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

Classifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area.  The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and

Background: Aflatoxin B1 (AFB1) is a widely distributed mycotoxin in nature. Several investigations have shown its biological effects on different organs and in different animal species. However, the effects of AFB1 on the rat kidney have not been much elucidated histologically.
Objective: This study aims to demonstrate the effects of AFB1 contaminated diet on the rat kidney from
the histological and morphometric aspects.
Method: Twelve mature albino rats were divided equally into a control group fed with usual diet and a treated group which was daily fed with diet contaminated with 20 mg AFB1/kg of body weight for 30 days. Semithin sections from renal cortex were stained with methylene blue and examined by l

Implementation of Warm Mix Asphalt concrete (WMA) is getting global acceptance due to the restrictions for protecting the environment and the requirements to reduce fuel consumption. In this investigation, two WMA mixtures have been prepared in the laboratory using medium curing cutback (MC-30) and Cationic emulsion asphalt. Hot Mix Asphalt (HMA) was also prepared for comparison. The cylinder specimens (63.5mm) in height and (101.6mm) in diameter were constructed from the mixtures and subjected to indirect tensile strength test to determine the Tensile Strength Ratio (TSR). The cylinder specimens of (101.6mm) in height and (101.6mm) in diameter were also constructed from those mixtures and subjected to static compressive

          The research aims to build a communication apprehention scale for student (females) from preparatory schools .research sample included (400)students (females) were selected from the preparatory . to build a tool for the researchers are several steps , todetermine the meaning of communication apprehention and formulation of the items of the seale according to the linkert method .

Rutting is mainly referring to pavement permanent deformation, it is a major problem for flexible pavement and it is a complicated process and highly observed along with many segments of asphalt pavement in Iraq. The occurrence of this defect is related to several variables such as elevated temperatures and high wheel loads. Studying effective methods to reduce rutting distress is of great significance for providing a safe and along-life road. The asphalt mixture used to be modified by adding different types of additives. The addition of additives typically excesses stiffness, improves temperature susceptibility, and reduces moisture sensitivity. For this work, steel fibres have been used for modifying asphalt mixture as they incorp

  This paper is concerned with Double Stage Shrinkage Bayesian (DSSB) Estimator for lowering the mean squared error of classical estimator ˆ q for the scale parameter (q) of an exponential distribution in a region (R) around available prior knowledge (q0) about the actual value (q) as initial estimate as well as to reduce the cost of experimentations.         In situation where the experimentations are time consuming or very costly, a Double Stage procedure can be used to reduce the expected sample size needed to obtain the estimator. This estimator is shown to have smaller mean squared error for certain choice of the shrinkage weight factor y( ) and for acceptance region R. Expression for