The historical document is a treasure. The frequent use of these documents requires having a nume... more The historical document is a treasure. The frequent use of these documents requires having a numeric copy. The use of these numeric documents requires developing techniques to facilitate their use. The search by content, the word spotting, and handwriting recognition became important points of research in document analysis. For this purpose, in this article is covered the recognition of the Arabic manuscript names extracted from the register of names of the Tunisian national archive. In the study, the authors have used several techniques for extracting knowledge, coding, and name recognition. The authors have also optimized the clonclas algorithm using the incremental principle from the i2gng algorithm. The results encourage continuing exploration.
Over-fitting avoidance in probabilistic neural networks
In this work, a new training algorithm for probabilistic neural networks (PNN) is presented. The ... more In this work, a new training algorithm for probabilistic neural networks (PNN) is presented. The proposed algorithm addresses one of the major drawbacks of probabilistic neural networks, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the new network is compared against performance of standard probabilistic neural networks for different databases from the UCI database repository. Results show an important gain in network size and performance.
Contribution to decision-making in the big data industry based on the multiparametric similarity measure for Pythagorean fuzzy sets
Journal of Logic and Computation, Jul 23, 2022
Big Data allows analysing and assessing all human production types with its 5Vs, which are Volume... more Big Data allows analysing and assessing all human production types with its 5Vs, which are Volume, Velocity, Variety, Veracity and Value. Big Data is useful to improve decision-making to adjust it better to market demand, specifically selection of supplier that is an important link to optimize the logistic chain of enterprises. In this case, leadership or decider is ahead one serious complex problem, inexact and fuzzy. Pythagorean fuzzy set (PFS) is disposing the indeterminacy data by the membership and the nonmembership functions; it is a generalization of the intuitionist fuzzy set when the last set is limited. First, some results for PFSs are displaying in this study as particular cases and generalization of some binary operations. After, an improved score function of Pythagorean fuzzy number is proposed to avoid the comparison problem in practice. In addition, an existing approach exploring the combined alternatives weight to settle Pythagorean fuzzy issue by multi-parametric similarity measure is applied with the new proposed score function to selection of supplier issue with five serious criteria as a Big Data industry decision-making problem in economic environment. Finally, a comparison of the presented method with some existing approaches has been executed in the light of counterintuitive phenomena for validating its advantages.
New Contribution to Adaptive Temporal Radial Basis Function Applied on TIMIT Corpus
International Conference on Cognitive Modelling, 2004
Introduction A successful speech recognition system has to determine features not only present in... more Introduction A successful speech recognition system has to determine features not only present in the input pattern at one point in time, but also features of input pattern changing over time ( e.g., Berthold, 1994; Benyettou, 1995). In network design, great importance must be attributed to correct choice of the number of hidden neurons, which helps avoiding problems of overfitting and contributes to reduce the time required for the training without significantly affecting the network performances (e.g., Colla & Reyneri & Sgarbi, 1999), but never looking to architecture adapting effect according to input. The goal to combine the approach of the RBF with the shift invariance features of the TDNN, can be get a new robust model, this is named temporal radial basis function “TRBF” (e.g., Mesbahi & Benyettou, 2003), but to be more efficient, we have adapt these networks so that they come more dynamic according to their behaviour and features of the object has study. It can be goes more clearly in continuous speech. Therefore in object to obtain an Adaptive TRBF, we must adapt the TRBF networks, consequently it was necessary to develop an algorithm that permits to solve this type of problem, this algorithm is called “DOLS” which means Dynamic Orthogonal Least Square, that will be presented in this paper.
The Cooperation of Immune Agents for Intrusion Detection System
Nowadays information technology and communication has evolve, computer networks became vulnerable... more Nowadays information technology and communication has evolve, computer networks became vulnerable faced to new forms of threats. In this article, a new model of intrusion detection based on multi-agents system and inspired from the biological immune system is presented. We begin through a presentation of the biological immune systems, followed by immune algorithm, a model of artificial immune system which is integrated in the behavior of distributed agents on the network is proposed in order to ensure a good intrusions detection. The internal structure of the immune agents and their capacity to distinguish between self and not-self is also presented. Agents are able to achieve simultaneous treatments, they are auto-adaptable to environment evolution and have also the property of distributed coordination.
On-Line Arabic Characters Recognition Using Enhanced Time Delay Neural Networks
International Journal on Communications Antenna and Propagation, Aug 31, 2017
This paper concerns with the online recognition of isolated hand writing Arabic characters, throu... more This paper concerns with the online recognition of isolated hand writing Arabic characters, through, the interpretation of a script presented by a pen trajectory. This technique was generally used in the electronic organizers of Personal Digital Assistant type. First of all, we have built a data base with several scripters using a graphic tablet which will be used in our application. In order to have a precise recognition of the isolated characters, it is important to model their structure the most correctly possible. In this work we present the study, the implementation and the result of the test of a particular neural network which is the Time Delay Neural Networks. We have followed a two steps approach, in the first one, the character characteristics are extracted, and in the second one, a temporal multi-layered perception is developed for a future classification. Our temporal approach with the adaptive topology, responds to the nature of the Arabic script during the acquisition phase while the use of different learning algorithms can minimize the cost function and improve recognition rates. The parameterization of these two parts will allow us to analyze the impact of the neural network topology on the results of character recognition rates.
Control of Nosocomial Infections by Data Mining - TI Journals
World Applied Programming, Nov 23, 2012
Abstract: These last 15 years have been rich in publishing high quality scientific studies evalua... more Abstract: These last 15 years have been rich in publishing high quality scientific studies evaluating the effectiveness of measures to prevent nosocomial infections, particularly in intensive care unit (ICU), comparison of the results of these studies and practices in intensive care units can now to better define a program for preventing nosocomial infections to develop in these services. Focused on managing the risk of infection and prevention of nosocomial infections, our study, using tools that use data mining methods, together proposals for how well resuscitation. Among the techniques we use in data mining classification, neural networks and decision trees that also use the description used for prevention or for the unsupervised classification and clustering, we estimate we have for the rules Association. These techniques are used with several algorithms that give different results and which are distinguished from each other.
Hybrid Intelligent Systems that combine knowledge-based and artificial neural network systems typ... more Hybrid Intelligent Systems that combine knowledge-based and artificial neural network systems typically have four phases involving domain knowledge representation, mapping of this knowledge into an initial connectionist architecture, network training, and rule extraction, respectively. The final phase is important because it can provide a trained connectionist architecture with explanation power and validate its output decisions. Moreover, it can be used to refine and maintain the initial knowledge acquired from domain experts. In this paper, we present three rule-extraction techniques. The first technique extracts a set of binary rules from any type of neural network. The other two techniques are specific to feedforward networks, with a single hidden layer of sigmoidal units. Technique 2 extracts partial rules that represent the most important embedded knowledge with an adjustable level of detail, while the third technique provides a more comprehensive and universal approach. A rule-evaluation technique, which orders extracted rules based on three performance measures, is then proposed. The three techniques area applied to the iris and breast cancer data sets. The extracted rules are evaluated qualitatively and quantitatively, and are compared with those obtained by other approaches.
Cardiovascular disease is a major public health problem and the leading cause of death in the wor... more Cardiovascular disease is a major public health problem and the leading cause of death in the world. An electrocardiogram (ECG) is a recording of the electrical activity of the heart as a function of time. Due to the speed of implementation, efficiency and especially reliability for diagnosis, the ECG plays an important role in monitoring and diagnosing patients today. The ECG is only effective when it is recorded over a long time. The analysis of such a recording requires methods of parameter optimization and automatic classification of heartbeats. In this paper, a genetic algorithm is used to optimize the ECG signal. The proposed method is able to optimize the ECG data and exceeds the result of related works which proves its effectiveness in ECG arrhythmias detection.
Brain computer interface provides communication opportunity between the brain and the environment... more Brain computer interface provides communication opportunity between the brain and the environment around a person with severe motor disabilities. However, the implementation of such interfaces requires a good signal processing scheme, whose performances depend principally on the technique used to select the best features, and the classification technique used to perform the discrimination between the different categories. This work proposes a new hybrid structure based on two stages with supervised and unsupervised learning. The first stage consists of a Self Organizing Map which allows to cluster the redundant irrelevant features and select the best descriptors. The second stage uses, a Probabilistic Quadratic Loss Multi-Class Support Vector Machine for the final classification. Five mental tasks namely: Baseline, Multiplication, Letter, Rotation, and Counting are considered, and an average accuracy between 81.73% and 91.90% is achieved. This result shows the effectiveness of the proposed method to enhance the performance of electroencephalogram classification problem.
The historical document is a treasure. The frequent use of these documents requires having a nume... more The historical document is a treasure. The frequent use of these documents requires having a numeric copy. The use of these numeric documents requires developing techniques to facilitate their use. The search by content, the word spotting, and handwriting recognition became important points of research in document analysis. For this purpose, in this article is covered the recognition of the Arabic manuscript names extracted from the register of names of the Tunisian national archive. In the study, the authors have used several techniques for extracting knowledge, coding, and name recognition. The authors have also optimized the clonclas algorithm using the incremental principle from the i2gng algorithm. The results encourage continuing exploration.
Over-fitting avoidance in probabilistic neural networks
In this work, a new training algorithm for probabilistic neural networks (PNN) is presented. The ... more In this work, a new training algorithm for probabilistic neural networks (PNN) is presented. The proposed algorithm addresses one of the major drawbacks of probabilistic neural networks, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the new network is compared against performance of standard probabilistic neural networks for different databases from the UCI database repository. Results show an important gain in network size and performance.
Contribution to decision-making in the big data industry based on the multiparametric similarity measure for Pythagorean fuzzy sets
Journal of Logic and Computation, Jul 23, 2022
Big Data allows analysing and assessing all human production types with its 5Vs, which are Volume... more Big Data allows analysing and assessing all human production types with its 5Vs, which are Volume, Velocity, Variety, Veracity and Value. Big Data is useful to improve decision-making to adjust it better to market demand, specifically selection of supplier that is an important link to optimize the logistic chain of enterprises. In this case, leadership or decider is ahead one serious complex problem, inexact and fuzzy. Pythagorean fuzzy set (PFS) is disposing the indeterminacy data by the membership and the nonmembership functions; it is a generalization of the intuitionist fuzzy set when the last set is limited. First, some results for PFSs are displaying in this study as particular cases and generalization of some binary operations. After, an improved score function of Pythagorean fuzzy number is proposed to avoid the comparison problem in practice. In addition, an existing approach exploring the combined alternatives weight to settle Pythagorean fuzzy issue by multi-parametric similarity measure is applied with the new proposed score function to selection of supplier issue with five serious criteria as a Big Data industry decision-making problem in economic environment. Finally, a comparison of the presented method with some existing approaches has been executed in the light of counterintuitive phenomena for validating its advantages.
New Contribution to Adaptive Temporal Radial Basis Function Applied on TIMIT Corpus
International Conference on Cognitive Modelling, 2004
Introduction A successful speech recognition system has to determine features not only present in... more Introduction A successful speech recognition system has to determine features not only present in the input pattern at one point in time, but also features of input pattern changing over time ( e.g., Berthold, 1994; Benyettou, 1995). In network design, great importance must be attributed to correct choice of the number of hidden neurons, which helps avoiding problems of overfitting and contributes to reduce the time required for the training without significantly affecting the network performances (e.g., Colla & Reyneri & Sgarbi, 1999), but never looking to architecture adapting effect according to input. The goal to combine the approach of the RBF with the shift invariance features of the TDNN, can be get a new robust model, this is named temporal radial basis function “TRBF” (e.g., Mesbahi & Benyettou, 2003), but to be more efficient, we have adapt these networks so that they come more dynamic according to their behaviour and features of the object has study. It can be goes more clearly in continuous speech. Therefore in object to obtain an Adaptive TRBF, we must adapt the TRBF networks, consequently it was necessary to develop an algorithm that permits to solve this type of problem, this algorithm is called “DOLS” which means Dynamic Orthogonal Least Square, that will be presented in this paper.
The Cooperation of Immune Agents for Intrusion Detection System
Nowadays information technology and communication has evolve, computer networks became vulnerable... more Nowadays information technology and communication has evolve, computer networks became vulnerable faced to new forms of threats. In this article, a new model of intrusion detection based on multi-agents system and inspired from the biological immune system is presented. We begin through a presentation of the biological immune systems, followed by immune algorithm, a model of artificial immune system which is integrated in the behavior of distributed agents on the network is proposed in order to ensure a good intrusions detection. The internal structure of the immune agents and their capacity to distinguish between self and not-self is also presented. Agents are able to achieve simultaneous treatments, they are auto-adaptable to environment evolution and have also the property of distributed coordination.
On-Line Arabic Characters Recognition Using Enhanced Time Delay Neural Networks
International Journal on Communications Antenna and Propagation, Aug 31, 2017
This paper concerns with the online recognition of isolated hand writing Arabic characters, throu... more This paper concerns with the online recognition of isolated hand writing Arabic characters, through, the interpretation of a script presented by a pen trajectory. This technique was generally used in the electronic organizers of Personal Digital Assistant type. First of all, we have built a data base with several scripters using a graphic tablet which will be used in our application. In order to have a precise recognition of the isolated characters, it is important to model their structure the most correctly possible. In this work we present the study, the implementation and the result of the test of a particular neural network which is the Time Delay Neural Networks. We have followed a two steps approach, in the first one, the character characteristics are extracted, and in the second one, a temporal multi-layered perception is developed for a future classification. Our temporal approach with the adaptive topology, responds to the nature of the Arabic script during the acquisition phase while the use of different learning algorithms can minimize the cost function and improve recognition rates. The parameterization of these two parts will allow us to analyze the impact of the neural network topology on the results of character recognition rates.
Control of Nosocomial Infections by Data Mining - TI Journals
World Applied Programming, Nov 23, 2012
Abstract: These last 15 years have been rich in publishing high quality scientific studies evalua... more Abstract: These last 15 years have been rich in publishing high quality scientific studies evaluating the effectiveness of measures to prevent nosocomial infections, particularly in intensive care unit (ICU), comparison of the results of these studies and practices in intensive care units can now to better define a program for preventing nosocomial infections to develop in these services. Focused on managing the risk of infection and prevention of nosocomial infections, our study, using tools that use data mining methods, together proposals for how well resuscitation. Among the techniques we use in data mining classification, neural networks and decision trees that also use the description used for prevention or for the unsupervised classification and clustering, we estimate we have for the rules Association. These techniques are used with several algorithms that give different results and which are distinguished from each other.
Hybrid Intelligent Systems that combine knowledge-based and artificial neural network systems typ... more Hybrid Intelligent Systems that combine knowledge-based and artificial neural network systems typically have four phases involving domain knowledge representation, mapping of this knowledge into an initial connectionist architecture, network training, and rule extraction, respectively. The final phase is important because it can provide a trained connectionist architecture with explanation power and validate its output decisions. Moreover, it can be used to refine and maintain the initial knowledge acquired from domain experts. In this paper, we present three rule-extraction techniques. The first technique extracts a set of binary rules from any type of neural network. The other two techniques are specific to feedforward networks, with a single hidden layer of sigmoidal units. Technique 2 extracts partial rules that represent the most important embedded knowledge with an adjustable level of detail, while the third technique provides a more comprehensive and universal approach. A rule-evaluation technique, which orders extracted rules based on three performance measures, is then proposed. The three techniques area applied to the iris and breast cancer data sets. The extracted rules are evaluated qualitatively and quantitatively, and are compared with those obtained by other approaches.
Cardiovascular disease is a major public health problem and the leading cause of death in the wor... more Cardiovascular disease is a major public health problem and the leading cause of death in the world. An electrocardiogram (ECG) is a recording of the electrical activity of the heart as a function of time. Due to the speed of implementation, efficiency and especially reliability for diagnosis, the ECG plays an important role in monitoring and diagnosing patients today. The ECG is only effective when it is recorded over a long time. The analysis of such a recording requires methods of parameter optimization and automatic classification of heartbeats. In this paper, a genetic algorithm is used to optimize the ECG signal. The proposed method is able to optimize the ECG data and exceeds the result of related works which proves its effectiveness in ECG arrhythmias detection.
Brain computer interface provides communication opportunity between the brain and the environment... more Brain computer interface provides communication opportunity between the brain and the environment around a person with severe motor disabilities. However, the implementation of such interfaces requires a good signal processing scheme, whose performances depend principally on the technique used to select the best features, and the classification technique used to perform the discrimination between the different categories. This work proposes a new hybrid structure based on two stages with supervised and unsupervised learning. The first stage consists of a Self Organizing Map which allows to cluster the redundant irrelevant features and select the best descriptors. The second stage uses, a Probabilistic Quadratic Loss Multi-Class Support Vector Machine for the final classification. Five mental tasks namely: Baseline, Multiplication, Letter, Rotation, and Counting are considered, and an average accuracy between 81.73% and 91.90% is achieved. This result shows the effectiveness of the proposed method to enhance the performance of electroencephalogram classification problem.
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