A Survey on Machine Learning Techniques for Multimodal Biomedical Signal Processing

In recent years, there has been a significant growth in the development of machine learning algorithms towards better experience in patient care. In this paper, a contemporary survey on the deep learning and machine learning techniques used in multimodal signal processing for biomedical applications is presented. Specifically, an overview of the preprocessing approaches and the algorithms proposed for five major biomedical applications are presented, namely detection of cardiovascular diseases, retinal disease detection, stress detection, cancer detection and COVID-19 detection. In each case, processing on each multimodal data type, such as an image or a text is discussed in detail. A list of various publicly available datasets for each of these applications is also presented. © 2023 IEEE.

A Long Short Term Memory Machine Learning Technique for Covid-19 Patient Count Prediction

The Corona-virus H19 pandemic is quickly spreading throughout the globe. Every three to four times, waves occur and have a major effect on people's lives. Other illnesses including covid disorders are misdiagnosed in this setting. There is no reliable statistics on the total number of covid patients in the nation, and no system exists to track them. This prevents the patients from receiving the necessary care and treatment. The number of patients in a given dataset may be determined with more precision using AI methods. In this article, we show how to forecast how many patients will be included in the Covid-19 database by using an adaptive method. Python spyder is used to run the simulation. . © 2023 IEEE.

A Predictive Model for Assessing Satisfaction with Online Learning for Higher Education Students During and After COVID-19 Using Data Mining and Machine Learning Techniques: A Case of Jordanian Institutions

Higher education institutions confronted an escalating unexpected pressure to rapidly transform throughout and after the COVID-19 pandemic, by replacing most of the traditional teaching practices with online-based education. Such transformation required institutions to frequently strive for qualities that meet conceptual requirements of traditional education due to its agility and flexibility. The challenge of such electronic learning styles remains in their potential of bringing out many challenges, along with the advantages it has brought to the educational systems and students alike. This research came to shed the light on several factors presented as a predictive model and proposed to contribute to the success or failure in terms of students' satisfaction with online learning. The study took the kingdom of Jordan as a case example country experiencing online education while and after the covid -19 intensive implementation. The study used a dataset collected from a sample of over "300” students using online questionnaires. The questionnaire included "25” attributes mined into the Knime analytics platform. The data was rigorously learned and evaluated by both the "Decision Tree” and "Naive Bayes” algorithms. Subsequently, results revealed that the decision tree classifier outperformed the naïve bayes in the prediction of student satisfaction, additionally, the existence of the sense of community while learning electronically among other reasons had the most contribution to the satisfaction. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Medical Image Diagnosis Using Deep Learning Classifiers for COVID-19

COVID-19 and other diseases must be precisely and swiftly classified to minimize disease spread and avoid overburdening the healthcare system. The main purpose of this study is to develop deep-learning classifiers for normal, viral pneumonia, and COVID-19 disorders using CXR pictures. Deep learning image classification algorithms are used to recognize and categorise image data to detect the presence of illnesses. The raw image must be pre-processed since deep neural networks perform the most important aspect of medical image identification, which includes translating the raw image into an intelligible format. The dataset includes three classifications, including normal and viral pneumonia and COVID-19. To aid in quick diagnosis and the proposed models leverage the performance validation of several models, which are summarised in the form of a recall, Fl-score, precision, accuracy, and AUC, to distinguish COVID-19 from other types of pneumonia. When all the deep learning classifiers and performance parameters were analyzed, the ResNetl0lV2 achieved the highest accuracy of COVID-19 classifications is 97.S2%, ResNetl0lV2 had the greatest accuracy of the normal categorization is 92.04% and the Densenet201 had the greatest accuracy of the pneumonia classification is 99.92%. The suggested deep learning system is an excellent choice for clinical use to aid in the COVID-19, normal, and pneumonia processes for diagnosing infections using CXR scans. Furthermore, the suggested approaches provided a realistic technique to implement in real-world practice, assisting medical professionals in diagnosing illnesses from CXR images. © 2023 IEEE.

Ischemic Heart Disease Prediction Using Machine Learning and Deep Learning Techniques

To have control over heart patient health, we need a capable detector which finds out based onhealth records. The idea is to work on coronary artery disease (CAD), which has been the majorhealth issue at present. We took a data set to train our system (machine learning algorithm) towork on the CAD and identify the user's health stage and provide the required information. Asper previous analysis, we got accuracy of 96% now with a minor modification we are trying to impact the accuracy. CAD has been the major health disease that is leading to death in world at present after COVID19, it is causing 33% of death rate by a survey by WHO. So, it is essentialto overcome the disease with proper analysis and prevention, which is all about our project. We are trying to make healthcare handy such that a person that analyze and know about his/her health condition from anywhere and at any time regardless of working hours. © 2023 IEEE.

Hybridized face-to-face and online laboratory content in the time of COVID-19.

This article describes strategies to adapt and ensure equivalency of content coverage for an existing protein assay laboratory practical for concurrent face-to-face and online deliveries during COVID-19 and beyond.

Machine Learning Techniques on X-ray Images for Covid-19 Classification

The Public Health Commission of Hubei Province, China, at the end of 2019reported cases of severe and unknown pneumonia, marked by fever, malaise, dry cough, dyspnea, and respiratory failure, that occurred in the urban area of Wuhan, according to the World Health Organization (WHO). The lung infection, SARS-CoV-2, also known as COVID-19, was caused by a brand-new coronavirus (coronavirus disease 2019). Since then, infections have increased exponentially, and the WHO labeled the outbreak a worldwide emergency at the beginning of March 2020. Infected and asymptomatic individuals who can spread the virus are the main sources of it. The transmission occurs mainly by airthrough the air through the droplets, however indirect transmission is also possible, such as through contact with infected surfaces. It becomes essential to identify viral carriers as soon as possible in order to stop the spread of the disease and reduce morbidity and mortality. Imaging examinations, which are among the specific tests used to make the definite diagnosis, are crucial in the patient's management when COVID-19 is suspected. Numerous papers that use machine learning techniques discuss the use of X-ray chest radiographs as a component that aids in diagnosis and permits disease follow-up. The goal of this work is to supply the scientific community with information on the most widely used Machine Learning algorithms applied to chest X-ray images. © 2022 IEEE.

Fusion-Based Segmentation and Classification of Novel Coronavirus or Pneumonia from Chest X-ray Images Using Machine Learning Techniques

The deadfall widespread of coronavirus (SARS-Co V-2) disease has trembled every part of the earth and has significant disruption to health support systems in different countries. In spite of such existing difficulties and disagreements for testing the coronavirus disease, an advanced and low-cost technique is required to classify the disease. For the sense of reason, supervised machine learning (ML) along with image processing has turned out as a strong technique to detect coronavirus from human chest X-rays. In this work, the different methodologies to identify coronavirus (SARS-CoV-2) are discussed. It is essential to expand a fully automatic detection system to restrict the carrying of the virus load through contact. Various deep learning structures are present to detect the SARS-CoV-2 virus such as ResNet50, Inception-ResNet-v2, AlexNet, Vgg19, etc. A dataset of 10,040 samples has been used in which the count of SARS-CoV-2, pneumonia and normal images are 2143, 3674, and 4223 respectively. The model designed by fusion of neural network and HOG transform had an accuracy of 98.81% and a sensitivity of 98.65%. © 2022 IEEE.

Human Activity Recognition Using Machine Learning Technique

COVID has made education shift towards online mode. In online mode, instructors have a hard time keeping track of their students. Students' performance in online classes falls considerably below the level of learning due to a lack of attention. This initiative aids in the supervision of students during online classes. Artificial Intelligence (AI) models are being developed to better recognize student activities during online sessions. Many applications rely on determining an individual's mental state. When evaluating which subtask is the most challenging, a quantitative measure of human activity while executing a task can be helpful. Thus, the goal of this research is to create an algorithm that uses EEG data gathered with a Muse headset to measure the amount of cognitive intelligence of students during online classes. The data collected by the Muse headset is multidimensional, and it is pre-processed before being fed into machine learning algorithms. Using feature selection, the dataset's dimension is now reduced. The model's precision and recall were calculated, and a confusion matrix was created. The Support Vector Machine produces better outcomes in the experiment. © 2022 IEEE.

Intelligent ResNet-18 based Approach for Recognizing and Assessing Arabic Children's Handwriting

Studies tackling handwriting recognition and its applications using deep learning have been promoted by developing advanced machine learning techniques. Yet, a shortage in research that serves the Arabic language and helps develop teaching and learning processes still exists. Moreover, COVID-19 pandemic affected the education system considerably in many countries and yielded an immediate shift to distance learning and extensive use of e-learning tools. An intelligent system was proposed and used in this paper to recognize isolated Arabic handwritten characters. Particularly, pre-trained CNN models were exploited and fine-tuned to meet the requirements of the considered application. Specifically, the designed system automatically supports teaching Arabic letters and evaluating children's writing skills. The Arabic Handwritten Character Dataset (AHCD) was used to train the models built upon ResNet-18 and assess the overall system performance. Furthermore, several models were investigated using various hyper-parameter settings in order to determine the most accurate one. The best model with the highest accuracy rate of 99% was used and integrated into the proposed system to recognize the Arabic alphabets. © 2023 IEEE.

The Techniques of Non-Muslim Students? in Learning Arabic Speaking Skills Online

The learning of Arabic in Malaysia, which was once exclusive for Muslim students, is now gaining popularity among local non-Muslims. Learning Arabic, even at the beginner's level, is not easy for non-Muslim students. The situation is even more challenging during the COVID-19 pandemic which witnessed a total shift to the online teaching and learning mode. The aim of this study is to identify the learning techniques employed by non-Muslim students to learn Arabic speaking skills online. This qualitative study interviewed six non-Muslim undergraduate students from Universiti Malaysia Sabah (UMS) who were taking a level 2 Arabic course. The results of shows that non -Muslim students were able to follow the process of learning speaking skills online, even though they had some difficulties at the beginning due to the lack of basics in Arabic. Their ability to follow the learning process were mainly the result of mastering the basic skills of reading Arabic texts and writing Arabic letters. Equipped with these two skills, the students demonstrated 22 learning techniques which can be divided into three categories according to their intended use, namely Malay-Arabic translation, reading Arabic texts without diacritical marks, and Arabic speech improvement. This study also suggests that fiurther research should be carried out to explore the potential of Asynchronous Multimedia-based Oral Communication (AMOC) in Arabic language learning for beginners.

Learning Techniques for Depression Detection: A Comparative Studies

Depression is a common mental problem that can fundamentally affect individuals' emotional wellness as well as their everyday lives. After COVID-19 other pandemics and subsequent social isolation this issue is more potent than ever. Numerous research works have been going on searching for methods that effectively recognize depression in order to detect depression. In this regard, a number of studies have been proposed. In this study, it examines a number of previous ones utilizing various Machine Learning (ML) and Artificial Intelligence (AI) methods for depression detection. In addition, various methods for determining an individual's mood and emotion are discussed. This study also discusses how facial expression, voice, gesture can be understood by chatbot and classified it as a depressed person or not. Addition to this, it reviews all the related research works and evaluates their methods to detect depression. © 2023 IEEE.

Covid-19 and Diabetes Risk Prediction for Diabetic Patient using Advance Machine Learning Techniques and Fuzzy Inference System

Diabetes has become a common and critical disease which generally occurs due to the presence of high sugar in blood for long time. A diabetic patient has to follow different rules and restrictions where he/she has to be under proper attention by measuring diabetes level frequently to avoid unexpected risk. The risk become more when patient even doesn't know that he/she is already having diabetes and doesn't follow those restrictions. To prevent this risk, everyone should check the diabetes status to be sure. With the same target different system using machine learning techniques have been introduced which can predict the diabetes status of a patient. But the challenging fact is that the performances and accuracy of those models are questionable where there may be a huge risk of patient's life. The conventional systems are not able to show that which level of diabetes a patient can have using the previous records. To solve this issue, through this paper an efficient system has been proposed with which the diabetes status can be predicted correctly. The proposed system can also show the complexity of diabetes as well as the Covid-19 risk percentage that can also be possible to measure. After comparing several machine learning techniques, the suitable model has been selected where high level of accuracy has been ensured in term of predicting the disease. © 2022 IEEE.

A Novel Real-time Automated Face Classification and Detection system using Machine Learning Technique

In recent days, Image classification and detection technique has become an important and more essential in the Image processing research field. Creating effective face detection is an essential aspect of handling the detection mechanism, Tracking mechanism and Validation mechanism. The classical methods used for face detection do not have sufficient output. This research paper presents various studies and how machine learning methods are become to solve many challenges present in the face detection system. The first phase of work has a classification model with support vector machines, decision trees and Hybrid Ensemble Transfer learning algorithm. The second phase of work is investigated with real-the world's most popular dataset from World Masked Face Image Dataset and Label Faces in the wild (RMFD). Moreover, the experiment, results show how better accuracy and fast computation which has been achieved by Hybrid Ensemble algorithm with SVM and Decision Trees machine learning techniques. This research helps to assist many social applications such as during pandemics like covid-19 and personal identity, it can be verifying the mask-worn persons. © 2023 IEEE.

The use of Deep Learning techniques in E-Learning systems and MOOCs

E-Learning and Massive Open Online Courses are old techniques, but since the Coronavirus, they have become more popular again. Students already suffer from a lack of concentration and motivation in traditional courses;thus, this lack affects online courses. Furthermore, another important Online Learning systems problem is the difference between learners in terms of Learning Styles, abilities, social characteristics as well as preferences, background, and other psychological and mental features. Generally, these features are not taken into account by scientists. Therefore, Deep Learning techniques and Datasets have been used to improve E-Learning systems and MOOCs in several aspects such as: predicting dropout, Learning Styles and performance of online learners, and even their attention after taking an online course. In this work, we have studied and analyzed many recent works in the area of using Deep Learning techniques to improve Online Learning systems and MOOCs. This analysis shows what researchers rely on to improve E-Learning and MOOCs and demonstrates that research does not use the definition of the appropriate Learning Style frequently. However, the most used ones are dropout and performance of learners. In another hand, learners' attention is still gap. © 2022 IEEE.

A New Respiratory Diseases Detection Model in Chest X-Ray Images Using CNN

Convolutional Neural Network (CNN)-based deep learning techniques have recently demonstrated increased potential and effectiveness in image recognition applications, such as those involving medical images. Deep-learning models can recognize targets with performance comparable to radiologists when used with CXR. The primary goal of this research is to examine a deep learning technique used on the radiography dataset to detect COVID-19 in X-ray medical images. The proposed system consists of several stages, from pre-processing, passing through the feature reduction using more than one technique, to the classification stage based on a proposed model. The test was applied to the COVID-19 Radiography dataset of normal and three lung infections (COVID-19, Viral Pneumonia, and Lung Opacity). The proposed CNN model has shown its ability to classify COVID, normal, and other lung infections with perfect accuracy of 99.94%. Consequently, the AI-based early-stage detection algorithms will be enhanced, increasing the accuracy of the X-raybased modality for the screening of various lung diseases. © 2023 Lavoisier. All rights reserved.

A Prototype for Interpretation of Sars-Cov-2 Tests Using Artificial Vision

Covid-19 has been declared a pandemic by the World Health Organization in March 2020, so science has been trying to help mitigate its effects from its various fields of study. Machine learning methods can play an important role in identifying test results that reveal whether an individual has the disease. This degree work presents a prototype based on computer vision and machine learning techniques to automatically detect SARS-CoV-2 serology tests. The goal of the prototype is to identify and classify the serology test cassette result by Immunoglobulin G and Immunoglobulin M indicators that are flagged after a test reaction time which is approximately 15 minutes. The results in the identification performed by the prototype are promising and ease its analysis, reducing the errors in the identification of the test and the interpretation of the results. The result is a prototype that allows to perform, simplify and improve the tasks of health professionals, which they must perform daily in the triage area. © 2022 IEEE.

COVID-19 detection on chest x-ray image using yolo based architecture

The whole world has been facing the problem of novel Coronavirus (COVID-19) since 2020. Over 88 million cases are confirmed and around 5 lacks deaths are accounted. Using the Lung-Computed Tomography (CT) Lesion Segmentation dataset, deep learning techniques may be used to quickly identify COVID-19 and the exact region that is infected. Based on CT, it is easy to identify the problem and the infected area, then assisting treatment of COVID-19. In the literature survey, research study has considered many research papers worked done work on identification of COVID-19 using chest/lungs X-ray image, and with that identified what are the deep learning-based models or methodology they have used for detecting COVID-19 result. To overcome their result, Authors have proposed a latest methodology of deep learning with the YOLO variant 7x to get optimum result of COVID -19 detection from lungs X-ray image. To identify COVID-19, Authors have applied proposed methodology on publically avail X-ray image-based dataset of COVID-19, proposed methodology has achieved good performance to detect COVID infection from lungs. © 2023 IEEE.

A Decision Support System using Machine Learning -IoT to tackle the Covid-19 pandemic in Uttarakhand

Covid-19 spread is worldwide;India is now at the second place where this epidemic is spreading with high rate. The state of Uttarakhand, a hilly state of India also has a significant impact of Covid-19. This paper suggests that machine learning techniques with IOT can equipped the doctors, and lab technicians to deal with this pandemic. Here, we also design a prediction system to help the doctors so that they can keep the records of infected patients. We used IoT, machine learning and ensemble methods for healthcare to store infected patient's data in the cloud database, and enable doctors/others to screen patient's data about their disease. We developed a decision support system to detect the diseases quickly and the treatment can be initiated immediately. © 2022 IEEE.

Proposed Experimental Design of a Portable COVID-19 Screening Device Using Cough Audio Samples

With the proliferation of COVID-19 cases, it has become indispensable to conceive of innovative solutions to abate the mortality count due to the pandemic. With a steep rise in daily cases, it is a known fact that the current testing capacity is a major hindrance in providing the right healthcare for the individuals. The common methods of detection include swab tests, blood test results, CT scan images, and using cough sounds paired with AI. The unavailability of data for the application of deep learning techniques has proved to be a major issue in the development of deep learning-enabled solutions. In this work, a novel solution of a screening device that is capable of collecting audio samples and utilizing deep learning techniques to predict the probability of an individual to be diagnosed with COVID-19 is proposed. The model is trained on public datasets, which is to be manually examined and processed. Audio features are extracted to create a dataset for the model which will be developed using the TensorFlow framework. The trained model is deployed on an ARM CortexM4 based nRF52840 microcontroller using the lite version of the model. The in-built PDM-based microphone is to be used to capture the audio samples. The captured audio sample will be used as an input for the model for screening. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.