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

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has

Abstract:

The models of time series often suffer from the problem of the existence of outliers ​​that accompany the data collection process for many reasons, their existence may have a significant impact on the estimation of the parameters of the studied model. Access to highly efficient estimators  is one of the most important stages of statistical analysis, And it is therefore important to choose the appropriate methods to obtain good  estimators. The aim of this research is to compare the ordinary estimators and the robust estimators of the estimation of the parameters of

One of the important goals in the learning process is to be effective learning through the self-direction of the learner , because it has an impact on the effort of learners , it is better to be a learner responsible for learning and independent of the acquisition of knowledge ,

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BN Rashid…, Special Education, 2022

War as a human phenomenon, has its own literature. Poetry is a major genre in this literature. This paper is an attempt to investigate and analyse some stylistic features in two selected, English and Arabic, war poems. These poems share the same theme.Both promote the principle of sacrificing one’s own life for the sake of homeland.  This paper limits itself to analyse, thecontent words, tenses, semantic grouping of vocabulary and foregrounding in the two poems. The areas of analysis show great similarities in distributing the general content words (nouns, verbs, adjectives, and adverbs). In the analysis of the semantic areas of each content word, these poems reveal some similarities and some differences in their frequency rates.

In the present study, a pressure drop technique was used to identify the phase inversion point of oil-in-water to water-in-oil flows through a horizontal pipe and to study the effect of additives (nanoparticles, cationic surfactant and blend  nanoparticles-surfactant) on the critical dispersed volume fraction (phase inversion point). The measurements were carried  for mixture velocity ranges from 0.8 m/sec to 2.3 m/sec. The results showed that at low mixture velocity 0.8 and 1 m/sec there is no effect of additives and velocity on phase inversion point, while at high mixture velocities the phase inversion point for nanoparticles and blend (nanoparticles/surfactant) systems was delayed (postponed) to a higher value of the dispers