As the number of scientific publications continues to increase at a dizzying rate, researchers face challenges related to spending too much time and effort searching for appropriate papers to cite in their work. Citation recommendation models have thus been developed to automatically generate a list of relevant papers for a specific text passage, thus helping to reduce the workload for scientists and contribute to better-quality research. Consequently, this research direction has recently attracted significant interest in the scientific community. However, the current citation recommendation models still focus primarily on the citation context and do not adequately address the metadata of papers, such as the citation links, publication time, and venue. To overcome these problems, in this study, we propose the SciBERT-GraphSAGE which is a hybrid deep learning-based model for recommending a list of academic papers by considering both the citation context and this article’s metadata. Our model has two important components: 1) SciBERT for text data representation learning and 2) GraphSAGE for learning the representations of this article’s citation links. We validate the effectiveness of our model on three benchmark datasets: 1) FullTextPeerRead; 2) ACL; and 3) RefSeer. The results from experiments demonstrate that our novel SciBERT-GraphSAGE model outperforms previous advanced models in terms of Recall@K, mean reciprocal rank (MRR), and mean average precision (MAP).

In the last few years, attackers have been shifting aggressively to the IoT devices in industrial Internet of things (IIoT). Particularly, IoT botnet has been emerging as the most urgent issue in IoT security. The main approaches for IoT botnet detection are static, dynamic, and hybrid analysis. Static analysis is the process of parsing files without executing them, while dynamic analysis, in contrast, executes them in a controlled and monitored environment (i.e., sandbox, simulator, and emulator) to record system’s changes for further investigation. In this article, we present a novel and advanced method for IoT botnet detection using dynamic analysis to improve graph-based features, which are generated based on static analysis. Specifically, dynamic analysis is used to collect printable string information that appears during the execution of the samples. Then, we use the printable string information to traverse the graph, which is obtained based on the static analysis, effectively, and ultimately acquiring graph-based features that can distinguish benign and malicious samples. In order to estimate the efficacy and superiority of the proposed hybrid approach, we conduct the experiment on a dataset of 8330 executable samples, including 5531 IoT botnet samples and 2799 IoT benign samples. Our approach achieves an accuracy of 98.1% and 91.99% for detecting and classifying IoT botnet, respectively. These results show that our approach has outperformed other existing contemporary counterpart methods in the aspects of accuracy and complexity. In addition, our experiments also demonstrate that hybrid graph-based features for IoT botnet family classification can further improve static or dynamic features’ performance individually.

This article proposes the Mamdani complex fuzzy inference system (Mamdani CFIS) to improve performance of the classical FIS and complex FIS. The applicability of the proposed CFIS is demonstrated by applying it to six commonly available datasets from UCI Machine Learning under the comparison with Mamdani FIS and the Adaptive Neuro Complex Fuzzy Inference System (ANCFIS). It is successfully proven that the proposed Mamdani CFIS is computationally less expensive and presents a more efficient method to handle time-series data and time-periodic phenomena, among all the fuzzy IS found thus far in the literature. Furthermore, the novelty of CFIS mainly lies in its implementation of the complex number throughout the entire procedures of computation. This gives much greater flexibility of implementing unexpected, nonlinear fluctuations.

Attribute reduction from decision tables has been much focused in recent years in which the incremental methods of the tradition rough set and extended models are mostly used for adding, removing, or updating the object or attribute set. However, when dealing with the dynamic decision tables, the existing incremental methods do not recalculate information which has been added into the decision table. In this article, we propose some new incremental methods using the hybrid filter-wrapper with fuzzy partition distance on fuzzy rough set. Experimental results indicate that the proposed algorithms decrease significantly the cardinality of reduct as well as achieve higher accuracy than the other filter incremental methods such as IV-FS-FRS-2, IARM, ASS-IAR, IFSA, and IFSD.

We propose in this paper an efficient heuristic method to learn a set of classification rules from a set of graph objects. Graph classification has various real-life applications, however, this is a very challenging problem due to the intrinsic complex structure of graphs. The proposed rule constru cting method is based on two lines of research. The first line of research is on Boosting [11] in which a weak-hypothesis is regarded as a rule and is assigned with a real-valued confidence. In our research, a rule is comprised by a set of subgraphs that maximize an objective function in each round of boosting. The second line of research is on utilizing the poset order of the Formal Concept Lattice of subgraphs to accelerate the process of generating rule candidates. The learned rule set is compact, comprehensible and obtains high classification accuracy on tested datasets.

Environment friendly production is an emerging trend in industrial production. Enhancing environmental protection and ensuring company's profitability have gained great attention from any manufacturers. The use of Overall Equipment Effectiveness (OEE) to reflect efficiency of production has been widely applied in the manufacturing process. However, the full advantage of OEE as a measure to simultaneously facilitate reduction of carbon dioxide (CO2) emissions and to sustain the existing profitability has not been utilized. This study aimed to investigate the use of OEE to improve the manufacturing process of corrugated paper production using two indicators including CO2emission and profitability. The research showed a model of measuring OEE, CO2emission and Profitability. The obtained results indicated that impact 1 and impact 2 could maintain the same profitability (9%) but OEE and CFP from impact 1 (37% and 26%, respectively) were higher than those from impact 2 (12% and 8%, respectively). The overall results revealed that when improving OEE, CO2emission per product (1m2) could be significantly reduced while profitability could be increased in the production of corrugated paper.