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IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Niranjana Govindan Bharathi Baskaran Azizul Azizan Fitri Yakub Inge Dhamanti C Dhaya Department of Computing Technologies Faculty of Engineering & Technology SRMIST Kattankulathur Chennai India Cyberphysical Research Group (Embedded AI & ML Science Lab) Faculty of Intelligence UTM Kuala Lumpur Malaysia Electronic System Engineering Department Malaysia-Japan International Institute of Technology UTM Kuala Lumpur Malaysia Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Department of Computer Science and Technology Adhiparasakthi Engineering College Melmaruvathur India

ISBN: (数字)9798350340204

ISBN: (纸本)9798350340211

Algorithmic trading has emerged as a powerful tool for making data-driven trading decisions in financial markets. In this paper, we present an innovative algorithmic trading model for stock price forecasting that integrates forest-based machine learning algorithms with the Golden Ratio strategy. Our proposed approach aims to leverage the predictive power of machine learning while incorporating insights from technical analysis to enhance trading performance and profitability. The methodology involves data collection and preprocessing, feature engineering, model development, and performance evaluation. Historical stock price data, financial indicators, and sentiment analysis data are gathered and pre-processed to ensure data quality. Feature engineering techniques are applied to extract relevant features, and forest-based machine learning algorithms are trained on the data to forecast stock prices. The Golden Ratio strategy is integrated into the trading model to identify the entry point and exit point. Empirical validation and performance evaluation demonstrate the effectiveness and robustness of the proposed approach. Through back testing and simulation-based testing, we evaluate the model's accuracy, profitability, and risk-adjusted returns. Our findings contribute to the advancement of algorithmic trading research and practice, offering insights into the potential benefits of combining machine learning algorithms with established trading strategies. The proposed hybrid model incorporates Golden Ratio Strategy, integrating Random Forest (RF). The results of the proposed random forest with Golden Ratio Strategy are compared with the results of individual model. This comparison helps in proving the efficiency of the proposed model.

关键词： Adaptation models Machine learning algorithms Soft sensors Forestry Reinforcement learning Predictive models Feature extraction Data models Forecasting Testing

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Device-Type Profiling for Network Access Control Systems using Clustering-Based Multivariate Gaussian Outlier Score 5

Device-Type Profiling for Network Access Control Systems usi...

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5th International Conference on Future Networks and Distributed Systems: The Premier Conference on Smart Next Generation Networking Technologies, ICFNDS 2021

作者： Muhammad, Musa Daniel Ani, Uchenna Abdullahi, Aminu Aliyu Radanliev, Petar Faculty of Engineering Environment and Computing Coventry University Scarborough United Kingdom Petras National Centre of Excellence for IoT Systems Cyber Security Department of Science Technology Engineering and Public Policy University College London United Kingdom Department of Computer Science Dutse Nigeria Oxford E-Research Centre Department of Engineering Sciences Oxford United Kingdom

ISBN: (纸本)9781450387347

Behaviour profiling is used in organisations to identify the working patterns of agents: humans or devices. It can be used to detect abnormal patterns of devices in an organisation's BYOD network to help control network access. Although BYOD offers great benefits of improving productivity and job satisfaction while reducing cost, it bears some security issues around access control with limitations in addressing insider threat scenarios. This means that motivated and determined attackers with valid access credentials can exploit the weaknesses to compromise the system. The limitation of mobile devices can mean that traditional network access control mechanism are ineffective in addressing insider threats, and can impact on device capacity and functionality. Thus, it is crucial to explore other ways of identifying insider threats from anomalous device behaviours. We propose a device-type profiling for threat detection which uses packet inter-arrival time patterns of devices for automatic identification of abnormal device-types. Experimental tests using clustering-based multivariate gaussian outlier score (CMGOS) to clearly distinguish and label normal and abnormal inter-arrival time patterns yielded promising results. This affirms the effectiveness of the proposed approach to support system administrators in monitoring and detecting insider threats for timely and effective access control response. © 2021 ACM.

关键词： Access control

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Sponge City As A Civil engineering Solution To Climate Change That Improves Urban Flood Resilience 10

Sponge City As A Civil Engineering Solution To Climate Chang...

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Shao, Yu-Tong Ma, Kevin Sheng-Kai Department of Bioenvironmental Systems Engineering National Taiwan University Taipei Taiwan Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan

ISBN: (纸本)9781665485579

Climate change and urbanization drive the demand for water management to make cities more water resilient. Climate change brings heavy precipitation and causes urban flooding, with conventional construction of cities no longer suitable given the rising risk of flooding. Characterized by constructing cities as large sponges that absorb and treat water, the idea of sponge cities is a solution to stormwater control and drainage water management, which can be achieved by creating water absorbing areas, increasing permeable surface, and integrating stormwater and drainage systems. To assess flood resilience, evaluation indicators have been introduced to measure the effectiveness of sponge cities. A case study was also provided in this review to demonstrate how the concept of sponge cities was put into practice. © 2022 IEEE.

关键词： Climate change

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Vessel's trim optimization using IoT data and machine learning models 13

Vessel's trim optimization using IoT data and machine learni...

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13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022

作者： Panagiotakopoulos, Theodor Filippopoulos, Ioannis Filippopoulos, Christos Filippopoulos, Evangelos Lajic, Zoran Violaris, Antonis Chytas, Sotirios Panagiotis Kiouvrekis, Yiannis Hellenic Open University School of Science and Technology Greece University of Nicosia Business School Cyprus Cyprus International Institute of Management Nicosia Cyprus Infralabs Ltd Nicosia Cyprus Angelicoussis Group Athens Greece University of Thessaly Department of Electrical and Computer Engineering Greece University of Thessaly Department of Public and One Health Greece

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

The shipping industry is an important source of greenhouse gas emissions, such as carbon dioxide, methane and nitrogen oxides. In the past few years, environmental and policy reasons dictate the immense reduction of greenhouse gas emissions in industries worldwide. Towards this direction, the shipping industry has focused on ship trim optimization in the last few years as an operational measure for better energy efficiency and thus a way to reduce consumption and energy-related emissions. In this paper, we present a machine learning solution to the problem of trim optimization. Specifically, we use Internet of Things (IoT) data for speed, draft, and trim in order to accurately predict shaft power. After our machine learning model is trained, we use its predicting capabilities to create the shaft power surface as part of the trim monitoring user interface of the maritime company infrastructure. © 2022 IEEE.

关键词： Machine learning

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Convolutional Bi-GRU-based prediction model for Climate Indicators

Convolutional Bi-GRU-based prediction model for Climate Indi...

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International Conference on Electronics Technology (ICET)

作者： Tsung-Ming Lo Hong-Tao Zhang Yun-Bing He Yi-Zhi Liu Wan-Ying Zhang Qiong-Dan Liang Zhen Bei Guangdong-Taiwan College of Industrial Science & Technology Dongguan University of Technology Dongguan China School of Traffic Information Engineering Guangxi Vocational and Technical College of Communications Nanning China Department of Public administration Nanfang College Guangzhou Guangzhou China School of Electrical and Computer Engineering Nanfang College-Guangzhou Guangzhou China Intelligent Manufacturing Engineering Dongguan University of Technology Dongguan China

ISBN: (数字)9798350363951

ISBN: (纸本)9798350363968

Climate prediction is critical to a wide range of fields, from agriculture to public health, but traditional methods face challenges due to the complexity and time-space characteristics of climate data. Machine learning techniques provide a promising solution by effectively extracting features from climate indicator data. In this study, we propose a new method for predicting climate indicators by combining CNN-Bi-GRU models with FNN networks. Through evaluation, our method demonstrates excellent prediction performance in terms of RMSE and MAE on both test and training sets, and robust convergence behavior is observed in model training. In addition, our method achieves high accuracy in predicting key climate metrics such as temperature and rain frequency, providing valuable information for understanding climate dynamics. Comparison experiments with LSTM-FNN, Double-LSTM, BiLSTM, and Bi-GRU models highlight the superiority of the CNNBi-GRU approach, especially in reducing temperature prediction errors. In conclusion, this study presents a promising approach to climate prediction that can enhance our understanding of climate variability and support informed decision making in various industries.

关键词： Training Measurement Accuracy Rain Predictive models Feature extraction Data models

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Adversarial attacks and defenses in physiological computing:a systematic review

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National Science Open 2023年第1期2卷 62-90页

作者： Dongrui Wu Jiaxin Xu Weili Fang Yi Zhang Liuqing Yang Xiaodong Xu Hanbin Luo Xiang Yu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China School of Civil and Hydraulic Engineering Huazhong University of Science and TechnologyWuhan 430074China College of Public Administration Huazhong University of Science and TechnologyWuhan 430074China Electrical Engineering and Computer Science Department University of MichiganAnn Arbor MI 48109USA School of Management and Sino-European Institute for Intellectual Property Huazhong University of Science and TechnologyWuhan 430074China

Physiological computing uses human physiological data as system inputs in real *** includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and physiological signal based *** computing increases the communication bandwidth from the user to the computer,but is also subject to various types of adversarial attacks,in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output,leading to possible user confusion,frustration,injury,or even ***,the vulnerability of physiological computing systems has not been paid enough attention to,and there does not exist a comprehensive review on adversarial attacks to *** study fills this gap,by providing a systematic review on the main research areas of physiological computing,different types of adversarial attacks and their applications to physiological computing,and the corresponding defense *** hope this review will attract more research interests on the vulnerability of physiological computing systems,and more importantly,defense strategies to make them more secure.

关键词： physiological computing brain-computer interfaces health informatics biometrics machine learning adversarial attack

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Assessing gender fairness in EEG-based machine learning detection of Parkinson's disease: A multi-center study 31

Assessing gender fairness in EEG-based machine learning dete...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Kurbatskaya, Anna Jaramillo-Jimenez, Alberto Ochoa-Gomez, John Fredy Brønnick, Kolbjørn Fernandez-Quilez, Alvaro Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway Stavanger University Hospital Stavanger Norway Department of Public Health Faculty of Health Sciences University of Stavanger Stavanger Norway Grupo de Neurociencias de Antioquia Universidad de Antioquia School of Medicine Medellín Colombia Grupo Neuropsicología y Conducta Universidad de Antioquia School of Medicine Medellín Colombia Department of Radiology Stavanger University Hospital Stavanger Norway

ISBN: (纸本)9789464593600

As the number of automatic tools based on machine learning (ML) and resting-state electroencephalography (rs-EEG) for Parkinson's disease (PD) detection keeps growing, the assessment of possible exacerbation of health disparities by means of fairness and bias analysis becomes more relevant. Protected attributes, such as gender, play an important role in PD diagnosis development. However, analysis of sub-group populations stemming from different genders is seldom taken into consideration in ML models' development or the performance assessment for PD detection. In this work, we perform a systematic analysis of the detection ability for gender sub-groups in a multi-center setting of a previously developed ML algorithm based on power spectral density (PSD) features of rs-EEG. We find significant differences in the PD detection ability for males and females at testing time (80.5% vs. 63.7% accuracy) and significantly higher activity for a set of parietal and frontal EEG channels and frequency sub-bands for PD and non-PD males that might explain the differences in the PD detection ability for the gender subgroups. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Electroencephalography

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Sensorless End-to-End Freehand Ultrasound with Physics Inspired Network

Sensorless End-to-End Freehand Ultrasound with Physics Inspi...

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IEEE Symposium (IUS) Ultrasonics

作者： Yimeng Dou Fangzhou Mu Yin Li Tomy Varghese Department of Electrical and Computer Engineering University of Wisconsin (UW)-Madison Madison WI USA Department of Medical Physics University of Wisconsin School of Medicine and Public Health Madison WI USA Department of Computer Science UW-Madison Madison WI USA Department of Biostatistics & Medical Informatics University of Wisconsin School of Medicine and Public Health Madison WI USA

Three-dimensional ultrasound (3D US) imaging finds broad applications in clinical practice. Compared to using 'wobbler' (motor embedded) or 'matrix array' transducers which suffers from a restricted field of view, freehand US offers more flexibility in mapping the 3D volume along the scanning path. Nevertheless, current methods for reconstructing 3D US volumes using freehand scanning are challenged by significant elevational shifts along the scanning trajectory. Previous research explored the integration of motion sensors to resolve transducer drift, yet frequent motion artifacts often compromise the benefits brought by these sensors. Recent work turned to deep neural networks (DNNs) for estimating the relative pose of imaging planes between frames in the scanning trajectory, allowing for sensorless freehand US. However, these data-driven models fall short in accuracy as they lack physical constraints, hence limiting their use in clinical practice. Inspired by the physical properties of US, we design a physics inspired DNN architecture that can better leverage the contextual cues between elevational frames for sensorless freehand 3D US reconstruction, and demonstrate substantially improving reconstruction accuracy.

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MABSplit: faster forest training using multi-armed bandits 22

MABSplit: faster forest training using multi-armed bandits

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Mo Tiwari Ryan Kang Je-Yong Lee Sebastian Thrun Chris Piech Ilan Shomorony Martin Jinye Zhang Department of Computer Science Stanford University Oxford University Electrical and Computer Engineering University of Illinois at Urbana-Champaign Department of Epidemiology Harvard T.H. Chan School of Public Health

ISBN: (纸本)9781713871088

Random forests are some of the most widely used machine learning models today, especially in domains that necessitate interpretability. We present an algorithm that accelerates the training of random forests and other popular tree-based learning methods. At the core of our algorithm is a novel node-splitting subroutine, dubbed MABSplit, used to efficiently find split points when constructing decision trees. Our algorithm borrows techniques from the multi-armed bandit literature to judiciously determine how to allocate samples and computational power across candidate split points. We provide theoretical guarantees that MABSplit improves the sample complexity of each node split from linear to logarithmic in the number of data points. In some settings, MABSplit leads to 100x faster training (an 99% reduction in training time) without any decrease in generalization performance. We demonstrate similar speedups when MABSplit is used across a variety of forest-based variants, such as Extremely Random Forests and Random Patches. We also show our algorithm can be used in both classification and regression tasks. Finally, we show that MABSplit outperforms existing methods in generalization performance and feature importance calculations under a fixed computational budget.

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Dynamic Charge Scheduling of Solar PV-Storage Hybrid Resources Based on Solar-Load Correlation

Dynamic Charge Scheduling of Solar PV-Storage Hybrid Resourc...

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Annual North-American Power Symposium

作者： Sarangan Rajendran Esther Liu Mary Peterson Visvakumar Aravinthan Al Tamimi Charles R. Yokley Electrical and Computer Engineering Department Wichita State University Wichita KS USA Wichita East High School Wichita KS USA Transmission Planning and Policy Sunflower Electric Power Corporation Wichita KS USA

Solar PV-storage hybrid resources are an attractive way of reducing grid dependency for project developers when collectively serving small communities. A well-structured charging schedule of the battery storage can improve the effectiveness of the hybrid system by reducing the grid dependency at the peak demand hours and improving the utilization of the battery. Studying the correlation between load and solar curves helps to identify periods at which short-term charging or discharging of the battery can aid to those benefits. In this paper, a dynamic charging schedule is proposed, where a nominal schedule is created based on full day forecasting of load and solar output, and further adjustments are made to the schedule based on the hourly expected correlation between load and solar curves. The proposed method is then tested on a test system based on actual data for a period of one year. The results show that the proposed approach can reduce the maximum power import from the grid, overall cost of imports and improve the battery utilization.

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