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VVoIP Quality Improvement Over IMS Network based on background subtraction 21

VVoIP Quality Improvement Over IMS Network based on backgrou...

引用

21st international learning and Technology conference, L and T 2024

作者： Ali, Hanafy M. Hussein, Aziza I. Ismail, Waled M. Hassan, Yahia B. Minia University Faculty of Engineering Computers and Systems Engineering Dept. El Minia Egypt Effat University College of Engineering Energy and Technology Research Center Saudi Arabia Telecom Egypt Co. Mobile Core Back Office Engineer Egypt Higher Institute of Engineering Electrical Eng. Dept Minia Egypt

ISBN: (纸本)9798350393569

The dependence on making Voice and Video over Internet Protocol (VVoIP) calls has increased dramatically in recent times. The use of VVoIP applications is not limited to social applications only but extended to business applications. The improvement of VVoIP call quality is one of the most important factors that enhance the spread of VVoIP services. In many cases, the high network traffic prevents the possibility of sending high data rate live video streams as sending high data rate live stream over high-traffic networks causes an increase in the data loss *** this paper, the codec switching techniques used to improve the quality under live networks with high traffic are discussed. In addition, A technique based on background subtraction to improve the quality of VVoIP calls using Python code to detect the moving pixels (foreground) and subtract them from the frame to discard the background is *** proposed technique was tested on large scale live network. The tested parameters impacting the Quality of Service (QoS) were packet loss and delay variation (Jitter). The study based on the comparison of packet loss and Jitter for the three common resolutions 480P, 720P and, 1080P over high traffic network without using background subtraction vs. using background subtraction. The results showed a significant improvement in different rates for each resolution and network condition. © 2024 IEEE.

关键词： Quality of service

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Hybrid Machine learning Approach for Microclimate Prediction in Equipment-Operated Open Ventilated Greenhouse 21

Hybrid Machine Learning Approach for Microclimate Prediction...

引用

21st international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Thwin, Kyaw Maung Maung Phatrapornnant, Teera Horanont, Teerayut Thepsilvisut, Ornprapa Nguyen Duy Hung Thammasat Univ Sirindhorn Int Inst Technol Sch ICT Pathum Thani Thailand Natl Elect & Comp Technol Ctr NECTEC Digital Agr Technol Res Team DAT Pathum Thani Thailand Thammasat Univ Agr Technol Fac Sci & Technol Pathum Thani Thailand

ISBN: (纸本)9798350381771;9798350381764

A proposed hybrid model did the assessment on microclimate prediction with high fluctuations of indoor temperature and humidity in equipment-operated open-ventilated greenhouses. Microclimate prediction for those greenhouses is required in order to optimize equipment operation effectiveness for the next step. The proposed model is structured at three levels and combined with three heterogeneous models: multilinear regression, gradient-boosting regression, and artificial neural networks. Accuracy performance was better than only using a single model, except for the artificial neural network in temperature prediction, and the model was also compared with multivariate LstM (long short-term memory) as well. The experiment setup configured different settings for the fans, water spray top, and sides of the three greenhouses. A remote access control system has already been implemented as a semi-automatic system for the configuration of the equipment operations in all of those greenhouses. The hybrid model achieved RMSE = 0.31 and R-2 = 0.980 for temperature and RMSE = 1.07 and R-2 = 0.96 for humidity.

关键词： Proposed Hybrid model Equipment operated Open-ventilated greenhouse Three Levels Threeheterogeneous models

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Federated learning in Clustered WSN for Natural Disaster Management 21

Federated Learning in Clustered WSN for Natural Disaster Man...

引用

2024 IEEE/ACS 21st international conference on Computer Systems and Applications, AICCSA 2024

作者： Belfeki, Zouheir Bouazizi, Mondher Krichen, Moez Zidi, Salah National Engineering School of Gabes Tunisia Keio University Faculty of Science and Technology Yokohama Japan Sfax University ReDCAD Laboratory Tunisia

ISBN: (纸本)9798331518240

Federated learning (FL) is a machine learning (ML) approach that allows a model to be trained across multiple decentralized devices holding local data samples without exchanging them. In the realm of Wireless Sensor Networks (WSNs), FL has not attracted much attention given that FL is typically meant to train models on data collected by much fewer and decently more powerful devices. However, given the potential of WSNs to collect diverse data in hazardous regions, we aim to explore how to employ FL to collect data in an area of interest where we have a natural disaster. In this paper, we introduce a novel task with regards to FL in the context of WSN for natural disaster management. Given a region where wireless sensors are deployed and data samples are distributed, we aim to cluster the sensors so that each cluster can be treated as a FL agent in a way that accelerates the process of FL. © 2024 IEEE.

关键词： Federated learning

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TIPICAL-Type Inference for Python in Critical Accuracy Level 21

TIPICAL-Type Inference for Python in Critical Accuracy Level

引用

21st IEEE/ACIS international conference on Software engineering Research, Management and Applications, SERA 2023

作者： Elkobi, Jonathan Gruner, Bernd Sonnekalb, Tim Brust, Clemens-Alexander UC San Diego GPS San Diego United States Institute of Data Science German Aerospace Center Jena Germany

ISBN: (纸本)9798350345889

Type inference methods based on deep learning are becoming increasingly popular as they aim to compensate for the drawbacks of static and dynamic analysis approaches, such as high uncertainty. However, their practical application is still debatable due to several intrinsic issues such as code from different software domains will involve data types that are unknown to the type inference *** order to overcome these problems and gain high-confidence predictions, we thus present TIPICAL, a method that combines deep similarity learning with novelty detection. We show that our method can better predict data types in high confidence by successfully filtering out unknown and inaccurate predicted data types and achieving higher F1 scores to the state-of-The-Art type inference method Type4Py. Additionally, we investigate how different software domains and data type frequencies may affect the results of our method. © 2023 IEEE.

关键词： Python

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Modified Term Frequency and Inverse Document Frequency with Optimized Deep learning Algorithm based Fake News Detection 1

Modified Term Frequency and Inverse Document Frequency with ...

引用

1st international conference on intelligent Systems and Computational Networks, ICISCN 2025

作者： Rana Veer Samara Sihman Bharattej, R. Prashanth, V. Alabdeli, Haideer Thottan, Sunaina Sangeet Ananthi, S. Saint Leo University Department of Computer Science FL United States Nitte Meenakshi Institute of Technology Department of Electrical and Electronics Engineering Bengaluru India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Dhanalakshmi Srinivasan College of Engineering and Technology Department of Artificial Intelligence and Data Science Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

The identification of real and fake news has become difficult task in the recent years due to more similar feature between them. As the issues of fake news are leading the miscommunication to the reads, various automated software has been introduced for the detection of fake news. The existing models tried to advance the identification of the fake new but failed to reach the expected outcomes due to overfitting issues and limited feature handling abilities. To overcome the existing issues the Latent Dirichlet Allocation based Term Frequency and Inverse Document Frequency with Brown-bear Optimization Algorithm-Bidirectional Long Short-Term Memory (LDA based TF-IDF with BOA-BiLstM) is developed for the detection of fake news. The news data is collected and get cleaned by tokenization and normalization techniques. Then the LDA based TF-IDF model is used for the extraction of significant details of the news text data. Further the BOA-BiLstM algorithm is used for the detection of fake news. The developed LDA based TF-IDF with BOA-BiLstM algorithm has exhibited the better result having accuracy of 0.9489 and recall of 0.9012 for ISOT fake news dataset compared to the existing Knowledge Graphs for multi-domain fake news detection (KG-MFEND) model. © 2025 IEEE.

关键词： Deep learning

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Adaptive PID Controller Using Neural Network for Pressure Drop in Nonlinear Fluid Systems 21

Adaptive PID Controller Using Neural Network for Pressure Dr...

引用

21st international Multi-conference on Systems, Signals and Devices, SSD 2024

作者： Bawazir, Aiman F. Al-Yazidi, Nezar M. Al-Dogail, Ala S. Bin Gaufan, Khaled S. Saif, Abdul-Wahid A. King Fahd University of Petroleum and Minerals Control and Instrumentation Engineering Department Dhahran31261 Saudi Arabia for Smart Mobility and Logistics King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia King Fahd University of Petroleum and Minerals Petroleum Engineering Department Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350374131

The majority of industrial processes display intrinsic nonlinear features. Therefore, conventional control procedures that rely on linearized structures are inadequate for obtaining optimal control. This research presents an approach that utilizes an artificial neural network (ANN) and reinforcement learning (RL) to regulate a nonlinear dynamic pressure drop. Regulation in systems involving the passage of many phases ANN exhibits proficiency in generalization, disturbances, and function approximations. This control technique utilizes the talents of artificial neural networks (ANN) in combination with the decision-making capabilities of reinforcement learning (RL) methodology. The study introduces two distinct machine-learning methodologies. Initially, a Hammerstein identification technique is employed to determine the mathematical representation of the multi-phase system based on the gathered experimental data. Actor-critical learning is employed to adjust the PID parameters in an adaptive manner, using the model-free and online learning capabilities of reinforcement learning. The simulation results demonstrate that the suggested controller is highly effective for complicated nonlinear systems, displaying exceptional adaptability and robustness. This surpasses the performance of a typical PID controller. © 2024 IEEE.

关键词： Reinforcement learning

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Ubiquitous, Secure, and Efficient Mobile Sensing Systems 23

Ubiquitous, Secure, and Efficient Mobile Sensing Systems

引用

21st Annual international conference on Mobile Systems, Applications and Services, MobiSys 2023

作者： Jia, Hong University of Cambridge Cambridge United Kingdom

ISBN: (纸本)9798400701108

The rapid development of mobile sensors and machine learning techniques has enabled the utilization of diverse sensor data in many applications. However, the design and implementation of these algorithms and systems to achieve ubiquitous, secure, and efficient real-world solutions remains challenging. This paper discusses various effective solutions to overcome these challenges and underscores potential future directions. © 2023 Owner/Author(s).

关键词： learning systems

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An Improved Attention-based BiLstM for PM2.5 Prediction in Air Environment 21

An Improved Attention-based BiLSTM for PM2.5 Prediction in A...

引用

21st international conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Kezhu An, Jing Wang, Zijian Wang, Kefan School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

ISBN: (纸本)9798350365221

The accurate prediction of PM2.5 concentrations in the air is crucial for air quality control and pollution prevention. However, the complexity of air environment data and its time-series characteristics pose significant challenges to this prediction task. This study first employs a random forest algorithm for feature selection to identify key features as input variables. Subsequently, a method combining a Butterworth filter with an attention-based bidirectional long short-term memory (BiLstM) network is proposed for predicting PM2.5 concentrations. The model smooths the input sequence using a Butterworth filter to remove outliers, and extracts useful information from complex long-term dependencies through a bidirectional long short-term memory (BiLstM) network based on the attention mechanism. Experimental results demonstrate that this method outperforms other similar approaches. © 2024 IEEE.

关键词： Butterworth filters

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Analysis of the Mechanism of Generative Adversarial Networks learning Based on Gray Image 3

Analysis of the Mechanism of Generative Adversarial Networks...

引用

3rd international conference on intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Geng, Chaozhi 21st Century International School Beijing100080 China

ISBN: (纸本)9781510661301

Machine learning has a crucial role in people's lives. Machine learning can be divided into four parts: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. It can help people with image recognition, stock prediction, etc. In addition, machine learning has a subset called deep learning (DL). DL in machine learning can help people optimize and learn models better. DL in machine learning can help people optimize and learn models better. One of them is generative adversarial networks (GANs). The other one is Convolutional Neural Networks (CNNs). First, GANs have achieved impressive results in image generation, data enhancement, etc. However, effective intervention and control of the results of generative adversarial networks is a challenging problem. Based on grayscale image data, this paper investigates the change patterns of the generation results of generative adversarial networks in the face of different input data. There are three groups in the experiment. The first group is to explore the effect of the size of the dataset on the generated images. The second group is to explore the effect of the diversity of the book dataset on the results. The third group was to explore the effect of the overall dataset on the generative images. The experimentally generated images revealed that data size and diversity are important factors that affect the quality of the results generated by generative adversarial networks. Among other things, the increase in the amount of data does not always positively affect the generative results. © 2023 SPIE.

关键词： Generative adversarial networks

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Research on intelligent Network Intrusion Detection Based on Gray and statistical Features 21

Research on Intelligent Network Intrusion Detection Based on...

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21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Chen, Hong Liu, Sisi Yuan, Xiaoshu Feng, Li Huang, YuanYuan Energy equipment Cyber Security Key Laboratory of Sichuan Province Chengdu610016 China Chengdu University of Information Technology China

ISBN: (纸本)9798331519254

This paper presents an intelligent network intrusion detection method based on gray and statistical features to enhance network security. As information technology advances, network attacks have become increasingly severe. Artificial intelligence, particularly deep learning, shows significant potential in this area. The method constructs a deep learning model by analyzing statistical characteristics of network traffic data and applying gray image processing, enabling effective detection of network attacks. Research indicates that advancements in deep learning for image recognition offer new insights for network defense. By calculating statistics such as mean, variance, and standard deviation, the model extracts important information about data distribution. Experimental results demonstrate that this method achieves high accuracy on the CICIDS2017 dataset, outperforming traditional convolutional neural networks by 7 percentage points. Additionally, the image conversion method using statistical features surpasses the original byte stream conversion in accuracy and F1 score, highlighting its effectiveness in network intrusion detection. © 2024 IEEE.

关键词： Network intrusion

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