Umamaheswari Ramalingam^1,*, Senthil Kumar Murugesan², Karthikeyan Lakshmanan², Chidhambararajan Balasubramaniyan³

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1733-1744, 2023, DOI:10.32604/iasc.2023.032108

Abstract A Deep Neural Sentiment Classification Network (DNSCN) is developed in this work to classify the Twitter data unambiguously. It attempts to extract the negative and positive sentiments in the Twitter database. The main goal of the system is to find the sentiment behavior of tweets with minimum ambiguity. A well-defined neural network extracts deep features from the tweets automatically. Before extracting features deeper and deeper, the text in each tweet is represented by Bag-of-Words (BoW) and Word Embeddings (WE) models. The effectiveness of DNSCN architecture is analyzed using Twitter-Sanders-Apple2 (TSA2), Twitter-Sanders-Apple3 (TSA3), and Twitter-DataSet (TDS). TSA2 and TDS consist of… More >

Qiuju Ji¹, Peipeng Yu¹, Zhihua Xia^{1, *}

Journal on Big Data, Vol.2, No.1, pp. 33-51, 2020, DOI:10.32604/jbd.2020.01004

Abstract Aprivacy-preserving search model for JPEG images is proposed in paper, which uses the bag-of-encrypted-words based on QDCT (Quaternion Discrete Cosine Transform) encoding. The JPEG image is obtained by a series of steps such as DCT (Discrete Cosine Transform) transformation, quantization, entropy coding, etc. In this paper, we firstly transform the images from spatial domain into quaternion domain. By analyzing the algebraic relationship between QDCT and DCT, a QDCT quantization table and QDTC coding for color images are proposed. Then the compressed image data is encrypted after the steps of block permutation, intra-block permutation, single table substitution and stream cipher. At… More >