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A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
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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 many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.</p>
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Publication Date
Wed Jan 01 2025
Journal Name
Heliyon
Treatment of hospital wastewater by anodic oxidation using a new approach made by combining rotation with pulsed electric current on Cu-SnO2–Sb2O5 rotating cylinder anode
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Publication Date
Wed Mar 02 2022
Journal Name
Journal Of Educational And Psychological Researches
The Role of School Administration in Achieving Educational and Learning Goals as Seen by Educational Supervisors in the Stage of Basic Education
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This study aimed to identify the role of school administration in achieving educational and learning goals from the point of view of educational supervisors in the stage of basic education. The descriptive method was adopted. As for the sample size, it has reached (59) educational supervisors. A questionnaire consisting of 29 paragraphs divided into four areas was used. The data were statistically analyzed on the Chi-square test, the percentage, and the mono-variance analysis. The result showed that the school administration contributes to achieving educational goals. It also works to solve problems in democratic ways, and in modern methods, there are differences in the criteria for choosing the headmaster. The study recommended that sch

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Publication Date
Thu Oct 01 2015
Journal Name
Journal Of Economics And Administrative Sciences
The impact of organizational learning in building intellectual capital in public organizations: comparative research between the universities of Baghdad and al-Mustansiriya
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   Organizational learning is one of the most important means of human resource development in organizations, but most of the organizations, especially public ones do not realize the importance of organizational learning enough, and estimated his role accurately in building intellectual capital, the resource competitive importantly for organizations of the third millennium and who suffers is other end of lack of understanding of its meaning and how to prove its presence and measured in public organizations, so there is the need for this research, which aims to investigate the effect of organizational learning its processes (knowledge acquisition, Information transfer, Interpreting the information, Organizational me

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Publication Date
Wed Mar 20 2024
Journal Name
Journal Of Petroleum Research And Studies
Advanced Machine Learning application for Permeability Prediction for (M) Formation in an Iraqi Oil Field
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Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy

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Publication Date
Mon Sep 09 2024
Journal Name
Научный Форум
the functioning of artificial intelligence for the development of communication skills among foreigners learning Russian
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Publication Date
Wed Nov 05 2025
Journal Name
Irrigation And Drainage
Predicting Potential Salinity in River Water for Irrigation Water Purposes Using Integrative Machine Learning Models
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ABSTRACT<p>Accurate prediction of river water quality parameters is essential for environmental protection and sustainable agricultural resource management. This study presents a novel framework for estimating potential salinity in river water in arid and semi‐arid regions by integrating a kernel extreme learning machine (KELM) with a boosted salp swarm algorithm based on differential evolution (KELM‐BSSADE). A dataset of 336 samples, including bicarbonate, calcium, pH, total dissolved solids and sodium adsorption ratio, was collected from the Idenak station in Iran and was used for the modelling. Results demonstrated that KELM‐BSSADE outperformed models such as deep random vector funct</p> ... Show More
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Publication Date
Wed Mar 16 2022
Journal Name
Journal Of Educational And Psychological Researches
Learning Cleverness of Physics Teachers in Secondary Stages According To the Variable Years of Service
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This current research aims to reveal the difference between the levels of teaching expertise of physics teachers according to the years of service by answering the following question: does the educational skill level of physics teachers work according to the service? The sample of the study consisted of (225) physics teachers by (125) females (56%), and (100) males (44%), distributed on (4) education directorates in Baghdad governorate on both sides of al-Karkh and al-Rusafa. In order to achieve the aim of the study, the researcher prepared a note card for expertise teaching, consisting of (39) paragraphs distributed into (5) fields. The apparent validity and reliability of the card were verified through an agreement (the researcher hers

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Publication Date
Sun May 15 2022
Journal Name
Al-kindy College Medical Journal
Assessment of Nurses’ Knowledge, Attitude, and Practices on Antibiotic Use and Resistance in Baghdad: A single-hospital study
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Background: There are so many evidences that there was antimicrobial resistance, and there were many strains that emerged which were difficult to treat. We are living in a situation that the dissemination of multiple drug resistant bacteria can lead us to the situation, in which no treatment could be offered for bacterial infection in future.

Aim of study: Assessment of nurses’ knowledge, attitude, and practices on antibiotic use and resistance in Fatima Al Zahra hospital in Baghdad.

Subjects and Methods:  A cross-sectional study. The study was carried on from 1st of February to 31st of March 2021.  A questionnaire was constructed by the research team based on literature review and was adapted to asses

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Publication Date
Wed May 10 2017
Journal Name
Journal Of The College Of Languages (jcl)
Procedimientos de traducción de los nombres propios del árabe al español: Fundamentos y Aplicaciones Procedures for translating proper noun from Arabic into Spanish: Fundamentals and Applications
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Los nombres propios nombran a un ser o a un objeto, distinguiéndolo de los demás seres de su misma clase, se escriben siempre con letra mayúscula a principio de palabra. Los lingüistas hacen mayor hincapié en las divergencias de referencia, entre nombres propios y nombres comunes. Así, suele decirse que el sustantivo propio no tiene como referente ningún concepto. El asunto de la traducción  de los nombres propios parecería una cuestión de gusto personal del traductor pero vemos también que en algunas épocas es más frecuente traducirlos, y en otras, por el contrario, se prefiere dejar esos nombres en su forma original, tal vez con algunas adaptaciones ortográficas. Parecería enton­ces cuestión de modas. Pero, eviden

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Publication Date
Sun Dec 29 2024
Journal Name
Iraqi Journal Of Agricultural Sciences
EFFECT OF DIFF. SUBS. AND SPRAYING WITH K2SIO3 ON THE ROOT AND VEGETATIVE GROWTH OF L. W. PLANTS IN BAGHDAD CITY
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This study was aimed to investigate the response of two types of ornamental herbaceous plants (Wedelia trilobata and Jacobaea maritima 'Cirrus') to different agricultural environments and the application of potassium silicates to the living walls system LWS (Felt layer system) under the climate conditions of Baghdad city. Each experiment involved the cultivation of a different plant species, and the study duration was from September 15, 2021, to August 1, 2022. A Strip-Plot Design experiment was conducted using two factors: factor M with four levels of substrates (50% peatmoss and perlite (M1), 50% Vermicompost and perlite (M2), 50% Water hyacinth compost and perlite (M3), 50% wheat straw compost and perlite (M4)) and factor S with

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