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To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted.

The Coronavirus Disease 2019 (COVID-19) pandemic has had social and clinical effects over the healthcare system. Globally, over 314.019.135 confirmed cases and 5.507.370 deaths have been recorded at 12 January 2022, based on the Dashboard updated by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The continuous rise in the number of asymptomatic, pre-symptomatic, and symptomatic patients has developed efficient and accessible models for prediction using Open Source Libraries and Cloud Services. This paper proposes different machine-learning algorithms to analyse the COVID-19 OpenData Resources from Mexico and Brazil, which represent the two major populations, economics and affected by disease countries in Latin America. This model uses only the COVID-19 patient's geographical, social conditions, economics conditions, clinical risk factors, symptom reports, and demographic data to predict recovery and death. The model of Mexico has an accuracy of 93% and the perceived mean of recall and the precision (F1 Score) of 0.79 on the dataset used. On the other hand, the model of Brazil has an accuracy of 69% and F1 score of 0.75 on the dataset studied. The result considers data from patients under the age of 0 and 120 years. The contribution of the work is the application of Big Data technologies and Machine Learning algorithms using Open Resource Libraries and Amazon Web Services (AWS) with the vision to improve the clinical diagnosis, even infectious disease with mathematical approaches. [ABSTRACT FROM AUTHOR]

Cloud computing refers to the on-demand availability of personal computer system assets, specifically data storage and processing power, without the client's input. Emails are commonly used to send and receive data for individuals or groups. Financial data, credit reports, and other sensitive data are often sent via the Internet. Phishing is a fraudster's technique used to get sensitive data from users by seeming to come from trusted sources. The sender can persuade you to give secret data by misdirecting in a phished email. The main problem is email phishing attacks while sending and receiving the email. The attacker sends spam data using email and receives your data when you open and read the email. In recent years, it has been a big problem for everyone. This paper uses different legitimate and phishing data sizes, detects new emails, and uses different features and algorithms for classification. A modified dataset is created after measuring the existing approaches. We created a feature extracted comma-separated values (CSV) file and label file, applied the support vector machine (SVM), Naive Bayes (NB), and long short-term memory (LSTM) algorithm. This experimentation considers the recognition of a phished email as a classification issue. According to the comparison and implementation, SVM, NB and LSTM performance is better and more accurate to detect email phishing attacks. The classification of email attacks using SVM, NB, and LSTM classifiers achieve the highest accuracy of 99.62%, 97% and 98%, respectively. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) is a field in machine learning that analyses and manipulate huge amounts of data and generates human language. There are a variety of applications of NLP such as sentiment analysis, text summarization, spam filtering, language translation, etc. Since privacy documents are important and legal, they play a vital part in any agreement. These documents are very long, but the important points still have to be read thoroughly. Customers might not have the necessary time or the knowledge to understand all the complexities of a privacy policy document. In this context, this paper proposes an optimal model to summarize the privacy policy in the best possible way. The methodology of text summarization is the process where the summaries from the original huge text are extracted without losing any vital information. Using the proposed idea of a common word reduction process combined with natural language processing algorithms, this paper extracts the sentences in the privacy policy document that hold high weightage and displays them to the customer, and it can save the customer's time from reading through the entire policy while also providing the customers with only the important lines that they need to know before signing the document. The proposed method uses two different extractive text summarization algorithms, namely LexRank and Kullback Leibler (KL) Summarizer, to summarize the obtained text. According to the results, the summarized sentences obtained via the common word reduction process and text summarization algorithms were more significant than the raw privacy policy text. The introduction of this novel methodology helps to find certain important common words used in a particular sector to a greater depth, thus allowing more in-depth study of a privacy policy. Using the common word reduction process, the sentences were reduced by 14.63%, and by applying extractive NLP algorithms, significant sentences were obtained. The results after applying NLP algorithms showed a 191.52% increase in the repetition of common words in each sentence using the KL summarizer algorithm, while the LexRank algorithm showed a 361.01% increase in the repetition of common words. This implies that common words play a large role in determining a sector's privacy policies, making our proposed method a real-world solution for environmental sustainability. [ABSTRACT FROM AUTHOR]