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检索条件"任意字段=International Conference on Machine Learning and Computing"

共 66300 条记录，以下是4761-4770 订阅

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Poisoning Attack based on Data Feature Selection in Federated learning 13

Poisoning Attack based on Data Feature Selection in Federate...

引用

13th international conference on Cloud computing, Data Science and Engineering, Confluence 2023

作者： Liu, Zhengqi Liu, Ziwei Yang, Xu University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Southwest Minzu University School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9781665462631

Federated learning is proposed as a typical distributed AI technique to protect user privacy and data security, and it is based on decentralized datasets that train machine learning models by sharing model gradients rather than sharing user data. However, while this particular machine learning approach safeguards data from being shared, it also increases the likelihood that servers will be attacked. Joint learning models are sensitive to poisoning attacks and can effectively pose a threat to the global model when an attacker directly contaminates the global model by passing poisoned gradients. In this paper, we propose a joint learning poisoning attack method based on feature selection. Unlike traditional poisoning attacks, it only modifies important features of the data and ignores other features, which ensures the effectiveness of the attack while being highly stealthy and can bypass general defense methods. After experiments, we demonstrate the feasibility of the method. © 2023 IEEE.

关键词： Deep learning

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2023 international conference on Cloud computing and Artificial Intelligence: Technologies and Applications, CloudTech 2023

2023 International Conference on Cloud Computing and Artific...

引用

6th IEEE international conference on Cloud computing and Artificial Intelligence: Technologies and Applications, CloudTech 2023

ISBN: (纸本)9798350303063

The proceedings contain 36 papers. The topics discussed include: towards an IoT architecture based on machine learning for missing data prediction on the edge;a survey on recent intrusion detection systems in cloud environment;advancing breast cancer diagnosis with machine learning: exploring data balancing, feature selection, and Bayesian optimization;using serverless FaaS for invoking long-running jobs;security governance in IOT environment: a state of art;data-driven approach for residential occupancy modeling using PIR sensors: a Moroccan case study;attempts in worst-case optimal joins on relational data systems: a literature survey;assessing digital government maturity model for developed countries. application to Singapore;evaluation and analysis of network safety mechanisms in SDN infrastructure;comparison of a FaaS- and SaaS-based ETL process in a cloud environment;and sentiment analysis applied to Arabic tweets using machine learning and deep learning.

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Margin Propagation based Analog Soft-Gates for Probabilistic computing 37

Margin Propagation based Analog Soft-Gates for Probabilistic...

引用

37th international conference on VLSI Design (VLSID) / 23rd international conference on Embedded Systems (ES)

作者： Nandi, Ankita Kumar, Pratik Chakrabartty, Shantanu Thakur, Chetan Singh Indian Inst Sci Dept Elect Syst Engn NeuRonICS Lab Bengaluru 560012 India Washington Univ Dept Elect & Syst Engn St Louis MO 63130 USA

ISBN: (纸本)9798350384406

Soft computing gates offer a promising approach for efficient and parallel processing of probabilistic signals. These gates are widely used in Bayesian networks and various machine learning models. However, unlike digital logic gates, the design and scaling of analog Soft-Gates is challenging due to analog artifacts, i.e., sensitivity to biasing, mismatch, and temperature variations. In this paper, we present a systematic framework for designing analog Soft-Gates that leverage the bias and temperature scalability of the Margin Propagation principle. Specifically, the paper proposes an adaptive design strategy to alleviate mismatch artifacts and to trade-off probabilistic computational accuracy, area efficiency, and power consumption. We demonstrate the design synthesis of a Soft-Gate and apply it to error correction decoding and filtering tasks. The reported Mean Square Error of the Soft-Gate is less than 10(-2), indicating its accuracy in probabilistic computations. For edge filtering applications, the proposed Soft-Gates can achieve an average Structural Similarity Index of 0.95. The estimated energy consumption in 180nm CMOS technology is in the order of pico-Joules, validating the gate's energy efficiency.

关键词： Analog Soft-Gates Generalized Margin Propagation Shape-based Analog computing Probabilistic Computation

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A prescriptive analytics approach for tool wear monitoring using machine learning techniques 3

A prescriptive analytics approach for tool wear monitoring u...

引用

3rd international conference on Secure Cyber computing and Communications, ICSCCC 2023

作者： Mohan, Soorya Prabha Jaya Nirmala, S. National Institute of Technology Tamilnadu Thiruchirapalli India

ISBN: (纸本)9798350300710

In machinery, unplanned machine downtime causes bottlenecks, hazardous incidents, and cut off the production process. Down the pike, it reduces delivery output;maximizes the costs, and leads to revenue losses. It is very important to bring back the failed machine to production with less downtime that is challenging. In practical, to bring the machine back to production with manual troubleshooting and repair to make the machine functional is not that much easy. Many machine-learning techniques used to predict machine failure already exists. However, the prescription to repair the machine and bring it into normal operating condition has not been researched much. Just predicting machine failure may not be helpful so much additionally, if we add the prescription followed by prediction helps to identify the influencing factor due to which machine will get fail. This will provide value add to the prediction. Therefore, prediction along with prescriptive analytics will reduce the MTTR (Mean Time To Repair) thereby reducing the machine downtime. © 2023 IEEE.

关键词： Wear of materials

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Risk Analysis of Enterprise Cross-network Data via DAE-BiLSTM and Fuzzy Bayesian Network 4

Risk Analysis of Enterprise Cross-network Data via DAE-BiLST...

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4th international conference on Intelligent Communications and computing

作者： Li, Wei Li, Jianjun Shan, Yuxiang Li, Junjie Nie, Guibing China Tobacco Zhejiang Ind Co Ltd Informat Ctr Hangzhou Peoples R China Zhejiang Univ Sch Engn Hangzhou Peoples R China Hangzhou DBAPPSecur Co Ltd Hangzhou Peoples R China

ISBN: (纸本)9798331530372;9798331530365

With the increasing severity of enterprise information security issues, the identification and evaluation of cross-network data risk has become one of the essential technologies to safeguard enterprise data and network security. However, traditional rule-based or signature-based anomaly detection methods are increasingly unable to cope with the complex and evolving forms of attacks, necessitating more intelligent and flexible detection mechanisms to counter these constantly changing threats. To address this, a method integrating denoising autoencoder (DAE) and bidirectional long short-term memory network (BiLSTM) is first proposed for monitoring the enterprise cross-network data traffic and identify the occurring abnormal risks. On this basis, we use a fuzzy Bayesian network to effectively measure these known abnormal risks. In the end, experimental results demonstrate that the proposed method outperforms traditional machine learning algorithms and other deep learning models across various evaluation metrics, exhibiting high accuracy and efficiency.

关键词： network security cross-network data risk analysis deep learning

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Predictive Modeling of Stability Indices in Wind-Integrated Multi-machines Power Systems Using machine learning Techniques 4

Predictive Modeling of Stability Indices in Wind-Integrated ...

引用

4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Dash, Binayak Tripathy, Manish Samal, Rajat Kanti Department of Electrical Engineering University of VSSUT Burla Burla India

ISBN: (纸本)9798331530402

The main goal of this work is to forecast Transient Energy Conversion (TEC) indicators used to evaluate the security and stability of wind-integrated power systems. Accurate stability assessments are crucial because wind power is intermittent, adding complexity to the system. TEC indices serve as important markers for determining whether a system is stable or unstable. Wind speed and system dynamics fluctuations introduce nonlinear characteristics that standard approaches struggle to handle effectively. Addressing these challenges is vital for ensuring the reliability and security of the electrical grid. machine learning(ML) algorithms, however, excel in learning from data patterns and adapting to complex, dynamic settings. First, various ML models are tested to predict TEC indicators. Second, polynomial feature transformation with a degree of 2 is applied to capture nonlinearity. Third, hyperparameter tuning is performed using GridSearchCV to optimize model performance. This approach enhances stability evaluation, ensuring the reliability of modern power grids under fluctuating renewable energy conditions. © 2025 IEEE.

关键词： Electric power transmission networks

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Exploring the design space of 32x32 Approximate Reduced Complexity Wallace Multipliers 13

Exploring the design space of 32x32 Approximate Reduced Comp...

引用

13th international conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Rizos, Ioannis Papatheodorou, Georgios Efthymiou, Aristides Univ Ioannina Dept Comp Sci & Engn Ioannina Greece

ISBN: (纸本)9798350385434;9798350385427

In the realm of nano-scale technology, integrated circuits are focused on enhancing speed while minimizing power consumption and physical footprint. This demand has given rise to the concept of approximate computing, which is particularly vital for error-tolerant applications such as multimedia, machine learning, signal processing, and scientific computing. This study delves into the characteristics of various approximate reduced complexity Wallace multipliers, evaluating their accuracy, area usage, power consumption and delay. Specifically, the research centers on approximating calculations during the partial product reduction phase by substituting conventional full adders with approximate ones. The findings highlight the variability in introduced error, area and power requirements based on the combination and placement of these approximate adders within the partial product reduction stage. Using a Genetic Algorithm, we successfully reduce the size of our approximate multiplier by 61% of the exact multiplier's size, and we also observe a power reduction by 65% of the exact multiplier's power consumption. The proposed methodology offers insights for designers in selecting the most suitable configuration of approximate adders for the partial product reduction stage in a reduced complexity Wallace multiplier, tailored to the specific demands of their application.

关键词： Adders approximate computing arithmetic circuits Wallace multiplier non-dominated multi-objective genetic algorithms design space exploration

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Enhancing Cardiovascular Disease Prediction Using Ensemble learning 7

Enhancing Cardiovascular Disease Prediction Using Ensemble L...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Ambade, Sharwari Chikmurge, Diptee School of Computer Engineering Mit Academy of Engineering Alandi India

ISBN: (纸本)9798350304268

The proposed ensemble learning framework integrates diverse machine learning algorithms. Each base model is trained on a diverse set of features derived from comprehensive patient data. To evaluate the ensemble model's performance, a large-scale dataset comprising anonymized electronic health records from a diverse patient population is employed. The dataset includes longitudinal data, allowing for the incorporation of temporal information. Preliminary results demonstrate that the ensemble learning approach outperforms individual models in terms of predictive accuracy and stability. Moreover, the model exhibits robustness across different subpopulations, indicating its potential for generalizability and applicability in diverse healthcare settings. The accuracy metrics is being utilized as a foundation for assessing the effectiveness of all classifications. With an accuracy of 93.01% the stacking classifier has proven to be the finest model. © 2023 IEEE.

关键词： Cardiovascular disease Ensemble learning machine learning algorithms

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Integrating Multi-Omics Data for Advanced Diabetes Prediction and Understanding 7

Integrating Multi-Omics Data for Advanced Diabetes Predictio...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Mishra, Soumya Ranjan Dash, Sachikanta Padhy, Sasmita Kumar, Naween Dash, Yajnaseni Giet University Gunupur India Giet University Department of Cse Gunupur India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Bhopal India Bennett University School of Computer Science Engineering and Technology Uttar Pradesh Greater Noida India Bennett University School of Artificial Intelligence Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

Diabetes is a major global health issue, requiring new approaches to understanding its complex etiology and improving predictions of effective management and treatment. Current methods typically use centralized models, which have privacy concerns and cannot fully capture correlations in multimodal datasets. To address these problems, we introduce a new model using integrated learning and causal theory. Our system uses three datasets: the Pima Indian Diabetes Dataset (PIDD), 130 U.S. Appl. Clinical Diabetes Dataset, and physiological summaries. For analysis in our proposed method, we use various machine learning methods with ensemble method. Here we achieve an accuracy of 93% for PIDD 92% for US 130 clinical datasets and 90% for physically collected datasets. By integrating genetic, proteomic, and metabolomic data, we better understand diabetic mechanisms and potential therapeutic targets. Our model addresses the limitations of current methods, improves prediction accuracy, and data privacy, and provides insight into the aetiology of diabetes. This approach provides a strong foundation for future study. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Cyclone Center Identification in Radar Images through Deep learning and Match Recognition 15

Enhancing Cyclone Center Identification in Radar Images thro...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shanmugam, Thirumurugan Chandrashekar, Chirag Subburaj, Maheswari Sivaraman, Arun Kumar Sultana, Ajmery Thangavel, Senthil Kumar University of Technology and Applied Sciences College of Computing and Information Sciences Suhar Oman Vellore Institute of Technology School of Computer Science and Engineering Chennai India Photon Interactive Inc Digital Engineering Solution Center ON Canada Algoma University Brampton School of Computer Science and Technology ON Canada Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone's center is vital for predicting its intensity and trajectory. However, challenges persist in automatically locating the center due to the diverse nature of cyclone morphology and structure. To address this, the deep convolutional network's capability is leveraged to capture various structural features in images by proposing two-step approach for cyclone center localization. Initially, a pre-trained EfficientDet model is employed using transfer learning to obtain weights. Subsequently, these weights, along with the data, are utilized in a deep learning model to provide precise coordinates of the cyclone center in the respective image. The effectiveness of existing deep learning and machine learning models show that the cyclone prediction systems have an accuracy ranging from 86% to 92% or better, and cyclone eye detection accuracy surpassing 87%. Experiment outcomes indicate that the proposed methodology outperforms conventional methods and existing works, showcasing its potential for enhancing cyclone monitoring and forecasting. © 2024 IEEE.

关键词： Cyclone detection deep learning efficient det and YOLO model transfer learning

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