Abstract

Abstract

Abstract

As in software development projects, the most critical activity in
Serious Game (SG) design process is the requirements specification due to SG's
multidisciplinary and characteristics complexity. In the literature, specific
design methodologies with requirements specification strategies are still needed
to achieve an engaged SG. This paper proposes an approach that assists
designers and design stakeholders when specifying required SG features and
their relationships. We shaped this approach into three stages with three
abstraction levels based on both characteristics taxonomy model and the SG
design challenges we propose in this work. We practiced the proposed process
in specifying an SG for health safety environment training for workers in fuel
storage sites. The feedback shows that such a strategy would be highly
beneficial for the participatory design process since it reduces game features'
complexity and thus their understanding by the design team members. It also
promotes game design artifacts evaluation and allows effective processing of
the game requirement changes.

Abstract

This paper models a transit control system for the management of traffic perturbations of public transport. The transit system data is voluminous and highly dynamic. Moreover, the transit domain has a remarkable lack of intelligent systems to monitor and maintain better performance. Consequently, realizing an intelligent transit control system has become a consistent need. The modeling of the system addresses a problem of optimizing performance measures based on key performance indicators. Its objective is to find the optimal control action in disturbance cases. The solution consists in combining all performance measures in a single measure by using fuzzification without neglecting the space and time requirements of the traffic. To model and implement our system we used a multi-agent approach. The experiments performed were based on real network traffic data. The obtained results demonstrate the relevance of the proposed fuzzy approach in our optimization problem and show the advantage of the multi-agent system in the modeling of our control system. We prove that the proposed control system achieves better results than certain existing fuzzy approaches and is able to manage disturbances with a better performance than the existing solutions.

Abstract

Serverless Computing, also named Function as a Service (FaaS) in the Azure cloud provider, is a new feature of cloud computing. This is another brick, after managed and fully managed services, allowing to provide on-demand services instead of provisioned resources and it is used to strengthen the company’s ability in order to master its IT system and consequently to make its business processes more profitable. Knowing that decision making is one of the important tasks in business processes, the improvement of this task was the concern of both the industry and the academy communities. Those efforts have led to several models, mainly the two Object Management Group (OMG) models: Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) in order to support this need. The DMN covers the decision-making task in business processes mainly the eligibility of business rules. This eligibility can be automated in order to help designers in the mastering of this important task by the running of an algorithm or a method such as the Multiple Criteria Decision Making (MCDM). This feature can be designed and implemented and deployed in various architectures to integrate it in existing Business Process Management Systems (BPMS). It could then improve supporting several business areas such as the Business Intelligence (BI) process. In this paper, our main contribution is the enrichment of the DMN model by the automation of the business rules eligibility through Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) using FaaS to further streamline the decision-making task in business processes. Results show to strengthen business-IT alignment and reduce the gap between the real world and associated IT solutions.

Abstract

Hyperheuristics represent a generic method that provides a high level of abstraction, enabling solving several problems in the combinatorial optimization domain while reducing the need for human intervention in parameters tuning. This category consists in managing a set of low-level heuristics and attempting to find the optimal sequence that produces high-quality results. This paper proposes a hyperheuristic that simulates the honey bees mating behavior called “Honey bee Mating Optimization HyperHeuristic”  to solve the Patient Admission Scheduling Problem (PASP). The PASP is an NP-hard problem that represents an important field in the health care discipline. In order to perceive the influence of low-level heuristics on the model’s performance, we implemented two versions of the hyperheuristic that each one works on a different set of low-level heuristics. The results show that one of the versions generates better results than the other, revealing the important role of low-level heuristics’ quality leading to enhancing the hyperheuristic performance.

Abstract

Abstract

Financial markets are dynamic and open systems. They are subject to the influence of environmental changes. For this reason, predicting stock market prices is a difficult task for investors due to the volatility of the financial stock markets nature. Stock market forecasting leads investors to make decisions with more confidence based on the prediction of stock market price behavior. Indeed, a lot of analysts are greatly focused in the research domain of stock market prediction. Generally, the stock market prediction tools are categorized into two types of algorithms: (1) linear models like Auto Regressive (AR), Moving Average (MA), Auto-Regressive Integrated Moving Average (ARIMA), and (2) non-linear models like Autoregressive Conditionally Heteroscedastic (ARCH), Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and recently Neural Network (NN)). This paper aspires to crucially predict the stock index movement for National Association of Securities Dealers Automated Quotations (NASDAQ) based on deep learning networks. We propose a hybrid stock price prediction model using Convolutional Neural Network (CNN) for feature selection and Neural Network models to perform the task of prediction. To evaluate the performance of the proposed models, we use five regression evaluation metrics: Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and R-Square (R2), and the Execution Time (ET) metric to calculate the necessary time for running each hybrid model. The results reveal that error rates in the CNN-BGRU model are found to be lower compared to CNN-GRU, CNN-LSTM, CNN-BLSTM and the the existing hybrid models. This research work produces a practical experience for decision makers on financial time series data.

Abstract

The prediction of stock price movements is one of the most challenging tasks in financial market field. Stock price trends depended on various external factors like investor’s sentiments, health and political crises which can make stock prices more volatile and chaotic. Lately, two crises affected the variation of stock prices, COVID-19 pandemic and Russia-Ukraine conflict. Investors need a robust system to predict future stock trends in order to make successful investments and to face huge losses in uncertainty situations. Recently, various machine learning (ML) models have been proposed to make accurate stock movement predictions. In this paper, a framework including five ML classifiers (Gaussian Naive Bayes (GNB), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM), and K-Nearest Neighbors (kNN))) is proposed to predict the closing price trends. Technical indicators are calculated and used with historical stock data as input. These classifiers are hybridized with Principal Component Analysis method (PCA) for feature selection and Grid Search (GS) Optimization Algorithm for hyper-parameters tuning. Experimental results are conducted on National Association of Securities Dealers Automated Quotations (NASDAQ) stock data covering the period from 2018 to 2023. The best result was found with the Random Forest classifier model which achieving the highest accuracy (61%).

Abstract

Since more and more organizations deploy their applications through the cloud, an increasing demand for using inter-cloud solutions is noticed. Such demands could inherently result in overutilization of resources, which leads to resource starvation that is vital for time-intensive and life-critical applications. In this paper, we are interested in the scheduling problem in such environments. On the one hand, a new taxonomy of criteria to classify task scheduling problems and resolution approaches in inter-cloud environments is introduced. On the other hand, a bi-level multi-follower model is proposed to solve the budget-constrained dynamic Bag-of-Tasks (BoT) scheduling problem in heterogeneous multi-cloud environments. In the proposed model, the upper-level decision maker aims to minimize the BoT’ makespan under budget constraints. While each lower-level decision maker minimizes the completion time of tasks it received. Experimental results demonstrated the outperformance of the proposed bi-level algorithm and revealed the advantages of using a bi-level scheme with an improvement rate of 32%, 29%, and 21% in terms of makespan for the small, medium, and big size instances, respectively.