Speech Recognition and Neural Networks based Talking Health Care Bot (THCB): Medibot

The COVID-19 pandemic has affected healthcare in several ways. Some patients were unable to make it to appointments due to curfews, transportation restrictions, and stay-at-home directives, while less urgent procedures were postponed or cancelled. Others steered clear of hospitals out of fear of contracting an infection. With the use of a conversational artificial intelligence-based program, the Talking Health Care Bot (THCB) could be useful during the pandemic by allowing patients to receive supportive care without physically visiting a hospital. Therefore, the THCB will drastically and quickly change in-person care to patient consultation through the internet. To give patients free primary healthcare and to narrow the supply-demand gap for human healthcare professionals, this work created a conversational bot based on artificial intelligence and machine learning. The study proposes a revolutionary computer program that serves as a patient's personal virtual doctor. The program was carefully created and thoroughly trained to communicate with patients as if they were real people. Based on a serverless architecture, this application predicts the disease based on the symptoms of the patients. A Talking Healthcare chatbot confronts several challenges, but the user's accent is by far the most challenging. This study has then evaluated the proposed model by using one hundred different voices and symptoms, achieving an accuracy rate of 77%. © 2023 IEEE.

Advances and Application of Artificial Intelligence and Machine Learning in the Field of Cardiovascular Diseases and Its Role During the Pandemic Condition

Artificial intelligence (AI) has shown an immense potential to affect diverse domains of healthcare during the COVID-19 pandemic. The applications of AI in the field of cardiovascular disorders during the COVID-19 pandemic were an added advantage to the cardiologists, as it helped in certain aspects of treatments digitally. This technology is constructive for providing sophisticated treatment in the area of cardiovascular medicine based on technology, because it may assist in assessing and measuring the human heart function. Artificial intelligence employs simulated neuronal program for predicting the survival of a COVID-19 patient affected with heart dysfunction. AI entails intricate algorithms for predicting successful evaluation and therefore the treatment protocol. AI utilizes various methods like cognitive computing, deep learning, and machine learning. It is integrated to make an assessment and determine multifaceted challenges. In humans, cardiovascular disease is still one of the major causes of death, and it is escalating for years together and is also very expensive. AI is employed to recognize new drug treatment and advance the efficacy of a clinician. AI is turning into a well-approved attribute of a variety of engineering and healthcare segments and is being expected to provide a feasible treatment stage. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Challenges in the Adoption of Artificial Intelligence and Machine Learning in Zimbabwe's Insurance industry

This study sought to investigate the challenges in the adoption of AI and ML in the Zimbabwean insurance industry. The TechnologyOrganisation-Environment (TOE) model was selected as the base theory underpinning the study. The study adopted a pragmatic research philosophy and a census was carried out on twenty insurance companies. Questionnaires were administered on operations managers representing their insurance companies. Interviews were used to collect data from 12 operation managers. NVivo version 16 was used to analyse the data thematically. The study results show that adoption of AI by the insurance sector in Zimbabwe is hindered by shortage of resources, lack of expertise and high cost of AI compliant products. These researchers recommend resource allocation, training of employees, culture change, and updated technological environment to ensure effective adoption of AI. This study will contribute to the body of knowledge, be significant to insurance practitioners and policy makers whilst giving direction for future studies. © 2022 IEEE.

Predicting Order Processing Times in E-Pharmacy Supply Chains During COVID Pandemic Using Machine learning—A Real-World Study

The purpose of this paper is to solve the problem of processing time prediction for orders for medical supplies placed through a large real-world e-Pharmacy—in a post-COVID-lockdown world—using artificial intelligence (AI) and machine learning (ML) techniques. We use an ensemble of ML regressors to predict the processing times of orders for medical supplies and an ensemble of ML classifiers to predict the shipment times of deliverables. We use intelligent model stacking methods to obtain performance improvements for our models. On exact match performance measurement scheme, our solution produces 548.49%, and on a 3-day range performance measurement scheme, our solution produces 25% improvement over the existing statistical solution implemented at the said e-Pharmacy. This is an important problem because when an e-Pharmacy can predict in advance the time elapsed between medical order placement and the time the order gets shipped out, the said e-Pharmacy can implement measures and controls to optimize the speed of fulfillment. We are one of the first to study real-world e-Pharmacy supply chain from the perspective of order processing time prediction under post-COVID-19-lockdown conditions and come up with a novel ML ensemble stacking approach to make predictions. The value this work provides is that we have shown that the adoption of AI and ML techniques in e-pharmacy supply chains would result in infusing certainty in the supply of therapeutics in these uncertain COVID lockdown times. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Detection of Covid-19 using Transfer Learning Technique

The whole world is suffering from the wave of the novel coronavirus that causes the large-scale death of a population and is proclaimed a pandemic by WHO. As RT-PCR tests to detect Coronavirus are costly and time taking. So now these days, the purpose of the researcher is to detect these diseases with the help of Artificial Intelligence or Machine learning-based models using CT scan images and X-rays images. So the testing cost, time taken and the number of data required could be minimized. In this paper, transfer learning based on three fine-tuned models has been proposed for Covid detection. The performance of these proposed fine-tuned models has been also compared with other competing models to check the accuracy and other matrices. © 2022 IEEE.

Analysis of the Regulatory Development Cryptocurrencies for Trading in Business with Deep Learning Techniques

As the number of people infected with COVID-19 continues to rise, a number of nations have implemented state wide quarantines. This has resulted in a global financial crisis that is having severe impacts on countries all around the world. As a direct consequence of the epidemic, unemployment rates have increased in a number of different regions, which has a substantial and detrimental effect on trade across the globe. In light of the current state of the economy, Artificial Intelligence (AI) is causing a shift in the manner in which businesses evaluate their bitcoin holdings. The application of AI in a commercial setting has the potential to produce a wide range of beneficial results. We are spared from completing as much manual labour as a direct result of the favourable effects that AI has had on technology. These consequences can be noticed in our day-to-day lives. In the event that there is a pandemic, having knowledge of AI and the various strategies it employs, such as the classifier model, could be beneficial. Humans will be better suited to make decisions if they have rapid access to the analyses and projections that are created by AI and big data. In order to be prepared for the arrival of the new world, the company is putting in more effort, in collaboration with small and medium-sized enterprises (SMEs) and start-ups, to improve the administration of virtual enterprises by having a presence on a variety of different e-trade systems. Artificial intelligence (AI) is currently being utilised in a variety of settings to assist with the process of identifying and implementing workable solutions to a variety of problems that can develop in the workplace. AI is being used to improve business operations in a wide variety of spheres, including marketing, fraud detection, algorithmic trading, customer assistance, portfolio management, and product recommendations based on customer preferences. These are just few of the sectors. These are just a few examples of the kinds of problems that artificial intelligence might be able to solve in the future. Given the present worth of cryptocurrencies, technological developments may also be made in order to improve the performance of the rules that have been provided and produce the most accurate conclusion that is possible. © 2022 IEEE.

Artificial Intelligence in the secondary education: the paradigm of Edu4AI

Technology Enhanced Learning (TEL) and Artificial Intelligence can substantially maximise the student learning experience and support students by acquiring both technical and traversal life skills. The COVID-19 pandemic in the last two years has accelerated the introduction of new methods and tools supplementing traditional practices. In this paper we present the methodology posed within the European funded project Edu4AI “Artificial Intelligence and Machine Learning to Foster 21st Century Skills in Secondary Education” to introduce artificial intelligence in secondary education curricula, based on the interdisciplinary cooperation of educational and technical partners from four European countries exploring how user-friendly and mostly graphical environments together with simple engaging pilot projects can be used for this purpose. Furthermore we present the criteria, selection methodology and description of these pilot projects and give an outlook for planned future work. © 2022 Copyright for this paper by its authors.

Integrating Artificial Intelligence into Cybersecurity Curriculum: New Perspectives

As societies rely increasingly on computers for critical functions, the importance of cybersecurity becomes ever more paramount. Even in recent months there have been attacks that halted oil production, disrupted online learning at the height of COVID, and put medical records at risk at prominent hospitals. This constant threat of privacy leaks and infrastructure disruption has led to an increase in the adoption of artificial intelligence (AI) techniques, mainly machine learning (ML), in state-of-the-art cybersecurity approaches. Oftentimes, these techniques are borrowed from other disciplines without context and devoid of the depth of understanding as to why such techniques are best suited to solve the problem at hand. This is largely due to the fact that in many ways cybersecurity curricula have failed to keep up with advances in cybersecurity research and integrating AI and ML into cybersecurity curricula is extremely difficult. To address this gap, we propose a new methodology to integrate AI and ML techniques into cybersecurity education curricula. Our methodology consists of four components: i) Analysis of Literature which aims to understand the prevalence of AI and ML in cybersecurity research, ii) Analysis of Cybersecurity Curriculum that intends to determine the materials already present in the curriculum and the possible intersection points in the curricula for the new AI material, iii) Design of Adaptable Modules that aims to design highly adaptable modules that can be directly used by cybersecurity educators where new AI material can naturally supplement/substitute for concepts or material already present in the cybersecurity curriculum, and iv) Curriculum Level Evaluation that aims to evaluate the effectiveness of the proposed methodology from both student and instructor perspectives. In this paper, we focus on the first component of our methodology - Analysis of Literature and systematically analyze over 5000 papers that were published in the top cybersecurity conferences during the last five years. Our results clearly indicate that more than 78% of the cybersecurity papers mention AI terminology. To determine the prevalence of the use of AI, we randomly selected 300 papers and performed a thorough analysis. Our results show that more than 19% of the papers implement ML techniques. These findings suggest that AI and ML techniques should be considered for future integration into cybersecurity curriculum to better align with advancements in the field. © American Society for Engineering Education, 2022

AI Techniques: Combating COVID-19 Pandemic

With COVID-19, more than millions of people from all over the world got infected due to this pandemic disease, has wrought havoc. Due to delay in detection of presence of COVID-19 in human body, it infected large number of people all around the globe. Besides all the available manual methods, Artificial Intelligence (AI) and Machine Learning (ML) can help in detecting, treating and monitoring the sternness of COVID-19. This paper intends to provide a complete overview of the role of AI and ML as one important tool for COVID-19 and associated epidemic screening, prediction, forecasting, contact tracing, and therapeutic development. AI is a game-changer in terms of disease diagnosis speed and accuracy. It's a promising technique for a fully transparent and autonomous monitoring system that can follow and cure patients remotely without transmitting the infection to others. AI Application areas in the field of health care are also identified. This paper examines the role of AI in combating the COVID-19 epidemic. We attempt to present a medical network architecture based on AI. The architecture employs artificial intelligence (AI) to efficiently and effectively carry out patient monitoring, diagnosis, and their cure. © 2022 IEEE.

AI to Machine Learning: Lifeless Automation and Issues

Machine learning makes the computer able to perform without explicit programming. So, machine learning is now applied to each and every field of our daily life. The broad range of applications of machine learning are disease detection, weather forecasting, gaming, political discussion, business analytics, acoustics, agriculture, energy forecasting, genomics, etc. The advances in artificial intelligence and machine learning is a combination of tools and techniques used together to solve cognitive problems. The concepts have been effectively executed through BERT and the GPT-2 architectures. Convolutional neural network which implements depthwise separable convolution and other neural networks are also used based on the requirement of the application. Machine learning strategies used for prediction and prognosis of the COVID-19 are partial derivative regression and nonlinear machine learning. Adaptive neural fuzzy inference system is used for wind power detection in power systems. Hence, it gives richer proposals and bits of knowledge for the ensuing decisions based on past information and activities with the extreme scope of production enhancement. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Artificial Intelligence and Machine Learning Techniques for COVID-19 Prediction

An Intelligent data processing is essential to create a large amount of data in Internet of things. We progress the consistent smooth and computerized uses of artificial intelligence, machine learning, deep Learning. To analyze the data using deep learning that is subcategory of machine learning techniques. This investigation designed and implemented the intelligent system that is used to detect the rise of Covid-19 cases using various artificial intelligent algorithms through machine learning. Here best algorithm is chosen for prediction of Covid 19 Omicron cases based on their accuracy of performance metrics. © 2022 IEEE.

Importance of Artificial Intelligence and Machine Learning in fighting with COVID-19 Epidemic

Novel coronavirus known as COVID-19 is spreading continuously with exponential rate in the world and till date we have no any proper treatment to fight and treat corona positive patients. The economy and employment of entire world lay down and collapsed due to COVID-19. As per WHO guidelines the entire world followed some precautionary measures only and therefore there is no cure mechanism and treatment to treat the COVID-19 patients. Entire health community treats the COVID-19 patient symptotically. The spreading momentum of COVID-19 is exponentially but fortunately the death rate of COVID-19 patient is very low. In such situation, Deep Learning (DL), Data Science, Machine Learning (ML) and Artificial Intelligence (AI) play vital role in cope up and deal with the COVID-19 patients. This paper focuses on predictions, challenges and dealing methods to fight COVID-19 patents with AI, ML and data science for lay hold of precautions and discover the vaccine and treatment. This paper also shows that how AI/ML should be engaged researchers, and governments to ensure the most effective responses and actions are taken. © 2021 IEEE.

The Role of Artificial Intelligence and Machine Learning in Covid-19 LED Pandemic

The leading life threatening and fatal virus which is spreaded all around the globe causing pandemic Covid-19 tends to originate in the wuhan city of China in Nov 2019 affecting the life of million every single day, Multiple clinical approaches were performed taking in consideration latest technology: AI and Ml have contributed a lot so as to control its wide spread. This paper presents some of the application of AI and ML which will help us to tackle this situation. There various branches of helping hands are the following: helped us by detection and testing of covid-19,building up of smart hospital using ML, mask detection using ML model and maintaining the social distancing and sanitization plays a crucial role for controlling the virus and lastly predicting the anxiety disorder is also important to understand the effects the lockdown has caused.We have also emphasised on the the challenges faced while predicting its accuracy of the model since the dataset wasn't up the mark due to absence of historical data it wasn't proficient, also considering the opportunity this pandemic has brought in our life's by introducing digital platforms facilities in everyday life by improving the quality services. Considering the future scope of this skill oriented technology, the world is going to experience a drastic transformation and we will hope scientists and researchers make utmost use of AI and ML to bring us the best potential resources from it. © 2021 IEEE.

Artificial Intelligence and Machine Learning as a Tool for Combating COVID-19: A Case Study on Health-Tech Start-ups

This research investigated the use of artificial intelligence (AI) and machine learning in health tech startups to manage coronavirus. The study uses the case study approach to provide valuable insights showing that the health tech start-ups are helping the community to fight the pandemic. This study established that certain development strategies such as app-based solution for the healthcare information, the Unstructured Supplementary Service Data (USSD), and the use of geo-mobility intelligence in controlling the spread of COVID-19 mainly in Rural India have proved to be successful. This study also highlights some of the health tech start-ups' (Aiisma, Qure.ai, and TruFactor) initiatives that are helping the community and health industry. These start-ups mainly made their focus on the use of AI, and machine learning that have been chosen to help in the examination of the current needs, use, and roles of technology in controlling the COVID-19 pandemic. © 2021 IEEE.

Deep Learning Application of Automated Facemask Classification and Physical-Distancing Detection

COVID-19 adversity has stopped the global economy and most of the countries in the entire world are still in locked down even though there is now a vaccine available to fight the COVID-19 diseases. Many nations including the Philippines proclaimed national emergencies to combat the further spread and to reduce the infections brought about by the COVID-19 virus. People are now getting vaccinated with the COVID-19 yet everyone is still in doubt when this catastrophe will end because the virus has evolved in many variants which is more infectious and deadly compared with the original virus. To fight this disease, health authorities implements wearing of facemask, avoiding crowds, cleaning and washing of hands often and staying at least 1-meter apart. This study aims to detect and classify facemask according to the types of facemask they are wearing and detects the physical distancing observe by the person in the area. Deep Learning is a family of Artificial Intelligence and Machine Learning that emulates the human brain in processing information and creates patterns to be use for decision-making. Dataset uses 4,000 images of the person wearing a facemask and not wearing a facemask and the type of facemask they are wearing including the surgical mask, N95 mask, and cloth mask. A real-time video is used to analyze the observation of physical distancing. The images were trained using the MobileNet model and recorded an accuracy rate of 94.50% during training. The trained model has effectively classified persons wearing a cloth mask, surgical mask, N95 mask, person not wearing a facemask, and detected the persons observing the physical distancing protocol. The study can be implemented in real-time to prevent the spread of COVID-19 by identifying the persons wearing the mask, classify the type of mask they are wearing, and detecting the physical distancing protocols. © 2021 IEEE.