Hand gesture is considered as one of the natural ways to interact with computers. The utility of hand gesture-based application is a recent trend and is an effective method for human-computer interaction. Though many ...
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Hand gesture is considered as one of the natural ways to interact with computers. The utility of hand gesture-based application is a recent trend and is an effective method for human-computer interaction. Though many static and other intelligent approaches using Machine learning (ML) are developed, still there is a marginal tradeoff between the computational cost and accuracy. In this paper, a Lightboost based Gradient boosting machine (LightGBM) is proposed for efficient hand gesture recognition. The hyper-parameters of the LightGBM are optimized with an improved memetic firefly algorithm. We have introduced a perturbation operator and incorporated it in the proposed memetic firefly algorithm for avoiding the local optimal solution in the traditional fireflyalgorithm. With comparative analysis among the proposed method and other competitive ML methods, the performance of the proposed method is found to be better in terms of various performance metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. The proposed memeticfirefly-based boosting approach is dominant over all the other considered methods with an accuracy of 99.36% and is robust for accurate hand gesture recognition. (C) 2021 Elsevier B.V. All rights reserved.
The increase in penetration levels of distributed generation (DG) into the grid has raised concern about undetected islanding operations. Islanding is a phenomenon in which the grid-tied inverter of a distributed gene...
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The increase in penetration levels of distributed generation (DG) into the grid has raised concern about undetected islanding operations. Islanding is a phenomenon in which the grid-tied inverter of a distributed generation system, and some of the local loads are disconnected from the grid. If this condition is not detected and the generation (e.g. from a photovoltaic energy source) remains operative, the isolated DG system will stay energised by the inverter. The phase mismatch between the grid and the inverter voltage makes this scenario undesirable since it could be hazardous for the maintenance operator and could harm the inverter and loads in the event of an unsynchronised reconnection of the grid. Consequently, the article presented a novel hybrid active anti-islanding approach for fast and reliably detecting unintended islanding. For the modelling and experimental setup, a multiphase grid-tied photovoltaic distributed generating system was employed, and it was regarded as a viable application. Initially, the study introduces a fault-tolerant control (FTC) technique of data-driven predictive control to limit the impact of grid faults on inverters. Furthermore, the article suggested the Sandia frequency and voltage shift (SFVS) approach for inverter-based distributed generation to identify an islanding state. The approach employs a positive feedback gain to minimise NDZ and THD;moreover, the system does not affect the power quality. To minimise system damage, this scenario necessitates the use of effective islanding detection algorithms. This study suggests using empirical mode decomposition (EMD) to enhance power quality to extract detailed coefficients, which are subsequently processed to detect common transient fluctuations during islanding. In the grid-tied inverter, random forest (RF) is also utilised to categorise the condition as islanding or non-islanding. This exhibits an acceptable trade-off between output power quality and islanding detection effecti
Malware is continuously evolving and becoming more sophisticated to avoid detection. Traditionally, the Windows operating system has been the most popular target for malware writers because of its dominance in the mar...
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Malware is continuously evolving and becoming more sophisticated to avoid detection. Traditionally, the Windows operating system has been the most popular target for malware writers because of its dominance in the market of desktop operating systems. However, despite a large volume of new Windows malware samples that are collected daily, there is relatively little research focusing on Windows malware. The Windows Registry, or simply the registry, is very heavily used by programs in Windows, making it a good source for detecting malicious behavior. In this paper, we present RAMD, a novel approach that uses an ensemble classifier consisting of multiple one-class classifiers to detect known and especially unknown malware abusing registry keys and values for malicious intent. RAMD builds a model of registry behavior of benign programs and then uses this model to detect malware by looking for anomalous registry accesses. In detail, it constructs an initial ensemble classifier by training multiple one-class classifiers and then applies a novel swarm intelligence pruning algorithm, called memeticfirefly-based ensemble classifier pruning (MFECP), on the ensemble classifier to reduce its size by selecting only a subset of one-class classifiers that are highly accurate and have diversity in their outputs. To combine the outputs of one-class classifiers in the pruned ensemble classifier, RAMD uses a specific aggregation operator, called Fibonacci-based superincreasing ordered weighted averaging (FSOWA). The results of our experiments performed on a dataset of benign and malware samples show that RAMD can achieve about 98.52% detection rate, 2.19% false alarm rate, and 98.43% accuracy.
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