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YOLOv3 with Asymmetric Intersection over Union Based Loss Function for Human Detection 21

YOLOv3 with Asymmetric Intersection over Union Based Loss Fu...

引用

5th international conference on machine learning and soft computing, icmlsc 2021

作者： Zhu, Heng Yang, Dongchao Huang, Geng Wu, Qingyuan Li, Teng Tao, Bo State Key Laboratory of Tribology Tsinghua University Beijing China

ISBN: (纸本)9781450387613

In order to improve the performance of the target detection model based on convolutional neural network, and make the bounding box representing the object location contain as complete original target information as possible, this paper improved the loss function of the bounding box regression part of the YOLOv3 network. A new metric, Asymmetric Intersection over Union (AIoU), is proposed to measure the similarity between the prediction box and the truth. Theoretical analysis shows that AIoU based loss function can better guide the direction of bounding box regression after introducing asymmetry. Experiments show that the improved YOLOv3 network achieves higher accuracy and maintains fast convergence in human target detection. The examples of bounding box regression on the test dataset show that the target information loss can be reduced by the proposed method. In addition, distinguishing between the prediction and the truth in calculation may have reference significance for the loss function design of network models for other tasks © 2021 ACM.

关键词： Deep learning

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Augmented Analytics an Innovative Paradigm 13th

Augmented Analytics an Innovative Paradigm

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13th international conference on Innovations in Bio-Inspired computing and Applications, IBICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

作者： Guarda, Teresa Lopes, Isabel Universidad Estatal Península de Santa Elena La Libertad Ecuador CIST – Centro de Investigación en Sistemas y Telecomunicaciones Universidad Estatal Península de Santa Elena La Libertad Ecuador Algoritmi Centre Minho University Guimarães Portugal Instituto Politécnico de Bragança Bragança Portugal UNIAG Instituto Politécnico de Bragança Bragança Portugal

ISBN: (纸本)9783031274985

Business intelligence (BI) and analytics are a set of techniques, methodologies, and tools used in the analysis of business data, which allow users (decision makers) to have a clearer view of the market, leveraging the decision-making process, allowing timely business decisions. Usually BI refers to Extract Transform and Load processes (ETL), Data Warehouse (DW), Data mining (DM), online analytical processing (OLAP), visualization tools, and reports. In turn, the analytics generally uses advanced techniques, providing BI users with Artificial Intelligence (AI) and machine learning (ML) techniques. In this context, Gartner introduced the term "augmented analytics" in 2017, making the line between BI and advanced analytics clear. The main objective of this work is to explore the area of Augmented Analytics in the context of BI, through the use of ML and Natural Language Processing (NLP) resources and capabilities, as an innovative paradigm of augmented analytics in the decision-making process. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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machine learning-Based Optimization of Network Security Protocols for Enhanced Threat Detection

Machine Learning-Based Optimization of Network Security Prot...

引用

Device Intelligence, computing and Communication Technologies, (DICCT), international conference on

作者： S. Palani A. Muthukumaravel Department of Computer Science Bharath Institute of Higher Education and Research Chennai Tamilnadu India Faculty of Arts and Science Bharath Institute of Higher Education and Research Chennai Tamilnadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Current cyber threats are relatively sophisticated, and this has put pressure on traditional network security protocols. It proposes an extended machine learning methodology using a supervised Random Forest algorithm for classification and an unsupervised Autoencoder for anomaly detection to improve network security measures. The consumes network traffic through a powerful preprocessing pipeline of feature selection, scaling, and encoding to increase the performance of a model. The proposed method was also tested on benchmark datasets like CICIDS2017, UNSW-NB15, and KDDCup99 with an accuracy of 98.76%, precision of 96.45%, false positive rate under 1.89%, and a latency of only 4.23ms better than other methods from 2021, 2022 and 2023. These results show that the ability of the developed framework is higher in terms of detection and more adaptable to real-world threats. It offers a transportable and secure solution for next-generation networks, which overcomes the gaps of the previous methods to make way for further developments.

关键词： Adaptation models Protocols Accuracy Autoencoders Network security Benchmark testing Threat assessment Optimization Random forests Next generation networking

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Customizable DDoS Attack Data Generation in SDN Environments for Enhanced machine learning Detection Models 5

Customizable DDoS Attack Data Generation in SDN Environments...

引用

5th international conference on Advanced Research in computing, ICARC 2025

作者： Gayantha, Nadeera Rajapakse, Chathura Senanayake, Janaka University of Kelaniya Dept. of Industrial Management Sri Lanka Robert Gordon University School of Computing Engineering and Technology Aberdeen United Kingdom

ISBN: (纸本)9798331530983

Distributed Denial of Service (DDoS) attacks are a critical threat to the security and reliability of software-Defined Networking (SDN) environments. Existing datasets for training machine learning (ML) models, such as KDDCup '99 and CICIDS 2017, are either outdated or fail to capture SDN-specific characteristics, limiting their effectiveness in detecting modern DDoS attacks. This paper proposes a framework for generating a comprehensive, SDN-specific dataset using a virtual environment that integrates Mininet, the Ryu controller, and Python-based automation. The dataset incorporates advanced flow-level metrics, including SYN counts, queue lengths, and real-time traffic dynamics, reflecting contemporary attack scenarios such as ICMP floods, TCP SYN floods, and UDP floods. By addressing the limitations of traditional datasets, this custom dataset enhances ML model training for DDoS detection in SDN environments, providing improved accuracy and adaptability. Contributions include a scalable SDN-based dataset generation framework, enriched feature sets for ML training, and a comprehensive approach to capturing both legitimate and malicious traffic dynamics. This study highlights the potential of SDN programmability in advancing security research and offers a robust tool for the development of reliable DDoS detection mechanisms. © 2025 IEEE.

关键词： software reliability

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A Peer learning Method for Building Robust Text Classification Models

A Peer Learning Method for Building Robust Text Classificati...

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IEEE international conference on Big Data and Smart computing (BigComp)

作者： Jeon, Hyun-Kyu Cheong, Yun-Gyung Sungkyunkwan Univ Dept AI Suwon South Korea Sungkyunkwan Univ Dept AI Convergence Program Social Innovat Suwon South Korea

ISBN: (纸本)9781728189246

Classilication is an essential task in many practical problems. A machine learning based classification model is built to minimize the error between actual labels and predicted labels generated by the model. When the model depends on only actual labels during the training, it can generate monotonous distributional predictions. In order to make a robust model, it needs to use other sources of information in addition to the original labels. To address this issue, we propose a peer learning method that enables the target model to reference multiple peer models and that can control the impact of peers on the target model during the training phase. The experiment results indicate that the proposed method is promising.

关键词： peer learning classification deep learning label refinery

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Identification of Risk Factors for Mortality after Myocardial Infarction Using machine learning Methods 24

Identification of Risk Factors for Mortality after Myocardia...

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24th international conference on soft computing and Measurements, SCM 2021

作者： Kashirina, Irina L. Firyulina, Maria A. Bondarenko, Yulia V. Desyatirikova, Elena N. Efimova, Olga E. Chernenkaya, Liudmila V. Voronezh State University Voronezh Russia Voronezh State Technical University Voronezh Russia Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russia

ISBN: (纸本)9781665439749

The article presents the results of a study on predicting the risk of mortality after myocardial infarction based on depersonalized data, including a description of 11457 cases of heart attacks in the Voronezh region in 2015-2017. There are some risk prediction methods were analyzed: Kaplan-Meier model, Cox model, logistic regression, gradient boosting model. To determine the significant factors affecting survival after myocardial infarction, several methods for determining the importance of signs are considered, and their comparative analysis is carried out. The results of the study demonstrate the decisive influence of the severity of myocardial infarction on the KILLIP scale, the age of the patient with percutaneous coronary intervention and the patient's history of arterial hypertension on the short-term 20-day prediction of survival after myocardial infarction. © 2021 IEEE.

关键词： Forecasting

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Fracture Modeling Method of Asmari Formation Carbonate Reservoir in G Oilfield, Middle East 13th

Fracture Modeling Method of Asmari Formation Carbonate Reser...

引用

13th international Field Exploration and Development conference, IFEDC 2023

作者： Sun, Fu-Ting Hui, Chen Tong, Yi Da, Li-Na CNOOC International Limited Beijing China

ISBN: (纸本)9789819704675

Fractures are developed in the carbonate reservoir of Asmari Formation in G oilfield in the Middle East, which aggravates the heterogeneity of the reservoir and is an important factor affecting the productivity of producers and the rule of water cut rising. Due to the lack of core and imaging logging data and weak seismic response of reservoir fractures, it is difficult to carry out fracture evaluation, prediction and modeling in G oilfield, which is a key problem restricting the efficient development of the reservoir. Firstly, an inversion method of fracture density based on formation anisotropy is established by using cross dipole array acoustic logging (CDAAL) data, which provides a reliable evaluation result of single well fracture density for fracture density modeling. Secondly, based on deep mining of fracture information in seismic attributes and integrating artificial intelligence algorithms, a machine learning based seismic multi-attribute fusion fracture prediction method is established to predict the distribution of fractures in the study area. Finally, a fracture density model is established with the single well fracture density as the hard data, the fracture planar distribution prediction as the soft constraint, then a discrete fracture network (DFN) model is established based on the fracture density model with fracture parametric statistics law as the constraint by random simulation method. Research has shown that the history matching result based on the dual-porosity model is significantly improved compared to the single-porosity model, which confirms the reliability of the fracture modeling method and results. At the same time, the research results have been applied to the optimization of well types and locations during the development of G oilfield. 4 horizontal wells have been implemented in areas with relatively developed fractures. The average initial productivity of them is 3–5 times that of the vertical wells in the same period, which has impro

关键词： Forecasting

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Deep Feature learning Network for Vehicle Retrieval 21

Deep Feature Learning Network for Vehicle Retrieval

引用

5th international conference on machine learning and soft computing, icmlsc 2021

作者： Phung, Trang T.T. Ly, Ngoc Q. Vo, Trung T. Ho, Minh T.N. VNUHCM-University of Science Ho Chi Minh City Viet Nam Vietnam and Saigon University Ho Chi Minh City Viet Nam SaiGon University Viet Nam

ISBN: (纸本)9781450387613

Vehicle Retrieval plays a very important role for traffic surveillance system which has been being developed in recent decades with innovative methods from traditional approach to deep learning approach. However, there are some challenges of Vehicle Retrieval which still need to be solved such as image resolution, image distortion, occlusion [1]. In this study, we utilized deep feature learning to build a vehicle retrieval system with two main tasks (1) deep feature learning network, (2) vehicle retrieval. Specifically, we adopted Faster R-CNN and SVM for building the feature network generating feature extraction and classification. Then, we used Approximate Nearest Neighbor (ANN) with tree-based approach for indexing feature vectors and searching. The proposed system solved some challenges such as dimensionality reduction of feature vector, improving the retrieval performance based on deep learning. The experimental results have shown that the proposed method is significantly outperforms the state of the art methods for vehicle retrieval on BIT-Vehicle dataset. © 2021 ACM.

关键词： Vehicles

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Research and application of data-driven carbon emission analysis and prediction 2

Research and application of data-driven carbon emission anal...

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2nd international conference on Cloud computing, Big Data Application and software Engineering, CBASE 2023

作者： Wei, Dongmei Meng, Dan Southwest University of Finance and Economics School of Management Science and Engineering Chengdu611130 China Xihua University School of Computer and Software Engineering Chengdu610039 China

ISBN: (纸本)9798350331448

The issue of carbon emissions has become an important policy direction for countries around the world to promote healthy socio-economic development and address climate change issues. This article conducts feature extraction and data analysis on various major energy data in China from 1997 to 2017, and constructs a model for verifying and predicting total carbon emissions. The model can not only accurately reflect China's carbon emissions over the past twenty years, but also predict future carbon emissions. This not only provides scientific basis for government decision-making, but also enriches the application of data-driven and artificial intelligence methods in this field. © 2023 IEEE.

关键词： machine learning

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machine learning Approach for Mitigating Security Threats in IoT Environment 5

Machine Learning Approach for Mitigating Security Threats in...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Saini, Harnit Bala, Saroj Ida, S.Jhansi Kumar, K. Suresh Swetha, Shankerpally Divyabarathi, P. Ajay Kumar Garg Engineering College Department of Cse Ghaziabad India Ajay Kumar Garg Engineering College Department of Mca Ghaziabad India R.M.K Engineering College Department of Computer Science and Business Systems Kavaraipettai India Kongu Engineering College Department of Cse Erode India Cvr College of Engineering Department of Information Technology Telangana India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India

ISBN: (纸本)9798350321425

In today's world, a huge amount of data and information is transferred through wireless networks. The Internet of Things (IoT) is a complex network that consists of diverse sensors, devices, and things, which often have limited resources and are susceptible to various security threats, including Distributed Denial of Service (DDoS) attacks. These networks are vulnerable to a variety of cyber threats and privacy issues. Intrusions are harmful activities that can damage a network. IoT networks are particularly vulnerable to security threats. The Bi-Layer Intrusion Detection Model (BIDM) identifies intrusions based on the optimal features selected in the Optimal Feature Vector Selection (OFVS) phase. This scheme not only prevents attacks, but also serves as a defense against legitimate threats. The proposed technique was evaluated using the KDD CUP 99 dataset, a well-known Intrusion Detection System (IDS) benchmark. The performance of the proposed scheme was further analyzed using the NSL-KDD and CICIDS-2017 datasets. The performance of OFVS was also investigated using the relatively new IoT Network Intrusion dataset. The implementation was carried out in Jupyter Notebook using the Python programming language. Matplotlib, Scikit-learn, pandas, and NumPy were some of the packages used in the implementation. The proposed framework achieved high accuracy rates, with NB at 97.4%, SVM at 96.1%, and DT at 98.1%. By utilizing this approach, the security of IoT networks can be significantly enhanced, and the risks associated with DDoS attacks can be mitigated. © 2023 IEEE.

关键词： Denial-of-service attack

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