scholarly journals PREDICTIVE MACHINE LEARNING (ML) ALGORITHM USING IOT FRAMEWORK FOR NOVEL CORONA VIRUS (COVID-19)

2021 ◽  
Vol 57 (9) ◽  
pp. 6328-6336
Author(s):  
G. S. N. Murthy, M. V. Sangameswar, Venubabu Rachapudi, Mylavarapu Kalyan Ram
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Research Work ◽  
Healthcare Industry ◽  
Blood Oxygen ◽  
Nearest Neighbour ◽  
Blood Oxygen Level ◽  
Clinical Methods ◽  
The World ◽  
The Internet Of Things

During earlier months of the pandemic COVID-19 with no recommended cure or vaccine available only solution to destroy the chain is self-isolation which can be maintained by physical distancing. This is now understood that the world require much faster solution to accommodate and deal with the future COVID-19 spread over the world by non-clinical methods namely data mining, augmented intelligence and several Artificial Intelligence (AI) techniques. It has become a huge hindrance to mitigate for the healthcare industry to provide more potential involved for patient's diagnosis and also for effective prognosis of 2019-CoV pandemic. Therefore, the proposed framework is implemented with the Internet of Things (IoTs) in healthcare industry for collecting the symptom data of real-time that is beneficial in predicting whether the person gets infected with COVID-19 virus or not. This can be done through various signs namely body temperature, blood oxygen level, headache, coughing patterns, etc. Thus, the research work focused on faster identification of COVID-19 virus infection cases potentially using Machine Learning (ML) algorithm from the real-time symptom data. Moreover, the obtained results have illustrated that K-Nearest Neighbour (KNN) algorithm is highly efficient while compared with other ML algorithms such as Naive Bayes and Logistic Regression (LR) in predicting the possible recovery of the infected patients from pandemic COVID-19 with the accuracy of 96.85%.

scholarly journals Design of an Intelligent Garbage Container that Allows to Monitor the Waste for its Proper Collection

2022 ◽  
Vol 12 (1) ◽  
pp. 31-39
Author(s):  
Robert Cerna Duran ◽  
◽  
Brian Meneses Claudio ◽  
Alexi Delgado
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Environmental Problem ◽  
Intelligent System ◽  
Research Work ◽  
The State ◽  
The Internet ◽  
Keywords Internet ◽  
The World ◽  
The Internet Of Things

The increase in garbage production today is due to the exponential growth of the population worldwide, due to the fact that thousands of tons of garbage are generated daily around the world, but the mismanagement that gives them has become an environmental problem since 33% of all the garbage generated is not recycled, for that reason it is estimated that within the next three decades the amount of waste worldwide will increase to 70%. That is why in the present research work it is proposed to make an intelligent system based on the Internet of Things (IoT) that allows monitoring the garbage containers in real time representing with percentages the state of these containers and these can be collected in time by garbage trucks, and thus avoid the increase of garbage in the streets and the various types of problems that these would cause. As a result, it was obtained that the System does comply with the established conditions because it allows to monitor in real time representing by percentages the state of the garbage container, which indicates 40% as almost full and 80% indicates that it is already available for collection. Finally, it is concluded that using the Garbage Container Monitoring System will allow to better optimize the collection process and, in addition, the problems that are usually perceived today due to the amount of garbage that are registered in the streets will decrease. Keywords-- Internet of Things; Intelligent system; Real time; Environmental Problem; Monitoror; Percentage.

scholarly journals Comparison of Machine Learning algorithm for COVID-19 Death Risk Prediction

2021 ◽  
Author(s):  
Praveeen Anandhanathan ◽  
Priyanka Gopalan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Decision Tree Algorithm ◽  
The Past ◽  
Random Forest Method ◽  
Learning Techniques ◽  
The World

Abstract Coronavirus disease (COVID-19) is spreading across the world. Since at first it has appeared in Wuhan, China in December 2019, it has become a serious issue across the globe. There are no accurate resources to predict and find the disease. So, by knowing the past patients’ records, it could guide the clinicians to fight against the pandemic. Therefore, for the prediction of healthiness from symptoms Machine learning techniques can be implemented. From this we are going to analyse only the symptoms which occurs in every patient. These predictions can help clinicians in the easier manner to cure the patients. Already for prediction of many of the diseases, techniques like SVM (Support vector Machine), Fuzzy k-Means Clustering, Decision Tree algorithm, Random Forest Method, ANN (Artificial Neural Network), KNN (k-Nearest Neighbour), Naïve Bayes, Linear Regression model are used. As we haven’t faced this disease before, we can’t say which technique will give the maximum accuracy. So, we are going to provide an efficient result by comparing all the such algorithms in RStudio.

Current Trends

2019 ◽  
pp. 181-198
Author(s):  
Jayanthi Jagannathan ◽  
Anitha Elavarasi S.
Keyword(s):  
Machine Learning ◽  
Manufacturing Industry ◽  
Predictive Analytics ◽  
Process Industry ◽  
Healthcare Industry ◽  
Current Trends ◽  
Smart Campus ◽  
Industry Automation ◽  
The Internet Of Things

This chapter addresses the key role of machine learning and artificial intelligence for various applications of the internet of things. The following are the most significant applications of IoT: (1) manufacturing industry: automation of industries is on the rise; there is an urge for analyzing the energy in the process industry; (2) anomaly detection: to detect the existing fault and abnormality in functioning by using ML algorithms thereby avoiding the adverse effect during its operation; (3) smart campus: in-order to efficiently handle the energy in buildings, smart campus systems are developed; (4) improving product decisions: with the help of the predictive analytics system products are designed and developed based on the user's requirements and usability; (5) healthcare industry: IoT with machine learning provides numerous ways for the betterment of the human wellbeing. In this chapter, the most predominant approaches to machine learning that can be useful in the IoT applications to achieve a significant set of outcomes will be discussed.

Prospects of Open Source Software for Maximizing the User Expectations in Heterogeneous Network

2021 ◽  
pp. 257-271
Author(s):  
Pushpa Singh ◽  
Rajeev Agrawal
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Real Time ◽  
Open Source Software ◽  
Heterogeneous Networks ◽  
Heterogeneous Network ◽  
Research Work ◽  
Software Tool ◽  
Knn Classifier ◽  
User Expectations

This article focuses on the prospects of open source software and tools for maximizing the user expectations in heterogeneous networks. The open source software Python is used as a software tool in this research work for implementing machine learning technique for the categorization of the types of user in a heterogeneous network (HN). The KNN classifier available in Python defines the type of user category in real time to predict the available users in a particular category for maximizing profit for a business organization.

scholarly journals Internet of Things: A Comprehensive Study on Machine Learning-based Intrusion Detection approaches

2021 ◽  
Author(s):  
Priyanka Gupta ◽  
Lokesh Yadav ◽  
Deepak Singh Tomar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Real Time ◽  
Detection System ◽  
Research Work ◽  
Security And Privacy ◽  
Iot Security ◽  
Advantages And Disadvantages ◽  
Iot Devices

The Internet of Things (IoT) connects billions of interconnected devices that can exchange information with each other with minimal user intervention. The goal of IoT to become accessible to anyone, anytime, and anywhere. IoT has engaged in multiple fields, including education, healthcare, businesses, and smart home. Security and privacy issues have been significant obstacles to the widespread adoption of IoT. IoT devices cannot be entirely secure from threats; detecting attacks in real-time is essential for securing devices. In the real-time communication domain and especially in IoT, security and protection are the major issues. The resource-constrained nature of IoT devices makes traditional security techniques difficult. In this paper, the research work carried out in IoT Intrusion Detection System is presented. The Machine learning methods are explored to provide an effective security solution for IoT Intrusion Detection systems. Then discussed the advantages and disadvantages of the selected methodology. Further, the datasets used in IoT security are also discussed. Finally, the examination of the open issues and directions for future trends are also provided.

Resource Management in Real Time Distributed System with Security Constraints

2012 ◽  
pp. 230-251
Author(s):  
Sarsij Tripathi ◽  
Rama Shankar Yadav ◽  
Ranvijay ◽  
Rajib L. Jana
Keyword(s):  
Resource Management ◽  
Load Balancing ◽  
Real Time ◽  
Distributed System ◽  
Research Work ◽  
Tracking Systems ◽  
Global Village ◽  
Future Directions ◽  
The World ◽  
Work Done

The world has become a global village. Today applications are developed which require sharing of resources dispersed geographically to fulfill the need of the users. In most cases applications turn out to be time bound thus leading to Real Time Distributed System (RTDS). Online Banking, Online Multimedia Applications, Real Time Databases, and Missile tracking systems are some examples of these types of applications. These applications face many challenges in the present scenario particularly in resource management, load balancing, security, and deadlock. The heterogeneous nature of the system exacerbates the challenges. This paper provides a widespread survey of research work reported in RTDS. This review has covered the work done in the field of resource management, load balancing, deadlock, and security. The challenges involved in tackling these issues is presented and future directions are discussed.

Real-Time Smart Parking: Challenges and Solution using Machine learning and IoT

2021 ◽  
pp. 451-458
Author(s):  
Biren Bishnu Prasad Sahoo ◽  
Shahjad ◽  
Prakash Singh Tanwar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Modern Society ◽  
The Internet ◽  
Challenging Issue ◽  
Smart Parking ◽  
The Internet Of Things ◽  
Modern Technologies

Modernization is something that everyone wants. With the increase in modernization, people are expecting to live a sustainable and hassle-free life. In this modern society, Parking is a major issue. Due to the growing number of vehicles in these cities, parking becomes a challenging task. We have tried to boost this particular system with the help of modern technologies i.e., the Internet of Things and Machine Learning. So that we can enhance the solution for this challenging issue efficiently.

Nuisance Flooding in Coastal Communities: Real-time Modeling and Decision Support to Improve Transportation Infrastructure Resilience

2020 ◽  
Author(s):  
Jonathan L. Goodall ◽  
Madhur Behl ◽  
Benjamin Bowes ◽  
Brad Campbell ◽  
Alex Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Real Time ◽  
The United States ◽  
Coastal Communities ◽  
Transportation Infrastructure ◽  
Study Region ◽  
Intense Storm ◽  
The World ◽  
Flooding Events

<p>Nuisance flooding, which is repetitive flooding caused by both tidal and rainfall-driven events, is increasing in frequency and severity for many coastal communities. As climate change causes sea level rise and more frequent and intense storm events, these nuisance flooding events are producing significant disruptions and impacts to coastal communities. The objective of this study is to improve modeling and decision support activities around nuisance flooding and, in particular, its impact on transportation infrastructure. Our study region and partner in the research is the City of Norfolk, Virginia, USA. Norfolk is home to the largest Navy base in the world, the second busiest port on the United States East Coast, and is the second most populous city in Virginia. It is also one of 100 Rockefeller Resilient Cities in the world, committed to taking progressive aims at combating nuisance flooding. Using real-time observational networks, crowdsourced data, physics-based and machine learning modeling approaches, model predictive control, and economic and social science methods, we are exploring ways to better understand and mitigate the impacts of street-scale flooding. Our research is showing how real-time control of stormwater infrastructure systems can help to improve the resilience of these systems during nuisance flooding events by strategically holding back rainfall runoff and preventing tidally driven stormwater backups. We are also showing physics-based and machine-learning methods can be combined for real-time decision support and how reputation system approaches can be used to measure trust in crowdsourced rainfall datasets. This presentation will provide an overview of these and related activities, each aimed at the common goal of leveraging real-time data from a variety of sources, innovative modeling techniques, and community-driven decision making to improve community resilience to nuisance flooding.</p>

Resource Management in Real Time Distributed System with Security Constraints

2011 ◽  
Vol 2 (2) ◽  
pp. 38-58 ◽  
Author(s):  
Sarsij Tripathi ◽  
Rama Shankar Yadav ◽  
Ranvijay ◽  
Rajib L. Jana
Keyword(s):  
Resource Management ◽  
Load Balancing ◽  
Real Time ◽  
Distributed System ◽  
Research Work ◽  
Global Village ◽  
Future Directions ◽  
The World ◽  
Heterogeneous Nature ◽  
Work Done

The world has become a global village. Today applications are developed which require sharing of resources dispersed geographically to fulfill the need of the users. In most cases applications turn out to be time bound thus leading to Real Time Distributed System (RTDS). Online Banking, Online Multimedia Applications, Real Time Databases, and Missile tracking systems are some examples of these types of applications. These applications face many challenges in the present scenario particularly in resource management, load balancing, security, and deadlock. The heterogeneous nature of the system exacerbates the challenges. This paper provides a widespread survey of research work reported in RTDS. This review has covered the work done in the field of resource management, load balancing, deadlock, and security. The challenges involved in tackling these issues is presented and future directions are discussed.

scholarly journals A Review on Identifying Suitable Machine Learning Approach for Internet of Things Applications

2021 ◽  
Vol 3 (3) ◽  
pp. 128-145
Author(s):  
R. Valanarasu
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Smart Transportation ◽  
Iot Applications ◽  
Enhance Efficiency ◽  
The Internet Of Things

Recently, IoT is referred as a descriptive term for the idea that everything in the world should be connected to the internet. Healthcare and social goods, industrial automation, and energy are just a few of the areas where the Internet of Things applications are widely used. Applications are becoming smarter and linked devices are enabling their exploitation in every element of the Internet of Things [IoT]. Machine Learning (ML) methods are used to improve an application's intelligence and capabilities by analysing the large amounts of data. ML and IoT have been used for smart transportation, which has gained the increasing research interest. This research covers a range of Internet of Things (IoT) applications that use suitable machine learning techniques to enhance efficiency and reliability in the intelligent automation sector. Furthermore, this research article examines and identifies various applications such as energy, high-quality sensors associated, and G-map associated appropriate applications for IoT. In addition to that, the proposed research work includes comparisons and tabulations of several different machine learning algorithms for IoT applications.

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