The bioinformatics field is concerned with processing medical data for information and knowledge extraction. The problem comes more interesting when dealing with uncertain data which is very common in the medicine diagnostic area. The medical data is processed inside the human brain to produce the appropriate diagnoses. The artificial neural networks are simulations to the human thinking. The rough neural networks are special networks that are capable of dealing with rough boundaries of uncertainness through rough neurons. This research tries to solve the diagnostic problems using the classification capabilities of the rough neural networks. The medical training data ,after preprocessing to remove unnecessary attributes, are applied to the rough neural network structure so as to update the connection weights iteratively and produce the final network that give a good accuracy rates. The testing data are used to measure these accuracy rates. The input data are transformed into its lower and upper boundaries by multiplying them by the input weights so there are no need for preparing rough data in advance. The illustrations of the proposed model and its sub modules along with the experimental results and comparisons with the neural network in diagnosing medical knowledge from the breast cancer data set for a number of experiments with different training set sizes are declared.

The need to handle uncertainty and vagueness in real world becomes a necessity for developing good and efficient systems. Fuzzy rules and their usage in fuzzy systems help too much in solving these problems away from the complications of probability mathematical calculations. Fuzzy rules deals will words and labels instead of values of the variables. These labels are called variable's subsets and needed to be prepared carefully to make sure that the fuzzy rules depend on accurate propositions. This research tries to design an efficient set of rules that is used later for inference by a hybrid model of Self Organized Features Maps and Parallel Genetic Algorithms. Self Organized Features Maps capabilities to cluster inputs using self adoption techniques have been very useful in generating fuzzy membership functions for the subsets of the fuzzy variables. Then the Parallel Genetic Algorithms use these membership functions along with the training data set to find the most fit fuzzy rule set from a number of initial sub populations according to the fitness function. The illustrations of the proposed model and its sub modules along with the experimental results and comparisons with previous techniques in generating rules from data sets are declared.

Abstract-Handling uncertain knowledge is a very tricky problem in the current world as the data, we deal with, is uncertain, incomplete and even inconsistent. Finding an efficient intelligent framework for this kind of knowledge is a challenging task. The knowledge based framework can be represented by a rule based system that depends on a set of rules which deal with uncertainness in the data. Fuzzy rough rules are a good competitive in dealing with the uncertain cases. They are consisted of fuzzy rough variables in both the propositions and consequences. The fuzzy rough variables represent the lower and upper approximations of the subsets of a fuzzy variable. These fuzzy variables use labels (fuzzy subsets) instead of values. An efficient fuzzy rough rule based system must depend on good and accurate rules. This system needs to be enhanced to view the future recommendations or in other words the system in time sequence. This paper tries to make a rule based system for uncertain knowledge using fuzzy rough theory to generate the desired accurate rules and then use fuzzy cellular automata parallel system to enhance the rule based system developed and find out what the system would look like in time sequence so as to give good recommendations about the system in the future. The proposed model along with experimental results and simulations of the rule based systems of different data sets in time sequence is illustrated.

Abstract-The automatic prediction and detection of breast cancer disease is an imperative, challenging problem in medical applications. In this paper, a proposed model to improve the accuracy of classification algorithms is presented. A new approach for designing effective pre-processing stage is introduced. Such approach integrates K-means clustering algorithm with fuzzy rough feature selection or correlation feature selection for data reduction. The attributes of the reduced clustered data are merged to form a new data set to be classified. Simulation results prove the enhancement of classification by using the proposed approach. Moreover, a new hybrid model for classification composed of K-means clustering algorithm, fuzzy rough feature selection and discernibility nearest neighbour is achieved. Compared to previous studies on the same data, it is proved that the presented model outperforms other classification models. The proposed model is tested on breast cancer dataset from UCI machine learning repository.

Planning Telecommunication Access Network (TAN) infrastructure is a real time problem that suffers from uncertainty and ambiguity. Fuzzy system is a discipline that proved its capability to deal with vague problems. Converting traditional data to suit fuzzy system calculations is called fuzzification process which is a crucial step affecting the whole system accuracy? Generating appropriate membership function for fuzzy variables is one of the most challenging issues in fuzzy systems design. This paper proposes a solution to generate membership function automatically. An integrated hybrid model (PSO-TE) is introduced which benefit the Particle Swarm Optimization (PSO) and the information theory measures (entropy and mutual information) as the fitness function to adjust particles (membership function parameters). The proposed algorithm is tested using realistic planning information to show its effectiveness and efficiency. During the comparison between the PSO-TE and the fuzzy C-mean algorithm, the proposed model proved its ability to produce stable membership function for the telecommunication data barriers.