Abstract

The pharmaceutical industry is facing challenges due to various factors such as supply chain disruptions, changing consumer behavior, and regulatory changes. Accurate demand forecasting is essential to ensure an adequate supply of drugs. The goal of this work is to forecast paracetamol product demand. For this purpose, we propose a hybrid forecasting model combining two effective forecasting techniques: SARIMA (Seasonal AutoRegressive Integrated Moving Average) and ANFIS (Adaptive Neuro-Fuzzy Inference System). This proposal consists of nonlinear components of time series by ANFIS and adjusting the result by the mean of the residuals of the SARIMA to improve the accuracy and performance of ANFIS predictions. Before the prediction phase, we preprocess our data and detect the anomalies in our dataset with Locally Selective Combination in Parallel Outlier Ensembles (LSCP). Then, by treating these anomalies as missing values, they are imputed using the combination of Fuzzy-Possibilistic c-means (FCM) with support vector regression (SVR) and a genetic algorithm (GA). Finally, we evaluate the performance of the model and some known models based on MAPE. We choose the hybrid model SARIMA-ANFIS that provides the most accurate and reliable forecasting.

Abstract

Sleep is a vital process for human well-being. Sleep scoring is performed by experts using polysomnograms, that record several body activities, such as electroencephalograms (EEG), electrooculograms (EOG), and electromyograms (EMG). This task is known to be exhausting, biased, time-consuming, and prone to errors. Current automatic sleep scoring approaches are mostly based on single-channel EEG and do not produce explainable results. Therefore, we propose a heterogeneous ensemble learning-based approach where we combine accuracy-based learning classifier systems with different algorithms to produce a robust, explainable, and enhanced classifier. The efficiency of our approach was evaluated using the Sleep-EDF benchmark dataset. The proposed models have reached an accuracy of 89.2% for the stacking model and 87.9% for the voting model, on a multi-class classification task based on the R&K guidelines.

Abstract

Supervised machine learning techniques include classification and regression. In regression, the objective is to map a real-valued output to a set of input features. The main challenge that existing methods for regression encounter is how to maintain an accuracy-simplicity balance. Since Regression Trees (RTs) are simple to interpret, many existing works have focused on proposing RT and Model Tree (MT) induction algorithms. MTs are RTs with a linear function at the leaf nodes rather than a numerical value are able to describe the relationship between the inputs and the output. Traditional RT induction algorithms are based on a top-down strategy which often leads to a local optimal solution. Other global approaches based on Evolutionary Algorithms (EAs) have been proposed to induce RTs but they can require an important calculation time which may affect the convergence of the algorithm to the solution. In this paper, we introduce a novel approach called Bi-level Evolutionary Model Tree Induction algorithm for regression, that we call BEMTI, and which is able to induce an MT in a bi-level design using an EA. The upper-level evolves a set of MTs using genetic operators while the lower-level optimizes the Linear Models (LMs) at the leaf nodes of each MT in order to fairly and precisely compute their fitness and obtain the optimal MT. The experimental study confirms the outperformance of our BEMTI compared to six existing tree induction algorithms on nineteen datasets.

Abstract

Since several decade, the Internet of Things IoT hasattracted enormous interest in the research communityand industry. However, IoT technologies has completelytransformed vehicular ad hoc networks (VANETs) intothe « Internet of Vehicles » IoV. In IoV networks, we needto integrate many different technologies, services andstandards. However, the heterogeneity and large numberof vehicles will increase the need of data security.The IoV security issues are critical because of the vulnerabilitiesthat exist during the transmission of informationthat expose the IoV to attacks. Each attack hasa security procedure. Many protocols and mechanismsexist to combat or avoid this communication securityproblem. One of these protocols is VIPER (a Vehicleto-Infrastructure communication Privacy EnforcementpRotocol). In our work, we try to improve this protocolby using Blockchain technology and certificationauthority.

Abstract

This paper introduces TradeStrat-ML, a novel framework for stock market trading. It integrates various techniques: technical analysis, hybrid machine learning models, multi-agent-based simulations (MABS), and financial modeling for stock market analysis and future predictions. The process involves using a Convolutional Neural Network (CNN) to extract features from preprocessed financial data. The output of this model is then combined with three machine learning models (Gated Recurrent Unit (GRU), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR)) to predict future stock price indices. Subsequently, the models are evaluated, compared, and the most accurate model is selected for stock market prediction. In the final stage, the selected model, along with the Simple Moving Average (SMA) indicator, is used to develop an optimized trading strategy. The TradeStrat-ML system is organized into four main layers and validated using MABS simulations. Comparative analysis and simulation experiments collectively indicate that this new combination prediction model is a potent and practical tool for informed investment decision-making.

Abstract

Abstract

Serious games (SGs) are valuable tools for learning, training, and improving skills in
various domains because they engage and motivate players to achieve planned processes to reach objectives. Several works provided methods, models, and frameworks
to support SG development. However, designers, developers, teachers, and
researchers face challenges in creating SG with entertainment and learning balance,
and many designed games still do not fulfill the main intended objectives. This paper
introduces an approach, called SGDA-IE with phases and steps to follow during the
entire SG design process. It was built on literature review and SG design challenges
designers need to consider from the early stages when creating SG. The proposed
approach is founded on three perspectives: software engineering best practices,
video game industry practices, and SG success factors and provides means to overcome the investigated design challenges. These are characteristics taxonomy model,
requirements specification approach, and artifacts iterative evaluation by designer,
domain expert, and players. To assess our approach efficacy, we conceived a health,
safety, and environment (HSE) training SG for workers on fuel storage sites and
petroleum installations. The feedback received is positive and indicates a favorable
specification method of the SG, effective participatory design, and control over
requirements evolution. The SG playtesting reveals a significant involvement of participants and efficient tracking of the knowledge acquisition.

Abstract

Predicting Gestational Diabetes Mellitus (GDM) is crucial for
pregnant women to enable regular monitoring of their blood sugar levels and
adherence to a healthy diet. Early intervention can significantly lower the risk
of developing this condition. To assess this risk, Machine Learning (ML) and
Deep Learning techniques are employed. However, traditional ML models often
face challenges in accurately predicting GDM risk due to the complex
processing required to optimize their hyperparameters for the best performance.
This study presents a feature selection for GDM prediction using AutoML-XAI
techniques (Automatic Machine Learning – eXplainable Artificial Intelligence
techniques) approach, which aims to automatically predict GDM risk as
accurate as possible while providing meaningful interpretations of the
predictive results used in feature selection. The AutoML models generated
utilize a Kaggle dataset and several combinations of features selected based on
their scores of importance determinated with XAI techniques such as SHAP

(Shapley Additive Explanations) and LIME (Local Interpretable Model-
agnostic Explanations). The proposed approach of autoML and features

selection with XAI techniques leads to the creation of a precise, efficient, and
easily interpretable model which surpasses other machine learning models in
predicting GDM risk without the need for human intervention. The scores of
importance of features are involved in the feature selection process and
multiple AutoML models are generated and assessed, with the optimal AutoML
model being established automatically.

Abstract

Intelligent Transportation Systems (ITS) are a promising research area that offers a variety of applications. The objective of these applications is to enhance road safety, to optimize traffic efficiency, and to provide a better driving experience. Yet, the efficiency of ITS applications, such as safety and driver assistance applications, relies essentially on the exchanged data between different entities of the network. Accordingly, trust management models are used to guarantee the quality of the data and to eliminate malicious and selfish nodes to secure vehicular communications. In this paper, we pay a special attention to the requirements of trust management models used in the context of ITS applications. We also dissected the trust model to extract the mechanisms used in the literature to fulfil the identified requirements. Furthermore, we present the most known simulators and evaluation metrics that are used to validate the proposed models. The aim of this study is to provide a global overview of the mechanisms that may be used to fulfil the crucial requirements of trust management models. For this purpose, we employed a systematic mapping study, through which we carefully analysed 60 selected articles. Through our analysis, five main requirements were identified: scalability, accuracy, robustness, privacy preservation, appropriate response time. Different mechanisms and techniques were applied to meet with the identified requirements. Two main findings are reported: (1) The accuracy and robustness requirements are the most considered requirements. On the other hand, the privacy requirement is the least covered by the publications, (2) the majority of the reviewed papers focus on addressing two or three requirements at most. A little number of publications covered all the requirements. Based on the identified research gaps, we highlight some future directions that may be investigated. We provide general recommendations that may serve as a guideline for researchers who want to design trust models that fulfil certain requirements.

Abstract

Stock markets have an important impact on economic growth of countries. The prediction
of stock market indexes has been a complex task for last years. Indeed, many researches and financial analysts are highly interested in the research area of stock market prediction. In this paper, we propose a novel framework, titled AutoCNN based on artificial intelligence techniques, to predict future stock market indexes. AutoCNN is composed mainly of three stages: (1) CNN for Automatic Feature Extraction, (2) The Halving Grid Search algorithm is combined to CNN model for stock indexes prediction and (3) Evaluation and recommendation. To validate our AutoCNN, we conduct experiments on two financial datasets that are extracted in the period between 2018 and 2023, which includes several events such as economic, health and geopolitical international crises. The performance of AutoCNN model is quantified using various metrics. It is benchmarked against different models and it proves strong prediction abilities. AutoCNN contributes to emerging technologies and innovation in the financial sector by automating decision-making, leveraging advanced pattern recognition, and enhancing the overall decision support system for investors in the digital economy.