Journal of Intelligent Learning Systems and Applications

Journal Information
ISSN / EISSN : 21508402 / 21508410
Current Publisher: Scientific Research Publishing, Inc. (10.4236)
Total articles ≅ 172
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Rixin Chen, Ruoxi Dai, MingYe Wang, Chen Rixin, Dai Ruoxi, Wang Mingye
Journal of Intelligent Learning Systems and Applications, Volume 12, pp 1-13; doi:10.4236/jilsa.2020.121001

Abstract:
Genome-wide epigenomic datasets allow us to validate the biological function of motifs and understand the regulatory mechanisms more comprehensively. How different motifs determine whether transcription factors (TFs) can bind to DNA at a specific position is a critical research question. In this project, we apply computational techniques that were used in Natural Language Processing (NLP) to predict the Transcription Factor Bound Regions (TFBRs) given motif instances. Most existing motif prediction methods using deep neural network apply base sequences with one-hot encoding as an input feature to realize TFBRs identification, contributing to low-resolution and indirect binding mechanisms. However, how the collective effect of motifs on binding sites is complicated to figure out. In our pipeline, we apply Word2Vec algorithm, with names of motifs as an input to predict TFBRs utilizing Convolutional Neural Network (CNN) to realize binary classification, based on the ENCODE dataset. In this regard, we consider different types of motifs as separate “words”, and their corresponding TFBR as the meanings of “sentences”. One “sentence” itself is merely the combination of these motifs, and all “sentences” compose of the whole “passage”. For each binding site, we do the binary classification within different cell types to show the performance of our model in different binding sites and cell types. Each “word” has a corresponding vector in high dimensions, and the distances between each vector can be figured out, so we can extract the similarity between each motif, and the explicit binding mechanism from our model. We apply Convolutional Neural Network (CNN) to extract features in the process of mapping and pooling from motif vectors extracted by Word2Vec Algorithm and gain the result of 87% accuracy at the peak.
Yong Zhang, Jing Zhang
Journal of Intelligent Learning Systems and Applications, Volume 12, pp 14-29; doi:10.4236/jilsa.2020.121002

Abstract:
Objective: The Chinese description of images combines the two directions of computer vision and natural language processing. It is a typical representative of multi-mode and cross-domain problems with artificial intelligence algorithms. The image Chinese description model needs to output a Chinese description for each given test picture, describe the sentence requirements to conform to the natural language habits, and point out the important information in the image, covering the main characters, scenes, actions and other content. Since the current open source datasets are mostly in English, the research on the direction of image description is mainly in English. Chinese descriptions usually have greater flexibility in syntax and lexicalization, and the challenges of algorithm implementation are also large. Therefore, only a few people have studied image descriptions, especially Chinese descriptions. Methods: This study attempts to derive a model of image description generation from the Flickr8k-cn and Flickr30k-cn datasets. At each time period of the description, the model can decide whether to rely more on images or text information. The model captures more important information from the image to improve the richness and accuracy of the Chinese description of the image. The image description data set of this study is mainly composed of Chinese description sentences. The method consists of an encoder and a decoder. The encoder is based on a convolutional neural network. The decoder is based on a long-short memory network and is composed of a multi-modal summary generation network. Results: Experiments on Flickr8k-cn and Flickr30k-cn Chinese datasets show that the proposed method is superior to the existing Chinese abstract generation model. Conclusion: The method proposed in this paper is effective, and the performance has been greatly improved on the basis of the benchmark model. Compared with the existing Chinese abstract generation model, its performance is also superior. In the next step, more visual prior information will be incorporated into the model, such as the action category, the relationship between the object and the object, etc., to further improve the quality of the description sentence, and achieve the effect of “seeing the picture writing”.
Asma Ben Khedher, Imène Jraidi, Claude Frasson
Journal of Intelligent Learning Systems and Applications, Volume 11, pp 1-14; doi:10.4236/jilsa.2019.111001

Xiyu Kang, Yiqi Wang, Yanrui Hu
Journal of Intelligent Learning Systems and Applications, Volume 11, pp 15-31; doi:10.4236/jilsa.2019.112002

Vladimir N. Shats
Journal of Intelligent Learning Systems and Applications, Volume 11, pp 65-75; doi:10.4236/jilsa.2019.114004

Abstract:
This paper proposes two new algorithms for classifying objects with categorical attributes. These algorithms are derived from the assumption that the attributes of different object classes have different probability distributions. One algorithm classifies objects based on the distribution of the attribute frequencies, and the other classifies objects based on the distribution of the pairwise attribute frequencies described using a matrix of pairwise frequencies. Both algorithms are based on the method of invariants, which offers the simplest dependencies for estimating the probabilities of objects in each class by an average frequency of their attributes. The estimated object class corresponds to the maximum probability. This method reflects the sensory process models of animals and is aimed at recognizing an object class by searching for a prototype in information accumulated in the brain. Because these matrices may be sparse, the solution cannot be determined for some objects. For these objects, an analog of the k-nearest neighbors method is provided in which for each attribute value, the class to which the majority of the k-nearest objects in the training sample belong is determined, and the most likely class value is calculated. The efficiencies of these two algorithms were confirmed on five databases.
Jiaxin Gao, Zirui Zhou, Jiangshan Ai, Bingxin Xia, Stephen Coggeshall
Journal of Intelligent Learning Systems and Applications, Volume 11, pp 33-63; doi:10.4236/jilsa.2019.113003

Ziming Chi, Bingyan Zhang
Journal of Intelligent Learning Systems and Applications, Volume 10, pp 121-134; doi:10.4236/jilsa.2018.104008

Tianquan Feng, Qingrong Chen, Ming Yi
Journal of Intelligent Learning Systems and Applications, Volume 10, pp 104-119; doi:10.4236/jilsa.2018.103007

Ramla Ghali, Hamdi Ben Abdessalem, Claude Frasson, Roger Nkambou
Journal of Intelligent Learning Systems and Applications, Volume 10, pp 93-103; doi:10.4236/jilsa.2018.103006

Vladimir N. Shats
Journal of Intelligent Learning Systems and Applications, Volume 10, pp 81-92; doi:10.4236/jilsa.2018.103005