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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33936 条记录，以下是1261-1270 订阅

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Promoting Security and Trust on Social Networks: Explainable Cyberbullying Detection Using Large Language Models in a Stream-Based machine learning Framework 11

Promoting Security and Trust on Social Networks: Explainable...

引用

11th IEEE international conference on Social Networks Analysis, Management and Security, SNAMS 2024

作者： Garcia-Mendez, Silvia De Arriba-Perez, Francisco Information Technologies Group-atlanTTic University of Vigo Vigo Spain

ISBN: (纸本)9798331518349

Social media platforms enable instant and ubiquitous connectivity and are essential to social interaction and communication in our technological society. Apart from its advantages, these platforms have given rise to negative behaviors in the online community, the so-called cyberbullying. Despite the many works involving generative Artificial Intelligence (AI) in the literature lately, there remain opportunities to study its performance apart from zero/few-shot learning strategies. Accordingly, we propose an innovative and real-Time solution for cyberbullying detection that leverages stream-based machine learning (ML) models able to process the incoming samples incrementally and Large Language Models (LLMS) for feature engineering to address the evolving nature of abusive and hate speech online. An explainability dashboard is provided to promote the system's trustworthiness, reliability, and accountability. Results on experimental data report promising performance close to 90 % in all evaluation metrics and surpassing those obtained by competing works in the literature. Ultimately, our proposal contributes to the safety of online communities by timely detecting abusive behavior to prevent long-lasting harassment and reduce the negative consequences in society. © 2024 IEEE.

关键词： Adversarial machine learning

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Integration of Optimization Techniques to Improve Performance of machine learning System 8

Integration of Optimization Techniques to Improve Performanc...

引用

8th international conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Shukla, Amogh Lodha, Nitin Vellore Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (纸本)9781665408165

machine learning (ML), a kind of artificial intelligence, may help software systems become better at anticipating outcomes without explicitly designing them (AI). machine learning systems use historical data to predict future values. Optimization strategies are a must-have toolkit for effectively managing an organization's resources and maximizing shareholder returns. This study's focus was on finding the best ways for developing machine learning systems. Researchers are using optimization methodologies to improve machine learning performance. Using optimization approaches in research is being explored with the purpose of boosting output. In order to help investors make better judgments, the collection of crypto currencies is currently being used. The optimization will assist in narrowing down the crypto currency dataset. Integrating an optimization method to boost a machine learning system's performance is what the proposed study aims to do. © 2022 IEEE.

关键词： machine learning

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EqNet-L: A new representation learning method for mathematical expression

EqNet-L: A new representation learning method for mathematic...

引用

international conference on Big data, Information and Computer Network (BDICN)

作者： Liu, Jiaxin Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9781665484763

In the field of machine learning, symbolic reasoning has not made substantial progress in the past decades compared to deep learning. Researchers believe that combining deep learning may help, and the key is that the neural network needs to learn the features in the mathematical expressions. EQNET was explicitly created for the representation learning of mathematical expressions. Although it has made significant progress in learning representations of mathematical expressions, we believe that there is still much work that can be done. On this basis, we propose EQNET-L. Its structure is mainly based on EQNET, and We applied Dropout in network, the module STACKED-SUBEXPAE is proposed to make sure the network could learn more semantic information. Compared with the previous models, EQNET-L achieved better results in training and testing with the same hyperparameters as EQNET.

关键词： component mathematical expression representation learning deep learning symbolic reasoning machine learning

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A Study on Predicting Skilled Employees’ Using machine learning Techniques 2nd

A Study on Predicting Skilled Employees’ Using Machine Lear...

引用

2nd international conference on Computational Intelligence in machine learning, ICCIML 2022

作者： Madana Kumar Reddy, C. Krishna, J. New Boyanapalli Andhra Pradesh Rajampet Annamaiah Dt. India Rajampet India

ISBN: (纸本)9789819979530

Management of human resource (HR) has become one of the key considerations of entrepreneurs and CEOs in nearly every industry, with the objective of encouraging practices for the proper disclosure of exceptionally qualified employees. Appropriately, the board is associated with these employees’ results. Especially to guarantee that the ideal individual is distributed with perfect timing to the convenient work. From here, data mining is getting popular as a viable option that its point is to find data from enormous measures of data. A model was developed as part of this study using data collection techniques to estimate the results of employees by using the dataset prepared by the Civil Aviation Ministry of Egypt, which had prepared a survey for 145 works and circulated it. In this, three key data mining methods have been utilized to foster the model of classification and to characterize the main factors that positively affect execution. The techniques are support vector machine (SVM), Naïve Bayes, and the decision tree (DT). To acquire an exceptionally exact model, a few experiments were completed in view of the previous strategies utilized in the WEKA tool to permit decision making, HR experts to expect and work on their employees’ productivity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

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Roses Greenhouse Cultivation Classification Using machine learning Techniques

Roses Greenhouse Cultivation Classification Using Machine Le...

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Albarico, Jennifer P. La Rosa, Glaiza Rein F. Santos, Regina Anne Dj. Tesorero, Alona Jane M. Magboo, Ma. Sheila A. Magboo, Vincent Peter C. Department of Physical Sciences and Mathematics University of the Philippines Manila Padre Faura St. Ermita Manila1000 Philippines

There is a demand for flowers globally all year round, more particularly roses, necessitating increased production for flowers. Demand for roses has increased due to their year-long availability as well as its uses in cosmetic, perfume, medicinal products, food raw materials and decoration industry. Rose plants are prone to drastic fluctuations in temperature, drought stress damages, and low precipitations. These resulted to an increase in greenhouse production to generate optimum supply to meet growing demands as controlled environment provides several advantages. In this study, four machine learning models (random forest, support vector machine, multinomial logistic regression, and artificial neural networks) were applied to roses greenhouse cultivation dataset. The study aims to classify the most suitable greenhouse environment to upgrade the roses state leading to the optimal production of roses. Four model configurations corresponding to the pre-processing techniques were tested. These were scaling only, scaling plus removal of outliers, scaling plus SMOTE, and scaling with removal of outliers plus SMOTE. Random forest with all pre-processing steps applied to the dataset obtained the best performance with the highest weighted F1- scores, weighted-average precision, weighted-average recall, and Cohen's kappa statistic. This indicates that machine learning models can predict corrective actions leading to improved conditions of roses. The notable contribution of this research is to find valid and reliable classification models that assist growers in predicting the best greenhouse micro-environment. © 2023 The Authors. Published by Elsevier B.V.

关键词： Cultivation

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Temperature Prediction for Stored Grain: A Multi-model Fusion Approach Based on machine learning 9

Temperature Prediction for Stored Grain: A Multi-model Fusio...

引用

9th international conference on Electronic Technology and Information Science (ICETIS)

作者： Chen, Donghao Liu, Binkun Henan Univ Technol Key Lab Grain Informat Proc & Control Zhengzhou Peoples R China Henan Univ Technol Henan Engn Lab Grain Condit Intelligent Detect & Zhengzhou Peoples R China

ISBN: (纸本)9798350388350;9798350388343

Temperature fluctuations significantly affect microorganism growth and pest activities in grain pile, precise monitoring and forecasting temperature of stored grain are essential for maintaining the quality and safety of grain storage. This paper proposes a multi-model fusion approach to predict grain temperature using historical temperature data of stored grain and meteorological data from the region. Firstly, four distinct machine learning models, namely Adaboost, decision tree, extra trees, and random forest, are fine-tuned through parameter optimization to enhance their predictive capabilities respectively;Subsequently, these optimized models are fused to form different ensemble models, which are compared for prediction accuracy to obtain the optimal fusion model. In essence, the fusion process integrates the predictions of each individual model as new feature inputs into the fusion models. Furthermore, random forest is utilized to identify the key factors influencing grain temperature, providing insights into the importance of different influencing factors. The experimental results demonstrate that the proposed fusion models can achieve higher prediction accuracy and robustness compared with the single-model prediction methods. Additionally, the analysis of feature importance also offers empirical evidence for understanding the factors influencing grain temperature.

关键词： Grain Temperature machine learning Multi-model Fusion Random Forest Feature Importance

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Cyberthreat Detection Using machine learning 2nd

Cyberthreat Detection Using Machine Learning

引用

2nd international conference on Emerging Trends in engineering and Technology, EGTET 2022

作者： Rajbangshi, Simanta Wangpan, Chemkai Chaudhury, Ayushman Choudhury, Nupur Mandal, Rupesh Assam Don Bosco University Guwahati India

ISBN: (纸本)9789819943616

Millions of users have been a victim of cyberattacks, and thousands of companies are affected as well. This paper proposes machine learning to be used as a method to improve the detection rates of cyberthreats in a network which is better than the traditional signature or anomaly-based methods. machine learning can be used to detect threats and protect systems in real time thereby reducing the damage caused by attacks to a very high extent. In this paper, five Supervised machine learning algorithms, Random Forest, Logistic Regression, SVM, Decision Tree and Naive Bayes, have been used with optimized parameters and tuning and lastly, a deep learning algorithm;Convolutional Neural Network (CNN) has been used, and the performances have been compared among them. The algorithms performed well with Random Forest model being the highest. The results achieved prove that machine learning can be implemented to develop a threat detection system for a network which would be much more secure compared to the existing methods of detection and prevention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Intrusion detection

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Online Hybrid Kernel learning machine with Dynamic Forgetting Mechanism 1st

Online Hybrid Kernel Learning Machine with Dynamic Forgettin...

引用

1st international conference on Emerging Networking Architecture and Technologies (ICENAT)

作者： Wang, Yuhua Li, Deyu Xu, Yuezhu Wang, Hao Harbin Engn Univ Coll Comp Sci & Technol Harbin Peoples R China Modeling & Emulat E Govt Natl Engn Lab Harbin Peoples R China

ISBN: (纸本)9789811996962;9789811996979

This paper, for the purpose of meeting challenges of fewer resources of storage and calculation in the detection of ICS intrusion as well as real-time requirements, has particularly designed an online hybrid kernel learning machine with dynamic forgetting mechanism. First, on the basis of online kernel limit learning machine, a dynamic forgetting mechanism is designed to dynamically adjust the amount of forgetting data according to the current block error, which reduces the system burden and improves the detection accuracy. Then, it replaces the former single kernel function with a hybrid kernel function, which successfully advances the accuracy rate and generalized performance. Finally, a hybrid noise-reducing autoencoder is created to perform dimensional reduction of industrial data with huge dimensions, resulting in the improvement of algorithm and efficiency. The validity and superiority of the proposed online hybrid kernel learning machine with dynamic forgetting mechanism are verified through simulation experiments.

关键词： Online learning Forgetting mechanism Noise-reducing autoencoder machine learning Intrusion detection Industry-oriented control system

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machine learning and Bayes Probability For Detecting Camouflaged Mini Pandemic at the Waves of Covid-19 24

Machine Learning and Bayes Probability For Detecting Camoufl...

引用

24th IEEE/ACIS international Winter conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2022

作者： Nieto-Chaupis, Huber Universidad Autónoma Del Perú Panamericana Sur Km. 16.3 Villa el Salvador Lima Peru

ISBN: (纸本)9798350310412

This paper present a methodology based at machine learning and a theory backed by the Bayes probability to identify rare strains that might not be in coherence with the corona virus. By using the criteria of Tom Mitchell applied on the data belonging to 2021-2022 period, the distributions of infections registered at the beginning of 2022 would not be in accordance to waves of pandemic as seen at 2020 and 2021. Therefore, algorithm of machine learning has yielded that the so-called Omicron variant would no be coherent with known mutations neither exhibiting same pattern of previous waves of pandemic. This creates a space to speculate about the origin of new strains that are camouflaged to central corona virus. From the results of this work, it is observed that Omicron might have nothing to do with Covid-19 pandemic, instead it have triggered a small pandemic of short duration as validated by global data. © 2022 IEEE.

关键词： machine learning

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Diabetes Prediction using machine learning Classification Algorithms

Diabetes Prediction using Machine Learning Classification Al...

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2022 international conference on Sustainable Computing and data Communication Systems, ICSCDS 2022

作者： Dharani, M.K. Thamilselvan, R. Komarasamy, Dinesh Uvetha, V. Swathy, G. Soundarya, M. Kongu Engineering College Department of Computer Science and Engineering Perundurai Erode 63806 India Kongu Engineering College Department of Information Technology Perundurai Erode 63806 India

ISBN: (纸本)9781665478847

Diabetes is one among the chronic diseases or metabolic diseases in which a person's blood glucose levels in the body gets increased. During this phase, the body cells will not respond properly to the insulin present in the body. Diabetes leads to high blood sugar and it is also considered as one of the deadliest diseases across the globe. Diabetes will also result in many problems if left untreated and undiagnosed. Hence, it has to be taken utmost care and a high level of accuracy is required in the diagnostic phase. With the development of the machine learning system, the researchers have gained the flexibility to predict the glucose level with utmost accuracy. In the existing system, various machine learning algorithms such as Support Vector machine [SVM], Naive Bayes [NB] and Random Forest [RF] have been separately used to predict the blood sugar and they have also achieved a prediction accuracy of up to 75%. In the proposed system, feature extraction has been included and a comparative analysis has been done with support vector machine, naive bayes and random forest algorithms. Then, the performance of the three algorithms is evaluated in various measures such as accuracy, precision, F-measure and recall. © 2022 IEEE.

关键词： Decision trees

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