Soosaimarian Peter Raja

In recent days, a spotlight has been thrown on microgrids as there has been more evolution of software systems, increasing customers’ thirst for sustainability, reliability, and cost predictability. For these reasons, there is a need for development of microgrids in remote areas. In this article, a hybrid microgrid system for a remote locality in the southern part of India, specifically Kavalur, Tamil Nadu, is presented. The primary objective is to perform sizing and cost optimization of the designed system. In order to design this system, commercial software named “HOMER Pro” was used to obtain the optimum solutions. The hybrid system consisted of a wind generator (WG), a solar photovoltaic (PV) array, a diesel generator (DG) system, and a battery storage system to fulfill the load requirements of the particular area. The sensitivity analysis was also carried out by considering different PV derated output powers at two different hub heights. It was found that the design consisting of a PV array, a wind turbine, a DG set, and a lead acid battery gave better optimum results, with a total net present cost (NPC) of US${\$}$340,287 with a PV derating of 80% and a hub height of 50 m. The cost of energy (CoE) was estimated as US${\$}$0.247 per unit.

In the evolving landscape of deploying blockchain technology for private applications within organizations and entities, ensuring security, scalability, and efficiency while providing decentralized decision making remains a major challenge. This research introduces an innovative hybrid framework that seamlessly merges the robustness of proof of work (PoW) with a novel approval node cluster based on proof of authority (PoA) that capitalizes on the strengths of both systems to make decisions. PoW’s puzzle-solving ensures decentralized security, while the approval node clusters validator-based approach expedites transaction validation and block addition. This addition increases overall security and adaptability, catering to the specific needs of different organizations. We demonstrate that the hybrid PoW–PoA framework significantly enhances system security, making it a viable solution for a wide range of applications.

This research intends to create a novel approach for solving fractional differential equations (FDEs) of both linear and nonlinear types utilizing the fractional shifted Legendre neural network alongside the extreme machine learning (ML) algorithm. The neural network serves as a trial solution to formulate the loss function where it is used to train the neural network through an extreme learning machine (ELM) to obtain the solution. The new approach is applied to the linear differential equations and the unknown coefficient matrix is identified using the psuedoinverse of the activation function output matrix whereas the nonlinear equations are handled by employing nonlinear least square perturb algorithm. An algorithm is provided for solving the FDEs. Theoretical analysis such as convergence, stability, and error estimates are studied. The proposed method is tested on various problems and is compared with the other algorithms discussed in the literature.

Automatic text summarization (ATS) deals with compressing a long document into a shorter version, retaining the key ideas of the original document. It aims to tackle the problem of information overload resulting from the continuous creation of documents. ATS helps people make news feeds, meeting reports, summarized legal and financial documents, study tools, article outlines, social media analysis, and so on. Extractive text summarization (ETS) is a type of ATS that creates summaries (extracts) by choosing sentences from the source without modifying the semantics and structure of the sentences. The main problem in ETS is to select the appropriate sentences that will constitute a coherent, concise, and nonredundant summary that covers the entire document. The literature suggests many optimization-based algorithms for the selection problem. Some authors have used binary particle swarm optimization (BPSO), genetic algorithm (GA), and artificial bee colony (ABC) algorithm to address the selection problem. However, these algorithms suffer from the problem of premature or slow convergence. This article proposes an ETS algorithm using BPSO and masked GA (EBPGA). The novelty of the proposed system is the usage of the masked GA (MGA), which leverages the properties of the document, namely, coherence, redundancy, relevance, and coverage factors, along with domain-specific mutation and crossover to tackle the premature convergence of the BPSO. Thus, the EBPGA algorithm selects the best sentences covering all the topics of the original document to create the summary that is coherent, relevant, and nonredundant. The proposed algorithm was evaluated by producing summaries of size 20%, 30%, and 40%, of the original document, and compared with the existing algorithms. The proposed summarization algorithm obtained a Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score that is comparable with the contemporary algorithms.

This research explores the growing threat of advanced persistent threats (APTs), which pose significant risks to national security, organisational operations and critical infrastructure. APTs have become increasingly sophisticated, targeting various sectors and demanding more effective defences to protect sensitive data and key systems. The focus of this paper is on addressing the rising frequency and complexity of APT attacks, aiming to provide a detailed analysis of their evolving tactics and the need for proactive security measures. Specifically, the paper examines current gaps in APT detection, from the initial stages of infiltration through to the complete removal of the threat. To address these challenges, the study introduces several detection strategies, including advanced correlation techniques, behavioural analysis of network traffic and user activity, and the application of machine learning and AI to improve threat identification. The paper analyses real-world APT incidents and discusses how monitoring and deception tactics can enhance security measures. It highlights the ongoing challenges presented by APTs, particularly their adaptive and dynamic attack methods, and emphasises the need for continuous improvement in defensive strategies. In conclusion, the paper outlines key areas for future research and stresses the importance of a proactive, evolving approach to counter the persistent and evolving nature of APTs.

Background Cardiovascular diseases (CVD) are a leading cause of morbidity and mortality globally, highlighting the urgent need for accurate risk prediction to improve early intervention and management. Traditional models have difficulty capturing the complex interactions between risk factors, which limits their predictive power. Objective This paper proposes the OptiStack Classifier, an optimized stacking framework developed to enhance CVD risk prediction through ensemble feature engineering and machine learning techniques. Methods The model uses dimensionality reduction and ensemble feature engineering methods, including polynomial expansion, binning and domain-specific feature transformation, to improve data representation. Principal Component Analysis (PCA) is used to dimensionality reduction, improving computational efficiency. A stacking framework integrates multiple machine learning algorithms as base learners, with Logistic Regression acting as the meta-classifier. Bayesian Optimization is applied for hyperparameter tuning, further boosting predictive performance. Results The proposed model shows significant improvements in predicting CVD risk, helping with early diagnosis and prevention, which can lead to better health outcomes for patients.