Modeling of an artificial intelligence based enterprise callbot with natural language processing and machine learning algorithms

The management of customer services by telephone encounters several problems: an uncontrollable flow of calls, complicated resource management, a very high cost of service, and more. Opportunities to improve the quality of service, save time and money triggered the widespread implementation of artificial intelligence (AI) based callbot. This article outlines the straightforward workflow developed to model the architecture of the callbot. Therefore, several algorithms were evaluated and compared based on real knowledge of a call center of an insurance society. The algorithms considered are: k-nearest neighbours (KNN), support vector machine (SVM), random forests (RF), logistic regression (LR), and Na¨ıve Bayes (NB). The comparison criteria are: correct responses, response time, accuracy, Cohen’s kappa and F1 score using n-gram (1.1) and (2.2). The results obtained show that the SVM (accuracy=70.29%) presents the best results on all the comparison criteria. The comparison between the results of the human agents and the callbot shows an improvement in several levels: the cost savings are greater than 80% on all the tests carried out, the holding time decrease to 0 seconds, and the processing time (almost a third or more). The results obtained sufficiently meet the objectives of this project

Artificial Intelligence Empowers Gamification: Optimizing Student Engagement and Learning Outcomes in E-learning and MOOCs

In this era of Artificial Intelligence (AI) growth, characterized by advances in the Large Language Models (LLMs) used by ChatGPT and Bard, this study examines the effects of gamification and Automatic Question Generation (AQG) on student engagement and learning outcomes in the context of a Massive Open Online Course (MOOC). AQG, implemented via a Moodle plugin, transforms conventional assessments into an interactive, gamified experience, leveraging the “test effect” to improve learning outcomes. Research with 100 fifth-graders in a primary and secondary school shows that gamified assessments significantly boost student motivation and learning outcomes compared with traditional methods. The custom Moodle plugin facilitates the AQG process, generating contextually relevant and grammatically correct Multiple-Choice Questions (MCQs) from course content. The result is a dynamic, personalized assessment experience aimed at optimizing student retention. This paper concludes by discussing the implications of the study for educators and highlighting potential directions for future research

Integrating AI-based speech recognition technology to enhance reading assessments within Morocco’s Tarl Program

This study examined the integration of artificial intelligence-powered speech recognition technology within early reading assessments in Morocco’s Teaching at the Right Level (TaRL) program. The purpose was to evaluate the effectiveness of an automated speech recognition tool compared to traditional paper- based assessments in improving reading skills among 100 Moroccan first to third-graders. The mixed- method approach combined pre-post standardized reading tests with qualitative feedback. Results showed students receiving the AI-enabled speech recognition assessments demonstrated significant gains in reading achievement compared to peers assessed via traditional methods. Qualitative findings revealed benefits of instant feedback and enhanced engagement provided by the speech recognition tool. This study contributes timely empirical evidence on adopting learning technologies, specifically AI-driven automated speech assessment instruments, to enhance foundational literacy development within under-resourced education systems implementing student-centered pedagogical techniques like TaRL. It provides valuable insights and guidance for integrating innovative speech analysis tools within localized teaching and learning frameworks to strengthen early reading instruction and monitoring

Customized dataset-based machine learning approach for black hole attack detection in mobile ad hoc networks

This article explores the application of machine learning (ML) algorithms to classify the black hole attack in mobile ad hoc networks (MANETs). Black hole attacks threaten MANETs by disrupting communication and data transmission. The primary goal of this study is to develop an intrusion detection system (IDS) to detect and classify this attack. The research process involves feature selection, the creation of a custom dataset tailored to the characteristics of black hole attacks, and the evaluation of four machine learning models: random forest (RF), logistic regression (LR), k-nearest neighbors (k-NN), and decision tree (DT). The evaluation of these models demonstrates promising results, with significant improvements in accuracy, precision, F1-score, and recall metrics. The findings underscore the potential of machine learning in enhancing the security of MANETs by providing an effective means of attack classification

Fuzzy Logic based Intrusion Detection System against Black Hole Attack in Mobile Ad Hoc Networks

A Mobile Ad hoc NETwork (MANET) is a group of mobile nodes that rely on wireless network interfaces, without the use of fixed infrastructure or centralized administration. In this respect, these networks are very susceptible to numerous attacks. One of these attacks is the black hole attack and it is considered as one of the most affected kind on MANET. Consequently, the use of an Intrusion Detection System (IDS) has a major importance in the MANET protection. In this paper, a new scheme has been proposed by using an Adaptive Neuro Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) for mobile ad hoc networks to detect the black hole attack of the current activities. Evaluations using extracted database from a simulated network using the Network Simulator NS2 demonstrate the effectiveness of our approach, in comparison to an optimized IDS based ANFIS-GA

Improving Performance of Mobile Ad Hoc Network Using Clustering Schemes

Mobile ad hoc network become nowadays more and more used in different domains, due to its flexibility and low cost of deployment. However, this kind of network still suffering from several problems as the lack of resources. Many solutions are proposed to face these problems, among these solutions there is the clustering approach. This approach tries to partition the network into a virtual group. It is considered as a primordial solution that aims to enhance the performance of the total network, and makes it possible to guarantee basic levels of system performance. In this paper, we study some schemes of clustering such as Dominating-Set-based clustering, Energy-efficient clustering, Low-maintenance clustering, Load-balancing clustering, and Combined-metrics based clustering

Artificial Intelligence System in Aid of Pedagogical Engineering for Knowledge Assessment on MOOC Platforms: Open EdX and Moodle

The aim of this research is to provide a novel educational model with the goal of reducing the expenses associated with manual question production and meeting the demand for a continual supply of new questions on MOOC platforms such as Moodle or Open EDX. We considered integrating machine-learning methods with natural language processing in order to increase the number and validity of assessing questions. To accomplish this, we developed a system that generates multilingual questions automatically. Various kinds of evaluation were conducted with two factors in mind: evaluating MOOC learners' competency and the similarity of the generated questions to those created by humans. The first evaluation is based on subjective judgment by three MOOC creators, while the second is based on replies from MOOC participants on machine-generated and human-created questions. Both evaluations revealed that the machine-generated questions performed on par with the human-created questions in terms of evaluating skills and similarity. Moreover, the results demonstrate that most of the produced questions (up to 82 percent) enhance e-assessment when the new suggested technology is used

Artificial Intelligence System in Aid of Pedagogical Engineering for Knowledge Assessment on MOOC Platforms: Open EdX and Moodle

The aim of this research is to provide a novel educational model with the goal of reducing the expenses associated with manual question production and meeting the demand for a continual supply of new questions on MOOC platforms such as Moodle or Open EDX. We considered integrating machine-learning methods with natural language processing in order to increase the number and validity of assessing questions. To accomplish this, we developed a system that generates multilingual questions automatically.&#x0D; Various kinds of evaluation were conducted with two factors in mind: evaluating MOOC learners' competency and the similarity of the generated questions to those created by humans. The first evaluation is based on subjective judgment by three MOOC creators, while the second is based on replies from MOOC participants on machine-generated and human-created questions. Both evaluations revealed that the machine-generated questions performed on par with the human-created questions in terms of evaluating skills and similarity. Moreover, the results demonstrate that most of the produced questions (up to 82 percent) enhance e-assessment when the new suggested technology is used.</jats:p