ABSTRACT
When one feels unwell, it is crucial to arrange a time as soon as possible to meet a doctor for early detection of potential health-related problems. However, a relatively large number of Vietnamese people usually avoid going to the hospital as they are afraid of long waits at such crowded places, while the current COVID-19 pandemic means being at those places poses a higher risk of contracting the disease. For simpler health problems, people would prefer a solution that, given their symptoms, provides a reliable diagnosis in a shorter time. This study presents an approach in building a deep-learning-based disease predictor of health conditions conducted from given symptoms in Vietnamese. The proposed method combines a tokenizer and bi-directional recurrent neural networks and achieved an accuracy of 98.96% (compared to a certified doctor's diagnosis) in selected test cases, demonstrating its promising capabilities in the task. The application is expected to easily be integrated into a mobile application and open the way for other deep-learning-based solutions which analyze people's symptoms to help them have their health conditions diagnosed at home. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Online examinations gradually become popular due to Covid 19 pandemic. Environmentally friendly, saving money, and convenient,.. are some of the advantages when taking exams online. Besides its major benefits, online examinations also have some serious adversities, especially integrity and cheating. There are some existing proctoring systems that support anti-cheating, but most of them have a low probability of predicting fraud based on students' gestures and posture. As a result, our article will introduce an online examination called ExamEdu that supports integrity, in which the accuracy of detecting cheating behaviors is 96.09% using transfer learning and fine-tuning for ResNet50 Convolutional Neural Network. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The COVID-19 is putting tremendous pressure on the medical facilities supply, as the demand for facilities has significantly outweighed the production capability. Several rogue traders have taken advantage of this issue to distribute counterfeit products. Moreover, some sellers advertise genuine products with unreasonably high prices. Our team believes that fake or overpriced facilities will significantly complicate the battle against COVID, thereby posing millions of lives to risk. That is why our team is developing V-Block. V-Block is a supply chain management system that harnesses the power of Blockchain. Its primary goals are to assist the government in tracking the product’s distribution process and help customers avoid questionable deals. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
When one feels unwell, it is crucial to arrange a time as soon as possible to meet a doctor for early detection of potential health-related problems. However, a relatively large number of Vietnamese people usually avoid going to the hospital as they are afraid of long waits at such crowded places, while the current COVID-19 pandemic means being at those places poses a higher risk of contracting the disease. For simpler health problems, people would prefer a solution that, given their symptoms, provides a reliable diagnosis in a shorter time. This study presents an approach in building a deep-learning-based disease predictor of health conditions conducted from given symptoms in Vietnamese. The proposed method combines a tokenizer and bi-directional recurrent neural networks and achieved an accuracy of 98.96% (compared to a certified doctor’s diagnosis) in selected test cases, demonstrating its promising capabilities in the task. The application is expected to easily be integrated into a mobile application and open the way for other deep-learning-based solutions which analyze people’s symptoms to help them have their health conditions diagnosed at home. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
To prevent the spread of the COVID-19 pandemic, 2019 has seen unprecedented demand for medical equipment and supplies. However, the problem of waste treatment has not yet been given due attention, i.e., the traditional waste treatment process is done independently, and it is not easy to share the necessary information. Especially during the COVID-19 pandemic, the interaction between parties is minimized to limit infections. To evaluate the current system at medical centers, we also refer to the traditional waste treatment processes of four hospitals in Can Tho and Ho Chi Minh cities (Vietnam). Almost all hospitals are handled independently, lacking any interaction between the stakeholders. In this article, we propose a decentralized blockchain-based system for automating waste treatment processes for medical equipment and supplies after usage among the relevant parties, named Medical-Waste Chain. It consists of four components: medical equipment and supplies, waste centers, recycling plants, and sorting factories. Medical-Waste Chain integrates blockchain-based Hyperledger Fabric technology with decentralized storage of medical equipment and supply information, and securely shares related data with stakeholders. We present the system design, along with the interactions among the stakeholders, to ensure the minimization of medical waste generation. We evaluate the performance of the proposed solution using system-wide timing and latency analysis based on the Hyperledger Caliper engine. Our system is developed based on the hybrid-blockchain system, so it is fully scalable for both on-chain and off-chain-based extensions. Moreover, the participants do not need to pay any fees to use and upgrade the system. To encourage future use of Medical-Waste Chain, we also share a proof-of-concept on our Github repository.
ABSTRACT
With recent emerging events such as the pandemic of coronavirus disease (COVID) 2019, human mobility has caused significant concern in the spread of this dangerous pandemic, so mobility prediction is considered as one of the crucial factors to prevent the pandemic. Therefore, there have been many proposed and highly functional studies. Applications of social networks have stored vast data of user movements and brought a vast of interesting research on human mobility. Friendship on social networks has also revealed some effects on the movement. In this study, we have attempted to explore the influence of friendships in location-based social networks on human mobility. We conduct the movement based on the K latest check-ins of friends of the user to predict mobility. We have deployed Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm (using Haversine distance) to cluster original check-in points and filtered the K latest friends’ check-ins of the user to predict the user’s next movement with the Random Forest algorithm. The prediction conducted from movement history of friends has obtained better performances compared to the prediction without considering the Friendship. The highest accuracy is 0.3176 (with a radius of 400 m and four latest check-ins of friends). Besides, we compare and evaluate the results of the proposed method with the clustered dataset with the original dataset. As observed from the experiments, clusters generated by DBSCAN with wider radii can reveal that their friends’ movements can influence users’ mobility on a location-based social network (LBSN). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The pain, namely “Covid-19 epidemic", has caused many sacrifices, loss, and loneliness. Only those who have experienced traumatic losses can fully understand the pain that is hard to erase by the epidemic. This study focuses on designing a remote medical assistance vehicle used in quarantine areas in Vietnam to support epidemic prevention with simple, cheap, easy-to-use, and multi-function criteria. The proposed system includes a 3-layer vehicle for transporting supplies controlled remotely via Radio Frequency (RF) signals to help limit cross-infection for medical staff and volunteers. The main component is the RF transceiver circuit, which transmits and receives data wirelessly over 2.4 GHZ RF using IC Nrf24l01, Nordic standard SPI interface for remote control. DC motor driver circuit BTS7960 43A controls the motor to prevent overvoltage and current drop. Moreover, the vehicle integrates an electric sprayer to support disinfecting spray a Xiaomi camera to stream video and communicate directly with patients and healthy in isolation. Ultrasonic sensors and infrared sensors aim to scan obstacles through reflected waves. The reflected signals received from the barrier objects are used as input to the microcontroller. The microcontroller is then used to determine the distance of objects around the vehicle. If an obstacle is detected, the disinfectant sprayer can stop for several seconds to ensure the safety of medical staff if there is a pass. The system has a built-in light sensor that works at night. The system is deployed at a low cost and is evaluated through some experiments. It is expected to be easy to use and is an innovative solution for hospitals. Once the outbreak is over, the product can still be used in infectious disease areas. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.