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  1. Presence of Amphibian Species Prediction Using Features Obtained From GIS and Satellite Images.Nadia Shaker Habib, Omar Kamal Abu Maghasib, Ahmed Rashad Al-Ghazali, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2020 - International Journal of Academic and Applied Research (IJAAR) 4 (11):13-22.
    The establishment of the transport infrastructure is usually preceded by an EIA procedure, which should determine amphibian breeding sites and migration routes. However, evaluation is very difficult due to the large number of habitats spread over a vast area and the limited time available for field work. An artificial Neural Network (ANN) is proposed for predicting the presence of amphibians species near the water reservoirs based on features obtained from GIS systems and satellite images. The dataset collected from UCI Machine (...)
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  2.  56
    Predicting the Age of Abalone From Physical Measurements Using Artificial Neural Network.Ghaida Riyad Mohammed, Jaffa Riad Abu Shbikah, Mohammed Majid Al-Zamili, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2020 - International Journal of Academic and Applied Research (IJAAR) 4 (11):7-12.
    Abalones have long been a valuable food source for humans in every area of the world where a species is abundant. Predicting the age of abalone is done using physical measurements. The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task. Other measurements, which are easier to obtain, are used to predict the age of abalone is using Artificial Neural Network (...)
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  3.  71
    ANN Model for Predicting Protein Localization Sites in Cells.Mohammed Nafez Abu Samra, Bilal Ezz El-Din Abed, Hossam Abdel Nasser Zaqout & Samy S. Abu-Naser - 2020 - International Journal of Academic and Applied Research (IJAAR) 4 (9):43-50.
    To automate examination of massive amounts of sequence data for biological function, it is important to computerize interpretation based on empirical knowledge of sequence-function relationships. For this purpose, we have been constructing an Artificial Neural Network (ANN) by organizing various experimental and computational observations as a collection ANN models. Here we propose an ANN model which utilizes the Dataset for UCI Machine Learning Repository, for predicting localization sites of proteins. We collected data for 336 proteins with known localization sites and (...)
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  4.  95
    ANN for Predicting Animals Category.Nassar Ibraheem & AlKahlout Mohammad - 2020 - International Journal of Academic and Applied Research (IJAAR) 3 (2):18-23.
    In this paper an Artificial Neural Network (ANN) model, was developed and tested for predicting the category of an animal. There is a number of factors that influence the classification of animals. Such as the existence of hair/ feather, if the animal gives birth or spawns, it is airborne, aquatic, predator, toothed, backboned, venomous, has –fins, has-tail, cat-sized, and domestic. They were then used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was developed (...)
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  5. Classification of Mushroom Using Artificial Neural Network.Alkronz Sameh, Meimeh Moghayer, Gazaz Mohanad & AlKahlout Mohammad - 2020 - International Journal of Academic and Applied Research (IJAAR) 3 (2):1-5.
    Predication is an application of Artificial Neural Network (ANN). It is a supervised learning due to predefined input and output attributes. Multi-Layer ANN model is used for training, validating, and testing of the data. In this paper, Multi-Layer ANN model was used to train and test the mushroom dataset to predict whether it is edible or poisonous. The Mushrooms dataset was prepared for training, 8124 instances were used for the training. JustNN software was used to training and validating the data. (...)
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  6.  38
    ANN for Predicting Overall Car Performance.Mubayyed Osamma & Gazaz Ahmed - 2020 - International Journal of Academic and Applied Research (IJAAR) 1 (3):1-4.
    In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.62 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method is (...)
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