Metal processing may benefit from innovative lean-and-green datacentric engineering techniques. Broad process improvement opportunities in the efficient usage of materials and energy are anticipated (United Nations Sustainable Development Goals #9, 1...
In recent years, the field of artificial intelligence has experienced significant growth, which has been primarily attributed to advancements in hardware and the efficient training of deep neural networks on graphics processing units. The development...
Artificial Intelligence (AI) has traditionally been developed according to a model-centric paradigm, in which progress is driven by increasingly sophisticated learning architectures applied to largely fixed datasets. However, this paradigm exhibits w...
This narrative critical review addresses class imbalance in medical imaging, particularly within the context of multi-sensor and multi-modal environments, poses a critical challenge to developing reliable AI diagnostic systems. The integration of het...
Limited training data is one of the biggest challenges in the industrial application of deep learning. Generating synthetic training images is a promising solution in computer vision; however, minimizing the domain gap between synthetic and real-worl...
The idea of a digital twin has recently gained widespread attention. While, so far, it has been used predominantly for problems in engineering and manufacturing, it is believed that a digital twin also holds great promise for applications in medicine...
Content-Centric Networking (CCN) is one of the emerging paradigms for the future Internet, which shifts the communication paradigm from host-centric to data-centric. In CCN, contents are delivered by their unique names, and a public-key-based signatu...
The latest industrial revolution 4 enabled significant performance improvement through technological advancements. Simultaneously, the industry is setting high-level expectations for changing business practices toward long-term benefits in all three...
Supply chain information systems increasingly need to support decisions, yet Internet of Things (IoT) and blockchain approaches remain largely data-centric. We propose a decision-centric system in which IoT data acquisition, an artificial intelligenc...
LambdaStore is a data-centric serverless platform that breaks the split between stateless functions and external storage in classic cloud computing platforms. By scheduling serverless invocations near data instead of pulling data to compute, LambdaSt...
Speed reducers (SR) and electric motors are crucial in modern manufacturing, especially within adhesive coating equipment. The electric motor mainly transforms electrical power into mechanical force to propel most machinery. Conversely, speed reducer...
Collaboration among individuals with diverse skills and personalities is crucial to producing high-quality software. The success of any software project depends on the team’s cohesive functionality and mutual complementation. This study introdu...
The On-Board Diagnostics II (OBD-II) system, driven by a wide range of embedded sensors, has revolutionized the automotive industry by enabling real-time monitoring of key vehicle parameters such as engine load, vehicle speed, throttle position, and...
This survey presents a comprehensive review of Human Pose Estimation spanning 2D and 3D settings, unifying prior work through a taxonomy of body representations (2D keypoints, 3D skeletons, dense meshes), processing flows (top-down vs. bottom-up), pr...
Background: This study deploys the Lean Six Sigma DMAIC framework to achieve systemic optimization of the school subscription process in Tunisia’s public transport service, a critical administrative operation affecting efficiency and customer s...
Research on technological infrastructures supporting open innovation ecosystems is evolving toward data-centric paradigms enabled by Artificial Intelligence (AI). Nevertheless, key limitations concern the continued reliance on syntactic rule-based se...
The effectiveness of machine learning and deep learning methods for network anomaly detection depends strongly on the quality and representativeness of the datasets used for training and evaluation. Despite recent advances, many publicly available be...
A physics-informed machine-learning model was created to forecast the dielectric constant of oxide materials. The study explores the physicochemical descriptors that govern the response of the materials. A set of 3304 non-metallic oxide compounds was...
Deep neural network (DNN) hardware (HW) accelerators have achieved great success in improving DNNs’ performance and efficiency. One key reason is the dataflow in executing a DNN layer, including on-chip data partitioning, computation parallelis...
Collaboration is a cornerstone of digital transformation in the Architecture, Engineering, and Construction (AEC) sector. Despite widespread adoption of building information modeling (BIM), a critical “human factor gap”—the disconne...
This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research ut...
Marine renewable energy (MRE) systems operate in harsh marine environments where long-term exposure to seawater leads to biofouling, resulting in increased surface roughness, hydrodynamic drag, added mass, structural loading, sensor degradation, and...
This work presents a data-centric method to use IoT data, generated from the site, to monitor core functions of safety barriers on a batch reactor. The approach turns process safety performance indicators (PSPIs) into online, globally available safet...
Automatic building extraction from high-resolution imagery remains constrained by limited training data and domain shifts across geographic regions and spatial resolutions. Although data augmentation is widely applied in semantic segmentation, its ca...
In a world increasingly dominated by data, graduates in STEM (Science, Technology, Engineering, and Mathematics) fields are in high demand. This focus often obscures the fact that a significant portion of digital data is social in nature, generated t...
Machine learning (ML) techniques discover knowledge from large amounts of data. Modeling in ML is becoming essential to software systems in practice. The accuracy and efficiency of ML models have been focused on ML research communities, while there i...
Water scarcity is a challenging global risk. Urban wastewater treatment technologies, which utilize processes based on single-stage ultrafiltration (UF) or nanofiltration (NF), have the potential to offer lean-and-green cost-effective solutions. Robu...
Electrodialysis (ED) may be designed to enhance wastewater recycling efficiency for crop irrigation in areas where water distribution is otherwise inaccessible. ED process controls are difficult to manage because the ED cells need to be custom-built...
Battery Energy Storage Systems (BESS), utilising chemistries based on Nickel Manganese Cobalt (NMC) containing lithium-ion devices, often present fire safety hazards that existing qualitative risk frameworks, including NFPA 855’s 5 × 5 co...
The use of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity, especially for creating Intrusion Detection Systems (IDSs), has become increasingly important. These systems are essential for detecting malicious behaviour, identify...