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检索条件"任意字段=Conference on Internet Quality and Performance and Control of Network Systems"

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Q-Learning Based Adaptive Flow control 5

Q-Learning Based Adaptive Flow Control

引用

5th International conference on Data-Driven Optimization of Complex systems, DOCS 2023

作者： Zhang, Xiaoping Wang, Yunlan Zhao, Tianhai Hu, Shuai Zhang, Hui School of Software Northwestern Polytechnical University Xi'an China School of Computer Science Northwestern Polytechnical University Xi'an China China Unicom Western Innovation Research Institute R&d Department 1 Xi'an China

ISBN: (纸本)9798350393521

With the rapid development of the internet and the expansion of its application scale, the volatility and complexity of network flow in highly concurrent environments have become increasingly prominent. Traditional static control strategies have been difficult to meet the actual needs. Traditional flow control requires manual parameter setting based on thresholds, which not only requires a large number of parameter settings but also affects system availability. This paper proposes an adaptive flow control algorithm based on q-learning, designs and implements a q-learning model for adaptive flow control, and considers the three elements of state, action and reward, where the state includes CPU utilization, average response time, requests per second, and concurrent threads;actions include flow control decisions, that is pass or reject;rewards represent the feedback results of each decision. The algorithm can use the trained q-learning model to select the best flow control decision based on the current state, so as to realize the adaptive adjustment and control of network flow. In the case of high network load, the algorithm will intelligently adjust the flow control strategy to reduce network congestion and delay, improve network performance and reliability, and solve the problem of system instability caused by high throughput under high concurrency. © 2023 IEEE.

关键词： Flow control

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Cloud controlled Home Safety and Management Solution for Equipment Automation via internet 7

Cloud Controlled Home Safety and Management Solution for Equ...

引用

7th International conference on Computing Methodologies and Communication, ICCMC 2023

作者： Kishore, D.V. Sai Babu, Y. Murali Mohan Blessy, Y.M. Anitha, K. Prabhakar, P.B. Edwin Department of Electronics and Communication Andhra Engineering Colleg Andhra Pradesh Nellore India Department of Electronics and Communication Engineering Chadalawada Ramanamma Engineering College Andhra Pradesh Tirupathi India Department of Electronics and Communication R.M.K. Engineering College Tamil Nadu Chennai India Tamil Nadu Perambalur India Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering & Technology Tamil Nadu Chennai India

ISBN: (纸本)9781665464086

Homes enhance people's quality of life by providing access to a range of services. One might think of a sensible home or machine-driven home. It strives to offer both simple work and enjoyment. The goal of this project is to effectively control home gadgets through the exploitation of internet of Things (IoT) apps. This paper presents a successful internet of Things-based system used to monitor and control home equipment via the internet. Handheld devices serve as the user interface for home control systems. This can be connected to the domestic automation network via a network pass, using protocols for connection, including Zigbee and Wi-Fi. This project is planned by using Wi-Fi as a networking protocol and raspberry pi as the server device to monitor home appliances via a Smartphone. The user travels around the network directly through the system, while house appliances, such as lighting, ventilators, and door locks, are remotely controlled through the simple website. Another feature improving firefighting safety is the ability to sludge the smoke and send a warning message and a picture to a Smartphone if there is a fire. The server is interfaced with relay hardware circuits that power home-based computers. The user can select the appropriate machine via the server contact. The user can choose the proper unit for the server correspondence. Relays are connected to the server. The built-in device board will operate the apps locally if the server or web affiliation goes down. This provides us with a cheap and climbable domestic automation gadget. © 2023 IEEE.

关键词： internet of things

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Detecting Plant Diseases Utilizing a Convolutional Neural network and Transfer-Learning Methodology 8

Detecting Plant Diseases Utilizing a Convolutional Neural Ne...

引用

8th IEEE International conference on Computing, Communication, control and Automation, ICCUBEA 2024

作者： Jane, Babita R. Dharmale, Gulbakshee J. Prasanth, P. Pimpri-Chinchwad College of Engineering Department of Information Technology Pune India

ISBN: (纸本)9798350391770

Timely detection of diseases in plants significantly impacts the production of high-quality and abundant grains. These precision techniques have shown impressive results in plant leaf-based image classification. This paper uses the Convolutional Neural network and Transfer-Learning application EfficientNetB7 to identify fresh and diseased leaves. Various parameters such as accuracy, block size, dropouts, loss, and time required for epochs were incorporated to appraise the model performance. This paper consists of several phases Implementation, i.e., creation of datasets, feature extraction, and training the classifier. Image dataset from Kaggle village plant datasets used for training and testing which contains several fresh and diseased leaves images. The deep CNN model is promising accurate results in identifying diseases has a notable impact on effective disease identification and could potentially enable disease detection in agricultural systems in real time. EfficientNetB7 compared to other Transfer-Learning applications such as ResNet, Inceptions, MobileNet, and DenseNet has a smaller number of parameters and achieved 99.89% performance accuracy is also higher than others. © 2024 IEEE.

关键词： Convolutional neural networks

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Application of distributed network in remote indoor temperature control and energy conservation system

Application of distributed network in remote indoor temperat...

引用

2023 International conference on Intelligent systems, Communications, and Computer networks, ISCCN 2023

作者： Jin, Sheng-Lang Que, Fei Tourism College Huangshan University Anhui Huangshan245041 China Zhejiang Institute of Economics and Trade Hangzhou310018 China

ISBN: (纸本)9781510666238

In the present work, a distributed network-based remote indoor temperature control and energy conservation system is proposed. The system provides such functions as remote data transfer and remote control, as well as a distributed information sharing and communication platform. Meanwhile, the system integrates an intelligent control system and a database management system, on which the data obtained from the internet are first assessed by the expert panel and then processed by the intelligent control system. In this way, real-Time intelligent control is achieved to allow the user to control the indoor temperature and production conditions of the greenhouse through the network. During the design of the system, a wireless monitoring system is developed as per the specific location and geographic conditions for remote surveillance. The field test shows that compared with the control plot, the experiment plot reduced the gas consumption by 2802.4 m3, reduced energy consumption by 24935.0 kW h. The work is expected to provide a solution for development of remote indoor temperature control and energy conservation systems in real-world scenarios. © 2023 SPIE. All rights reserved.

关键词： Energy conservation

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A Fuzzy Convolutional Neural network for the Classification of Aerosol Particle Mass Spectral Patterns Generated by Single-Particle Mass Spectrometry

A Fuzzy Convolutional Neural Network for the Classification ...

引用

International Joint conference on Neural networks (IJCNN)

作者： Wang, Guanzhong Ruser, Heinrich Schade, Julian Passig, Johannes Zimmermann, Ralf Dollinger, Guenther Adam, Thomas Univ Bundeswehr Munich Inst Appl Phys & Measurement Technol Neubiberg Germany Univ Rostock Joint Mass Spectrometry Ctr Rostock Germany Univ Bundeswehr Munich Inst Chem & Environm Neubiberg Germany

ISBN: (纸本)9798350359329;9798350359312

Air quality control is essential for assessing the impact on human health, environment and climate. Single-particle mass spectrometry (SPMS) is a powerful measurement tool for providing the chemical composition of air-transported particle matter (PM) in real-time. Common methods to classify PM according to characteristic ion patterns in their mass spectra are based on clustering methods which generally require manual postprocessing and are not suitable for real-time automated air quality monitoring. A number of automated classification models trained on labeled SPMS data were proposed recently by the authors. As it appeared, the most advanced methods of them, based on deep-learning convolutional neural networks (CNN), still have difficulties in distinguishing between classes of similar but distinctive mass spectra. In this work, we propose a novel fuzzy convolutional neural network (FCNN) combining fuzzy network and CNN to accurately classify particle mass spectral patterns. FCNN models integrate the respective advantages of fuzzy and neural networks to effectively separate non-isolated and overlapping features of similar patterns through fuzzy information, and also inherently optimize the fuzzy rule parameters through NN back propagation. To validate the performance of FCNN, a benchmark dataset with 37,406 samples in 13 particle classes was created. Compared to CNN, with FCNN 10 out of 13 classes could be classified with higher accuracy, especially those distinguishing subtle differences in the mass spectra. Applied to automated SPMS analysis, the proposed FCNN tackles the classification challenges posed by the chemical complexity of aerosol particles and opens up ways to foster the development of specific, real-time air quality monitoring and pollution source identification systems.

关键词： Fuzzy network CNN mass spectrometry aerosol particles environmental monitoring

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performance Analysis of Multipath TCP Congestion control Algorithms 14

Performance Analysis of Multipath TCP Congestion Control Alg...

引用

14th IEEE International conference on Communication systems and network Technologies, CSNT 2025

作者： Biswal, Subhra Priyadarshini Chatterjee, Akash Alanam, Khair Patel, Sanjeev Department of Computer Science and Engineering National Institute of Technology Rourkela Odisha 769008 India

ISBN: (纸本)9798331531935

In modern high-speed networks, providing reliable and high-quality internet services is essential. However, maintaining consistent network performance in dynamic environments with varying scenarios presents significant challenges. To address these, Multipath TCP (Transmission control Protocol) offers a viable solution by aggregating multiple network paths, resulting in improved bandwidth utilization, higher throughput, and reduced delay. This approach overcomes the limitations of traditional single-path TCP by enhancing responsiveness, friendliness, and reducing window oscillation. Moreover, MPTCP incorporates congestion control mechanisms that couple several subflows, ensuring efficient traffic distribution across multiple paths, thereby improving overall network performance and fairness. In this paper, we analyze the performance of LIA (Linked Increases Algorithm), OLIA (Opportunistic Linked Increases Algorithm), and BALIA (Balanced Linked Adaptation) congestion control algorithms using the ns-3 network simulator. The results show that BALIA achieves 21% higher throughput compared to LIA. © 2025 IEEE.

关键词： Transmission control protocol

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Artificial Intelligence in Data Science 14

Artificial Intelligence in Data Science

引用

14th International conference on Advances in Computing, control, and Telecommunication Technologies, ACT 2023

作者： Ahmed, Abdul Mateen Sultana, Ayesha Imran, Mohammed Khadheja, Syeda Begum, Najma Ahmedi, Madeha Qureshi, Samiya ISL Engineering College Department of ECE Hyderabad India ISL Engineering College Department of IT Hyderabad India

This paper introduces Current Wireless systems which works on Automatic gain control (AGC), Adaptive Modulation and Coding Schemes and channel quality Indicators (CQI). AI-Enabled Wireless CQI Monitoring and Diagnosis System for 6G Cellular network. The Proposed AI in Data science provides a collaborative process to a systematic process that is adopted in wireless systems that leverages the network performance. © Grenze Scientific Society, 2023.

关键词： Adaptive control systems

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PINet: Towards Effective and Efficient Industrial control Protocol Identification

PINet: Towards Effective and Efficient Industrial Control Pr...

引用

2023 International conference on Artificial Intelligence of Things and systems, AIoTSys 2023

作者： Jin, Jianxin Sha, Wanli Zhao, Chaoyang Wang, Zhan Zhong, Yaoyi Zhou, Junliang Zhejiang Zheneng Jiahua Power Co. Ltd. China Zhejiang Energy Digital Technology Co. Ltd. China Zhejiang Petroleum Comprehensive Energy Sales Co. Ltd. China

ISBN: (纸本)9798350312270

Accurate identification of industrial control protocols plays a pivotal role in ensuring the secure and reliable operation of Industrial internet of Things (IIoT) systems. In this paper, we propose, PINet, a novel model that employs a combination of a Gated Recurrent Unit (GRU) network and a multi-head attention mechanism for industrial control protocol identification. PINet effectively captures the underlying patterns within the input sequence by utilizing the hidden state representations obtained from the GRU network. Furthermore, the multi-head attention mechanism facilitates the extraction of crucial information from these hidden states. Ultimately, a classifier linear layer is employed to map the extracted feature vectors onto the probability distribution of protocol categories. Experimental evaluations demonstrate that PINet achieves outstanding performance and accuracy in classifying industrial control protocols, showcasing its effectiveness and reliability in practical applications. © 2023 IEEE.

关键词： Probability distributions

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Hybrid Quantum-Classical Convolutional Squeeze Excitation Neural network based HEVC Video quality Enhancement 2

Hybrid Quantum-Classical Convolutional Squeeze Excitation Ne...

引用

2nd International conference on Self Sustainable Artificial Intelligence systems, ICSSAS 2024

作者： Arun Shalin, L.V. Kansal, Vipashi Nikitha, M. Nandini, V. Ronald, Bindu Soudagar, Manzoore Elahi M. Karunya Institute of Technology and Sciences Division of Data Science and Cybersecurity Tamil Nadu Coimbatore641 114 India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun248 002 India Bannari Amman Institute of Technology Department of Information Technology Tamil Nadu Erode638401 India Brindavan College of Engineering Department of Chemistry Dwaraka Nagara Yelahanka Karnataka Bengaluru560063 India Vimannagar Maharashtra Pune411014 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350368413

Due to the popularity of video streaming on the internet, there is a high utilization of HEVC because of the high compression ratio it delivers in video quality. However, the compression process causes distorting elements that have a negative impact on the quality of the videos, which presents much complication in preserving the content integrity as it was decoded from the videos. To overcome these issues, this manuscript proposes a Hybrid Quantum-Classical Convolutional Squeeze Excitation Neural network for HEVC Video quality Enhancement through a two-phase approach: There are two areas namely, (i) Quantization Bit Stream and (ii) post-processing. In the first phase, for example, a Squeeze Excitation Multi-Head Attention-Based Encoder and Decoder radically effects a video frame compression by utilising possibilities of prediction, transformation, quantization, and entropy coding, though it distorts at the same time. The decoder then unpacks the frames thus created to the format that the enhancement module uses as input. The second phase makes use of a Hybrid Quantum-Classical Convolutional Neural network (HQ2C2N). This approach involves a Feature Extraction Layer that employs quantum circuits to optimize the feature extraction step, a Feature Enhancement Layer that applies classical convolutional networks to eliminate artifacts that have been introduced to the video and a Post-Processing Layer that allows for tuning of end frames to the desired quality standards typical of plyback or further processing. The experiments of the proposed HQ2C2N method are conducted using MATLAB programming environment. The HQ2C2N significantly improves HEVC video quality by effectively reducing compression artifacts, resulting in a higher PSNR of 75 dB and a lower MSE of 0.1%, outperforming previous methods in visual fidelity and performance metrics. © 2024 IEEE.

关键词： MATLAB

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Smart Healthcare - IoT and Artificial Neural network(ANN) 15

Smart Healthcare - IoT and Artificial Neural Network(ANN)

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15th International conference on Computing Communication and networking Technologies, ICCCNT 2024

作者： Sutar, Laxmikanta Chinara, Suchismita National Institute of Technology Rourkela Dept. of Computer Science and Engineering Rourkela India

ISBN: (纸本)9798350370249

With the use of the internet of Medical Things (IoMT), the growing accessibility of wearable devices, and the continual reduction in the cost of sensor-based technologies and green computing, internet-connected sensors allow users to track, analyze, and share welfare information employing a cloud-based platform. Artificial Neural networks (ANN) find extensive use across various applications, including healthcare systems, drawing inspiration from the biological concept of neurons. This study explores the prediction of heart attack risks using a combination of internet of Things (IoT) sensors and artificial neural networks (ANN). Through the integration of IoT sensors such as the pulse sensor and LM35 temperature sensor, essential physiological data related to heart rate and body temperature was collected. An ANN model was developed, focusing on predictive capabilities using the sigmoid function, feedforward and backpropagation techniques, and random weight initialization. The investigation further utilized Keras with TensorFlow and optimized model parameters using Adam Optimization and Later with One Hot Encoding plus a Genetic Algorithm (GA). Additionally, the study involved training neural networks on higher-quality UCI datasets to evaluate performance. Scatterplots were generated to visualize relationships between variables. Results demonstrated promising performance improvements, with the model achieving close to 90% accuracy on the testing dataset. Encoding techniques were employed to handle categorical variables effectively, contributing to the model's robustness and generalization. Overall, this research offers valuable insights into leveraging IoT sensors and ANN for heart attack risk prediction. © 2024 IEEE.

关键词： Adam Optimisation ANN Back Propagation Dense Layers Feed Forwarding Fuzzy Genetic Algorithm HTTP IoT MQTT One Hot Encoding Protocols UCI Heart Data

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