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A Survey on Wireless Network Traffic Analysis using machine learning Algorithms 3

A Survey on Wireless Network Traffic Analysis using Machine ...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Praveen Kumar, M. Ashwitha Noble, P. Esha Malavika, V.S. Geethanjali, G. Farheen, A.S. Sri Eshwar College of Engineering Department of CSE Coimbatore India

ISBN: (纸本)9798350343632

Analyzing and forecasting network traffic for different applications has gained popularity in recent years. Several tests have been carried out to find issues with the computer network apps that are in use today. Anticipating and evaluating network traffic can assist in implementing preemptive steps to guarantee safe, dependable, and excellent network connection. Numerous methods, including data mining approaches and neural network-based techniques, have been suggested and assessed for the purpose of predicting patterns of network traffic. For this goal, other linear and nonlinear models have also been developed. The main objectives of combining different network analysis and prediction techniques are efficiency and optimization. The goal of this survey study is to present a thorough review of the variety of methods used in traffic prediction and network analysis. The paper delves into the distinctive features and findings of previous research efforts and brings to light the patterns that have emerged from these studies. Furthermore, it provides an overview of the various areas in which network traffic analysis and prediction have shown their effectiveness. © 2023 IEEE.

关键词： data mining Techniques dataset NTA Preprocessing

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Citrus Detection Application of Based on Improved YOLOv3 Algorithm 3

Citrus Detection Application of Based on Improved YOLOv3 Alg...

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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Long, Jinhui Wang, Xiaokan Henan Electrical and Mechanical Vocational College Xinzheng451191 China

ISBN: (纸本)9781510664814

In order to solve the problem of missed detection and false detection caused by light change, leaf occlusion, fruit overlap and other citrus detection factor, an improved YOLOv3 citrus detection depth learning algorithm is proposed to achieve accurate and rapid recognition of citrus in natural environment. The algorithm uses image scaling and rotation transformation, homomorphic filtering processing and other methods to process the citrus object pictures. The 1− IOU value is the distance function, and the genetic algorithm based on K-means clustering is used to optimize the initial anchor box size, all that are enriched the citrus image data set. So the algorithm achieves effective image enhancement and improves the detection resolution and accuracy. The experimental results show that the average accuracy of the citrus detection algorithm reaches 88.7%, the average detection time of single width pixels 640×448 pixels is 131 ms and the system efficiency increases by 9.6%. © 2023 SPIE.

关键词： Genetic algorithms

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3rd international conference on Computing and Communication Networks, ICCCN 2023

3rd International Conference on Computing and Communication ...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

ISBN: (纸本)9789819726707

The proceedings contain 60 papers. The special focus in this conference is on Computing and Communication Networks. The topics include: Enhancing Security in Wireless Sensor Networks: A Broadcast/Multicast Authentication Framework with Identity-Based Signature Schemes;TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments;tracking Climatic Variations Through Smart IoT-Driven Approach: An Exploratory Analysis;Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM Communications and IoT Technology;a Comparative Analysis of Optimized Routing Protocols for High-Performance Mobile Ad Hoc Networks;integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor Networks;the Effects of Weather Conditions on data Transmission in Free Space for Wireless Sensor Networks;Using Wireless Sensor Network Performance and Optimizing in Underground Mines with Virtual MIMO Antenna;a Novel Logistic Regression-Based Fire Detection Model Using IoT in Underground Coal Mines;An MQTT IoT Intrusion Detection System Using Deep-learning;multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep learning;application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society;insurance 4.0: Smart Insurance Technologies and Challenges Ahead;review on Vision Transformer for Satellite Image Classification;primitive Roots and Euler Totient Function-Based Progressive Visual Cryptography;approach for Fire Detection Using Image Processing;review of Various Approaches for Authorship Identification in Digital Forensics;machine learning Model for Traffic Prediction and pattern Extraction in High-Speed Optical Networks;improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption.

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2023 3rd international conference on Applied Artificial Intelligence, ICAPAI 2023

2023 3rd International Conference on Applied Artificial Inte...

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3rd international conference on Applied Artificial Intelligence, ICAPAI 2023

ISBN: (纸本)9798350328929

The proceedings contain 11 papers. The topics discussed include: metaheuristic firefly and C5.0 algorithms based intrusion detection for critical infrastructures;a novel spatial-temporal deep neural network for electricity price forecasting;temporal-spatial graph neural network for wind power forecasting considering the blockage effects;European standardization efforts from fair toward explainable-AI-ready data documentation in materials modelling;detection of pneumonia from chest X-ray images using convolutional neural network (CNN);SVM at edge: low cost caching prediction for connected edge intelligence in federated machine learning;staying ahead of the curve: early prediction of academic probation among first-year cs students;facial expression recognition using deep neural network;human posture detection on lightweight DCNN and SVM in a digitalized healthcare system;and fishing trawler event detection: an important step towards digitization of sustainable fishing.

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Research on the High Resolution Remote Sensing Image Target Detection Based on machine learning 3rd

Research on the High Resolution Remote Sensing Image Target ...

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3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Fu, Yanli Sun, Yingying Li, Shuyao Deng, Rui Linlin, Hou Engineering University of CAPF Shaanxi Xian China

ISBN: (纸本)9783031631351

With the development of computer technology and the national aerospace industry, remote sensing satellites have been successively projected into the air, and the resolution of remote sensing images is becoming higher and clearer. The surface information contained in remote sensing images can be used not only in civil fields such as forestry, agriculture, water conservancy, but also in the military and national defense fields. Current target detection technologies have achieved good results in natural image detection, but their performance in remote sensing images is poor. This is due to the high resolution of remote sensing images themselves, the small size of key targets in the image, and the dense distribution in multiple directions, while the background information is very complex and covers the entire range of the image, resulting in low detection accuracy and slow speed issues when using traditional methods for target detection. High resolution remote sensing images have a wide range of utilization scenarios. The traditional remote sensing image target detection based on artificial vision recognition and processing has been difficult to effectively cope with and meet the practical needs of large-scale high-resolution remote sensing impact target recognition. Based on this, this paper first analyzes remote sensing image information extraction methods based on machine learning, then studies high-resolution remote sensing image target detection methods based on machine learning, and finally presents the development trend of high-resolution remote sensing image target detection based on machine learning. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Remote sensing

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Traffic lights recognition in foggy weather based on YOLOv5 3

Traffic lights recognition in foggy weather based on YOLOv5

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3rd international conference on Signal Processing and machine learning, CONF-SPML 2023

作者： Zhang, Huiwen University of Electronic Science and Technology of China China

Autonomous driving technology has gradually matured. The main application is unmanned vehicles, which have begun to test on the road. However, there is a critical technical problem with mitigating interfering factors in efficient traffic light detection and recognition. Due to the size of traffic lights and the complex road conditions, the detection accuracy is not up to satisfactory. This paper proposes a Traffic Light recognition system based on YOLOv5, which has high speed and accuracy. The system also tests foggy data which gain from image processing. The experimental results show that the method achieved 0.9943 in normal conditions and 0.9937 in foggy conditions. Overall, the efficiency of YOLOv5 meets the practical application requirements. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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ML-Based AI Conceptual Framework for Assessing SMEs Digitalization Through the Lens of Sentiment Analysis 3

ML-Based AI Conceptual Framework for Assessing SMEs Digitali...

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3rd international conference on Digitalization and Management Innovation, DMI 2024

作者： Pelova, Boryana Hristova, Gloria Faculty of Economics and Business Administration Sofia University "St. Kliment Ohridski" Bulgaria

ISBN: (纸本)9781643685823

Tools from the machine learning and data mining domain become even more popular in the fields of economics, entrepreneurship, and policy-making. At the same time, the research on small and medium-sized enterprises (SMEs) is getting amplifying importance for governments and policy-makers especially when it comes to support of SME’s digitalization. A good understanding of the current level of digitalization of SMEs by industries is a prerequisite for design and integration of effective national policies. The goal of this paper is to design the architecture of an ML-based AI conceptual framework for assessing SMEs digitalization. We do this from the perspective of customers assuming that their preferences are absorbed in the publicly available (online) data that they generate in social media and community forums. This approach forms a significant contribution of this paper. Furthermore, we define an algorithm for data preparation, and we develop an algorithm based on sentiment analysis, which generates a set of industry-specific digitalization indices, which is another important contribution of this paper. © 2025 The Authors.

关键词： Adversarial machine learning

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data mining and Visualization to Understand Employee Attrition and Work Performance 3

Data Mining and Visualization to Understand Employee Attriti...

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3rd international conference on Computing and Information Technology, ICCIT 2023

作者： Gollapalli, Mohammed Rahman, Ata-Ur Osama, Abdurrahman Alfaify, Ahmad Yassin, Muhammad Alabdullah, Ahmad Imam Abdulrahman Bin Faisal University College of Computer Science & Information Technology Department of Computer Information Systems Dammam31441 Saudi Arabia Imam Abdulrahman Bin Faisal University College of Computer Science & Information Technology Department of Computer Science Dammam31441 Saudi Arabia

ISBN: (纸本)9798350321487

Throughout the course of careers, a large number of employees encounter a variety of normal life events that can have an impact on their performance and job environment satisfaction. Under these circumstances, some employees are compelled to leave their positions, not because of issues with their work environment, but due to personal circumstances. This is what we refer to as Employee Attrition, a pervasive phenomenon affecting numerous businesses and work environments in the present day. Employee attrition could occur in a variety of ways and be determined by several entities. Therefore, we look into various conditions, causes, indicators, and entities in order to study the attrition problem and determine its answers and enhancements. This study investigated the attrition of employees based on IBM data repository, together with the machine learning based experimental analysis and knowledge discoveries, to identify the most effective approaches to the attrition problem. The experimental data contained 1,471 records with 35 distinct characteristics. We formally employed Nave Bayes Classifier in this study using WEKA datamining software, and adequate knowledge has been extracted to better comprehend employee turnover and their work performance facets. © 2023 IEEE.

关键词： data mining

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Third international conference on Digital Technologies, Optics, and Materials Science, DTIEE 2024

Third International Conference on Digital Technologies, Opti...

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3rd international conference on Digital Technologies, Optics, and Materials Science, DTIEE 2024

ISBN: (纸本)9781510681590

The proceedings contain 40 papers. The topics discussed include: calculation of soft magnetic high-entropy alloys systems;enhancement of tribological properties of tool metal-ceramic materials by high-power ion beams;some features of the formation of gas hydrates in solutions of common salt;study of the features of formation of gas hydrates in solutions of common salt and calcium chloride;development of mathematical models for nonlinear nonstationary random processes;detecting anomalies in forest input data in a supply chain system using machine learning;grid approximation in the problems of synthesis of UAV control systems;development of an emotion and age recognition model based on the combination of local binary patterns and squeeze-and-excitation block method;and simulation modeling as a decision support system tool for improving the environmental safety of road transport.

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Multimodal Biometrics Classification Using Long Short Term Memory with Sigmoid Functions 3

Multimodal Biometrics Classification Using Long Short Term M...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Archana Reddy, R. Kurian, Smitha Ramadan, Ghazi Mohamad Abood, Baydaa Sh. Z. Habelalmateen, Mohammed I. School of Computer Science and Artificial Intelligence SR University Telangana Warangal India HKBK. College of Engineering Department of Computer Science Bengaluru India College of MLT Ahl Al Bayt University Karbala Iraq National University of Science and Technology Dhi Qar Iraq The Islamic University Najaf Iraq

ISBN: (纸本)9798350317022

In the evolving fields of biometric authentication and recognition, ongoing efforts are aimed at overcoming various challenges related to accuracy, robustness, computational complexity, and generalization. Notably, there are common challenges such as the need for image augmentation, dataset size impact, and architectural limitations in deep learning approaches. To address these issues, this paper leverages two diverse datasets, the CASIA dataset, and the UCI-HAR dataset, each offering unique biometric data sources. The proposed method employs Long Short Term Memory (LSTM) with a sigmoid neural network for classification, effectively capturing temporal patterns and enhancing biometric recognition. This approach has the potential to revolutionize biometric classification and authentication, offering enhanced accuracy, robustness, and applicability across various scenarios. The performance of this method is exemplary, achieving an accuracy rate of 99.80%. Furthermore, the proposed method is compared against established techniques, including Support Vector machine (SVM), K-Nearest Neighbor (K-NN), and Deep Convolutional Neural Network, highlighting its superior performance. By effectively addressing the prevalent challenges in the field, it paves the way for more reliable and secure biometric recognition systems. © 2023 IEEE.

关键词： Biometric authentication Deep learning Long Short Term Memory Sigmoid Neural Network Support Vector machine

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