Sally M. El-Ghamrawy

AN AGENT DECISION SUPPORT MODULE BASED ON GRANULAR ROUGH MODEL

A multi-agent system (MAS) is a branch of distributed artificial intelligence, composed of a number of distributed and autonomous agents. In a MAS, effective coordination is essential for autonomous agents to achieve their goals. Any decision based on a foundation of knowledge and reasoning can lead agents into successful cooperation; to achieve the necessary degree of flexibility in coordination, an agent must decide when to coordinate and which coordination mechanism to use. The performance of any MAS depends directly on the decisions made by the agents. The agents must therefore be able to make correct decisions. This paper proposes a decision support module in a distributed MAS that is concerned with two main decisions: the decision needed to allocate a task to specific agent/s and the decision needed to select the appropriate coordination mechanism when agents must coordinate with other agent/s to accomplish a specific task. An algorithm for the task allocation decision maker (TADM) and the coordination mechanism selection decision maker (CMSDM) algorithm are proposed that are based on the granular rough model (GRM). Furthermore, a number of experiments were performed to validate the effectiveness of the proposed algorithms; the efficiency of the proposed algorithms is compared with recent works. The preliminary results demonstrate the efficiency of our algorithms.

An Automatic Label Extraction Technique for Domain-Specific Hidden Web Crawling (LEHW)

General-purpose search engines (e.g. Google and Yahoo) ignore valuable data that represent 80% of the content on the Web, this portion of Web called hidden Web (HW). Pages in the hidden Web are dynamically generated in response to queries submitted via the search forms. In this paper, a new algorithm for extracting labels from multi-attribute (M-A) search form fields is proposed. A technique for automatic query generation for single-attribute (S-A) search forms is also provided in order to enhance the performance of the overall domain-specific hidden Web crawlers. The innovation of (LEHW) algorithm is its capability to distinguish between (S-A) and (M-A) forms; so that the capability of dealing with both of them, unlike most hidden Web crawlers that ignore either of them. Embedding of the proposed algorithm within the overall framework of the HW crawler is evaluated through experiments using real Web sites. The preliminary results demonstrate the accuracy and precision of the proposed approach for most of the sites considered

A Knowledge Management Framework for imbalanced data using Frequent Pattern Mining based on Bloom Filter

Managing medical environments and organizations performance depend directly on the knowledge management (KM) systems. Knowledge Discovery (KD) is responsible for digging information from datasets and finding internal knowledge within organizations or external sources. Data mining (DM) is the core of KD process. Although recent mining techniques have proven their accuracy in discovering the knowledge from balanced data, where the class distribution is balanced, the problem of discovering knowledge from unbalanced data is still a challenge that needs to be addressed. A Clustered Knowledge Management Framework (CKMD) is presented in this paper, for enhancing the performance of KD from unbalanced data. A Simple Hybrid Sampling Approach (SHSA) is proposed to reduce the adverse impacts of imbalanced data. Mining frequent pattern process plays an important role in KD process. Moreover, a Frequent Pattern Mining algorithm based on Bloom Filter (FPMBF) is proposed to discover items that frequently co-occur in the data using the bloom filter, that requires a single scan of the data, which leads to less time consuming in discovering knowledge for imbalanced data. Finally, the performance of the proposed methods is evaluated using real datasets and comparative experiments.

Distributed multi-agent communication system based on dynamic ontology mapping

Communication is the most important feature for meaningful interaction among agents in distributed multi-agent systems. Communication enables agents interaction to achieve their goals. Agent communication languages provide a standard in the protocol and language used in the communication, but cannot provide a standard in ontology, because ontology depends on the subject and concept of the communication. This lack of standardisation is known as interoperability problem. In order to obtain semantic interoperability, agents need to agree on the basis of different ontologies. In this paper, a communication layer is proposed to outline the communication between agents, multiplatform communication system (MPCS) architecture is proposed to provide a highly flexible and scalable system. In addition a dynamic ontology mapping system for agent communication (DOMAC) is proposed based on different mapping approaches.

Prediction of Liver Diseases Based on Machine Learning Technique for Big Data

Liver diseases have produced a big data such as metabolomics analyses, electronic health records, and report including patient medical information, and disorders. However, these data must be analyzed and integrated if they are to produce models about physiological mechanisms of pathogenesis. We use machine learning based on classifier for big datasets in the fields of liver to Predict and therapeutic discovery. A dataset was developed with twenty three attributes that include the records of 7000 patients in which 5295 patients were male and rests were female. Support Vector Machine (SVM), Boosted C5.0, and Naive Bayes (NB), data mining techniques are used with the proposed model for the prediction of liver diseases. The performance of these classifier techniques are evaluated with accuracy, sensitivity, specificity.