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Fulltext Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review

Ravichandran BD, Keikhosrokiani P

Neural Comput Appl, 2023;35(1):699-717.
PMID: 36159189 DOI: 10.1007/s00521-022-07797-y

The spread of Covid-19 misinformation on social media had significant real-world consequences, and it raised fears among internet users since the pandemic has begun. Researchers from all over the world have shown an interest in developing deception classification methods to reduce the issue. Despite numerous obstacles that can thwart the efforts, the researchers aim to create an automated, stable, accurate, and effective mechanism for misinformation classification. In this paper, a systematic literature review is conducted to analyse the state-of-the-art related to the classification of misinformation on social media. IEEE Xplore, SpringerLink, ScienceDirect, Scopus, Taylor & Francis, Wiley, Google Scholar are used as databases to find relevant papers since 2018-2021. Firstly, the study begins by reviewing the history of the issues surrounding Covid-19 misinformation and its effects on social media users. Secondly, various neuro-fuzzy and neural network classification methods are identified. Thirdly, the strength, limitations, and challenges of neuro-fuzzy and neural network approaches are verified for the classification misinformation specially in case of Covid-19. Finally, the most efficient hybrid method of neuro-fuzzy and neural networks in terms of performance accuracy is discovered. This study is wrapped up by suggesting a hybrid ANFIS-DNN model for improving Covid-19 misinformation classification. The results of this study can be served as a roadmap for future research on misinformation classification.
Predicting behavioral intention to use e-learning system: A case-study in Begum Rokeya University, Rangpur, Bangladesh

Humida T, Al Mamun MH, Keikhosrokiani P

Educ Inf Technol (Dordr), 2021 Aug 14.
PMID: 34413694 DOI: 10.1007/s10639-021-10707-9

Digital transformation and emerging technologies open a horizon to a new method of teaching and learning and revolutionizes the e-learning industry. The goal of this study is to scrutinize a proposed research model for predicting factors that influence student's behavioral intention to use e-learning system at Begum Rokeya University, Bangladesh. The study used quantitative approach and developed a research model based on several technological acceptance models. In order to test the model, a survey was conducted to obtain data from 262 university students. SEM-PLS, a multivariate statistical analysis technique, was used to analyze the responses to examine the model, factors, structural relationships, and hypotheses. The result shows that 'perceived usefulness' and 'perceived ease of use' positively and significantly influenced by 'perceived enjoyment'. Furthermore, 'perceived usefulness', 'perceived ease of use' and 'facilitating condition' have a significant impact to predict behavioral intention to use e-learning. The results of mediation analysis show that 'perceived usefulness' and 'perceived ease of use' have mediating effects between the predictors and the outcome. Finally, 'facilitating condition' have a remarkable moderating effect to predict the student's behavioral intention in using e-learning. The findings have a noteworthy empirical implication for educational institutions to introduce e-learning system as one of the teaching and learning tools.
Fulltext A systematic literature review for APT detection and Effective Cyber Situational Awareness (ECSA) conceptual model

Salim DT, Singh MM, Keikhosrokiani P

Heliyon, 2023 Jul;9(7):e17156.
PMID: 37449192 DOI: 10.1016/j.heliyon.2023.e17156

Advancements in computing technology and the growing number of devices (e.g., computers, mobile) connected to networks have contributed to an increase in the amount of data transmitted between devices. These data are exposed to various types of cyberattacks, one of which is advanced persistent threats (APTs). APTs are stealthy and focus on sophisticated, specific targets. One reason for the detection failure of APTs is the nature of the attack pattern, which changes rapidly based on advancements in hacking. The need for future researchers to understand the gap in the literature regarding APT detection and to explore improved detection techniques has become crucial. Thus, this systematic literature review (SLR) examines the different approaches used to detect APT attacks directed at the network system in terms of approach and assessment metrics. The SLR includes papers on computer, mobile, and internet of things (IoT) technologies. We performed an SLR by searching six leading scientific databases to identify 75 studies that were published from 2012 to 2022. The findings from the SLR are discussed in terms of the literature's research gaps, and the study provides essential recommendations for designing a model for early APT detection. We propose a conceptual model known as the Effective Cyber Situational Awareness Model to Detect and Predict Mobile APTs (ECSA-tDP-MAPT), designed to effectively detect and predict APT attacks on mobile network traffic.
Fulltext Evaluating online health information quality using machine learning and deep learning: A systematic literature review

Baqraf YKA, Keikhosrokiani P, Al-Rawashdeh M

Digit Health, 2023;9:20552076231212296.
PMID: 38025112 DOI: 10.1177/20552076231212296

BACKGROUND: Due to the large volume of online health information, while quality remains dubious, understanding the usage of artificial intelligence to evaluate health information and surpass human-level performance is crucial. However, the existing studies still need a comprehensive review highlighting the vital machine, and Deep learning techniques for the automatic health information evaluation process.
OBJECTIVE: Therefore, this study outlines the most recent developments and the current state of the art regarding evaluating the quality of online health information on web pages and specifies the direction of future research.
METHODS: In this article, a systematic literature is conducted according to the PRISMA statement in eight online databases PubMed, Science Direct, Scopus, ACM, Springer Link, Wiley Online Library, Emerald Insight, and Web of Science to identify all empirical studies that use machine and deep learning models for evaluating the online health information quality. Furthermore, the selected techniques are compared based on their characteristics, such as health quality criteria, quality measurement tools, algorithm type, and achieved performance.
RESULTS: The included papers evaluate health information on web pages using over 100 quality criteria. The results show no universal quality dimensions used by health professionals and machine or deep learning practitioners while evaluating health information quality. In addition, the metrics used to assess the model performance are not the same as those used to evaluate human performance.
CONCLUSIONS: This systemic review offers a novel perspective in approaching the health information quality in web pages that can be used by machine and deep learning practitioners to tackle the problem more effectively.
A proposal to design a Location-based Mobile Cardiac Emergency System (LMCES)

Keikhosrokiani P, Mustaffa N, Zakaria N, Sarwar MI

Stud Health Technol Inform, 2012;182:83-92.
PMID: 23138083

Healthcare for elderly people has become a vital issue. The Wearable Health Monitoring System (WHMS) is used to manage and monitor chronic disease in elderly people, postoperative rehabilitation patients and persons with special needs. Location-aware healthcare is achievable as positioning systems and telecommunications have been developed and have fulfilled the technology needed for this kind of healthcare system. In this paper, the researchers propose a Location-Based Mobile Cardiac Emergency System (LMCES) to track the patient's current location when Emergency Medical Services (EMS) has been activated as well as to locate the nearest healthcare unit for the ambulance service. The location coordinates of the patients can be retrieved by GPS and sent to the healthcare centre using GPRS. The location of the patient, cell ID information will also be transmitted to the LMCES server in order to retrieve the nearest health care unit. For the LMCES, we use Dijkstra's algorithm for selecting the shortest path between the nearest healthcare unit and the patient location in order to facilitate the ambulance's path under critical conditions.
Fulltext Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony

Keikhosrokiani P, Naidu A/P Anathan AB, Iryanti Fadilah S, Manickam S, Li Z

Digit Health, 2023;9:20552076221150741.
PMID: 36655183 DOI: 10.1177/20552076221150741

Cardiovascular disease is one of the main causes of death worldwide which can be easily diagnosed by listening to the murmur sound of heartbeat sounds using a stethoscope. The murmur sound happens at the Lub-Dub, which indicates there are abnormalities in the heart. However, using the stethoscope for listening to the heartbeat sound requires a long time of training then only the physician can detect the murmuring sound. The existing studies show that young physicians face difficulties in this heart sound detection. Use of computerized methods and data analytics for detection and classification of heartbeat sounds will improve the overall quality of sound detection. Many studies have been worked on classifying the heartbeat sound; however, they lack the method with high accuracy. Therefore, this research aims to classify the heartbeat sound using a novel optimized Adaptive Neuro-Fuzzy Inferences System (ANFIS) by artificial bee colony (ABC). The data is cleaned, pre-processed, and MFCC is extracted from the heartbeat sounds. Then the proposed ABC-ANFIS is used to run the pre-processed heartbeat sound, and accuracy is calculated for the model. The results indicate that the proposed ABC-ANFIS model achieved 93% accuracy for the murmur class. The proposed ABC-ANFIS has higher accuracy in compared to ANFIS, PSO ANFIS, SVM, KSTM, KNN, and other existing studies. Thus, this study can assist physicians to classify heartbeat sounds for detecting cardiovascular disease in the early stages.