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1.
Recent Advances in Computer Science and Communications ; 15(5):748-764, 2022.
Article in English | Scopus | ID: covidwho-1834116

ABSTRACT

Introduction: Today, technology and internet both are proliferating due to which information access is becoming easier, and is creating new challenges and opportunities in all fields, especially when working in the field of education. For example, the e-learning education system can be personalized in order to acquire knowledge level and learner’s requirements in a learning process. The learning experience, as per the individual learner’s goals, should be adopted. Background: In the current educational environment, e-learning plays a significant role. For many researchers, it has become one of the most important subjects, as through the use of e-learning, the whole education system would revolutionize. There are many areas of e-learning in which research work is being carried out, such as Mass Communication, Information and Technology (IT), Education and Distance Education. Objectives: To meet the various needs of the learners such as talents, interests and goals an e-learning system needs to be designed as a personalized learning system by considering various educational experiences. Many methods such as ontologies, clustering, classification and association rules have been used along with filtering techniques to enhance the personalization and performance of the learner. Methods: This paper presents a detailed review of the literature of previous work that has been conducted in e-learning area, especially in the recommendation system. Current research works on e-learning has been discussed in this work in order to discover the research developments in this discipline. Conclusions: One of the vital functions of the current e-learning system is creating a personalized resource recommendation system. In this paper, we reviewed some crucial papers on both e-learning and recommendation systems. Future research work of this paper would be designing efficient and precise e-learning and recommendation system to deal with the problem of substantial personalized information resources as e-learning plays a vital role in preventing virus spread during COVID-19 pandemic. © 2022 Bentham Science Publishers.

2.
Information ; 13(3):128, 2022.
Article in English | ProQuest Central | ID: covidwho-1765737

ABSTRACT

In the age of the digital revolution and the widespread usage of social networks, the modalities of information consumption and production were disrupted by the shift to instantaneous transmission. Sometimes the scoop and exclusivity are just for a few minutes. Information spreads like wildfire throughout the world, with little regard for context or critical thought, resulting in the proliferation of fake news. As a result, it is preferable to have a system that allows consumers to obtain balanced news information. Some researchers attempted to detect false and authentic news using tagged data and had some success. Online social groups propagate digital false news or fake news material in the form of shares, reshares, and repostings. This work aims to detect fake news forms dispatched on social networks to enhance the quality of trust and transparency in the social network recommendation system. It provides an overview of traditional techniques used to detect fake news and modern approaches used for multiclassification using unlabeled data. Many researchers are focusing on detecting fake news, but fewer works highlight this detection’s role in improving the quality of trust in social network recommendation systems. In this research paper, we take an improved approach to assisting users in deciding which information to read by alerting them about the degree of inaccuracy of the news items they are seeing and recommending the many types of fake news that the material represents.

3.
12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1752356

ABSTRACT

With the onset of lockdown in the COVID-19 scenario, people were forced to confine themselves within the four walls of their rooms which in the meantime invited mood disorders like depression, anxiety etc. Music has proven to be a potential empathetic companion in this tough time for all. The proposed emotion-based music recommendation system uses aser emotion as an input to recommend songs that are-ascertained using faciai expression or using direct inputs from the user. The model uses a Random Forest classifier and XGBoost algorithm to identify the song's emotion considering various features like instruineiitainess, energy, acoustics, liveness, etc, and lyrical similarity among songs with the help of Term-Frequency times Inverse Document-Frequency (TF-IBF). The results of comprehensive experiments on reai data confirm the accuracy of the proposed emotion classification system that can be integrated into any recommendation engine. © 2021 IEEE.

4.
Methods ; 198: 3-10, 2022 02.
Article in English | MEDLINE | ID: covidwho-1721113

ABSTRACT

The coronavirus disease 2019 (COVID-19) has outbreak since early December 2019, and COVID-19 has caused over 100 million cases and 2 million deaths around the world. After one year of the COVID-19 outbreak, there is no certain and approve medicine against it. Drug repositioning has become one line of scientific research that is being pursued to develop an effective drug. However, due to the lack of COVID-19 data, there is still no specific drug repositioning targeting the COVID-19. In this paper, we propose a framework for COVID-19 drug repositioning. This framework has several advantages that can be exploited: one is that a local graph aggregating representation is used across a heterogeneous network to address the data sparsity problem; another is the multi-hop neighbors of the heterogeneous graph are aggregated to recall as many COVID-19 potential drugs as possible. Our experimental results show that our COVDR framework performs significantly better than baseline methods, and the docking simulation verifies that our three potential drugs have the ability to against COVID-19 disease.


Subject(s)
COVID-19 , Pharmaceutical Preparations , Antiviral Agents , Drug Repositioning , Humans , Molecular Docking Simulation , SARS-CoV-2
5.
3rd International Conference on Advancements in Computing, ICAC 2021 ; : 365-370, 2021.
Article in English | Scopus | ID: covidwho-1714009

ABSTRACT

Pharmacy services are a paramount important pillar of health. People must keep social distance due to the COVID-19 pandemic, hence the availability of online services to give medicine is vital. Due to the quarantine measures implemented in and by various countries to prevent the virus's breaking out and online pharmacies have become an exceptionally popular way to obtain accurate medication. Currently, in Sri Lanka, there are a few mobile applications separately owned by each of the pharmacies to provide online pharmaceutical services for their customers. But all the medicines the customer needs might not be available in a single pharmacy. PharmaGo provides with its cooperation to the customers to get medicines of his necessity at a single pharmacy, as against avoiding him roaming from pharmacy to pharmacy. Similarly, pharmacy owners can read the prescription by using image processing mechanisms and doubtlessly identify the required medicines. In addition, the system analyzes previous sales records and provides predictions regarding the future demand for drugs to the pharmacy owners. PharmaGo includes a highly trained AI-powered medical chatbot to guide the customers throughout the process. PharmaGo provides a reliable platform for both pharmacy users and pharmacists to fulfill the unique needs of pharmacy services. © 2021 IEEE.

6.
International Journal of Advanced Computer Science and Applications ; 13(1):369-375, 2022.
Article in English | Scopus | ID: covidwho-1687562

ABSTRACT

Semester planner plays an essential role in students’ society that might help students have self-discipline and determination to complete their studies. However, during the COVID-19 pandemic, they faced difficulty organizing time management and doing a manual schedule. It resulted in substantial disruptions in learning, internal assessment disturbances, and the cancellation of public evaluations. Hence, this research aims to optimize the recommended semester planner, Timetable Generator using a greedy algorithm to increase student productivity. We identified three-set control functions for each entered information: 1) validation for the inserted information to ensure valid data and no redundancy, 2) focus scale, and 3) the number of hours to finish the activity. We calculate the priority task sequence to achieve the best optimal solution. The greedy algorithm can solve the optimization problem with the best optimal solution for each situation. Then, we executed it to make a recommended semester planner. From the test conducted, the functionality shows all the features successfully passed. We validate using test accuracy for the system’s reliability by evaluating it compared to the Brute Force algorithm, and the trends increase from 60% to 100%. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

7.
2021 International Conference on Electronics, Communications and Information Technology, ICECIT 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1685083

ABSTRACT

Blood or plasma transmission is one of the most effective treatments for critical diseases like Covid 19. Nowadays, voluntary blood donation has become the major source of blood supply. Several mobile applications are currently available to establish the initial communication between blood donors and receivers. Recommending the right potential donor during a blood search can save the life of a critical patient with an immediate response from the donor. However, the requirement of an advanced recommendation system has not been addressed by any of the existing mobile applications. In our research work, we have designed a real-time, intelligent, and rational recommendation system using sentiment analysis of the user's feedback, response rate of the donor, and the current geo-location information and finally develop a cross-platform application for blood collection and distribution system. To process and generate features from the user feedback, we have designed a Bi-directional LSTM-based deep learning model. The quality of the recommendation of the potential donors has significantly improved. Moreover, we have conducted rigorous requirement analysis from real users and evaluated the performance of our application through both indoor and outdoor testing. © 2021 IEEE.

8.
International Journal of Circuits, Systems and Signal Processing ; 16:122-131, 2022.
Article in English | Scopus | ID: covidwho-1663038

ABSTRACT

At present, personalized recommendation system has become an indispensable technology in the fields of e-commerce, social network and news recommendation. However, the development of personalized recommendation system in the field of education and teaching is relatively slow with lack of corresponding application.In the era of Internet Plus, many colleges have adopted online learning platforms amidst the coronavirus (COVID-19) epidemic. Overwhelmed with online learning tasks, many college students are overload by learning resources and unable to keep orientation in learning. It is difficult for them to access interested learning resources accurately and efficiently. Therefore, the personalized recommendation of learning resources has become a research hotspot. This paper focuses on how to develop an effective personalized recommendation system for teaching resources and improve the accuracy of recommendation. Based on the data on learning behaviors of the online learning platform of our university, the authors explored the classic cold start problem of the popular collaborative filtering algorithm, and improved the algorithm based on the data features of the platform. Specifically, the data on learning behaviors were extracted and screened by knowledge graph. The screened data were combined with the collaborative filtering algorithm to recommend learning resources. Experimental results show that the improved algorithm effectively solved the loss of orientation in learning, and the similarity and accuracy of recommended learning resources surpassed 90%. Our algorithm can fully satisfy the personalized needs of students, and provide a reference solution to the personalized education service of intelligent online learning platforms. © 2022, North Atlantic University Union NAUN. All rights reserved.

9.
Multimed Tools Appl ; 80(24): 33329-33355, 2021.
Article in English | MEDLINE | ID: covidwho-1363759

ABSTRACT

The education system worldwide has been affected by the Corona Virus Diseases 2019 (COVID-19) pandemic, resulting in the interruption of all educational institutions. Moreover, as a precautionary measure, the lockdown has been imposed that has severely affected the learning processes, especially assessment activities, including exams and viva. In such challenging situations, E-learning platforms could play a vital role in conducting seamless academic activities. In spite of all the advantages of remote learning systems, many hurdles and obstacles, like a selection of suitable learning resources/material encounter by individual users based on their interests or requirements. Especially those who are not well familiar with the internet technology in developing countries and are in need of a platform that could help them in resolving the issues related to the online virtual environment. Therefore, in this work, we have proposed a mechanism that intelligently and correctly predicts the appropriate preferences for the selection of resources relevant to a specific user by considering the capabilities of diverse perspectives users to provide quality online education and to make work from home policy more effective and progressive during the pandemic. The proposed system helps teachers in providing quality online education, familiarizing them with advanced technology in the online environment. It also semantically predicts the preferences for virtual assistance of those users who are in need of learning the new tools and technologies in short time as per their institutional requirements in order to meet the quality standards of online education. The experimental and statistical results have demonstrated that the proposed virtual personalized preferences system has improved overall academic activities as compared to the current method. The proposed system enhanced user's learning abilities and facilitated them in selecting short courses while using different online education tools adopted/suggested by the institutions to conduct online classes/seminars/webinars etc., as compared to the conventional classes/activities.

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