The Paleocene benthic foraminiferal zonation of the Umm Er Rhadhuma Formation from the borehole (K.H 12/7), South Anah City (Western Iraq), has been re-studied and re-analyzed precisely based on the large benthic foraminifera (LBF). They are represented by two biozone Rotorbinella hensoni Partial Range Zone, recorded from the Lower and middle parts of the Umm Er Rhadhuma Formation and Lockhartia praehaimei Partial Range Zone determined Uppermost of this unit, and dated to be the Selandian – Thanetian stage. Almost all the biogenic (micro and macro) and non-biogenic constituents, including large benthic foraminifera, Algae, Echinoderm, Bryozoans, Oyster, Gastropod fragments, and peloids, in addition to lithofacies types, indicate t

In this paper, some necessary and sufficient conditions are obtained to ensure the oscillatory of all solutions of the first order impulsive neutral differential equations. Also, some results in the references have been improved and generalized. New lemmas are established to demonstrate the oscillation property. Special impulsive conditions associated with neutral differential equation are submitted. Some examples are given to illustrate the obtained results.

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

Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration

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

Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

In the present work, heterojunction diode detectors will be prepared using germanium wafers as a substrate material and 200 nm tin sulfide thickness will be evaporated by using thermal evaporation method as thin film on the substrate. Nd:YAG laser (λ=532 nm) with different energy densities (5.66 J/cm2 and 11.32 J/cm2) is used to diffuse the SnS inside the surface of the germanium samples with 10 laser shots in different environments (vacuum and distilled water). I-V characteristics in the dark illumination, C-V characteristics, transmission measurements, spectral responsivity and quantum efficiency were investigated at 300K. The C-V measurements have shown that the heterojunction were of abrupt type and the maximum value of build-in pot