Network traffic classification based on ensemble learning and co-training

Classification of network traffic is the essential step for many network researches. However,with the rapid evolution of Internet applications the effectiveness of the port-based or payload-based identifi-cation approaches has been greatly diminished in recent years. And many researchers begin to turn their attentions to an alternative machine learning based method. This paper presents a novel machine learning-based classification model,which combines ensemble learning paradigm with co-training tech-niques. Compared to previous approaches,most of which only employed single classifier,multiple clas-sifiers and semi-supervised learning are applied in our method and it mainly helps to overcome three shortcomings:limited flow accuracy rate,weak adaptability and huge demand of labeled training set. In this paper,statistical characteristics of IP flows are extracted from the packet level traces to establish the feature set,then the classification model is created and tested and the empirical results prove its feasibility and effectiveness.     

Effect of the fluctuation range of formation temperature on the preparation and superconductivity of Bi(Pb)2223 single-phase

Effects of the fluctuation range of formation temperature on preparation and superconducting properties of the Bi(Pb)2223 phase have been investigated in detail. Our results show that knowledge about the temperature distribution and fluctuation in the muffle furnace is necessary for preparation of the Bi(Pb)2223 single-phase. Single-phase samples from Bi_0.17Pb_0.3Sr₂Ca₂Cu₃O_y were obtained by a solid-state reaction in air at 835±5C. The best superconducting property of the as-prepared samples shows a one-step transition of a.c. susceptibility withT _c=109 K. Using this optimum sintering temperature and keeping the temperature fluctuation less than 5C, we have prepared pure Bi(Pb)2223 single-phase samples with good reproducibility.     

Balancing MXene Surface Termination and Interlayer Spacing Enables Superior Microwave Absorption

Surface chemistry and interlayer engineering determines the electrical properties of 2D MXene. However, it remains challenging to regulate the surface and interfacial chemistry of MXene simultaneously. Herein, simultaneous modulation of Ti₃C₂T_x MXene surface termination and layer spacing by alkali treatment are achieved. The electrical and electromagnetic properties of Ti₃C₂T_x are investigated in detail with respect to KOH and ammonia concentration dependence. A high concentration of KOH caused the Ti₃C₂T_x layer spacing to expand to 13.7 Å and the surface O/F ratio to increase to 33.84. Because of its weaker ionization effect, ammonia provides finer tuning compared to the drastic intercalation of KOH with a thorough sweeping of the F-containing groups. Ti₃C₂T_x is enriched with conductive -OH termination after ammonia treatment, which achieves an effective balance with the increased interlayer resistance. Therefore, NH₃H₂O-Ti₃C₂T_x achieves broad-band impedance matching and exhibits an efficient microwave loss of −49.1 dB at a low thickness of 1.7 mm, with an effective frequency bandwidth of 3.9 GHz. The results herein optimize the electrical properties of Ti₃C₂T_x using surface and interfacial chemistry to achieve broad microwave absorption, providing a framework for enhancing the electromagnetic wave loss of intrinsic MXene.     

Glycyrrhetinic Acid Liposomes Encapsulated Microcapsules from Microfluidic Electrospray for Inflammatory Wound Healing

In diabetic wound healing, M1 macrophage accumulation and elevated inflammation are prevalent issues. Intelligent delivery systems that can sustainably release antioxidizing and anti-inflammatory ingredients are expected for effective wound healing. Herein, a novel glycyrrhetinic acid (GA) liposomes encapsulated microcapsules delivery system that has desired features for inflammatory wound repair is presented. As the bacteria could break down the alginate shells, the GA liposomes could be controllably released from the microcapsules, which promotes M2 macrophage polarization and regulate their responses in the inflammatory wound microenvironment. Based on these, it is demonstrated that the GA liposomes encapsulated microcapsules delivery system exhibits an anti-inflammatory and immunomodulatory effect for diabetic wound healing in a full-thickness defect model in diabetic rats. These results indicate that the immunomodulatory capabilities of the microcapsules can be unitized for efficient wound repair, and such a delivery system is valuable for clinical wound healing applications.     

Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI

Journal of Signal Processing Systems - Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful...     

Proposition of new computer artificial intelligence models for shear strength prediction of reinforced concrete beams

Engineering with Computers - The study is investigated the capacity of new artificial intelligence (AI) methodologies for shear strength (Vs) computation of reinforced concrete (RC) beams. The...     

A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19

The Journal of Supercomputing - This paper designs and develops a computational intelligence-based framework using convolutional neural network (CNN) and genetic algorithm (GA) to detect COVID-19...     

Self-reinforcing mechanisms for cellularisation: a longitudinal case study

Firms that adopt cellular manufacturing (CM) often need to restructure their products, personnel, equipment, factory management and organisation culture. Despite voluminous literature on CM, only a few case studies related to cellularisation exist. Therefore, to enhance understanding in this area, this paper presents a longitudinal case study on cellularisation in an electronic assembly plant. Six years of data were collected from the observations and discussions of an industrialist and an academician. Events instrumental to the persistence of CM were highlighted, with reference to path dependence analysis. Cellularisation is not entirely evolutionary or teleological but is an interplay of the two; it is significantly influenced by the positive feedback of self-reinforcing mechanisms after departing from critical junctures. The observed self-reinforcing mechanisms were expounded and divided into six categories.