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

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A Hybrid learning Model for Detecting Attacks in Cloud Computing 3

A Hybrid Learning Model for Detecting Attacks in Cloud Compu...

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3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Ponnapalli, Sudhir Dornala, Raghunadha Reddi Sai, Kalakoti Thriveni Lead DevOps Engineer United States Cloud Architect United States CNA United States

ISBN: (纸本)9798350396157

Cloud computing has revolutionized how organizations manage and deliver IT services, offering unprecedented scalability and flexibility. However, with this transformation, the cloud infrastructure has become a prime target for cyber-attacks. Detecting and mitigating attacks in cloud computing environments is paramount to ensure data security and business continuity. This paper presents a novel Hybrid learning Model (HLM) for detecting attacks in cloud computing. The proposed HLM leverages the strengths of both supervised and unsupervised learning techniques to enhance attack detection accuracy and reduce false positives. The proposed approach is the combination of Iterative Intrusion Detection system (IIDS) combined with random forest (RF) as the training model and iterative reinforcement learning based Support Vector machine (SVM) with transfer learning is used to classify the types of attacks. The preprocessing techniques used in this paper are Modified Z-Score and SMOTE (Synthetic Minority Over-sampling Technique) used to clear the data. A feature selection technique Correlation-based Feature Selection (CFS) is also used to select the accurate features from the data. It combines the discriminative power of supervised machine learning with the anomaly detection capabilities of unsupervised learning. The model is trained on a diverse dataset of regular and attack traffic to learn the patterns and behaviors of various attacks in a cloud environment. The HLM is evaluated using real-world datasets from cloud environments and compared to traditional detection methods. Our results show that the Hybrid learning Model significantly improves attack detection accuracy while reducing false positives. This research contributes to the ongoing efforts to enhance the security of cloud computing infrastructures and protect critical data assets. © 2024 IEEE.

关键词： Support vector machines

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Enhancing Organ Donation: Web-based System for Promoting Awareness with machine learning for Optimized Matching 3

Enhancing Organ Donation: Web-based System for Promoting Awa...

引用

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

作者： Patil, Aditi Mahamuni, Chaitanya V Patil, Kaustubh Kunjumon, Roy Nadar, Kingston Dept. of Computer Engineering Fcrit Vashi Navi Mumbai India

ISBN: (纸本)9798350375190

Thisresearch work presents a web-based system designed to enhance organ donation by optimizing donor-recipient matching and raising public awareness. Motivated by the urgent need to improve transplant success rates amidst organ shortages, our work integrates machine learning to optimize the matching process. Multiple linear regression is utilized for pairing donors and recipients due to its capability to model the complex relationship between multiple attributes, such as blood type compatibility and age, thereby optimizing the matching process for organ transplantation. The system uses HTML, CSS, JavaScript, PHP, and SQL technologies to facilitate donor registration, matching algorithms, and hospital workflows. Results demonstrate improved efficiency in donor-recipient pairing, showcasing potential impact on transplant outcomes. Future work includes algorithm refinement, expanded outreach efforts, and implementation of robust privacy measures. This research contributes to the organ donation field by offering technological solutions to enhance transplant success rates and address organ shortage challenges. © 2024 IEEE.

关键词： Geographic information systems

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BiLSTM Model for Electricity Consumption Prediction: A Comparative Study with Traditional machine learning Approaches 3

BiLSTM Model for Electricity Consumption Prediction: A Compa...

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3rd IEEE Delhi Section Flagship conference, DELCON 2024

作者： Chauhan, Neetu Bansal, Shweta Malik, Meenakshi School of Engineering and Technology K. R. Mangalam University Gurugram India School of Engineering and Technology Bml Munjal University Gurugram India

ISBN: (纸本)9798331518592

The reliability of power systems depends on accurate electricity load forecasting, as the demand for residential electricity rises annually as a result of infrastructure expansion and population expansion. While some traditional models based on machine learning have shown improvement in many areas, their accuracy in predicting energy consumption is still not up to par. Residential electricity consumption patterns are irregular and uneven, making precise power load assessment more difficult. U sing methods such as gated recurrent unit (GRU), convolutional neural network (CNN), eXtreme Gradient Boosting (XGB) regressor, bidirectional long short-term memory (BiLSTM), and Convolutional LSTM (ConvLSTM), the research focuses on building and assessing energy consumption prediction models. Four performance metrics namely, mean absolute error, root mean square error, mean square error and R2 are considered to check the potency of different machine learning algorithms but after the data preprocessing step i.e. filling missing values and removing outliers in the dataset. In this research, among all short-term prediction algorithms, BiLSTM performance surpassed good results in energy consumption prediction with values 0.0281, 0.0163, 0.0007 and 0.9197 of RMSE, MAE, MSE and R2 respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Multivariate time series forecasting model based on sliding window machine learning 3

Multivariate time series forecasting model based on sliding ...

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3rd international conference on Electronics Technology and Artificial Intelligence, ETAI 2024

作者： Bai, Hexiang Yu, Peiwen Yang, Zhenshan Xing, Ruijia Rui, Zhang College of Computer Science and Technology Jilin University Changchun China College of Software Jilin University Changchun China Financial Technology Department BANK OF JILIN CO. Ltd. Changchun China

ISBN: (纸本)9781510683143

This paper introduces a multivariate time series forecasting model that combines a sliding window technique with machine learning, incorporating a convolutional neural network to extract spatio-temporal features. This integration boosts predictive accuracy and robustness. Applied to the Jilin credit card installment dataset, our SHRF method improves AUC PRECISION and F1 scores. Validations on the BeijingAirQuality dataset using the SHL model show our model reduces RMSE per time step to 15.18, demonstrating excellent performance in multivariable time series forecasting. © 2024 SPIE.

关键词： Adversarial machine learning

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Construction and Optimization of English machine Translation Model Based on Hybrid Intelligent Algorithm 3rd

Construction and Optimization of English Machine Translation...

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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： He, Lirong Xie, Chun Deng, Yuan College of International Studies and Education Tongren University Guizhou Tongren445300 China GuilinUniversityofTechnology Guangxi Guilin541004 China

ISBN: (纸本)9783031631351

English grammar is a prerequisite for mastering English, and its complexity and learning difficulty perplex most English learners. Most English learners will encounter all kinds of difficulties in the stage of English learning, so English grammar learning has become a module for English learners to invest energy in learning. The goal correction is to automatically correct grammar errors in the text through computer programs, that is, to identify and correct grammar errors based on the analysis of the context of the input text, so as to ensure the accuracy of the output text and not affect its own semantics. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Error correction

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Towards Explainability Using ML And Deep learning Models For Malware Threat Detection 3

Towards Explainability Using ML And Deep Learning Models For...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Snehal, Mattaparti Satya Chandana Nagoor, Veeraboina Rohit, Sugrivu Raghunandan, S. Thangavel, Senthil Kumar Srinivasan, Kartik Kapoor, Pratyul Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Sr. Architect IBM Security IBM India Pvt Ltd India

ISBN: (纸本)9798350372120

In response to the escalating threat of Android malware, this research proposes a hybrid model for malware detection and classification using a combination of machine learning (ML) and deep learning techniques. Leveraging Random Forest for feature extraction, the model efficiently processes the complex feature spaces present in the CICMaldroid dataset. Simultaneously, the Long Short-Term Memory (LSTM) CNN, Bidirectional LSTM (BILSTM), Bidirectional LSTM (BILSTM) CNN are employed for further analysis. The proposed approach integrates predictions from both the machine learning and deep learning models to achieve comprehensive results. data preparation involves outlier removal and Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance. Random Forest analysis guides feature selection, optimizing the model's efficiency. Cross-validation ensures robustness, and Explainable AI techniques, specifically SHAP, enhance interpretability. Experimental results demonstrate the model's effectiveness, achieving high overall accuracy and specific identification of Android malware types. The proposed methodology provides a streamlined and transparent approach to early malware detection, contributing to a deeper understanding of the decision-making process behind each prediction. © 2024 IEEE.

关键词： Adaptive boosting

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Domain Adaptation Method for Gear RUL Prediction Integrating Convolutional Attention and Adversarial learning 3

Domain Adaptation Method for Gear RUL Prediction Integrating...

引用

3rd IEEE Industrial Electronics Society Annual On-Line conference, ONCON 2024

作者： Chen, Yuanhai Jiang, Kai Lin, Jing School of Reliability and System Engineering Beihang University Beijing China

ISBN: (纸本)9798331540319

In recent years, deep learning-based methods for predicting gear remaining useful life (RUL) have garnered widespread attention. However, these approaches continue to encounter various obstacles, such as fluctuating operational environments, interference from noise, and a lack of adequate lifespan data samples. To address these issues, this paper proposes an adaptive gear RUL prediction model that combines convolutional block attention mechanisms (CBAM) with adversarial learning. First, a network structure combining a CBAM and temporal convolutional network (CBAM-TCN) is designed. Then, a deep feature extraction network is constructed using CBAM-TCN. Finally, by incorporating Domain Adversarial Neural Networks (DANN), the feature extraction network is optimized to enhance the cross-domain invariance and trend of deep features. The prediction accuracy of the proposed RUL prediction method is validated through multiple cross-condition RUL prediction tasks, demonstrating superior performance compared to other RUL prediction methods. © 2024 IEEE.

关键词： Adversarial machine learning

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Cultural-Aware AI Model for Emotion Recognition 13

Cultural-Aware AI Model for Emotion Recognition

引用

13th Iranian/3rd international machine Vision and Image Processing conference (MVIP)

作者： Baradaran, Mehrdad Zohari, Payam Mahyar, Abtin Motamednia, Hossein Rahmati, Dara Gorgin, Saeid Shahid Beheshti Univ Dept Comp & Data Sci Tehran Iran Khaje Nasir Univ Technol Dept Comp Engn Tehran Iran Inst Res Fundamental Sci Sch Comp Sci High Performance Comp Lab Tehran Iran Shahid Beheshti Univ Fac Comp Sci & Engn Tehran Iran Chosun Univ Dept Comp Engn Gwangju 61452 South Korea

ISBN: (纸本)9798350350494;9798350350500

Emotion AI is a research domain that aims to understand human emotions from visual or textual data. However, existing methods often ignore the influence of cultural diversity on emotional interpretation. In this paper, we propose a multi-modal deep learning model that integrates cultural awareness into emotion recognition. Our model uses images as the primary data source and comments from individuals across different regions as the secondary data source. Our results show that our model achieves robust performance across various scenarios. Our contribution is to introduce a novel fusion approach that bridges cultural gaps and fosters a more nuanced understanding of emotions. Due to the best of our knowledge, few works are using this approach, for Emotion AI, combining different types of data sources and models. We evaluate our model on the ArtELingo dataset, which contains image-comment pairs with Chinese, Arabic, and English annotations. The experimental results in the evaluation phase demonstrate an impressive 80% recognition accuracy for the model that merges image-text features.

关键词： Emotion Recognition DeepFeature Extraction Image Representation Text Representation

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Research on Rail Corrugation Identification Algorithm Based on Vibration data of Operating Vehicles 3

Research on Rail Corrugation Identification Algorithm Based ...

引用

3rd international conference on Rail Transportation, ICRT 2024

作者： Liu, Silei Cheng, Zhongning Xing, Hailing Shan, Taotao Technical Research Centre of Shanghai Shentong Metro Group Co. Ltd. Shanghai China Key Laboratory of Road and Traffic Engineering of the Ministry of Education Shanghai Key Laboratory of Rail Transit Structure Endurance and System Safety Tongji Univ. Shanghai China

ISBN: (数字)9780784485941

ISBN: (纸本)9780784485941

The track is the core foundation of the subway system, and the steel rails are the most important components of the track structure. The diseases of steel rails directly affect the safety of train operation. If not treated in time, it will not only affect the comfort of the vehicle but also cause damage to the track and vehicle structural components, thereby endangering the safety of train operation. Based on the data of the online monitoring system of the operating train, this paper proposes a new method for the identification of steel rail corrugation based on multichannel sensor data, combining wavelet transformation and machine learning technology to achieve efficient identification and continuous tracking monitoring of steel rail corrugation under normal operating conditions. The identification of corrugation based on the vibration data of operating vehicles can improve the efficiency of steel rail corrugation detection and provide data support for tracking the development process of steel rail corrugation. © ASCE.

关键词： Wavelet transforms

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Research on Digital Media Image Processing and Enhancement System for Computer Deep learning Vision 3

Research on Digital Media Image Processing and Enhancement S...

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3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Lin, Huiyu School of Information Engineering Guangzhou Huashang Vocational College Guangdong Guangzhou510000 China

ISBN: (纸本)9798350380989

Combining the object-oriented technology and tree view function of VB6, a graphical control method based on tree view is presented. The information about the algorithm is wrapped in these graphical controls by means of wrapping. The boundary characteristics of the original image are extracted by filtering it based on the deep belief net. The mathematical model of deep belief net and its probability distribution function are established. In this project, the method of combining neural network transformation and sparse representation is proposed to carry out multi-feature fusion of image boundaries, and sigma function is added to the output layer of deep confidence network model to reduce the complexity of iterative training process. By extracting the shape gradient feature of the image boundary, the edge discrete feature is removed and the digital image boundary enhancement is realized. Experiments show that the improved image has better boundary characteristics. Under the same sample conditions, the number of iterative training steps is less, and the image edge processing effect is better than the traditional scheme. © 2024 IEEE.

关键词： deep learning digital image edge enhancement graphic algorithm control image processing platform Visual programming

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