E-Learning Readiness Assessment Using Machine Learning Methods

Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students' readiness. This paper presents an effective approach for assessing e-learning readiness by combining the ADKAR model and machine learning-based feature importance identification methods. The motivation behind using machine learning approaches lies in their ability to capture nonlinearity in data and flexibility as data-driven models. This study surveyed faculty members and students in the Economics faculty at Tlemcen University, Algeria, to gather data based on the ADKAR model's five dimensions: awareness, desire, knowledge, ability, and reinforcement. Correlation analysis revealed a significant relationship between all dimensions. Specifically, the pairwise correlation coefficients between readiness and awareness, desire, knowledge, ability, and reinforcement are 0.5233, 0.5983, 0.6374, 0.6645, and 0.3693, respectively. Two machine learning algorithms, random forest (RF) and decision tree (DT), were used to identify the most important ADKAR factors influencing e-learning readiness. In the results, ability and knowledge were consistently identified as the most significant factors, with scores of ability (0.565, 0.514) and knowledge (0.170, 0.251) using RF and DT algorithms, respectively. Additionally, SHapley Additive exPlanations (SHAP) values were used to explore further the impact of each variable on the final prediction, highlighting ability as the most influential factor. These findings suggest that universities should focus on enhancing students' abilities and providing them with the necessary knowledge to increase their readiness for e-learning. This study provides valuable insights into the factors influencing university students' e-learning readiness.

Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the core technologies of the fourth industrial revolution that have become vital in decision making. However, in the era of epidemics and big data, the volume of data has increased dramatically while the sources have become progressively more complex, making data distribution highly susceptible to change. These situations can easily lead to concept drift, which directly affects the effectiveness of prediction models. How to cope with such complex situations and make timely and accurate decisions from multiple perspectives is a challenging research issue. To address this challenge, we summarize concept drift adaptation methods under the deep learning framework, which is beneficial to help decision makers make better decisions and analyze the causes of concept drift. First, we provide an overall introduction to concept drift, including the definition, causes, types, and process of concept drift adaptation methods under the deep learning framework. Second, we summarize concept drift adaptation methods in terms of discriminative learning, generative learning, hybrid learning, and others. For each aspect, we elaborate on the update modes, detection modes, and adaptation drift types of concept drift adaptation methods. In addition, we briefly describe the characteristics and application fields of deep learning algorithms using concept drift adaptation methods. Finally, we summarize common datasets and evaluation metrics and present future directions.

Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study

We study the behavior of an economic platform (e.g., Amazon, Uber Eats, Instacart) under shocks, such as COVID-19 lockdowns, and the effect of different regulation considerations. To this end, we develop a multi-agent simulation environment of a platform economy in a multi-period setting where shocks may occur and disrupt the economy. Buyers and sellers are heterogeneous and modeled as economically-motivated agents, choosing whether or not to pay fees to access the platform. We use deep reinforcement learning to model the fee-setting and matching behavior of the platform, and consider two major types of regulation frameworks: (1) taxation policies and (2) platform fee restrictions. We offer a number of simulated experiments that cover different market settings and shed light on regulatory tradeoffs. Our results show that while many interventions are ineffective with a sophisticated platform actor, we identify a particular kind of regulation - fixing fees to the optimal, no-shock fees while still allowing a platform to choose how to match buyers and sellers - as holding promise for promoting the efficiency and resilience of the economic system. © 2023 ACM.

S-Nav: Safety-Aware IoT Navigation Tool for Avoiding COVID-19 Hotspots

In this article, we present a [Formula Omitted]-learning-enabled safe navigation system—S-Nav—that recommends routes in a road network by minimizing traveling through categorically demarcated COVID-19 hotspots. S-Nav takes the source and destination as inputs from the commuters and recommends a safe path for traveling. The S-Nav system dodges hotspots and ensures minimal passage through them in unavoidable situations. This feature of S-Nav reduces the commuter's risk of getting exposed to these contaminated zones and contracting the virus. To achieve this, we formulate the reward function for the reinforcement learning model by imposing zone-based penalties and demonstrate that S-Nav achieves convergence under all conditions. To ensure real-time results, we propose an Internet of Things (IoT)-based architecture by incorporating the cloud and fog computing paradigms. While the cloud is responsible for training on large road networks, the geographically aware fog nodes take the results from the cloud and retrain them based on smaller road networks. Through extensive implementation and experiments, we observe that S-Nav recommends reliable paths in near real time. In contrast to state-of-the-art techniques, S-Nav limits passage through red/orange zones to almost 2% and close to 100% through green zones. However, we observe 18% additional travel distances compared to precarious shortest paths.

A multi-agent reinforcement learning model for inventory transshipments under supply chain disruption

The COVID-19 pandemic has significantly disrupted global supply chains (SCs), emphasizing the importance of SC resilience, which refers to the ability of SCs to return to their original or more desirable state following disruptions. This study focuses on collaboration, a key component of SC resilience, and proposes a novel collaborative structure that incorporates a fictitious agent to manage inventory transshipment decisions between retailers in a centralized manner while maintaining the retailers' autonomy in ordering. The proposed collaborative structure offers the following advantages from SC resilience and operational perspectives: (1) it facilitates decision synchronization for enhanced collaboration among retailers, and (2) it allows retailers to collaborate without the need for information sharing, addressing the potential issue of information sharing reluctance. Additionally, this study employs non-stationary probability to capture the deeply uncertain nature of the ripple effect and the highly volatile customer demand caused by the pandemic. A new reinforcement learning (RL) algorithm is developed to handle non-stationary environments and to implement the proposed collaborative structure. Experimental results demonstrate that the proposed collaborative structure using the new RL algorithm achieves superior SC resilience compared with centralized inventory management systems with transshipment and decentralized inventory management systems without transshipment using traditional RL algorithms. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation

The spread of online misinformation threatens public health, democracy, and the broader society. While professional fact-checkers form the first line of defense by fact-checking popular false claims, they do not engage directly in conversations with misinformation spreaders. On the other hand, non-expert ordinary users act as eyes-on-the-ground who proactively counter misinformation - recent research has shown that 96% counter-misinformation responses are made by ordinary users. However, research also found that 2/3 times, these responses are rude and lack evidence. This work seeks to create a counter-misinformation response generation model to empower users to effectively correct misinformation. This objective is challenging due to the absence of datasets containing ground-truth of ideal counter-misinformation responses, and the lack of models that can generate responses backed by communication theories. In this work, we create two novel datasets of misinformation and counter-misinformation response pairs from in-the-wild social media and crowdsourcing from college-educated students. We annotate the collected data to distinguish poor from ideal responses that are factual, polite, and refute misinformation. We propose MisinfoCorrect, a reinforcement learning-based framework that learns to generate counter-misinformation responses for an input misinformation post. The model rewards the generator to increase the politeness, factuality, and refutation attitude while retaining text fluency and relevancy. Quantitative and qualitative evaluation shows that our model outperforms several baselines by generating high-quality counter-responses. This work illustrates the promise of generative text models for social good - here, to help create a safe and reliable information ecosystem. The code and data is accessible on https://github.com/claws-lab/MisinfoCorrect. © 2023 Owner/Author.

The Study of 'Preparation Guidelines' Towards Phetchabun Government Officers and Health Massage Establishments

The purpose of research was to study practices, barriers, and solutions of Phetchabun health massage establishments under COVID-19 situation. Non-participant observation, informal interview, in-depth interview, and participation observation were carried out respectively. Purposive sampling was used with 15 health consumer protection officers in charge;11 district level, 2 provincial level, 2 regional level as well as each representative of 11 districts. Results after implementation of "Preparation Guidelines for Health Spa, Health Massage, and Beauty Massage to Promote Health Tourism During COVID-19 Pandemic" were categorized into two sections. Firstly, the practices, barriers, and solutions of government officer performances included preparation for reopening, monitoring of the provider practices, and performance report. Secondly, the provider operations consisted of doing "Self-Assessment of Health Establishment", logging-in webpage before reopening, and practices for clients included screening and report of patients under investigation, establishment monitor, service, and establishment cleaning. In summary, the preparation guidelines were purposed to reopen their business with numerous contents and messages written by official language, it caused establishment providers and practitioners difficultly understood when applying. LINE Application and making calls were easy and accessible methods for their communication to reach current data and to ensure exact information. Various encouragements and having compliments were also considerable to form trust and confidence among them, they also raised their proud.Copyright © 2023, World Informations Syndicate. All rights reserved.

A Deep Learning-Based Preventive Measures of COVID-19 in a crowd using Reinforcement Model over GAN for Enhanced efficiency

The fast human-to-human spread of COVID-19 has caused significant lifestyle changes for many individuals. At the end of January 2020, the pandemic began, and many nations responded with varying degrees of testing, sanitation, lockdown, and quarantine centers. New normals of testing, sanitization, social separation, and lockdown are being implemented, and people are gradually returning to work and other daily routines. The COVID-19 infected population is monitored by testing individuals regularly. But it's a resource-heavy endeavor to test everyone without good reason. An optimum strategy is required to efficiently identify persons who are most likely to test positive for COVID-19. Sanitation is utilized for both persons and public spaces to eliminate germs. However, the disruption of governmental operations and economic development makes the use of lockdown and quarantine centers a resource-intensive endeavor. Conversely, it degrades the standard of living across a society. Furthermore, keeping people inside their houses or quarantine centers for an unlimited amount of time would not allow the government to care for everyone. These variables impact virus propagation, human health and happiness, available resources, and the economy's health, making their management resource-intensive. counting and density estimation are both attempts to create clever and efficient algorithms that can interpret the data provided by images to carry out Efficiency. GANs have been proven to have promising applications in overcoming the data dearth problem in COVID-19 lung image analysis. The Convolutional Neural Network (CNN) models built for the diagnosis of COVID-19 have benefited from the GAN-generated data used to refine their training. Moreover, GANs have helped improve the performance of CNNs by super-resolving pictures and performing segmentation. This work highlights the Reinforcement deep learning model over the fundamental constraints of the possible transformation of GANs-based approaches. This work proposes the model be developed with a new intelligent approach using RL to quantify these different types of testing considered for social distancing, face mask detection, limiting the gathering, and locking the location using the Q Learning technique. Different RL algorithms are implemented, and agents are equipped with these algorithms so that they may interact with the environment and learn the optimum method for doing so. © 2023 IEEE.

5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network.

The COVID-19 outbreak has stimulated the digital transformation of antiquated healthcare system to a smart hospital, enabling the personalised and remote healthcare services. To augment the functionalities of these intelligent healthcare systems, 5G & B5G heterogeneous network has emerged as a robust and reliable solution. But the pivotal challenge for 5G & B5G connectivity solutions is to ensure flexible and agile service orchestration with acknowledged Quality of Experience (QoE). However, the existing radio access technology (RAT) selection strategies are incapacitated in terms of QoE provisioning and Quality of Service (QoS) maintenance. Therefore, an intelligent QoE aware RAT selection architecture based on software-defined wireless networking (SDWN) and edge computing has been proposed for 5G-enabled healthcare network. The proposed model leverages the principles of invalid action masking and multi-agent reinforcement learning to allow faster convergence to QoE optimised RAT selection policy. The analytical evaluation validates that the proposed scheme outperforms the other existing schemes in terms of enhancing personalised user-experience with efficient resource utilisation.

Improving New Patient Appointment Utilisation in Rheumatology Advanced Practice Physiotherapy

Background/Aims Long Rheumatology waiting lists in the UK were further affected by the COVID-19 pandemic;resulting in negative impacts upon the timeliness and efficiency of patient care. The use of Advanced Practitioners within Rheumatology care pathways has been shown to be safe and effective;they can support the Rheumatology workforce and expedite care where patients are appropriately triaged to them. As part of a service provision change in a NHS Trust, an Advanced Practice Physiotherapist (APP) post was funded with the intent to harness these benefits. Initial utilisation of the APP appointments within the Rheumatology provision was found to be low and could be improved. A Quality Improvement (QI) Project was initiated, with the aim to increase APP appointment utilisation to at least 85% over a period of four months, and for at least 75% of these appointments to contain patients who had been appropriately triaged. Methods The 'Model for Improvement' was chosen as the QI approach. The project was led by an APP. Firstly, a stakeholder analysis was performed to identify staff with influence and interest in the project. A root cause analysis found lack of awareness of triaging clinicians and challenges with booking processes as potential reasons for lowerthan- expected appointment utilisation. Change interventions were devised and tested over three Plan, Do, Study, Act (PDSA) cycles. PDSA one developed communication with booking and triage staff to clarify these processes with them. PDSA two educated clinical staff about the APP role, triage criteria and the booking procedures confirmed in PDSA one. PDSA three focused upon sustaining change by reinforcement of the topics established in PDSA two among staff. Outcome measures used were the percentage of available APP appointments utilised per week, and the percentage of these which contained patients who were appropriately triaged. Results APP appointment utilisation increased from a mean of 22% pre-project to 61% during the change intervention period. Sixty-three patients were seen over the 17-week change intervention period;of which 86% had been appropriately triaged. Data showed that 70% of the patients directed to the APP were managed by them (24% discharged and 46% reviewed). Of the remaining patients, 13% were followed up by a Rheumatologist, 12% did not attend and 5% had an alternative outcome such as awaiting advice. Conclusion This QI project led to an improvement in Rheumatology care provision locally. Engagement with support staff, education of clinical staff and implementation of clear standard operating procedures improved the utilisation of the Rheumatology APP resource. Results suggest that the APP role was effective locally in managing appropriately triaged patients, without a negative effect on patient care or other services. Continuing to improve utilisation will support management of the Rheumatology waiting list and improve patient care.

Tackling hepatitis B co-infection in PLHIV after COVID: an audit and updated guidelines

Background: 5-20% of people living with HIV (PLWH) are co-infected with Hepatitis B (HBV) and coinfection is associated with an increased risk of cirrhosis and hepatocellular carcinoma (HCC), incidence of which is 5 to 6 times higher. COVID-19 led to a lapse in surveillance of this population, warranting a reassessment. Method(s): BHIVA and EACS guidelines were combined to create a standard to audit against. All people under the care of the HIV team with co-infection were included, and analysed for the prior six months. Local ethics approval was granted. The results were then presented to clinicians, and local guidelines created to reflect the most recent research on co-infection which were shared with the department. A re-audit was then conducted against the modified guidelines. Result(s): 42 people were living with co-infection of HBV and HIV, with a 50:50 gender split;32 were of Black African ethnicity (76%). The median age was 50.5. Nobody had a HBV resistance profile done at baseline. 3 people did not have suppressed HIV viral load (VL), and 8 people did not have a suppressed HBV VL. In the previous 6 months only 26 (62%) had had a HBV VL, 20 (48%) had had an alfa-fetoprotein (AFP) check, and 21 (36%) had had an ultrasound liver. An US had been requested in 21 (50%) of patients. 100% were on a tenofovir-containing drug regimen. Following presentation and rewriting of guidelines, performance of investigations improved. An US had been requested in 26 (62%) cases although only performed in 16 (38%) and an AFP had been measured in 25 (60%). Vaccination of partners had also improved. Conclusion(s): The provision of care of those with coinfection was significantly impacted by the COVID pandemic, but reinforcement of information, and re-issuing of guidelines improved patient care. Attendance of appointments for blood tests and scans remains a major challenge for improving patient care. Literature aimed at our local population to reinforce the importance of HCC screening is being developed.

HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control.

Infectious diseases, such as Black Death, Spanish Flu, and COVID-19, have accompanied human history and threatened public health, resulting in enormous infections and even deaths among citizens. Because of their rapid development and huge impact, laying out interventions becomes one of the most critical paths for policymakers to respond to the epidemic. However, the existing studies mainly focus on epidemic control with a single intervention, which makes the epidemic control effectiveness severely compromised. In view of this, we propose a Hierarchical Reinforcement Learning decision framework for multi-mode Epidemic Control with multiple interventions called HRL4EC. We devise an epidemiological model, referred to as MID-SEIR, to describe multiple interventions' impact on transmission explicitly, and use it as the environment for HRL4EC. Besides, to address the complexity introduced by multiple interventions, this work transforms the multi-mode intervention decision problem into a multi-level control problem, and employs hierarchical reinforcement learning to find the optimal strategies. Finally, extensive experiments are conducted with real and simulated epidemic data to validate the effectiveness of our proposed method. We further analyze the experiment data in-depth, conclude a series of findings on epidemic intervention strategies, and make a visualization accordingly, which can provide heuristic support for policymakers' pandemic response.

Hierarchical Multi-agent Model for Reinforced Medical Resource Allocation with Imperfect Information

With the advent of the COVID-19 pandemic, the shortage in medical resources became increasingly more evident. Therefore, efficient strategies for medical resource allocation are urgently needed. However, conventional rule-based methods employed by public health experts have limited capability in dealing with the complex and dynamic pandemic-spreading situation. In addition, model-based optimization methods such as dynamic programming (DP) fail to work since we cannot obtain a precise model in real-world situations most of the time. Model-free reinforcement learning (RL) is a powerful tool for decision-making;however, three key challenges exist in solving this problem via RL: (1) complex situations and countless choices for decision-making in the real world;(2) imperfect information due to the latency of pandemic spreading;and (3) limitations on conducting experiments in the real world since we cannot set up pandemic outbreaks arbitrarily. In this article, we propose a hierarchical RL framework with several specially designed components. We design a decomposed action space with a corresponding training algorithm to deal with the countless choices, ensuring efficient and real-time strategies. We design a recurrent neural network-based framework to utilize the imperfect information obtained from the environment. We also design a multi-agent voting method, which modifies the decision-making process considering the randomness during model training and, thus, improves the performance. We build a pandemic-spreading simulator based on real-world data, serving as the experimental platform. We then conduct extensive experiments. The results show that our method outperforms all baselines, which reduces infections and deaths by 14.25% on average without the multi-agent voting method and up to 15.44% with it.

Personal qualities are malleable and fixed: Ambivalent mindset, capability ranking reinforcement, and parent-child relationship among Hong Kong Chinese parents

Past research showed that people may hold contradictory ideas about something or someone. Mindset ambivalence refers to the psychological state in which a person holds contradictory beliefs about the malleability of a valued attribute and spontaneously expresses agreement with both the fixed and growth mindsets. Our past findings showed that a sizable proportion of Hong Kong Chinese adults possess the ambivalent mindset. In the present study, 101 Hong Kong Chinese parents completed a survey during the COVID-19 pandemic. The findings provided further support for the prevalence of the ambivalent mindset. In addition, we found that parents with the ambivalent mindset tended to support several parental practices that would reinforce the relative ability rankings of their children. These practices included person praise, mobilization of effort to compensate for low ability, and lowering of expectation to avoid future failures. Finally, the use of these parental practices was accompanied by deterioration of parent-child relationship when children displayed undesirable self-regulatory behaviors. We discuss these findings' implications for growth mindset interventions in Chinese societies.

Same-day delivery with fair customer service

The demand for same-day delivery (SDD) has increased rapidly in the last few years and has particu-larly boomed during the COVID-19 pandemic. The fast growth is not without its challenge. In 2016, due to low concentrations of memberships and far distance from the depot, certain minority neighborhoods were excluded from receiving Amazon's SDD service, raising concerns about fairness. In this paper, we study the problem of offering fair SDD service to customers. The service area is partitioned into differ-ent regions. Over the course of a day, customers request for SDD service, and the timing of requests and delivery locations are not known in advance. The dispatcher dynamically assigns vehicles to make de-liveries to accepted customers before their delivery deadline. In addition to overall service rate ( utility ), we maximize the minimal regional service rate across all regions ( fairness ). We model the problem as a multi-objective Markov decision process and develop a deep Q-learning solution approach. We introduce a novel transformation of learning from rates to actual services, which creates a stable and efficient learn-ing process. Computational results demonstrate the effectiveness of our approach in alleviating unfairness both spatially and temporally in different customer geographies. We show this effectiveness is valid with different depot locations, providing businesses with an opportunity to achieve better fairness from any location. We also show that the proposed approach performs efficiently when serving heterogeneously wealthy districts in the city.(c) 2022 Elsevier B.V. All rights reserved.

Shifting the paradigm: ankylosis arthroplasty for the proximal interphalangeal joint with a novel collateral ligament reconstruction.

Silicone arthroplasty for proximal interphalangeal joint ankylosis is rarely performed, partly due to the potential for lateral joint instability. We present our experience performing proximal interphalangeal joint arthroplasty for joint ankylosis, using a novel reinforcement/reconstruction technique for the proper collateral ligament. Cases were prospectively followed-up (median 13.5 months, range 9-24) and collected data included range of motion, intraoperative collateral ligament status and postoperative clinical joint stability; a seven-item Likert scale (1-5) patient-reported outcomes questionnaire was also completed. Twenty-one ankylosed proximal interphalangeal joints were treated with silicone arthroplasty, and 42 collateral ligament reinforcements undertaken in 12 patients. There was improvement in range of motion from 0° in all joints to a mean of 73° (SD 12.3); lateral joint stability was achieved in 40 out of 42 of collateral ligaments. High median patient satisfaction scores (5/5) suggest that silicone arthroplasty with collateral ligament reinforcement/reconstruction should be considered as a treatment option in selected patients with proximal interphalangeal joint ankylosis.Level of evidence: IV.

Multi-objective route recommendation method based on Q-learning algorithm

With the continuous expansion of city scale and the advancement of transportation technology, route recommendations have become an increasingly common concern in academic and engineering circles. Research on route recommendation technology can significantly satisfy the travel demands of residents and city operations, thereby promoting the construction of smart cities and the development of intelligent transportation. However, most current route recommendation methods focus on generating a route satisfying a single objective attribute and fail to comprehensively consider other types of objective attributes or user preferences to generate personalized recommendation routes. This study proposes a multi-objective route recommendation method based on the reinforcement learning algorithm Q-learning, that comprehensively considers multiple objective attributes, such as travel time, safety risk, and COVID-19 risk, and generates recommended routes that satisfy the requirements of different scenarios by combining user preferences. Simultaneously, to address the problem that the Q-learning algorithm has low iteration efficiency and easily falls into the local optimum, this study introduces the dynamic exploration factor σ and initializes the value function in the road network construction process. The experimental results show that, when compared to other traditional route recommendation algorithms, the recommended path generated by the proposed algorithm has a lower path cost, and based on its unique Q-value table search mechanism, the proposed algorithm can generate the recommended route almost in real time. [ FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Applying the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders in Children and Adolescents to Misophonia: A Case Example

Misophonia is a condition in which individuals suffer a wide range of intense emotions in response to sound triggers. Emotions such as anxiety, irritability, and disgust may lead individuals to engage in avoidance behaviors to escape or suppress sound triggers. Transdiagnostic treatment may serve as a practical intervention for misophonia as it addresses a broad scope of emotions and physiological sensations. This paper presents the first reported case example of misophonia treated with a transdiagnostic treatment protocol, the Unified Protocol for Emotional Disorders in Adolescents (UP-A). In this case, the UP-A was efficacious in treating a client with autism spectrum disorder, comorbid misophonia and anxiety symptoms. The client evidenced reliable change in misophonia and related problems. Future research should investigate the efficacy of the UP-A in a larger sample of youth with misophonia, as well as assess mechanisms of change in transdiagnostic treatment of this disorder in youth.Copyright © 2022 Society of Clinical Child & Adolescent Psychology.

Enhancing Immersive User Experience Quality of StudoBot Telepresence Robots with Reinforcement Learning

The pandemic situation (Covid 19) brought new challenges in the education sector while simultaneously presenting unique opportunities for technology enabled services. The use of Mobile Robotic Telepresence systems in educational sector is promising as it provides means to significantly enhance the involvement and benefits to stakeholders involved in such interactions. An immersive user interaction with such a system depends on many aspects which are both static and dynamic. We approach the dynamic aspect of such interactions recognizing that the video and audio aspects of such a system will require fine tuning and adaptation. Closely related is the aspect of maintaining the necessary quality of network connection. Considering each of these aspects a reinforcement learning mechanism is incorporated to improve the overall user experience with such a system. A working system is built and experiments performed to demonstrate the effectiveness of the approach. Reward generation matrix, a crucial piece of data gathering from the environment, takes about 45 minutes, offline training time is less than a second, while the robot is able to cover the workspace in slightly less than a minute. The system is not limited to educational sector alone and provides a foundational framework to extend the concepts and principles to adjacent markets. © 2023 IEEE.

Computer-Aided System for COVID-19 Using Semi-supervised-based Ensemble Learning and Reinforcement Learning

Coronavirus Disease 2019(COVID-19) has shocked the world with its rapid spread and enormous threat to life and has continued up to the present. In this paper, a computer-aided system is proposed to detect infections and predict the disease progression of COVID-19. A high-quality CT scan database labeled with time-stamps and clinicopathologic variables is constructed to provide data support. To our knowledge, it is the only database with time relevance in the community. An object detection model is then trained to annotate infected regions. Using those regions, we detect the infections using a model with semi-supervised-based ensemble learning and predict the disease progression depending on reinforcement learning. We achieve an mAP of 0.92 for object detection. The accuracy for detecting infections is 98.46%, with a sensitivity of 97.68%, a specificity of 99.24%, and an AUC of 0.987. Significantly, the accuracy of predicting disease progression is 90.32% according to the timeline. It is a state-of-the-art result and can be used for clinical usage. © 2022 IEEE.