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.

Abstract

Stock markets are highly volatile, complex, non-linear, and stochastic. Therefore, predicting stock market behavior is one of finance’s most complex challenges. Recently, political, health, and economic crises have profoundly impacted global stock prices, leading researchers to use machine learning to predict prices and analyze financial data. This article delves into two primary facets: firstly, examining stock price responses to the Russia-Ukraine war and the COVID-19 pandemic by assessing volatility and draw-downs from 2018 to 2023. Secondly, we introduce a framework named StockPredCris, designed to predict future stock indices employing two machine learning algorithms: Support Vector Regression (SVR) and eXtreme Gradient Boosting (XGBoost). Our experiments are conducted on four major stock market indices (NASDAQ, Russell 2000, DAX, and SSE) using a combination of historical stock index data and COVID-19 pandemic data. The performance of the implemented models is evaluated using five regression metrics along with the CPU time metric. The results of SVR demonstrates superior performance and signifcantly outperforms the other considered models for benchmarking. For instance, SVR achieved 0.0 MSE and 99.99% R for the four stock indices, with training times between 0.005 and 0.007 seconds. We recommend SVR for predicting future stock prices during crises. This study offers valuable insights for financial decision-makers, guiding them in making informed choices by understanding stock market reactions to major global crises, while addressing the uncertainty and fear of investors.

Abstract

The stock market which is a particular type of financial market, has attracted nowadays the attention of financial analysts and investors, as it is recognized one of the most challenging and unpredictable market. Recently, this kind of market is well known by its extreme volatility and instability due to the health (COVID-19), the geopolitical (the Russian, Ukraine, European, and American conflict), and the economic crises. This situation intensified the uncertainty and fear of investors; they need an intelligent and stable decision support system to assist them to foresee the future variations of stock prices. To address this issue, our paper proposes a hybrid ensemble-learning model that integrates different methods. (1) The Singular Spectrum Analysis (SSA) is used to eliminate the noise from financial data. (2) The Convolutional Neural Network (CNN) is applied to handle the task of automatic feature extraction. (3) Three machine-learning predictors (Random Forest (RF), Gradient Boosting (GB), and Extra Trees (ET)) are merged together and optimized using the Halving Grid Search (HGS) algorithm to obtain collective final predictions. To verify the validity of the proposed model, two major indices of Chinese and American stock markets, namely SSE and NASDAQ, were used. The proposed model is evaluated using RMSE, MAE, MAPE and CPU time metrics. Based on experiments, it is proven that the achieved results are better than other comparative prediction models used for benchmarking.

Abstract

Financial markets operate as dynamic systems susceptible to ongoing changes influenced by recent crises, such as geopolitical and health crises. Due to these factors, investor uncertainty has increased, making it challenging to identify trends in the stock markets. Predicting stock market prices enhances investors’ ability to make accurate investment decisions. This paper proposes an intelligent financial system named Pred-IFDSS, aiming to recommend the best model for accurate predictions of future stock market indexes. Pred-IFDSS includes seven machine learning models: (1) Linear Regression (LR), (2) Support Vector Regression (SVR), (3) eXtreme Gradient Boosting (XGBoost), (4) Simple Recurrent Neural Network (SRNN), (5) Gated Recurrent Unit (GRU), (6) Long Short-Term Memory (LSTM), and (7) Artificial Neural Network (ANN). Each model is tuned using the grid search strategy, trained, and evaluated. Experiments are conducted on three stock market indexes (NASDAQ, S&P 500, and NYSE). To measure the performance of these models, three standard strategic indicators are employed (MSE, RMSE, and MAE). The outcomes of the experiments demonstrate that the error rate in SRNN model is very low, and we recommend it to assist investors in foreseeing future trends in stock market prices and making the right investment decisions.

Abstract

Bi-level optimization problems (BLOPs) is a class of challenging problems with two levels of optimization tasks. The particular structure of the bi-level optimization model facilitates the formulation of several practical situations that involve hierarchical decision-making process where lower-level decisions depend on upper-level actions. In this context, a hierarchical production–distribution (PD) planning problem in supply management is addressed. These two entities (production and distribution) are naturally related; however, in most practical situations, each decision entity concentrates on optimizing its process one at a time, independently on other related decisions. In this chapter, we considered a new formulation of the PD system using the bi-level framework under the constraints of shared depots resources in the distribution phase. To this end, a mixed integer bi-level formulation is proposed to model the problem, and a cooperative decomposition-based algorithm is developed to solve the bi-level model. Statistical experimental results show that our proposed algorithm gives competitive and better results with respect to the competitor algorithm. Indeed, allowing flexible choice of the stop depot reveals the outperformance of our proposal in reducing total traveling cost of generated solution compared to the baseline problem.

Abstract

The increasing demand for efficient global supply chain management and faster product delivery has led to a rise in the use of multimodal transportation systems (MFT). One of the key challenges in multimodal transportation is selecting the appropriate freight mode. This decision depends on several factors such as cost, transit time, reliability, mode availability, service frequency, and cargo characteristics. However, existing research often focuses on only two modes, namely trucks and trains, which fails to capture the complexities of real-world freight transportation decisions. Moreover, while reducing travel time and cost are primary objectives for service providers and researchers, other important considerations such as environmental impact are often overlooked. To this end, in this work, the researchers take into account four major modes of transportation (Air, Road, Rail, and Sea/Water) in a multimodal freight context aiming to optimize three distinct objectives: overall transportation cost, transportation time, and CO2 emissions. To solve this problem, the researchers adopt two the well-known metaheuristic algorithms: Tabu Search and the Genetic Algorithm through an experimental study demonstrating the efficacy of these evolutionary solution methods in tackling such challenging optimization problems.

Abstract

Home Health Care (HHC) aims to provide medical care and support services directly to patients in their own homes. The demand for HHC services is steadily increasing due to demographic trends, with a growing preference for receiving care in the home. This trend pushes organizations providing home health care services, to optimize their activities in order to meet this increasing demand efficiently. For this purpose, we propose in this work a new bi-level modeling of the problem, that we termed Bi-level Home Health Care Problem Considering Patients Preferences (Bi-HHCPP) aiming to find an efficient solution corresponding to this design. Existing research studies have focused on optimizing the problem considering only one decision-maker that optimizes both routing and scheduling entities imposed by the problem. This paper is the first to shed light on a new bi-level modeling of the problem involving two hierarchical decision entities: (1) a scheduling entity, and (2) a routing one. The proposed model primarily accounts for nurse qualification, travel costs, and patient preferences on visited nurses. Besides, the proposed mathematical formulation of the problem is tested using the CBC (Coin-or Branch and Cut) optimization solver.