With the advent of the era of big data, data mining has become one of the key technologies in the field of research and business. In order to improve the efficiency of data mining, this paper studies data mining based on the intelligent recommendation system. Firstly, this paper makes mathematical modeling of the intelligent recommendation system based on association rules. After analyzing the requirements of the intelligent recommendation system, Java 2 Platform, Enterprise Edition, technology is used to divide the system architecture into the presentation layer, business logic layer, and data layer. Recommendation module is divided into three substages: data representation, model learning, and recommendation engine. Then, the fuzzy clustering algorithm is used to optimize the system. After the system is built, the performance of the system is evaluated, and the evaluation indexes include accuracy, coverage, and response time. Finally, the system is put into a trial operation of an e-commerce platform. The click-through rate and purchase conversion rate of recommended products before and after the operation are compared, and a questionnaire survey is randomly launched to the platform users to analyze the user satisfaction. The experimental data show that the MAE of this system is the lowest, maintained at about 0.73, and its accuracy is the highest; before the recommended threshold exceeds 0.5, the average coverage rate of this system is the highest: 0.75; in Q1–Q5 subsets, the shortest response time of the system is 0.2 s. Before and after the operation of the system, the average click-through rate increased by 11.04%, and the average purchase rate increased by 9.35%. Among the 1216 users, 43% of the users were satisfied with 4 and 9% with 1. This shows that the system algorithm convergence speed is fast; it can recommend products more in line with user needs and interests and promote higher click-through rate and purchase rate, but user satisfaction can be further improved.

/pdf/intelligent-recommendation-system-based-on-mathematical-573kwgz630.pdf

Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

Interests play an essential role in the process of learning, thereby enriching learners 'interests will yield to an enhanced experience in MOOCs. Learners interact freely and spontaneously on social media through different forms of user-generated content which contain hidden information that reveals their real interests and preferences. In this paper, we aim to identify and extract the topical interest from the text content shared by learners on social media to enrich their course preferences in MOOCs. We apply NLP pipeline and topic modeling techniques to the textual feature using three well-known topic models: Latent Dirichlet Allocation, Latent Semantic Analysis, and BERTopic. The results of our experimentation have shown that BERTopic performed better on the scrapped dataset. 

/pdf/identifying-learners-topical-interests-from-social-media-2mx0en5i.pdf

Identifying learners’ topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques

There has been a lot of work to optimize the performance of relational data warehouses. Three major techniques can be used for this objective: enhanced index schemes (join indexes, bitmap indexes), materialized views, and data partitioning. The existing research prototypes or products use materialized views alone or indexes alone or combination of them, but none of the prototypes use all three techniques together for optimizing the performance of the relational data warehouses. In this paper we show by a systematic experiment evaluation that the combination of these three techniques reduces the query processing cost and the maintenance overhead significantly. We conduct several experiments and analyse the situations where the data partitioning gives better performance than the materialized views and indexes. Based on rigorous experiments, we recommend the tuning parameters for better utilization of data partitioning, join indexes and materialized views to optimize the total cost.

Bringing together partitioning, materialized views and indexes to optimize performance of relational data warehouses

Field Programmable Gate Array (FPGA) draws a significant attention from both industry and academia by accelerating computationally expensive applications and achieving low power consumption. FPGAs are interesting due to the flexibility and reconfigurabiltiy of their device. Cloud computing becomes a major trend towards infrastructure and computing resources dematerialization. It provides “unlimited” storage capacities and a large number of data and applications that make collaboration easier between multiple (not domain specific) designers. Many papers in the literature have surveyed Cloud and FPGA separately and, more precisely, their services and challenges. The acceleration of applications by FPGA and the unlimited capacities of the cloud are expected to be more and more pervasive. As more and more FPGA are being deployed in traditional cloud, it is appropriate to clarify what is the cloud FPGA and which drawbacks of using FPGA in local are resolved. We present a survey of the cloud FPGA works that have been proposed to exploit the advantages of using FPGA in the cloud. We classify these studies in three services to highlight their benefits and limitations. This survey aims at motivating further researches in cloud FPGA.

/pdf/from-fpga-to-support-cloud-to-cloud-of-fpga-state-of-the-art-2xlt5p5m4z.pdf

From FPGA to Support Cloud to Cloud of FPGA: State of the Art

Digital transformation in e-health is a well-known challenge problem reported from several studies and from several dimensions. In addition, it has been verified a gap in the utilization of new technologies as differential tool in the war against the Covid-19 pandemic. In this paper, we present an ongoing research effort which is characterized for supporting a digital transformation gap found in a public primary healthcare system. Therefore, it can be seen as an interesting case study approach to tackle some challenges found in Covid-19. Utilizing smart bands by groups of different type of voluntaries, where vital signals were collected in a digital data fashion and then evaluated in public health unit. A recommendation system (RS) algorithm was also developed to understand users´ behaviors, based upon their vital signals. In addition, we utilized a simulator software to highlight people movement and predictable scenarios of Covid-19 contamination. This last effort provides a visualization on how the proposal could also help in a real ordinary monitoring scenario. Initial results from this research work indicates a differentiated approach to tackle challenges in digital transformation in a public health scenario, especially in a pandemic. In addition, our experiments illustrate that the adoption of some computational technologies require mainly changes on the present behavior, from governments and people, to be successful approaches to individual protection inside public environments.

/pdf/covid-19-a-digital-transformation-approach-to-a-public-2zybes4rak.pdf

Covid-19: A Digital Transformation Approach to a Public Primary Healthcare Environment

Integrating Artificial Intelligence (AI) technologies implied significant growth in variousdomains. Furthermore, many companies integrate AI technologies into their products toenhance the quality of their services. Chatbots are among the AI technologies widely used inseveral areas, especially E-learning. Chatbots support learners in their learning processes byhelping them to find the appropriate answers to their questions. We aim to conduct a systematicliterature review (SLR) to uncover the use of AI chatbots to offload teachers from repetitive andmassive tasks. This article surveys the literature over the period 2016–2022 on the use of AIchatbots in the E-learning domain as they automatically answer learners’ questions. Thus, weidentify, collect, and synthesize multiple research studies on the application of AI chatbots in theE-learning field. Based on the renowned frameworks, PRISMA and PICO, we have succeeded in(1) Developing our research questions and (2) Automatically implementing a solution based onPython language to analyze selected papers, highlighting research gaps, and opening new windowsto guide our future works. Our study shows that chatbots effectively interact with learners.However, there are some drawbacks: (1) Educational chatbots are still limited in their localKnowledge Base (KB), which makes them unable to answer students’ questions correctly. Thus,Chatbot’s KB needs to be extended through external sources, enabling the chatbot to update itsKB over time, making it rich and saving time. (2) Lack of reliable external sources to enrich thechatbot’s KB and make it up to date. (3) Lack of educational chatbots with smart services suchas speech recognition and sentiment analysis to boost the user experience and make learningeasier. In our SLR, we discuss these limitations and propose some solutions to fill the gap. 

SMART Chatbots in the E-learning Domain: A Systematic Literature Review

We witness, today, a strong evolution of learning environments. In parallel, a problem has emerged, consisting in how to capitalize the production of resources when switching from one environment to another. The heterogeneity of the environments, the evolution of the platforms and the will to reuse the educational resources already produced pushed us to design an intelligent system based on cases. In this study, we will focus on the need for resource indexing to facilitate the task of researching and recommending educational resources for authors regardless of the learning environment used. In the literature, this representation can take two forms: Standards or ontologies. The use of standards has partially solved our problem since it is very beneficial for systems that are under construction. On the other hand, it is more interesting to go through the ontologies for systems that are already designed, that we wish to reuse, especially for those that have shown, through the authors, a great satisfaction in the field of knowledge management. Indeed, their use does not require an investment in the environments concerned by the reuse.

Pedagogical Resources Indexation Based on Ontology in Intelligent Recommendation System for Contents Production in d-Learning Environment

The sustainability of any Data Warehouse System (DWS) is closely correlated with user satisfaction. Therefore, analysts, designers and developers focused more on achieving all its functionality, without considering others kinds of requirement such as dependability s aspects. Moreover, these latter are often considered as properties of the system that will must be checked and corrected once the project is completed. The practice of "fix it later" can cause the obsolescence of the entire Data Warehouse System. Therefore, it requires the adoption of a methodology that will ensure the integration of aspects of dependability since the early stages of project DWS. In this paper, we first define the concepts related to dependability of DWS. Then we present our approach inspired from the MDA (Model Driven Architecture) approach to model dependability s aspects namely: availability, reliability, maintainability and security, taking into account their interaction.

Toward a New Approach for Modeling Dependability of Data Warehouse System

The expanding Cloud computing's services offers great opportunities for consumers to find the best service and best cost. It offers a computing power and a storage space adapted especially for Big Data processing. However, it raises new challenges on how to select the best service out of the huge pool. It is time-consuming for consumers to collect the necessary information and analyze all service providers to make the right decision. Moreover, it'is a highly demanding task from a computational perspective, because the same computations may be conducted repeatedly by multiple consumers who have similar requirements. Therefore, in this paper, we propose an approach based on Analytic Hierarchy Process (AHP) method, which manages the selection of the Cloud Service adequate to Big Data based on its parameters and criteria. We applied this approach on a case study in order to validate its efficity. The studied case is about the selection of the adequate Cloud Service for Big Data in the context of National Health Service (NHS) of United Kingdom (UK).

pdf/an-approach-for-selecting-cloud-service-adequate-to-big-data-6cgwf0nneg.pdf

An Approach for Selecting Cloud Service Adequate to Big Data Case Study: E-health Context

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