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A Hybrid stock Price Prediction Model Based on KELM and BiLstM 21

A Hybrid Stock Price Prediction Model Based on KELM and BiLS...

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21st OITS international conference on Information Technology, OCIT 2023

作者： Jena, Ajay Kumar Parida, Ajaya Kumar Rajput, Prakhar KIIT Deemed to Be University School of Computer Engineering Odisha Bhubaneshwar India

ISBN: (纸本)9798350358230

In recent years, stock forecasting has gained significant importance in the financial world. It is considered to be an essential financial technique that enables the development of effective stock exchange strategies. With the advent of technological advancements and the availability of enormous amounts of data, stock prediction has become an area of active research. In this paper, a novel hybrid model is proposed for stock price prediction by combining the KELM (Kernel-Extreme learning Machine) with BiLstM (Bidirectional Long Short-Term Memory) and GWO (Grey Wolf Optimizer) techniques. We first evaluate the performance of the individual models for short and long-Term predictions using test data. Further, we develop a hybrid model by combining the KELM and BiLstM models and use the GWO algorithm for optimization. The proposed model leverages the strengths of each of these models and provides better results compared to the individual models. We demonstrate the effectiveness of our hybrid model by comparing the minimized errors of both the short and long-Term predictions with the other models. Our results shows that the proposed hybrid model outperforms the state-of-The-Art models. Therefore, the proposed model can be considered a valuable tool for predicting stock prices accurately and aiding investors in making informed decisions. © 2023 IEEE.

关键词： Fuzzy neural networks

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Constructing Future Vehicle Digital Twins with Insights from Cloud-Edge Collaboration 21

Constructing Future Vehicle Digital Twins with Insights from...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Shi, Xin Zhao, Liang Lin, Na Xu, Lexi Hawbani, Ammar Bi, Yuanguo Shenyang Aerospace University Shenyang China China Unicom Beijing China Northeastern University Shenyang China

ISBN: (纸本)9798350329223

intelligent Transportation System (ITS) is embarking on a new development road, in which Digital Twins (DT) provides crucial support for improving the smartness standard of vehicles and realizing a high-level of traffic management in this process. However, the existing solutions face challenges such as limited computing resources, data scarcity and data distribution, which hinder the application prospects of DT for ITS. First, we propose the concept of future-oriented DT aim to address these issues. Specifically, lightweight local DTs are deployed at the different edge servers to provide services for vehicles, and a strong performance global DT is deployed in the cloud server to manage and optimize the local DTs. Then, we introduce a collaborative learning method between two models to construct the DT network architecture and realize the vision of the future-oriented. This method makes use of rich global knowledge and local data, so that the model can dexterously integrate personalization and generalization capabilities. Finally, We use an Incremental learning algorithm to improve the ability of the model that adapt to new scenarios and changing input data. All experiments are based on real traffic data, and trajectory prediction task as application carrier. Experimental results demonstrate the effectiveness of the proposed method, and the stability and performance of DTs have achieved excellent performance in the construction process. © 2023 IEEE.

关键词： learning systems

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Autoencoder Neural Network for Detecting Non-human Web Traffic 21st

Autoencoder Neural Network for Detecting Non-human Web Traff...

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21st international conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Gabryel, Marcin Lada, Dawid Kocic, Milan Spark Digitup Plac Woln 13 Lok 10 PL-31060 Krakow Poland

ISBN: (纸本)9783031234798;9783031234804

In this paper, a neural network model is presented to identify fraudulent visits to a website, which are significantly different from visits of human users. Such unusual visits are most often made by automated software, i.e. bots. Bots are used to perform advertising scams or to do scraping, i.e., automatic scanning of website content frequently not in line with the intentions of website authors. The model proposed in this paper works on data extracted directly from a web browser when a user or a bot visits a website. This data is acquired by way of using JavaScript. When bots appear on the website, collected parameter values are significantly different from the values collected during usual visits made by human website users. However, just knowing what values these parameters have is simply not enough to identify bots as they are being constantly modified and new values that have not yet been accounted for appear. Thus, it is not possible to know all the data generated by bots. Therefore, this paper proposes a neural network with an autoencoder structure that makes it possible to detect deviations in parameter values that depart from the learned data from usual users. This enables detection of anomalies, i.e., data generated by bots. The effectiveness of the presented model is demonstrated on authentic data extracted from several online stores.

关键词： Autoencoder Neural networks Deep learning Web traffic Ad fraud

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stylized Avatar Animation Based on Expression Recognition Mapped Deep learning 21

Stylized Avatar Animation Based on Expression Recognition Ma...

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21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Dantong, Feng Ying, Zhong Xu, Ji Yijie, Ai School of Automation and Electrical Engineering Chengdu Technological University Chengdu611730 China

ISBN: (纸本)9798331519254

The generation of animated character expressions not only requires the reproduction of facial feature point information but also the vivid representation of facial expressions. There are differences in parameter rules between facial expressions and animated character models, and directly generating expressions may lead to exaggerated and unnatural expressions. This article proposes a technique for generating animated character expressions based on facial expression recognition limitations. Firstly, recognize and classify facial expressions based on the deep learning network, and then extract facial feature points. The expression generation model is established by using animated character rig parameters and training on a large amount of data, and the parameter mapping restriction rules are obtained based on facial key points distance. In the process of generating animated expressions, the parameter rules are effectively restricted according to different expression categories, and a script is established to achieve the generation of animated character expressions. © 2024 IEEE.

关键词： Animation

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Alzheimer’s Disease Detection Using Resnet 1st

Alzheimer’s Disease Detection Using Resnet

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1st international conference on intelligent Computing for Sustainable Development, ICICSD 2023

作者： Patel, Priyanka Patil, Rohini Department of Computer Engineering TEC Maharashtra Kharghar India

ISBN: (纸本)9783031612862

Alzheimer’s disease is incurable. Early Alzheimer’s diagnosis helps with treatment and brain tissue preservation. statistics and machine learning methods have been applied to diagnose AD. Clinical research uses MRI to diagnose AD. Advanced deep learning approaches have recently proven equivalent or even superior human-level performance. In addition to that, the strong computing power available today has had a huge impact on the algorithms used in deep learning, including medical picture processing. This paper aims to provide improved model performance for the early-stage diagnosis of AD. Using brain MRI data processing, we propose a deep CNN ResnetAD model for AD diagnosis. The result shows the best model accuracy of 90%, a training loss of 0.3924, a K-fold accuracy of 0.579, and a validation loss of 40%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diagnosis

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Heart Disease Prediction using Machine learning Algorithms from ECG images: A short Summary 21

Heart Disease Prediction using Machine Learning Algorithms f...

引用

21st OITS international conference on Information Technology, OCIT 2023

作者： Nandini, Annam Behera, Arati Mishra, Tapas Kumar SRM University-AP Department of Computer Science and Engineering Andhra Pradesh Amaravati India

ISBN: (纸本)9798350358230

Heart disease refers to a group of disorders that affect the heart's ability to circulate blood as well as oxygen throughout the body. Heart rhythm problems to more serious conditions like coronary artery disease or heart failure can all be the result of several illnesses, including heart disease. This paper's main goal is to use image processing to categorize Coronary Artery Disease (CAD). Further, the major contribution is to transform Electrocardiogram(ECG) recordings into a 1-D signal using image segmentation. ECG image dataset that includes P, QRS, and T waves is used. Several classifiers that includes k-nearest neighbours(KNN), Logistic Regression (LR), Support Vector Machine(SVM), Random Forest (RF), and Decision Tree(DT) are experimented. The performance of the classifier is evaluated using widely-Accepted standard metrics, including recall, accuracy, precision, and f1-score. Finally, the performance is analyzed and presented for further research. The further research will be focused on data extraction from the image and minority eradication of the dataset. © 2023 IEEE.

关键词： Support vector machines

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1st international Workshop on Big data-driven Edge Cloud Services, BECS 2021 held in conjunction with 21st international conference on Web engineering, ICWE 2021

1st International Workshop on Big data-driven Edge Cloud Ser...

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1st international Workshop on Big data-driven Edge Cloud Services, BECS 2021 held in conjunction with 21st international conference on Web engineering, ICWE 2021

ISBN: (纸本)9783030922306

The proceedings contain 8 papers. The special focus in this conference is on Big data-driven Edge Cloud Services. The topics include: Towards Proactive Context-Aware IoT Environments by Means of Federated learning;real-Time Deep learning-Based Anomaly Detection Approach for Multivariate data streams with Apache Flink;a Novel Approach to Dynamic Pricing for Cloud Computing Through Price Band Prediction;learning-Based Activation of Energy Harvesting Sensors for Fresh data Acquisition;exploiting Triangle Patterns for Heterogeneous Graph Attention Network;towards Seamless IoT Device-Edge-Cloud Continuum:: Software Architecture Options of IoT Devices Revisited.

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Employing Convolutional Neural Networks for Continual learning 21st

Employing Convolutional Neural Networks for Continual Learni...

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21st international conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Jasinski, Marcin Wozniak, Michal Wroclaw Univ Sci & Technol Dept Syst & Comp Networks Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

ISBN: (纸本)9783031234910;9783031234927

The main motivation for the presented research was to investigate the behavior of different convolutional neural network architectures in the analysis of non-stationary data streams. learning a model on continuously incoming data is different from learning where a complete learning set is immediately available. However, streaming data is definitely closer to reality, as nowadays, most data needs to be analyzed as soon as it arrives (e.g., in the case of anti-fraud systems, cybersecurity, and analysis of images from on-board cameras and other sensors). Besides the vital aspect related to the limitations of computational and memory resources that the proposed algorithms must consider, one of the critical difficulties is the possibility of concept drift. This phenomenon means that the probabilistic characteristics of the considered task change, and this, in consequence, may lead to a significant decrease in classification accuracy. This paper pays special attention to models of convolutional neural networks based on probabilistic methods: Monte Carlo dropout and Bayesian convolutional neural networks. Of particular interest was the aspect related to the uncertainty of predictions returned by the model. Such a situation may occur mainly during the classification of drifting data streams. Under such conditions, the prediction system should be able to return information about the high uncertainty of predictions and the need to take action to update the model used. This paper aims to study the behavior of the network of the models mentioned above in the task of classification of non-stationary data streams and to determine the impact of the occurrence of a sudden drift on the accuracy and uncertainty of the predictions.

关键词： Continous learning Concept drift Bayesian convolutional neural network Deep learning

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Privacy-Preserving Federated learning through Clustered Sampling on Fine-Tuning Distributed non-iid Large Language Models 21

Privacy-Preserving Federated Learning through Clustered Samp...

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作者： Yun, Sheng Bhuiyan, Zakirul Alam Sadi, Md. Taufiq Al Hasib Su, Shen Fordham University Department of Computer and Information Sciences New York10458 United States North South University Department of Electrical & Computer Engineering Dhaka Bangladesh Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou China

ISBN: (纸本)9798350329223

Recently, Large Language Models (LLMs) have been a phenomenal trend in the Artificial intelligence field. However, training and fine-tuning can be challenging because of privacy concerns and limited computing resources. Federated learning (FL) has emerged as a novel machine learning framework offering privacy protection. The challenges in applying FL to real-world applications include dealing with heterogeneous data, poor client updates, and client selection. This paper introduces Privacy-preserving Federated learning through Clustered Sampling on LLMs (FCLM), a framework that clusters models by their distribution similarity. It helps the model group similar models to improve text data heterogeneity handling and privacy concerns in distributed machine-learning environments. The FCLM framework is implemented and evaluated using popular Language models and text data. The framework shows a robust performance over the heterogeneous text data, which can further extend to the use of more complex LLMs. © 2023 IEEE.

关键词： Privacy-preserving techniques

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state of Charge Estimation for Lithium-Ion Batteries Based on Informer-LstM Hybrid Network 21

State of Charge Estimation for Lithium-Ion Batteries Based o...

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21st international System-on-Chip Design conference, ISOCC 2024

作者： Song, Ningfei Jin, Nanlin Wang, Jingchen Zhang, Jie Man, Ka Lok Smith, Jeremy S. Yue, Yutao Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou215123 China The University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3BX United Kingdom JITRI Institute of Deep Perception Technology Wuxi China

ISBN: (纸本)9798350377088

Accurate state of Charge (SoC) estimation is essential for efficient battery management systems (BMS). In this study, we propose a novel hybrid neural network architecture, combining the Informer and Long Short-Term Memory (LstM) networks. Our hybrid network captures temporal dependencies and nonlinear characteristics inherent in battery data, enhancing sequence integration capabilities and computational efficiency. Experimental results on battery datasets demonstrate the effectiveness of our approach, with the proposed method achieving a maximum Mean Absolute Error (MAE) of 1.395% and a maximum Root Mean Square Error (RMSE) of 1.593%. Our findings suggest that the Informer-LstM hybrid network holds promise for improving battery SoC estimation accuracy and enhancing battery management systems. © 2024 IEEE.

关键词： state of charge

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