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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
Sun Mar 17 2019
Journal Name
Baghdad Science Journal
A New Cipher Based on Feistel Structure and Chaotic Maps
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Chaotic systems have been proved to be useful and effective for cryptography. Through this work, a new Feistel cipher depend upon chaos systems and Feistel network structure with dynamic secret key size according to the message size have been proposed. Compared with the classical traditional ciphers like Feistel-based structure ciphers, Data Encryption Standards (DES), is the common example of Feistel-based ciphers, the process of confusion and diffusion, will contains the dynamical permutation choice boxes, dynamical substitution choice boxes, which will be generated once and hence, considered static,

            While using chaotic maps, in the suggested system, called

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Publication Date
Tue Jul 18 2023
Journal Name
Journal Of Current Researches On Social Sciences
A Study on Sociocultural Features among Turkish and Iraqi Women
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SM ADAI, BN RASHID, Journal of Current Researches on Social Sciences, 2023

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Publication Date
Tue Feb 18 2025
Journal Name
International Journal Of Scientific Research In Science, Engineering And Technology
A Comprehensive Review on Cryptography Algorithms: Methods and Comparative Analysis
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The evolution of cryptography has been crucial to preservation subtle information in the digital age. From early cipher algorithms implemented in earliest societies to recent cryptography methods, cryptography has developed alongside developments in computing field. The growing in cyber threats and the increase of comprehensive digital communications have highlighted the significance of selecting effective and robust cryptographic techniques. This article reviews various cryptography algorithms, containing symmetric key and asymmetric key cryptography, via evaluating them according to security asset, complexity, and execution speed. The main outcomes demonstrate the growing trust on elliptic curve cryptography outstanding its capabi

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Publication Date
Tue Jun 25 2024
Journal Name
Biomedical And Pharmacology Journal
Psidium guajava: A Review on Its Pharmacological and Phytochemical Constituents
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Psidium guajava, belonging to the Myrtaceae family, thrives in tropical and subtropical regions worldwide. This important tropical fruit finds widespread cultivation in countries like India, Indonesia, Syria, Pakistan, Bangladesh, and South America. Throughout its various parts, including fruits, leaves, and barks, guava boasts a rich reservoir of bioactive compounds that have been traditionally utilized as folkloric herbal medicines, offering numerous therapeutic applications. Within guava, an extensive array of Various compounds with antioxidative properties and phytochemical constituents are present, including essential oils, polysaccharides, minerals, vitamins, enzymes, triterpenoids, alkaloids, steroids, glycosides, tannins, fl

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Publication Date
Wed Sep 30 2015
Journal Name
College Of Islamic Sciences
A consensus on measurement
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Many letters and theses written on the subject of consensus, as well as in measurement,
But we tried to address a topic of consensus

Building a blind measuring guide.
 We have tried to explain the meaning of convening, then the statement of consensus in language and terminology and then the statement of measurement
Also, we have shown the types of consensus mentioned by the jurists, and this is how much was in the first topic, either
The second section included the statement of the doctrines of the blind in the matter, and then the evidence of each doctrine and discussed.
We followed it with the most correct opinion statement and concluded the research with some of the conclusions we reached through
search.

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Publication Date
Sat Apr 30 2022
Journal Name
Revue D'intelligence Artificielle
Performance Evaluation of SDN DDoS Attack Detection and Mitigation Based Random Forest and K-Nearest Neighbors Machine Learning Algorithms
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

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Publication Date
Fri Oct 01 2021
Journal Name
Applied Thermal Engineering
Simultaneous and consecutive charging and discharging of a PCM-based domestic air heater with metal foam
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Publication Date
Fri Jun 01 2012
Journal Name
Journal Of Economics And Administrative Sciences
The Effect of the Stability of Some Commodity Activities in Iraq on the Estimation of the Statistical Data Models for the Period (1988-2000)
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There is an assumption implicit but fundamental theory behind the decline by the time series used in the estimate, namely that the time series has a sleep feature Stationary or the language of Engle Gernger chains are integrated level zero, which indicated by I (0). It is well known, for example, tables of t-statistic is designed primarily to deal with the results of the regression that uses static strings. This assumption has been previously treated as an axiom the mid-seventies, where researchers are conducting studies of applied without taking into account the properties of time series used prior to the assessment, was to accept the results of these tests Bmanueh and delivery capabilities based on the applicability of the theo

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Publication Date
Fri May 10 2019
Journal Name
Research Journal Of Chemistry And Environment
Solid Phase Extraction of Theophylline in Aqueous Solutions by Modified Magnetic Iron Oxide Nanoparticles as an Extractor Material and Spectrophotometry Technique for the Determination
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new, simple and fast solid-phase extraction method for separation and preconcentration of trace theophylline in aqueous solutions was developed using magnetite nanoparticles (MIONPs) coated with aluminium oxide (AMIONPs) and modified with palmitate (P) as an extractor (P@AMIONPs). It has shown that the developed method has a fast absorbent rate of the theophylline at room temperature. The parameters that affect the absorbent of theophylline in the aqueous solutions have been investigated such as the amount of magnetite nanoparticle, pH, standing time and the volume, concentration of desorption solution. The linear range, limit of quantification (LOQ) and limit of detection (LOD) for the determination of theophylline were 0.05-2.450 μg mL-

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Publication Date
Thu Apr 01 2021
Journal Name
Biochem. Cell. Arch
Biological Therapy for Patients With Rheumatoid Arthritis and Its Effect on Periodontal and Immunological Parameters
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Rheumatoid arthritis and periodontitis use analogous effector destructive procedures, in that the inflammatory cells and pro-inflammatory cytokines that drive chronic bone erosion in RA and chronic periodontal destruction in Periodontitis are alike. Periodontitis (PD) has appeared as a hazard factor in a number of health situations as rheumatoid arthritis (RA). To determine the effect of anti-tumor necrosis factor alpha biological treatment (methotrexate and Enbrel or infliximab) on periodontal status of patients having rheumatoid arthritis with periodontitis in comparison to those having periodontitis without rheumatoid arthritis and control healthy subjects and to determine the serum levels of anti-cyclic citrullinated peptide (ACCP) in t

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