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Статті в журналах з теми "Decision-gated framework"
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Liyakathunisa, Abdullah Alsaeedi, Saima Jabeen, and Hoshang Kolivand. "Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques." Journal of Ambient Intelligence and Smart Environments 14, no. 1 (January 20, 2022): 5–23. http://dx.doi.org/10.3233/ais-210162.
Повний текст джерелаАнотація:
Due to the increase in the global aging population and its associated age-related challenges, various cognitive, physical, and social problems can arise in older adults, such as reduced walking speed, mobility, falls, fatigue, difficulties in performing daily activities, memory-related and social isolation issues. In turn, there is a need for continuous supervision, intervention, assistance, and care for elderly people for active and healthy aging. This research proposes an ambient assisted living system with the Internet of Medical Things that leverages deep learning techniques to monitor and evaluate the elderly activities and vital signs for clinical decision support. The novelty of the proposed approach is that bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques with mutual information-based feature selection technique is applied to select robust features to identify the target activities and abnormalities. Experiments were conducted on two datasets (the recorded Ambient Assisted Living data and MHealth benchmark data) with bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques and compared with other state of art techniques. Different evaluation metrics were used to assess the performance, findings reveal that bidirectional Gated Recurrent Unit deep learning techniques outperform other state of art approaches with an accuracy of 98.14% for Ambient Assisted Living data, and 99.26% for MHealth data using the proposed approach.
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Servant, Mathieu, Gabriel Tillman, Jeffrey D. Schall, Gordon D. Logan, and Thomas J. Palmeri. "Neurally constrained modeling of speed-accuracy tradeoff during visual search: gated accumulation of modulated evidence." Journal of Neurophysiology 121, no. 4 (April 1, 2019): 1300–1314. http://dx.doi.org/10.1152/jn.00507.2018.
Повний текст джерелаАнотація:
Stochastic accumulator models account for response times and errors in perceptual decision making by assuming a noisy accumulation of perceptual evidence to a threshold. Previously, we explained saccade visual search decision making by macaque monkeys with a stochastic multiaccumulator model in which accumulation was driven by a gated feed-forward integration to threshold of spike trains from visually responsive neurons in frontal eye field that signal stimulus salience. This neurally constrained model quantitatively accounted for response times and errors in visual search for a target among varying numbers of distractors and replicated the dynamics of presaccadic movement neurons hypothesized to instantiate evidence accumulation. This modeling framework suggested strategic control over gate or over threshold as two potential mechanisms to accomplish speed-accuracy tradeoff (SAT). Here, we show that our gated accumulator model framework can account for visual search performance under SAT instructions observed in a milestone neurophysiological study of frontal eye field. This framework captured key elements of saccade search performance, through observed modulations of neural input, as well as flexible combinations of gate and threshold parameters necessary to explain differences in SAT strategy across monkeys. However, the trajectories of the model accumulators deviated from the dynamics of most presaccadic movement neurons. These findings demonstrate that traditional theoretical accounts of SAT are incomplete descriptions of the underlying neural adjustments that accomplish SAT, offer a novel mechanistic account of decision-making mechanisms during speed-accuracy tradeoff, and highlight questions regarding the identity of model and neural accumulators. NEW & NOTEWORTHY A gated accumulator model is used to elucidate neurocomputational mechanisms of speed-accuracy tradeoff. Whereas canonical stochastic accumulators adjust strategy only through variation of an accumulation threshold, we demonstrate that strategic adjustments are accomplished by flexible combinations of both modulation of the evidence representation and adaptation of accumulator gate and threshold. The results indicate how model-based cognitive neuroscience can translate between abstract cognitive models of performance and neural mechanisms of speed-accuracy tradeoff.
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Meng, Zhaorui, and Xianze Xu. "A Hybrid Short-Term Load Forecasting Framework with an Attention-Based Encoder–Decoder Network Based on Seasonal and Trend Adjustment." Energies 12, no. 24 (December 4, 2019): 4612. http://dx.doi.org/10.3390/en12244612.
Повний текст джерелаАнотація:
Accurate electrical load forecasting plays an important role in power system operation. An effective load forecasting approach can improve the operation efficiency of a power system. This paper proposes the seasonal and trend adjustment attention encoder–decoder (STA–AED), a hybrid short-term load forecasting approach based on a multi-head attention encoder–decoder module with seasonal and trend adjustment. A seasonal and trend decomposing technique is used to preprocess the original electrical load data. Each decomposed datum is regressed to predict the future electric load value by utilizing the encoder–decoder network with the multi-head attention mechanism. With the multi-head attention mechanism, STA–AED can interpret the prediction results more effectively. A large number of experiments and extensive comparisons have been carried out with a load forecasting dataset from the United States. The proposed hybrid STA–AED model is superior to the other five counterpart models such as random forest, gradient boosting decision tree (GBDT), gated recurrent units (GRUs), Encoder–Decoder, and Encoder–Decoder with multi-head attention. The proposed hybrid model shows the best prediction accuracy in 14 out of 15 zones in terms of both root mean square error (RMSE) and mean absolute percentage error (MAPE).
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Wali, S., M. H. U. Haq, M. Kazmi, and S. A. Qazi. "An End-to-End Machine Learning based Unified Architecture for Non-Intrusive Load Monitoring." Engineering, Technology & Applied Science Research 11, no. 3 (June 10, 2021): 7217–22. http://dx.doi.org/10.48084/etasr.4142.
Повний текст джерелаАнотація:
Non-Intrusive Load Monitoring (NILM) or load disaggregation aims to analyze power consumption by decomposing the energy measured at the aggregate level into constituent appliances level. The conventional load disaggregation framework consists of signal processing and machine learning-based pipelined architectures, respectively for explicit feature extraction and decision making. Manual feature selection in such load disaggregation frameworks leads to biased decisions that eventually reduce system performance. This paper presents an efficient End-to-End (E2E) approach-based unified architecture using Gated Recurrent Units (GRU) for NILM. The proposed approach eliminates explicit feature engineering and has a unified classification and prediction model for appliance power. This eventually reduces the computational cost and enhances response time. The performance of the proposed system is compared with conventional algorithms' with the use of recall, precision, accuracy, F1 score, the relative error in total energy and Mean Absolute Error (MAE). These evaluation metrics are calculated on the power consumption of top priority appliances of Reference Energy Disaggregation Dataset (REDD). The proposed architecture with an overall accuracy of 91.2 and MAE of 25.23 outperforms conventional methods for all electrical appliances. It has been showcased through a series of experiments that feature extraction and event-based approaches for NILM can readily be replaced with E2E deep learning techniques allowing simpler and cost-efficient implementation pathways.
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Singaravelan, Anandakumar, Chung-Ho Hsieh, Yi-Kai Liao, and Jia-Lien Hsu. "Predicting ICD-9 Codes Using Self-Report of Patients." Applied Sciences 11, no. 21 (October 27, 2021): 10046. http://dx.doi.org/10.3390/app112110046.
Повний текст джерелаАнотація:
The International Classification of Diseases (ICD) is a globally recognized medical classification system that aids in the identification of diseases and the regulation of health trends. The ICD framework makes it easy to keep track of records and evaluate medical data for evidence-based decision-making. Several methods have predicted ICD-9 codes based on the discharge summary, clinical notes, and nursing notes. In our study, our approach only utilizes the subjective component to predict ICD-9 codes. Data cleaning and segmentation, and Natural Language Processing (NLP) techniques are applied on the subjective component during the pre-processing. Our study builds the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU) to develop a model for predicting ICD-9 codes. The ICD-9 codes contain different ICD levels such as chapter, block, three-digit code, and full code. The GRU model scores the highest recall of 57.91% in the chapter level and the top-10 experiment has a recall of 67.37%. Based on the subjective component, the model can help patients in the form of a remote assistance tool.
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Zeng, Hong, Chen Yang, Hua Zhang, Zhenhua Wu, Jiaming Zhang, Guojun Dai, Fabio Babiloni, and Wanzeng Kong. "A LightGBM-Based EEG Analysis Method for Driver Mental States Classification." Computational Intelligence and Neuroscience 2019 (September 9, 2019): 1–11. http://dx.doi.org/10.1155/2019/3761203.
Повний текст джерелаАнотація:
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. The comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI).
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Wang, Dongjie, Pengyang Wang, Kunpeng Liu, Yuanchun Zhou, Charles E. Hughes, and Yanjie Fu. "Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4410–17. http://dx.doi.org/10.1609/aaai.v35i5.16567.
Повний текст джерелаАнотація:
In this paper, we study the problem of mobile user profiling, which is a critical component for quantifying users' characteristics in the human mobility modeling pipeline. Human mobility is a sequential decision-making process dependent on the users' dynamic interests. With accurate user profiles, the predictive model can perfectly reproduce users' mobility trajectories. In the reverse direction, once the predictive model can imitate users' mobility patterns, the learned user profiles are also optimal. Such intuition motivates us to propose an imitation-based mobile user profiling framework by exploiting reinforcement learning, in which the agent is trained to precisely imitate users' mobility patterns for optimal user profiles. Specifically, the proposed framework includes two modules: (1) representation module, that produces state combining user profiles and spatio-temporal context in real-time; (2) imitation module, where Deep Q-network (DQN) imitates the user behavior (action) based on the state that is produced by the representation module. However, there are two challenges in running the framework effectively. First, epsilon-greedy strategy in DQN makes use of the exploration-exploitation trade-off by randomly pick actions with the epsilon probability. Such randomness feeds back to the representation module, causing the learned user profiles unstable. To solve the problem, we propose an adversarial training strategy to guarantee the robustness of the representation module. Second, the representation module updates users' profiles in an incremental manner, requiring integrating the temporal effects of user profiles. Inspired by Long-short Term Memory (LSTM), we introduce a gated mechanism to incorporate new and old user characteristics into the user profile. In the experiment, we evaluate our proposed framework on real-world datasets. The extensive experimental results validate the superiority of our method comparing to baseline algorithms.
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Arzo, Sisay Tadesse, Zeinab Akhavan, Mona Esmaeili, Michael Devetsikiotis, and Fabrizio Granelli. "Multi-Agent-Based Traffic Prediction and Traffic Classification for Autonomic Network Management Systems for Future Networks." Future Internet 14, no. 8 (July 28, 2022): 230. http://dx.doi.org/10.3390/fi14080230.
Повний текст джерелаАнотація:
Recently, a multi-agent based network automation architecture has been proposed. The architecture is named multi-agent based network automation of the network management system (MANA-NMS). The architectural framework introduced atomized network functions (ANFs). ANFs should be autonomous, atomic, and intelligent agents. Such agents should be implemented as an independent decision element, using machine/deep learning (ML/DL) as an internal cognitive and reasoning part. Using these atomic and intelligent agents as a building block, a MANA-NMS can be composed using the appropriate functions. As a continuation toward implementation of the architecture MANA-NMS, this paper presents a network traffic prediction agent (NTPA) and a network traffic classification agent (NTCA) for a network traffic management system. First, an NTPA is designed and implemented using DL algorithms, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), multilayer perceptrons (MLPs), and convolutional neural network (CNN) algorithms as a reasoning and cognitive part of the agent. Similarly, an NTCA is designed using decision tree (DT), K-nearest neighbors (K-NN), support vector machine (SVM), and naive Bayes (NB) as a cognitive component in the agent design. We then measure the NTPA prediction accuracy, training latency, prediction latency, and computational resource consumption. The results indicate that the LSTM-based NTPA outperforms compared to GRU, MLP, and CNN-based NTPA in terms of prediction accuracy, and prediction latency. We also evaluate the accuracy of the classifier, training latency, classification latency, and computational resource consumption of NTCA using the ML models. The performance evaluation shows that the DT-based NTCA performs the best.
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Le, Thi-Thu-Huong, Yongsu Kim, and Howon Kim. "Network Intrusion Detection Based on Novel Feature Selection Model and Various Recurrent Neural Networks." Applied Sciences 9, no. 7 (April 3, 2019): 1392. http://dx.doi.org/10.3390/app9071392.
Повний текст джерелаАнотація:
The recent increase in hacks and computer network attacks around the world has intensified the need to develop better intrusion detection and prevention systems. The intrusion detection system (IDS) plays a vital role in detecting anomalies and attacks on the network which have become larger and more pervasive in nature. However, most anomaly-based intrusion detection systems are plagued by high false positives. Furthermore, Remote-to-Local (R2L) and User-to-Root (U2R) are two kinds of attack which have low predicted accuracy scores in advance IDS methods. Therefore, this paper proposes a novel IDS framework to overcome these IDS problems. The proposed framework including three main parts. The first part is to build SFSDT model which is the feature selection model. SFSDT is to generate the best feature subset from the original feature set. This model is a hybrid Sequence Forward Selection (SFS) algorithm and Decision Tree (DT) model. The second part is to build various IDS models to train on the best-selected feature subset. The various Recurrent Neural Networks (RNN) are traditional RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Two IDS datasets are used for the learned models in experiments including NSL-KDD in 2010 and ISCX in 2012. The final part is to evaluate the proposed model by comparing the proposed models to other IDS models. The experimental results show the proposed models achieve significantly improved accuracy detection rate as well as attack types classification. Furthermore, this approach can reduce the computation time by memory profilers measurement.
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Proctor, Philippe, Christof Teuscher, Adam Hecht, and Marek Osiński. "Proximal Policy Optimization for Radiation Source Search." Journal of Nuclear Engineering 2, no. 4 (September 30, 2021): 368–97. http://dx.doi.org/10.3390/jne2040029.
Повний текст джерелаАнотація:
Rapid search and localization for nuclear sources can be an important aspect in preventing human harm from illicit material in dirty bombs or from contamination. In the case of a single mobile radiation detector, there are numerous challenges to overcome such as weak source intensity, multiple sources, background radiation, and the presence of obstructions, i.e., a non-convex environment. In this work, we investigate the sequential decision making capability of deep reinforcement learning in the nuclear source search context. A novel neural network architecture (RAD-A2C) based on the advantage actor critic (A2C) framework and a particle filter gated recurrent unit for localization is proposed. Performance is studied in a randomized 20×20 m convex and non-convex simulation environment across a range of signal-to-noise ratio (SNR)s for a single detector and single source. RAD-A2C performance is compared to both an information-driven controller that uses a bootstrap particle filter and to a gradient search (GS) algorithm. We find that the RAD-A2C has comparable performance to the information-driven controller across SNR in a convex environment. The RAD-A2C far outperforms the GS algorithm in the non-convex environment with greater than 95% median completion rate for up to seven obstructions.
Дисертації з теми "Decision-gated framework"
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Newman, David John. "Decision-Making for Oil and Gas Projects: Using Front End Loading and Decision Analysis More Effectively." Thesis, 2019. http://hdl.handle.net/2440/121701.
Повний текст джерелаАнотація:
The oil & gas industry has a history of projects not achieving the outcomes promised at sanction. It is well-known that good Front End Loading (FEL) will increase the likelihood of project success. However, despite this, a significant number of projects proceed with insufficient FEL. This research aims to find out why this is, and to develop ways of influencing decision makers so that FEL will be used more effectively in future. Making high quality decisions is the best way of maximising the likelihood of achieving desirable outcomes. Decision Analysis (DA) is a pragmatic methodology for making high quality decisions that has been around for many decades. However, it is not always used when making key decisions on oil and gas projects. This research aims to determine why this is, and to find ways to influence decision makers to use DA more effectively. Interviews and a survey were carried out with senior personnel from oil and gas companies to determine their knowledge and understanding of FEL and DA, how they think they should be used and how they are used in practice. These studies demonstrated a strong belief that FEL must be carried out if a project is to be successful, and that DA needs to be applied for major decisions - but these only happen in practice around half of the time. A follow up survey was carried out to clarify issues outstanding from the interviews and the initial survey, and to determine the likely uptake of proposals to encourage better use to be made of FEL and DA. There was strong support for the proposals which included developing a simple tool to give a pragmatic assessment of FEL, having performance incentives based on achieving good FEL and high Decision Quality, and undertaking training on project decision making. An experiment was set up to investigate how training, and the way a decision is framed, influence the approach taken for project decision making. Half of the participants received training by watching three short online videos, the other half received no training. They all then answered questions on three decision making scenarios for projects. The results showed that training influenced decisionmakers to take a more structured and process-based approach, and that the way a decision is framed by an authority figure has a strong influence on the approach taken for project decision making. An alternative way of assessing FEL has been developed to encourage FEL to be used more effectively and increase the likelihood of delivering better project outcomes. It is a simple, decision-based approach to assessing FEL which can be carried out in-house. It is proposed that it is used in conjunction with FEL benchmarking to gain the benefits of both approaches, provide a better understanding of FEL, and have a stronger basis for decision-making.Thesis (Ph.D.) -- University of Adelaide, Australian School of Petroleum, 2019
Тези доповідей конференцій з теми "Decision-gated framework"
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Clark, Hamish P., and Orlando Castellanos Diaz. "Technology Development Framework: Moving From Qualitative to Quantitative Decision-Making." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210290-ms.
Повний текст джерелаАнотація:
Abstract Technology development in the energy sector typically suffers from an ad hoc approach to making decisions about the key questions of "What is the next de-risking step?" and "At what scale?". This lack of structure in decision making occurs despite the technology development process usually being within a stage gated protocol. This often leads to significant waste of time and resources and plenty of "surprises" about the extent of additional testing required. The approach proposed in this manuscript is relatively simple to use, scalable, and transferable across industry sectors. Consistent application of the methodology has the potential to bring a quantitative rigor that can lead to more effective and efficient technology development and improve both intellectual and monetary capital allocation. Additionally, the framework provided facilitates a more structured and faster learning curve for people moving into technology development roles. Starting with a high-level understanding of the technology and business case, the methodology identifies possible commercial deployments and associates key technical and commercial risks that can be barriers to commercialization. Then, using a "Risk Burndown" approach based around an initial assessment of Probability of Success (POS), different Paths to Deployment (PTD) are developed. A Value of Information (VOI) approach is used to evaluate key metrics. This leads to a diverse set of de-risking options with associated trade-offs for informed decision making. The methodology has been developed and deployed since 2017 across a portfolio of both incremental improvement and game-changing subsurface technologies at Suncor Energy, a large integrated Canadian energy company. Over 25 technologies have been progressed using elements of this workflow with 7 of the more complex and expensive projects employing the full quantitative VOI approach. These analyses have been used to inform decision making on over CAD 400 million of proposed de-risking activity spend.