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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6844 条记录，以下是851-860 订阅

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DDoS Attack Classification Leveraging data Balancing and Hyperparameter Tuning Approach Using Ensemble machine learning with XAI 3

DDoS Attack Classification Leveraging Data Balancing and Hyp...

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3rd international conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Jiyad, Zakaria Masud Maruf, Abdullah Al Haque, Md. Mahmudul Gupta, Mrityunjoy Sen Ahad, Abdul Aung, Zeyar Department of Computer Science & Engineering Dhaka Bangladesh Khalifa University Department of Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350349207

A distributed denial-of-service (DDoS) attack is a cyber-attack that aims to disrupt the regular traffic of a targeted server, service, or network by inundating the target or its surrounding infrastructure with a flood of Internet traffic. DDoS attacks can cause significant harm to the security of the network environment. There are several works on the classification of DDoS attacks using machine learning (ML) and Deep learning (DL). However, some improvement is needed, and in-depth research is necessary with the rapidly changing DDoS attack types. This study presents a novel ensemble model that can identify DDoS attacks. The approach leverages ML algorithms such as Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting (XGBoost) classifiers to detect and classify these malicious attacks effectively. The hyper-tuning process plays a significant role in increasing the performance of our proposed model and reducing overfitting. In our research, we use the potent eXplainable Artificial Intelligence (XAI) models SHAP (Shapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). By utilizing SHAP and LIME's capabilities, we improve our ML models' readability and transparency, giving us a better understanding of difficult predictions and model behavior. The evaluation results demonstrate that the XGBoost ensemble model outperforms other classifiers, achieving an impressive accuracy rate of 97 %, with an outstanding F -score of 97%. The precision and recall are accordingly 98% and 96%. © 2024 IEEE.

关键词： Denial-of-service attack

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A Predictive Analysis of Life Cycle Emissions of CO2 in Electric Vehicles Using machine learning 3

A Predictive Analysis of Life Cycle Emissions of CO2 in Elec...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Koilery, Shriya Sanjay Narikutty Muhammad, Ridha Karalakath, Nabeel Faisal Payyani Rasheed, Azeem A. Revathi, M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

Electric vehicles (EVs) have emerged as a quick solution and eco-friendly alternative to internal combustion engine vehicles (ICEVs), offering fewer emissions and better energy consumption. Lithium-ion batteries are used to give power to electric vehicles, but their production and disposal add a lot to the upstream Carbon Dioxide (CO2) emissions, which is often otherwise, neglected by the society while considering EV as a 0 to minimal CO2 emission substitute to fossil fuels. This is where a proper understanding of EV's life-cycle emissions is necessary. This paper proposes a novel approach to estimating the CO2 emissions of an EV considering its life-cycle emission, addressing limitations in the already available datasets by creating a new and comprehensive dataset. Four machine learning models, i.e., Linear Regression, Random Forest, Support Vector Regression (SVR) and Bi-LSTM, are evaluated for their accuracy in predicting emissions. These results indicate that Linear Regression and SVR struggled with the dataset's non-linearity, whereas Random Forest performed well but faced issues with unnecessary data. The Bi-LSTM model was the most effective, offering the best accuracy and efficiency in the prediction of CO2 emission by making use of long-term dependencies. The research emphasizes the importance of considering the entire life-cycle emissions of EVs to assess their impact on the environment and highlights Bi-LSTM's efficiency in accurately predicting CO2 emissions. © 2024 IEEE.

关键词： Low emission

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Less is More: Revisiting the Role of Batch Normalization in Test-time Adaptation 3

Less is More: Revisiting the Role of Batch Normalization in ...

引用

3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Gu, Xin College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798331506582

This Continual Test-Time Adaptation (CTTA) enables pre-trained models to adapt to evolving data distributions without the need of source data for retraining. Current methods often involve resource-intensive updates or full model modifications, which can lead to error accumulation and catastrophic forgetting. We propose LessBN, a simple yet effective approach that selectively adjusts shallow Batch Normalization (BN) layers at test time, minimizing prediction entropy to boost model confidence. This adjustment preserves deep feature knowledge while enhancing efficiency and mitigating forgetting. The benchmark experiments on CIFAR10C and CIFAR100C demonstrate that LessBN matches or surpasses existing methods with significantly faster adaptation speed, validating its practicality and effectiveness. It can be easily plugged into off-the-shelf models due to its simplicity and generalizability. © 2024 IEEE.

关键词： batch normalization catastrophic forgetting continual learning test-time adaptation

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Advanced Eye Disease Classification using the EfficientNetB3 Deep learning Model 3

Advanced Eye Disease Classification using the EfficientNetB3...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Soni, Tanishq Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331532420

This research study introduces a novel approach for the classification of ocular diseases that employs the EfficientNetB3 architecture. The backbone of our model is EfficientNetB3, which has a total of 42 layers and a well-optimized layer structure. The model's feature extraction capabilities are optimized for medical image data by utilizing pre-trained weights from ImageNet. The model effectively reduces overfitting and enhances generalization across the dataset by incorporating supplementary layers, including Batch Normalization, Dense, and Dropout. The softmax activation function is employed by the final output layer to forecast class probabilities, which is customized to the precise number of eye disease classes. The proposed model's potential for reliable eye disease classification in clinical contexts was demonstrated by its impressive accuracy of 93.8% on the test dataset. © 2024 IEEE.

关键词： Deep learning

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Smart Crop Cultivation: Harnessing machine and Deep learning for Informed Agricultural Practices 3

Smart Crop Cultivation: Harnessing Machine and Deep Learning...

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Meghani, Smit Vele, Jay Madrecha, Kunal Salunke, Abhijeet Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350375190

Amongst various occupations in India, farming is the most common. It boosts the Indian economy significantly and provides Indians with a wide range of job opportunities. The ideal crop for each type of soil is no longer chosen by farmers. This has an immediate effect on agricultural yields. It is causing serious financial losses for the farmers in the area. Cultivating a certain crop on a particular type of soil now involves several factors to take into account. The optimal crop to plant is determined by taking into account a number of soil specifications. This novel opinion mining method will consider the following parameters: temperature, air humidity, soil pH, nitrogen, potassium, phosphorus, and annual rainfall. The model aids in determining the kind of crop that has to be planted by taking into account the data collected from prior years. In order to support the mission, various algorithms are analyzed and evaluated on multiple fronts, including comparative research and analysis. In addition, the ideal fertilizer will be recommended depending on crop-related environmental factors, temperature, pH level, and soil moisture content. © 2024 IEEE.

关键词： Crops

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CTCRNN: A Hybrid CNN-BiLSTM Based Classification Method for Industrial IoT Encrypted Traffic Based on Spatiotemporal Features 24

CTCRNN: A Hybrid CNN-BiLSTM Based Classification Method for ...

引用

3rd international conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE)

作者： Zhao, Huiqi Sun, Shunfa Wang, Rui Fan, Fang Liu, Lu Shandong Univ Sci & Technol Coll Intelligent Equipment Tai An 271000 Shandong Peoples R China State Grid Shandong Elect Power Res Inst Jinan 250003 Shandong Peoples R China

ISBN: (纸本)9798400718212

The proliferation of smart devices in the industrial Internet of Things (IIoT) will generate large volumes of data traffic in the 5G era. Encrypting network traffic helps to secure the data security of smart devices while also raising the complexity of classification. Due to the issue of traditional machine learning methods depending on expert experience and the fact that most current research only uses a single CNN and RNN network to detect encrypted traffic without taking forward and backward correlations between data packets into consideration, it is challenging to identify encrypted traffic features effectively. This paper proposes CNN-BiLSTM, a method that integrates spatiotemporal characteristics with attention mechanisms. In particular, we utilize 1D-CNN and BiLSTM to extract spatial and temporal features from encrypted traffic;we then apply the CBAM block and TPA block to weight the extracted features, allowing the model to concentrate on highly discriminative features;additionally, we applied the ELU activation function in the CNN model to enhance the model's convergence speed and avoid gradient vanishing issues. This study was validated experimentally using the ISCX2016 public dataset, and the experimental findings demonstrate that, in comparison to current mainstream methods, the model performs exceptionally well in categorization.

关键词： Encrypted Traffic Attention Mechanism Deep learning Spatiotemporal Features

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Privacy-preserving data Splitting Based on machine learning 3

Privacy-preserving Data Splitting Based on Machine Learning

引用

3rd international conference on Electronic Information engineering and Computer, EIECT 2023

作者： Ruan, Huafeng Dongguan University of Technology School of Computer Science and Technology School of Software School of Cyberspace Security Dongguan China

ISBN: (纸本)9798350357707

Privacy-preserving data splitting is an effective means of storing parts of data across different servers to safeguard user privacy. Current privacy protection schemes that employ data splitting for privacy protection assume that data owners are already aware of the constraints between attributes, which limits their applicability. To address this challenge, we propose a scheme that utilizes machine learning to segment anonymous data and securely store it. Initially, we employ machine learning algorithms to recursively compute the relationships between attributes. Subsequently, we introduce a greedy algorithm that leverages the relationships derived in the previous step to calculate a data fragmentation approach that ensures privacy safety. Experimental results indicate that our scheme can successfully derive privacy-safe fragmentation methods using various machine learning algorithms. Among the four machine learning algorithms tested, Lasso regression demonstrated the fastest computation speed. © 2023 IEEE.

关键词： Privacy-preserving techniques

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CIBIP-AI: Development of California Invasive Bird Identification and Prediction Artificial Intelligence System 3

CIBIP-AI: Development of California Invasive Bird Identifica...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Chen, Kayla Shao, Claire Harker Sch San Jose CA 95129 USA Lynbrook High Sch San Jose CA USA

ISBN: (纸本)9798350372977;9798350372984

Invasive species are detrimental to native wildlife and pose a major threat to biodiversity. In California alone, municipalities and conservation authorities are estimated to spend approximately $50.8 million per year on invasive species. Effective management of bio-invasions depends on reliable identifying the invasive threats and predicting areas at risk of invasion. Yet, there is no efficient solution to address the issue. This paper proposes the first California Invasive Bird Identification and Prediction AI System (CIBIP-AI). The CIBIP-AI system utilizes a deep learning solution to automatically identify and predict two California invasive birds, the mute swan and brown-headed cowbird. The CIBIP-AI system uses a hybrid deep learning architecture based on VGG16 and ResNet50 for identification. The CIBIP-AI system can also predict the geographical areas at risk of invasion based on a set of the birds' physiology features and microclimate conditions. The CIBIP-AI database is created for storing data and continuous training. The initial experimental results show that the classification accuracy for the image input reached 96%. The audio input was more challenging to classify, with 73% accuracy. After applying knowledge-based filtering and common pattern extraction, the accuracy was improved to 77%. The video was split into multiple images and audio and input into the hybrid models. The classification accuracy of the hybrid models reached 97%. The work shows that the proposed CIBIP-AI system provides significant aid to the early detection and prevention of invasive bird species.

关键词： Invasive species artificial intelligence deep learning ecology mute swan brown-headed cowbird

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Comparison of machine learning models 3

Comparison of machine learning models

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3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Zhou, Shigang Kang, Jinjia Dalian Polytechnic University Dalian116034 China South China Normal University Foshan528225 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

As the machine learning system becomes increasingly mature, the advantage of each learning model has corresponding surveys and research. Gradually, a mass of learning models has been developed to address different problems and applications. In this paper, we compare each classical machine learning model on a few datasets to catch features of these common models that could be used in both regression and classification problems. Basically, we choose different scales of datasets with different characteristics and then reproduce all these models through our programming and get the evaluation criteria after training them on the samples to gain the performance. Models like Linear Regression, Support Vector machine, Decision Tree, Random Forest, Gradient Boost, K-nearest Neighbors, Ridge Regression, Least Angle Regression, and so on are used and compared in this paper based on different tasks (Classification and Regression are discussed respectively). The performing criteria we chose are Mean Square Error, Mean Absolute Error, R squared for Regression (prediction problems), and accuracy, precision, and recall for Classification. Through the comparison, we could figure out in which condition that the model can perform better and how to select efficient machine learning Models for users to deal with specific data without fitting parameters. Anyway, this study contributes to a deeper understanding of machine learning models and assists practitioners in selecting the most suitable model for their particular application. Finally, we discuss the improvements that we could make in the future to compare them more comprehensively and deeper in specific regions. © 2025 SPIE.

关键词： Contrastive learning

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Enhanced Gym Performance Prediction using machine learning: A Comprehensive Model for Personalized Fitness 3

Enhanced Gym Performance Prediction using Machine Learning: ...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Lincy, R. Babitha Akkshara, S.S. Chandarshini, S. Prasad, K.S. Dev, R. Prabu Department of CCE Sri Eshwar College of Engineering TamilNadu Coimbatore India

ISBN: (纸本)9798331532420

In recent years, gadgets have emerged as a growing generation of health and fitness, providing personalized insights to enhance overall fitness and performance. This article provides an in-depth analysis of how to consume algorithms to create and improve fitness performance based on different physiological parameters and variety of techniques are used to set these parameters, keeping in mind more accurate performance prediction by data affecting heart rate, frame structure, body composition, exercise intensity, and the exercise logs model provides real-time records, providing accurate and personalized advice to maximize training results. The proposed system objective is to enhance customers’ overall health and development, reduce the threat of damage, and improve productiveness. Its high specificity and scalability make it a valuable tool for fitness self-management structures, private trainers and health enthusiasts. In order to provide individualized solutions, this app also offers personalized solutions. © 2024 IEEE.

关键词： Electronic health record

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