The task of assessing, grouping and arranging data into meaningful groups or clusters based on their similarities/dissimilarities measures known as cluster analysis. Thereby, there are numerous clustering algorithms: hierarchical and partitional. In the last decade, clustering using bio-inspired algorithms received more attention, specifically the ant clustering algorithms. Regardless, they have required a lot of processing power due to the massive amount of data that has been generated during the last years. As a consequence, determining the computational cost of these algorithms is one of the most interesting tasks in the quest for optimal clustering solutions in a real-time system. This study presents a research guide for the researchers working in the same field. A series of experiments are elaborated to investigate the computational complexity of the most promising algorithms applied to students grouping problem. The results indicate two challenges that arise when using ant clustering algorithms: the difficulty in adjusting parameters and extended computation time.

Writer identification/recognition from off-line Arabic handwriting on sentence-level is still a tough task. In this paper, we start by investigating the performance of textural extractors for writer identification of divergent writing types. Taking into account their strengths and limits, we propose a new method that keeps the main features of the writing and handles the sensitivity of systems towards the available samples of text at the pre-processing phase. We also analyze the influence of the handwriting types on the efficiency of the writer identification process. In this regard, we perform a comparative study between handcrafted and automated features. Under multiple classifiers (RF, XGB, KNN and SVM). We find that writers with good and well clear handwriting have fewer similarities, thus, provides enhanced experimental identification rates. However, Bad handwriting presents more similarities between the writers, which explains the reduction in the identification rate.

There is strong evidence that emotional states affect the Human's performance and decision-making. Therefore, understanding Human emotions has become of great concern in the field of Human-Computer Interaction (HCI). One way to an online emotion recognition is through keystroke dynamics. Keystroke Dynamics focuses on the particular way a person types on a keyboard. To provide insight, assess recent works, and guide future researches in this field, a Systematic Literature Review (SLR) is conducted. An SLR adopts a rigorous procedure with the definition of a formal review protocol. The primary aim of this paper is to highlight the effectiveness of using keystroke dynamics biometrics in recognizing emotions by systematically presenting research efforts in the past decade. We provide insight into such an approach by summarizing and discussing the data acquisition procedures, datasets, extracted features, classification methods, and performance measures used in previous researches. In light of these findings and the noticed scarcity of datasets related to emotion recognition through keystroke dynamics, we develop and host an interactive web application to construct a new dataset. The findings of this work reveal new interesting research directions and may motivate the research community to develop automatic emotion recognition systems based on keystroke dynamics.

The new Information, communication, and mobile technologies empower the users to learn anywhere and anytime. They also need conversational systems that could be aware of their mobile context in order to adjust it dynamically. The actual research field is focusing on adaptive conversational systems, especially in the case of Mobile-learning. This paper presents a comparative study of some related works. Then, it proposes an M-learning architecture based on hybrid cooperative agents (mobile, conversational, cognitive), with the possibility integrating Multi-Language ontology for the development of a Conversational Agent (CA) speaking Multi-Language especially Arabic language. A prototype of text-to-speech mobile agent (MobiSpeech) is presented and discussed. MobiSpeech is intended for mobile users, and provide all services to read any text and any text file extension for the user while considering the existing context-awareness.

Assessing E-commerce web sites quality is essential not only to have recommendations for improvement but also to make comparisons with competitors. In this paper, the aim is to know the best criteria for the evaluation and obtain a weight for them using fuzzy Analytic Network Process (fuzzy ANP). The subjective judgments of the decision maker are expressed by fuzzy numbers. The decision making problem is solved by making fuzzy pairwise comparisons and a feedback between the criteria.

Multiple Criteria Decision Making (MCDM) is widely used in everyday life especially to make decisions between conflicted criteria. In this paper, MCDM is explored in the Web domain that plays a major role in the modern society. A web site can provide or not users' needs. Assess the quality of web sites requires a list of criteria and sub-criteria, they depend also on web site category. So, to qualify criteria versus others it is important to resort to Multi-Criteria decision Making method. Weighing criteria, ranking web sites or other purposes of assessment can be resolved by MCDM. This paper identifies and discusses some methods and techniques in this area; it proposes also a comparative study between them and concludes with some findings discussions and future issues.

Autonomous MultiRobot Systems are developing useful capabilities in several fields of applications as surveillance, exploration and space cleaning. Moreover, important features are of robotics' environments like avoid collision and planning should be handled. Furthermore, the distributed planning approaches, considered as MultiAgent planning, can be thought as a specialization of distributed problem solving. Therefore, this paper started by propounds a review on some planning approaches for MultiRobot Systems and describes the subsumption architecture of the mobile robot control in the MultiRobot system by highlighting the lowest level which is the obstacle avoidance using the soft computing technique. We present also in this research a simulation of MultiRobot for parallel spaces cleaning.

The amount of circulating data on the internet has witnessed a considerable increase during the last decades. A web site is the main source that provides users' needs. However, some of the existing web sites are not well intentioned by users. Many studies have treated the problem of assessing the web sites' quality of different categories such as ecommerce, education, entertainment, health, etc. The problematic implies a multiple criteria decision making (MCDM) due to the multiple conflicting criteria for assessment. Existing methods are mainly based on making a hierarchy to divide high level criteria, sub-level criteria and alternatives. There is no standard until now that defines important criteria for evaluation. Indeed, this paper presents a process of collecting and extracting data from a list of studies according to a Systematic Literature Review (SLR) method. In fact, it is necessary to know frequent criteria used in the literature for establishing the task of assessment. This paper proposes also a determination of an association rules' set extracted from a set of criteria by applying an Apriori method.

This paper presents an intelligent hybrid system to support the planning for a mobile robot motion in unknown and dynamic environment. Called Fuzzy-MARCoPlan (Fuzzy-MultiAgent Remote Control motion Planning), this system optimizes the path by the introduction of sub-goals and through a multiagent cooperation based on fuzzy reasoning. In fact, we propose to agentify the surrounding zones of the robot; these zone agents compete for attracting the sub-goal. A planning agent, fortified with a fuzzy rule based system, decides on the best sub-goal to reach. Fuzzy-MARCoPlan is simulated and tested on several navigation environments which are generated randomly under the multiagent platform MadKit. These tests confirm the robustness of the proposed system in terms of path optimality in a dynamic environment. Moreover, the obtained results reinforce the advantage of a multiagent planning hybridized with fuzzy reasoning for mobile robot motion planning.

Since swarm intelligence allows self-organization into an unfamiliar environment and adapting behaviors through simple individuals' interactions, we propose to realize a swarm multirobot organization with a fuzzy control. We introduce in this paper a fuzzy system for avoiding the collaboration stagnation and to improve the counter-ant algorithm (CAA). The robots' collaborative behavior is based on a hybrid approach combining the CAA and a fuzzy system learned by MAGAD-BFS (Multi-agent Genetic Algorithm for the Design of Beta Fuzzy System). A series of simulations enables us to discuss and validate both the effectiveness of the hybrid approach to the problem of environment exploration (i.e., for the purpose of cleaning an area) as well as the usefulness of MAGAD-BFS for learning the fuzzy knowledge base while tuning it and reducing its number of rules.

In view of the high dynamicity of traffic flow and the polynomial increase in the number of vehicles on road networks, the route choice problem becomes more complex. A classical shortest path algorithm based only on road length is no longer relevant. We propose in this paper an adaptive vehicle guidance system instigated from the ants behavior, well known for its good adaptativity; this system allows adjusting intelligently and promptly the route choice according to the real-time changes in the road network situations, such as new congestions and jams. This method is implemented as a deliberative module of a vehicle ant agent in a collaborative multiagent system representing the entire road network. Series of simulations, under a multiagent platform, allow us to discuss the improvement of the global road traffic quality in terms of time, fluidity, and adaptativity.

Proposes a multi-agent approach (MA) for genetic algorithms (GA) applied to the training of Beta basis function neural networks (BBFNN). This approach, called the multi-agent distributed genetic algorithm (MADGA) has two advantages. First, thanks to the GAs' efficiency, it allows us to design a suitable architecture for the Beta system. Second, it improves the GAs' convergence by reducing their temporal complexity thanks to distributed implementation of the MA system. Agents, which are managed dynamically, interact to provide an optimal solution in order to obtain the best neural network that is considered as a compromise between network performances and structures. For illustration and discussion, we used BBFNN training sets with two space dimensions.