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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Abdelaziz Marzak
University Hassan II
Computer Science & IT Engineering

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Cluster-based fuzzy regression trees for software cost prediction Assia Najm; Abdelali Zakrani; Abdelaziz Marzak
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp1138-1150

Abstract

The current paper proposes a novel type of decision tree, which is never used for software development cost prediction (SDCP) purposes, the cluster-based fuzzy regression tree (CFRT). This model uses the fuzzy k-means (FKM), which deals with data uncertainty and imprecision. The tree expansion is based on the variability measure by choosing the node with the highest value of granulation diversity. This paper outlined an experimental study comparing CFRT with four SDCP methods, notably linear regression, multi-layer perceptron, K-nearest-neighbors, and classification and regression trees (CART), employing eight datasets and the leave-one-out cross-validation (LOOCV). The results show that CFRT is among the best, ranked first in 3 datasets according to four accuracy measures. Also, according to the Pred(25%) values, the proposed CFRT model outperformed all the twelve compared techniques in four datasets: Albrecht, constructive cost model (COCOMO), Desharnais, and The International Software Benchmarking Standards Group (ISBSG) using LOOCV and 30-fold cross-validation technique.
Guide to choosing internet of things protocols Sakina Elhadi; Loubna Chhiba; Nawal Sael; Abdelaziz Marzak
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1567-1575

Abstract

The internet of things (IoT) is a global infrastructure for the information society, enabling advanced services by interconnecting objects (physical or virtual) through existing or evolving interoperable information and communication technologies. Among the main keys to the IoT is the widespread adoption of clearly defined protocols. The implementation of its applications requires protocols capable of effectively managing these conditions, namely network protocols and applications. Considering the importance of using protocols in IoT applications, many protocols have been developed and used by various organizations according to their needs. However, choosing an adaptable, standard, and efficient protocol is a difficult decision, for all organizations and researchers. This difficulty, due to the complex nature of the IoT system and its requirements. Consequently, we propose a model for the use of IoT protocols based on criteria and metrics that will evolve the protocols. we call these models by the model of good practice of protocols of the Internet of things. Then, we implement these models in the form of a tool for choosing IoT (Networks and application) protocols. This study will allow researchers and developers to choose the appropriate protocols for an IoT application by allowing the result before the realization of the application.
An efficient method to improve machine learning decoders using automorphisms group Imrane Chemseddine Idrissi; Said Nouh; El Mehdi Bellfkih; Mohammed El Assad; Abdelaziz Marzak
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp547-558

Abstract

The decoding of error-correcting codes (ECCs) is a critical aspect of communication systems, yet traditional decoding techniques can often be computationally demanding or ineffective for certain codes, necessitating innovative approaches. In this study, we introduce a hybrid approach that combines machine learning and automorphism techniques to optimize the decoding process. Specifically, we train multilayer perceptron (MLP) models to learn the mapping between error syndromes and their corresponding errors. While these models exhibit robust learning capabilities, their performance sometimes does not reach 100%. To mitigate this limitation, we exploit the automorphism group of the code—a set of structure-preserving transformations—to convert the errors that the MLP struggles to decode into ones it can process more effectively. We use a minimum number of p permutations, pre-calculating and storing all possible automorphisms to ensure computational efficiency. Our experimental results reveal that this hybrid approach substantially enhances the decoding performance of the MLP model, presenting a promising avenue for decoding ECCs. Importantly, this approach is not limited to MLP models and can be applied to any machine learning model with a learning score less than 100%, broadening its applicability and impact. By integrating machine learning with traditional algebraic coding theory, we propose a new paradigm that holds the potential to revolutionize the design of decoding systems, making them more efficient and effective.
Co-Authors Abdelali Zakrani Assia Najm El Mehdi Bellfkih Imrane Chemseddine Idrissi Loubna Chhiba Mohammed El Assad Nawal Sael Said Nouh Sakina Elhadi