Machine Learning based Diabetes Prediction and Development of Smart Web Application

Abstract Counterfeiting is an increasing concern for businesses and governments as greater numbers of counterfeit integrated circuits (IC) infiltrate the global market. There is an ongoing effort in experimental and national labs inside the United States to detect and prevent such counterfeits in the most efficient time period. However, there is still a missing piece to automatically detect and properly keep record of detected counterfeit ICs. Here, we introduce a web application database that allows users to share previous examples of counterfeits through an online database and to obtain statistics regarding the prevalence of known defects. We also investigate automated techniques based on image processing and machine learning to detect different physical defects and to determine whether or not an IC is counterfeit.

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Prediction of aircraft estimated time of arrival using machine learning methods

The Aeronautical Journal ◽

10.1017/aer.2021.13 ◽

2021 ◽

pp. 1-15

Author(s):

O. Basturk ◽

C. Cetek

Keyword(s):

Machine Learning ◽

Web Application ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Weather Data ◽

Time Of Arrival ◽

Learning Models ◽

Trajectory Data ◽

Different Sources ◽

Machine Learning Models

ABSTRACT In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.

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PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features

International Journal of Molecular Sciences ◽

10.3390/ijms22052704 ◽

2021 ◽

Vol 22 (5) ◽

pp. 2704

Author(s):

Andi Nur Nilamyani ◽

Firda Nurul Auliah ◽

Mohammad Ali Moni ◽

Watshara Shoombuatong ◽

Md Mehedi Hasan ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Web Application ◽

Computational Prediction ◽

Vital Role ◽

Machine Learning Algorithms ◽

Recursive Feature Elimination ◽

Post Translational Modification ◽

Multiple Sequence ◽

Sequence Features

Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.

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Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

Applied Sciences ◽

10.3390/app11094266 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4266

Author(s):

Md. Shahriare Satu ◽

Koushik Chandra Howlader ◽

Mufti Mahmud ◽

M. Shamim Kaiser ◽

Sheikh Mohammad Shariful Islam ◽

...

Keyword(s):

Machine Learning ◽

Web Application ◽

Learning Models ◽

Short Term ◽

Infected People ◽

Infected Case ◽

First Case ◽

Novel Coronavirus ◽

Short Term Prediction ◽

Machine Learning Models

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

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Signature-based and Machine-Learning-based Web Application Firewalls: A Short Survey

Procedia Computer Science ◽

10.1016/j.procs.2021.05.105 ◽

2021 ◽

Vol 189 ◽

pp. 359-367

Author(s):

Simon Applebaum ◽

Tarek Gaber ◽

Ali Ahmed

Keyword(s):

Machine Learning ◽

Web Application ◽

Short Survey

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Machine Learning for Diabetes Prediction

10.1109/ictc52510.2021.9621066 ◽

2021 ◽

Author(s):

Usman Ahmed ◽

Chunxiao Li

Keyword(s):

Machine Learning ◽

Diabetes Prediction

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Analysis of Classifier Models and Boosting a Weak Classifier for Diabetes Prediction through Machine Learning

SSRN Electronic Journal ◽

10.2139/ssrn.3852889 ◽

2021 ◽

Author(s):

Kranti Wagle ◽

Simren Batra ◽

Viola Dsouza ◽

Prarthana Shetty

Keyword(s):

Machine Learning ◽

Weak Classifier ◽

Diabetes Prediction

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Algorithm for detecting attacks on Web applications based on machine learning methods and attributes queries

Journal of Science and Technology on Information security ◽

10.54654/isj.v2i14.118 ◽

2022 ◽

Vol 2 (14) ◽

pp. 26-34

Author(s):

Nguyen Manh Thang ◽

Tran Thi Luong

Keyword(s):

Machine Learning ◽

Web Application ◽

Cloud Storage ◽

Web Applications ◽

Detection Algorithm ◽

Learning Methods ◽

Google Docs ◽

Effective Work ◽

Web Resources ◽

Machine Learning Methods

Abstract—Almost developed applications tend to become as accessible as possible to the user on the Internet. Different applications often store their data in cyberspace for more effective work and entertainment, such as Google Docs, emails, cloud storage, maps, weather, news,... Attacks on Web resources most often occur at the application level, in the form of HTTP/HTTPS-requests to the site, where traditional firewalls have limited capabilities for analysis and detection attacks. To protect Web resources from attacks at the application level, there are special tools - Web Application Firewall (WAF). This article presents an anomaly detection algorithm, and how it works in the open-source web application firewall ModSecurity, which uses machine learning methods with 8 suggested features to detect attacks on web applications. Tóm tắt—Hầu hết các ứng dụng được phát triển có xu hướng trở nên dễ tiếp cận nhất có thể đối với người dùng qua Internet. Các ứng dụng khác nhau thường lưu trữ dữ liệu trên không gian mạng để làm việc và giải trí hiệu quả hơn, chẳng hạn như Google Docs, email, lưu trữ đám mây, bản đồ, thời tiết, tin tức,... Các cuộc tấn công vào tài nguyên Web thường xảy ra nhất ở tầng ứng dụng, dưới dạng các yêu cầu HTTP/HTTPS đến trang web, nơi tường lửa truyền thống có khả năng hạn chế trong việc phân tích và phát hiện các cuộc tấn công. Để bảo vệ tài nguyên Web khỏi các cuộc tấn công ở tầng ứng dụng, xuất hiện các công cụ đặc biệt - Tường lửa Ứng dụng Web (WAF). Bài viết này trình bày thuật toán phát hiện bất thường và cách thức hoạt động của tường lửa ứng dụng web mã nguồn mở ModSecurity khi sử dụng phương pháp học máy với 8 đặc trưng được đề xuất để phát hiện các cuộc tấn công vào các ứng dụng web.

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