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Mohab gawish

For full functionality of ResearchGate it is necessary to enable JavaScript. Here are the instructions how to enable JavaScript in your web browser. Mohammed Abdel-Megeed Mohammed Salem. In the last years, various reasoning methodologies have been proposed by the researchers in order to develop intelligent learning systems. These systems are based on the concepts and theories of the artificial intelligence AI science and technology. Many types of learning systems are in existence today and are applies to different domains and tasks, e. From the AI point of view, the research in the reasoning paradigms cover a variety of approaches. A number of computational models of analogy have been employed in a wide variety of research ALSs in different fields, acquiring features of how human compare representations, retrieve source analogues from memory, and learn from the results. This paper investigates the main features of some ARTs namely, structured production rules, fuzzy rules, cognitive scripts, cases, and semantic networks of used for the development of ALSs from the AI perspective. DNA Methylation is a process by which cell assure the regulation of gene expression. Improved methods of detecting methylated sites are needed, instead of experimental methods which are very expensive and time consuming. In this paper, metaheuristic techniques namely; Genetic Algorithm, Artificial Immune System, and Hybrid Immune Genetic Algorithm are implemented to solve the problem of feature selection and select the susceptible CpG sites from dataset. Reducing the dimensionality of the dataset by applying previous algorithms resulting the following sets. A new signature set was created by gathering all the common CpG sites from the three generated sets. Its size is equal to 0. Then it is used to generate the proteins regulatory network. Creating artificial intelligence AI agents with computational models based on human cognitive abilities is an ongoing research area. This paper proposes a new evolutionary cognitive model for cross-domain learning, which aims to improve the cognitive learning process by extracting new experienced knowledge from pre-existing socio-cultural cognitive scripts. This knowledge is necessary for the AI agents to develop learning for the current faced social situation Target. The model depends basically on two phases; the retrieval phase and the learning phase. In the retrieval phase, Pharaoh algorithm is utilized to retrieve the most relevant cognitive script Base to the target script considering the context. Whereas, the learning phase employs evolutional processes to enrich the target script. Finally, the enriched script replaces the target script in the evolved script-base in order to be used in the learning and retrieval phases again. Cognitive scripts can act as a basis for representing behavioral tasks and domain knowledge in cognitive systems. Each event in a cognitive script is either temporally or causally linked with preceding and succeeding events. This temporal progression of events is what provides context to a particular cognitive script. In other words, it is this linking that provides a deeper explanation of a key event by defining the settings in which this event occurs i. Contextual information plays a significant role in the retrieval process of cognitive scripts and needs to be considered in the retrieving process of cognitive scripts from large search spaces. Standard retrieval methods have been used on various unstructured data objects, such as text documents, images, audio, mind maps or videos. Other representations appear in logic-based languages that provide a structure that supports information retrieval based on logical reasoning, such as the Web Ontology Language. However, the application of these methods to structured cognitive scripts is not ideal because of the type of contextual information in cognitive scripts. This article presents Pharaoh, a novel context-based retrieval algorithm for cognitive scripts that can be employed in cognitive systems. Pharaoh relies on semantic structure and keyword-based retrieval to retrieve similar cognitive scripts based on a novel similarity measure between a structured query cognitive script and registered cognitive scripts.

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