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The dynamic and distributed nature of telecommunication networks makes complex the design of model-based approaches for network fault diagnosis. Most model-based approaches assume the prior existence of the model which is reduced to a static image of the network. Such models become rapidly obsolete when the network changes. We propose in this paper a 3-layered self-reconfigurable generic model of...
To determine the severity of metal-loss defects in oil and gas pipelines, the depth of potential defects, along with their length, needs first to be estimated. For this purpose, pipeline engineers use intelligent Magnetic Flux Leakage (MFL) sensors that scan the metal pipelines and collect defect-related data. However, due to the huge amount of the collected MFL data, the defect depth estimation task...
CREDO is a framework for understanding and designing cognitive systems. It has evolved through a long research program starting from empirical studies of human medical expertise, and including extensive theoretical studies and practical development of AI systems in medicine. The results are now being successfully exploited in a wide range of clinical applications, some on a large scale. The unique...
Prediction financial time series (stock index price) is the most challenging task. Support vector regression (SVR), Support vector machine (SVM) and back propagation neural network (BPNN) are the most popular data mining techniques in prediction financial time series. In this paper a hybrid combination model is introduced to combine the three models and to be most beneficial of them all. Quantization...
This paper presents a practical usage of a machine learning algorithm for increasing sales in a car repair service. The application is implemented for iPad and stores records of the performed reparations by previous clients. The proposed solution computes the costs of reparation, generates a number of promotional packages and offers a sales simulation for proposed packages with a certain discount...
Case based reasoning (CBR) is frequently used for data classification problems, it can be considered as similarity based reasoning but equal importance are assigned to every attribute in the dataset. By identifying the features which are more important in the process of classification we can have better accuracy of CBR system. This paper proposes use of ranked attribute selection on the basis of their...
This paper describes fast, efficient and global optimization method called wind driven optimization (WDO) algorithm for nulling pattern synthesis of uniformly spaced linear array having maximum side lobe level (SLL) suppression, restricted dynamic range ratio (DRR), beam width and null control by controlling the array elements amplitude-only. A broad null is placed in the direction of maximum interference...
In this work, we adapt the fine tuning algorithm of Naïve Bayes (NB) for Tree Augmented Naïve Bayes (TAN). The adapted algorithm, takes into consideration the differences in structure between NB and TAN. The algorithm augments the regular TAN learning phase with a fine tuning phase in which the probability terms are fine tuned to give better classification accuracy. The fine tuning algorithm is applied...
The paper deals with a non-linear relationship between 3 groups of factors and one dependent variable, namely the success of innovation management of projects. A framework is created using the technique of fuzzy logic using Neural Networks and multi perceptron method. The three groups of 17 factors each are used for business users, management personnel and technical personnel, respectively. Neural...
Electricity forecasting is a big deal for companies, and so the energy planning is needed in the short, medium and long term. In this way, it is important that the prediction remains relevant taking into account different parameters as GDP (Gross Domestic Product), weather, and so on. This work focuses on forecasting medium and long terms of Algerian electrical load using information from past consumption...
In this work we present a method based on association rules for the prediction of bladder cancer recurrence. Our objective is to provide a system which is on one hand comprehensible and on the other hand with a high sensitivity. Since data are not equitably distributed among the classes and since errors costs are asymmetric, we propose to handle separately the cases of recurrence and those of no-recurrence...
This paper investigates an intelligent search technique for the design of a robust H∞ controller for a class of uncertain networked control systems (NCS) with random packet losses, occurring simultaneously between the sensor and controller and between the controller and actuator. The parameter uncertainties are norm bounded and packet dropout are assumed to obey Bernoulli random binary distribution...
Loan provision is associated with a credit risk. Banks assess the creditworthiness of potential borrowers to lower a credit risk. Creditworthiness assessment is carried out by credit scoring methods. Most of these methods classify individuals into two categories: ‘good’ or ‘bad’ creditworthiness. Decision support system for loan granting based on these methods fail to differentiate the loan price...
Complaints Management (CM) is one of the important elements in Customer Relationship Management (CRM) system of any organization which helps in customer retention for the longest possible period of time. In this research, a system called Complaint Classification System (CCS) is implemented to discuss how Data Mining Techniques (DMT) can be used to classify and direct complaints to the departments...
The purpose of this paper is to integrate the concept of Supervised Learning Algorithms in Engine tuning. These days Machine learning has become a very valuable tool for prediction. A given subset of this domain involves using supervised algorithms to intake data, analyze the data and ‘learn’ from it. The more the data that is processed by it (training stage), the better it learns (Fitting Parameters...
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