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检索条件"任意字段=International Conference on Advances in Machine Learning and Systems Engineering"

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Leveraging machine learning Models for Proactive Disaster Forecasting 1

Leveraging Machine Learning Models for Proactive Disaster Fo...

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1st international conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Affan, Daimi Syed Ahmed, Askari Arslaan Maaz, Ahmed Syed Asma, Iqbal Computer Science and Engineering Muffakham Jah College of Engineering and Technology Hyderabad India Electronics and Communication Engineering Deccan College of Engineering and Technology Hyderabad India

ISBN: (纸本)9798350355611

The increasing frequency and intensity of natural disasters, such as earthquakes, tsunamis, floods, and forest fires, necessitate the development of advanced early warning systems. Current disaster prediction systems are hampered by limitations in sensor technology, which is often expensive and primarily effective for large-scale disasters. This project aims to enhance disaster prediction accuracy and timeliness by utilizing real-time data and advanced machine learning algorithms, including XGBoost, Voting classifier, and Random Forest classifier. Additionally, recognizing the long-term health impacts of poor air quality, the project extends its scope to predict the Air Quality Index (AQI). The project seeks to enable proactive risk management and effective disaster response by integrating real-time environmental data and machine learning models. We aim to improve proactive risk management and disaster response by employing real-time data. The outcomes of this project promise significant advancements in disaster preparedness, ultimately safeguarding human lives and infrastructure. © 2025 IEEE.

关键词： Risk management

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machine learning-Based Fault Diagnosis Approach for Geosynchronous Satellite Power systems 10

Machine Learning-Based Fault Diagnosis Approach for Geosynch...

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10th international conference on Recent advances in Air and Space Technologies (RAST)

作者： Eyupoglu, Can Natl Def Univ Turkish Air Force Acad Dept Comp Engn Istanbul Turkiye

ISBN: (纸本)9798350323023

Power systems, known as the hearth of satellites, have a direct impact on how long the satellite will operate. An on-orbit mission might fail due to several problems with the satellite power system. Thus, during the entire lifespan of the satellite, the diagnostics of power system faults is crucial. In this study, a new machine learning-based fault diagnosis approach has been proposed for geosynchronous (GEO) satellite power systems. In the feature extraction step of the approach, principal component analysis (PCA) technique is used. Then, LogitBoost with random forest classifier is utilized for the aim of classification. The experimental results show that the proposed model is effective and can be used for fault diagnosis of GEO satellite power systems.

关键词： GEO satellite power systems machine learning fault diagnosis PCA LogitBoost random forest

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Enhancing Cybersecurity: A Multilayered Approach to Phishing Website Detection Using machine learning

Enhancing Cybersecurity: A Multilayered Approach to Phishing...

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2023 IEEE international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Lakshmi Narayanaa, T. Skandarsini, R.R. Ida, S. Jhansi Dept. of Computer Science and Business Systems RMK Engineering College Thiruvallur India

ISBN: (纸本)9798350305708

This paper employs machine learning techniques to combat the escalating threat of phishing attacks in the digital realm. The research builds a predictive model capable of differentiating between phishing and legitimate websites by examining a broad range of information retrieved from website URLs, including address bar-based properties, domain characteristics, and webpage content. To ascertain their effectiveness in this task, six well-known machine learning algorithms-Decision Tree, Random Forest, XGBoost, Deep learning, Autoencoder Neural Network, and Support Vector machines-are rigorously investigated. Notably, the Multilayer Perceptrons algorithm emerges as the standout performer, achieving an 86.4% accuracy in identifying phishing websites. This endeavor not only advances the field of cybersecurity but also empowers individuals to proactively safeguard themselves against the pervasive threat of phishing attacks in an increasingly interconnected digital landscape. © 2023 IEEE.

关键词： Cybersecurity Dataset machine learning model Phishing attacks prediction

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Pointwise Reliability of machine learning Models: Application to Cardiovascular Risk Assessment 9th

Pointwise Reliability of Machine Learning Models: Applicatio...

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9th European Medical and Biological engineering conference (EMBEC)

作者： Henriques, Jorge Rocha, Teresa Paredes, Simao Gil, Paulo Loureiro, Joao Petrella, Lorena Univ Coimbra Ctr Informat & Syst CISUC Coimbra Portugal Coimbra Inst Engn IPC ISEC Polytech Inst Coimbra Coimbra Portugal NOVA Sch Sci & Technol Dept Elect & Comp Engn CTS Lisbon Portugal

ISBN: (纸本)9783031616273;9783031616280

machine learning has made significant advances in many areas, particularly in the healthcare domain. However, despite the advances, the implementation of these models in clinical scenarios is still limited due to several challenges, including the lack of trust. Standard performance measures, such as sensitivity, specificity and confidence intervals can be used to evaluate the reliability of a model, but these are overall performance metrics and do not provide insight into the performance of individual instances. Moreover, these estimates are typically calculated during the training phase and are not easily generalized to new, unseen instances, occurring in the deployment phase. As result, besides the prediction outcome, the existence of a measure of reliability in the prediction of individual estimations would add a layer of security, increasing trust in human-AI interaction, as well as it could also be helpful to support the improvement of the model. This study proposes a reliability measure, combining density and local fit principles, to estimate the confidence of individual predictions in the deployment phase. When applied to a machine learning model in the cardiovascular risk assessment context, the method demonstrates the ability to distinguish between reliable and unreliable predictions, as well as aiding in the stratification of the population.

关键词： machine learning reliability measures cardiovascular risk stratification decision support systems

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An Analysis of Irrigation Management for Crops using machine learning Algorithms 1

An Analysis of Irrigation Management for Crops using Machine...

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1st IEEE international conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Thamilselvan, R. Natesan, P. Rajalaxmi, R.R. Prasanth, D. Nithish Swaminathan, S. Yuvapriya, R. Kongu Engineering College Computer Science and Engineering Perundurai Erode India Kongu Engineering College Department of Computer Applications Perundurai638 060 India

ISBN: (纸本)9798350372748

In every nation, agriculture has boosted the economy. Agriculture is currently dealing with a number of difficulties, such as irrigation and water management. Crop irrigation plays a crucial role in agricultural production by providing water to crops in regions where rainfall alone is insufficient to meet their needs. This abstract provides an overview of crop irrigation, its importance, and various irrigation methods used in agriculture. Irrigation is a critical component of agricultural production, and advancements in machine learning algorithms have the potential to optimize irrigation practices for crop cultivation. Additionally, machine learning has developed in several industries to improve quality. machine learning algorithms like Random Forest, K-Nearest Neighbor, Support Vector machine and Decision Tree are used to find the various results. Internet of Things also plays a vital role in irrigation by making use of the sensors. This review's investigation was conducted using a variety of machine learning algorithms. © 2024 IEEE.

关键词： Support vector machines

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Using Multi-Modal Data to Cluster Student's Behavior within Embodied learning Context 15

Using Multi-Modal Data to Cluster Student's Behavior within ...

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15th international conference on Information and Communication systems (ICICS)

作者： Chettaoui, Neila Atia, Ayman Bouhlel, Med Salim Univ Sfax SETIT Lab Natl Sch Engineers Sfax Tunisia October Univ Modern Sci & Arts HCI Lab Fac Comp & Artificial Intelligence Helwan Univ October City Egypt Univ Sfax SETIT Lab Higher Inst Biotechnol Sfax Tunisia

ISBN: (纸本)9798331540777;9798331540760

advances in sensor technology have supported collecting multimodal data to examine the synergistic relationship between students' interaction behavior and learning performance. However, using real-time students-generated multimodal data to analyze different students' interaction behavior during a multimodal-based embodied learning activity is under-explored. Thus, this paper explores capturing multimodal data to analyze students' interaction behaviors within an embodied learning context. We conducted an in-situ study with 40 primary school students (aged 11-12) engaging in a multimodal activity on electric circuits. We captured students' embodied interaction by collecting their eye-tracking data, emotions, gesture trials, learning performance, and time to complete the activity. Three clusters were identified using K-means clustering. Using supervised machine learning, we have identified three learning profiles based on interaction behavior features: low, medium, and high performers. The results highlighted a significant difference in the learning performance between clusters, as determined by one-way ANOVA. Our findings suggest that multimodal data representing interaction traces can encode the impact of embodied interaction behavior on learning performance.

关键词： embodied learning multimodal data behavioral patterns cluster analysis

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Train-On-Request: An On-Device Continual learning Workflow for Adaptive Real-World Brain machine Interfaces

Train-On-Request: An On-Device Continual Learning Workflow f...

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2024 Biomedical Circuits and systems conference

作者： Mei, Lan Cioflan, Cristian Ingolfsson, Thorir Mar Kartsch, Victor Cossettini, Andrea Wang, Xiaying Benini, Luca ETU Zurich Dept ITET Zurich Switzerland Univ Bologna DEI Bologna Italy

ISBN: (纸本)9798350354966;9798350354959

Brain-machine interfaces (BMIs) are expanding beyond clinical settings thanks to advances in hardware and algorithms. However, they still face challenges in user-friendliness and signal variability. Classification models need periodic adaptation for real-life use, making an optimal re-training strategy essential to maximize user acceptance and maintain high performance. We propose TOR, a train-on-request workflow that enables user-specific model adaptation to novel conditions, addressing signal variability over time. Using continual learning, TOR preserves knowledge across sessions and mitigates inter-session variability. With TOR, users can refine, on demand, the model through on-device learning (ODL) to enhance accuracy adapting to changing conditions. We evaluate the proposed methodology on a motor-movement dataset recorded with a non-stigmatizing wearable BMI headband, achieving up to 92% accuracy and a re-calibration time as low as 1.6 minutes, a 46% reduction compared to a naive transfer learning workflow. We additionally demonstrate that TOR is suitable for ODL in extreme edge settings by deploying the training procedure on a RISC-V ultra-low-power SoC (GAP9), resulting in 21.6 ms of latency and 1 mJ of energy consumption per training step. To the best of our knowledge, this work is the first demonstration of an online, energy-efficient, dynamic adaptation of a BMI model to the intrinsic variability of EEG signals in real-time settings.

关键词： brain-machine interface EEG wearable EEG wearable healthcare transfer learning continual learning ondevice learning

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Privacy-Preserving machine learning Models

Privacy-Preserving Machine Learning Models

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Chodey, Madhuri Devi Gouthami, E. Rao, KakanaboyinaUpendra Ramana, G.V. Shirisha, N. Kumar, Nellore Manoj Department of Electronics and Communication Engineering Karnataka 584101 India Lendi College of Engineering and Technology Department of Science and Humanities Andhra Pradesh Vizianagaram535005 India Anurag Engineering College Department of Computer Science and Engineering Telangana 508206 India Aditya University Department of Mathematics Andhra Pradesh Surampalem533437 India Mlrit Department of Computer Science and Engineering Telangana Hyderabad500043 India Department of Mathematics Thandalam Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331509828

The rapid adoption of machine learning (ML) across several businesses raises serious concerns about data privacy, particularly when sensitive data is involved. By integrating trustworthy techniques in the preprocessing, feature selection, and classification stages, this research provides a comprehensive methodology for developing machine learning models that preserve privacy. Preprocessing uses data anonymisation techniques like k-anonymity and 1-diversity to protect sensitive properties and maintain data utility. Privacy-aware feature selection, or PAFS, is used to identify and retain characteristics that maximise model performance without compromising privacy. In the classification stage, privacy-preserving neural networks handle anonymised or encrypted input while using secure computing frameworks to provide accurate predictions. For privacy-sensitive applications like healthcare and banking, this integrated approach provides a scalable solution, tackling significant challenges in finding a compromise between privacy and usefulness. Experimental results illustrate the utility of the proposed framework and how it could support ethical and safe machine learning in real-world applications. © 2025 IEEE.

关键词： Adversarial machine learning

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Anomaly Detection from Web Log Data Using machine learning Model 7

Anomaly Detection from Web Log Data Using Machine Learning M...

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7th international conference on Computer Applications in Electrical engineering-Recent advances, CERA 2023

作者： Mishra, Amit Kumar Bagla, Piyush Sharma, Ravi Pandey, Neeraj Kumar Tripathi, Neha Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India National Institute of Technology Department of Computer Science & Engineering Jalandhar India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350305005

The information in the logs produced by the servers, devices, and applications can be utilized to assess the system's health. It's crucial to manually review logs, for instance, during upgrades, to verify whether the update and data movement went smoothly. Manual testing is insufficiently trustworthy, and manual log examination takes much time and effort. In this paper, we propose to search log files for anomalous sequences using the machine learning methods K-Means and DBSCAN. The two data representation approaches examined in this study were feature vector representation and IDF representation. The effectiveness of the deployed machine learning algorithms was examined using evaluation measures like F1 score, recall, and precision. The study found considerable differences in the algorithms' capacities to spot anomalies, with some algorithms being better at seeing various types of abnormal arrangements than their overall prevalence. By using the study's findings, the user might be able to spot strange arrangements after manually sifting through the log file. © 2023 IEEE.

关键词： Anomaly detection

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Biomarker Trajectory Prediction and Causal Analysis of the Impact of the Covid-19 Pandemic on CVD Patients using machine learning

Biomarker Trajectory Prediction and Causal Analysis of the I...

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9th IEEE/ACM international conference on Connected Health - Applications, systems and engineering Technologies (CHASE)

作者： Inekwe, Trusting Mkandawire, Winnie Wee, Brian Agu, Emmanuel Colubri, Andres Worcester Polytech Inst Comp Sci Dept Worcester MA 01609 USA Univ Massachusetts Chan Med Sch Genom & Computat Biol Worcester MA 01655 USA Harvard Univ Chem & Phys Biol Cambridge MA 02138 USA

ISBN: (纸本)9798350345025;9798350345018

Background: The COVID-19 pandemic disrupted healthcare services, increasing the susceptibility of high-risk patients including those with cardiovascular Diseases (CVDs), to adverse outcomes. Biomarkers provide insights into patients' underlying health status. However, few studies have investigated the effects of the COVID-19 pandemic on CVD biomarker trajectories using predictive modeling and causal analyses frameworks. Prior research explored the impacts of the COVID-19 pandemic on CVD severity and prognosis but did not investigate biomarker trajectories using machine learning (ML), which can discover complex multivariate relationships in multi-modal data. Objective: This study aimed to compare six ML regression models to select the best performing models for predicting biomarker trajectories in CVD patients using retrospective data. Subsequently, these models were used to assess the COVID-19 pandemic's impact on CVD patients and for causal analyses Approach: Using ML regression and causal inference, this study investigated the pandemic's impact on biomarker values of 80,917 CVD patients and 77,332 non-CVD controls, treated at two hospitals in Central Massachusetts between May 2018 and December 2021. ML regression algorithms, including Neural Networks (NN), Decision Trees (DT), Random Forests (RF), XGBoost, CATBoost and ADABoost, were trained and compared. Important CVD biomarkers (HbA1c, LDL cholesterol, BMI, and BP) were predicted as outcome variables with patients' risk factors (age, race, gender, socioeconomic status) as input variables. Shapley feature importance analyses identified the most predictive features, which were then utilized in Causal Analysis. A Difference-in-Differences (DID) approach within a Double/Debiased machine learning (DML) method isolated the pandemic's impact on biomarkers, while minimizing the effects of confounding factors. Results: CATBoost and XGBoost were the most predictive ML models for LDL cholesterol and HbA1c, yielding R-2 val

关键词： COVID-19 Cardiovascular Diseases biomarkers machine learning regression predictive modeling causal inference Difference-in-Differences SHAP value Double/Debiased machine learning

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