With the arrival of new technologies in modern smart factories, automated predictive maintenance is also related to production robotisation. Intelligent sensors make it possible to obtain an ever-increasing amount of data, which must be analysed efficiently and effectively to support increasingly complex systems' decision-making and management. The paper aims to review the current literature concerning predictive maintenance and intelligent sensors in smart factories. We focused on contemporary trends to provide an overview of future research challenges and classification. The paper used burst analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis. The results show the increasing number of papers related to key researched concepts. The importance of predictive maintenance is growing over time in relation to Industry 4.0 technologies. We proposed Smart and Intelligent Predictive Maintenance (SIPM) based on the full-text analysis of relevant papers. The paper's main contribution is the summary and overview of current trends in intelligent sensors used for predictive maintenance in smart factories.

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review.

Single-Electron Majority Logic Circuits

Diabetes is one among many chronic diseases. It is the most common disease and lots of peoples are affected by this. There are many things that are liable for diabetes, mainly age, obesity, weakness, sudden weight loss, and many more. Diabetes patients have high risk of diseases like cardiopathy, renal disorder, stroke, nerve damage, eye damage, etc. Detection of the disease isn’t very easy and prediction is additionally costlier. In today’s situation, hospitals are extremely busy due to COVID-19 pandemic, and it might be revolutionary if one could know if they’re at risk of being diabetic without visiting a doctor. But the rise in Artificial Intelligence techniques can be used for disease prognosis. The objective of this study is to develop a model with significant accuracy to diagnose diabetes in patients. Moreover, this paper also presents an effective diabetes prediction model for better classification of diabetes and to enhance the accuracy in diabetes prediction using several machine learning algorithms. Different machine learning algorithms are utilized for early stage diabetes prediction, namely, Logistic Regression, Random Forest Classifier, Support Vector Machine, Decision Trees, K-Nearest Neighbors, Gaussian Process Classifier, AdaBoost Classifier, and Gaussian Naive Bayes. The performances of these models are measured on respective criteria like Accuracy, Precision, Recall, F-Measure, and Error. For this research work, latest available dataset dated 22nd July, 2020, is being utilized. Latest updated dataset will show comparatively better result.

Machine Learning Technique to Prognosis Diabetes Disease: Random Forest Classifier Approach

It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of machine learning in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using machine learning models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a machine learning based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.

Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things

Applications of ML/DL in the management of smart cities and societies based on new trends in information technologies: A systematic literature review

A substantial technological growth of the photovoltaic systems has occurred around the world during the recent years enhancing the availability of electric energy in an environment friendly way. Internet of Things (IoT) is one of the emerging technologies having a great potential to apply in the area of renewable energy, especially on solar PV cell. Maximum power production is the general criteria for any solar PV cell. The maximum power point tracking technique enables maximization of the energy production of PV cell during partial shading conditions. Thus, the overall efficiency of the photovoltaic energy production system is increased. Numerous techniques have been proposed during the last decade for implementing the maximum power point tracking process in a photovoltaic system. This article proposed a novel idea of interfacing IOT system with PV cell in partial shading condition and applied MPPT method to analyze and demonstrate their performance features.

IOT based MPPT for Performance Improvement of Solar PV Arrays Operating under Partial Shade Dispersion

Smart manufacturing and data analytics can help in manufacturing sector, where the current scenarios transmission and data analysis from across the plant create manufacturing intelligence; it can be used to have a good impact across all side of operations. Predictive analysis helps that in a lot of ways in smart manufacturing. In this work, authors have tried to take values of a smart manufacturing company and have done predictive analysis to take out the energy consumption based on historical data.

Predictive Data Analysis for Energy Management of a Smart Factory Leading to Sustainability

Autonomous Vehicles are expected to change the future of worldwide transportation system. As self-driving cars are facing a lot of engineering challenges, it is one of the hottest topics in recent research. One such challenge is to build models to predict the movements of traffic agents such as cars, cyclists, pedestrians etc around the self-driving cars. The objective of this paper is to analyse the prediction efficiency of various deep learning models by calculating root mean square error score. This deep learning models takes a current state of the surrounding and depending on that predict the motion for the agents.

Motion Prediction for Autonomous Vehicles from Lyft Dataset using Deep Learning

Pulmonary fibrosis is a progressive lungs disease which usually gets worse over time. Once this disease damages the lungs, it cannot be cured totally. But early detection and proper diagnosis can help to keep this disease in control. It causes scarring in the lungs over time. As an effect, people face breathing difficulty. It can cause shortness of breath, even at rest. The general causes of pulmonary fibrosis can be exposure to toxic element like coal dust, asbestos fibres, silica dust, hard metal dusts etc. But in majority of the cases, the doctor cannot figure out the exact cause of this disease. That's why this disease is termed as Idiopathic Pulmonary Fibrosis. The objective of this paper is to analyse and compare the performance of various machine leaning models by predicting the final forced volume capacity measurements for each patient and a confidence value. It can be deployed on any computer to predict a patient's severe condition regarding lungs function which is based on a CT scan of the lungs of the patients. Lung function is checked out based on a spirometer output that measures the forced vital capacity (FVC) of the lungs. In the future, early diagnosis of pulmonary fibrosis should be possible. Machine learning model is helping to use the human resources efficiently and it is also reducing the expanses spent on the social and healthcare aspects of this deadly disease.

Ankush Ghosh

Papers

Machine Learning Technique to Prognosis Diabetes Disease: Random Forest Classifier Approach

IOT based MPPT for Performance Improvement of Solar PV Arrays Operating under Partial Shade Dispersion

Predictive Data Analysis for Energy Management of a Smart Factory Leading to Sustainability

Motion Prediction for Autonomous Vehicles from Lyft Dataset using Deep Learning

Prediction Analysis of Idiopathic Pulmonary Fibrosis Progression from OSIC Dataset