Artificial Intelligence By Saroj Kaushik Pdf
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Testing was performed using an exact algorithm and meta-heuristic algorithm on random generated network instances. The problem consists of finding the k optimal paths that minimizes a metric such as distance, time, etc. The meta-heuristic we propose introduces new hybrid genetic algorithm named IOGA. We study the traffic network in large scale routing problems as a field of application. Therefore, traffic management has become a major problem. The number of vehicles on the road has increased incredibly. The problem of finding the shortest paths is a combinatorial optimization problem which has been well studied from various fields. This paper introduces a new approach of hybrid meta-heuristics based optimization technique for decreasing the computation time of the shortest paths algorithm. The underestimated features of user profiles have been enhanced after term-term relation analysis which results in improved similarity estimation of relevant items with the user profiles.The experimentation result proves that the proposed methodology improves the overall search and retrieval results as compared to the state-of-art algorithms.
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It associates terms that are semantically related in real world or are used inter-changeably such as synonyms. In the proposed methodology, the semantic relationship is analyzed by estimating the explicit and implicit relationship between terms. This paper proposes the new methodology using Non-IIDness based semantic term-term coupling from the content referred by users to enhance recommendation results. It suggests items such as news, documents, articles, webpages, journals, and more to users as per their inclination by comparing the key features of the items with key terms or features of user interest profiles. Also, the proposed method shows an improved result with another state of work.Ĭontent-based recommender system is a subclass of information systems that recommends an item to the user based on its description. Experimental results prove that the proposed method outperforms the other transform-based compression in terms of PSNR, CR, and Complexity. It has been investigated with various metaheuristic algorithms. Set Partitioning in Hierarchical Trees (SPIHT) is used for encoding the significant coefficients to achieve efficient image compression.
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It opts for a decomposing using optimal scheme for achieving the input image into a sparse representation which gives a much-detailed performance for texture and edge information better than wavelet transform.
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This paper proposes a compression algorithm that uses a Haar based wavelet transform called Tetrolet transform, which reduces the noise on the input images and decomposes with a 4 x 4 blocks of equal squares called tetrominoes. Storing and transferring the huge volume of images becomes complicated without an efficient image compression technique. Over recent times, medical imaging plays a significant role in clinical practices.
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