Gene Selection is very important problem in the classification of serious diseases in clinical information systems. A limitation of these gene selection methods is that they may result in gene sets with some redundancy and yield an unnecessary large number of candidate genes for classification analysis. In the current work, a hybrid approach is presented in order to classify diseases, such as colon cancer, leukemia, and liver cancer, based on informative genes. This hybrid approach uses clustering (K-means) with statistical analysis (ANOVA) as a preprocessing step for gene selection and Support Vector Machines (SVM) to classify diseases related to microarray experiments. To compare the performance of the proposed methodology, two kinds of comparisons were achieved: 1) applying statistical analysis combined with clustering algorithm (K-means) as a preprocessing step and 2) comparing different classification algorithms: decision tree (ID3), naïve bayes, adaptive naïve bayes, and support vector machines. In case of combining clustering with statistical analysis, much better classification accuracy is given of 97% rather than without applying clustering in the preprocessing phase. In addition, SVM had proven better accuracy than decision trees, Naïve Bayes, and Adaptive Naïve Bayes classification.

Gene Selection is very important problem in the classification of serious diseases in clinical information systems. A limitation of these gene selection methods is that they may result in gene sets with some redundancy and yield an unnecessary large number of candidate genes for classification analysis. In the current work, a hybrid approach is presented in order to classify diseases, such as colon cancer, leukemia, and liver cancer, based on informative genes. This hybrid approach uses clustering (K-means) with statistical analysis (ANOVA) as a preprocessing step for gene selection and Support Vector Machines (SVM) to classify diseases related to microarray experiments. To compare the performance of the proposed methodology, two kinds of comparisons were achieved: 1) applying statistical analysis combined with clustering algorithm (K-means) as a preprocessing step and 2) comparing different classification algorithms: decision tree (ID3), naïve bayes, adaptive naïve bayes, and support vector machines. In case of combining clustering with statistical analysis, much better classification accuracy is given of 97% rather than without applying clustering in the preprocessing phase. In addition, SVM had proven better accuracy than decision trees, Naïve Bayes, and Adaptive Naïve Bayes classification.

Ontology has become a very vital issue to solve important issues regarding human diseases through data integration of chemical and biological data. Mining such data discovers highly important knowledge about diseases can give an important insight to arrive to new drug targets and assist in personalized medicine. In the current paper, a mining technique for diseases is developed based on integrated ontology and association rule mining algorithm. To perform mining, the semantic web, as a knowledge representation methodology is used to integrate data. In addition, an Ontology Association Rule Mining algorithm (OARM) is developed since existing algorithms cannot be applied because of the ontology nature of data containing several types of relations. To test our performance, prostate cancer data is obtained from NCI, which is related to 279 genes and 89 genes (from prostate cancer pathway).

Ontology has become a very vital issue to solve important issues regarding human diseases through data integration of chemical and biological data. Mining such data discovers highly important knowledge about diseases can give an important insight to arrive to new drug targets and assist in personalized medicine. In the current paper, a mining technique for diseases is developed based on integrated ontology and association rule mining algorithm. To perform mining, the semantic web, as a knowledge representation methodology is used to integrate data. In addition, an Ontology Association Rule Mining algorithm (OARM) is developed since existing algorithms cannot be applied because of the ontology nature of data containing several types of relations. To test our performance, prostate cancer data is obtained from NCI, which is related to 279 genes and 89 genes (from prostate cancer pathway).

Abstract. In this paper, we propose two methods to improve the Ad-Hoc On-Demand Distance-Vector (AODV) protocol. The main goal in the design of the protocol was to reduce the routing overhead, buffer overflow, end-to-end delay and increase the performance. A multi-path routing protocol is proposed which is based on AODV and Ant Colony Optimization(ACO). This protocol is refereed to Multi-Route AODV Ant routing (MRAA). Also we propose a load balancing method that uses all discovered paths simultaneously for transmitting data. In this method, data packets are balanced over discovered paths and energy consumption is distributed across many nodes through network. This protocol is refereed to Load Balanced Multi- Route AODV Ant routing algorithm (LBMRAA)

Abstract. In this paper, we propose two methods to improve the Ad-Hoc On-Demand Distance-Vector (AODV) protocol. The main goal in the design of the protocol was to reduce the routing overhead, buffer overflow, end-to-end delay and increase the performance. A multi-path routing protocol is proposed which is based on AODV and Ant Colony Optimization(ACO). This protocol is refereed to Multi-Route AODV Ant routing (MRAA). Also we propose a load balancing method that uses all discovered paths simultaneously for transmitting data. In this method, data packets are balanced over discovered paths and energy consumption is distributed across many nodes through network. This protocol is refereed to Load Balanced Multi- Route AODV Ant routing algorithm (LBMRAA)

Abstract. In this paper, we propose two methods to improve the Ad-Hoc On-Demand Distance-Vector (AODV) protocol. The main goal in the design of the protocol was to reduce the routing overhead, buffer overflow, end-to-end delay and increase the performance. A multi-path routing protocol is proposed which is based on AODV and Ant Colony Optimization(ACO). This protocol is refereed to Multi-Route AODV Ant routing (MRAA). Also we propose a load balancing method that uses all discovered paths simultaneously for transmitting data. In this method, data packets are balanced over discovered paths and energy consumption is distributed across many nodes through network. This protocol is refereed to Load Balanced Multi- Route AODV Ant routing algorithm (LBMRAA)