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In the information age, the analysis of human behavioral patterns is of significant interest, and extensive data collection is crucial in the context of privacy protection. From 2011 to 2023, the authors collaborated with more than 20 volunteers to construct and collect 30,791 lifelog using mobile devices; these lifelog included a sizable amount of multi-type data containing behavioral activity information, temporal location information, textual description information, and media images. In this paper, we propose a DCP-BiLSTM (Description + Cluster + Position) And Bidirectional Long-Short Term Memory (BiLSTM) personal big data behavior classification model for the lifelog dataset, which uses text description information + GPS information classified by DB-SCAN + location information, it is validated on Liulifelog to better address the behavior prediction problem of lifelog data. Unlike the traditional text classification approach, which is based on a unique lifelog dataset, BiLSTM can combine bidirectional semantic variables comprising description information and location information into the DCP-BiLSTM model. Evaluation of the DCP-BiLSTM demonstrates that can increase the performance and accuracy of behavior prediction, and the BiLSTM that incorporates description and location information to build, not only identifies daily user activity but also corrects the bias caused by manual analysis's one-sidedness and has greater prediction performance. [ABSTRACT FROM AUTHOR]

Many current intrinsically interpretable machine learning models can only handle the data that are linear, low-dimensional, and relatively independent attributes and often with discrete attribute values, while the models that are capable of handling high-dimensional nonlinear data, like deep learning, have very poor interpretability. Based on the geometric characteristics, a new idea of accurately wrapping the data region with minimum-volume geometry is proposed for pattern classification. The Variance Hyper Rectangle (VHR) model presented in this paper is a realization of the idea. The VHR model uses the minimum-volume hyper rectangles, obtained through projection variance calculation, to wrap the regions occupied by a category of data, hence it has strong and clear geometric interpretability. In addition, the VHR model is well suited for large data volume, as it approaches the linear complexity in both time and space. Extensive qualitative and quantitative experiments are performed on seven real-world data sets, demonstrating that VHR outperforms the state-of-the-art interpretable methods while running quickly. [Display omitted] • VHR has strong geometric interpretability, and is much reliable and trustworthy. • VHR can provide a clear range of values in each direction for a category of data. • VHR naturally supports incremental learning without any extra processing. • VHR has great performance and stability, and is able to handle big data. [ABSTRACT FROM AUTHOR]

Nowadays, internet is mostly used communication tool worldwide. However, the major problem of the internet is to provide security. To provide internet security, many researches and papers have been suggested about information and network security. The commonly used system against network attacks is firewalls. In this study, a novel firewall data classification approach is presented. This approach uses 10 cases to obtain numerical results. The proposed approach consists of data acquisition from Firewall, feature selection and classification steps. Firstly, the Firewall data were gathered from a Firewall. Then, the redundant features are eliminated and these features are normalized using min–max normalization. The obtained final feature sets are forwarded to classifiers. In the cases defined, Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (Bi-LSTM) and Support Vector Machine (SVM) are utilized as classifiers. It was seen from the results, the deep learning approach are more successful than SVM classifier and the highest classification accuracy was calculated as 97.38% by using Bi-LSTM-LSTM hybrid network. The proposed method has several advantages and these are (1) the proposed method achieved high success rates using hybrid deep learning approaches (2) the training time of the proposed method is short (3) an intelligent network security monitoring method is presented using basic methods and deep learning. In addition, a useful approach has been presented to achieve high success rate at the end of the faster training process than traditional machine learning methods. Briefly, an intelligent monitoring system is proposed for network security. • Hybrid deep learning networks are presented for firewall data classification. • 10 cases are defined to comprehensively evaluate. • An artificial intelligent based internet security system is presented. [ABSTRACT FROM AUTHOR]