Protecting a sensitive dataset using a time based password in big data
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Publication Date
Tue Oct 01 2024
Journal Name
Separation And Purification Technology
A comprehensive review on the use of Ti3C2Tx MXene in membrane-based water treatment
2D materials
Membrane-based processes
MXenes
Ti3C2Tx
Water treatment
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Publication Date
Thu Dec 01 2022
Journal Name
Ieee Transactions On Human-machine Systems
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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
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Publication Date
Tue Apr 27 2021
Journal Name
Cells
DNMT3B Is an Oxygen-Sensitive De Novo Methylase in Human Mesenchymal Stem Cells
The application of physiological oxygen (physoxia) concentrations is becoming increasingly commonplace within a mammalian stem cell culture. Human mesenchymal stem cells (hMSCs) attract widespread interest for clinical application due to their unique immunomodulatory, multi-lineage potential, and regenerative capacities. Descriptions of the impact of physoxia on global DNA methylation patterns in hMSCs and the activity of enzymatic machinery responsible for its regulation remain limited. Human bone marrow-derived mesenchymal stem cells (BM-hMSCs, passage 1) isolated in reduced oxygen conditions displayed an upregulation of SOX2 in reduced oxygen conditions vs. air oxygen (21% O2, AO), while no change was noted for either OCT-4 or NA
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(8)
Publication Date
Thu Jul 01 2021
Journal Name
University Of Northampton Pue
Validating a Proposed Data Mining Approach (SLDM) for Motion Wearable Sensors to Detect the Early Signs of Lameness in Sheep
motion wearable sensors
sensor data mining
supervised machine learning
CART ensemble classifier
sheep lameness detection
sheep behaviour classification
Publication Date
Sat Feb 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Revealing the potentials of 3D modelling techniques; a comparison study towards data fusion from hybrid sensors
Abstract<p>The vast advantages of 3D modelling industry have urged competitors to improve capturing techniques and processing pipelines towards minimizing labour requirements, saving time and reducing project risk. When it comes to digital 3D documentary and conserving projects, laser scanning and photogrammetry are compared to choose between the two. Since both techniques have pros and cons, this paper approaches the potential issues of individual techniques in terms of time, budget, accuracy, density, methodology and ease to use. Terrestrial laser scanner and close-range photogrammetry are tested to document a unique invaluable artefact (Lady of Hatra) located in Iraq for future data fusion sc</p>
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Publication Date
Sun May 01 2011
Journal Name
Information Sciences
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Publication Date
Thu Jan 01 2015
Journal Name
Journal Of Engineering
A Visual Interface Design for Evaluating the Quality of Google Map Data for some Engineering Applications
data quality
positional accuracy
VGI
formal data
Google Map Maker.
Today, there are large amounts of geospatial data available on the web such as Google Map (GM), OpenStreetMap (OSM), Flickr service, Wikimapia and others. All of these services called open source geospatial data. Geospatial data from different sources often has variable accuracy due to different data collection methods; therefore data accuracy may not meet the user requirement in varying organization. This paper aims to develop a tool to assess the quality of GM data by comparing it with formal data such as spatial data from Mayoralty of Baghdad (MB). This tool developed by Visual Basic language, and validated on two different study areas in Baghdad / Iraq (Al-Karada and Al- Kadhumiyah). The positional accuracy was asses
... Show MorePublication Date
Fri Apr 14 2023
Journal Name
Journal Of Big Data
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Abstract<p>Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for</p>
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Publication Date
Fri Oct 31 2025
Journal Name
Mathematical Modelling Of Engineering Problems
Heterogeneous Traffic Management in SDN-Enabled Data Center Network Using Machine Learning-SPIKE Model
Software-Defined Networking (SDN) has evolved network management by detaching the control plane from the data forwarding plane, resulting in unparalleled flexibility and efficiency in network administration. However, the heterogeneity of traffic in SDN presents issues in achieving Quality of Service (QoS) demands and efficiently managing network resources. SDN traffic flows are often divided into elephant flows (EFs) and mice flows (MFs). EFs, which are distinguished by their huge packet sizes and long durations, account for a small amount of total traffic but require disproportionate network resources, thus causing congestion and delays for smaller MFs. MFs, on the other hand, have a short lifetime and are latency-sensitive, but they accou
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