Laure Pauline Fotso

Modeling for effective collaboration in telemedicine

Requirements and modeling for supporting collaborative activities in telemedicine.Identification of types of exchanged data for information sharing in telemedicine.Functional specifications associated with achieving an effective telemedicine work.Semantic formalization to give good meaning and substance to shared information.Management of the acquired knowledge through the capitalization of the reasoning. Telemedicine is a remote medical practice, which utilizes advanced telecommunications and information technologies for the delivery of healthcare and the exchange of heath information across distances. The development of telemedicine has significantly changed the medical collaborative decision making and doctor-patient relationships and has an impact on the responsibilities of physicians to patients and how to treat them. Moreover, in the chain of medical care information exchanged between a requesting physician and medical expert should assist in decision making. In this regard, we propose means for the formalization of exchanges, which is very important because it facilitates a transparent and traceable understanding of the remote process. So, we engage knowledge-based modeling for supporting collaborative activities in telemedicine. This includes the engagement of formal ontologies to ensure structuration of terminology and identification across all entities in a domain so that multiple sources of data can be aggregated through comparable reference terms. The provided benefit is the generated support for logic-based intelligent applications that are able to perform complex reasoning tasks such as checking for errors and inconsistencies and deriving logical inferences.

A quadratic edge-finding filtering algorithm for cumulative resource constraints

The cumulative scheduling constraint, which enforces the sharing of a finite resource by several tasks, is widely used in constraint-based scheduling applications. Propagation of the cumulative constraint can be performed by several different filtering algorithms, often used in combination. One of the most important and successful of these filtering algorithms is edge-finding. Recent work by Vilím has resulted in a 𝒪 (kn log n) algorithm for cumulative edge-finding (where n is the number of tasks and k is the number of distinct capacity requirements), as well as a new related filter, timetable edge-finding, with a complexity of 𝒪(n2). We present a sound 𝒪(n2) filtering algorithm for standard cumulative edge-finding, orthogonal to the work of Vilím; we also show how this algorithm’s filtering may be improved by incorporating some reasoning from extended edge-finding, with no increase in complexity. The complexity of the new algorithm does not strictly dominate previous edge-finders for small k, and it sometimes requires more iterations to reach the same fixpoint; nevertheless, results from Project Scheduling Problem Library benchmarks show that in practice this algorithm consistently outperforms earlier edge-finding filters, and remains competitive with timetable edge-finding, despite the latter algorithm’s generally stronger filtering.

Towards A “Deep” Ontology for African Traditional Medicine

The increasing interest on ontologies as the backbone technology for knowledge based systems implies the refinement of ontologies development methods. In this paper we propose a new approach to develop an ontology for African Traditional Medicine. The aim of our approach is to build a deep ontology by deepening concepts descriptions to formally represent all the semantics underlying the concepts used in African traditional medicine. We use a description logics language to formalize our approach.

An ontology-based computer-aided diagnosis system in African traditional medicine: At the Sorcerer’s Stone

Purpose – The purpose of this paper is to describe an AI (Artificial Intelligence) that can “think like an African traditional doctor”. The system proposes to model and to use attitudes taken and concepts used by African traditional doctors when facing cases. It is designed to go deep into the concepts of African traditional medicine (ATM) by dealing with all the possible interpretations of those concepts, and to produce more much satisfying and accurate support for medical diagnosis and prescription than existing systems. Design/methodology/approach – To take into account the sometimes strange concepts used and attitudes taken by African traditional healers, including mystical considerations, the system relies on a deep ontology describing all those concepts and attitudes in a more computer readable manner allowing a multi-agent system to have full access to ATM knowledge. Ethnological inquiries, literary analysis and interviews of traditional doctors (the holders of African medicine knowledge) were perf...

A quadratic extended edge-finding filtering algorithm for cumulative resource constraints

Edge-finding, extended edge-finding, not-first/not-last and energetic reasoning are well-known filtering rules used in constraint-based scheduling problems for propagating constraints over disjunctive and cumulative resources. In practice, these filtering algorithms frequently form part of a sequence to form a more powerful propagator, thereby helping to reduce search tree size. In this paper, we propose a sound O(n2) extended edge-finding algorithm for cumulative resources, where n is the number of tasks sharing the resource. This algorithm uses the notion of minimum slack to detect when extended edge-finding justifies a strengthening of a domain, and it is more efficacious when executed on a domain already at the fix point of standard edge-finding. Previously, the best known complexity for filtering extended edge-finding on cumulative resources was O(kn2) (where k is the number of distinct capacity requirements). Experimental results on resource constrained scheduling benchmarks confirm that the new algorithm outperforms previous extended edge-finding algorithms, and sometimes results in better performance than standard edge-finding alone. Furthermore, we show that our method is competitive with the current state-of-the-art in edge-finding-based algorithms.

Using Boolean factors for the construction of an artificial neural networks

ABSTRACT We propose a novel approach to define Artificial Neural Network(ANN) architecture from Boolean factors. ANNs are a subfield of machine learning applicable to several areas of life. However, defining its architecture for solving a given problem is not formalized and remains an open research problem. Since it is difficult to look into the network and figure out exactly what it has learnt, the complexity of such a technique makes its interpretation more tedious. We propose in this paper to build feedforward ANNs using the optimal factors obtained from the Boolean context representing a data. Since optimal factors completely cover the data and therefore give an explanation to these data, We could give an interpretation to the neurons activation and justify the presence of a neuron in our proposed neural network. We show through experiments and comparisons on the use data sets that this approach provides relatively better results for some key performance measures.

Boolean factors based Artificial Neural Network

Due to its ability to solve nonlinear problems, Artificial Neural Network (ANN) could be applied in several areas of life. However, defining its architecture for solving a given problem is not formalized and remains an open research problem. On the other hand the complexity of such a technique due to its “black box” aspect, makes its interpretation more tedious. Since optimal factors completely cover the data and therefore give an explanation to these data, we propose in this paper to build feedforward ANNs using the optimal factors obtained from the boolean context representing a data. We show through experiments and comparisons on the use datasets that this approach provides relatively better results than those existing in the literature.

New Classes of Graceful Unicyclic Graphs

Abstract A C n -unicyclic graph is a unicyclic graph where the cycle has n ≥ 3 vertices. A caterpillar R with spine P n = v 0 v 1 ⋯ v n − 1 is denoted by R ( v 0 v 1 ⋯ v n − 1 ) . A cycle with a pendant caterpillar is obtained by identifying a vertex of the cycle with a leaf of R ( v 0 v 1 ⋯ v n − 1 ) that is adjacent to v 0 (or v n − 1 ). In this paper, we investigate the gracefulness of unicyclic graphs with pendant caterpillars at two adjacent vertices of the cycle, and pendant edges at some other vertices of the cycle.