Focusing on emotion recognition, this paper addresses the task of emotion classification and its performance with respect to accuracy, by investigating the capabilities of a distributed ensemble model using precision-based weighted blending. Research...
In this paper, we present several optimization strategies that can improve the overall performance of the distributed in-memory computing system, “Apache Spark”. Despite its distributed memory management capability for iterative jobs and intermediate...
Data anonymization strategies such as subtree generalization have been hailed as techniques that provide a more efficient generalization strategy compared to full-tree generalization counterparts. Many subtree-based generalizations strategies (e.g.,...
Resilience-based decision-making for urban water distribution systems (WDSs) is a challenge when WDS sensing data contain incomplete or missing values. This study investigated the impact of missing data imputation on a WDS resilience evaluation depen...
The features of a dataset play an important role in the construction of a machine learning model. Because big datasets often have a large number of features, they may contain features that are less relevant to the machine learning task, which makes t...
Alternating Direction Method of Multipliers (ADMM) is a widely used machine learning tool in distributed environments. In the paper, we propose an ADMM-based differential privacy learning algorithm (FDP-ADMM) on penalized quantile regression for dist...
The frequent occurrence of extreme weather events (EWEs) in recent years has posed major hazards to urban transportation as well as socioeconomic impacts. A quantitative evaluation of the urban transportation resilience to minimize the impact caused...
This study investigates rural settlement patterns and land suitability in Rwanda’s Western Highlands, a mountainous region highly vulnerable to geohazards like landslides and flooding. Its primary aim is to inform sustainable, climate-resilient...
The proliferation of indoor and outdoor tracking devices has led to a vast amount of spatial data. Each object can be described by several trajectories that, once analysed, can yield to significant knowledge. In particular, pattern analysis by cluste...
Nearly 25% of all lakes on earth are located at high latitudes. These lakes are formed by a combination of thermokarst, glacial, and geological processes. Evidence suggests that the origin of periglacial lake formation may be an important factor cont...
This study examines the impact of data integrity attacks on Federated Learning (FL) for load forecasting in smart grid systems, where privacy-sensitive data require robust management. While FL provides a privacy-preserving approach to distributed mod...
The rapid evolution of malware continues to pose severe challenges to cybersecurity, highlighting the need for accurate and efficient detection systems. Traditional signature- and heuristic-based methods are increasingly inadequate against sophistica...
Debris flow events are complex natural phenomena that are challenging to predict, especially when data are limited or uncertain. This study presents a novel probabilistic approach using Bayesian Neural Networks (BNN) to predict possible volumes of de...
In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the...
Colonia communities, which host forgotten Americans, lack essential services such as portable water, adequate wastewater and solid waste disposal, adequate drainage, and adequate paved roads. The aim of this study is to investigate five key aspects o...
Efficient and secure data sharing is paramount for advancing modern digital ecosystems, especially within edge computing environments characterized by resource-constrained nodes and dynamic network topologies. In such settings, privacy preservation,...
The Smart Grid aims to enhance the electric grid’s reliability, safety, and efficiency by utilizing digital information and control technologies. Real-time analysis and state estimation methods are crucial for ensuring proper control implementa...
Estimating confidence intervals in small or noisy datasets is a recurring challenge in biomolecular research, particularly when data contain outliers or exhibit high variability. This study introduces a robust statistical method that combines a hybri...
Background: Major challenges in the application of intensity-modulated proton therapy (IMPT) for lung cancer patients include the uncertainties associated with breathing motion, its mitigation and its consideration in IMPT optimization. The primary o...
This study introduces an orbital monitoring system designed to quantify non-technical losses (NTLs) within electricity distribution networks. Leveraging Sentinel-2 satellite imagery alongside advanced techniques in computer vision and machine learnin...
UAVs typically rely on satellite navigation for positioning, yet this method proves ineffective in instances where the signal is inadequate or communication is disrupted. Visually based positioning technology has emerged as a reliable alternative. In...
In this paper, we present a novel blockchain-enabled approach to opportunistic federated learning (OppCL) for intelligent transportation systems (ITS). Our approach integrates blockchain with OppCL to streamline the learning of autonomous vehicle mod...
The phenomenon of big data has occurred in many fields of knowledge, one of which is astronomy. One example of a large dataset in astronomy is that of numerically integrated time series asteroid orbital elements from a time span of millions to billio...
The study analyzes the heterogeneous impacts of the COVID-19 pandemic on financial performance across five strategic sectors of Slovakia’s economy. Using a longitudinal dataset of 500 companies (100 per sector) spanning 2015–2022, we exam...
The continuously evolving cyber threat landscape necessitates not only resilient defense mechanisms but also the sustained capacity development of security personnel. However, conventional training pipelines are predominantly dependent on static real...
The Unit Inverse Maxwell–Boltzmann (UIMB) distribution is introduced as a novel single-parameter model for data constrained within the unit interval (0,1), derived through an exponential transformation of the Inverse Maxwell–Boltzmann dis...
Frequent itemset mining (FIM) is a common approach for discovering hidden frequent patterns from transactional databases used in prediction, association rules, classification, etc. Apriori is an FIM elementary algorithm with iterative nature used to...
Gross primary production (GPP) is a key component in assessing the global change in carbon uptake and in evaluating the impacts of climate change on terrestrial ecosystems. A decrease in the photosynthetic rate due to stomata closing by vegetation co...
In the present study, a total of 360 FE analyses were carried out on tubular X-joints strengthened with collar plates under brace tension under laboratory testing conditions (20 °C) and various fire conditions. The generated FE models were valida...
Smart cities necessitate ultra-secure and scalable communication frameworks to manage billions of interconnected IoT devices, particularly in the face of the emerging quantum computing threats. This paper proposes the QESIF, a novel Quantum-Enhanced...
Recent studies in data anonymization techniques have primarily focused on MapReduce. However, these existing MapReduce based approaches often suffer from many performance overheads due to their inappropriate use of data allocation, expensive disk I/O...
Identifying a soil core microbiome is crucial to appreciate the established microbial consortium, which is not usually subjected to change and, hence, possibly resistant/resilient to disturbances and a varying soil context. Fungi are a major part of...
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At the core of housing and welfare research is a premise that stable residential environments are important to children’s health and development. The relationship between housing stability and health outcomes for children is, however, complex;...
The reliability and robustness of global electrical networks are being impacted by the reciprocal effects of climate change and severe weather events. This article assesses research and collaborative trends to further these concerns. This study attem...
Residential water demand exhibits marked variability over time and across users, with direct implications for the sustainable and robust design of water distribution networks. In this study, a high-resolution experimental dataset is analysed to chara...
An important application of airborne- and satellite-based hyperspectral imaging is the mapping of the spatial distribution of vegetation biophysical and biochemical parameters in an environment. Statistical models, such as Gaussian processes, have be...
The increasing potential of infrastructure disruptions calls for high-quality infrastructure models to be used in resilience analysis and decision making. Unfortunately, many utilities and communities do not have access to accurate and detailed model...
Machine learning (ML) is efficiently disrupting and modernizing cities in terms of service quality for mobility, security, robotics, healthcare, electricity, finance, etc. Despite their undeniable success, ML algorithms need crucial computational eff...
Utilization of remote sensing-derived meteorological data is a valuable alternative for tropical insular territories such as Puerto Rico (PR). The study of ecosystem resilience in insular territories is an underdeveloped area of investigation. Little...
We present an enhanced Extreme Value Analysis (EVA) framework designed to improve the forecasting of extremely low-production events in photovoltaic (PV) systems and to reveal the key inter-variable relationships governing performance under extreme c...
Water distribution networks are often susceptible to pipeline leaks caused by mechanical damages, natural hazards, corrosion, and other factors. This paper focuses on the detection of leaks in water distribution networks (WDN) using a data-driven app...
This study investigates how climate gradients shape tick–host associations, testing the hypothesis that variations in climate leverage some associations, which can be ecosystem-specific. To test this hypothesis, we modelled tick–host asso...
Rapid and accurate anomaly traffic detection is one of the most important research problems in cyberspace situational awareness. In order to improve the accuracy and efficiency of the detection, a two-stage anomaly detection method based on user pref...
Logistics operations demand real-time visibility and rapid response, yet minute-level traffic speed forecasting remains challenging due to heterogeneous data sources and frequent distribution shifts. This paper proposes a Deep Operator Network (DeepO...
The primary objective of this study is to assess the techno-economic feasibility of an innovative solar energy generation system with a rainwater collection feature to generate electrical energy and meet irrigation needs in agriculture. The proposed...
In this study, a cross-hierarchical intelligent modeling framework integrating an ecological semantic encoder, a distribution-aligned contrastive loss, and a disturbance-aware attention mechanism was developed to address the semantic alignment challe...
The cyberspace is a convenient platform for creative, intellectual, and accessible works that provide a medium for expression and communication. Malware, phishing, ransomware, and distributed denial-of-service attacks pose a threat to individuals and...
The diverse usage of Unmanned Aerial Vehicles (UAVs) across commercial, military, and civil domains has significantly heightened the need for robust cybersecurity mechanisms. Given their reliance on wireless communications, real-time control systems,...
In this study, we explore the implications of advancing AI technology on the safety of machine learning models, specifically in decision-making across diverse applications. Our research delves into the domain of network intrusion detection, covering...
As online social networks play a more and more important role in public opinion, the large-scale simulation of social networks has been focused on by many scientists from sociology, communication, informatics, and so on. It is a good way to study rea...
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