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The current study aimed to investigate the most common pathogenic bacteria that are naturally infecting wild marine fishes collected at different localities along the coastline zone of Hurghada City, Egypt. A total of 300 samples of marbled spine foot Siganus rivulatus and the Haffara Seabream Rhabdosargus haffara were subjected to clinical and bacteriological examinations. The examined fishes showed the characteristic clinical signs and postmortem lesions of vibriosis and photobacteriosis. Based on the morpho-chemical characterization, bacterial isolates retrieved from the naturally infected fishes were identified as Vibrio spp. and Photobacterium spp. Through sequencing 16S rRNA genes, the identities of bacterial isolates were confirmed as V. alginolyticus, V. vulnificus, P. damselae subsp. damselae and P. damselae subsp < em> piscicida. Vibrio alginolyticus was the most frequent isolated bacterial pathogen and represented 54.4% and 46.7% of the total isolates recovered from S. rivulatus and R. haffara, respectively. Thus, the current study confirmed that Vibrio and Photobacterium species remain the most prevalent bacterial pathogens infecting Egyptian Red Sea fishes.  From food safety perspective, these types of infections could pose potential public health hazards.

With the continuous growth in the World Wide Web, the need arises for indexing and classifying Web documents for fast retrieval of relevant information accessible through it. Traditionally, classification has been accomplished manually. A recent study revealed that, there exist about 29.7 billion pages on the Web in February 2007, which means that manual classification would be infeasible and reflects the need for automated techniques for accomplishing this task. Though Web documents should follow the basic definitions on Hypertext Markup Language, they are known to be unstructured or semi-structured, which imposes new challenges to Web classification especially in the area of feature selection. The objective of this paper is to investigate Web document classification approaches, and compare between recent techniques proved promising in literature within this field. Traditionally, automatic classification is performed by extracting information for representing a web document from the document hypertext itself. Several approaches are used for Web classification; the simplest one uses text features only, known as Text-Only approach. Other approaches include Hypertext approach, Link Analysis approach, and Neighborhood Category approach, depending on the type of context features used. Techniques used with each approach as we as their result, and a final conclusion will be presented.

Feature selection is a typical search problem where each state in the search space represents a subset of features candidate for selection. Out of n features, 2n subsets can be constructed, hence, an exhaustive search of all subsets becomes infeasible when n is relatively large. Therefore, Feature selection is done by employing a heuristic search algorithm that tries to reach the optimal feature subset. Here, we propose a new wrapper feature selection and weighting algorithm called Artificial Immune Feature Selection Algorithm (AIFSA); the algorithm is based on the metaphors of the Clonal Selection Algorithm (CSA). AIFSA, by itself, is not a classification algorithm, rather it utilizes well-known classifiers to evaluate and promote candidate feature subset. Experiments were performed on textual datasets like WebKB and Syskill&Webert web page ratings. Experimental results showed AIFSA competitive performance over traditional well-known filter feature selection approaches as well as some wrapper approaches existing in literature.