This research proposes an alternative Load Flow Controller (LFC) to power systems stability studies based on the population-oriented mathematical optimizer called the PI-Rao. This approach is applied to stabilizing frequency deviations in single-area power systems and compared with a basic PI controller. The research employed a dynamic systems model for a singular area system to capture the dynamics of an LFC. The system is composed of two parts: Part 1 is the LFC optimization systems part which makes a global search for the optimal P & I factors based on some prespecified frequency fluctuation. In contrast, Part 2 is the METARPHOLESS-PI part which uses a population-oriented mathematical logic. The presented optimization in (Part 1) follows classical methods inspired by evolutionary approaches to search for the optimal set of fitting parameters. The minimization of area control error (ACE) is considered an objective function involving the minimization of an Integral Time multiplied Absolute Error (ITAE). The results of simulations have shown the superiority of the proposed solution considering the loading changes of 0.02p.u to 0.1p.u and at intervals of 0.02p.u. Thus, the PI-Rao should serve as another potential solution for power systems LFC applications. The current challenges and future research directions for the existing and prospective projects of the power system operators seek microprocessor-based fault analysis solutions.

This paper presents a machine learning-based fraud detection model to combat the SMS based mobile money fraud. The systems use an XGBoost classifier to process SMS message content real-time and boast a high level of accuracy with predicting malicious intent. Built using Flask framework, the system allows administrators to monitor in real-time all reported alarms for specific transactions regarding fraud and keep statistical records. Future research includes improving the interpretability of models and making them both more scalable as well as robust to adversarial attacks for better fraud detection in mobile money services.

According to the conjecture that P ¹ NP, we recall in this paper that class NP includes P, NP-intermediate and NP-complete problems (some of Co-NP and NP-hard problems also belong to NP). It is obvious that if a single problem belonging to NP is formally proved non-polynomial, then P ¹ NP no longer remains a conjecture but rather becomes a formal statement. In this purpose, we formally prove that the Graph-isomorphism problem (belonging to class NP) is non-polynomial time, which leads that P ¹ NP is a formal statement, not a conjecture.

Detecting the spread of pandemics will greatly reduce human and economic loss. Existing Epidemiological models used for coronavirus disease 2019 (COVID-19) prediction models are too slow and fail to capture epidemic development thoroughly. This research presents a Physics-based Machine Learning Architecture (PMLA) to improve the processing speed and accuracy of epidemic forecasting governed by susceptible–exposed–infected–recovered–deceased (SEIRD) model equations. The dynamics of the epidemic were extracted using Convolutional Neural Networks (CNN) and Deep Reinforcement Learning (Deep RL) from data simulated with Partial Differential Equations (PDEs). The PMLA accuracy is measured using mean squared error. The PMLA prediction model enhances the ability of health authorities to predict the spread of COVID-19 in real time efficiently and effectively.

Keywords: COVID-19, Convolutional Neural Networks, Deep Reinforcement Learning, Partial Differential Equations, Machine learning, Finite Element Method.