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检索条件"任意字段=International Conference on Machine Learning and Computing（ICMLC 2009）"

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Multimodal Approach for Portfolio Optimization Using Deep Reinforcement learning

Multimodal Approach for Portfolio Optimization Using Deep Re...

引用

international conference on Smart computing & Communications (ICSCC)

作者： Elizabeth Isaac Jimson Mathew Surekha Mariam Varghese Sohail PM Jaimy Simon Anupama Ajith Dept. of CSE M A College of Engineering Kothamangalam Kerala India Indian Institute of Technology- Patna Patna Bihar India Dept. of CSE Mar Baselios Institute Of Technology And Science Kothamangalam Kerala India

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

Portfolio optimization stands as a fundamental pillar in financial management, aimed at maximizing returns while concurrently minimizing risks. This paper presents an innovative approach to portfolio optimization by leveraging a blend of historical stock data and fundamental financial indicators, utilizing the deep reinforcement learning (DRL) framework of the Advantage Actor-Critic (A2C) model. The objective of this research is to demonstrate the superiority of the A2C model in enhancing profit returns and risk-adjusted performance compared to traditional strategies. Our methodology involves the following steps: collecting historical stock data and fundamental financial indicators for the Dow Jones Industrial Average (DJIA) stocks from 2009 to 2022, framing the stock trading challenge as a Markov Decision Process (MDP), training the A2C model within a bespoke stock trading environment crafted using the OpenAI Gym toolkit, and conducting rigorous backtesting to evaluate the model's performance. The outcomes of our rigorous backtesting reveal that the A2C model significantly improves profit returns and risk-adjusted performance over traditional methods. These results underscore the pivotal role of integrating fundamental data within the DRL framework, showcasing the potential of combining fundamental analysis with state-of-the-art machine learning techniques for informed trading decisions.

关键词： Analytical models Adaptation models Soft sensors Deep reinforcement learning Data models Robustness Real-time systems Portfolios Optimization Investment

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A Matrix-Based Granular Reduct Approach in Formal Contexts

A Matrix-Based Granular Reduct Approach in Formal Contexts

引用

international conference on machine learning and Cybernetics (icmlc)

作者： Cheng-Ling Zhang Eric C.C. Tsang Wei-Hua Xu Yi-Dong Lin Faculty of Information Technology Macau University of Science and Technology Taipa Macau School of Artificial Intelligence Southwest University Chongqing China School of Mathematics and Statistics Minnan Normal University Zhangzhou China

ISBN: (纸本)9781665466097

Granular reduction is a crucial methodology for knowledge representation and data analysis in formal concept analysis (FCA for short). In this article, applying with the matrix the-ory, the notions and properties of the object granular matrix and the granular consistent set stemed from the relation ma-trix are presented. Furthermore, the discernibility attribute vector and the discernibility matrix are constructed to calcu-late a minimal granular reduct. And the corresponding algorithm is proposed to explain reduction approach. Eventually, numerical experiments results carried out on eight datasets from UCI and show the proposed algorithm is an excellent granular reduction algorithm.

关键词： Formal concept analysis Data analysis Granular computing machine learning Knowledge representation Cybernetics

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Accelerator on Multi-Granularity Attribute Reduction for Continuous Parameters

Accelerator on Multi-Granularity Attribute Reduction for Con...

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international conference on machine learning and Cybernetics (icmlc)

作者： Da-Sheng Zhao Jing-Jing Song Tai-Hua Xu Eric C.C. Tsang School of Computer Jiangsu University of Science and Technology Zhenjiang China Faculty of Information Technology Macau University of Science and Technology Macau China

ISBN: (纸本)9781665466097

Currently, in the field of Granular computing, parameterized granularity has been widely explored by many scholars. Notably, the multi-granularity model for continuous parameters has also been attracted extensive attention. Based on such a model, it is often necessary to consider multiple granularities to evaluate the significance of candidate condition attributes in solving problems oriented to attribute reduction, which may lead to high time consumption. In view of this, one acceleration algorithm based on multi-granularity attribute reduction (MGAR) for continuous parameters is designed to decrease the time consumption of acquiring reduct. In fact, the acceleration method we proposed is on the basis of the thinking of positive approximation. Specifically, the iterations of evaluating and selecting condition attributes are carried out on the progressively decreased universe instead of the entire sample space. The experimental results over 10 UCI datasets show that the acceleration algorithm not only can effectively accelerate the process of searching reduct, but also can generate the same reduct with traditional approach.

关键词： machine learning algorithms Granular computing Computational modeling machine learning Approximation algorithms Cybernetics

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Detection of modulated motor cortex using anodal and cathodal TDCS based neurofeedback 19

Detection of modulated motor cortex using anodal and cathoda...

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19th international conference on machine learning and Cybernetics, icmlc 2020

作者： Chowdhury, Zarif Ahmed Nahidul Alam, Dewan Fattah Hossain Bhuiyan Nahid, Md. Abu Anisur Rahman, M.D. Parvez, Mohammad Zavid BRAC University Department of Computer Science and Engineering Bangladesh Charles Sturt University School of Computing Mathematics Australia

ISBN: (纸本)9780738124261

Over the centuries, human aimed to achieve the ability to understand the inner functions of the mind and brain. One of the techniques to understand such functions is the application of neurofeedback. Neurofeedback is the procedure which has an influence on physiological brain conditions that takes place by allowing self-regulation of brain activities. Several techniques have been used in the application of neurofeedback to improve different kinds of brain-related conditions including attention capacity and other disabilities. However, literature shows that there are still chances of further improvement in this field, since neurofeedback often causes complications anxiety, discontent and discomfort. Therefore in this paper, we proposed a method to detect modulated motor cortex using anodal and cathodal tDCS based neurofeedback to achieve a better result in the application of neurofeedback. The proposed method showed a higher percentage of accuracy (98.67%) for both anodal and cathodal using Electroencephalography(EEG) based neurofeedback data for twenty subjects. The accuracy of our proposed method is better than three other existing techniques on neurofeedback application. The experimental results demonstrate that our proposed method is suitable in the application of neurofeedback. © 2020 IEEE.

关键词： Electroencephalography

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Proceedings - 2021 international conference on Intelligent computing, Automation and Applications, ICAA 2021

Proceedings - 2021 International Conference on Intelligent C...

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2021 international conference on Intelligent computing, Automation and Applications, ICAA 2021

ISBN: (纸本)9781665437301

The proceedings contain 176 papers. The topics discussed include: feature detection method of assembly position based on machine vision;image segmentation based on watershed algorithm;comparative analysis of machine learning models for recidivism prediction based on chi-square test;survey on virtualization of high performance computing;application of differential evolution algorithm to power system economic dispatch problem;research on automatic polarization adjustment technology of satellite antenna;statistical analysis and regular research on Chongqing coal mine accidents;the threshold characteristics of the poverty reduction effect of educational financial expenditure-analysis of panel data in Chongqing, China from 2009 to 2018;and research on target ranging method based on binocular stereo vision.

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3D printed brain-controlled robot-arm prosthetic via embedded deep learning from sEMG sensors 19

3D printed brain-controlled robot-arm prosthetic via embedde...

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19th international conference on machine learning and Cybernetics, icmlc 2020

作者： Lonsdale, David Zhang, L.I. Jiang, Richard Northumbria University Computer and Info Sciences Newcastle Upon Tyne United Kingdom Lancaster University Computing and Communication Bailrigg Lancaster United Kingdom

ISBN: (纸本)9780738124261

In this paper, we present our work on developing robot arm prosthetic via deep learning. Our work proposes to use transfer learning techniques applied to the Google Inception model to retrain the final layer for surface electromyography (sEMG) classification. Data have been collected using the Thalmic Labs Myo Armband and used to generate graph images comprised of 8 subplots per image containing sEMG data captured from 40 data points per sensor, corresponding to the array of 8 sEMG sensors in the armband. Data captured were then classified into four categories (Fist, Thumbs Up, Open Hand, Rest) via using a deep learning model, Inception-v3, with transfer learning to train the model for accurate prediction of each on real-time input of new data. This trained model was then downloaded to the ARM processor based embedding system to enable the brain-controlled robot-arm prosthetic manufactured from our 3D printer. Testing of the functionality of the method, a robotic arm was produced using a 3D printer and off-the-shelf hardware to control it. SSH communication protocols are employed to execute python files hosted on an embedded Raspberry Pi with ARM processors to trigger movement on the robot arm of the predicted gesture. © 2020 IEEE.

关键词： Prosthetics

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A deep learning based wearable healthcare iot device for ai-enabled hearing assistance automation 19

A deep learning based wearable healthcare iot device for ai-...

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19th international conference on machine learning and Cybernetics, icmlc 2020

作者： Young, Fraser Zhang, Li Jiang, Richard Liu, Han Wall, Conor Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Lancaster University School of Computing and Communications Faculty of Science and Technology Lancaster United Kingdom Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

ISBN: (纸本)9780738124261

With the recent booming of artificial intelligence (AI), particularly deep learning techniques, digital healthcare is one of the prevalent areas that could gain benefits from AI-enabled functionality. This research presents a novel AI-enabled Internet of Things (IoT) device operating from the ESP-8266 platform capable of assisting those who suffer from impairment of hearing or deafness to communicate with others in conversations. In the proposed solution, a server application is created that leverages Google's online speech recognition service to convert the received conversations into texts, then deployed to a micro-display attached to the glasses to display the conversation contents to deaf people, to enable and assist conversation as normal with the general population. Furthermore, in order to raise alert of traffic or dangerous scenarios, an 'urban-emergency' classifier is developed using a deep learning model, Inception-v4, with transfer learning to detect/recognize alerting/alarming sounds, such as a horn sound or a fire alarm, with texts generated to alert the prospective user. The training of Inception-v4 was carried out on a consumer desktop PC and then implemented into the AI-based IoT application. The empirical results indicate that the developed prototype system achieves an accuracy rate of 92% for sound recognition and classification with real-time performance. © 2020 IEEE.

关键词： Internet of things

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Detection of stress for visually impaired people using EEG signals based on time-frequency domain analysis 19

Detection of stress for visually impaired people using EEG s...

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19th international conference on machine learning and Cybernetics, icmlc 2020

作者： Sultana, Samia Rahman, Md Anisur Zavid Parvez, Mohammad BRAC University Software Engineering and HCI Research Group Department of Computer Science and Engineering Bangladesh Charles Sturt University School of Computing and Mathematics Australia

ISBN: (纸本)9780738124261

Stress refers to body's physical, emotional and psychological reaction to any environmental change needing adjustment with major impact on human psychology. Stress is specially difficult to manage for visually impaired people (VIP) as they can become easily stressed in unknown situations. Electroencephalogram (EEG) signals can be used to detect stress as it basically represents the ongoing electrical signal changes in human brain. Literature shows that the stress detection techniques are mostly based on either time or frequency domain analysis. However, using either time or frequency domain analysis may not be sufficient to provide appropriate outcome for stress detection. Hence, in this paper a method is proposed using empirical mode decomposition (EMD) and short-term Fourier transform (STFT) are used to extract features considering spatio-temporal information from EEG signals. In the EMD, the signal is first decomposed into intrinsic mode functions (IMFs) representing a finite number of signals while maintaining the time domain and STFT is used to convert time domain to time-frequency domain. Support vector machine (SVM) is applied to classify the stress of VIP in unfamiliar indoor environments. The performance of the proposed method is compared with a state-of-the-art technique for stress detection. The experimental results demonstrate the superiority of the proposed technique over the existing technique. © 2020 IEEE.

关键词： Empirical mode decomposition

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Novel class detection using hybrid ensemble 19

Novel class detection using hybrid ensemble

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19th international conference on machine learning and Cybernetics, icmlc 2020

作者： Pandit, Diptangshu Zhang, Li Mistry, Kamlesh Jiang, Richard Teesside University Department of Computer Science and Information Systems Middleborough United States Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Lancaster University School of Computing and Communications Faculty of Science and Technology Lancaster United Kingdom

ISBN: (纸本)9780738124261

In this research, we propose a hybrid meta-classifier for novel class detection. It is able to efficiently detect the arrival of novel unseen classes as well as tackle real-time data stream classification. Specifically, the proposed hybrid meta-classifler includes three ensemble models, i.e. class-specific, cluster-specific and complementary boosting ensemble classifiers. Distinctive training strategies are also proposed for the generation of effective and diversified ensemble classifiers. The weights of the above ensemble models and the threshold of the novel class confidence are subsequently optimized using a modified Firefly Algorithm, to enhance performance. The above proposed ensemble and optimization algorithms cooperate with each other to conduct the detection of novel unseen classes. Several UCI databases are employed for evaluation, i.e. The KDD Cup, Image Segmentation, Soybean Large, Glass and Iris databases. In comparison with the baseline meta-algorithms such as Boosting, Bagging and Stacking, our approach shows significantly enhanced performance with the increment of the number of novel classes for all the test data sets, which poses great challenges to the existing baseline ensemble methods. © 2020 IEEE.

关键词： Image segmentation

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Detection of brain tumor and identification of tumor region using deep neural network on FMRI images 19

Detection of brain tumor and identification of tumor region ...

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19th international conference on machine learning and Cybernetics, icmlc 2020

作者： Mashiat, Afsara Akhlaque, Reza Rifat Fariha, Fahmeda Hasan Reza, Tanzim Rahman, Md Anisur Parvez, Mohammad Zavid Brac University Software Engineering and HCI Research Group Department of Computer Science and Engineering Bangladesh Charles Sturt University School of Computing and Mathematics Australia

ISBN: (纸本)9780738124261

As brain is the most vital organ of the human body, the affects of brain related diseases can be severe. One of the most harmful diseases is brain tumor, which results in a very short life expectancy of the affected patient. Detection of brain tumor is a challenging task in the early stages. Still, with the help of modern technology and machine learning algorithms, it has become a matter of great interest for research. While detecting the brain tumor of an affected person, we are considering the fMRI data of the patient. Our aim is to identify whether the tumor is present in the patient's brain or not. We use a Convolutional Neural Network(CNN) that is good enough to generate high accuracy. We have used some deeper architecture design VGG16, VGG19, and Inception v3 for better accuracy. Three classification techniques are used namely binary classification, lobe based classification, and position based classification. The main contribution of our proposed work is that we have identified the specific region of the brain where the tumor is located. The region-based classification distinguishes our work from others that are applied on the same dataset. For binary classification, we found approximately 95% accuracy from all the three architectures. Furthermore, we found approximately 78% accuracy for lobe based classification and approximately 97% accuracy for position based classification. The experimental results indicate the superiority of our proposed method in terms of identifying the brain tumor. © 2020 IEEE.

关键词： Brain

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