International Journal of Cognitive Informatics and Natural Intelligence

Includes bibliographical refences.

Article 1. Deep Convolutional Neural networks for customer churn prediction analysis -- Article 2. Detecting DDoS Attacks using polyscale analysis and deep learning -- Article 3. Distributional semantic model based on convolutional neural network fpr arabic textual similarity -- Article 4. Generalized ordered weighted simplified neutrosophic cosine similarity measure for multiple attribute group decision making -- Article 5. Moving target detection and tracking based on improved FCM Algorithm.

[Article Title: Deep Convolutional Neural networks for customer churn prediction analysis / Alae Chouiekh and El Hassane Ibn El Haj, p. 1 - 16] Abstract: Several machine learning models have been proposed to address customer churn problems. In this work, the authors used a novel method by applying deep convolutional neural networks on a labeled dataset of 18,000 prepaid subscribers to classify/identify customer churn. The learning technique was based on call detail records (CDR) describing customers activity during two-month traffic from a real telecommunication provider.;[Article Title: Detecting DDoS Attacks using polyscale analysis and deep learning / Maryam Ghanbari and Witold Kinsner, p. 17 - 34] Abstract: Distributed denial-of-service (DDoS) attacks are serious threats to the availability of a smart grid infrastructure services because they can cause massive blackouts. This study describes an anomaly detection method for improving the detection rate of a DDoS attack in a smart grid. This improvement was achieved by increasing the classification of the training and testing phases in a convolutional neural network (CNN). A full version of the variance fractal dimension trajectory (VFDTv2) was used to extract inherent features from the stochastic fractal input data.;[Article Title: Distributional semantic model based on convolutional neural network for arabic textual similarity / Adnen Mahmoud and Mounir Zrigui, p. 35 - 50] Abstract: The problem addressed is to develop a model that can reliably identify whether a previously unseen document pair is paraphrased or not. Its detection in Arabic documents is a challenge because of its variability in features and the lack of publicly available corpora. Faced with these problems, the authors propose a semantic approach. At the feature extraction level, the authors use global vectors representation combining global co-occurrence counting and a contextual skip gram model. At the paraphrase identification level, the authors apply a convolutional neural network model to learn more contextual and semantic information between documents.;[Article Title: Generalized Ordered Weighted Simplified Neutrosophic Cosine Similarity Measure for Multiple Attribute Group Decision Making / Jun Ye, p. 51 - 62] Abstract: The paper proposes a generalized ordered weighted simplified neutrosophic cosine similarity (GOWSNCS) measure by combining the cosine similarity measure of simplified neutrosophic sets (SNSs) with the generalized ordered weighted averaging (GOWA) operator and investigates its properties and special cases. Then, the author develops a simplified neutrosophic group decision-making method based on the GOWSNCS measure to handle multiple attribute group decision-making problems with simplified neutrosophic information.;[Article Title: Moving Target Detection and Tracking Based on Improved FCM Algorithm / Wang Ke Feng and Sheng Xiao Chun, p. 63 - 74] Abstract: With the rapid development of computer intelligence technology, the majority of scholars have a great interest in the detection and tracking of moving targets in the field of video surveillance and have been involved in its research. Moving target detection and tracking has also been widely used in military, industrial control, and intelligent transportation. With the rapid progress of the social economy, the supervision of traffic has become more and more complicated.