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.
The aim of this study is to find a relationship between oxidative stress and adiponectin in Iraqi patients with acromegaly. The present study included 30 patients with acromegaly disease attending at Al-Yarmuk teaching hospital , and 30 healthy individuals as a control group.The two groups with ages ranging (30-55) years. The results revealed a highly significant elevation in all parameters (GH,IGF-1 , adiponectin , malondialdehyde , and peroxynitrite ) levels in sera of patients when compared with healthy control .It can be concluded that oxidative stress (malondialdehyde and peroxynitrite ) may be valuable in detecting of endocrine diseases like acromegaly .
The aim of this study is to find a relationship between oxidative stress and adiponectin in Iraqi patients with acromegaly. The present study included 30 patients with acromegaly disease attending at Al-Yarmuk teaching hospital , and 30 healthy individuals as a control group.The two groups with ages ranging (30-55) years. The results revealed a highly significant elevation in all parameters (GH,IGF-1 , adiponectin , malondialdehyde , and peroxynitrite ) levels in sera of patients when compared with healthy control .It can be concluded that oxidative stress (malondialdehyde and peroxynitrite ) may be valuable in detecting of endocrine diseases like acromegaly .
A polypyrrole‐graphene oxide (PPy/GO) nanocomposite was made chemically using a polymerization method in deep eutectic solvents made up of choline chloride and propionic acid in a 1:2 molar ratio. The purpose of this study is to examine the feasibility and efficacy of PPy/GO nanocomposite in the detection of metronidazole in pharmaceutical formulations utilizing spectrofluorometric techniques. Characterization of the synthesized PPy/GO nanocomposite was conducted using several methods such as transmission electron microscopy (TEM), scanning electron microscopy (SEM), Fourier‐transform infrared spectroscopy (FTIR), thermal gravimetric analysis (TGA), and X
Education around the world has been negatively affected by the new coronavirus disease (COVID-19) pandemic. Many institutions had to transition to distance learning in compliance with the enforced safety measures. Distance learning might work well for settings with stable internet connections, professional technical teams, and basic implementation of technology in education. In contrast, distance learning faces serious challenges in less fortunate settings with inferior infrastructure. This report aims to shed light on the immediate action steps taken at a leading pharmacy school in Iraq to accommodate for the enforced changes in pharmacy education. The University of Baghdad College of Pharmacy went from less than minimal technology impl
... Show MoreA novel ligand, (E)-5-((2-hydroxy-4,6-dimethylphenyl)diazenyl)-2,3-dihydrophthalazine-1,4- dione, was synthesized through the reaction of 3,5-dimethylphenol with the diazonium salt of 5-amino-2,3-dihydrophthalazine-1,4-dione. The ligand underwent characterization through the utilization of diverse spectroscopic methods, including UV-Vis, FT-IR, 13C, and 1H-NMR, alongside Mass spectroscopy and micro elemental analysis (Carbon, Hydrogen, Nitrogen, and Oxygen). Metal chelates of transition metals were prepared and analyzed using elemental analysis, mass spectra, atomic absorption, UV-Vis, FT-IR spectral analysis, as well as conductivity and magnetic measurements. The investigation into the compounds’ nature was conducted by utilizing mole r
... Show MoreRe-use of the byproduct wastes resulting from different municipal and industrial activities in the reclamation of contaminated water is real application for green projects and sustainability concepts. In this direction, the synthesis of composite sorbent from the mixing of waterworks and sewage sludge coated with new nanoparticles named “siderite” (WSSS) is the novelty of this study. These particles can be precipitated from the iron(II) nitrate using waterworks sludge as alkaline agent and source of carbonate. Characterization tests using X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) mapping revealed that the coating process was c