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.
Multiple linear regressions are concerned with studying and analyzing the relationship between the dependent variable and a set of explanatory variables. From this relationship the values of variables are predicted. In this paper the multiple linear regression model and three covariates were studied in the presence of the problem of auto-correlation of errors when the random error distributed the distribution of exponential. Three methods were compared (general least squares, M robust, and Laplace robust method). We have employed the simulation studies and calculated the statistical standard mean squares error with sample sizes (15, 30, 60, 100). Further we applied the best method on the real experiment data representing the varieties of
... Show MoreObjectives: This study aimed to identify and study most properties of the specific and general health-related
quality-of-life (HRQoL) in prostate cancer patients, as well as creating a new measurement scale for assessing QoL
among prostate cancer patients.
Methodology: A cross sectional (descriptive) study was conducted to evaluate General Quality of life in patients
with prostate cancer. A sample of 100 prostate cancer patients from Al-Amal National hospital for cancer
management and Oncology Center in Baghdad Medical City. This study applied format of General World Health
Organization Quality of Life-BERF questionnaire. The methods used descriptive statistics to evaluate the General
QoL-Improvements, as well as inf
Most reinforced concrete (RC) structures are constructed with square/rectangular columns. The cross-section size of these types of columns is much larger than the thickness of their partitions. Therefore, parts of these columns are protruded out of the partitions. The emergence of columns edges out of the walls has some disadvantages. This limitation is difficult to be overcome with square or rectangular columns. To solve this problem, new types of RC columns called specially shaped reinforced concrete (SSRC) columns have been used as hidden columns. Besides, the use of SSRC columns provides many structural and architectural advantages as compared with rectangular columns. Therefore, this study was conducted to explain the structura
... Show MoreThe present study aims to estimating the prevalence of autoimmune thyroid disorders in Iraqi infertile women with polycystic ovary syndrome (PCOS). Eighty-five Iraqi women, with age range (19-45) years, were divided into three groups; first group included 33 women with PCOS; second group included 30 women without PCOS; while third group included 22 fertile women as controls. The clinical data [age, body mass index (BMI), and menstrual status] have been recorded. Blood samples were collected to determine the levels of reproductive hormones [estradiol (E2), luteinizing hormone (LH), and follicle stimulating hormone (FSH)]; and thyroid hormones [triiodothyronine (T3) and thyroxin (T4)]. Also, autoimmune thyroid antibodies assessment h
... Show MoreThe use of Near-Surface Mounted (NSM) Carbon-Fiber-Reinforced Polymer (CFRP) strips is an efficient technology for increasing flexural and shear strength or for repairing damaged Reinforced Concrete (RC) members. This strengthening method is a promising technology. However, the thin layer of concrete covering the NSM-CFRP strips is not adequate to resist heat effect when directly exposed to a fire or at a high temperature. There is clear evidence that the strength and stiffness of CFRPs severely deteriorate at high temperatures. Therefore, in terms of fire resistance, the NSM technique has a significant defect. Thus, it is very important to develop a set of efficient fire protection systems to overcome these disadvantages. This pape
... Show MoreThis work is an experimental investigation for single basin-single slope solar still coupled with an evacuated tube solar collector. The work is carried out under the climatic conditions of Baghdad city (33.2456º North and East latitude, 44.3337º longitude) through certain days of the months of the year 2019 to study the impact of using evacuated tube solar collector on the daily productivity and efficiency under the outdoors climatic conditions. It was found that using the evacuated tube solar collector increase daily productivity from 2.175 kg/ to 2.95 kg/ for 9 hours (35.63 %) for clear days, also an enhancement about 10.97 % in daily efficiency.
Quantum channels enable the achievement of communication tasks inaccessible to their
classical counterparts. The most famous example is the distribution of secret keys. Unfortunately, the rate
of generation of the secret key by direct transmission is fundamentally limited by the distance. This limit
can be overcome by the implementation of a quantum repeater. In order to boost the performance of the
repeater, a quantum repeater based on cut-off with two different types of quantum memories is suggestd,
which reduces the effect of decoherence during the storage of a quantum state.
In this paper, we introduce the concept of cubic bipolar-fuzzy ideals with thresholds (α,β),(ω,ϑ) of a semigroup in KU-algebra as a generalization of sets and in short (CBF). Firstly, a (CBF) sub-KU-semigroup with a threshold (α,β),(ω,ϑ) and some results in this notion are achieved. Also, (cubic bipolar fuzzy ideals and cubic bipolar fuzzy k-ideals) with thresholds (α,β),(ω ,ϑ) are defined and some properties of these ideals are given. Relations between a (CBF).sub algebra and-a (CBF) ideal are proved. A few characterizations of a (CBF) k-ideal with thresholds (α, β), (ω,ϑ) are discussed. Finally, we proved that a (CBF) k-ideal and a (CBF) ideal with thresholds (α, β), (ω,ϑ) of a KU-semi group are equivalent relations.