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检索条件"任意字段=6th Asia Conference on Machine Learning and Computing, ACMLC 2024"

共 606 条记录，以下是51-60 订阅

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the State of the Art in machine learning-Based Information Security: Identifying the Gaps 6

The State of the Art in Machine Learning-Based Information S...

引用

6th International conference on Intelligent computing in Data Sciences, ICDS 2024

作者： Langa, thobeka Venter, Hein S. University of Pretoria Dep. of Computer Science Pretoria South Africa

ISBN: (纸本)9798350351200

machine learning (ML) has emerged as a constantly evolving and powerful technology with the potential to transform the field of information security. Its ability to handle enormous quantities of data at high speeds, detect abnormalities, and automate efficient responses to security risks makes ML very promising in this domain. However, its application in the field of information security has not been extensively investigated, and a comprehensive understanding of existing research gaps in ML-based information security is still lacking. To accomplish this objective, the authors use previous publications to conduct an analysis of the machine learning techniques that have been adopted or explored within the field of information security. the outcome is then used to produce a state-of-the-Art paper, identifying areas where more research is required. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

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machine learning Detection Approach Against Trust Manipulation Attack in Trust Management Systems 6

Machine Learning Detection Approach Against Trust Manipulati...

引用

6th International conference on Intelligent computing in Data Sciences, ICDS 2024

作者： Baalla, Mohcine Driss, Bouzidi National School of Computer Science and Systems Analysis ENSIAS Mohammed v University in Rabat Rabat Morocco

ISBN: (纸本)9798350351200

Trust Management Systems (TMS) play a crucial role in establishing and maintaining trust among nodes in various contexts. However, some nodes may adopt suspicious behaviors by strategically alternating between trustworthy and malicious actions to evade detection, an attack called Trust Manipulation Attack (TMA) or on-off attack. this paper addresses the challenge of detecting such suspect behaviors in TMS and proposes a machine learning-based approach for TMA identification. To validate our contribution, we utilize a Vehicular Ad Hoc Network (VANET) dataset generated through a series of simulations. the results obtained from our experiments demonstrate the effectiveness of the proposed approach in accurately identifying nodes with alternating trust patterns, thus effectively detecting suspect behaviors within the TMS. In a machine learning model, the accuracy reached up to 97.78%. © 2024 IEEE.

关键词： Adversarial machine learning

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Virtual learning Environment to Predict Students' Academic Performance Using Deep learning Technique 6

Virtual Learning Environment to Predict Students' Academic P...

引用

6th International conference on Intelligent computing in Data Sciences, ICDS 2024

作者： Al Husaini, Yousuf Nasser Ahmad Shukor, Nur Syufiza Arab Open University Faculty of Computer Studies Muscat Oman Universiti Selangor Faculty of Communication Visual Art and Computing Shah Alam Malaysia

ISBN: (纸本)9798350351200

Data mining in education has gained importance in recent decades due to the abundance of educational data available. the purpose of this study is to investigate deep learning and machine learning methods for predicting students' academic performance. the study employed machine learning techniques, such as the decision tree, random forest, and logistic regression, and deep learning techniques, such as Deep learning Long Short-Term Memory (LSTM). And it found that the LSTM model was best for sequential classification of time series and used the Open University learning Analytics Dataset (OULAD) to develop predictive models. the LSTM achieved the highest accuracy, with an impressive 94.4%. this performance outperformed the decision tree, random forest, and logistic regression, models. the exceptional accuracy of the LSTM suggests that it can be a valuable tool for identifying at-risk students early and targeting interventions to improve their academic performance. this study recommends using this model in universities and colleges to predict the students' performance, and the prediction results would help improve the policymakers and decision-making. © 2024 IEEE.

关键词： Adversarial machine learning

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Smart room for patient monitoring based on IoT technologies 6

Smart room for patient monitoring based on IoT technologies

引用

6th Artificial Intelligence and Cloud computing conference (AICCC) / 5th asia Digital Image Processing conference

作者： Zhukova, Nataly Motienko, Anna Levonevskiy, Dmitriy Subbotin, Alexey Pham Tuan Anh St Petersburg Fed Res Ctr Russian Acad Sci SPC RA St Petersburg Inst Informat & Automat Russian Aca Lab Big Data Technol Sociocyberphys Syst St Petersburg Russia St Petersburg State Elect Univ Fac Comp Technol & Informat St Petersburg Russia ITMO Univ Fac Software Engn & Comp Syst St Petersburg Russia

ISBN: (纸本)9798400716225

this article presents a method for building a hospital room with extended intelligent features based on Internet of things (IoT) technologies. We begin by reviewing existing vision-based health care systems and identifying important and useful functions that they should have. We then present a method for building a smart room that allows increase the level of the intelligence of the existing hospital rooms due to extensive use of the up-to-date machine learning methods. the proposed solution assumes using readily available hardware devices to reduce the cost of smart room building. Compared to commercially available assistive technologies, the proposed smart room turn out to have high level of intelligence and to be very cost effective. the system's non-intrusive nature makes it easy to use it both in hospitals and at home for patient care.

关键词： smart ward smart room automation in medicine Internet of things IoT intellectual room machine learning

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Predicting Diabetes Using machine learning : XGBoost and the PIMA Dataset 6

Predicting Diabetes Using Machine Learning : XGBoost and the...

引用

6th International conference on Intelligent computing in Data Sciences, ICDS 2024

作者： El Arni, Maroua Lahsen-Cherif, Iyad Bellafkih, Mustapha National Institute of Posts and Telecommunication Rabat Morocco

ISBN: (纸本)9798350351200

Diabetes is a significant health issue that needs innovative approaches to be managed and detected early. Ma-chine learning (ML) has demonstrated a significant potential in improving its prediction and diagnosis. the present paper highlights a comprehensive comparison of different machine learning algorithms (eXtreme Gradient Boosting (XGBoost), Random Forest (RF), K-Nearest Neighbor (KNN) and C-Support Vector Classification (SVC)) applied to the Pima Indian dataset, a well known resource in the diabetes research. After rigorous experimentation and evaluation, we have identified the most effective ML algorithm that surpasses existing models in accuracy, precision and recall. this work employs a supervised ML approach, XGBoost, that achieved the best accuracy after exploring its parameters. © 2024 IEEE.

关键词： Adversarial machine learning

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Leveraging EEG Signals and machine learning for Schizophrenia Classification 6

Leveraging EEG Signals and Machine Learning for Schizophreni...

引用

6th International conference on computing and Informatics, ICCI 2024

作者： Elfarsy, Abdelrahman El-Metwally, Sara Medical Informatics Program Faculty of Computers and Information Mansoura University Mansoura Egypt Computer Science Department Faculty of Computers and Information Mansoura University Mansoura Egypt

ISBN: (纸本)9798350373875

Schizophrenia is a neurological disorder known for its potential to disrupt brain function and cause erratic behavior. Timely diagnosis and intervention are crucial for improving patient outcomes. this paper conducts a comprehensive comparative study of three machine learning models: Logistic Regression (LR), Support Vector machine (SVM), and Random Forest (RF) to classify EEG signals associated with schizophrenia. the dataset utilized in this study comprises EEG data obtained from 28 individuals, data preprocessing techniques, including artifact removal, filtering, and normalization, were applied to enhance data quality. A set of informative statistical features was extracted from the EEG signals to capture relevant information. the three machine learning models are trained and evaluated using various performance metrics, including accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). Random Forest achieved the highest accuracy (96%), while SVM demonstrated strong precision and recall (95%). these findings highlight the potential of machine learning in aiding early schizophrenia diagnosis through EEG signal analysis. © 2024 IEEE.

关键词： Support vector machines

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Utilizing Deep learning and machine learning Methods to Forecast Market Performance 6

Utilizing Deep Learning and Machine Learning Methods to Fore...

引用

6th International conference on Intelligent computing in Data Sciences, ICDS 2024

作者： El Mahjouby, Mohamed El Fahssi, Khalid Bennani, Mohamed Taj Lamrini, Mohamed El Far, Mohamed Faculty of Science Dhar El Mahraz Department of Computer Science Fez Morocco

ISBN: (纸本)9798350351200

Forecasting the stock market has become one of the most challenging problems for the AI research community. Stock market investment strategies are complex and depend on analyzing amounts of data. Recently, machine learning techniques have increasingly evaluated and enhanced market predictions compared to traditional methods. this interest is driven by the time-dependent nature of stock prices, making their predictions essential in data mining and a subject of significant interest and effort over the past decades. this challenge remains difficult due to the intrinsic characteristics of time series data, such as its high dimensionality, large volume, and constant updates. Researchers have explored machine learning and deep learning methods to improve the effectiveness of traditional approaches. In this document, we aim to forecast stock market performance at the end of the trading day by applying various machine learning algorithms to two datasets, 'CoinMarketCap' and 'CryptoCurrency,' and analyzing the prediction outcomes of these models. © 2024 IEEE.

关键词： Adversarial machine learning

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RB-Net: Rule-Based Neural Networks with Interpretable Models 6

RB-Net: Rule-Based Neural Networks with Interpretable Models

引用

6th International conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Xue, Zhehang Zheng, Yangfei North China Institute of Computing Technology Beijing China

ISBN: (纸本)9798350388374

In the current landscape of machine learning, black-box models face certain limitations in applications such as healthcare and law due to their opacity. Consequently, there's a growing preference for interpretable models to address these issues, with rule-based models being a significant category among them. Traditional rule-based models often rely on heuristic search techniques customized for particular rule-learning scenarios. these methods contrast sharply with the gradient-based approaches typical in neural machine learning. Recent efforts to bridge the gap between neural and symbolic machine learning have led to the exploration of gradient-based rule learning within neural network frameworks. these innovative approaches enable the application of neural network learning techniques to the realm of rule learning. In this work, we propose RB-Net, a neural network-based rule-generating interpretable model. We demonstrate its competitive performance in classification tasks compared to similar algorithms, particularly excelling when the number of rules is limited. © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient Financial Fraud Detection: An Empirical Study using Ensemble learning and Logistic Regression 6

Efficient Financial Fraud Detection: An Empirical Study usin...

引用

6th IEEE International conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Wang, Kewei Redmond United States

ISBN: (纸本)9798350374315

In the financial sector, fraud detection remains a crucial yet challenging task due to the increasing volume and complexity of transactions. this study focuses on developing an efficient fraud detection model using a highly realistic financial transaction dataset. the dataset, characterized by significant class imbalance, undergoes comprehensive preprocessing including PCA for feature extraction and various sampling techniques for balancing. We employ a range of machine learning classifiers, including logistic regression, K-Nearest Neighbors, support vector machine, and decision tree, alongside ensemble learning approaches to enhance detection performance. A comparative analysis is conducted, highlighting that ensemble models, including stacking with logistic regression and LightGBM, demonstrate superior performance over individual classifiers. the results show notable improvements in AUC-ROC and other metrics, underscoring the efficacy of combined approaches in detecting fraudulent activities with high accuracy and robustness. the findings offer valuable insights into the design of advanced fraud detection systems capable of mitigating financial losses and enhancing system security. © 2024 IEEE.

关键词： Logistic regression

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Comparative Analysis of machine learning and Traditional Forecasting Methods in Bitcoin Price Prediction 6

Comparative Analysis of Machine Learning and Traditional For...

引用

6th International conference on Blockchain computing and Applications, BCCA 2024

作者： Shen, Ying Qi, Zhengxin Martinez, Fernando Rahouti, Mohamed Hsu, D. Frank Oliveira, Diogo CIS Department Fordham University New YorkNY10023 United States College of IST Pennsylvania State University MonacaPA15601 United States

ISBN: (纸本)9798350351538

In an era where digital currencies are becoming increasingly prevalent, accurately predicting the price fluctuations of cryptocurrencies like Bitcoin is crucial for investors and analysts alike. this paper delves into this challenge by comparing the efficacy of traditional time series forecasting methods against modern machine learning (ML) techniques. Utilizing a rich dataset that includes Bitcoin’s technical indicators and publicly accessible financial data, the study methodically assesses the predictive performance of each model. the results underscore the superior forecasting capabilities of ML algorithms, with the best-performing model significantly outstripping traditional methods in accuracy. this advancement not only showcases the potential of ML in enhancing financial market predictions but also sets a new benchmark for future research in cryptocurrency price forecasting. © 2024 IEEE.

关键词： Adversarial machine learning

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