Showing papers in "Studies in computational intelligence in 2021"

Emergence of Drug Discovery in Machine Learning

Abstract: The discovery of drugs and its pipelines formulation is tough, long, and depend on various factors. Machine Learning comes as a savior to this field and supplies different tools and techniques that are used to drastically improve the discovery as well as the decision-making capabilities for questions that have been well specified and already have an abundant amount of high quality data. There are several possibilities in each and every stage of drug discovery to apply Machine Learning tools and techniques. The applications of Machine Learning have proved from time to time that with some of its approaches generating accurate predictions and insights are very easy. Thus its application has not only been limited to the theoretical part of drug discovery, but has also proven that it can be used in practical conditions too. But there are certain challenges that we need to face that come with Machine Learning. Its inadequacy in terms of interpreting and repeating in its results may sometimes pose as a hindrance to their application in some areas. It also requires consistent and comprehensive high-quality data in almost all areas. There are continuous efforts being put to find a solution for such problems and also growing the reach of machine learning to new fields. Machine Learning can boost data-driven decision-making in this field and has the capability to accelerate the whole process and lessen the failure rates in drug discovery and development.

A Review on COVID-19

Abstract: Background: Coronavirus is a family of viruses, and they are named coronavirus based on the crown-like spikes they have on their surface. The word “Corona” is a Latin word that means “crown.” Recently a virus of the corona family emerged in Wuhan, Hubei, China. On December 31, China informed WHO about some patients having unidentified pneumonia. It was initially named novel coronavirus because of its uniqueness. But later the coronavirus study group of the International Committee on Taxonomy of Viruses designated it as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 has affected the entire world, infected more than a million people till now, and claimed more than 235,288 lives so far.

Computational Intelligence in Vaccine Design Against COVID-19

Abstract: The pandemic Novel coronavirus disease (COVID-19) was aggressively expanding throughout the world, and no effective vaccines and drugs are available. Giant pharmaceutical industries and researchers use computer intelligence coupled with bioinformatics knowledge to accelerate the development process of designing an effective vaccine against SARS-CoV-2, a time-consuming, complicated, intricate, and complex process. Supercomputers are used to give power to the Artificial Intelligence (AI) and Machine Learning (ML) assistance for structural modeling of unresolved protein, molecular dynamics simulation (MD) of the modeled protein structure, target finding, selection of B and T cell epitopes and simulations study for vaccine development. In this chapter, we described an in-depth overview on the use and impact of various revolutionary and game-changing technology of computer intelligence like AI and ML which with the guide of computational biology, bioinformatics, structural biology, and genomics paved the way in understanding, design, and development of vaccines at a much diminished time and minimal cost. Various software and tools used in the developmental process are also consolidated here. Finally, the limitations and future prospects of overcoming the global crisis and tackling pandemics with the help of computational intelligence are speculated here.

Mobile Technology Solution for COVID-19: Surveillance and Prevention

Abstract: The twenty-first century, a century would be known for profound technological advancements and unfortunately also for a global economic and health crisis due to SARS-CoV2, the causal organism of respiratory syndrome COVID-19. Due to the huge crisis in every sector, ‘Technological or Digital way’ is the brightest hope to fight this pandemic. Analysis of the data obtained from the past few infectious months, the spread is more likely to become a seasonal threat to mankind. As an effort to level up the technology and other associated aspects, various researchers and developers are developing mobile applications and mobile controllable devices to provide quality information which can help in flattening the curve of this pandemic. Practically, it becomes a great method to prevent close contacts with diseased individuals by providing virtual visits and through robotic technologies. In the current chapter, the technological background of computational intelligence-controlled smartphone applications for monitoring an individual’s health and tracking the geographical spread of the virus, along with the research scope and data security concerned with these applications have been discussed. This could help the government to understand the potential risk circumstances towards early exposure and timely medical intervention to prevent it from further spread to other regions.

Development of an IoT Based Systems to Mitigate the Impact of COVID-19 Pandemic in Smart Cities

Abstract: The dramatic growth of urbanization in modern cities calls for smart strategies to resolve crucial problems such as transportation, healthcare, energy, and civil infrastructure. The Internet of Things (IoT) is one of the most exciting enabling technologies to address smart city problems by creating a large global network of interconnected physical objects embedded with electronics, software, sensors, and network connectivity. Since the end of 2019, the world has been confronted with the challenge of the COVID-19 virus originating in Wuhan, China. Precautionary measures are expected to be needed in the world to combat the pandemic of COVID-19 until an effective vaccine is developed. The disease has already proven the value of modern smart healthcare which plays a significant role in preventing COVID-19. The aim of this research is to develop the COVID-19 cluster tracking system, and face mask detection system for public safety. The proposed cluster tracking system was validated for 26 test subjects in an experimental scenario of a potential COVID-19 cluster. The accuracy of the proposed face mask detection system was 86.96% observed at 0.9756 precision. According to the testing results, the proposed system was showed promising outcomes for the prevention of the COVID-19 pandemic in a smart city.

The Role of Internet of Things (IoT) in the Containment and Spread of the Novel COVID-19 Pandemic

Abstract: The novel COVID-19 pandemic is hitting the strongest economies in an unprecedented manner leading to the crippling of most economic sectors globally. Movement restriction order profoundly affected many industries, including manufacturing, transportation, aviation, education, tourism, and trade and investment, among others. The consequences resulted in people losing their jobs, corporate organizations and the Government experiencing a sharp drop in income and revenue. Similarly, the global crude oil market prices crash to the lowest rate of less than USD30/barrel. In recent times, the world has not witnessed a pandemic that threatened human existence without any sigh of relief as no cure has been found for the disease. The most effective recommended measure in containing the chain of transmitting the virus is through social distancing as a large gathering of people is highly discouraged. Internet of Things (IoT) alongside other related technologies such as artificial intelligence (AI), drones, robotics, Big Data, and e-learning related technologies were found as platforms that can play a critical role in breaking the chain of the virus transmission. This study highlighted the role of IoT related technologies as a measure that enhances human-machine interaction, which supports the social distancing among people.

Artificial Intelligence and Financial Technology FinTech: How AI Is Being Used Under the Pandemic in 2020

Abstract: The chapter is about the development of Artificial Intelligence (AI) technology in finance, especially under the case of the COVID-19 pandemic in 2020. It does not only present the applications, but also the regulation of AI and Financial Technology FinTech. An innovation regulatory framework at the regulation level and compulsory restrictive guidance and supervision for AI-based technology to allow sustainable growth will promote the accelerated growth of AI in finance. The AI in the financial industry itself focuses on the main characteristics of “digitalization”, “onlineization”, “remoteization”, “visualization”, and “intelligence”, building a multi-functional, all-process end-to-end system based on data, enabling multi-user multi-terminal concurrent office, intelligently assisting in dealing with problems and giving solutions. The advent of AI and its ever-broader effects on other industries demands an assessment of its influence on achieving sustainable development goals.

Factual and Counterfactual Explanation of Fuzzy Information Granules

Abstract: In this chapter, we describe how to generate not only interpretable but also self-explaining fuzzy systems. Such systems are expected to manage information granules naturally as humans do. We take as starting point the Fuzzy Unordered Rule Induction Algorithm (FURIA for short) which produces a good interpretability-accuracy trade-off. FURIA rules have local semantics and manage information granules without linguistic interpretability. With the aim of making FURIA rules self-explaining, we have created a linguistic layer which endows FURIA with global semantics and linguistic interpretability. Explainable FURIA rules provide users with evidence-based (factual) and counterfactual explanations for single classifications. Factual explanations answer the question why a particular class is selected in terms of the given observations. In addition, counterfactual explanations pay attention to why the rest of classes are not selected. Thus, endowing FURIA rules with the capability to generate a combination of both factual and counterfactual explanations is likely to make them more trustworthy. We illustrate how to build self-explaining FURIA classifiers in two practical use cases regarding beer style classification and vehicle classification. Experimental results are encouraging. The generated classifiers exhibit accuracy comparable to a black-box classifier such as Random Forest. Moreover, their explainability is comparable to that provided by white-box classifiers designed with the Highly Interpretable Linguistic Knowledge fuzzy modeling methodology (HILK for short) in terms of explainability.

Digital Transformation and Emerging Technologies for COVID-19 Pandemic: Social, Global, and Industry Perspectives

Abstract: COVID-19 disease pandemic is affecting the lives of millions of people in one or another manner. To handle the COVID-19 pandemic situation, technological aspects play a vital role in parallel to medical and healthcare facilities. With the use of existing infrastructure, technologies such as artificial intelligence, neural network, blockchain technology, cloud computing, drone-based monitoring, etc. have given the important observations and awareness to many. It is observed that with the combined efforts of technology and healthcare system, recognition of the outbreak is much faster compared to earlier infections. However, many are working continuously to collect and analyze the available COVID-19-related data and introspect the future. The whole of this work is performed to maximize the use of technology and reduce the risk of a continuous outbreak. This work has discussed the recent work done over the use of technologies in handling the COVID-19 scenario. Here, a comparative analysis of various parameters in each technological aspect is discussed to have an understanding of the preferred approaches in different places. Further, brief surveys are conducted in each technological aspect for a better understanding of technological advantage in handling pandemic.

Drone-Based Social Distancing, Sanitization, Inspection, Monitoring, and Control Room for COVID-19

Abstract: In recent times, the COVID-19 pandemic has affected billions of people worldwide and has resulted in the slowing down of the economy, industry shutdown, job losses, etc. Every country has taken appropriate measures to fight against pandemic by keeping in mind that health is the primary concern for human beings. This work introduces the COVID-19 pandemic and discusses its types, influence over mankind, prevention methods, and latest observations. Further, this study has designed drone-based case studies for pandemic monitoring, social distance measurements, the necessity of the control room, etc. The simulation is designed to have a single-layer drone movement strategy with a fixed distance. The simulation experimentation is derived from real-time drone movement and area coverage for sanitization. The drone movement and collision avoidance strategy are pre-emptive in nature, i.e., drones are derived to move to a fixed location and execute its functionality. At the ground level, service is designed for which people make queues and maintain social distance before being served. This case study shows its successful execution and can be mapped to a real-time environment. Further, a case study is extended to observe the real-time ambulance monitoring for patient pickup and drop at the hospital. Results show its successful working and continuous operation.

Integrating M-Health with IoMT to Counter COVID-19

Abstract: With the alarming escalation of COVID infection across the globe, there seems a dire need of upgrading the conventional medical practices and technologies in order to battle the pandemic. There are many technological advancements in the direction of integrating emergent technologies with the current medical practices for efficient treatment of COVID crisis and other therapeutic strategies. The proposed intent is aimed to explain such technological advancements like IoMT (Internet of Medical Things) and mHealth and their combined utilization in combating the COVID-19 pandemic. This chapter highlights the works done in integrating Medical Science with emergent technologies such as Robotics, Cognitive Radio System, Wearable and Ingestible Sensory devices, Mobile phones and their significance in providence of medical facilities at doorstep, and 5G technologies in Medical applications. All these elucidations are followed by highlighting their utilization in combating the COVID pandemic through various approaches. This chapter also discusses their current limitations in practical medical world and future aspects of advancements in the medical science.

A Review on Predictive Systems and Data Models for COVID-19

Abstract: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or novel Coronavirus, responsible for the transmission of Coronavirus disease (COVID-19), represents the causative agent of a conceivably deadly sickness, and a global public health concern. In December 2019 in China (Wuhan), the spread of SARS-CoV-2 has taken the shape of a pandemic and affects the respiratory system and manifests as pneumonia in humans, influencing more than 216 nations so far. On January 12th, 2020, the World Health Organization (WHO) gave the name “2019-nCoV” for 2019 novel Coronavirus, and the infection further on February 11th, 2020, is authoritatively named as COVID-19. Instead of using various predictive systems and data models, the prevalence of COVID-19 is continuously increasing, affecting millions of individuals. This chapter focuses on predictive systems and data models utilized from the beginning of COVID-19 outbreak that helped in predicting the cases and deaths qualities of COVID-19 in the desire for giving a reference to future investigations and help in controlling the spread of further epidemics. And also suggest how these data models can help and enable policymakers to plan the regional and national healthcare systems required and design monitored active plans.

COVID-19: Hard Road to Find Integrated Computational Drug and Repurposing Pipeline

Abstract: Shedding of infectious coronavirus disease (COVID-19) is affecting 215 countries and territories; quickly circulates continuously in worldwide. The vital scientific communities are rigorously looking at these public health challenges, global crisis and finding new ways to deal with this pandemic disease. Currently, there is no specific effective approved drug or vaccine available in the market to treat or prevent COVID-19. Thus, there is an urgent need for more and better research to boost up the development of effective therapeutic vaccines and drugs against this virus. Numerous solidarity clinical trial studies, high-level effort and investigations are underway. The repurposing drugs such as chloroquine and its derivatives, remdesivir, favipiravir, darunavir, umifenovir, nitazoxanide and thalidomide are being used globally for clinical trial studies to test their safety and efficacy in this pandemic virus treatment, some of which are already being tested in COVID-19 patients. The computational intelligence methods including machine learning has been useful in computer-aided drug design and drug repurposing. This chapter focus on strengthening the current understanding of the selected number of repurposing antivirals, antiretroviral, antimalarial, and anti-inflammatory drugs that can fight with COVID-19 infection. Further, we look forward to an insightful piece of drug compounds that can be used either individually or in combination.

An Introduction to Computational Intelligence in COVID-19: Surveillance, Prevention, Prediction, and Diagnosis

Abstract: Coronavirus disease 2019 (COVID-19) has been declared as pandemic which took the lives of more than 500 thousand people till mid of 2020 worldwide. Since the coronavirus is highly contagious in nature, COVID-19 is spreading rapidly despite observing social distancing and taking other recommended precautionary measures. Computational intelligence, a powerful tool that mimics human intelligence and learns specific tasks using data, is widely deployed to combat the COVID-19. This chapter briefly covers the computational intelligence methods and its applications in the surveillance, prevention, prediction, and diagnosis of COVID-19. Further, the limitation of current systems and prospects are also discussed.

Interpretability Constraints and Criteria for Fuzzy Systems

Abstract: Fuzzy systems are commonly considered suitable tools to express knowledge in a human comprehensible fashion This kind of characterization makes them eligible for being applied in several contexts where interpretability is a major issue and humans may profit from a self-explanatory form of automatic computation However, fuzzy systems are not interpretable per se and the simple adoption of a natural language representation does not guarantee full acceptance and plain confidence among human experts A number of constraints and criteria have been proposed in literature to drive the design and the construction of fuzzy systems so that they can be deemed interpretable The aim of this chapter is to provide a thorough exposition of constraints and criteria which have been variously adopted in the research community in this context of investigation Due to their heterogeneity and multiplicity, we resorted to a particular arrangement of their presentation By following a hierarchical organization, we start from the basic constituents of a fuzzy system (namely, the fuzzy sets) and we go through the other design levels where compound elements are involved: for each level, an exhaustive review of interpretability constraints and criteria is expounded supplying formal definitions, illustrative examples, and bibliographical references

Computational Intelligence in Drug Repurposing for COVID-19

Abstract: Severe acute respiratory syndrome is a viral respiratory infection known as COVID-19, which is caused by a novel coronavirus, called SARS-associated coronavirus-2 (SARS-CoV-2). Considering it as an international concern, WHO declared COVID-19 as the “sixth public health emergency” and has termed it as ‘pandemic”. Currently, no specific drugs are available, and studies about COVID-19 treatment are still in progress. As the world is facing a major challenge in trying to adapt and defend itself against this new pandemic disease, computational intelligence offers a new hope that a cure to this disease might be developed faster than ever before. Many targets for the design of drugs have been already identified, and studies are in progress to explore these potential targets. Computational approaches like virtual screening, molecular docking, machine learning, deep learning and natural language processing (NLP) play a vital role in drug repurposing studies. Repurposing drugs involves discovering novel drug-target interactions and their use against the treatment of different diseases. This strategy has regained significant interest to develop a drug against the COVID-19 considering this pandemic scenario, and offers the best chance to identify potent drugs from the list of approved drugs. Various research efforts are currently focusing on the identification of existing drugs which might be useful in mitigating the infection and some compounds namely favipiravir, remdesivir, lopinavir, hydroxychloroquine etc. are in the final stage of human testing.

Internet of things in healthcare: A survey of telemedicine systems used for elderly people

Abstract: Internet of Things (IoT) in telemedicine delivers immediate treatments to patients, continuously monitors critically ill patients, and keeps tracks of records of each patient. The main target groups have become senior citizens for service providers in telemedicine technology. Getting older may lead to the loss of individuals’ physical and mental stability when compared to the young population. Therefore, it is necessary to have a system to monitor their daily activities continuously. This study aims to investigate the telemedicine systems based on the IoT used in the healthcare sector while identified the cost-effective and comprehensible telemedicine system. The data from 26 peer-reviewed publications were reviewed and assembled according to different parameters: telecare system platform, algorithm, encryption method, IoT hub system, operating system, communication system, sensors, storage, network system, and hardware. According to the results, electrocardiogram (ECG) is the conventional sensors type, used in healthcare systems, and it was 22%. Also, the use of motion and temperature sensors was recorded as 18% and 20%, respectively. It was reported 8% of the Ubuntu/Linux users and 4% of infrared (IR) users. Similarly, 25% of the healthcare systems use Wi-Fi as the communication system, while 21% use the Bluetooth as the communication system. In total, 4% of the healthcare system intended to use a GPRS communication system. Furthermore, 38% of the healthcare systems use the Android operating system, and 23% of the user implemented the iOS operating system. JAVA operating system has popular among 4% of the users in the healthcare system. The analysis showed that the highest percentage of the sensors used in healthcare systems is ECG sensors, and most of the healthcare systems use Wi-Fi as the communication system and Android as the operating system.

Pandemic Drone with Thermal Imaging and Crowd Monitoring System (DRISHYA)

Abstract: The idea proposed here is the introduction of an Unmanned Aerial Vehicle (UAV) named “Drishya” to tackle the situation of the spread of novel coronavirus. The UAV has been equipped with all the necessary technologies to cater to such adverse situations. Drishya’s main objective is to monitor social distancing and trace COVID-19 infected suspects with the help of two cameras, one for normal vision and the other for high range thermal optimization. The thermal camera will be able to detect any suspect with a temperature higher than normal and hence can be sent for screening. With such technology least human intervention is required on the field which makes it more safe. This process is a better alternative due to its high efficiency and least human involvement. It is most applicable for high transmission areas or rather the areas symbolized as a red zone. It also acts as a helping hand for the medical teams out for screening person to person.

Revisiting Indexes for Assessing Interpretability of Fuzzy Systems

Abstract: Interpretability is one of the most valuable properties of fuzzy systems. Despite the effort made by the research community for characterizing interpretability, there is not a consensus about how to measure interpretability yet. It is admitted that the analysis of interpretability is subjective because it depends on the background of the person who makes the assessment. Accordingly, an index flexible enough to fit with preferences and expectations would be appreciated from the designer but also from the user viewpoints. Hence, subjective indexes become essential for customization purposes. However, they are not enough for measuring interpretability in the broad sense. There is also a need of objective indexes to make feasible fair comparisons among different fuzzy systems designed for solving a given problem. Thus, it is necessary to look for two kinds of complementary indexes, subjective and objective ones. Moreover, they should tackle with interpretability from readability and comprehensibility viewpoints. They also must consider both structural and semantic interpretability properties. In this chapter, we give an overview on existing indexes for assessing all aspects related to interpretability of fuzzy systems. In addition, we present a conceptual framework for assessing interpretability with a customizable index which combines subjective and objective ones.

Artificial intelligence based multinational corporate model for EHR interoperability on an e-health platform

Abstract: This study explores the improvement of efficiency in e-Health by standardizing access to electronic health records (EHRs). Without overlaid organizations, EHR will remain an uneven and fragmented network of lagging systems unable to achieve accuracy and consistency, thus efficiencies. A multinational corporation (MNC) model is proposed to reduce healthcare costs, and implement a coherent system where data, technology and training are uniformly upgraded to alleviate interoperability issues. The conclusion revealed from our review of literature suggests that EHR interoperability issues may be mitigated by creating common architectures that enable fragmented systems to interoperate under supra organizations. As a result, an Artificial Intelligence based model is proposed to facilitate the improvement of the efficacy of e-Health to standardize HER.

An Overview of Fuzzy Systems

Abstract: Fuzzy systems have found widespread application in several contexts and proved their suitability in tackling a number of diverse real-world problems. However, the realization of such systems must be well grounded on some solid theoretical bases that scientists and developers should properly master. In this chapter we discuss the key elements of fuzzy systems, as well as the different instances of their implementation. To this aim, an overview of Fuzzy Set Theory is preliminarily proposed, as an indispensable foundation which is useful to fix the basic concepts, the mathematical underpinning, and the formal notation. In this way, we are able to suitably introduce the concept of fuzzy rule-based systems, illustrating the different components involved in their structure. We review also various types of fuzzy rule-based systems, focusing a special attention on details related to the design of interpretable models.

Human-Centered Design Smart Clothing for Ambient Assisted Living of Elderly Users: Considerations in the COVID-19 Pandemic Perspective

Abstract: World median age population growth during the last decades brought with it an improvement of the relationship between Design Research and older users. The balance between users’ requirements and Human-Centered Design (HCD) strategies to satisfy them has been destabilized by the COVID-19 pandemic. Human-Centered Design smart clothing is becoming an essential tool for Ambient Assisted Living (AAL) of elderly users improving their life quality, lifestyle, and health. These devices allow the monitoring of the person’s daily habits, health status, and well-being in a non-intrusive way, enhancing autonomy and independence. By 2030, aging will affect more and more the human being of the smart society. Future perspective needs to be addressed to empowering AAL where HCD smart clothing will become an essential tool for protecting senior citizens.

Designing Interpretable Fuzzy Systems

Abstract: Fuzzy sets and fuzzy logic are powerful tools widely used to represent human knowledge and mimic human reasoning capabilities, being the main constituents of fuzzy systems. Among the different approaches to fuzzy systems, fuzzy rule-based systems represent the one offering a better framework for interpretability considerations. Their applications range from classification to modeling and control. Independently on its purpose, the behavior of a fuzzy rule-based system can be evaluated in terms of its reliability and comprehensibility, two concepts usually represented by accuracy and interpretability in the context of fuzzy systems. Indeed, achieving the highest possible levels of accuracy and interpretability is one of the central aspects of designing fuzzy systems. The present chapter will go through the design process considering its different steps and analyzing the multiple options allowing us to improve interpretability or to achieve a better interpretability-accuracy balance, in the search for interpretable fuzzy systems which represent an interesting approach in the framework of explainable Artificial Intelligence. We will consider questions related to the knowledge extraction and refinement process; some examples are complexity reduction and semantic improvement. We will also analyze other questions linked to the design of the processing structure, such as the effects of applying different aggregation and implication operators.

Toward Explainable Artificial Intelligence Through Fuzzy Systems

Abstract: Explainable Artificial Intelligence is a novel paradigm conjugating the effectiveness of machine learning with the new requirements coming from the integration of intelligent systems in the human society Explainable Artificial Intelligence can find successful application in a plethora of contexts, endowing classical intelligent systems with a crucial added value: the possibility for users to interact with machines, validate their results and ultimately trust their behavior Fuzzy Set Theory provides a mathematical framework which is especially suitable to model concepts and perceptions of physical reality, thus injecting a kind of common-sense reasoning into machine learning algorithms and realizing a human-centric information processing which is the core of the Granular Computing paradigm In particular, the exploitation of natural language enables Fuzzy Set Theory to act as a key-element for designing explainable models which can be able to provide explanations useful for human beings We argue on those topics in order to show how the development of explainable fuzzy systems is a promising direction for paving the way from interpretable fuzzy systems to explainable intelligent systems

Application of AI Techniques for COVID-19 in IoT and Big Data Era: A Survey

Abstract: The infectious novel coronavirus (COVID-19) is said to have originated from China. The COVID-19 pandemic has spread over a hundred nations and regions on the planet and has fundamentally influenced each part of our day-by-day lives. As of present, the quantities of COVID-19 cases and deaths despite everything increment fundamentally and do not indicate a very much controlled circumstance; over a thousand cases have been accounted for around the world. Artificial intelligence (AI) goal is to adapt to human conceptual cutoff points. It is getting an outlook on human organizations, filled by the developing accessibility of helpful clinical information and the snappy movement of keen systems. Inspired by ongoing progress and uses of the artificial intelligence (AI) and Big Data in different territories, in this survey we target their underlying significance in reacting to the coronavirus flare-up and forestalling the extreme effect of the epidemic. In this survey, we initially summarize the current territory of AI applications in clinical organizations while combating COVID-19. Besides, we feature the use of Big Data while cubing this infection. We additionally review the feature, difficulties, and issues related to discovering solutions. An overview was made in ordering AI and Big Data, at that point distinguishing their applications in battling against COVID-19. Likewise, an accentuation has been made on districts that use cloud computing in battling different comparable infections to COVID-19 and COVID-19 itself. The explored strategies put forth propel clinical data investigation with a precision of up to 90%. We further end up with a point-by-point conversation about how AI usage can be in a favorable position in fighting different comparative infections. This paper gives specialists and researchers new bits of knowledge into the manners in which AI and Big Data can be used in improving the COVID-19 circumstance and drive further examinations in halting the outbreak of the virus.

IoT-Based Computational Frameworks in Disease Prediction and Healthcare Management: Strategies, Challenges, and Potential

Abstract: In this fastest-growing digitalization and technology, the application of the Internet of Things (IoT) in the healthcare industry brings revolutionary changes in the development and advancement in disease prediction, health monitoring, mobile health, and healthcare management. The main objective of this chapter is to focus on the various computational frameworks that are available for IoT-based healthcare system, by incorporating the findings demonstrated in the recently published research papers/reviews. This chapter highlights the strategic development of IoT-based computational framework or network for the advancement in disease prediction, monitoring, treatment strategies and drug monitoring and provides a ubiquitous healthcare system. The use of Internet-assisted healthcare networks, sensor-based devices, web servers, smartphone applications, big data, and cloud computing systems effectively limitless resources for generating massive datasets and digital health records can be used for remote monitoring and mobile health. The development of smartphone applications increases the efficiency and accessibility of IoT-based healthcare system to the user. Furthermore, this study also focuses on the application of Internet-assisted technology to provide better healthcare platform and overcome the worldwide pandemic emergency of COVID-19. In summary, this chapter enlightens the potentials and promises of various IoT-based computational frameworks for the development of an easily accessible, simple to handle, time-efficient, and almost low-cost healthcare system with its technological constraints and future advancement.

A Comparative Study of the SIR Prediction Models and Disease Control Strategies: A Case Study of the State of Kerala, India

Abstract: The Novel Coronavirus (nCoV or COVID-19) that hit the City of Wuhan in the Hubei Province of China in December last year has become the greatest concern throughout the world. The countries in the world have shown a significant difference in the control of the spread of disease and the mortality rate. Kerala—a southern state in India—has shown notable performance in the field of disease control of COVID-19. Various measures of disease control are proved effective in the containment of COVID-19. A study of the situation in Kerala after the outbreak of COVOD-19 is used to analyze the effect of the control strategies. In this chapter, the main focus is on a comparative study of the predictions of the SIR model and the actual performance made by the state in controlling the disease.

Computational Intelligence Approach for Prediction of COVID-19 Using Particle Swarm Optimization

Abstract: Computational Intelligence (CI) is predicated on naturally motivated computational procedures. The strategic supports that comprise this arena are Genetic Algorithms, Neural Networks, and Fuzzy Systems. Neural Networks are procedures that will be recycled for function classification or estimation difficulties. They embrace Supervised, Unsupervised, and Reinforcement Learning. Genetic Algorithms, conversely, the pursuit of procedures motivated by gradual genetics. Cross-over and Mutation are the two important trust operators. The populace of people signifying resolutions to the matter is twisted over numerous cohorts. The procedure customs an arbitrary steered methodology to optimize problems supported by a fitness function. Fuzzy Logic is predicated on Fuzzy Set theory so on embracing cognitive that’s fluid or imprecise slightly than secure and precise. Fuzzy Logic variables consume certainty standards extending in score between 0 and 1 which may also switch restricted certainty Computational Intelligence methods are effectively utilized in various real-life applications during a diversity of commercial and medical problems. Data processing is designated as mining of pertinent info as of copious capacities of knowledge to market the commercial buzz and deciding proficiency. The empirical analysis of data may be an analogous performance for succinct and classifying the designs within the data. This empirical data exploration may be statistical, a regression model, a discriminate model, or a clustering model, which is produced using the data and is used for forecast or prediction, classification, or hypothesis verification. We introduced a standard rule detection process reinforced swarm intelligence to advert the eminence creation of rules and smear the rule pruning appliance to condense the rule. The predictable technique usages COVID-19 and Mammographic Corpus Statistics. This procedure, while smeared on COVID-19, initiate to be extra precise associated with the prevailing Classification procedures explicitly Classification using Decision Trees. This performance analysis has been achieved with slightly more balanced trees using entropy-based Information gain measures.

Leveraging Big Data to Accelerate Supply Chain Management in Covid-19

Abstract: Pre Covid 19 (CoV), supply chains over the globe were getting progressively mind boggling, on account of globalization and the ever-changing elements of supply and demand. During Covid-19, as a precautionary measure, governments all across world announced full lockdown. This lockdown during CoV, disrupted well established supply chains throughout the world, as movement of people was restricted, factories were shut down, transportation facility came to stand still. With unlock of manufacturing activities in many countries, the organizations are tackling the intensity of technology innovation, or all the more decisively: big data analytics, to acquire troublesome changes at all degrees of Supply Chain. In this context, big data allows vast amounts of organized and unstructured data from multiple sources to be seen rapidly. This will help improve perceptibility for the supply chain, and provide additional bits of information to the entire supply chain. Looking at the potential benefits of big data, the aim of this chapter is to discuss the numerous inefficiencies in the supply chain and their negative effect on the competitiveness of companies. The chapter then evaluates how Big Data advancements could positively impact the supply chain inefficiencies. It also discusses what challenges organizations have to face for implementing big data framework in their organization structure. In the last, it discusses the future prospects for Big Data in supply chain and how Big Data will impact the future of the industry workforce. The chapter takes into consideration the issues of global supply chain vis-a-vis the Indian Supply Chain Industry.

Role of Computational Intelligence Against COVID-19

Abstract: The recent COVID-19 pandemic caused due to the notorious SARS-CoV-2 virus has caused widespread loss of human lives across the globe. It has affected more than 213 countries, with a count of 11,669,259 cases and 539,906 deaths, as per WHO report on 9 July 2020. The epidemic has brought the world to a halt and has pointed out the shortcomings of the healthcare system and the flaws in epidemic management. The traditional ways of healthcare management have collapsed under these exigent circumstances. In these trying times, there is a dire need for better implementation of available resources and technology to accelerate the management of the pandemic. Hence, a systematic and thorough assessment of available technology, resources, tools, and techniques will point us in the right direction for finding potential solutions for the control of the severity or spread of the disease and ultimately finding a cure. The use of the advanced field of computational intelligence, which includes Artificial Intelligence, Machine Learning, and Big Data analytics, for clinical/healthcare data can serve substantial solutions in the current scenarios. This chapter aims to discuss the role of AI, ML, as well as Big Data analytics, in healthcare and epidemic management. The chapter elaborates on various applications of computational intelligence in speed tracking of spread, identifying patients with critically low immunity/high-risk patients, assistance in treatment and diagnosis, controlling the spread, and future predictions using the current dataset. The chapter addresses a significant application of Computational intelligence in the research area of drug discovery and repurposing in the hunt for a cure. It briefly discusses technologies like Blockchain and AI-empowered image acquisition that have proved their significance in diagnosis and treatment in various countries, globally. The chapter proposes short-term action plans for combating the virus using computational intelligence while outlining future strategies of applying CI in healthcare management and its long-term benefits.