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检索条件"任意字段=5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022"

共 261 条记录，以下是211-220 订阅

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Airline Flight Delay Prediction Using machine learning Models 21

Airline Flight Delay Prediction Using Machine Learning Model...

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Proceedings of the 2021 5th international conference on E-Business and Internet

作者： Yuemin Tang University of Southern California USA

ISBN: (纸本)9781450385657

Flight delays are gradually increasing and bring more financial difficulties and customer dissatisfaction to airline companies. To resolve this situation, supervised machine learning models were implemented to predict flight delays. the data set that records information of flights departing from JFK airport during one year was used for the prediction. Seven algorithms (Logistic Regression, K-Nearest Neighbor, Gaussian Naïve Bayes, Decision Tree, Support Vector machine, Random Forest, and Gradient Boosted Tree) were trained and tested to complete the binary classification of flight delays. the evaluation of algorithms was fulfilled by comparing the values of four measures: accuracy, precision, recall, and f1-score. these measures were weighted to adjust the imbalance of the selected data set. the comparative analysis showed that the Decision Tree algorithm has the best performance with an accuracy of 0.9777, and the KNN algorithm has the worst performance with an f1-score of 0.8039. Tree-based ensemble classifiers generally have better performance over other base classifiers.

关键词： classification algorithms flight delay prediction machine learning data analytics airline transport

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Application of Deep Q-Network in Portfolio Management 5

Application of Deep Q-Network in Portfolio Management

引用

5th IEEE international conference on Big data analytics (ICBDA)

作者： Gao, Ziming Gao, Yuan Hu, Yi Jiang, Zhengyong Su, Jionglong Xian Jiaotong Liverpool Univ Dept Math Suzhou Peoples R China

ISBN: (纸本)9781728141114

machine learning algorithms and Neural Networks are widely applied to many different areas such as stock market prediction, facial recognition and automatic machine translation. this paper introduces a novel strategy based on the classic Deep Reinforcement learning algorithm, Deep Q-Network, for stock market portfolio management. It is a type of deep neural network which is optimized by Q learning. To adapt the Deep Q-Network for stock market production, we first discretize the action space so that portfolio management becomes a problem that Deep Q-Network can solve. Following this, we combine the Convolutional Neural Network and dueling Q-Net to enhance the recognition ability of the algorithm. We choose five low-relevant American stocks to test our model. It is found that the Deep Q-Network based strategy outperforms the ten other traditional strategies. the profit of Deep Q-Network algorithm is 30% more than the profit of other strategies. Moreover, the Sharpe ratio and Max Drawdown demonstrates that the risk of policy associated with Deep Q-Network is the lowest.

关键词： Q learning convolutional neural network portfolio management

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Securing Smart City Networks - Intelligent Detection Of DDoS Cyber Attacks

Securing Smart City Networks - Intelligent Detection Of DDoS...

引用

international conference on Contemporary Computing and Informatics (IC3I)

作者： Ms. Deepthi Tabitha Bennet Ms. Preethi Samantha Bennet D Anitha Department of Applied Mathematics and Computational Science Thiagarajar College of Engineering Madurai India

A distributed denial-of-service (DDoS) is a malicious attempt by attackers to disrupt the normal traffic of a targeted server, service or network. this is done by overwhelming the target and its surrounding infrastructure with a flood of Internet traffic. the multiple compromised computer systems (bots or zombies) then act as sources of attack traffic. Exploited machines can include computers and other network resources such as IoT devices. the attack results in either degraded network performance or a total service outage of critical infrastructure. this can lead to heavy financial losses and reputational damage. these attacks maximise effectiveness by controlling the affected systems remotely and establishing a network of bots called bot networks. It is very difficult to separate the attack traffic from normal traffic. Early detection is essential for successful mitigation of the attack, which gives rise to a very important role in cybersecurity to detect the attacks and mitigate the effects. this can be done by deploying machine learning or deep learning models to monitor the traffic data. We propose using various machine learning and deep learning algorithms to analyse the traffic patterns and separate malicious traffic from normal traffic. Two suitable datasets have been identified (DDoS attack SDN dataset and CICDDoS2019 dataset). All essential preprocessing is performed on both datasets. Feature selection is also performed before detection techniques are applied. 8 different Neural Networks/ Ensemble/ machine learning models are chosen and the datasets are analysed. the best model is chosen based on the performance metrics (DEEP NEURAL NETWORK MODEL). An alternative is also suggested (Next best - Hypermodel). Optimisation by Hyperparameter tuning further enhances the accuracy. Based on the nature of the attack and the intended target, suitable mitigation procedures can then be deployed.

关键词： Deep learning Smart cities Computational modeling Neural networks Traffic control Denial-of-service attack Chatbots

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LDSQP: Scalable and Efficient Log data Storage and Query Processing Scheme for Cloud data Centers 21

LDSQP: Scalable and Efficient Log Data Storage and Query Pro...

引用

5th international conference on High Performance Compilation, Computing and Communications, HP3C 2021

作者： Zhang, Jilian Deng, Xuelian College of Information Science and Technology Jinan University Guangzhou China Faculty of Computer Science Guangxi University of Chinese Medicine China

ISBN: (纸本)9781450389648

Cloud data centers are one of the most important information infrastructures for most real-world applications involving information technology, and serves millions of user requests daily. To provide various services with Quality-of-Service (QoS) guarantees to users, such as low network latency and fast response time, it is vital that cloud data centers should (1) manage its resources efficiently, and (2) avoid resource overprovisioning in situation such as virtual machine allocation. To achieve these goals, cloud data centers usually employ modern techniques from artificial intelligence or machine learning community to analyze status information of cloud data center, so as to find actionable strategies. In this paper, we propose LDSQP, an efficient scheme for storing and querying status information in cloud data centers, i.e., log data that contains enriched information collected from servers, virtual machines, and containers in cloud data centers. We design scalable cluster structure and row key format for log data record in data storage, and we also propose a compact and efficient query expression to capture user's data requirement. We introduce an efficient query processing algorithm that can minimize the number of table partitions to scan. Experiments on a real cluster show that our proposed LDSQP is efficient in log data management. © 2021 ACM.

关键词： Query processing

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A modular approach for integrating data science concepts into multiple undergraduate STEM+C courses 129

A modular approach for integrating data science concepts int...

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129th ASEE Annual conference and Exposition: Excellence through Diversity, ASEE 2022

作者： Naseri, Mohammad Yunus Snyder, Caitlin McLoughlin, Brendan Bhandari, Sambridhi Aryal, Niroj Biswas, Gautam Henrick, Erin Hotchkiss, Erin R. Jha, Manoj K. Jiang, Steven Kern, Emily Lohani, Vinod K. Marston, Landon Todd Vanags, Christopher Xia, Kang

With increasingly technology-driven workplaces and high data volumes, instructors across STEM+C disciplines are integrating more data science topics into their course learning objectives. However, instructors face significant challenges in integrating additional data science concepts into their already full course schedules. Streamlined instructional modules that are integrated with course content, and cover relevant data science topics, such as data collection, uncertainty in data, visualization, and analysis using statistical and machine learning methods can benefit instructors across multiple disciplines. As part of a cross-university research program, we designed a systematic structural approach-based on shared instructional and assessment principles-to construct modules that are tailored to meet the needs of multiple instructional disciplines, academic levels, and pedagogies. Adopting a research-practice partnership approach, we have collectively developed twelve modules working closely with instructors and their teaching assistants for six undergraduate courses. We identified and coded primary data science concepts in the modules into five common themes: 1) data acquisition, 2) data quality issues, 3) data use and visualization, 4) advanced machine learning techniques, and 5) miscellaneous topics that may be unique to a particular discipline (e.g., how to analyze data streams collected by a special sensor). these themes were further subdivided to make it easier for instructors to contextualize the data science concepts in discipline-specific work. In this paper, we present as a case study the design and analysis of four of the modules, primarily so we can compare and contrast pairs of similar courses that were taught at different levels or at different universities. Preliminary analyses show the wide distribution of data science topics that are common among a number of environmental science and engineering courses. We identified commonalities and differences i

关键词： data visualization

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Predicting Building Energy Consumption by Time Series Model Based on machine learning and Empirical Mode Decomposition 5

Predicting Building Energy Consumption by Time Series Model ...

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5th IEEE international conference on Big data analytics (ICBDA)

作者： Liu, Dandan Yang, Qiangqiang Yang, Fang Shanghai Univ Elect Power Coll Elect & Informat Engn Shanghai Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9781728141114

In this paper, the prediction method for building energy consumption was discussed. the building energy consumption data was regarded as time series, which was usually nonlinear and non-stationary. the traditional time series analysis model has lower prediction accuracy for nonlinear and non-stationary time series. So the joint algorithm using support vector regression (SVR) and empirical mode decomposition (EMD) was applied. EMD method decomposed the non-stationary and nonlinear energy consumption time series into several Intrinsic Mode Functions (IMFs). And support vector regression (SVR) was used to predict the decomposed time series. the sum of every predicted subsequence was final forecasting result. the experimental results showed the prediction accuracy of the combination model is better than SVR forecasting model.

关键词： time series analysis energy consumption empirical mode decomposition support vector regression

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An E-Commerce Prototype for Predicting the Product Return Phenomenon Using Optimization and Regression Techniques 5th

An E-Commerce Prototype for Predicting the Product Return Ph...

引用

5th international conference on Advances in Computing and data Sciences, ICACDS 2021

作者： Rajasekaran, Vidya Priyadarshini, R. B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai Tamilnadu India

ISBN: (纸本)9783030882433

E-Commerce product returns are considered as a major disease and is also a very challenging issue that greatly impacts the revenue of the E-Commerce firm. Most of the E-Commerce firms consider 10% of the return rates to be normal, but in cases when the product return rate exceeds 10%, the investigation to such cases are further needed. the rising of the return rate is considered as a big threat to the e-commerce industry, which needed to be slowed down. Predictions can be carried out to overcome the return issues in advance and measures can be taken to decrease the return rate. In this paper, the work focuses on developing a prototype model for predicting the return rate of any particular product in advance. In the existing system the return volume management is predicted based on the dependent variable of the manufacturer’s production process and their resources alone. Our work focuses on finding the return rate by including a few more parameters which in turn enhances the prediction accuracy. the proposed work is tested on different machine learning algorithms for optimizing the results. the results can be applied to overcome the major return and loss percentage by the e-commerce industry to enhance their future revenue. © 2021, Springer Nature Switzerland AG.

关键词： data analytics

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Effective Training-Time Stacking for Ensembling of Deep Neural Networks 22

Effective Training-Time Stacking for Ensembling of Deep Neur...

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Proceedings of the 2022 5th international conference on Artificial Intelligence and Pattern Recognition

作者： Polina Proskura Alexey Zaytsev Skoltech Russia Laboratory of applied research for structured data statistics Skoltech Russia

ISBN: (纸本)9781450396899

Ensembling is a popular and effective method for improving machine learning (ML) models. It proves its value not only in classical ML but also for deep learning. Ensembles enhance the quality and trustworthiness of ML solutions, and allow uncertainty estimation. However, they come at a price: training ensembles of deep learning models eat a huge amount of computational resources. A snapshot ensembling collects models in the ensemble along a single training path. As it runs training only one time, the computational time is similar to the training of one model. However, the quality of models along the training path is different: typically, later models are better if no overfitting occurs. So, the models are of varying utility. Our method improves snapshot ensembling by selecting and weighting ensemble members along the training path. It relies on training-time likelihoods without looking at validation sample errors that standard stacking methods do. Experimental evidence for Fashion MNIST, CIFAR-10, and CIFAR-100 datasets demonstrates the superior quality of the proposed weighted ensembles c.t. vanilla ensembling of deep learning models. the suggested approach spends on training of N models N times less time than classical ensemble and provides significantly higher quality compared to the single model.

关键词： Deep learning Ensembles Image classification Neural network

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Personal Loan Fraud Detection Based on Hybrid Supervised and Unsupervised learning 5

Personal Loan Fraud Detection Based on Hybrid Supervised and...

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5th IEEE international conference on Big data analytics (ICBDA)

作者： Wen, Hanlin Huang, Fangming Huazhong Univ Sci & Technol Dept Artificial Intelligence & Automat Wuhan Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9781728141114

In recent years, we have been witnessing a dramatic increase on the personal loan for consumption, due to the rapid development of e-services, including e-commerce, e-finance and mobile payments. Resulting from the lack of effective grid verification and supervision, it inevitably leads to large-scale losses caused by credit loan fraud [1]. Considering the difficulty of manual inspection and verification on the large amount of credit card transactions, machine learning methods are commonly used to detect fraudulent transactions automatically. this article has applied the Extreme Gradient Boosting(XGBoost) model for data mining and analysis, which is inspired by its brilliant reputation in various data mining contests. With people's growing concern about privacy protection, how can we apply data mining techniques while taking consideration into privacy terms is one problem. Additionally, according to current loan fraud detection studies, some features are considered to contain little information or a bit of redundancy, whereas others hold the critical information which makes things harder when feature engineering. In order to filter useless information and preserve the useful information without knowing the meaning of our data, this paper combines Kernel Principal Component Analysis (Kernel PCA) together with XGBoost algorithm and proposes a new hybrid unsupervised and supervised learning model, KP-XGBoost. We use grid search to avoid over-fitting and compare the performance of both XGBoost and P-XGBoost and other classical machine learning methods. It turns out that P-XGBoost outperforms XGBoost in fraud detection, which provides a new perspective to detecting the fraud behaviour while protecting clients' privacy.

关键词： supervised learning unsupervised learning Extreme Gradient Boosting principal component analysis

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Full-Rotation Quantum Convolutional Neural Network for Abnormal Intrusion Detection System 22

Full-Rotation Quantum Convolutional Neural Network for Abnor...

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Proceedings of the 2022 5th international conference on Artificial Intelligence and Pattern Recognition

作者： Suya Chao Guang Yang Min Nie Yuanhua Liu Meiling Zhang School of Communication and Information Engineering & School of Artificial Intelligence Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

Intrusion detection system (IDS) is a significant mechanism to improve network security. As a promising technique, machine learning (ML) methods has been applied in IDS to obtain high classification accuracy. However, classical ML based IDS methods hit a bottleneck in computing performance in case of huge network traffic and complex high-dimensional data. Due to the parallelism, superposition, entanglement of quantum computing, quantum computing provides a new solution to speed up the classical ML algorithms. this paper proposes a novel IDS scheme based on full-rotation quantum convolutional neural network (FR-QCNN). the key component of the FR-QCNN is the quantum convolution filter, which is composed of coding layer, variational layer and measurement layer. Different from the traditional quantum convolutional neural network, a full-rotation quantum circuit is used in the variational layer of the FR-QCNN, realizing a complete parameter update in the model training. Experiment on dataset from KDD Cup shows that the IDS classification accuracy of FR-QCNN is higher than classical ML models such as convolutional neural network (CNN), decision tree (DT) and support vector machine (SVM), as well as higher than traditional quantum convolutional neural network(QCNN). Meanwhile, FR-QCNN and QCNN have lower space complexity and time complexity than classical ML methods.

关键词： FR-QCNN machine learning Network intrusion QCNN

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