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14 results on '"Hicham, Attariuas"'

5. Development of a sustainable strategy model for predicting optimal container stacking locations in container yards using artificial intelligence and cubic data.

Author

Moqbel Saleh, Mohammed Ahmed, Hicham, Attariuas, Ben Maâti, M. L., Taha, Hatem, and Mohammed, Yahya M. A.

Subjects
*ARTIFICIAL intelligence, *SUSTAINABLE development, *SMART structures, *CONTAINERS, *CONTAINER terminals, *DYNAMIC models, *PROBLEM solving, *SHIPPING containers
Abstract

Transporting containers advances the economy forward and hastens global transformation. Despite this swift change, ports face many difficulties, including how to store and extract containers. We provide a strategic, sustainable, dynamic model inspired by cubic data and artificial intelligence to solve the problem of container storage. This study creates a smart model based on cubic data and spatiotemporal data by integrating matrices into a multidimensional structure to overcome the uncertainty. The model proactively conjures data expressing yard components and examines what is compatible with or serves the situation it is trying to solve. This approach tries to bring together various concerned entities in the terminal yard, determine their properties and attributes, and process them in real-time. Moreover, the results of this study showed good agreement when compared with previous publications. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Academic Failure at University and Data Processing Methods Based on Decision Trees and Neural Networks: Research Methodology

Author

Hicham Attariuas and Smail Admeur

Subjects
Data processing, Artificial neural network, Computer science, business.industry, Research methodology, College Failure, Work Methodology, Data Processing, Decision Tree, Artificial Neural Networks, Decision tree, General Medicine, Artificial intelligence, Machine learning, computer.software_genre, business, computer
Abstract

For a long time, academic failure among university students sparked heated controversy. Many educational psychologists try to figure it out and then explain it. Statisticians have tried to predict it. Our research (article) aims to classify students into several categories, as well as to use the decision tree and artificial neural networks to classify first-year students and identify variables that may explain the problem.

Published
2021

10. An Intelligent Model for Enterprise Resource Planning Selection Based on BP Neural Network

Author

Amine Elyacoubi, Hicham Attariuas, and Noura Aknin

Subjects
Ideal (set theory), Artificial neural network, business.industry, Computer science, Effective method, Artificial intelligence, business, Machine learning, computer.software_genre, Enterprise resource planning, computer, Selection (genetic algorithm), Back office
Abstract

Enterprise resource planning (ERP) is the managing business system that allows an enterprise of any organization to utilize a collection of integrated applications to manage its business and automate many back office functions related to technology. The selection itself of a suitable ERP is one of the most important parts in the implementation. This paper attempts to use artificial neural networks to choose an ideal ERP for any enterprise. This paper constructs a three-level BP neural network to analyze the principle and model of a suitable ERP. By using the samples to train and inspect the BP neural network, we conclude that the application of BP neural networks is an effective method to forecast suitable ERP. Thus the purpose of this study is to requite mainly three factors among the many others that influence the choice of a suitable ERP. By using statistics in several investigation-filled samples, we can collect a database for many cases that can in return help us create a model that manages the choice of an ideal ERP for the company and reduces the costs of failure.

Published
2018

14. An Improved Approach Based on Fuzzy Clustering and Back-Propagation Neural Networks with Adaptive Learning Rate for Sales Forecasting.

Author

Hicham, Attariuas, Mohamed, Bouhorma, and Abdellah, El Fallahi

Subjects
FUZZY clustering technique, BACK propagation, ARTIFICIAL neural networks, INSTRUCTIONAL systems, SALES forecasting, SMOOTHING (Numerical analysis), PRINTED circuits industry, COMPUTER architecture
Abstract

This paper describes new hybrid sales forecasting system based on fuzzy clustering and Back-propagation (BP) Neural Networks with adaptive learning rate (FCBPN).The proposed approach is composed of three stages: (1) Winter's Exponential Smoothing method will be utilized to take the trend effect into consideration; (2) utilizing Fuzzy C-Means clustering method (Used in an clusters memberships fuzzy system (CMFS)), the clusters membership levels of each normalized data records will be extracted; (3) Each cluster will be fed into parallel BP networks with a learning rate adapted as the level of cluster membership of training data records. Compared to many researches which use Hard clustering, we employ fuzzy clustering which permits each data record to belong to each cluster to a certain degree, which allows the clusters to be larger which consequently increases the accuracy of the proposed forecasting system . Printed Circuit Board (PCB) will be used as a case study to evaluate the precision of our proposed architecture. Experimental results show that the proposed model outperforms the previous and traditional approaches. Therefore, it is a very promising solution for industrial forecasting. [ABSTRACT FROM AUTHOR]

Published
2012

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