Search

Most standard classification algorithms are difficult to effectively learn and predict from imbalanced network traffic data, which usually leads to lower classification accuracy. To analyze the influence of imbalanced network traffic data on the performance of standard classification algorithms, the imbalanced data augmentation algorithms are first designed to obtain the imbalanced network traffic data set with gradually varying Imbalance Ratio (IR) and belonging to the same distribution. Then, to obtain more objective classification result and simplify the evaluation process, the evaluation metric AFG is used to evaluate the classification performance of standard classification algorithms based on area under the receiver operating characteristic curve (AUC), F-measure and G-mean. Finally, based on AFG and coefficient of variation (CV), performance stability of standard classification algorithms on imbalanced network traffic data is obtained. Experiments of eight widely used standard classification algorithms on 25 different imbalanced network traffic data demonstrate that the classification performance of GNB, RF and DT is unstable, while BNB, KNN, LR, GBDT, and SVC are relatively stable and not susceptible to imbalanced data. Especially, the KNN has the most stable classification performance. Also, the results are statistically confirmed by Friedman and Nemenyi post hoc statistical tests. [ABSTRACT FROM AUTHOR]

Increasing spectrum efficiency in new-generation communication networks is possible by raising the operating frequencies or increasing the number of serving cells. Both of the solutions indirectly lead to a decrease in the cell size. By shrinking the coverage area of the cells, the mobility management becomes a critical issue in order to provide seamless connectivity. The main tool for mobility management in connected mode is the handover procedure, which conventionally is triggered based on the user equipment's (UE's) measurements of serving and neighboring cells' signal quality. The performance of the methods provided in the 3GPP standard for setting up the handover is based on a comparison between the quality of the received signal from the serving cell and the neighbor cells, which is severely sensitive to the appropriate setting of threshold values and waiting time for setting up the handover. The remarkable methods in the literature to improve the handover performance in the 3GPP standard either utilize positional information or are based on complex algorithms. Positional information parameters such as speed, location, direction of movement, etc., need extra measurement modules. Also, complex algorithms like deep learning are not suitable for a wide range of active devices in 5 G networks with limited resources. In this paper, a novel fuzzy logic-based method is proposed to trigger the handover procedure based on estimated serving cell's and neighbor cells' radio link quality (RLQ) values. The proposed system consists of two stages. A second-order regressor beside a simple fuzzy logic system is introduced to predict the serving cell's and neighbor cells' RLQ. The final handover trigger decision is made with the help of another cascade fuzzy logic system, which is responsible for eliminating too-early, too-late, and ping-pong handovers. Considering the uncertainty handling feature of the interval type II fuzzy logic systems versus type I, we implement both methods and compare their results. Finally, we simulate the proposed algorithm using the ns-3 LTE module with a very tight setting for detecting radio link failure to meet 5 G strict standards. Proposed method succeeded in improving the performance of the handover process at high-speed scenarios by 50%, only with the help of radio link quality information. The main advantage of the proposed method is the proper management of the handover procedure, independent of UE's velocity, as well as its simple structure with few rules, which makes it suitable for use in UAV and IoT devices. [ABSTRACT FROM AUTHOR]

Parkinson's disease is one of the most common degenerative conditions that affect people aged 60 and older. The illness is normally diagnosed by clinical indicators that develop as a variety of movement symptoms and medical observations. Conventional diagnostic methods, on the other hand, rely on the detection of small motions, which are notoriously difficult to pin down with absolute precision. This makes it possible for these methods to lend themselves to subjective interpretations. This is because traditional diagnostic methods rely on the interpretation of motions. Image categorization is performed using an altered version of the ResNeXt model in the proposed model. The proposed ResNeXt extends version of ResNet architecture by introducing a new block called the "cardinality block." The cardinality block consists of multiple parallel branches, each with its own set of convolutional layers. These branches are then combined by concatenation before being passed to the next layer. The key idea behind the cardinality block is to increase the capacity of the network without significantly increasing the number of parameters. By using parallel branches with different filter sizes, ResNeXt is able to capture a wider range of features in the input image, leading to better performance on image classification tasks. In order to enhance the performance of the standard ResNeXt model, certain extra dense and dropout layers have been included. The size of the final model is reduced by pruning the model in order to improve the efficiency of the network connections and minimize the overall size of the model. The proposed method is compared to a number of deep learning models that are already in use, and it is shown that the acquired results are superior to those of the deep learning models that are already in use. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced, memory-assisted version of the multi-verse optimizer (MVO) is proposed for the shape and size optimization of various types of truss structures. The cosmology-based MVO processes the interaction of the multiple universes through white, black, and wormholes to solve the target optimization problem. However, the MVO has drawbacks such as slow convergence speed, easy to fall into local optimum, and low efficiency in the case of continuous and discrete variables existing in structural optimization. This paper aims to modify the Multi-verse Optimizer and propose an enhanced version, named as memory-assisted adaptive multi-verse optimizer (MAMVO). The enhanced algorithm seeks to reinforce the performance of the standard algorithm using two mechanisms: (I) Using an adaptive pseudorandom transfer strategy in cases with both continuous and discrete variables to strike a better balance between the diversification and the intensification tasks (II) using a multi-elite memory, by which several best so far solutions are saved and exchanged with several worst ones for better convergence. The performance of the proposed MAMVO is compared with those of the standard MVO and some other optimization methods through various types of truss benchmarks (beams, towers, and bridges) under different constraints such as displacement, stress, buckling, or frequency. Results confirm that MAMVO significantly outperforms the standard MVO and has comparable or superior performance to the other methods. [ABSTRACT FROM AUTHOR]

In data mining, mining high utility itemset (HUI) is one among the recent thrust area that receives several approaches for solving it in an effective manner. In the past decade, addressing optimization problems using evolutionary algorithms are an unavoidable strategy due to its convergence towards optimal solution within the stipulated time. The results of evolutionary algorithms on various optimization problems are far effective when compared to the exhaustive approaches with respect to computational time. The problem with HUI is discovering a set of items from a transactional database that possess high level of utility when compared with other distinctive sets. This problem becomes harder while addressing the count of items in the database while its higher and computational time to solve this problem using exhaustive search becomes exponential as proposition of items in transaction database increases. In this paper, an optimization model based on the biological behaviour of grey wolf is proposed; the model namely grey wolf optimization algorithm is used to solve HUI using five different Boolean operations. The proposed model is evaluated using standard performance metrics over synthetic datasets and real-world datasets. The proposed model results are then compared with recent HUIM models to show the significance. [ABSTRACT FROM AUTHOR]

Construction of an effective model for regression to fit data samples with noise or outlier is a challenging work. In this paper, in order to reduce the influence of noise or outlier on regression and further improve the prediction performance of standard twin support vector regression (TSVR), we proposed a fast clustering-based weighted TSVR, termed as FC-WTSVR. First, we use a fast clustering algorithm to quickly classify samples into different categories based on their similarities. Secondly, to reflect the prior structural information and distinguish contributions of samples located at different positions to regression, we introduce the covariance matrix and weighted diagonal matrix into the primal problems of FC-WTSVR, respectively. Finally, to shorten the training time, we adopt the successive over-relaxation algorithm to solve the quadratic programming problems. The results show that the proposed FC-WTSVR can obtain better prediction performance and anti-interference capability than some state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The number of jobs that takes place entirely or partially in a computer is nowadays very significant. These workplaces, as many others, often offer the key ingredients for the emergence of stress and the performance drop of its long-term effects: long hours sitting, sustained cognitive effort, pressure from competitiveness, among others. This has a toll on productivity and work quality, with significant costs for both organizations and workers. Moreover, a tired workforce is generally more susceptible to negative feelings and mood, which results in a negative environment. This paper contributes to the current need for the development of non-intrusive methods for monitoring and managing worker performance in real time. We propose a framework that assesses worker performance and a case study in which this approach was validated. We also show the relationship between performance and mood. [ABSTRACT FROM AUTHOR]