Recently, telemedicine solutions have become a new trend in remote medical treatment. Many diseases are originated from abnormal variation of biological processes, especially in nucleosome positioning. Thus, effective prediction of nucleosome positioning becomes a hotspot in the research of telemedicine. In this paper, a novel method is provided to compare varies of sequences firstly. This method, which is called fractal entropy increment of diversity (FEID), is based on information entropy and increment of diversity. Then, a novel nucleosome positioning method is provided by using FEID into the data set of diversiform DNA sequences of human, worm, fly, and yeast. Moreover, experimental results show that FEID is an effective nucleosome positioning method by compared with other methods on several benchmark data sets. Finally, the most important nucleotide sequence in nucleosome positioning is provided based on calculated contribution rates of nucleotide sequences. © 2013 IEEE.

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Lu M

Liu S

Zhou Y

Pan Z

Zuo Y.

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Nucleosome Positioning With Fractal Entropy Increment of Diversity in Telemedicine

IEEE Access

In recommender systems (RS), many models are designed to predict ratings of items for the target user. To improve the performance for rating prediction, some studies have introduced tags into recommender systems. Tags benefit RS considerably, however, they are also redundant and ambiguous. In this paper, we propose a hybrid deep learning model TRSDL (tag-aware recommender system based on deep learning) to improve the performance of tag-aware recommender systems (TRS). First, TRSDL uses pre-trained word embeddings to represent user-defined tags, and constructs item and user profiles based on the items' tags set and users' tagging behaviors. Then, it utilizes deep neural networks (DNNs) and recurrent neural networks (RNNs) to extract the latent features of items and users, respectively. Finally, it predicts ratings from these latent features. The model not only addresses tag limitations and takes advantage of semantic tag information but also learns more advanced implicit features via deep structures. We evaluated our proposed approach and several baselines on MovieLens-20 m, and the experimental results demonstrate that TRSDL significantly outperforms all the baselines (including the state-of-the-art models BiasedMF and I-AutoRec). In addition, we also explore the impacts of network depth and type on model performance. © 2018 by the authors.

Applied Sciences

TRSDL: Tag-Aware Recommender System Based on Deep Learning–Intelligent Computing Systems

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Predicting Antimicrobial Peptides by Using Increment of Diversity with Quadratic Discriminant Analysis Method

2018 1st International Conference on Bioinformatics, Biotechnology, and Biomedical Engineering - Bioinformatics and Biomedical Engineering

Multimedia Tools and Applications

Exploring a smart pathological brain detection method on pseudo Zernike moment

Computers & Electrical Engineering

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Journal	Data powered by TypesetIEEE Access
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers (IEEE)
ISSN	21693536
Open Access	Yes