Integrating Two-Level Reinforcement Learning Process for Enhancing Task Scheduling Efficiency in a Complex Problem-Solving Environment

Task scheduling scenarios require the system designers to have complete information about the resources and their capabilities, along with the tasks and their application-specific requirements. An effective task-to-resource mapping strategy will maximize resource utilization under constraints, while minimizing the task waiting time, which will in-turn maximize the task execution efficiency. In this work, a two-level reinforcement learning algorithm for task scheduling is proposed. The algorithm utilizes a deep-intensive learning stage to generate a deployable strategy for task-to-resource mapping. This mapping is re-evaluated at specific execution breakpoints, and the strategy is re-evaluated based on the incremental learning from these breakpoints. In order to perform incremental learning, real-time parametric checking is done on the resources and the tasks;and a new strategy is devised during execution. The mean task waiting time is reduced by 20% when compared with standard algorithms like Dynamic and Integrated Resource Scheduling, Improved Differential Evolution, and Q-learning-based Improved Differential Evolution;while the resource utilization is improved by more than 15%. The algorithm is evaluated on datasets from different domains like Coronavirus disease (COVID-19) datasets of public domain, National Aeronautics and Space Administration (NASA) datasets and others. The proposed method performs consistently on all the datasets. © 2023 IETE.

An Incremental Learning-based Framework for Non-stationary Traffic Representations Clustering and Forecasting

To curb the growth of COVID-19, many rules, including a work-from-home policy, were issued in 2020. While these limits successfully prevented the virus's transmission, they completely altered original mobility patterns, resulting in considerable reductions in travel time and vehicle miles traveled. Under this non-stationary data stream, the US Department of Transportation struggled to anticipate future traffic conditions. Obviously, two essential challenges need to be addressed immediately: 1) it is challenging for transportation agencies to learn representative traffic patterns from the continually changing traffic circumstances. And 2) when and how should the forecasting model be updated to learn new patterns without forgetting previous tasks? We proposed an incremental learning-based framework for non-stationary data clustering and forecasting in transportation scenarios to tackle the issues mentioned above. It is a dual-module architecture that includes a Temporal Neighborhood Clustering module and an Incremental Learning module. The objective of the first component is to dynamically detect the optimal boundary for clustering statistically similar neighbors by enlarging both the in-group similarity and between-group dissimilarity. The second module applies the online-EWC approach, which is commonly used in image classification tasks but rarely in regression models, to learn new tasks and avoid catastrophic forgetting, which is a typical occurrence while training neural networks with multiple tasks. Experiments on the Greater Seattle Area employed loop detector data collected in 2020 yielded reliable prediction performance in both robustness and accuracy. The dual-module framework can generate promising results from pre-COVID-19 to post-COVID-19 time frames. This framework would aid government agencies and the general public in developing long-term policies and strategies for post-pandemic intelligent transportation systems. © 2022 IEEE.

Dynamic Classification of Bank Clients by the Predictability of Their Transactional Behavior

We propose a method for dynamic classification of bank clients by the predictability of their transactional behavior (with respect to the chosen prediction model, quality metric, and predictability measure). The method adopts incremental learning to perform client segmentation based on their predictability profiles and can be used by banks not only for determining predictable (and thus profitable, in a sense) clients currently but also for analyzing their dynamics during economical periods of different types. Our experiments show that (1) bank clients can be effectively divided into predictability classes dynamically, (2) the quality of prediction and classification models is significantly higher with the proposed incremental approach than without it, (3) clients have different transactional behavior in terms of predictability before and during the COVID-19 pandemics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

SpamFender: A Semi-supervised Incremental Spam Classification System across Social Networks

Social network users receive a large amount of social data every day. These data may contain malicious unwanted social spams, even though each social network has its social spam filtering mechanism. Moreover, spammers may send spam to multiple social networks concurrently, and the spam on the same topic from different social networks has similarities. Therefore, it is crucial to building a universal spam detection system across different social networks that can effectively fend off spam continuously. In this paper, we designed and implemented a tool Spam-Fender to facilitate spam detection across social networks. In order to utilize the raw social data obtained from multiple social networks, we utilized a semi-supervised learning method to convert unlabelled data into usable data for training the model. Moreover, we developed an incremental learning method to enable the model to learn new data continuously. Performance evaluations demonstrate that our proposed system can effectively detect social spam with satisfactory accuracy levels. In addition, we conducted a case study on the COVID-19 dataset to evaluate our system.

The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images

The new Coronavirus or simply Covid-19 causes an acute deadly disease. It has spread rapidly across the world, which has caused serious consequences for health professionals and researchers. This is due to many reasons including the lack of vaccine, shortage of testing kits and resources. Therefore, the main purpose of this study is to present an inexpensive alternative diagnostic tool for the detection of Covid-19 infection by using chest radiographs and Deep Convolutional Neural Network (DCNN) technique. In this paper, we have proposed a reliable and economical solution to detect COVID-19. This will be achieved by using X-rays of patients and an Incremental-DCNN (I-DCNN) based on ResNet-101 architec-ture. The datasets used in this study were collected from publicly available chest radiographs on medical repositories. The proposed I-DCNN method will help in diagnosing the positive Covid-19 patient by utilising three chest X-ray imagery groups, these will be: Covid-19, viral pneumonia, and healthy cases. Furthermore, the main contribution of this paper resides on the use of incremental learning in order to accommodate the detection system. This has high computational energy requirements, time consuming challenges, while working with large-scale and reg-ularly evolving images. The incremental learning process will allow the recognition system to learn new datasets, while keeping the convolutional layers learned pre-viously. The overall Covid-19 detection rate obtained using the proposed I-DCNN was of 98.70% which undeniably can contribute effectively to the detection of COVID-19 infection. © 2022, Brno University of Technology. All rights reserved.

Modelling the COVID-19 Virus Evolution With Incremental Machine Learning

The investment of time and resources for better strategies and methodologies to tackle a potential pandemic is key for dealing with potential outbreaks of new variants or other viruses in the future. In this work, we recreated the scene of 2020 for the fifty countries with more COVID-19 cases reported. We performed some experiments to compare state-of-the-art machine learning algorithms, such as LSTM, against online incremental learning methods (ILMs) in terms of how well they adapted to the daily changes in the spread of the disease and predict future COVID-19 cases. To compare the methods, we performed two experiments: In the first experiment, we trained the models using only data from the country we predicted. In the second one, we used data from the fifty countries to train and predict each one of them. In these two experiments, we used a static hold-out approach for all the methods. Results show that ILMs are a promising approach to model the disease changes over time;ILMs are always up-to-date with the latest state of the data distribution, and they have a significantly lower computational cost than other techniques such as LSTMs. © 2021 CEUR-WS. All rights reserved.

A novel adaptive deep learning model of Covid-19 with focus on mortality reduction strategies.

We employ deep learning to propose an Artificial Neural Network (ANN) based and data stream guided real-time incremental learning algorithm for parameter estimation of a non-intrusive, intelligent, adaptive and online analytical model of Covid-19 disease. Modeling and simulation of such problems pose an additional challenge of continuously evolving training data in which the model parameters change over time depending upon external factors. Our main contribution is that in a scenario of continuously evolving training data, unlike typical deep learning techniques, this non-intrusive algorithm eliminates the need to retrain or rebuild the model from scratch every time a new training data set is received. After validating the model, we use it to study the impact of different strategies for epidemic control. Finally, we propose and simulate a strategy of controlled natural immunization through risk-based population compartmentalization (PC) wherein the population is divided in Low Risk (LR) and High Risk (HR) compartments based on risk factors (like comorbidities and age) and subjected to different disease transmission dynamics by isolating the HR compartment while allowing the LR compartment to develop natural immunity. Upon release from the preventive isolation, the HR compartment finds itself surrounded by enough number of immunized individuals to prevent the spread of infection and thus most of the deaths occurring in this group are avoided.