This paper investigates the semantic search performance of search engines. Initially, three keyword-based search engines (Google, Yahoo and Msn) and a semantic search engine (Hakia) were selected. Then, ten queries, from various topics, and four phrases, having different syntax but similar meanings, were determined. After each query was run on each search engine; and each phrase containing a query was run on the semantic search engine, the first twenty documents on each retrieval output was classified as being "relevant" or "nonrelevant". Afterwards, precision and normalized recall ratios were calculated at various cut-off points to evaluate keyword-based search engines and the semantic search engine. Overall, Yahoo showed the best performance in terms of precision ratio, whereas Google turned-out to be the best search engine in terms of normalized recall ratio. However, it was found that semantic search performance of search engines was low for both keyword-based search engines and the semantic search engine.

Abstract. This is an investigation of information retrieval performance of Turkish search engines with respect to precision, normalized recall, coverage and novelty ratios. We defined seventeen query topics for Arabul, Arama, Netbul and Superonline. These queries were carefully selected to assess the capability of a search engine for handling broad or narrow topic subjects, exclusion of particular information, identifying and indexing Turkish characters, retrieval of hub/authoritative pages, stemming of Turkish words, correct interpretation of Boolean operators. We classified each document in a retrieval output as being "relevant" or "nonrelevant" to calculate precision and normalized recall ratios at various cut-off points for each pair of query topic and search engine. We found the coverage and novelty ratios for each search engine. We also tested how search engines handle meta-tags and dead links. Arama appears to be the best Turkish search engine in terms of average precision and normalized recall ratios, and the coverage of Turkish sites. Turkish characters (and stemming as well) still cause bottlenecks for Turkish search engines. Superonline and Netbul make use of the indexing information in metatag fields to improve retrieval results.

Multimodal biometric systems are preferred as a defense compared to unimodal systems. This study introduces an open access multimodal vein database named FYO with each letter dedicated to each author's name. The database involves three biometric traits; palm vein, dorsal vein and wrist vein of the same individuals, to explore and enhance research in the area of using these traits to create a spoof-proof multimodal authentication system. The vein images of FYO are acquired using medical vein finder in a controlled environment. Comparisons are performed to show the differences with the existing well known databases and the state-of-the-art recognition algorithms. Hand-crafted feature extractors such as Binarized Statistical Image Features (BSIF), Gabor filter and Histogram of Oriented Gradients (HOG) are applied to show the viability of the vein datasets. Additionally, a deep learning based Convolutional Neural Networks (CNN) architecture is proposed with two models using decision-level fusion of palmar, dorsal and wrist biometric traits on vein images. Unimodal systems, multimodal systems and the proposed architecture are tested on several vein datasets including palmar, dorsal and wrist vein images. Experimental results based on accuracy and computation time on our FYO datasets showed competitive output with that of other databases such as Tongji Contactless Palm Vein database, VERA, PUT, Badawi and Bosphorus hand vein databases. Moreover, the proposed CNN architecture on three vein biometric traits show superior performance compared to hand-crafted methods. INDEX TERMS Data fusion, deep learning, hand-crafted features, vein recognition, multimodal biometrics.

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