Method towards reconstructing collaborative business processes with cloud services using evolutionary deep Q-learning

Tan, Wenan; Huang, Li; Kataev, Mikhail Yu.; Sun, Yong; Zhao, Lu; Zhu, Hai Ping; Guo, Kai; Xie, Na

doi:10.1016/j.jii.2020.100189

Cited by 20 publications

(6 citation statements)

References 32 publications

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“…Many authors explored the deep Q learning algorithm and exploited it in a wide variety of complex applications, like collaborative business processes with cloud services, manufacturing assembly programs, many robotic applications, automated trading in equity stock markets, and many more [24][25][26][27][28]. Chatterjee et al [29] discussed deep reinforcement learning for the application where the most phishing activities are taking place on websites and detecting malicious URLs.…”

Section: Review Of Related Workmentioning

confidence: 99%

RDQN: ensemble of deep neural network with reinforcement learning in classification based on rough set theory for digital transactional fraud detection

Tekkali

Natarajan

2023

Complex Intell. Syst.

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All financial sectors are facing the most common frauds, which are digital transactional frauds. Fraudsters have always engaged in illegal activities such as stealing personal information and logging in with unauthorised credentials. Many machine learning algorithms predict whether the transaction is factual or nonfactual but fail to decrease the processing time. Hybrid models are used in this case to identify the fraud in a quick and efficient manner. This article demarcates to construct a novel model, RDQN, i.e., deep reinforcement learning, that combines with the rough set theory. This article has three steps, including data pre-processing to determine the quality of the data, which affects the learning ability of the model, determining the structural relationship and gaining useful features from the data set using rough set theory, and doing a hybridization of DNN (deep neural network) and Q learning, which is called DQN. It uses the MISH activation function and the ReLU activation function in different layers for training dynamics in the neural network. The proposed model classifies and predicts that the transaction belongs to the category implemented by the agents by activating the reward function. The reinforcement-learning agent’s performance improves based on reward assessment. This reward function gives a more precise value for each transaction, and no fraudster can escape from the agent’s sight. This novel approach improves accuracy and reduces processing time by considering the best feature selection during the process.

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Section: Review Of Related Workmentioning

confidence: 99%

RDQN: ensemble of deep neural network with reinforcement learning in classification based on rough set theory for digital transactional fraud detection

Tekkali

Natarajan

2023

Complex Intell. Syst.

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“…Chen et al (2022): Proposed a deep Q-learning approach to address same-day delivery with vehicles and drones, leveraging the strengths of different fleets [6]. Tan et al (2021): Introduced a hybrid Evolutionary Deep Q-Learning based BPaaS reconstruction algorithm called EDQL-BPR. It optimizes the reengineering of collaborative business processes using Particle Swarm Optimization [7].…”

Section: Literature Surveymentioning

confidence: 99%

Predictive Analytics for Stock and Demand Balance Using Deep Q-Learning Algorithm

2023

datake

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Predicting and managing stock and demand balance is a critical aspect of supply chain management. In this paper, we propose a predictive analytics approach that leverages deep Q-learning algorithm to optimize stock and demand balance. Traditional forecasting methods often struggle to capture the complex dynamics and uncertainties associated with stock and demand fluctuations. The deep Q-learning algorithm, a reinforcement learning technique, offers a promising solution by learning optimal decision-making policies through interaction with the environment. The proposed approach utilizes historical stock and demand data to train a deep Q-learning model, enabling it to make accurate predictions about future stock levels and demand patterns. By considering factors such as seasonality, trends, and external variables, the model learns to adjust stock levels to meet demand while minimizing excess inventory or stock outs. To validate the effectiveness of the proposed approach, a case study conducted using real-world data from a supply chain network. The results demonstrate that the deep Q-learning algorithm outperforms traditional forecasting methods, achieving higher accuracy in predicting stock and demand balance. The implications of this research are significant for supply chain managers and decision-makers. By incorporating predictive analytics with the deep Q-learning algorithm, companies can enhance their inventory management strategies, reduce holding costs, minimize stock outs, and improve customer satisfaction.

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“…This ability reduces the customization need for algorithms to work on a wide range of prediction problems. Based on deep learning, sequence models can process sequence data that is sensitive to sequential order and can be applied in many fields, such as biological data analysis (Khan & Baik, 2020), violence detection (Khan et al, 2019), and image retrieval (Khan, Hussain, et al, 2021), which also demonstrate promising applications in BPM (Tan et al, 2021). Compared to earlier process mining techniques, deep learning techniques capture potential data relationships concealed in event logs utilizing multiple hidden layers with higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

Next activity prediction of ongoing business processes based on deep learning

et al. 2023

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Next activity prediction of business processes (BPs) provides valid execution information of ongoing (i.e., unfinished) process instances, which enables process executors to rationally allocate resources and detect process deviations in advance. Current researches on next activity prediction, however, concentrate mostly on model construction without in‐depth analysis of historical event logs. In this article, we are dedicated to proposing an approach to forecast the next activity effectively in BPs. After in‐depth analysis of historical event logs, three types of candidate activity attributes are defined and calculated as additional input for the prediction based on three essential elements, that is, frequent activity patterns, trace similarity and position information. Furthermore, we construct an effective hybrid prediction model combining the popular convolutional neural network (CNN) and bidirectional long short‐term memory (Bi‐LSTM) with self‐attention mechanism. Specifically, CNN is used to extract the temporal features before importing into Bi‐LSTM for accurate prediction, and self‐attention mechanism is applied to strengthen features that have decisive effects on the prediction results. Comparison experiments on four real‐life datasets demonstrate that our hybrid model with selected attributes achieves better performance on next activity prediction than single models, and improves the prediction accuracy by 2.98%, 6.05%, 2.70% and 5.26% on Helpdesk, Sepsis, BPIC2013 Incidents and BPIC2012O datasets than the state‐of‐the‐art methods, respectively.

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Method towards reconstructing collaborative business processes with cloud services using evolutionary deep Q-learning

Cited by 20 publications

References 32 publications

RDQN: ensemble of deep neural network with reinforcement learning in classification based on rough set theory for digital transactional fraud detection

RDQN: ensemble of deep neural network with reinforcement learning in classification based on rough set theory for digital transactional fraud detection

Predictive Analytics for Stock and Demand Balance Using Deep Q-Learning Algorithm

Next activity prediction of ongoing business processes based on deep learning

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