A novel approach to dynamic profiling of e-customers considering click stream data and online reviews

In this paper, we present an approach for mining change in customer’s behavior for the purpose of maintaining robust profiling model over time. Most of previous studies leave important questions unanswered: In developing B2C e-commerce strategies, how do managers implicitly load customer’s profiles based on their satisfaction over the online store characteristics? And: What kind of feedback segments do they have? Our proposed approach does not force customers to explicitly express their preference information over the online service but rather capture their preference from their online activities. The challenge does not only lay in analyzing how customer’s classifier model change and when it does so but also to adapt it to the customer’s click stream data using a new decision tree generation algorithm which takes as inputs new set of variables; categorical, continuous and fuzzy variables. Customer’s online reviews rates are considered as classes. Experiments show that this work performed well in identifying relevant customer’s stream data to judge the chinese e-commerce website “Tmall”. The extracted values of the website’s features are also useful to identifying the satisfaction level when the customer’s rate is not available.

Distributed Multi-Label Classification Approach For Textual Big Data

With the increased generation of data, classification still a hot research topic in machine learning. Although a lot of works in literature are interested in single-label classification, the huge amount of dimensionality of data requires new approaches. Thus, multi-label classification has attracted significant attention in the research community over the last years. This task which is an extension of the single-label classification, consists of associating an instance of data (document) with multiple labels; which is practical in many domains such as image analysis, bio-informatics, and text categorization, among others. Besides that multi-label classification is a challenging task, the high dimensionality requires the use of distributed environment to manage data effectively and efficiently. Thus, in this work we propose a distributed system to classify documents using Hadoop framework. Documents are given to the MapReduce framework which assigns the set of positive labels to the documents using a distributed approach based on the Label Powerset method. Experiments on real-life data were carried out to show that the proposed approach can effectively reduce redundant attributes and improve multi-label classification accuracy

Neuro-Fuzzy Combination for Reactive Mobile Robot Navigation: A Survey

Autonomous navigation of mobile robots is a fruitful research area because of the diversity of methods adopted by artificial intelligence. Recently, several works have generally surveyed the methods adopted to solve the path-planning problem of mobile robots. But in this paper, we focus on methods that combine neuro-fuzzy techniques to solve the reactive navigation problem of mobile robots in a previously unknown environment. Based on information sensed locally by an onboard system, these methods aim to design controllers capable of leading a robot to a target and avoiding obstacles encountered in a workspace. Thus, this study explores the neuro-fuzzy methods that have shown their effectiveness in reactive mobile robot navigation to analyze their architectures and discuss the algorithms and metaheuristics adopted in the learning phase

Design and development of a fuzzy explainable expert system for a diagnostic robot of COVID-19

Expert systems have been widely used in medicine to diagnose different diseases. However, these rule-based systems only explain why and how their outcomes are reached. The rules leading to those outcomes are also expressed in a machine language and confronted with the familiar problems of coverage and specificity. This fact prevents procuring expert systems with fully human-understandable explanations. Furthermore, early diagnosis involves a high degree of uncertainty and vagueness which constitutes another challenge to overcome in this study. This paper aims to design and develop a fuzzy explainable expert system for coronavirus disease-2019 (COVID-19) diagnosis that could be incorporated into medical robots. The proposed medical robotic application deduces the likelihood level of contracting COVID-19 from the entered symptoms, the personal information, and the patient's activities. The proposal integrates fuzzy logic to deal with uncertainty and vagueness in diagnosis. Besides, it adopts a hybrid explainable artificial intelligence (XAI) technique to provide different explanation forms. In particular, the textual explanations are generated as rules expressed in a natural language while avoiding coverage and specificity problems. Therefore, the proposal could help overwhelmed hospitals during the epidemic propagation and avoid contamination using a solution with a high level of explicability

Mercury pollution in beachrocks from the Arzew gulf (West of Algeria) Pollution mercurielle des grès formés sur les plages du golf d'Arzew (Ouest Algérien)

Abstract. The gulf or Arzew extends between the towns of Stidia and Mostaganem on the west coast of Algeria. The gulf receives the discharge of many industries. Mercury contamination level of beachrocks has been assessed through the determination of mercury concentration (Hg) in sandstones formed on the beach. The sandstone samples were collected along the coast, dried, ground and digested in order to be analyzed by atomic fluorescence spectrometry. The analyses showed the presence of mercury in beachrocks with an average geochemical index of 4.1 and high mercury concentrations up to 5.0 µg.g -1 , well above average in the Earth's crust. The geo-accumulation index revealed severe and intense mercury pollution due to anthropogenic activities. The high Hg concentration in beachrocks suggests that mercury accumulated and circulated freely in the gulf before lithification and cementation of beach sediment. This contamination may affect the coastal ecosystem and even human health via the food chain. Keywords : Pollution, mercury, beachrock, geo-accumulation, Gulf of Arzew, Algeria. Résumé. Le golfe d'Arzew s'étend entre les villes de Stidia et Mostaganem sur la côte ouest algérienne. Il reçoit la décharge de nombreuses industries. La présente étude démontre la détermination de la concentration en mercure (Hg) dans les grès formés sur la plage. Les échantillons de grès ont été prélevés dans 5 stations échelonnées sur une distance de 25 km environ le long de la côte. Séchés et broyés, ces échantillons sont analysés par spectrométrie de fluorescence atomique. Le mercure est piégé dans ces roches sédimentaires avec un indice géochimique moyen de 4,1 et des concentrations voisines de 5,0 μg.g -1 . Les valeurs dépassent largement la moyenne signalée dans la croûte terrestre. L'indice géo-accumulation révèle une pollution sévère et intense par le mercure due aux activités anthropiques. La forte concentration de mercure dans les "Beachrocks" admet que cet élément toxique s'accumule et circule librement dans le Golfe d'Arzew avant la lithification et la cimentation de sédiments de la plage. Cette contamination pourrait affecter les composantes biologiques de l'écosystème côtier et même la santé humaine via la chaîne alimentaire

The concept of negotiation from the Islam perspective (in the Islamic organization)

The discussion of the negotiation process and outcome concept has been largely discussed based on different perspectives that informed by its value system.Meanwhile, what has remained unexplored is the possibility of another approach which is the Islamic perspective, giving rise to similar concepts of negotiation in practice. The dearth of literature on negotiation from Islamic approach can be seen to be has caused lack of attention which is assumed lead to lack of understanding of the issue among the Islamic organization. Therefore, the purpose of this paper is to identify the concept of negotiation from the Islamic perspective.A series of interviews were conducted to drive data from Informants of this study.A set of thematic data analysis was directed by the assistance of the NVIVO 8 Software.Finding identified that negotiation from the Islamic approach means a platform of arguments between two parties or more within the Shariah teachings to obtain spiritual satisfaction

Chemical Composition, Antibacterial, Antifungal and Antidiabetic Activities of Ethanolic Extracts of Opuntia dillenii Fruits Collected from Morocco

peer reviewedOpuntia dillenii (Ker Gawl.) Haw. belongs to the Cactaceae family and is native to the arid and semi-arid regions of Mexico and the southern United States. O. dillenii are now used as medicinal plants in various countries. In this study, we investigated the chemical composition of ethanolic extracts obtained from seeds, juice, and peel of O. dillenii fruits collected from Morocco, and we evaluated their antibacterial, antifungal, and antidiabetic activities. Phytochemical screening revealed high quantities of polyphenols (193.73 ± 81.44 to 341.12 ± 78.90 gallic acid eq [g/100 g dry weight]) in the extracts. The major phenolic compounds determined by HPLC were gallic acid, vanillic acid, and syringic acid. Regarding flavonoids, quercetin 3-O-β-D-glucoside and kaempferol were the predominant molecules. Juice extracts showed weak to moderate antibacterial activity against the bacteria species Listeria monocytogenes, Escherichia coli, and Salmonella braenderup. All tested extracts displayed a significant inhibitory effect on α-glucosidase and α-amylase activities in vitro, with the peel extracts showing the greatest inhibitory effects. Together, these findings suggest that O. dillenii fruits are a promising source for the isolation of novel compounds with antibacterial or antidiabetic activities. For the most abundant phytochemicals identified in O. dillenii peel ethanolic extract, molecular docking simulations against human pancreatic α-amylase enzyme were performed. These indicated the presence of bioactive compounds in the extract with a better potential to decrease the enzyme activity than the commercial drug acarbose

Immunogenicity of toxins during Staphylococcus aureus infection

AB - BACKGROUND: Toxins are important Staphylococcus aureus virulence factors, but little is known about their immunogenicity during infection. Here, additional insight is generated. METHODS: Serum samples from 206 S. aureus-infected patients and 201 hospital-admitted control subjects were analyzed for immunoglobulin (Ig) G binding to 20 toxins, using flow-cytometry based technology. Antibody levels were associated with p

Twelve numerical, symbolic and hybrid supervised classification methods

International audienceSupervised classification has already been the subject of numerous studies in the fields of Statistics, Pattern Recognition and Artificial Intelligence under various appellations which include discriminant analysis, discrimination and concept learning. Many practical applications relating to this field have been developed. New methods have appeared in recent years, due to developments concerning Neural Networks and Machine Learning. These "hybrid" approaches share one common factor in that they combine symbolic and numerical aspects. The former are characterized by the representation of knowledge, the latter by the introduction of frequencies and probabilistic criteria. In the present study, we shall present a certain number of hybrid methods, conceived (or improved) by members of the SYMENU research group. These methods issue mainly from Machine Learning and from research on Classification Trees done in Statistics, and they may also be qualified as "rule-based". They shall be compared with other more classical approaches. This comparison will be based on a detailed description of each of the twelve methods envisaged, and on the results obtained concerning the "Waveform Recognition Problem" proposed by Breiman et al which is difficult for rule based approaches