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

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

Emotion Recognition from Facial Expression Based on Fiducial Points Detection and using Neural Network

The importance of emotion recognition lies in the role that emotions play in our everyday lives. Emotions have a strong relationship with our behavior. Thence, automatic emotion recognition, is to equip the machine of this human ability to analyze, and to understand the human emotional state, in order to anticipate his intentions from facial expression. In this paper, a new approach is proposed to enhance accuracy of emotion recognition from facial expression, which is based on input features deducted only from fiducial points. The proposed approach consists firstly on extracting 1176 dynamic features from image sequences that represent the proportions of euclidean distances between facial fiducial points in the first frame, and faicial fiducial points in the last frame. Secondly, a feature selection method is used to select only the most relevant features from them. Finally, the selected features are presented to a Neural Network (NN) classifier to classify facial expression input into emotion. The proposed approach has achieved an emotion recognition accuracy of 99% on the CK+ database, 84.7% on the Oulu-CASIA VIS database, and 93.8% on the JAFFE database

An improved Arabic text classification method using word embedding

Feature selection (FS) is a widely used method for removing redundant or irrelevant features to improve classification accuracy and decrease the model’s computational cost. In this paper, we present an improved method (referred to hereafter as RARF) for Arabic text classification (ATC) that employs the term frequency-inverse document frequency (TF-IDF) and Word2Vec embedding technique to identify words that have a particular semantic relationship. In addition, we have compared our method with four benchmark FS methods namely principal component analysis (PCA), linear discriminant analysis (LDA), chi-square, and mutual information (MI). Support vector machine (SVM), k-nearest neighbors (K-NN), and naive Bayes (NB) are three machine learning based algorithms used in this work. Two different Arabic datasets are utilized to perform a comparative analysis of these algorithms. This paper also evaluates the efficiency of our method for ATC on the basis of performance metrics viz accuracy, precision, recall, and F-measure. Results revealed that the highest accuracy achieved for the SVM classifier applied to the Khaleej-2004 Arabic dataset with 94.75%, while the same classifier recorded an accuracy of 94.01% for the Watan-2004 Arabic dataset

Development of an injectable composite for bone regeneration

With the development of minimally invasive surgical techniques, there is a growing interest in the research and development of injectable biomaterials especially for orthopedic applications. In a view to enhance the overall surgery benefits for the patient, the BIOSINJECT project aims at preparing a new generation of mineral-organic composites for bone regeneration exhibiting bioactivity, therapeutic activity and easiness of use to broaden the application domains of the actual bone mineral cements and propose an alternative strategy with regard to their poor resorbability, injectability difficulties and risk of infection. First, a physical-chemical study demonstrated the feasibility of self-setting injectable composites associating calcium carbonate-calcium phosphate cement and polysaccharides (tailor-made or commercial polymer) in the presence or not of an antibacterial agent within the composite formulation. Then, bone cell response and antimicrobial activity of the composite have been evaluated in vitro. Finally, in order to evaluate resorption rate and bone tissue response an animal study has been performed and the histological analysis is still in progress. These multidisciplinary and complementary studies led to promising results in a view of the industrial development of such composite for dental and orthopaedic applications

A New Big Data Feature Selection Approach for Text Classification

Feature selection (FS) is a fundamental task for text classification problems. Text feature selection aims to represent documents using the most relevant features. This process can reduce the size of datasets and improve the performance of the machine learning algorithms. Many researchers have focused on elaborating efficient FS techniques. However, most of the proposed approaches are evaluated for small datasets and validated using single machines. As textual data dimensionality becomes higher, traditional FS methods must be improved and parallelized to handle textual big data. This paper proposes a distributed approach for feature selection based on mutual information (MI) method, which is widely applied in pattern recognition and machine learning. A drawback of MI is that it ignores the frequency of the terms during the selection of features. The proposal introduces a distributed FS method, namely, Maximum Term Frequency-Mutual Information (MTF-MI), based on term frequency and mutual information techniques to improve the quality of the selected features. The proposed approach is implemented on Hadoop using the MapReduce programming model. The effectiveness of MTF-MI is demonstrated through several text classification experiments using the multinomial Naïve Bayes classifier on three datasets. Through a series of tests, the results reveal that the proposed MTF-MI method improves the classification results compared with four state-of-the-art methods in terms of macro-F1 and micro-F1 measures

Conception et élaboration d'un système à base de règles floues pour le traitement d'information chimique

PARIS-BIUSJ-Thèses (751052125) / SudocPARIS-BIUSJ-Mathématiques rech (751052111) / SudocSudocFranceF

Molecular descriptor selection using decision trees: application to structure-sandalwood odour relationships

International audienc

Man-machine interaction for odor prediction

International audienc