When resolving logical contradictions in ontologies, Reiter's hitting set tree algorithm is often applied to satisfy the minimal change principle. To improve the efficiency, the researchers have proposed various algorithms by using a scoring function, defining new semantics or applying some heuristic strategies. However, these algorithms either sacrifice minimal change or are designed for less expressive ontologies like DL-Lite. In this paper, we propose a mathematic approach based on integer linear programming, which is an optimization problem of maximizing or minimizing a linear objective function, to deal with DL ontologies. Specifically, we define the integer linear programming-based model to resolve logical contradictions. To realize the model, we propose one algorithm to find a cardinality-minimal solution and two algorithms dealing with weighted ontologies. Our experiments are conducted over 70 real-life and artificial ontologies to compare our algorithms with those hitting set tree-based ones. Through the experiments, the prominent efficiency and effectiveness have been exhibited by our algorithms. They usually take about 0.4 s to find a solution while others spend more than 100 s in many cases. The experimental results also show that the first two algorithms could find the cardinality-minimal solutions and those with a minimal sum of weights, respectively. INDEX TERMS Logical contradictions, inconsistency handling, ontology repair, semantic web, integer linear programming.
Background: From the epidemiological point of view, certain factors involved in the appearance of varicose veins are preponderant such as multiple pregnancies, age, and also certain races. Physiologically, venous valve dysfunction can also be a factor. Here, radiologists intervene to determine venous insufficiency. Doppler ultrasound (US) and tomography are often the most used in this detection. Certain other factors contribute to their recidivism. Aims: Some factors that occur in the recurrence of varicose veins are extrinsic such as age, sex, or genetic factor. On the other hand, certain factors are linked to an inadequate surgical procedure that can be partly explained by a poor radiological or methodological reading. The aim of this study is to prevent recurring complications that may occur the analysis of the factors of these is necessary. Materials and Methods: In our study, 62 patients were operated in our general surgery department during the period from January 2016 to September 2017. The pre-operative clinical examination included, among others, the radiological examination using a Doppler US. Patients who have had a recurrence are classified from the identification of the possible causes. Since the causes are complex and vary from one person to another, this makes them very difficult to analyze by conventional methods. We proposed an intelligent system based on artificial neural networks. Results: Once the system is established, this will identify the most important factor in the recurrence of varicose veins. By randomly changing the parameters at the input one by one and we record the effect that each produces on the recurrence rate at the output. Conclusion: The proposed system with its very strong inters connectivity, and the support of all possible combinations with the weight of each factor makes it possible to extract the predominant cause. With its learning from the real values recorded, and the optimal function created between the two input-output spaces, it becomes very easy to identify the main cause that leads to recidivism.
Aim: Nowadays, functional MRI is widely used in the study of brain functions. If it has the advantage of being non-invasive and allows delimiting the zones that are activated at each stimulus in 3D, it presents several deficiencies. On the psychotic studies, the zones which activate induce the analyst in error in view of the complexity and the differences between individuals and their states of anxiety. Even minimal movements of the head influence the result; the response of the vascular system signal is delayed after the stimulus...etc. A heavy numerical processing in particular in statistical analyzes is necessary to refine the images. Despite this, difficulties persist. Method:In this study, a fuzzy logic system in this analysis is proposed. Viewing the complexity of the system, the variables that define the constructed image are considered as inaccurate variables and therefore fuzzy variable. The motor and emotional stimulus, the parasites movements, the anxiety state are considerate as inputs system. The quality of image constructed is the output system. The data base constructed permits adjusting input variables for optimal image. Conclusion:The proposed system allows defining the optimal interaction of the different factors for an optimal image. Considering the variables of input and output as fuzzy variables thus imprecise, this makes it possible to overcome the deficiencies of the system
The rise of the Internet and social media (i.e. reviews, forum discussions, blogs and social networks) constituted an interesting source to detect user opinion trends. This study examines the global publication output on opinion mining and sentiment analysis from documents published in 2000 to 2020. Bibliometric indicators on the trends, most cited papers, authors, institutions, countries, funding agencies and research subject areas were independently screened and analysed using bibliometrix package in R. A total of 7603 eligible documents were identified from 2000 to 2020. The total number of citations for all publications was 129,251, with an average of 17.0 citations per publication. About 14,629 authors wrote those documents with 1.93 authors per document and a collaboration index of 1.98. The most prolific author was Cambria Erik, with 47 publications and h-index of 42. The leading countries for research were China with n = 824, India with n = 576 and the United States with n = 244 publications. Lecture Notes in Computer Science proceedings was the top-ranked venue for publications with n = 434, h-index of 32 and 4598 total citation scores. National Natural Science Foundation of China was the top-ranked funding agency for research, and most of the publications were computer science ( n = 6320) documents. The study provides an in-depth assessment of the landmark of the hot research topic and acknowledges the contribution of the most productive and active authors across different countries in the world. In addition, the findings could support the younger scholars in their future research direction and improve the efficiency in potential future research collaborations and projects.
Aim: Often post-mortem radiography as a judicial procedure is intended to know the causes of death. X-rays are systematic on putrefied, charred or severely altered bodies when identifying a body. Nowadays other radiological techniques are used in post mortem recognition. In the case of collective disasters (war, air accident, or industrial ...etc.) the task is easy when comparing ante-mortem radiographs. In the absence of these, vestibular craniography and positional morpho-metric analysis is necessary. Specific characters of a skull are taken into account in this study. It refers us to his race as the first identification. Method:In this study, a database is based on the data that specifying each ethnic group (Gallo-Romans, Japanese, Ainu, Amerindians, Melanesians, African Blacks, Australians, Tasmanians ...). Each group is distinguished by specific characters (the shape of the structures and for their position in the axes, their structure and their reciprocal articulation). From measurements made on radiography skull and artificial neural network analysis, it will be possible to attribute this to the ethnic group to which it belongs. Conclusion:In this study, we consider these characters (distances, circumferences, curve, volumes, and angles) are considered as input variables of the network. These variables are related to an output variable that refers to the individual race. This can be a valuable tool for identification in forensic medicine.
Abstract. The causes of chronic diseases, including cancers, are extremely complex. Throughout their lives, individuals who differ in genetic makeup and sensitivity are exposed to a wide variety of carcinogens. Some chemicals by themselves are safe but can act as synergists or promoters together with other chemicals to cause the disease. The problem of the harmful effects of pesticides is the long-term consequences. If the acute effects are well known, the long-term projections remain to be elucidated. Surface water has a direct influence on the degree of contamination by pesticides in groundwater. These are contaminated by significant infiltrations. In order to establish a direct relationship between the possible chronic effects of pesticides on public health, it is essential to consider a set of factors that intervene in the process. Some factors are related to climatic conditions, others to the nature of the soil and others to the nature of the pesticide used. Other factors are related to the people who consume these waters in terms of period, age, sex ... etc. Making a classical mathematical analysis becomes very difficult and even impossible. We propose in this study the analysis of these variables by the techniques of artificial intelligence mainly fuzzy inference. The variables analyzed are considered as fuzzy variables. This makes it possible to take care of these uncertainties and vagueness. A database is established, the resulting algorithm will allow us to read instantly the outcome which expresses the degree of contamination on public health from the introduction of the random variables to the input of the system.
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