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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Arun Kumar S Anand Kumar M Robin Rohit Vincent Anusha M S Uttam Upendra Hegde N Nauman Pasha Dept. of Computer Studies Symbiosis Institute of Computer Studies and Research (SICSR) Pune India Dept. of Information Science and Engineering Networks & IoT Lab Presidency University Bangaluru India Dept. of Computer Science and Engineering Bio-Intelligence Lab Presidency University Bangaluru India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

This study introduces a deep learning-based method for classifying brain tumors using a pre-trained VGG19 convolutional neural network (CNN). By leveraging transfer learning, we adapted the VGG19 model with custom fully connected layers and trained it on MRI images to differentiate between tumor and non-tumor cases. The model’s architecture includes dropout layers to minimize overfitting and enhance generalization while maintaining the robust feature extraction capabilities of the VGG19 base. We developed a Flask web application interface to make the model accessible via web-based user input, allowing users to upload MRI images and receive immediate predictions on tumor presence. Preliminary results show that proposed model can accurately classify brain tumor images with high accuracy, highlighting the potential of deep learning in automating brain tumor diagnostics. This tool could significantly aid clinicians in making rapid and accurate diagnoses, ultimately improving patient outcomes.

关键词： Deep learning Adaptation models Accuracy Magnetic resonance imaging Transfer learning Brain tumors Predictive models Brain modeling Convolutional neural networks Reliability

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A Dynamic Resource-Aware Routing Protocol in Resource-Constrained Opportunistic networks

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computers, Materials & Continua 2022年第2期70卷 4147-4167页

作者： Aref Hassan Kurd Ali Halikul Lenando Slim Chaoui Mohamad Alrfaay Medhat A.Tawfeek Department of Computer Systems and Communication Technologies Faculty of Computer Science and Information TechnologyUniversiti Malaysia Sarawak(UNIMAS)Kota Samarahan94300Malaysia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka2014KSA Unit-Lab of Sciences of Electronics Technologies of Information and TelecommunicationsSfax UniversitySfaxTunisia Department of Basic Sciences First Common Year DeanshipJouf UniversitySakaka2014KSA Department of Computer Sciences College of Computer and Information SciencesJouf UniversitySakaka2014KSA Department of Computer Sciences Faculty of Computers and InformationMenoufia UniversityEgypt

Recently,Opportunistic networks(OppNets)are considered to be one of the most attractive developments of Mobile Ad Hoc networks that have arisen thanks to the development of intelligent *** are characterized by a rough and dynamic topology as well as unpredictable contacts and contact *** is forwarded and stored in intermediate nodes until the next opportunity ***,achieving a high delivery ratio in OppNets is a challenging *** is imperative that any routing protocol use network resources,as far as they are available,in order to achieve higher network *** this article,we introduce the Resource-Aware Routing(ReAR)protocol which dynamically controls the buffer usage with the aim of balancing the load in resource-constrained,stateless and non-social *** ReAR protocol invokes our recently introduced mutual informationbased weighting approach to estimate the impact of the buffer size on the network performance and ultimately to regulate the buffer consumption in real *** proposed routing protocol is proofed conceptually and simulated using the Opportunistic Network Environment *** show that the ReAR protocol outperforms a set of well-known routing protocols such as EBR,Epidemic MaxProp,energy-aware Spray and Wait and energy-aware PRoPHETin terms of message delivery ratio and overhead ratio.

关键词： Opportunistic networks mobile ad hoc networks routing protocols resource-constrained networks load balancing buffer management

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Microservice-Based Fog Testbed for 6G Applications 26

Microservice-Based Fog Testbed for 6G Applications

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26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Kuzmina, Ekaterina Tefikova, Meriem Volkov, Artem Muthanna, Ammar Ateya, Abdelhamied A. Koucheryavy, Andrey Saint-Petersburg State University of Telecommunications Department of Telecommunication Networks and Data Transmission Saint Petersburg Russia Zagazig University Department of Electronics and Communications Engineering Zagazig44519 Egypt College of Computer and Information Sciences Prince Sultan University Eias Data Science Lab Riyadh11586 Saudi Arabia

ISBN: (纸本)9791188428120

This paper provides a real-time fog computing model based on a microservice architecture that enables testing and modeling of eventual implementations of ultra-reliable low-latency communications (uRLLC) services. The work provides fog-based architecture for sixth-generation cellular (6G) applications, including telepresence and uRLLC. A testbed of a robot swarm was developed to prototype the proposed network architecture. Computing tasks are offloaded and handled based on a proposed microservice scheme introduced to meet the 6G requirements. Furthermore, we developed a novel migration scheme for the proposed architecture to support the mobility of end devices. The optimum server for migrating computing tasks is allocated by solving a proposed optimization problem using particle swarm optimization (PSO). All proposed algorithms were implemented in the developed prototype. The proposed work is introduced to provide an architectural foundation for testing fog-based 6G applications and services and to implement and test novel network methods in the future. © 2024 Global IT Research Institute - GIRI.

关键词： Fog

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Automated Lane Changing Through Learning-Based Control: An Experimental Study

Automated Lane Changing Through Learning-Based Control: An E...

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International Conference on Intelligent Transportation

作者： Won Yong Ha Sayan Chakraborty Yujie Yu Samin Ghasemi Zhong-Ping Jiang Department of Electrical and Computer Engineering Control and Networks Lab New York University Brooklyn NY U.S.A.

This paper presents a learning-based methodology for developing an optimal lane-changing control policy for a Remote Controlled (RC) car using real-time sensor data. The RC car is equipped with sensors including GPS, IMU devices, and a camera integrated in an Nvidia Jetson AGX Xavier board. By a novel Adaptive Dynamic Programming (ADP) algorithm, our RC car is capable of learning the optimal lane-changing strategies based on the real-time processed measurement from the sensors. The experimental outcomes show that our learning-based control algorithm can be effectively implemented, adapt to parameter changes, and complete the lane changing tasks in a short learning time with satisfactory performance.

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Hybrid Deep Learning based Attack Detection and Classification Model on IoT Environment 5

Hybrid Deep Learning based Attack Detection and Classificati...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Lal, Jaya Dipti Ayoub, Shahnawaz Hakim, Prashant D. Prabagar, S. Dwivedi, Vijay Kumar Tiwari, Mohit Shri G S Institute of Technology and Science Department of Electronics and Communication Engineering Madhya Pradesh Indore India Shri Venkateshwara University Department of Computer Science Uttar PradeshGujrala Amroha India Thakur College of Science and Commerce Department of Information Technology Maharashtra Mumbai India School of Computer Science and Engineering & Information Science Presidency University Networks and IoT Lab Department of Information Science Engineering Karnataka Bengaluru India Vishwavidyalaya Engineering College Department of Mathematics Chhattisgarh Ambikapur India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665493604

The Internet of Things (IoT) is becoming an active research area because of its largescale challenges and implementation. But security is the major concern while seeing the dramatic expansion in its applications and size. It is challenging to independently put security mechanism in all the IoT devices and upgrade it according to newer threats. Furthermore, machine learning (ML) techniques could better apply the massive quantity of data produced by IoT devices. Thus, several Deep Learning (DL) based algorithms were introduced for detecting attacks in IoT. Therefore, this study develops a galactic swarm optimization with Deep Learning based Attack Detection and Classification (GSODL-ADC) Model in IoT Environment. The presented GSODL-ADC technique concentrates on the identification of attacks in the IoT environment. The presented GSODL-ADC technique utilizes deep autoencoder (DAE) as a classifier model which properly recognizes the attacks in the IoT environment. Followed by this, the GSO approach is utilized for the optimum hyperparameter adjustments of the DAE model, which leads to enhanced attack detection efficacy. The experimental evaluation of the GSODL-ADC algorithm is tested against benchmark dataset. The obtained experimental values signify the betterment of the GSODL-ADC technique for attack recognition purposes. © 2023 IEEE.

关键词： Internet of things

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Extremum Seeking and Adaptive Dynamic Programming for Distributed Feedback Optimization

Extremum Seeking and Adaptive Dynamic Programming for Distri...

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IEEE Conference on Decision and Control

作者： Tong Liu Miroslav Krstić Zhong-Ping Jiang Department of Electrical and Computer Engineering Tandon School of Engineering Control and Networks Lab New York University Brooklyn NY USA Department of Mechanical and Aerospace Engineering University of California San Diego CA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper studies the distributed feedback optimization problem for linear multi-agent systems without precise knowledge of local costs and agent dynamics. The proposed solution is based on a hierarchical approach that uses upper-level coordinators to adjust reference signals toward the global optimum and lower-level controllers to regulate agents’ outputs toward the reference signals. In the absence of precise information on local gradients and agent dynamics, an extremum-seeking mechanism is used to enforce a gradient descent optimization strategy, and an adaptive dynamic programming approach is taken to synthesize an internal-model-based optimal tracking controller. The whole procedure relies only on measurements of local costs and input-state data along agents’ trajectories. Moreover, under appropriate conditions, the closed-loop signals are bounded and the output of the agents exponentially converges to a small neighborhood of the desired extremum. A numerical example is conducted to validate the efficacy of the proposed method.

关键词： Adaptation models Costs Distributed feedback devices Cost function Dynamic programming Trajectory Numerical models Signal design Multi-agent systems Convergence

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Developing High Confinement and Low-loss Silicon Nitride Photonic Devices for Optical Coherence Tomography (Invited)

Developing High Confinement and Low-loss Silicon Nitride Pho...

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Novel Techniques in Microscopy, NTM 2023 - Part of Optica Biophotonics Congress: Optics in the Life Sciences 2023

作者： Ji, Xingchen Chen, Junzhuo Ling, Yuye Su, Yikai John Hopcroft Center for Computer Science School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China State Key Lab of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

There is a surging interest in developing integrated Optical Coherence Tomography (OCT) system. However, most components are based on silicon which cannot be used for wavelength below 1.2 µm. Here, we discuss the... 详细信息

ISBN: (纸本)9781957171210

关键词： Breast cancer imaging Frequency combs Optical coherence tomography Optical imaging Photonic devices Supercontinuum sources

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Parameter Estimation of Synchronous Generator Using Neural Controlled Differential Equations

Parameter Estimation of Synchronous Generator Using Neural C...

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International Conference on Control and Automation (ICCA)

作者： Zhun Yin Hong Wang Zhong-Ping Jiang Department of Electrical and Computer Engineering Control and Networks Lab Tandon School of Engineering New York University Brooklyn NY USA Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This paper introduces a synchronous generator modeling method based on neural controlled differential equations (neural CDEs) using online sampled data. This method begins with a fifth-order generator model, where every trainable parameter is regarded as one parameter of the fifth-order generator model. The objective is to use the real-time data to learn these parameters. A training algorithm has been formulated, and it has been shown that the combination of the proposed neural CDEs and the fifth-order model can produce desired online parameter estimations for the synchronous generator. The simulation results show that the proposed method can generate a very accurate estimation and model predictions with the mean absolute percentage error of 0.04638%.

关键词： Training Parameter estimation Accuracy Voltage Differential equations Predictive models Mathematical models

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SAFLA: Semantic-Aware Full Lifecycle Assurance for Intent-Driven networks

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Kou, Shiwen Yang, Chungang Gurusamy, Mohan Xidian University The State Key Laboratory on Integrated Services Networks Xi’an710071 China Communication & Networks Lab Department of Electrical and Computer Engineering National University of Singapore SG117583 Singapore

Intent-Driven networks (IDNs) are designed to improve network management efficiency by transforming high-level intents into actionable configurations. Due to evolving user requirements and network dynamics, a semantic gap has emerged between declarative intents and underlying configurations, posing significant challenges to the assurance and reliability of IDNs. Despite promising preliminaries, existing intent assurance approaches, which mainly focus on monitoring intents’ Key Performance Indicators (KPIs), fail to bridge the semantic gap. This gap can result in configurations that technically meet KPIs from a top-down perspective but fail to accurately reflect the original network intent from a bottom-up view, leading to serious security risks and undermining overall objectives. To address this issue, we introduce SAFLA, a semantic-aware framework designed to ensure the intent’s full lifecycle. By seamlessly integrating top-down and bottom-up approaches, SAFLA not only enhances intent assurance by monitoring intent performance but also bridges the semantic gap between high-level intents and low-level configurations. This integration facilitates a self-healing mechanism for IDNs, reducing the need for manual intervention even under evolving requirements and network reconfigurations. Experimental results on software-defined Networking demonstrate the SAFLA’s feasibility and efficiency in intent assurance. © 2015 IEEE.

关键词： Network management

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Strategic Demand-Planning in Wireless networks: Can Generative-AI Save Spectrum and Energy?

arXiv

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arXiv 2024年

作者： Çiloğlu, Berk Koç, Görkem Berkay Shamsabadi, Afsoon Alidadi Ozturk, Metin Yanikomeroglu, Halim Electrical and Electronics Engineering Ankara Yıldırım Beyazıt University Ankara Turkey Non-Terrestrial Networks Lab Systems and Computer Engineering Carleton University Ottawa Canada

Generative-AI (GenAI), a novel technology capable of producing various types of outputs, including text, images, and videos, offers significant potential for wireless communications. This article introduces the concept of strategic demand-planning through demand-labeling, demand-shaping, and demand-rescheduling. Accordingly, GenAI is proposed as a powerful tool to facilitate demand-shaping in wireless networks. More specifically, GenAI is used to compress and convert the content of various types (e.g., from a higher bandwidth mode to a lower one, such as from a video to text), which subsequently enhances performance of wireless networks in various usage scenarios, such as cell-switching, user association and load balancing, interference management, as well as disasters and unusual gatherings. Therefore, GenAI can serve a function in saving energy and spectrum in wireless networks. With recent advancements in AI, including sophisticated algorithms like large language models and the development of more powerful hardware built exclusively for AI tasks, such as AI accelerators, the concept of demand-planning, particularly demand-shaping through GenAI, becomes increasingly relevant. Furthermore, recent efforts to make GenAI accessible on devices, such as user terminals, make the implementation of this concept even more straightforward and feasible. Copyright © 2024, The Authors. All rights reserved.

关键词： Demand response

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