Cross project defect prediction: a comprehensive survey with its SWOT analysis

Innovations in Systems and Software Engineering - Software fault prediction (SFP) refers to the process of identifying (or predicting) faulty modules based on its characteristics/software metrics....     

EarNet: Biometric Embeddings for End to End Person Authentication System Using Transient Evoked Otoacoustic Emission Signals

Neural Processing Letters - Transient Evoked Otoacoustic Emissions (TEOAE) are a class of oto-acoustic emissions that are generated by the cochlea in response to an external stimulus. The TEOAE...     

Smart Edge Computing for 5 g/6 g Satellite IOT for Reducing Inter Transmission Delay

Mobile Networks and Applications - 5G/6G communication are first generation high speed wireless communication network which integrates the aerial data, terrestrial data and maritime data via...     

Domain Adaptation for POS Tagging with Contrastive Monotonic Chunk-wise Attention

Neural Processing Letters - Part of Speech (POS) tagging is a sequential labelling task and one of the core applications of Natural Language Processing. It has been a challenging problem for the...     

Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions

In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability.     

Resource management in UAV-assisted MEC: state-of-the-art and open challenges

Wireless Networks - Unmanned aerial vehicles (UAV) have been widely used in various fields because of their high mobility and portability. At the same time, due to the rapid development of...     

Nutrient enrichment of dairy curd by incorporation of whole and ruptured microalgal cells (Nannochloropsis salina)

This work investigated incorporation of Nannochloropsis salina into renneted dairy gels and curd. Whole and ruptured microalgal cells did not impair κ-casein macropeptide cleavage by the rennet enzyme. However, insoluble components of ruptured cells impeded gelation, presumably by hindering interactions between renneted casein micelles. Confocal imaging showed that whole cells were retained and homogenously distributed within the protein network of the gels and cooked curd, whereas ruptured algae formed large aggregates that altered the protein matrix. Eicosapentaenoic acid (EPA) in the whole microalgal cells was incorporated within the curds, with considerably less EPA retained for ruptured cells. Soluble algal debris did not impair gelation, however EPA wasn't retained in the curd. The study demonstrates that nutrient enrichment of renneted dairy products is possible by incorporating whole microalgal cells to displace milk fat with protein and the beneficial long-chain omega-3 fatty acid EPA. Future research into the optimisation of product organoleptic properties is required.     

Behavior-based detection and classification of malicious software utilizing structural characteristics of group sequence graphs

Journal of Computer Virology and Hacking Techniques - In this work we present a graph-based approach for behavior-based malware detection and classification utilizing the Group Relation Graphs...     

DFT-based no-reference quality assessment of blurred images

Multimedia Tools and Applications - In this paper, blind image quality assessment (IQA) of Gaussian blurred images based on Discrete Fourier Transform (DFT) is proposed. The proposed work is based...     

Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.