases. The outcomes ensure 24/7 support by delivering substantial enhancements in power transfer capability, enabling stable operation during both local and inter-area oscillations. © 2025 ieee.

关键词： Electric power transmission networks

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YOLOv8 for Personal Device Screen Detection to Preserve Data Privacy: Enhancing Security Measures in real-time Monitoring systems 10

YOLOv8 for Personal Device Screen Detection to Preserve Data...

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10th international conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Nimkoompai, Apichaya Nilsiam, Yuenyong Nuchitprasitchai, Siranee Sirikongtham, Puwadol Thai-Nichi Institute of Technology Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Information Technology and Digital Innovation Department of Information Technology Bangkok Thailand

ISBN: (纸本)9798331543273

In public and semi-public environments, personal device usage often exposes sensitive information, presenting a challenge to user privacy when such data is captured by surveillance systems. This study investigates the application of YOLOv8, an advanced object detection model, to identify and obscure personal electronic device screens, particularly mobile phones, in CCTV footage. Utilizing real-time blurring with a Gaussian kernel, this approach ensures that sensitive information displayed on these devices remains unreadable in video footage. The study aims to address the following research questions: (1) Each version of YOLOv8 varies in its effectiveness for detecting and identifying mobile device screens? (2) What level of Gaussian blurring is sufficient to obscure sensitive information without compromising processing efficiency? Experimental results demonstrate that YOLOv8m consistently excels, particularly in precision and mAP50, making it the ideal choice for applications where high accuracy is essential. Meanwhile, YOLOv8n, as the smallest model, delivers promising outcomes in terms of processing speed. Additionally, a 35x35 Gaussian kernel achieves an optimal balance between privacy protection and computational efficiency. These findings support the feasibility of integrating YOLOv8-based real-time blurring into surveillance systems, providing a robust solution for privacy protection in high-risk surveillance areas. © 2025 ieee.

关键词： Sensitive data

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FedEnv-Net: Federated Neural Network for Decentralized Health Risk Prediction in Dynamic Urban Environments

FedEnv-Net: Federated Neural Network for Decentralized Healt...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Priyadharshini, M. Murugesh, V. Chowdhury, Subrata Dash, Bibhuti Bhsan Chandra De, Utpal Patra, Sudhansu Shekhar Department of Computer Science and Engineering Hyderabad501 203 India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India Sreenivasa Institute of Technology and Management Studies Department of Cse Anthra Pradesh Chittoor India Deemed to Be University School of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798331508685

The predictions of health risks in urban settings are constrained by factors such as pollution and temperature which change over time. Centralized models have problems with privacy and scalability that often do not allow implementing solutions in real-world large, and distributed systems. To this end, we introduce the FedEnv-Net which is a federated neural network to predict health risk in a decentralized manner. Using federated learning, FedEnv-Net train's models directly on edge devices, where data is trained and only updates are shared. Dynamics of temporal patterns and features are predicted by LSTM branch and Dense branch respectively in the model. A Meta-Learner merges such branches for prediction and a tuned loss function for conservative health risk estimations. Although it ensures privacy of data, FedEnv-Net yields an R2 score of 0.92 and an MAE of 0.14, which determines its predictive capability. Future work includes increasing the efficiency of the communication between the system components, broadening the number of input data sources and improving the level of privacy to make FedEnv-Net a versatile, privacy-preserving solution for real-time health risk assessment. © 2025 ieee.

关键词： Decentralized Model Federated Learning Health Risk Prediction LSTM Privacy-Preserving real-time Monitoring Urban Health

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Blockchain-Enhanced 5G Networks: Securing IoTDriven Medical Supply Chains 1

Blockchain-Enhanced 5G Networks: Securing IoTDriven Medical ...

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1st international conference on AIML-applications for Engineering and Technology, ICAET 2025

作者： Nagargoje, Yogesh Shravan, A. Ambudkar, Bhavna Pune India Symbiosis Skills and Professional University Pune India Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798350355611

Managing medical supply chains powered by the Internet of Things (IoT) may be radically altered by integrating blockchain technology with 5G networks, which might radically alter communication and security. An essential component of real-time healthcare applications, ultra-reliable, low-latency communication is promised by the rapidly improving 5G networks. In delicate settings, such as medical supply chains, where interference or manipulation might have serious repercussions, the increasing connection brings additional concerns about the confidentiality, availability, and integrity of data. Blockchain technology provides a strong answer to these problems because to its decentralised, irreversible, and transparent attribute. Examining the ways in which 5G networks and blockchain may function together, this article delves into the ways in which blockchain can improve the safety of medical supply chains that rely on the Internet of Things (IoT). Specifically, it explains how blockchain can guarantee data integrity, prohibit unauthorised access, and make medical items traceable. By leveraging smart contracts, the proposed solution can automate various processes, improving efficiency and reducing human error. We also discuss the scalability of blockchain in handling the vast amounts of data generated in 5G-enabled IoT systems and examine the potential for enhancing trust among stakeholders in the healthcare ecosystem. The findings suggest that blockchain-enhanced 5G networks offer a promising framework for securing and optimizing IoTdriven medical supply chains in the digital age. © 2025 ieee.

关键词： 5G mobile communication systems

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Blockchain Integrated Security Solutions for the Internet of Vehicles: Addressing DoS Attack in Vehicle to Vehicle under AODV 1

Blockchain Integrated Security Solutions for the Internet of...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Meshram, Tanuj Dasgupta, Mou National Institute of Technology Raipur Department of Computer Applications Raipur India

ISBN: (纸本)9798331529246

The Internet of Vehicles (IoV) has become a critical target for security threats;vehicles have heightened the need for robust security mechanisms, particularly against Denial of Service (DoS) attacks. We proposed a blockchain based framework for enhancing IoV security by decentralized event logging in tamper-proof storage, integrated with reputation mechanisms. It detects real-time anomalies by using the novel Drop-to-Forward Ratio (D/F Ratio) metric of the proposed system and enforces penalties on malicious nodes. The performance of the proposed system is evaluated based on simulations conducted for various attack scenarios that ensure a remarkable improvement in the Packet Delivery Ratio (PDR) from as low as 19.14% (under attack) to over 83.74%, and reduces End-to-End (E2E) delay from a maximum of 0.6982 seconds to as low as 0.0016 seconds, ensuring robust and efficient network performance. The results confirm the ability of the framework to maintain network performance and resilience while addressing challenges related to cost-efficiency and scalability. It presents a trustworthy and secure solution that could help reduce the threat of DoS attacks in IoV networks toward much safer vehicular communications. © 2025 ieee.

关键词： Denial-of-service attack

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Building Scalable Airlines Reservation systems: A Microservices Approach Using AI and Deep Learning for Enhanced User Experience 4

Building Scalable Airlines Reservation Systems: A Microservi...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Barua, Biman Mozumder, Mohammad Jashim Uddin Kaiser, M. Shamim Barua, Imon Institute of Information Technology Jahangirnagar University Dhaka Bangladesh Bgmea University of Fashion & Technology Department of Cse Dhaka Bangladesh Bangabandhu Sheikh Mujib Medical University Dhaka Bangladesh SYSystems Co. Ltd. Nagoya Japan

ISBN: (纸本)9798331523923

This study investigates the design and implementation of a scalable airline reservation system through a microservice architecture with AI-based personalization and real-time updates. This architecture, on the other hand, breaks the various core functionalities into independent services so as to deliver a statement of flexibility, scalability, and fault tolerance. This system uses *** as its front end for providing dynamic and user-friendly interfaces for applications while SQL Server will act as the back-end database to store data reliably and manage it effectively. Personalization and optimizations like flight recommendations and fraud detections are made using AI-based frameworks like TensorFlow and PyTorch. The real-time airlines databases and APIs keep flight updates accurate and fresh for the users. A case study prototype simulation where immediate user experience through notifications and personalized flight recommendations can be judged based on the performance of the proposed system pertaining to load handling, latency, and fault-tolerance simulation as compared to traditional reservation systems demonstrates such performance. The former win will subsequently reveal superior scalability, with better optimized response times and resilience at peak times of traffic durability. Also, instantaneous notifications and personalized flight recommendations improve user satisfaction through deep learning models. It can thus be concluded that this system improves significantly in scalability, efficiency, and user engagement by providing very solid and future-proof solutions for modern airline reservations. On one hand by microservices usage and AI, the system provides a pretty much efficient, fault-tolerant, and scalable replacement over conventional reservation frameworks. © 2025 ieee.

关键词： Reservation systems

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Intrusion Detection at the Edge computing: A Deep Learning Approach Using the UNSW-NB15 Dataset

Intrusion Detection at the Edge Computing: A Deep Learning A...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Vipin Kumar Vivek Kumar Abhay Pratap Singh Bhadauria Jay Dixit Amit Kumar Department of Computer Science Gurukula Kangri (Deemed to be University) Haridwar India Department of Computer Engineering & Applications GLA University Mathura Department of Computer Engineering & Applications GLA University Mathura UP

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Edge computing has transformed technology by enabling seamless connections between IoT devices, but it also introduces significant security challenges. EC is crucial for providing minimal latency processing and reducing the load on centralized servers, ensuring timely and scalable intrusion detection for IoT environments. The findings underscore the potential of combining edge computing, IoT, and deep learning to address evolving security challenges in distributed systems. To mitigate these risks, efficient models for detecting malicious activities are crucial. This study investigates the application of edge computing, Internet of Things (IoT), and deep learning to improve the performance of Intrusion Detection systems (IDS). By leveraging the UNSW-NB15 dataset, the research assesses the capability of deep learning models to identify and mitigate cyber threats effectively. The results demonstrate better performance, showing their suitability for real-time security applications. These findings highlight the potential of intelligent intrusion detection systems powered by robust datasets and deep learning for addressing evolving security threats in edge computing environments.

关键词： Deep learning Image edge detection Computational modeling Intrusion detection real-time systems Internet of Things Security Servers Long short term memory Edge computing

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