Abstract

Biclustering is an unsupervised classification technique that plays an increasingly important role in the study of modern biology. This data mining technique has provided answers to several challenges raised by the analysis of biological data and more particularly the analysis of gene expression data. It aims to cluster simultaneously genes and conditions. These unsupervised techniques are based essentially on the assumption that the extraction of the co-expressed genes allows to have co-regulated genes. In addition, the integration of biological information in the search process may induce to the extraction of relevant and non-trivial biclusters. Therefore, this work proposes an evolutionary algorithm based on local search method that relies on biological knowledge. An experimental study is achieved on real microarray datasets to evaluate the performance of the proposed algorithm. The assessment and the comparison are based on statistical and biological criteria. A cross-validation experiment is also used to estimate its accuracy. Promising results are obtained. They demonstrate the importance of the integration of the biological knowledge in the biclustering process to foster the efficiency and to promote the discovery of non-trivial and biologically relevant biclusters.

Abstract

Code smells (a.k.a. anti-patterns) are manifestations of poor design solutions that could deteriorate the software maintainability and evolution. Despite the high number of existing detection methods, the issue of class label uncertainty is usually omitted. Indeed, two human experts may have different degrees of uncertainty about the smelliness of a particular software class not only for the smell detection task but also for the smell type identification one. Thus, this uncertainty should be taken into account and then processed by detection tools. Unfortunately, these latter usually reject and/or ignore uncertain data that correspond to software classes (i.e. dataset instances) with uncertain labels. This practice could considerably degrade the detection/identification process effectiveness. Motivated by this observation and the interesting performance of the Possibilistic K-NN (PK-NN) classifier in dealing with uncertain data, we propose a new possibilistic evolutionary detection method, named ADIPOK (Anti-patterns Detection and Identification using Possibilistic Optimized K-NNs), that is able to deal with label uncertainty using some concepts stemming from the Possibility theory. ADIPOK is validated using a possibilistic base of smell examples that simulates the subjectivity of software engineers’ opinions’ uncertainty. The statistical analysis of the obtained results on a set of comparative experiments with respect to four state-of-the-art methods show the merits of our proposed method.

Abstract

Like the case of any software, Web Services (WSs) developers could introduce anti-patterns due to the lack of experience and badly-planned changes. During the last decade, search-based approaches have shown their outperformance over other approaches mainly thanks to their global search ability. Unfortunately, these approaches do not consider the uncertainty of class labels. In fact, two experts could be uncertain about the smelliness of a particular WS interface but also about the smell type. Currently, existing works reject uncertain data that correspond to WSs interfaces with doubtful labels. Motivated by this observation and the good performance of the possibilistic K-NN classifier in handling uncertain data, we propose a new evolutionary detection approach, named Web Services Anti-patterns Detection and Identification using Possibilistic Optimized K-NNs (WS-ADIPOK), which can cope with the uncertainty based on the Possibility Theory. The obtained experimental results reveal the merits of our proposal regarding four relevant state-of-the-art approaches.

Abstract

Abstract

Recently, decomposition has gained a wide interest in solving multi-objective optimization problems involving more than three objectives also known as Many-objective Optimization Problems (MaOPs). In the last few years, there have been many proposals to use decomposition to solve unconstrained problems. However, fewer is the amount of works that has been devoted to propose new decomposition-based algorithms to solve constrained many-objective problems. In this paper, we propose the ISC-Pareto dominance (Isolated Solution-based Constrained Pareto dominance) relation that has the ability to: (1) handle constrained many-objective problems characterized by different types of difficulties and (2) favor the selection of not only infeasible solutions associated to isolated sub-regions but also infeasible solutions with smaller CV (Constraint Violation) values. Our constraint handling strategy has been integrated into the framework of the Constrained Non-Dominated Sorting Genetic Algorithm-III (C-NSGA-III) to produce a new algorithm called Isolated Solution-based Constrained NSGA-III (ISC-NSGA-III). The empirical results have demonstrated that our constraint handling strategy is able to provide better and competitive results when compared against three recently proposed constrained decomposition-based many-objective evolutionary algorithms in addition to a penalty-based version of NSGA-III on the CDTLZ benchmark problems involving up to fifteen objectives. Moreover, the efficacy of ISC-NSGA-III on a real world water management problem is showcased.

Abstract

In the pharmaceutical industry, the production of psychiatric drugs has been seriously disrupted since the appearance of COVID’19. For that, Demand Forecasting of psychiatric drugs is among the big challenges in this industry. The objective is to avoid an excess of stock and, at the same time, to ensure that a stock rupture does not occur. Based on analysis of psychiatric drugs data, we compare in this paper several forecasting techniques which are Exponential Smoothing, seasonal ARIMA (i.e. SARIMA), SARIMAX, enhanced with the integration of exogenous (explanatory) variables, and LSTM. Through all the done tests, we make a comparison study of the results to identify the most promising models.

Abstract

In front of the continued growth of autistics number in the world, intelligent systems can be used by non-specialists such as educators or general physicians in autism screening. Moreover, it can assist psychiatrists in the diagnosis of autism to detect it as early as possible for early intervention. We propose in this chapter a tool for the diagnosis of autism: DAS-Autism. It is a knowledge-based system that handles qualitative knowledge in the multi-valued context. For this, we use our knowledge-based system shell RAMOLI, and its inference engine executes an approximate reasoning based on linguistic modifiers that we have introduced in a previous work. We have built a knowledge base that represents the domain expertise, in collaboration with a child psychiatry department of Razi hospital, the public psychiatric hospital in Tunisia. We have then conducted an experimental study in which we compared the system results to expert’s diagnoses. The results of this study were very satisfactory and promising.

Abstract

Abstract

Sleep is an essential element that affects directly our daily life thus sleep analysis is a very interesting field. Sleep stages classification represents the base of all sleep analysis activities. However, the classification of sleep stages suffers from high uncertainty between its stages which could lead to degrade the performance of classification methods. To cope partially with this issue, we propose a new approach that deals with uncertainty especially with imprecision. Our method integrates the belief function theory in eXtended Classifier System (XCS). The proposed approach shows a good performance ability comparing to classical methods.

Abstract

Nowadays, companies are more and more adopting cloud technologies in the management of their business processes rising, then, the Business Process as a Service (BPaaS) model. In order to guarantee the consistency of the provisioned BPaaS, cloud providers should ensure a strategical management (e.g., allocation, migration, etc.) of their available resources (e.g., computation, storage, etc.) according to services requirements. Existing researches do not prevent resource provision problems before they occur. Rather, they conduct a real-time allocation of cloud resources. This paper makes use of historical resource usage information for providing enterprises and BPaaS providers with predictions of cloud availability zones’ states. For that, we first propose a Neural Network-based prediction model that exploits the superposition power of Quantum Computing and the evolutionary nature of the Genetic Algorithm, in order to optimize the accuracy of the predicted resource utilization. Second, we define a placement algorithm that, based on the prediction results, chooses the optimal cloud availability zones for each BPaaS fragment, i.e. under-loaded servers. We evaluated our approach using real cloud workload data-sets. The obtained results confirmed the effectiveness and the performance of our NNQGA approach, compared to traditional techniques.