Through social media platforms, massive amounts of data are being produced. As a microblogging social media platform, Twitter enables its users to post short updates as “tweets” on an unprecedented scale. Once analyzed using machine learn...
Supply chain agility has become a key success factor for businesses trying to handle upheavals and uncertainty in today’s quickly changing business environment. Proactive risk reduction is essential for achieving this agility. To facilitate rea...
The rapid growth of Internet banking has necessitated advanced systems for secure, real-time decision making. This paper introduces BankNet, a predictive analytics framework integrating big data tools and a BiLSTM neural network to deliver high-accur...
Real-time spaceborne bistatic SAR imaging could significantly reduce the whole processing time and can enhance the spaceborne SAR mission availability. Onboard real-time SAR imaging relies on the Doppler parameters estimated from the real-time onboar...
We study big-data hybrid-data-processing lambda architecture, which consolidates low-latency real-time frameworks with high-throughput Hadoop-batch frameworks over a massively distributed setup. In particular, real-time and batch-processing engines a...
Emerging applications of IoT (the Internet of Things), such as smart transportation, health, and energy, are envisioned to greatly enhance the societal infrastructure and quality of life of individuals. In such innovative IoT applications, cost-effic...
In today’s business environments, data-driven decision-making has played a crucial role in bringing real-time sustainability to organizational behavior. Furthermore, the Internet of Things (IoT) has been widely adopted recently, but studies inv...
The accurate measurement of audience engagement in real-world live events remains a significant challenge, with the majority of existing research confined to controlled environments like classrooms. This paper presents a comprehensive survey of Compu...
In data-intensive real-time applications, e.g., cognitive assistance and mobile health (mHealth), the amount of sensor data is exploding. In these applications, it is desirable to extract value-added information, e.g., mental or physical health condi...
Students’ affective states describe their engagement, concentration, attitude, motivation, happiness, sadness, frustration, off-task behavior, and confusion level in learning. In online learning, students’ affective states are determinati...
Twitter is a microblogging platform that generates large volumes of data with high velocity. This daily generation of unbounded and continuous data leads to Big Data streams that often require real-time distributed and fully automated processing. Has...
The rapid proliferation of Internet of Things (IoT) devices across industries has created a need for robust, scalable, and real-time data processing architectures capable of supporting intelligent analytics and predictive maintenance. This paper pres...
The exponential growth of video data across domains such as surveillance, transportation, and healthcare has raised critical challenges in scalability, real-time processing, and privacy preservation. While existing studies have addressed individual a...
The integration of cloud computing, IoT (Internet of Things), and artificial intelligence (AI) is transforming precision agriculture by enabling real-time monitoring, data analytics, and dynamic control of environmental factors. This study develops a...
Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on comput...
While technology has helped improve process efficiency in several domains, it still has an outstanding debt to education. In this article, we introduce NAIRA, a Multimodal Learning Analytics platform that provides Real-Time Feedback to foster collabo...
The volatile nature of cryptocurrency markets demands real-time analytical capabilities that traditional centralized computing architectures struggle to provide. This paper presents a novel hybrid cloud–edge computing framework for cryptocurren...
Traditional academic monitoring practices rely on retrospective data analysis, generally identifying at-risk students too late to take meaningful action. To address this, this paper proposes a real-time, rule-based decision support system designed to...
Integration of machine learning (ML) and artificial intelligence (AI) into point-of-care (POC) sensor systems represents a transformative advancement in healthcare. This integration enables sophisticated data analysis and real-time decision-making in...
This study proposes a novel framework integrating Building Information Modeling (BIM) and Geographic Information Systems (GIS) with real-time crowd analytics from Closed-Circuit Television (CCTV) for quantitative walkability assessment. The framework...
Images captured in rainy weather conditions often suffer from contamination, resulting in blurred or obscured objects, which can significantly impact detection performance due to the loss of identifiable texture and color information. Moreover, the q...
As natural disasters and crises increase globally in both frequency and severity, researchers have been exploring innovative technological solutions to manage them effectively. This systematic review examines the integration of artificial intelligenc...
To ensure the reliability and security of Colombia’s national power system, there is an ongoing necessity for upgrades in monitoring and protection mechanisms. Approximately sixteen years ago, the introduction of synchrophasor measurements enab...
The rapid proliferation of wearable sensors and advanced tracking technologies has revolutionized data collection in elite sports, enabling continuous monitoring of athletes’ physiological and biomechanical states. This study proposes a compreh...
The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are...
Digitalization has shaped every part of our world, from education to healthcare, from finance to entertainment, and from manufacturing to social interactions. This digital transformation has put tremendous power in the hands of individuals and delive...
Disasters are sudden and catastrophic events with fatal consequences. Time-sensitive information collection from disaster zones is crucial for improved and data-driven disaster response. However, information collection from disaster zones in a prompt...
This paper investigates the transformative potential of integrating technical and methodological tools such as GraphQL, openEHR, Redis, and Pervasive Business Intelligence in healthcare. Modern healthcare systems face data silos, interoperability, an...
This study evaluates input error function observers for tracking of stiffness variation in real-time. The input error function is an Analytical Redundancy (AR)-based diagnosis method and necessitates a mathematical model of the system and system iden...
Driver drowsiness is a major contributor to road accidents, often resulting from delayed reaction times and impaired cognitive performance. This study introduces EffRes-DrowsyNet, a hybrid deep learning model that integrates the architectural efficie...
Traffic crashes are a leading cause of death and injury worldwide, with far-reaching societal and economic consequences. To effectively address this global health crisis, researchers and practitioners rely on the analysis of crash data to identify ri...
Cognitive biases continue to pose significant challenges in executive decision-making, often leading to strategic inefficiencies, misallocation of resources, and flawed risk assessments. While traditional decision-making relies on intuition and exper...
The transition from Industry 4.0 to Industry 5.0 gives more prominence to human-centered and sustainable manufacturing practices. This paper proposes a conceptual design framework based on Vision Transformers (ViTs) and digital twins, to meet the dem...
We propose a hybrid CNN-LSTM architecture for energy-efficient spatiotemporal modeling in sports venue analytics, addressing the dual challenges of computational efficiency and prediction accuracy in dynamic environments. The proposed method integrat...
Enormous heterogeneous sensory data are generated in the Internet of Things (IoT) for various applications. These big data are characterized by additional features related to IoT, including trustworthiness, timing and spatial features. This reveals m...
This paper examines the combination of traditional banking credit assessment techniques with contemporary internal sales accounting systems in Kazakhstan, aiming to augment the precision and resilience of financial assessments pertaining to SMEs. The...
Learner-centered coaching and feedback are relevant to various educational contexts. Spaced retrieval enhances long-term knowledge retention. We examined the efficacy of Blank Slate, a novel spaced retrieval software application, to promote learning...
Blasingame production decline is an effective method to obtain permeability and single-well controlled reserves. The accurate Blasingame production decline curve needs an accurate wellbore pressure approximate solution of the real-time domain. Theref...
The global transition from fossil fuels to renewable energy has become a critical priority in addressing environmental and sustainability challenges. However, the intermittent nature of renewable energy sources, such as solar and wind, poses signific...
Elevated temperature in the machining process is detrimental to cutting tools—a result of the effect of thermal softening and material diffusion. Material diffusion also deteriorates the quality of the machined part. Measuring or predicting mac...
In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of IoT application systems. It provides various integration capabilities for the collection, d...
The integration of artificial intelligence (AI) agents with the Internet of Things (IoT) has marked a transformative shift in environmental monitoring and management, enabling advanced data gathering, in-depth analysis, and more effective decision ma...
Lubricating greases enclose oil in porous structures of aggregated thickener particles. Their tendency to separate oil under static conditions is evaluated according to DIN 51817 or DIN ISO 22285 in tests of up to 168 h with the mass fraction of sepa...
The increasing scale and complexity of data mining outputs, such as frequent itemsets, association rules, sequences, and subgraphs have made efficient pattern retrieval a critical, yet underexplored challenge. This review addresses the organisation,...
The mass of a direct-drive generator is often defined by the requirements for structural stiffness to meet the magnetic stiffness between the rotor and stator surfaces. This paper analyses this magnetic stiffness and estimates the structural stiffnes...
22 January 2026
(1) Background: The convergence of Big Data and the Internet of Things (IoT) is transforming digital accounting from retrospective documentation into real-time operational intelligence. This systematic review examines how Industry 4.0 technologies&md...
Fitting of analytic forms to point or triangle sets is central to computer-aided design, manufacturing, reverse engineering, dimensional control, etc. The existing approaches for this fitting assume an input of statistically strong point or triangle...
In recent years, advances in artificial intelligence, machine vision, and the Internet of Things have significantly impacted sports analytics, particularly basketball, where accurate measurement and analysis of player performance have become increasi...
The increasing development of urban centers brings serious challenges for traffic management. In this paper, we introduce a smart visual sensor, developed for a pilot project taking place in the Australian city of Liverpool (NSW). The project’s...
The volume of streaming sensor data from various environmental sensors continues to increase rapidly due to wider deployments of IoT devices at much greater scales than ever before. This, in turn, causes massive increase in the fog, cloud network tra...
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