Expert systems have been widely used in medicine to diagnose different diseases. However, these rule-based systems only explain why and how their outcomes are reached. The rules leading to those outcomes are also expressed in a machine language and confronted with the familiar problems of coverage and specificity. This fact prevents procuring expert systems with fully human-understandable explanations. Furthermore, early diagnosis involves a high degree of uncertainty and vagueness which constitutes another challenge to overcome in this study. This paper aims to design and develop a fuzzy explainable expert system for coronavirus disease-2019 (COVID-19) diagnosis that could be incorporated into medical robots. The proposed medical robotic application deduces the likelihood level of contracting COVID-19 from the entered symptoms, the personal information, and the patient's activities. The proposal integrates fuzzy logic to deal with uncertainty and vagueness in diagnosis. Besides, it adopts a hybrid explainable artificial intelligence (XAI) technique to provide different explanation forms. In particular, the textual explanations are generated as rules expressed in a natural language while avoiding coverage and specificity problems. Therefore, the proposal could help overwhelmed hospitals during the epidemic propagation and avoid contamination using a solution with a high level of explicability.
Comprehensive study: machine learning approaches for COVID-19 diagnosis IJECEIAES
Coronavirus disease 2019 (COVID-19) is caused a large number of death since has declared as an international pandemic in December 2019, and it is spreading all over the world (more than 200 countries). This situation puts the health organizations in an aberrant demand for urgent needs to develop significant early detection and monitoring smart solutions. Therefore, that new system or solution might be capable to identify COVID-19 quickly and accurately. Nowadays, the science of artificial intelligence (AI), and internet of things (IoT) techniques have an extensive range of applications, it can be initiated a possible solution for early detection and accurate decisions. We believe, combine both of the IoT revolution and machine learning (ML) methods are expected to reshape healthcare treatment strategies to provide smart (diagnosis, treatments, monitoring, and hospitals). This work aims to overview the recent solutions that have been used for early detection, and to provide the researchers a comprehensive summary that contribute to the pandemic control such AI, IoT, cloud, fog, algorithms, and all the dataset and their sources that recently published. In addition, all models, frameworks, monitoring systems, devices, and ideas (in four sections) have been sufficiently presented with all clarifications and justifications. Also, we propose a new vision for early detection based on IoT sensors data entry using 1 million patients-data to verify three proposed methods.
Early coronavirus disease detection using internet of things smart systemIJECEIAES
The internet of things (IoT) is quickly evolving, allowing for the connecting of a wide range of smart devices in a variety of applications including industry, military, education, and health. Coronavirus has recently expanded fast across the world, and there are no particular therapies available at this moment. As a result, it is critical to avoid infection and watch signs like fever and shortness of breath. This research work proposes a smart and robust system that assists patients with influenza symptoms in determining whether or not they are infected with the coronavirus disease (COVID-19). In addition to the diagnostic capabilities of the system, the system aids these patients in obtaining medical care quickly by informing medical authorities via Blynk IoT. Moreover, the global positioning system (GPS) module is used to track patient mobility in order to locate contaminated regions and analyze suspected patient behaviors. Finally, this idea might be useful in medical institutions, quarantine units, airports, and other relevant fields.
NEW CORONA VIRUS DISEASE 2022: SOCIAL DISTANCING IS AN EFFECTIVE MEASURE (COV...IRJET Journal
The document describes a proposed real-time system to monitor social distancing using computer vision and deep learning techniques. The system would use a camera to detect individuals and calculate distances between them in order to identify instances where social distancing guidelines are breached. When a breach is detected, an audio-visual cue would be emitted to alert individuals without identifying or saving personal data. The system aims to help reduce the spread of COVID-19 while respecting privacy and avoiding overreach. It outlines the technical approach including camera calibration, region of interest definition, object detection using YOLOv3, distance calculation techniques, and system architecture at a high level.
Role of Machine Learning Techniques in COVID-19 Prediction and DetectionIRJET Journal
This document summarizes a research paper that examines the role of machine learning techniques in predicting and detecting COVID-19. It discusses how machine learning algorithms like convolutional neural networks can be applied to chest X-ray images to diagnose COVID-19. The document also explores how machine learning can be used to predict the spread of COVID-19 cases, recoveries, and deaths. It analyzes several studies that have used techniques like deep learning and data augmentation to accurately detect COVID-19 in medical images with up to 98% accuracy.
This document provides an overview of artificial intelligence systems that have been developed to automatically detect coronavirus using medical imaging techniques like CT scans and x-rays. It discusses how AI techniques like deep learning and convolutional neural networks can be used to analyze medical images and diagnose COVID-19 infection faster than traditional PCR testing. The document aims to inform researchers about current automatic detection systems and analyze their features to help build better diagnostic tools.
Covid-19 Data Analysis and VisualizationIRJET Journal
This document summarizes a research paper that analyzes COVID-19 data using machine learning algorithms. It first introduces the authors and provides an abstract describing the project's goal of gaining insights from COVID-19 data using Python and Tableau visualization tools. It then reviews related work applying models and algorithms to infectious disease data. The methodology section outlines the process used: collecting data from government websites, cleaning the data, performing data visualization, calculating accuracy of different algorithms (logistic regression, KNN, random forest, decision tree), and using the most accurate algorithm to predict if a person is COVID-19 positive based on symptoms.
Efficient machine learning classifier to detect and monitor COVID-19 cases b...IJECEIAES
In this research work, coronavirus disease 2019 (COVID-19) has been considered to help mankind survive the present-day pandemic. This research is helpful to monitor the patients newly infected by the virus, and patients who have already recovered from the disease, and also to study the flow of virus from similar health issues. In this paper, an internet of things (IoT) framework has been developed for the early detection of suspected cases. This framework is used for collecting and uploading symptoms (data) through sensor devices to the physician, data analytics center, cloud, and isolation/health centers. The symptoms of the first wave, second wave, and omicron are used to identify the suspects. Five machine learning algorithms which are considered to be the best in the existing literature have been used to find the best machine learning classifier in this research work. The proposed framework is used for the rapid detection of COVID-19 cases from real-world COVID-19 symptoms to mitigate the spread in society. This model also monitors the affected patient who has undergone treatment and recovered. It also collects data for analysis to perform further improvements in algorithms based on daily updated information from patients to provide better solutions to mankind.
INSIGHT ABOUT DETECTION, PREDICTION AND WEATHER IMPACT OF CORONAVIRUS (COVID-...ijaia
The document summarizes research using machine learning models to analyze the impact of weather factors on the COVID-19 pandemic and to detect COVID-19 from chest X-rays. It describes using decision tree regressors to determine that temperature, humidity, and sun exposure have 85.88% impact on COVID-19 spread and 91.89% impact on COVID-19 deaths. It also details using pre-trained convolutional neural networks like VGG16 and VGG19 on chest X-rays to classify images as normal, pneumonia, or COVID-19 with over 92% accuracy. Finally, it mentions using logistic regression to predict an individual's risk of death from COVID-19 based on attributes like age, gender, and location, achieving 94.
Comprehensive study: machine learning approaches for COVID-19 diagnosis IJECEIAES
Coronavirus disease 2019 (COVID-19) is caused a large number of death since has declared as an international pandemic in December 2019, and it is spreading all over the world (more than 200 countries). This situation puts the health organizations in an aberrant demand for urgent needs to develop significant early detection and monitoring smart solutions. Therefore, that new system or solution might be capable to identify COVID-19 quickly and accurately. Nowadays, the science of artificial intelligence (AI), and internet of things (IoT) techniques have an extensive range of applications, it can be initiated a possible solution for early detection and accurate decisions. We believe, combine both of the IoT revolution and machine learning (ML) methods are expected to reshape healthcare treatment strategies to provide smart (diagnosis, treatments, monitoring, and hospitals). This work aims to overview the recent solutions that have been used for early detection, and to provide the researchers a comprehensive summary that contribute to the pandemic control such AI, IoT, cloud, fog, algorithms, and all the dataset and their sources that recently published. In addition, all models, frameworks, monitoring systems, devices, and ideas (in four sections) have been sufficiently presented with all clarifications and justifications. Also, we propose a new vision for early detection based on IoT sensors data entry using 1 million patients-data to verify three proposed methods.
Early coronavirus disease detection using internet of things smart systemIJECEIAES
The internet of things (IoT) is quickly evolving, allowing for the connecting of a wide range of smart devices in a variety of applications including industry, military, education, and health. Coronavirus has recently expanded fast across the world, and there are no particular therapies available at this moment. As a result, it is critical to avoid infection and watch signs like fever and shortness of breath. This research work proposes a smart and robust system that assists patients with influenza symptoms in determining whether or not they are infected with the coronavirus disease (COVID-19). In addition to the diagnostic capabilities of the system, the system aids these patients in obtaining medical care quickly by informing medical authorities via Blynk IoT. Moreover, the global positioning system (GPS) module is used to track patient mobility in order to locate contaminated regions and analyze suspected patient behaviors. Finally, this idea might be useful in medical institutions, quarantine units, airports, and other relevant fields.
NEW CORONA VIRUS DISEASE 2022: SOCIAL DISTANCING IS AN EFFECTIVE MEASURE (COV...IRJET Journal
The document describes a proposed real-time system to monitor social distancing using computer vision and deep learning techniques. The system would use a camera to detect individuals and calculate distances between them in order to identify instances where social distancing guidelines are breached. When a breach is detected, an audio-visual cue would be emitted to alert individuals without identifying or saving personal data. The system aims to help reduce the spread of COVID-19 while respecting privacy and avoiding overreach. It outlines the technical approach including camera calibration, region of interest definition, object detection using YOLOv3, distance calculation techniques, and system architecture at a high level.
Role of Machine Learning Techniques in COVID-19 Prediction and DetectionIRJET Journal
This document summarizes a research paper that examines the role of machine learning techniques in predicting and detecting COVID-19. It discusses how machine learning algorithms like convolutional neural networks can be applied to chest X-ray images to diagnose COVID-19. The document also explores how machine learning can be used to predict the spread of COVID-19 cases, recoveries, and deaths. It analyzes several studies that have used techniques like deep learning and data augmentation to accurately detect COVID-19 in medical images with up to 98% accuracy.
This document provides an overview of artificial intelligence systems that have been developed to automatically detect coronavirus using medical imaging techniques like CT scans and x-rays. It discusses how AI techniques like deep learning and convolutional neural networks can be used to analyze medical images and diagnose COVID-19 infection faster than traditional PCR testing. The document aims to inform researchers about current automatic detection systems and analyze their features to help build better diagnostic tools.
Covid-19 Data Analysis and VisualizationIRJET Journal
This document summarizes a research paper that analyzes COVID-19 data using machine learning algorithms. It first introduces the authors and provides an abstract describing the project's goal of gaining insights from COVID-19 data using Python and Tableau visualization tools. It then reviews related work applying models and algorithms to infectious disease data. The methodology section outlines the process used: collecting data from government websites, cleaning the data, performing data visualization, calculating accuracy of different algorithms (logistic regression, KNN, random forest, decision tree), and using the most accurate algorithm to predict if a person is COVID-19 positive based on symptoms.
Efficient machine learning classifier to detect and monitor COVID-19 cases b...IJECEIAES
In this research work, coronavirus disease 2019 (COVID-19) has been considered to help mankind survive the present-day pandemic. This research is helpful to monitor the patients newly infected by the virus, and patients who have already recovered from the disease, and also to study the flow of virus from similar health issues. In this paper, an internet of things (IoT) framework has been developed for the early detection of suspected cases. This framework is used for collecting and uploading symptoms (data) through sensor devices to the physician, data analytics center, cloud, and isolation/health centers. The symptoms of the first wave, second wave, and omicron are used to identify the suspects. Five machine learning algorithms which are considered to be the best in the existing literature have been used to find the best machine learning classifier in this research work. The proposed framework is used for the rapid detection of COVID-19 cases from real-world COVID-19 symptoms to mitigate the spread in society. This model also monitors the affected patient who has undergone treatment and recovered. It also collects data for analysis to perform further improvements in algorithms based on daily updated information from patients to provide better solutions to mankind.
INSIGHT ABOUT DETECTION, PREDICTION AND WEATHER IMPACT OF CORONAVIRUS (COVID-...ijaia
The document summarizes research using machine learning models to analyze the impact of weather factors on the COVID-19 pandemic and to detect COVID-19 from chest X-rays. It describes using decision tree regressors to determine that temperature, humidity, and sun exposure have 85.88% impact on COVID-19 spread and 91.89% impact on COVID-19 deaths. It also details using pre-trained convolutional neural networks like VGG16 and VGG19 on chest X-rays to classify images as normal, pneumonia, or COVID-19 with over 92% accuracy. Finally, it mentions using logistic regression to predict an individual's risk of death from COVID-19 based on attributes like age, gender, and location, achieving 94.
Advancing the cybersecurity of the healthcare system with self- optimising an...Petar Radanliev
This article advances the knowledge on teaching and training new artificial intelligence algorithms, for securing, preparing,
and adapting the healthcare system to cope with future pandemics. The core objective is to develop a concept healthcare
system supported by autonomous artificial intelligence that can use edge health devices with real-time data. The article constructs two case scenarios for applying cybersecurity with autonomous artificial intelligence for (1) self-optimising predictive cyber risk analytics of failures in healthcare systems during a Disease X event (i.e., undefined future pandemic), and (2) self-adaptive forecasting of medical production and supply chain bottlenecks during future pandemics. To construct the two testing scenarios, the article uses the case of Covid-19 to synthesise data for the algorithms – i.e., for optimising and securing digital healthcare systems in anticipation of Disease X. The testing scenarios are built to tackle the logistical challenges and disruption of complex production and supply chains for vaccine distribution with optimisation algorithms.
COVID-19 FUTURE FORECASTING USING SUPERVISED MACHINE LEARNING MODELSIRJET Journal
This document describes a study that uses machine learning models to forecast factors related to the COVID-19 pandemic over the next 10 days. It trains and tests four supervised learning regression models - linear regression, least absolute shrinkage and selection operator, support vector machine, and exponential smoothing - on a dataset of daily COVID-19 case statistics. The models predict the number of new confirmed cases, deaths, and recoveries. The results show that exponential smoothing performs best, accurately forecasting the trends in new cases, death rate, and recovery rate. Linear regression and LASSO also perform well for these predictions, while support vector machine performs poorly.
COVID-19 knowledge-based system for diagnosis in Iraq using IoT environmentnooriasukmaningtyas
The importance and benefits of healthcare mobile applications is increasing rapidly, especially when such applications are connected to the internet of things (IoT). This paper describes a smart knowledge-based system (KBS) that helps patients showing symptoms of Influenza verify being infected with Coronavirus, commonly known as COVID-19. In addition to the systems’ diagnostic functionality, it helps these patients get medical assistance fast by notifying medical authorities using the IoT. This system displays patient’s location, phone number, date and time of examination. During the applications’ development, the developers used Twilio, short message service (SMS), WhatsApp, and Google map applications.
Deep Learning Approaches for Diagnosis and Treatment of COVID-19IRJET Journal
The document discusses using deep learning approaches for diagnosing and treating COVID-19. It first provides background on deep learning and convolutional neural networks. It then discusses challenges in early COVID-19 diagnosis and the need for computer-assisted diagnosis methods. The paper reviews several existing studies that used deep learning on CT scans and X-rays to classify COVID-19. It proposes developing a COVID-19 diagnosis system using a lung CT image dataset and deep learning models. The system would be designed, implemented and tested to efficiently detect COVID-19 infections from CT scans.
MediBot: A Primary Telemedicine Approach for Basic AilmentsIRJET Journal
This document describes a medical chatbot called MediBot that can provide basic medical advice and recommendations for minor ailments. MediBot uses natural language processing and text or voice inputs to understand patients' symptoms and determine an appropriate diagnosis. It then provides audio and visual outputs to inform patients about their condition and recommend home treatments or medicines. The goal is to give patients access to basic medical guidance without needing to visit a doctor in person. The document outlines how MediBot was developed using Python programming and discusses its potential to improve healthcare access, especially in rural areas where medical services are limited.
All submitted research articles are subjected to immediate rapid screening by the editors, in consultation with the Editorial Board or others working in the field as appropriate, to ensure they are likely to be of the level of interest and importance appropriate for the journal. ijerst offers a fast publication schedule whilst maintaining rigorous peer review; the use of recommended electronic formats for article delivery expedites the process.
IJERST offers a fast publication schedule whilst maintaining rigorous peer review; the use of recommended electronic formats for article delivery expedites the process. International Journal of Engineering Research and Science & Technology (IJERST) is an international online journal in English published Quarterly. All submitted research articles are subjected to immediate rapid screening by the editors, in consultation with the Editorial Board or others working in the field as appropriate, to ensure they are likely to be of the level of interest and importance appropriate for the journal.
scope of a journal in the field of Information Technology and Computer Engineering. This journal accepts various types of articles, including research articles, review articles, and short communications.
PREDICTION OF COVID-19 USING MACHINE LEARNING APPROACHESIRJET Journal
This document summarizes a research paper that used machine learning models to predict the spread of COVID-19. The researchers used various machine learning algorithms like SVM, random forest, decision tree, and linear regression on COVID-19 case data. SVM had the highest error in predictions, while random forest and decision tree performed best with lowest error. The models were developed using Python and deployed on cloud platforms. The study aimed to accurately predict COVID-19 trends to help governments respond better to the pandemic.
A novel predictive model for capturing threats for facilitating effective soc...IJECEIAES
Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches.
Nobody can predict the future, however by following trends, we can navigate the direction in which we’re heading. Trends are dictated by a wide range of economic and political factors, and often they are propelled by innovations. The newest technological trends owe themselves to necessary innovations in the healthcare industry, spurred by the Covid-19 pandemic.
With the Covid-19 pandemic revealing the gaps and inefficiencies of healthcare systems around the world, the newest developments in healthcare technologies are suddenly getting a lot more attention. This is useful, because the executives who are often hesitant in changing long-standing healthcare practices must revaluate and evolve in order to provide the most effective treatment plans for their patients.
The document describes a Bluetooth-based contact tracing app called COCOA that is being used in Japan to track the spread of COVID-19. The app prioritizes user privacy while improving the ability to balance health care system loads and speed up pandemic responses. It uses a decentralized peer-to-peer system to notify users of potential exposure risks without revealing identities. For the app to be effective, modeling shows participation needs to be near 90% to limit virus spread in Japan.
This document discusses the use of information and communication technologies (ICT) in healthcare. It begins with an introduction to ICT technologies and their role in healthcare. It then discusses how ICT is used in healthcare for health education, hospital management systems, health research, and health data management. The document also discusses future and emerging ICT technologies like augmented reality, virtual reality, artificial intelligence, and robotic process automation and how they are impacting healthcare. It concludes by discussing the use of computer simulations for healthcare education and training, including examples of simulations used for intra-oral radiography, cervical spine procedures, and surgical training.
COVID-19 VACCINATION CLASSIFICATION OF OPINION MINING WITH SEMANTIC KNOWLEDGE...dannyijwest
The Covid-19 ontology is to classify the data using a supervised learning approach in machine learning,
which has been preprocessed. Afterthe classification is done, with thehelp of opinion mining with decisionmaking, the classified data is stored in the database using semantic webontology using the protégé tool.
The data will be retrieved through SPARQL which helps to retrieve complex queries, followed by the
output based on the given query. This Covid-19 ontology helps in analyzing the risk factors and treatment
plans for the respective individuals i.e., students based on their given details which include diagnosis,
symptoms, and vaccination history. The information given by the students can be automatically processed
and with the help of SWRL (Semantic Web Rule Language), the risk factor and treatment plans for the
students are inferred from the given knowledge.
AI, IoMT and Blockchain in Healthcare.pdfrectified
This document discusses the application of artificial intelligence, internet of medical things, and blockchain technology in healthcare. Specifically, it covers:
1) How AI, IoMT, and blockchain can enhance patient outcomes, reduce costs, and improve efficiencies in healthcare.
2) Examples of current applications of these technologies, including in breast cancer diagnosis, PCOS diagnosis, and dementia detection. Machine learning algorithms are shown to outperform humans in some medical image analysis and diagnosis tasks.
3) Challenges and future research areas around implementing these technologies, such as ensuring patient privacy and data security.
An innovative IoT service for medical diagnosis IJECEIAES
The document proposes an innovative IoT service for medical diagnosis that utilizes IoT and cloud infrastructure to provide a shared environment for medical data between patients and doctors, predicts medical diagnoses and treatments based on multiple classifiers to ensure high accuracy, and includes functionalities such as searching for scientific papers and disease descriptions for unrecognized symptom combinations.
Covid 19 Prediction in India using Machine LearningYogeshIJTSRD
Various computational models are used around the world to predict the number of infected individuals and the death rate of the COVID 19 outbreak 3 . Machine learning based models are important to take proper actions. Due to the ample of uncertainty and crucial data, the aerodynamic models have been challenged regarding higher accuracy for long term prediction of this disease 1 . By researching the COVID19 problem, it is observed that lockdown and isolation are important techniques for preventing the spread of COVID 19 2 . In India, public health and the economical condition are impacted by COVID 19, our goal is to visualize the spread of this disease 5 . Machine Learning Algorithms are used in various applications for detecting adverse risk factors. Three ML algorithms we are using that is Logistic Regression LR , Support Vector Machine SVM , and Random Forest Classifier RFC . These machine learning models are predicting the total number of recovered patients as per the date of each state in India 8 . Sarfraj Alam | Vipul Kumar | Sweta Singh | Sweta Joshi | Madhu Kirola "Covid-19 Prediction in India using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology - 2021 , May 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42458.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/42458/covid19-prediction-in-india-using-machine-learning/sarfraj-alam
1. Researchers developed an X-ray disease identifier using a deep learning model to analyze chest X-ray images and diagnose diseases.
2. They used the VGG19 classification model to process X-ray images from the NIH dataset and diagnose diseases, achieving over 60% accuracy for most diseases.
3. The system aims to assist radiologists by providing automated disease diagnoses from X-ray images to reduce their workload and enable diagnoses in remote areas.
Cloud Computing: A Key to Effective & Efficient Disease Surveillance Systemidescitation
Cloud computing, a future generation concept
characterized by three entities: Software, hardware &
network designed to enhance the capacity building
simultaneously increasing the throughput by extending the
reach for any system without having heavy investment of
infrastructure and training new personnel. It is becoming
a major building block for any sort of businesses across the
globe. This paper likes to propose a cloud as a solution for
having an effective disease surveillance system. Till now,
multiple surveillance systems come into play but still they
lack sensitivity, specificity & timeliness.
seminar report iot based health monitoring system 2023.pdfriddheshbore97
This document provides an overview of a seminar report on an IoT-based health monitoring system. The report was submitted by Prerna Ravi Shirsath for their Bachelor of Engineering degree. The report discusses the development of a system that measures a patient's body temperature, heartbeat, and oxygen saturation levels using sensors and sends the data to a mobile application via Bluetooth. It presents the architecture of an IoT health monitoring system which includes medical sensors, a smart gateway, and a back-end system. The report also covers the advantages of such systems in enabling remote monitoring and prevention, reducing healthcare costs, and improving treatment management. Some disadvantages around security, risk of failure, and cost are also discussed.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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This article advances the knowledge on teaching and training new artificial intelligence algorithms, for securing, preparing,
and adapting the healthcare system to cope with future pandemics. The core objective is to develop a concept healthcare
system supported by autonomous artificial intelligence that can use edge health devices with real-time data. The article constructs two case scenarios for applying cybersecurity with autonomous artificial intelligence for (1) self-optimising predictive cyber risk analytics of failures in healthcare systems during a Disease X event (i.e., undefined future pandemic), and (2) self-adaptive forecasting of medical production and supply chain bottlenecks during future pandemics. To construct the two testing scenarios, the article uses the case of Covid-19 to synthesise data for the algorithms – i.e., for optimising and securing digital healthcare systems in anticipation of Disease X. The testing scenarios are built to tackle the logistical challenges and disruption of complex production and supply chains for vaccine distribution with optimisation algorithms.
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COVID-19 knowledge-based system for diagnosis in Iraq using IoT environmentnooriasukmaningtyas
The importance and benefits of healthcare mobile applications is increasing rapidly, especially when such applications are connected to the internet of things (IoT). This paper describes a smart knowledge-based system (KBS) that helps patients showing symptoms of Influenza verify being infected with Coronavirus, commonly known as COVID-19. In addition to the systems’ diagnostic functionality, it helps these patients get medical assistance fast by notifying medical authorities using the IoT. This system displays patient’s location, phone number, date and time of examination. During the applications’ development, the developers used Twilio, short message service (SMS), WhatsApp, and Google map applications.
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The document discusses using deep learning approaches for diagnosing and treating COVID-19. It first provides background on deep learning and convolutional neural networks. It then discusses challenges in early COVID-19 diagnosis and the need for computer-assisted diagnosis methods. The paper reviews several existing studies that used deep learning on CT scans and X-rays to classify COVID-19. It proposes developing a COVID-19 diagnosis system using a lung CT image dataset and deep learning models. The system would be designed, implemented and tested to efficiently detect COVID-19 infections from CT scans.
MediBot: A Primary Telemedicine Approach for Basic AilmentsIRJET Journal
This document describes a medical chatbot called MediBot that can provide basic medical advice and recommendations for minor ailments. MediBot uses natural language processing and text or voice inputs to understand patients' symptoms and determine an appropriate diagnosis. It then provides audio and visual outputs to inform patients about their condition and recommend home treatments or medicines. The goal is to give patients access to basic medical guidance without needing to visit a doctor in person. The document outlines how MediBot was developed using Python programming and discusses its potential to improve healthcare access, especially in rural areas where medical services are limited.
All submitted research articles are subjected to immediate rapid screening by the editors, in consultation with the Editorial Board or others working in the field as appropriate, to ensure they are likely to be of the level of interest and importance appropriate for the journal. ijerst offers a fast publication schedule whilst maintaining rigorous peer review; the use of recommended electronic formats for article delivery expedites the process.
IJERST offers a fast publication schedule whilst maintaining rigorous peer review; the use of recommended electronic formats for article delivery expedites the process. International Journal of Engineering Research and Science & Technology (IJERST) is an international online journal in English published Quarterly. All submitted research articles are subjected to immediate rapid screening by the editors, in consultation with the Editorial Board or others working in the field as appropriate, to ensure they are likely to be of the level of interest and importance appropriate for the journal.
scope of a journal in the field of Information Technology and Computer Engineering. This journal accepts various types of articles, including research articles, review articles, and short communications.
PREDICTION OF COVID-19 USING MACHINE LEARNING APPROACHESIRJET Journal
This document summarizes a research paper that used machine learning models to predict the spread of COVID-19. The researchers used various machine learning algorithms like SVM, random forest, decision tree, and linear regression on COVID-19 case data. SVM had the highest error in predictions, while random forest and decision tree performed best with lowest error. The models were developed using Python and deployed on cloud platforms. The study aimed to accurately predict COVID-19 trends to help governments respond better to the pandemic.
A novel predictive model for capturing threats for facilitating effective soc...IJECEIAES
Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches.
Nobody can predict the future, however by following trends, we can navigate the direction in which we’re heading. Trends are dictated by a wide range of economic and political factors, and often they are propelled by innovations. The newest technological trends owe themselves to necessary innovations in the healthcare industry, spurred by the Covid-19 pandemic.
With the Covid-19 pandemic revealing the gaps and inefficiencies of healthcare systems around the world, the newest developments in healthcare technologies are suddenly getting a lot more attention. This is useful, because the executives who are often hesitant in changing long-standing healthcare practices must revaluate and evolve in order to provide the most effective treatment plans for their patients.
The document describes a Bluetooth-based contact tracing app called COCOA that is being used in Japan to track the spread of COVID-19. The app prioritizes user privacy while improving the ability to balance health care system loads and speed up pandemic responses. It uses a decentralized peer-to-peer system to notify users of potential exposure risks without revealing identities. For the app to be effective, modeling shows participation needs to be near 90% to limit virus spread in Japan.
This document discusses the use of information and communication technologies (ICT) in healthcare. It begins with an introduction to ICT technologies and their role in healthcare. It then discusses how ICT is used in healthcare for health education, hospital management systems, health research, and health data management. The document also discusses future and emerging ICT technologies like augmented reality, virtual reality, artificial intelligence, and robotic process automation and how they are impacting healthcare. It concludes by discussing the use of computer simulations for healthcare education and training, including examples of simulations used for intra-oral radiography, cervical spine procedures, and surgical training.
COVID-19 VACCINATION CLASSIFICATION OF OPINION MINING WITH SEMANTIC KNOWLEDGE...dannyijwest
The Covid-19 ontology is to classify the data using a supervised learning approach in machine learning,
which has been preprocessed. Afterthe classification is done, with thehelp of opinion mining with decisionmaking, the classified data is stored in the database using semantic webontology using the protégé tool.
The data will be retrieved through SPARQL which helps to retrieve complex queries, followed by the
output based on the given query. This Covid-19 ontology helps in analyzing the risk factors and treatment
plans for the respective individuals i.e., students based on their given details which include diagnosis,
symptoms, and vaccination history. The information given by the students can be automatically processed
and with the help of SWRL (Semantic Web Rule Language), the risk factor and treatment plans for the
students are inferred from the given knowledge.
AI, IoMT and Blockchain in Healthcare.pdfrectified
This document discusses the application of artificial intelligence, internet of medical things, and blockchain technology in healthcare. Specifically, it covers:
1) How AI, IoMT, and blockchain can enhance patient outcomes, reduce costs, and improve efficiencies in healthcare.
2) Examples of current applications of these technologies, including in breast cancer diagnosis, PCOS diagnosis, and dementia detection. Machine learning algorithms are shown to outperform humans in some medical image analysis and diagnosis tasks.
3) Challenges and future research areas around implementing these technologies, such as ensuring patient privacy and data security.
An innovative IoT service for medical diagnosis IJECEIAES
The document proposes an innovative IoT service for medical diagnosis that utilizes IoT and cloud infrastructure to provide a shared environment for medical data between patients and doctors, predicts medical diagnoses and treatments based on multiple classifiers to ensure high accuracy, and includes functionalities such as searching for scientific papers and disease descriptions for unrecognized symptom combinations.
Covid 19 Prediction in India using Machine LearningYogeshIJTSRD
Various computational models are used around the world to predict the number of infected individuals and the death rate of the COVID 19 outbreak 3 . Machine learning based models are important to take proper actions. Due to the ample of uncertainty and crucial data, the aerodynamic models have been challenged regarding higher accuracy for long term prediction of this disease 1 . By researching the COVID19 problem, it is observed that lockdown and isolation are important techniques for preventing the spread of COVID 19 2 . In India, public health and the economical condition are impacted by COVID 19, our goal is to visualize the spread of this disease 5 . Machine Learning Algorithms are used in various applications for detecting adverse risk factors. Three ML algorithms we are using that is Logistic Regression LR , Support Vector Machine SVM , and Random Forest Classifier RFC . These machine learning models are predicting the total number of recovered patients as per the date of each state in India 8 . Sarfraj Alam | Vipul Kumar | Sweta Singh | Sweta Joshi | Madhu Kirola "Covid-19 Prediction in India using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology - 2021 , May 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42458.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/42458/covid19-prediction-in-india-using-machine-learning/sarfraj-alam
1. Researchers developed an X-ray disease identifier using a deep learning model to analyze chest X-ray images and diagnose diseases.
2. They used the VGG19 classification model to process X-ray images from the NIH dataset and diagnose diseases, achieving over 60% accuracy for most diseases.
3. The system aims to assist radiologists by providing automated disease diagnoses from X-ray images to reduce their workload and enable diagnoses in remote areas.
Cloud Computing: A Key to Effective & Efficient Disease Surveillance Systemidescitation
Cloud computing, a future generation concept
characterized by three entities: Software, hardware &
network designed to enhance the capacity building
simultaneously increasing the throughput by extending the
reach for any system without having heavy investment of
infrastructure and training new personnel. It is becoming
a major building block for any sort of businesses across the
globe. This paper likes to propose a cloud as a solution for
having an effective disease surveillance system. Till now,
multiple surveillance systems come into play but still they
lack sensitivity, specificity & timeliness.
seminar report iot based health monitoring system 2023.pdfriddheshbore97
This document provides an overview of a seminar report on an IoT-based health monitoring system. The report was submitted by Prerna Ravi Shirsath for their Bachelor of Engineering degree. The report discusses the development of a system that measures a patient's body temperature, heartbeat, and oxygen saturation levels using sensors and sends the data to a mobile application via Bluetooth. It presents the architecture of an IoT health monitoring system which includes medical sensors, a smart gateway, and a back-end system. The report also covers the advantages of such systems in enabling remote monitoring and prevention, reducing healthcare costs, and improving treatment management. Some disadvantages around security, risk of failure, and cost are also discussed.
Similar to Design and development of a fuzzy explainable expert system for a diagnostic robot of COVID-19 (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
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Design and development of a fuzzy explainable expert system for a diagnostic robot of COVID-19
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 6, December 2023, pp. 6940~6951
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i6.pp6940-6951 6940
Journal homepage: http://ijece.iaescore.com
Design and development of a fuzzy explainable expert system
for a diagnostic robot of COVID-19
Omar El Beggar, Mohammed Ramdani, Mohamed Kissi
Laboratory of Intelligence Machine, Faculty of Sciences and Techniques of Mohammedia, Hassan II University of Casablanca,
Casablanca, Morocco
Article Info ABSTRACT
Article history:
Received Dec 29, 2022
Revised Apr 19, 2023
Accepted Apr 24, 2023
Expert systems have been widely used in medicine to diagnose different
diseases. However, these rule-based systems only explain why and how their
outcomes are reached. The rules leading to those outcomes are also
expressed in a machine language and confronted with the familiar problems
of coverage and specificity. This fact prevents procuring expert systems with
fully human-understandable explanations. Furthermore, early diagnosis
involves a high degree of uncertainty and vagueness which constitutes
another challenge to overcome in this study. This paper aims to design and
develop a fuzzy explainable expert system for coronavirus disease-2019
(COVID-19) diagnosis that could be incorporated into medical robots. The
proposed medical robotic application deduces the likelihood level of
contracting COVID-19 from the entered symptoms, the personal
information, and the patient's activities. The proposal integrates fuzzy logic
to deal with uncertainty and vagueness in diagnosis. Besides, it adopts a
hybrid explainable artificial intelligence (XAI) technique to provide different
explanation forms. In particular, the textual explanations are generated as
rules expressed in a natural language while avoiding coverage and
specificity problems. Therefore, the proposal could help overwhelmed
hospitals during the epidemic propagation and avoid contamination using a
solution with a high level of explicability.
Keywords:
Coronavirus disease-2019
Diagnostic robot
Expert system
Explainable artificial
intelligence
Fuzzy logic
This is an open access article under the CC BY-SA license.
Corresponding Author:
Omar El Beggar
Laboratory of Intelligence Machine, Faculty of Sciences and Techniques of Mohammedia, Hassan II
University of Casablanca
BP 146 Mohammedia 28806, Morocco
Email: omar.elbeggar@fstm.ac.ma
1. INTRODUCTION
Medical diagnosis has drawn great interest from researchers and industrialists from different
specialties. It consists of determining the disease from which a patient suffers to prescribe the appropriate
treatment. In other words, it is based on finding out the causes and symptoms of the disease [1]. Among the
dangerous viral diseases that have appeared in the last few years, coronavirus disease-2019 (COVID-19)
caused by the SARS-CoV-2 virus [2]. Due to its quick and large transmission, this virus has impacted
harmfully public health and the international economy since 2020 [3]. It has affected over 468 million people
worldwide and has caused over 6 million deaths by the end of March 2022 [4]. For the same period, in
Morocco only, over 1 million confirmed cases and over 16 thousand deaths have been reported by the
Moroccan Ministry of Health [5]. According to the World Health Organization (WHO) [6], the symptoms of
COVID-19 might vary from one person to another. However, fever, cough, tiredness, and loss of taste and
smell are the most common. Other symptoms could be appeared according to the cases' severities. Moreover,
2. Int J Elec & Comp Eng ISSN: 2088-8708
Design and development of a fuzzy explainable expert system for a diagnostic … (Omar El Beggar)
6941
the same source claimed that persons aged over 60 years or suffering from chronic diseases are more at risk
of contracting COVID-19. They might also develop severe symptoms. Notwithstanding COVID-19
asymptomatic cases, a safe and early diagnosis of persons who show some signs is of great importance.
Indeed, making the diagnosis of COVID-19 a safe process is a relevant challenge that should be taken up.
Using medical robots would be among the most efficient ways of that. Nowadays, many robots are
used in the healthcare sector. Meanwhile, their first emergence was in the 1980s with robotic surgeries [7].
Over the years, artificial intelligence (AI) has expanded robot capabilities to cover many fields in healthcare
like diagnosis, therapy, rehabilitation, and clinic reception. Despite performing a safe process, a diagnostic
robot for COVID-19 presents other benefits, like providing impartial medical diagnosis, supporting clinicians
and alleviating their workload, and saving time and money. The final objective of our studies is to build this
robot and employ it in Moroccan hospitals.
The hardware architecture of the robot is composed of three main blocs (intelligence bloc,
navigation bloc, and control bloc) as described in Figure 1. The intelligence block holds the expert system
responsible for COVID-19 diagnosis, sound and image processing, and data transmission to the cloud. The
navigation bloc ensures the robot's autonomous navigation thanks to the obstacle sensors and the radar
signals that map the travel environment. The control bloc is responsible for servomotors' control and energy
management supplied by the batteries. Given the key role of the expert system in the intelligence bloc, this
study will be focused mainly on designing and developing this system. A diagnostic expert system is a
computer-based system that could assist medical specialists or even imitate their behaviors of diagnosing [8].
The proposed expert system infers the likelihood of contracting COVID-19 from a set of input data:
symptoms severities, age, possibility of contacting positive cases in the last 14 days, last time immunity has
been acquired either from direct infection by COVID-19 or from a vaccine and the maximum risk level
deduced from the recently visited places. Besides, diagnosis involves a high degree of uncertainty and
vagueness. It is a medical field whereby information is mainly linguistic, particularly during the first
consultation. Even worse, there is often confusion between different diseases while diagnosing [9].
Since fuzzy logic is an effective tool that deals with uncertain and vague data [8], it is ideal to use in
the design and development of such systems. Furthermore, a fuzzy expert system is not entirely transparent
for end-users, and the reasoning behind its outcome is still incomprehensible to humans without an
explanation facility [10]. Formerly, the most explanation facilities of the first expert systems are focused on
the rule trace-based strategy that answers why and how the system decision is reached [11]. However,
expressing the rules that led to a conclusion in a machine language did not provide fully human-
understandable explanations. Thereby, an auto-generation of those rules in natural language (NL) is required.
This will improve rule expressiveness and enable our future medical robots properly communicate
explanations to humans. Nowadays, much attention is paid to explain ability in AI, especially with the
increasing use of opaque machine learning algorithms like deep learning [12]. Despite expert systems are not
considered opaque, new efforts should be deployed on explainable artificial intelligence (XAI) and its
application on those systems to improve their level of transparency and explicability.
In this paper, we attempt to enrich the system explanation facility with a hybrid XAI technique that
provides different explanation forms and allows getting a so-called fuzzy explainable expert system (FEES).
The rest of the paper is structured as follows. Section 2 discusses the related works. Section 3 presents the
proposed method to design and develop the system-to-be and how it addresses the disease diagnosis.
Section 4 describes the proposal results and provides some discussions. Finally, conclusions and perspectives
are listed in section 5.
Figure 1. The hardware architecture for the diagnostic robot of COVID-19
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2. RELATED WORKS
Expert systems have been widely used in medicine to diagnose different diseases as stated in [8].
COVID-19 is no different because many works have been performed since the epidemic's emergence. In this
section, we will focus only on research that has proposed an expert system to diagnose this disease.
Nema et al. [13] developed a knowledge-based system for COVID-19 diagnosis in Iraq using
internet of things (IoT) technologies. IoT is used to determine contact and location history. However, the
authors did not treat uncertainty and vagueness in the diagnosis process. Instead, Shatnawi et al. [14]
proposed a fuzzy inference system based merely on symptoms to diagnose COVID-19. Unfortunately, this
system has developed using MATLAB and could not be employed directly by healthcare specialists.
Al Hakim et al. [2] proposed an android-based expert system to make self-diagnoses of this disease
in Indonesia. Besides, their approach applied certainty factors (CFs) to facts and rules to overcome the
uncertainty problem. However, these CFs are still crisp values and should be calculated using mathematical
formulas (the authors used Microsoft Excel for that).
Raihan et al. [15] a framework consisting of a web and mobile application has been developed to
allow COVID-19 prognostication. Rules and features have been deduced using classification algorithms. The
objective of this computer-aided framework is COVID-19 risk prediction thanks to the input symptoms and
the clinical measures, like respiratory and pulse rates. However, the lack of such measurable data might lead
to an error in the final prediction. Furthermore, using opaque or black-box models to build the expert system
will not allow getting fully interpretable system conclusions.
Banjar et al. [16] proposed a prototype for the diagnosing and monitoring of COVID-19 cases in
Saudia Arabia. This prototype comprises an expert system responsible for recommendations providing, such
as risk classification, treatment plans, and the required laboratory tests and chest images. The knowledge base
is adaptively updated by learning new medical guidelines. The authors chose the following system inputs:
symptoms, hospitalization history, epidemiological information, and contact exposure. They also consecrated
an interface called “knowledge inbox” to present system explanations, although they are not well-detailed.
Overall, most of the studied expert systems used the COVID-19 symptoms revealed by WHO,
personal information and location history as common inputs. Nonetheless, they have ignored the pertinent
factor of immunity that has to be included. Indeed, a long period of acquiring immunity either from infection
or from vaccine increases the possibility of contracting COVID-19, although symptoms severities are slight.
To the most of our knowledge, almost studied systems offer no explanation facility compared to our
approach. The main reason claimed by Dhaliwal and Tung [17] is that the development of explanation
facilities has received lesser attention than the development of the expert systems themselves.
In particular, our approach and that of Banjar et al. [16] give explanations of the system decisions.
However, the proposed explanations in [16] are limited to expressing rules in a pseudo-NL, and plots of
fuzzy sets corresponding to user inputs. The paper also lacks details about the fuzzification process, including
the method adopted to generate fuzzy rules and their expressiveness in NL.
Another drawback of these works is related to the number and length of the rules that might be
presented as explanations of the system outcomes. Despite the coverage and specificity problems decreasing
significantly the system explicability, any of the studied expert systems have been interested in these
problems. The main similarities and differences between the studied approaches and our proposal are
provided in Table 1.
Table 1. Comparison between the studied expert systems for COVID-19 diagnosis
Approach Criteria
Diagnosis factors Uncertainty
support
Epidemic
data update
Dynamic
extensibility
Decision
explicability
Tool
Nema et al.
[13]
Symptoms contact history
location history
No No No No python
+CLIPS
Shatnawi et al.
[14]
Symptoms Fuzzy logic No No No MATLAB
toolbox
Al Hakim et al.
[2]
Symptoms, contact history,
location history age
Certainty factors No No No Mobile
App
Raihan et al.
[15]
Symptoms, measures Triangular fuzzy
numbers
No No No Mobile and
web apps
Banjar et al.
[16]
Symptoms hospitalization
history, epidemiological info,
contact exposure
Fuzzy logic No Yes Clinical rules +
fuzzy sets plots
Web App
Our approach Symptoms, contact history,
location history, age,
immunity period
(vaccine/infection)
Fuzzy logic Yes Yes (new rules,
variables)
Yes (hybrid XAI) Morfees-
C19
4. Int J Elec & Comp Eng ISSN: 2088-8708
Design and development of a fuzzy explainable expert system for a diagnostic … (Omar El Beggar)
6943
3. PROPOSED METHOD
The proposal aims to develop a FEES for early diagnosis of COVID-19. It contains three main
components: a knowledge base, an inference engine, and user interfaces. The knowledge base includes facts
and rules. Rules are represented in form of IF-THEN statements and either defined by experts or collected
from reliable sources. The inference engine is the rule interpreter that provides inferences by selecting the
rules from the knowledge base that should be executed with regard to their priorities or weights. The user
interface is essential for users to interact with the system and could be extended with an explanation facility
to supply user explanations [10]. The FEES starts with the fuzzification of the crisp input data. Next, it
evaluates the fuzzy rules using the inference engine and aggregates their outputs. Finally, it defuzzifies the
outcome and provides explanations.
Our methodology to design and implement this system contains three main phases described as:
i) definition of fuzzy variables: creating membership functions associated with input and output variables,
ii) definition of fuzzy rules: expressing fuzzy rules gathered from discussions with experts and external
sources, and iii) generation of explanations using a hybrid XAI technique: providing different forms of
explanations related to the system conclusions.
Throughout the rest of the paper, the proposed FEES will be referred to as the Moroccan fuzzy
explainable expert system for diagnosing COVID-19 (MORFEES-C19). The main functionality of
MORFEES-C19 is the diagnosis of COVID-19. However, we have tried to produce our solution with
additional features that make it more attractive and easier to use. In fact, MORFEES-C19 is multilingual and
supports English, French and Arabic languages. The system also supplies user explanations and helps
patients to get quick medical assistance or notify the medical authorities by email. Moreover, actualizing
epidemiological data through communication with external sources allows a reliable diagnosis. As well, the
MORFEES-C19 could be extended with additional rules making thus the system even more scalable. Overall,
the main functionality concerning COVID-19 diagnosis is performed by MORFEES-C19 according to the
following three steps:
Step 1: in a chosen language, the patient enters his temperature and birth date. The system deduces
his age and records his visited places or localizations in the last 14 days. A location could concern a living, a
working, or a travelling place. The patient can submit his locations as many as he desires during this period.
The objective is to check whether he has visited a region with a high risk of contamination. For each location,
the system calculates the corresponding risk level from a dedicated table “region_stats”, and assigns it to the
submitted localization. This table is updated periodically from the external source “COVID19-geomatic.ma”
[18]. The website provides a GeoJSON file containing data on the epidemic evolution in Morocco by region.
The data herein concern the confirmed cases and the number of deaths recorded daily by region. A region
risk R is calculated per 100K people for the last 14 days using the (1),
𝑅 =
100 000
𝑃𝑜𝑝
∑ (𝑁𝐶𝑜𝑛𝑓𝑖𝑟𝑚𝑒𝑑𝑐𝑎𝑠𝑒𝑠𝑗 + 𝑁𝑑𝑒𝑎𝑡ℎ𝑠𝑗)
14
𝑗=1
(1)
where NConfirmedcasesj and Ndeathsj represent the number of confirmed cases and deaths recorded in a
region on a jth
day respectively. Pop is the region's total population. In this way, the system determines which
location visited by the patient presents the maximum risk of contamination.
Step 2: the second step consists of entering the symptoms’ severities according to [6], as well as the
contact exposure in the last 14 days, and the period separating either the possible infection by COVID-19 or
the vaccination against this disease.
Step 3: MORFEES-C19 fuzzy files all entries and fires next the appropriate fuzzy rules to provide
the patient's likelihood level of contracting COVID-19 and the different explanations related to this result.
The different performed diagnoses are saved in a relational data base. The diagnosis functionality could be
synthesized by algorithm 1. Besides, Figure 2 shows the software architecture of MORFEES-C19.
3.1. Definition of fuzzy variables
The first phase of the proposed method consists of creating membership functions required to
convert the input data into fuzzy sets. In this paper, the triangular (2), Gaussian (3) and trapezoidal (4)
functions are used to this purpose:
μA(x) = {
x−a
b−a
, a ≤ x ≤ b
c−x
c−b
, b ≤ x ≤ c
0, x < aorx > c
(2)
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6944
𝜇𝐴(𝑥) = 𝑒𝑥𝑝 [−
1
2
(
𝑥−𝑐
𝜎
)
2
] (3)
𝜇𝐴(𝑥) =
{
𝑥−𝑎
𝑏−𝑎
, 𝑎 ≤ 𝑥 ≤ 𝑏
1, 𝑏 ≤ 𝑥 ≤ 𝑐
𝑑−𝑥
𝑑−𝑐
, 𝑐 ≤ 𝑥 ≤ 𝑑
0, 𝑥 < 𝑎𝑜𝑟𝑥 > 𝑑
(4)
The fuzzy variables used in the proposal are symptoms, age, Max_Locs_Risk, Pos_Contact, and
Time_ Immunity. The following is a brief explanation of them:
a. Symptoms: In 2021, WHO classified COVID-19 symptoms into three categories [6]: i) main symptoms
are listed as fever, dry cough, tiredness, and loss of taste and smell; ii) less common symptoms include
diarrhea, headache, sore throat, rashes, muscle pains, and conjunctivitis; and iii) Serious Symptoms
comprise confusion, chest pain, and dyspnea. Three levels are defined to describe the severity of those
symptoms: slight, moderate, and severe. Figure 3(a) illustrates the fuzzy variable fever according to the
corporal temperature. The severities of the other symptoms are represented in a range of 0-3 using the
Gaussian function, such as headache shown in Figure 3(b).
b. Age: it could be described by five linguistic terms: child, young, middle-aged, old, and senile as described
in Figure 3(c).
c. Max_Locs_Risk: indicates the maximum risk deduced from the visited places in the last 14 days using
(1). The following terms: very low, low, medium, high, and very high have been defined for scoring and
ranking this variable, as shown in Figure 3(d).
d. Pos_Contact: indicates the possibility of contact with confirmed cases in the last 14 days. Like
Figure 3(b), this variable is ranked using the Gaussian function into three linguistic terms: unlikely,
likely, and very likely.
e. Time_Immunity: indicates the last time a person developed immunity to COVID-19 from either a direct
infection or a vaccination. Four linguistic terms are used to represent it Figure 3(e): very small, small,
average, and long.
Moreover, Cov19_Likelihood is the fuzzy output variable that will be derived from the fuzzy input
variables previously described. It represents a likelihood level of contracting COVID-19 expressed as a
percentage and ranked into seven linguistic terms as seen in Figure 3(f): extremely low, very low, low, medium,
high, very high, and extremely high. Table 2 describes the all-fuzzy variables of the proposal.
Algorithm 1. Diagnosis functionality of MORFEES-C19
Input: bdate, syms, locs, temp, posc, timm /*birth date, symptoms, visited
localizations, temperature, possible contact and time immunity*/
Output: likelihood //a percentage of possible contracting of COVID-19
Data: maxRisk, age
Procedure submitInputs(bdate,syms,locs,temp,posc,timm)
//check either input is out of range or not and get data (age and MaxRisk)
age=calculateAge(bdate)
foreach L in locsdo
// get risk form Table Region stats
L.risk=findRiskfromDB(L.name)
// locs implements a Comparator by risk
maxRisk=Collections.max(locs)
Procedure fuzzifyInputs(age,syms,posc,maxRisk,timm)
Fuzzy_age=membership_age(age)
fuzzy_fever=membership_temp(temp)
fuzzy_MaxRisk=membership_risk(MaxRisk)
fuzzy_posc=membership_contact(posc)
fuzzy_timm=membership_immunity(timm)
fori=1→syms.sizedo
fuzzy_Si.sev=membership_severity(Si.sev)
Function calculatelikelihood():real
// using a Mamdani model
outputsrules=fireRules(fuzzy_ age, fuzzy_ fever, fuzzy_ MaxRisk,
fuzzy_ posc,fuzzy_timm,fuzzy_S1.sev,...,fuzzy_Sn.sev)
fuzzy_likelihood=aggregate(Outputsrules)
likelihood=defuzzify(fuzzy_ likelihood)
return likelihood
Procedure generateExplanations()
convertToNL(Rule)
displayVisulisations()
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Design and development of a fuzzy explainable expert system for a diagnostic … (Omar El Beggar)
6945
Figure 2. The software architecture of MORFEES-C19 and its different components
(a) (b)
(c) (d)
(e) (f)
Figure 3. The used membership functions of the fuzzy variables:(a) fever, (b) headache, (c) age, (d) max risk,
(e) time immunity, and (f) likelihood of contracting COVID-19
Table 2. Fuzzy variables of the proposal
Linguistic variable Linguistic terms Universe of discourse
Fever slight, moderate, and severe From 36 to 43 ℃
The rest of symptoms slight, moderate, and severe from 0 to3
Age child, young, middle-aged, old, and senile greater than 0 years old
Max locs risk very low, low, medium, high, and very high greater than 1 case per 100K
Pos Contact unlikely, likely, and very likely from 0 to 3
Time Immunity Very small, small, average, and long from 0 to 12 months
Cov19_Likelihood extremely low, very low, low, medium, high, very high,
and extremely high
from 0% to 100%
3.2. Definition of fuzzy rules
The proposed system is a Mamdani system [19] with a forward chaining inference engine [20]. This
kind of system is well-fitting to human inputs and more interpretable than other ones [21]. In a Mamdani
model, the output of each fuzzy rule is also a fuzzy set. The form of a fuzzy rule according to this model:
Fever
Universe
Membership
Grade
0.0
0.6
38 39 40 41 42 43
slight moderate severe
Headache
Universe
Membership
Grade
0.0
0.4
0.8
0.0 0.5 1.0 1.5 2.0 2.5 3.0
slight moderate severe
Age
Universe
Membership
Grade
0.0
0.4
0.8
0 20 40 60 80 100 120
child young middle_age old senile
Max_Locs_Risk
Universe
Membership
Grade
0.0
0.4
0.8
0 50 100 150 200 250 300 350
v_low low medium high v_high
Time_Immunity
Universe
Membership
Grade
0.0
0.4
0.8
0 2 4 6 8 10 12
v_small small average long
Cov19_Likelihood
Universe
Membership
Grade
0.0
0.4
0.8
0 20 40 60 80 100
e_low v_low low medium high v_high e_high
Discourse
Universe
Discourse
Universe
Discourse
Universe
Discourse
Universe
Discourse
Universe
Discourse
Universe
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𝐼𝐹 𝑥 𝑖𝑠 𝐴 𝑎𝑛𝑑 𝑦 𝑖𝑠 𝐵 𝑇𝐻𝐸𝑁 𝑧 𝑖𝑠 𝐶 (5)
where A and B are fuzzy sets of the rule antecedents, while C is a fuzzy set of the rule consequent.
In Mamdani model, the conjunction (AND) between rules’ antecedents and implication are evaluated with
the t-norm operator. Meanwhile, t-conorm is used for disjunction (OR) and aggregation [19].
The second phase of our proposal consists of defining the fuzzy rules according to the Mamdani
model. Based on the fuzzy variables already defined in the previous phase, the fuzzy rules are designed and
built with the assistance of a domain expert (an internist) and expressed using fuzzy control language (FCL)
[22]. FCL supports the IEC 61131-7 specifications, and allows getting portable fuzzy rules. Our choice is
motivated by the language independency of systems suppliers and the ability to exchange programs control
between different platforms [23]. We can also assign a weight and support degrees to a fuzzy rule,
representing its importance and support respectively. Both degrees lie between 0 and 1. The support degree is
deduced from the evaluation of all memberships present in the rule antecedents and the connection methods
used between them (MIN, MAX, and PROD). Some machine learning algorithms could be used to adjust
these degrees and optimize the FEES accuracy. A sample of the defined fuzzy rules in FCL is shown in
Figure 4.
Figure 4. The sample defined fuzzy rules
To create the fuzzy inference rules, we have based on the basic principle that considers symptoms of
the first category [6] as determinist factors of the likelihood of contracting COVID-19. As far as the remaining
inputs only increase this likelihood. Besides, weights are gradually assigned to fuzzy rules to prioritize those
that provide high likelihood values in the final aggregation. The inference engine generates the output fuzzy sets
from the fuzzy rules whose premises match the input fuzzy sets. All output fuzzy sets are aggregated next into a
single fuzzy set. At this stage, the combined output fuzzy set constitutes the diagnostic result. This fuzzy result
cannot be interpreted easily by healthcare specialists. Thereby it should be converted into a crisp value using the
defuzzifier component. There are many techniques for defuzzification, such as the center of gravity or Maxima
[24]. In this paper, we have chosen the center of gravity presented in (6).
𝑐 =
∫ 𝑥𝜇(𝑥)𝑑𝑥
𝑚𝑎𝑥
𝑚𝑖𝑛
∫ 𝜇(𝑥)𝑑𝑥
𝑚𝑎𝑥
𝑚𝑖𝑛
(6)
3.3. Generation of explanations with a hybrid XAI technique
Explainable Artificial Intelligence (XAI) consists of understanding the decisions made by machines
and making their behaviors more intelligible to humans [25]. The literature distinguishes between transparent
or white-box models that are interpretable by design, and those considered opaque or black-box models that
can be explained through XAI techniques [26]. Although rule-based systems are considered transparent by
design, an explanation facility module is required to increase their level of transparency. Indeed, the
explanation facility is the FEES component that might interact with the user interface and knowledge base to
RULE 1: IF Fever IS slight AND Tiredness IS slight AND Dry_cough IS slight AND Loss_taste_smell IS slight
THEN Cov19_ Likelihood IS extremely low WITH 0.1;
RULE 2: IF Fever IS slight AND Tiredness IS slight AND Dry_cough IS slight AND Loss_taste_smell IS slight AND (Age
IS old OR Age IS senile) THEN Cov19_ Likelihood IS very low WITH 0.5;
RULE 3: IF Fever IS slight AND Tiredness IS slight AND Dry_cough IS slight AND Loss_taste_smell IS slight
AND Time_ Immunity IS long THEN Cov19_ Likelihood IS very low WITH 0.5;
RULE 4: IF Fever IS slight AND Tiredness IS slight AND Dry_cough IS slight AND Loss_taste_smell IS slight AND
(Pos_Contact IS likely OR Pos_ Contact IS very likely) THEN Cov19_ Likelihood IS low;
RULE 5: IF Fever IS slight AND Tiredness IS slight AND Dry_cough IS slight AND Loss_taste_smell IS slight
AND (Max_ Locs_ Risk IS high OR Max_ Locs_ Risk IS very high) THEN Cov19_ Likelihood IS low;
RULE 6: IF Fever IS slight AND Tiredness IS slight AND Dry_cough IS slight AND Loss_taste_smell IS slight AND
(Diarrhoea IS NOT slight OR Conjunctivitis IS NOT slight OR Headache IS NOT slight OR Muscle pains IS NOT slight OR
Sore throat IS NOT slight OR Rashes IS NOT slight) THEN Cov19_ Likelihood IS very low WITH 0.5;
RULE 7: IF Fever IS slight AND Tiredness IS slight AND Dry_cough IS slight AND Loss_taste_smell IS slight AND
(Confusion IS NOT slight OR Chest_ pain IS NOT slight OR Dyspnea IS NOT slight) THEN Cov19_ Likelihood IS low;
RULE 8: IF Fever IS NOT slight AND Tiredness IS slight AND Dry_cough IS slight AND Loss_taste_smell IS slight THEN
Cov19_ Likelihood IS very low WITH 0.1;
RULE 9: IF Fever IS NOT slight AND Tiredness IS NOT slight AND Dry_cough IS slight AND Loss_taste_smell IS slight
THEN Cov19_ Likelihood IS low WITH 0.1;
RULE 10: IF Fever IS NOT slight AND Tiredness IS NOT slight AND Dry_cough IS NOT slight AND Loss_taste_smell IS
slight THEN Cov19_ Likelihood IS medium WITH 0.1;
RULE10: IFFeverISNOTslightANDTirednessISNOTslightANDDry_coughISNOTslightANDLoss_taste_smellISslightTHEN
Cov19_ Likelihood IS medium WITH 0.1;
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Design and development of a fuzzy explainable expert system for a diagnostic … (Omar El Beggar)
6947
provide explanations. These explanations should take a form understandable by users. As stated by [26]–[28],
different explanation forms could be generated in XAI: text explanations [29], visualizations [30], local
explanations [28], Counterfactual explanations [27], explanations by comparison [31], explanations by
example [26], explanations by simplification [26] and feature relevance [32].
Explainability in an expert system could be confronted with two major problems that are coverage and
specificity [26]. Coverage corresponds to the number of rules. As long as the expert system uses a lot of rules,
its performance will be increased to the detriment of its explicability. The specificity problem relates to the rule
length. When the rule’s antecedents or consequents are too-long, the explainability also diminishes.
The last phase of the proposal consists of providing an explanation facility that adopts a hybrid XAI
technique and solves the coverage and specificity problems. Our hybrid XAI comprises text and visual
explanations as well as feature relevance of local rules. Local rules describe the rules that led to a given
prediction [28], while their associated weights and degrees of support determine their impact or relevance in
the final prediction. Despite accommodating fuzzy logic with rules’ expressiveness, this is not enough to
provide explanations in a fully-comprehensible manner. Automatic generation of rules in NL could make
system decisions more familiar to users and easy to understand. For that, the explanation facility converts the
fuzzy rules expressed in Mamdani form to an NL form according to the following template:
I found patient’s IN_VAR1 is Term_VAR1 from user input
And I also found patient’s IN_VAR2 is Term_VAR2 from user input
And I also found patient’s IN_VARn is Term_VARn from user input
Therefore, the OUT_VAR is Term_VAR from the activation rule N.
Term_VARi and Term_VAR represent the linguistic terms associated with input and output variables
respectively. N is the number or the noun of the locale rule. An example of expressing rule 1 according to
this template:
I found patient’s fever is severe from user input
And I also found patient’s tiredness is severe from user input
And I also found patient’s dry cough is moderate from user input
And I also found patient’s loss of taste and smell is moderate from user input
Therefore the likelihood of contracting COVID-19 is high from the activation rule 1.
The functions convertToNL(R:Rule), getRightTerm(V:Variable) and getActiveVariable(vars:Set) are
used to support conversion. Indeed, convertToNL(RuleR) is the main function of conversion and the other
ones are considered helpers. It is important to note that the coverage problem is solved using the operators
OR and NOT that allow building combined premises, minimizing thus the number of rules. However, this is
at the stake of compromising with the specificity problem, since rules could become longer. For this reason,
the aforementioned helpers solve the problem of specificity. The function getRightTerm(V:Variable) returns
the appropriate linguistic term when the operators OR or NOT are present in the antecedent or the consequent
of a rule. It selects the term representing the maximum membership value according to the user input as seen
in algorithm 2. For example, the expression “Age IS NOT young”, with 65 years old as user input, will be
“Age is middle-aged”. Also, the function getActiveVariable(Vars:Set) returns the set of variables that
correspond to the symptoms whose associated premises are satisfied as seen in algorithm 3. Another example
concerning the rule antecedent with the expression “AND Confusion IS NOT slight OR Chest pain IS NOT
slight OR Dyspnea IS NOT slight, with 2.7 as an input of Dyspnea severity, will be “Dyspnea IS severe”.
Algorithm 2. Conversion of a premise with NOT/OR operator
Function getRightTerm(V:Variable):string
ch, md, ol, se, maxi: real; term : string
/* obtain membership values from the user input for each fuzzy set */
ch=V .getMembershipValue(“child”)
md=V .getMembershipValue (“middle-aged”)
ol=V .getMembershipValue (“old”)
se=V .getMembershipValue(“senile”)
maxi = max{se, max{ol, max{ch, md}}}/* get the maximum membership value */
if maxi== se then
term=“senile”
else if maxi == ol then
term=“old”
else if maxi==md then
term=“middle-aged”
else
term=“child”
return term
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Concerning the visual explanations, the facility explanation of MORFEES-C19 provides different
plots. For instance, the aggregated fuzzy set and its center of gravity are shown in a plot to highlight the
system outcome. Other plots are also available to discover the different ranges and fuzzy sets equivalent to
user inputs. Besides, a bar and pie charts are proposed to allow comparisons between locations ‘risks and
rules supports respectively. Some FEES could contain input variables whose values come from external
sources and could be updated over time, such as Max_Locs_Risk. Monitoring the evolution of this variable
over time is very important for users to recognize the system’s conclusions. Understanding further when such
time-observable variables have repeated behaviors or spikes might improve the system’s explicability.
Algorithm 3. Conversion of a rule antecedent containing OR operator
Function getActiveVariable(V ars:Set): Set
max,mo,se,sl: float; actives: Set; // Diagnostic.getDetails() returns the diagnostic
details, i.e symptoms and their severities entered by the end-user
foreach D in Diagnostic.getDetails() do
foreach V in vars do
/* lingterm is the linguistic term used in the premise */
if D.Symptom.name == V.name AND V.getMembershipValue(lingterm)>0 then
actives.add(V);
return actives
Within this context, time will be an additional explanatory element. In considering this element, we
have proposed a time series chart to illustrate the evolution of this kind of variable at equal intervals of time.
It is well-known that a time series is a potential tool for data analysis and forecasting. The main features of
time series are level, trend, seasonality, and noise which could be analyzed to make a deep understanding of
variables. Overall, the early expert systems such as MYCIN [11] were based on the paradigm why/how or
rule-trace to provide explanations. In the proposal, thanks to XAI techniques, we have extended this
paradigm with new questions “How much” and “When”. In fact, how much relevance is made on a rule to
get the output value? The support degrees and weights assigned to rules give a clear answer to this question.
When a variable has a remarkable change? This question is answered using the time series chart.
4. RESULT AND DISCUSSION
MORFEES-C19 is developed with Java language and uses different Java API, such as JavaMail, Gson,
jfreeChart, JPA, and jFuzzyLogic. The latter is an open-source API to integrate fuzzy logic in Java applications
[22]. It supports developing fuzzy expert systems and defining fuzzy rules with FCL. Figure 5(a) represents the
home frame of MORFEES-C19 displayed in English, while Figure 5(b) shows the Arabic version.
This frame contains the following inputs: the patient temperature, birth date and visited locations in
the last 14 days. The next step consists of submitting the symptoms’ severities, the possibility of contact
exposure to positive cases and the last period of developed immunity. Herein, three main symptoms: fever,
dry cough, and tiredness are defined with moderate severity, while loss of taste and smell is severe. Besides,
the immunity is developed from nine months with a likely possibility of contacting positive cases. Figure 6(a)
illustrates these input values submitted to MORFEES-C19. The latter predicts in the last frame as shown in
Figure 6(b) a likelihood of contracting COVID-19 of 92.97% and generates the locale rules in English NL
with the associated visualizations.
(a) (b)
Figure 5. Home frames of MORFEES-C19 in (a) English and (b) Arabic languages
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(a) (b)
Figure 6. Input and output frames of MORFEES-C19 (a) user input frame and (b) output frame with
explanations
MORFEES-C19 provides important visual explanations thanks to the available hypertexts in the
explanation interface. For instance, the hypertext “compare rules support” allows comparing the support of
local rules through a pie chart as seen in Figure 7(a). When clicking on the time series icon that marks the
visited location with high risk, it appears as shown in Figure 7(b). This chart draws the evolution of the
location risk in the last six months. The data is extracted in real-time from a GeoJSON file provided by the
website [18]. About symptoms hypertexts, they show the corresponding fuzzy sets plots and the current user
inputs.
(a) (b)
Figure 7. Visual explanations (a) pie chart of rules supports and (b) time series of risk evolution
5. CONCLUSION
In this article, we have proposed a fuzzy explainable expert system for early diagnosis of
COVID-19. This system infers the likelihood of contracting COVID-19 based on the patient’s symptoms,
personal information and activities. This expert system is a medical robotic application that we will attempt
to integrate into medical robots. The objective is to assist overwhelmed hospitals with COVID-19 cases and
protect the lives of both clinicians and patients. This robotic application integrates fuzzy logic to handle
uncertainty and vagueness during diagnosing. For this reason, different membership functions are defined for
the system variables in addition to the fuzzy rules required to infer the output variables.
Furthermore, our proposal adopts a hybrid XAI technique that provides different explanation forms,
mainly textual and visual ones. The text explanations are generated in NL through an automatic rule-
generation algorithm. In the meantime, the coverage and specificity problems are solved using specified
helpers. This fact improves rule expressiveness and gives robots the ability to communicate rules to humans
in a proper manner. Moreover, the visual explanations are represented by different plots that depict system
inputs, outcomes, and inferred rules. In particular, plots drawing the local rules relevance and risk evolution
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by localization provide a new explanation paradigm “how much/when”. The latter extends the rule trace-
based paradigm “how/why” of old expert systems to get thus a solution with a high level of explicability.
In future works, we plan to install and deploy this medical robotic application on the robot operating
system Raspbian and test it in some clinical settings. The system can be extended with some clinical
measures, such as blood pressure, respiratory rate, oxygen saturation, and chest images. However, this
implies that the system- to-be should be connected with the clinic information system. As well, one of the
interesting future directions is to apply machine learning (ML) algorithms to auto-generate the fuzzy rules.
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BIOGRAPHIES OF AUTHORS
Omar El Beggar received the Engineering degree in Computer Science from
ENSIAS, University Mohammed V, Morocco in 2002, and his Ph.D. in Computer Science
from FSTS, University Hassan I, Morocco in 2013. He obtained afterwards his HDR in Soft
Computing and Meta-modelling of decisional support systems from FSTM, University Hassan
II, Morocco. Currently, he is a full Professor at the Department of Computer Science at the
same faculty and the Pedagogical Director of the engineering department “software
engineering and IT systems integration” (ILISI) since 2021. His research interests include
green IT, explainable artificial intelligence, MDA, multi-criteria decision aid, and fuzzy logic.
He is member of IEEE CIS and author of many publications in relevant international journals
and conferences. He can be contacted at email: omar.elbeggar@fstm.ac.ma.
Mohammed Ramdani received his Ph.D. in fuzzy machine learning in 1994,
and his HDR in perceptual computation in 2001, at the University of Paris VI, France. Since
1996, he is a full Professor at the FSTM, University Hassan II of Casablanca, Morocco. In the
same faculty, for the periods 1996-1998 and 2003-2005, he held the position of head of the
Computer Science department. Between 2008 and 2014, he was the Pedagogical Director of
the engineering department ILISI. Since 2006, he is the Director of the Computer Science Lab.
With many relevant publications in indexed journals and conferences, his research interests
include explanation in ML, perceptual computation with fuzzy logic and big data mining. He
can be contacted at email: mohammed.ramdani@fstm.ac.ma.
Mohamed Kissi received in 2004 his Ph.D. in Computer Science from the
UPMC, France. Currently, He is a full Professor in the Department of Computer Science,
FSTM, University Hassan II, Morocco. His current research interests include machine
learning, data and text mining (Arabic) and big data. He is the author of many research papers
published in conference proceedings and international journals about Arabic text mining,
bioinformatics, genetic algorithms and fuzzy sets and systems. He is member of IEEE CIS and
expert of the scientific committee of the faculty. He can be contacted at email:
mohamed.kissi@fstm.ac.ma.