OPEES: Online Proctored Entrance Examination System with Degree Program Recommender for Colleges and Universities

The college entrance examination is vital for program admission. Typically, entrance examinations are conducted onsite using paper and pens. When the COVID-19 pandemic hit, the entrance examination was lifted and physical gatherings were prohibited. Since many schools cannot offer an online admissions exam, they rely on grades and interviews to admit and qualify students for degree programs. However, academic standards differ between schools, and grades may not be enough to assess students' capacity. Thus, this study aims to develop an Online Proctored Entrance Examination System (OPEES) with Degree Program Recommender for colleges and universities to help institutions administer onsite or online entrance tests and generate course suggestions using a rulebased algorithm. The study employed the scrum methodology in software development. OPEES allows applicants to submit applications online, and institutions can manage user accounts, tailor exams and degree programs' criteria, manage exam dates, and assign proctors. Online proctoring using Jitsi, an opensource multiplatform voice, video, and instant messaging tool with end-to-end encryption, ensures exam integrity. The system's features were evaluated by 102 respondents, comprised of end-users (students and school personnel) and IT professionals, using the FURPS (Functionality, Usability, Reliability, Performance, and Supportability) software quality model. In the software evaluation, the overall system proved to be functional as perceived by the respondents, as manifested by the mean rating of 4.61. In conclusion, the system's architecture was deemed feasible and offers a better way to streamline admission examinations and determine a student's applicable degree program by enabling institutions to customize their exams and degree program requirements. It will be beneficial to look into recommendation system algorithms and historical enrollment data to improve the system's use case. © 2022 IEEE.

Therapist Feedback and Implications on Adoption of an Artificial Intelligence-Based Co-Facilitator for Online Cancer Support Groups: Mixed Methods Single-Arm Usability Study.

BACKGROUND: The recent onset of the COVID-19 pandemic and the social distancing requirement have created an increased demand for virtual support programs. Advances in artificial intelligence (AI) may offer novel solutions to management challenges such as the lack of emotional connections within virtual group interventions. Using typed text from online support groups, AI can help identify the potential risk of mental health concerns, alert group facilitator(s), and automatically recommend tailored resources while monitoring patient outcomes. OBJECTIVE: The aim of this mixed methods, single-arm study was to evaluate the feasibility, acceptability, validity, and reliability of an AI-based co-facilitator (AICF) among CancerChatCanada therapists and participants to monitor online support group participants' distress through a real-time analysis of texts posted during the support group sessions. Specifically, AICF (1) generated participant profiles with discussion topic summaries and emotion trajectories for each session, (2) identified participant(s) at risk for increased emotional distress and alerted the therapist for follow-up, and (3) automatically suggested tailored recommendations based on participant needs. Online support group participants consisted of patients with various types of cancer, and the therapists were clinically trained social workers. METHODS: Our study reports on the mixed methods evaluation of AICF, including therapists' opinions as well as quantitative measures. AICF's ability to detect distress was evaluated by the patient's real-time emoji check-in, the Linguistic Inquiry and Word Count software, and the Impact of Event Scale-Revised. RESULTS: Although quantitative results showed only some validity of AICF's ability in detecting distress, the qualitative results showed that AICF was able to detect real-time issues that are amenable to treatment, thus allowing therapists to be more proactive in supporting every group member on an individual basis. However, therapists are concerned about the ethical liability of AICF's distress detection function. CONCLUSIONS: Future works will look into wearable sensors and facial cues by using videoconferencing to overcome the barriers associated with text-based online support groups. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/21453.

Competitive gamification in crowdsourcing-based contextual-aware recommender systems.

During the COVID-19 outbreak, crowdsourcing-based context-aware recommender systems (CARS) which capture the real-time context in a contactless manner played an important role in the "new normal". This study investigates whether this approach effectively supports users' decisions during epidemics and how different game designs affect users performing crowdsourcing tasks. This study developed a crowdsourcing-based CARS focusing on restaurant recommendations. We used four conditions (control, self-competitive, social-competitive, and mixed gamification) and conducted a two-week field study involving 68 users. The system provided recommendations based on real-time contexts including restaurants' epidemic status, allowing users to identify suitable restaurants to visit during COVID-19. The result demonstrates the feasibility of crowdsourcing to collect real-time information for recommendations during COVID-19 and reveals that a mixed competitive game design encourages both high- and low-performance users to engage more and that a game design with self-competitive elements motivates users to take on a wider variety of tasks. These findings inform the design of restaurant recommender systems in an epidemic context and serve as a comparison of incentive mechanisms for gamification of self-competition and competition with others.

Recommender System for Government Service in Smart Cities

significant recommender systems (RS) development has occurred along with the Internet of Things (IoT) development in recent years. Recommender systems have been widely spread across diverse fields, including environmental preservation, e-commerce, healthcare, social and governance systems. There has been a growing focus on e-government as part of smart city initiatives in today's world of connected devices and infrastructure, especially after the COVID-19 pandemic. With the use of information and communication technologies (ICTs), the government can enhance the delivery of public services, increase transparency, accountability, and credibility, as well as engage citizens in the decision-making process. To facilitate 'smart' governance, one of a smart city initiative's objectives is integrating e-government into the city's governance framework. The lack of personalized services for particular stakeholders is one of the most significant limitations of e-governance. There are a number of open challenges coupled with interesting opportunities, making this a very promising and exciting area for research to shape recommendation systems for urban environments. Considering the overwhelming amount of information, services, and tasks available through smart government applications, it is a greater chance of providing personalized recommendations for different stakeholders and tasks within multi-faceted and multi-dimension. There is still a lot of research to be done on recommendation systems in the context of smart cities or smart government. This paper survey the existing studies on recommendation systems for smart governance. The study aims to address smart city challenges to considered when designing and implementing recommendations for e-governance and the target stakeholder's interests. © 2022 IEEE.

A drug recommender system Based on Collaborative Filtering for Covid-19 patients

The epidemic caused by a new mutation of the coronavirus family called Covid-19 has created a global crisis involving all the world's countries. This disease has become a severe danger to everyone due to its unknown nature, high spread, and inability to detect the infected. In this regard, one of the important issues facing patients with Covid-19 is the prescription of Drugs according to the severity of the disease and considering the records of underlying diseases in people. In recent years, recommender systems have been developed significantly along with the advancement in information technology and artificial intelligence, which is one of its applications in various fields of medical sciences. Among them, we can refer to recommending systems for the prevention, control, and treatment of diseases. In this research, using the collaborative filtering approach as one of the types of recommender systems as well as the K-means clustering algorithm, a Drug recommendation system for patients with Covid-19 in the treatment stage of the disease is presented. The results of this research show that this recommender system has an acceptable performance based on the evaluation criteria of precision, recall, and F1-score compared to the opinions of experts in this field. © 2023 IEEE.

A Session-Based Recommender System for Learning Resources

Learning resource recommendation systems can help learners find suitable resources (e.g., books, journals,.) for learning and research. In particular, in the context of online learning due to the impact of the COVID-19 pandemic, the learning resource recommendation is very necessary. In this study, we propose using session-based recommendation systems to suggest the learning resources to the learners. Experiments are performed on a learning resource dataset collected at a local university and a public dataset. After preprocessing the data to convert it to session form, the Neural Attentive Session-based Recommendation (NARM) and Recurrent Neural Networks (GRU4Rec) models were used for training, testing, and comparison. The results show that recommending learning resources according to the NARM model is more effective than that of the GRU4Rec model, and thus, using the session-based recommendation system would be a promising approach for learning resource recommendation.

Collaborative E-Learning Process Discovery in Multi-tenant Cloud

With the appearance of the COVID-19 pandemic, the practice of e-learning in the cloud makes it possible to: avoid the problem of overloading the institutions infrastructure resources, manage a large number of learners and improve collaboration and synchronous learning. In this paper, we propose a new e-leaning process management approach in cloud named CLP-in-Cloud (for Collaborative Learning Process in Cloud). CLP-in-Cloud is composed of two steps: i) design general, configurable and multi-tenant e-Learning Process as a Service (LPaaS) that meets different needs of institutions. ii) to fulfill the user needs, developpe a functional and non-functional awareness LPaaS discovery module. For functional needs, we adopt the algorithm A* and for non-functional needs we adopt a linear programming algorithm. Our developed system allows learners to discover and search their preferred configurable learning process in a multi-tenancy Cloud architecture. In order to help to discover interesting process, we come up with a recommendation module. Experimentations proved that our system is effective in reducing the execution time and in finding appropriate results for the user request.

A naturopathy knowledge graph and recommendation system to boost the immune system: KISS: Knowledge-based Immune System Suggestion

Because of COVID-19 worldwide pandemic, there is a need for any complementary solutions to boost the immune system. Nowadays, healthy lifestyle, fitness, and diet habits have become central applications in our daily life. We designed a Naturopathy Knowledge Graph for a recommender system to boost the immune system (KISS: Knowledge-based Immune System Suggestion). The Naturopathy Knowledge Graph is built from more than 50 ontology-based food projects, also released as the LOV4IoT-Food ontology catalog. The naturopathy data set is referenced on the Linked Open Data (LOD) cloud. The LOV4IoT-Food ontology catalog encourages researchers to follow FAIR principles and share their reproducible experiments by publishing online their ontologies, data sets, rules, etc. The set of the ontology code shared online can be semiautomatically processed, if not available, the scientific publications describing the food ontologies are semiautomatically processed with Natural Language Processing (NLP) techniques. We build the naturopathy recommender system that will suggest food to boost the immune system. The recommender system can be extended to address other advice such as aromatherapy and take into consideration medical devices to monitor patients' vital signals. © 2022 Elsevier Inc. All rights reserved.

Memory Augmented Distributed Monte Carlo Tree Search Algorithm-Based Content Popularity Aware Content Recommendation Using Content Centric Networks

The rapid advancements of social media networks have created the problem of overloaded information. As a result, the service providers push multiple redundant contents and advertisements to the users without adequate analysis of the user interests. The content recommendation without user interests reduces the probability of users reading them and the wastage rate of network load increases. This problem can be alleviated by providing accurate content recommendations with consideration of users' precise interests and content similarity. Content centric networking has been developed as the trending framework to satisfy these requirements and improve access to relevant information and reception by the desired user. The uses of message entity by giving a proper name, the users' real-time interests are identified and then the accurate and popular contents with high contextual similarity are recommended. An efficient content recommendation scheme is presented in this paper using Memory Augmented Distributed Monte Carlo Tree Search (MAD-MCTS) algorithm for ensuring minimum energy consumption in the CCN. The big data context of the users' social media data is considered in this study so that the complexity can be visualized and controlled to minimize the network complexities. Experiments are conducted on a benchmark as well as an offline collected Twitter dataset on Covid-19 and the results implied that the accuracy and convergence of the proposed MAD-MCTS outperform the other content recommendation algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

MCMARS: Hybrid Multi-criteria Decision-Making Algorithm for Recommender Systems of Mobile Applications

A mobile application (app) recommender system needs to support both developers and users. Existing recommender systems in the literature are based on single-criterion analysis, which is insufficient for producing better recommendations. Moreover, recommendations do not reflect the user's perspectives. To address these issues, in this paper, we present a Multi-Criteria Mobile App Recommender System (MCMARS) that assists developers in improving their apps and recommends the top-performing apps to users. We define the performance score of an app based on four criteria attributes: risk assessment score, functionality score, user rating, and the app's memory size. We define the risk assessment score for each app using multi-perspective analysis and the functionality score by assigning preference weights to the services of apps in the same category. We evaluate optimal weights of the criteria by integrating the entropy method and the extended Best-Worst method (BWM) using Hesitant-Triangular-Fuzzy information with group-decisions. Finally, the TOPSIS uses these weights to assess the app's performance. To validate our MCMARS, we prepared a dataset of 124 government-approved COVID-19 Android apps from 80 countries and made it available on GitHub for the research community. Finally, we perform a fine-grained analysis of the app's performance based on the criteria attributes that help the developers to improve their apps. The experimental results show that two independent attributes, "risk assessment score” and "functionality score”, significantly measure the app's performance. According to our findings, only 12.5% of the apps in the experimental dataset provide high-performance, high-functionality, and low-risk. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Big Data Analytics Based Recommender System for Tele-Communication Industry

In recent times, the amount of data sent and received through wireless networks has grown quickly. Smartphones and the growth of Internet access around the world are two big reasons for this volume. Due to the current state of global health, which is mostly caused by Covid-19, telecommunications companies have a great chance to find new ways to make money by using Big Data Analytics (BDA) solutions. This is because data traffic has gone up. After all, more customers are using telecommunications services. As most of the world's data is now made by smartphones and sent through the telecom network, telecom operators are facing an information explosion that makes it harder to make decisions based on the data they need to predict how people will act. This problem was solved by making a system that sorts through information and makes suggestions based on how people have behaved in the past. Content-based filtering, collaborative filtering, and a hybrid approach are the three main ways that recommender systems filter data to solve the problem of too much data and give users relevant recommendations based on their interests and the data that is being created in real-time. Distance algorithms like Cosine, Euclidean, Manhattan, and Minkowski are at the heart of the suggested recommender system, which aims to research and design an effective recommendation strategy. The suggested model suggests different telecom packages to meet the needs of users to increase revenue per subscriber and get consumers, telecom providers, and corporations to sign long-term contracts. © 2022 IEEE.

E-Commerce Platform with Recommender System and Android Mobile Application

E-commerce shows steady growth in the marketplace since it makes the lives of people easier. Today's generation is more inclined toward convenience in purchasing goods. The COVID-19 shut down many food establishments across the Philippines, resulting in an online bakeries boom. Pastries are one of the most sought goods online, especially with the pandemic surge where physical stores are seldomly open. E-commerce with a recommender system is the trend that helps customers choose products, which helps in decision-making on what to purchase. On the other hand, a mobile application counterpart could increase brand recognition and customer engagement as it is now the most effective, direct, and personalized way to deliver product information. In this study, the descriptive research method was used, with a questionnaire using the functionality, usability, reliability, performance and supportability (FURPS) serving as the instrument for testing the acceptability. The overall quality of the system was given an acceptable rating with a weighted mean of 4.07, indicating that the system's functions were well integrated, that navigation was simple, performed consistently, and that the system was accessible regardless of device. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Healthcare Recommender System Based on Medical Specialties, Patient Profiles, and Geospatial Information

The global outburst of COVID-19 introduced severe issues concerning the capacity and adoption of healthcare systems and how vulnerable citizen classes might be affected. The pandemic generated the most remarkable transformation of health services, appropriating the increase in new information and communication technologies to bring sustainability to health services. This paper proposes a novel, methodological, and collaborative approach based on patient-centered technology, which consists of a recommender system architecture to assist the health service level according to medical specialties. The system provides recommendations according to the user profile of the citizens and a ranked list of medical facilities. Thus, we propose a health attention factor to semantically compute the similarity between medical specialties and offer medical centers with response capacity, health service type, and close user geographic location. Thus, considering the challenges described in the state-of-the-art, this approach tackles issues related to recommenders in mobile devices and the diversity of items in the healthcare domain, incorporating semantic and geospatial processing. The recommender system was tested in diverse districts of Mexico City, and the spatial visualization of the medical facilities filtering by the recommendations is displayed in a Web-GIS application. © 2022 by the authors.

AgroSupportAnalytics: big data recommender system for agricultural farmer complaints in Egypt

The world's agricultural needs are growing with the pace of increase in its population. Agricultural farmers play a vital role in our society by helping us in fulfilling our basic food needs. So, we need to support farmers to keep up their great work, even in difficult times such as the coronavirus disease (COVID-19) outbreak, which causes hard regulations like lockdowns, curfews, and social distancing procedures. In this article, we propose the development of a recommender system that assists in giving advice, support, and solutions for the farmers' agricultural related complaints (or queries). The proposed system is based on the latent semantic analysis (LSA) approach to find the key semantic features of words used in agricultural complaints and their solutions. Further, it proposes to use the support vector machine (SVM) algorithm with Hadoop to classify the large agriculture dataset over Map/Reduce framework. The results show that a semantic-based classification system and filtering methods can improve the recommender system. Our proposed system outperformed the existing interest recommendation models with an accuracy of 87%.

YouTube's Social Media Analytics as an Evaluation of Educational Teaching Videos

During the COVID-19 lockdowns in South Africa undergraduate laboratory sessions were forbidden, in turn, video-based tutorials were proposed as a tentative solution to address the lack of in-person practical demonstration sessions. Five videos were filmed on electrical engineering topics, uploaded, and then publicly shared on YouTube. An investigation was then conducted as to whether videos may be useful for the teaching of practical engineering content in the university context. This article is a report back on the findings of using YouTube as a platform for sharing and evaluating engineering educational practical tutorial videos. The gaol of this article is to introduce YouTube's social media analytics as a tool for educators to evaluate their educational videos. The findings suggest that educators may consider evaluating their videos using social media analytics, but these analytics should be reviewed critically and should comprise of several metrics measured temporally. Understanding YouTube's recommender system and its influence on the platform is also an important factor in evaluating one's video content. © 2022 IEEE.

AgroSupportAnalytics: big data recommender system for agricultural farmer complaints in Egypt

The world's agricultural needs are growing with the pace of increase in its population. Agricultural farmers play a vital role in our society by helping us in fulfilling our basic food needs. So, we need to support farmers to keep up their great work, even in difficult times such as the coronavirus disease (COVID-19) outbreak, which causes hard regulations like lockdowns, curfews, and social distancing procedures. In this article, we propose the development of a recommender system that assists in giving advice, support, and solutions for the farmers' agricultural related complaints (or queries). The proposed system is based on the latent semantic analysis (LSA) approach to find the key semantic features of words used in agricultural complaints and their solutions. Further, it proposes to use the support vector machine (SVM) algorithm with Hadoop to classify the large agriculture dataset over Map/Reduce framework. The results show that a semantic-based classification system and filtering methods can improve the recommender system. Our proposed system outperformed the existing interest recommendation models with an accuracy of 87%. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

A Session-Based Recommender System for Learning Resources

Learning resource recommendation systems can help learners find suitable resources (e.g., books, journals, …) for learning and research. In particular, in the context of online learning due to the impact of the COVID-19 pandemic, the learning resource recommendation is very necessary. In this study, we propose using session-based recommendation systems to suggest the learning resources to the learners. Experiments are performed on a learning resource dataset collected at a local university and a public dataset. After preprocessing the data to convert it to session form, the Neural Attentive Session-based Recommendation (NARM) and Recurrent Neural Networks (GRU4Rec) models were used for training, testing, and comparison. The results show that recommending learning resources according to the NARM model is more effective than that of the GRU4Rec model, and thus, using the session-based recommendation system would be a promising approach for learning resource recommendation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Multi-Criteria Recommender System for Determining Risk Level of Covid-19

The coronavirus pandemic is a global disease outbreak causing countless loss of lives and also threatening the economic, social, religious, education, and other key sectors of nations. This highly infectious virus continues to spread rapidly and therefore, the need to develop innovative strategies and policies to curb the growing effects becomes very crucial. One significant approach is the introduction of lockdown measures, although this instrument is not completely dependable, due to possible adverse effects on societal activities. Prior to deployment, a number of criteria are taken into account, including demographic conditions, healthcare options, and Covid-19 case data. Depending on the influencing factors, a lockdown decision is typically made by assessing the different danger levels of a certain place. Consequently, this research propose a multi-criteria recommender system to determine the worth and risk of various regions, based on several constraints and databases. The model, which utilized the analytical network process (ANP) to discover interconnectedness and feedback, also included the weighting technique. In this study carried out in 27 districts and cities in West Java, Indonesia, 15% of the selected locations were categorized as high-risk levels. Meanwhile, 63% and 22% were associated with medium and low risk, respectively. © 2022 IEEE.

IRRMA: An Image Recommender Robot Meeting Assistant

The number of people who attend virtual meetings has increased as a result of COVID-19. In this paper, we present a system that consists of an expressive humanoid social robot called QTRobot, and a recommender system that employs natural language processing techniques to recommend images related to the content of the presenter’s speech to the audience in real time. This is achieved utilising the QTRobot’s platform capabilities (microphone, computation power, and Wi-Fi). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity.

Background: Hospitals face a significant problem meeting patients' medical needs during epidemics, especially when the number of patients increases rapidly, as seen during the recent COVID-19 pandemic. This study designs a treatment recommender system (RS) for the efficient management of human capital and resources such as doctors, medicines, and resources in hospitals. We hypothesize that a deep learning framework, when combined with search paradigms in an image framework, can make the RS very efficient. Methodology: This study uses a Convolutional neural network (CNN) model for the feature extraction of the images and discovers the most similar patients. The input queries patients from the hospital database with similar chest X-ray images. It uses a similarity metric for the similarity computation of the images. Results: This methodology recommends the doctors, medicines, and resources associated with similar patients to a COVID-19 patients being admitted to the hospital. The performance of the proposed RS is verified with five different feature extraction CNN models and four similarity measures. The proposed RS with a ResNet-50 CNN feature extraction model and Maxwell-Boltzmann similarity is found to be a proper framework for treatment recommendation with a mean average precision of more than 0.90 for threshold similarities in the range of 0.7 to 0.9 and an average highest cosine similarity of more than 0.95. Conclusions: Overall, an RS with a CNN model and image similarity is proven as an efficient tool for the proper management of resources during the peak period of pandemics and can be adopted in clinical settings.