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Barriers to deep learning implementation in orthodontics: data, methodological, and translational challenges—a scoping review
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Abstract Background Deep learning (DL) has attracted increasing attention in orthodontics, with many studies reporting high diagnostic and predictive accuracy. However, despite these promising results, routine clinical adoption remains limited. Objectives This scoping review aimed to explore the main barriers that restrict the translation of DL systems from research environments into everyday orthodontic practice. Methodology The review was conducted in accordance with PRISMA-ScR guidelines. Electronic searches were performed in PubMed, Scopus, Web of Science, and IEEE Xplore for English-language studies published between 2015 and June 2025. Studies reporting quantitative performance of DL-based orthodontic applications were included. Data were extracted using a standardized charting form and synthesized using a barrier-centered thematic approach. Results From 612 records, 26 studies were included. While most demonstrated high internal performance, consistent barriers to clinical implementation were identified. These primarily involved limited dataset diversity, single-center data sourcing, absence of external validation, and methodological heterogeneity. In addition, reliance on controlled research settings and insufficient real-world testing further restricted translation into routine orthodontic practice. Conclusion Although deep learning shows promising technical performance in orthodontics, meaningful clinical integration remains limited. Overcoming current barriers will require stronger validation standards, greater transparency, collaborative multicenter research, and implementation strategies that align with real clinical workflows.

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Publication Date
Sun Sep 01 2024
Journal Name
Results In Engineering
Implementation of a control and monitoring system for a cathodic protection cell to mitigate localized corrosion in fixed and mobile steel structures
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The current study introduces a novel technique to handle electrochemical localized corrosion in certain limited regions rather than applying comprehensive cathodic protection (CP) treatment. An impressed current cathodic protection cell (ICCPC) was fabricated and firmly installed on the middle of a steel structure surface to deter localized corrosion in fixed or mobile steel structures. The designed ICCPC comprises three essential parts: an anode, a cathode, and an artificial electrolyte. The latter was developed to mimic the function of the natural electrolyte in CP. A proportional-integrated-derivative (PID) controller was designed to stabilize this potential below the ICCPC at a cathodic potential of −850 mV, which is crucial for prote

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Scopus (7)
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Publication Date
Thu Oct 08 2026
Journal Name
Al–bahith Al–a'alami
The Challenges of Forming A Culture of Change in Iraq (Self-Obstacles to the Media Role)
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The Iraqi culture faced a set of challenges that can be diagnosed with the most prominent features as follows:

  1. The dominance of authoritarian political systems which entails authoritarian regimes with the absence of contemporary political concepts of human rights.
  2. The prevalence of non- informed cultural systems which have the shortage of capabilities that enable them to activate cultural elements in positive references, historical, or seclusion on itself and not be able to interact with the current active cultures.
  3. Stagnant economic conditions have not had a decent life for individuals, or a certain level of well-being, as well as poor services and others.
  4. Social life controlled by the prevai

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Publication Date
Tue Jan 14 2025
Journal Name
South Eastern European Journal Of Public Health
Deep learning-based threat Intelligence system for IoT Network in Compliance With IEEE Standard
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The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre

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Publication Date
Tue Jan 01 2019
Journal Name
Ieee Access
Implementation of Univariate Paradigm for Streamflow Simulation Using Hybrid Data-Driven Model: Case Study in Tropical Region
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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Oil spill classification based on satellite image using deep learning techniques
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 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

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Publication Date
Sat Apr 01 2023
Journal Name
The Ocular Surface
Detecting dry eye from ocular surface videos based on deep learning
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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
Indoor/Outdoor Deep Learning Based Image Classification for Object Recognition Applications
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With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor se

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Publication Date
Wed Mar 15 2023
Journal Name
International Journal Of Advances In Intelligent Informatics
An automatic lip reading for short sentences using deep learning nets
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One study whose importance has significantly grown in recent years is lip-reading, particularly with the widespread of using deep learning techniques. Lip reading is essential for speech recognition in noisy environments or for those with hearing impairments. It refers to recognizing spoken sentences using visual information acquired from lip movements. Also, the lip area, especially for males, suffers from several problems, such as the mouth area containing the mustache and beard, which may cover the lip area. This paper proposes an automatic lip-reading system to recognize and classify short English sentences spoken by speakers using deep learning networks. The input video extracts frames and each frame is passed to the Viola-Jone

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Scopus (13)
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Publication Date
Fri Jul 21 2023
Journal Name
Journal Of Engineering
Design and Implementation of a Vlsm Simulator
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Variable-Length Subnet Masks (VLSM), often referred to as "subnetting a subnet", is used to maximize addressing efficiency. The network administrator is able to use a long mask on networks with few hosts, and a short mask on subnets with many hosts. This addressing scheme allows growth and does not involve wasting addresses. VLSM gives a way of subnetting a network with
minimal loses of IP addresses for a specific range. Unfortunately, the network administrator has to perform several mathematical steps (or use charts) to get the required results from VLSM. In this paper, a simple graph simulator is proposed (using Visual Basic 6.0 Language) to perform all the required mathematical steps and to display the obtained required informatio

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Publication Date
Wed Sep 12 2012
Journal Name
Journal Of Engineering
Design and Implementation of a Vlsm Simulator
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Variable-Length Subnet Masks (VLSM), often referred to as "subnetting a subnet", is used to maximize addressing efficiency. The network administrator is able to use a long mask on networks with few hosts, and a short mask on subnets with many hosts. This addressing scheme allows growth and does not involve wasting addresses. VLSM gives a way of subnetting a network with minimal loses of IP addresses for a specific range. Unfortunately, the network administrator has to perform several mathematical steps (or use charts) to get the required results from VLSM. In this paper, a simple graph simulator is proposed (using Visual Basic 6.0 Language) to perform all the required mathematical steps and to display the obtained required information (the

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