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

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

Self-driving automobiles are prominent in science and technology, which affect social and economic development. Deep learning (DL) is the most common area of study in artificial intelligence (AI). In recent years, deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. Different studies investigated a variety of significant technologies for autonomous vehicles, including car navigation systems, path planning, environmental perception, as well as car control. End-to-end learning control directly converts sensory data into control commands in autonomous driving. This research aims to identify the most accurate pre-trained Deep Neural Network (DNN) for predicting the steerin

   This research studies the rheological properties ( plastic viscosity, yield point and apparent viscosity) of Non-Newtonian fluids under the effect of temperature using different chemical additives, such as (xanthan gum (xc-polymer), carboxyl methyl cellulose ( High and low viscosity ) ,polyacrylamide, polyvinyl alcohol, starch, Quebracho and Chrome Lignosulfonate). The samples were prepared by mixing 22.5g of bentonite with 350 ml of water and adding the additives in four different concentrations (3, 6, 9, 13) g by using Hamilton Beach mixer. The rheological properties of prepared samples were measured by using Fan viscometer model 8-speeds. All the samples were subjected to Bingham plastic model. The temperature range studi

      This research investigates the type and the significant relationship between roaming management and self-efficacy and its impact on excellence in providing hotel service. To achieve this, the applied approach was adopted through  A questionnaire was designed and developed for the collected data. It has consisted of three parts. The firsts section included nine questions to measure the dimensions of management by roaming. The second section includes nine questions to measure the effectiveness of the two employees. The last section includes   12   questions to measure the excellence of the hotel service. The research sample included  43  employees' responding to this

Age and growth of Varicorhinus damascinus (Val.) in Tigris river at Salahuldin province have been investigated. Monthly samples were taken during the period from September 1999 to August 2000, using small-meshed gill nets. The age data showed that there were six age groups and the dominant age groups were (III – IV) for both sexes. The results of the present study revealed that the increment in length of V. damascinus at the sites of study showed a tendency to decrease with the increase in age, after the third year of life in both sexes. The length-weight relationship of males and females were calculated and demonstrated in a straight line logarithmic formula, as follows: Log W = 1.5404 + 2.6885 log L for males r = 0.95 (P > 0.05) Log