Low power and Lossy Networks (LLN) are the backbone of the blooming Internet of Things (IoT) technology. The routing protocols play crucial role in the efficiency and management of the scarce resources of LLNs. RPL is...
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Low power and Lossy Networks (LLN) are the backbone of the blooming Internet of Things (IoT) technology. The routing protocols play crucial role in the efficiency and management of the scarce resources of LLNs. RPL is the popular routing protocol for LLNs and its efficiency depends on the routing metrics used. The default objective functions of RPL use hop count and Expected Transmission Count (ETX). In denser networks, congestion is caused, and the objective functions require optimization. This paper aims at providing an optimization of RPL by creating a new strategy of amalgamating the existing sigma routing metric and enhanced routing metric. Contiki Cooja simulator is used for simulating the results. The results are evaluated with the default objective functions, sigma routing, and enhanced objective function. The neo-hybrid composite routing metric (NCRM) outperforms all the other in packet delivery ratio, network life time, end-to-end delay and energy consumption.
In this paper, a new kind of convergence of complex uncertain variable convergence that complete convergence and convergence of p-distance were presented. Then the relationships among complete convergence, convergence...
In this paper, a new kind of convergence of complex uncertain variable convergence that complete convergence and convergence of p-distance were presented. Then the relationships among complete convergence, convergence of p-distance, convergence in measure, convergence in almost surely, convergence in uniformly almost surely were investigated.
The properties of matrices as a subject in linear algebra are used in the topic of image processing via employing many algebraic methods in several areas, including security. The digital image watermarking techniques ...
The properties of matrices as a subject in linear algebra are used in the topic of image processing via employing many algebraic methods in several areas, including security. The digital image watermarking techniques represent the security technique type addressed in this work. This paper aims to implement and demonstrate the possibility of successfully using the algebraic Hessemberge decomposition method (HDM) for the first time in building a zero watermarking technique as a transform to extract the features of the image without using any of the customary transformations. Also, the improvement of the results of the technique is performed by applying discrete wavelet transform (DWT) as an additional transform to increase the accuracy in the extraction of the watermark. Two techniques are adopted to achieve the aim. The basic idea behind both techniques is to make use of the algebraic Hessemberge decomposition method (HDM) as a mathematical tool to transform the image into another domain. In the first one, the YCbCr is applied to the color image to obtain the Y component. The HDM is carried out to create one matrix from the Hi matrices to get the master-secret. In the second technique, the DWT is utilized on the Y component to obtain the low-frequency band (LL). which is a low-resolution approximation of the original image. After that, the HDM is used to create one matrix from all the Hi matrices to get the master-secret. The experimental results show that the NC values under a lot of the attacks are improved in the second technique. But the NC values of salt and pepper attack in the first technique are higher. Some attacks retained the same values of the NC in both techniques.
Class imbalance is one of the major challenges faced by the machine learning research community. The imbalance in the data degrades the performance of the model. The widely used resampling methods can miss important i...
Class imbalance is one of the major challenges faced by the machine learning research community. The imbalance in the data degrades the performance of the model. The widely used resampling methods can miss important instances during undersampling. On the other hand, oversampling can cause bias in the data that lead to overfitting of the model. To avoid this problem an alternate solution of estimating the threshold for an imbalanced dataset was adapted. In general, the default threshold is fixed to 0.5, which is not suitable for imbalanced datasets. The optimal threshold is estimated by maximizing the f1-score and Geometric mean (G-mean), the two widely used metrics for imbalanced learning. The proposed approach preserves the original data without any alteration. To compare the performance six ensemble classifiers such as Adaptive Boosting (AdaBoost), Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Random Forest (RF) and Classic Gradient Boosting (CatBoost) are selected. The IEEE-CIS fraud detection dataset from Kaggle having an imbalance ratio of 27.58 was used for this study. Experimental results show that the precision, recall and f1-score are improved by shifting the threshold values for binary classification. We observed that the performance of XGBoost (0.7325) and LGBM (0.7123) was comparably high in terms of f1-score. Hence, the selection of optimal threshold helps to provide betterfindings.
In healthcare industry, the usage of automated diagnostic systems has become common in recent years. These systems offer several advantages during diagnosis. This system can minimize the operator-dependent nature inhe...
In healthcare industry, the usage of automated diagnostic systems has become common in recent years. These systems offer several advantages during diagnosis. This system can minimize the operator-dependent nature inherent in medical imaging systems and can make the diagnostic process reproducible. And also helps to improve the accuracy of diagnosis. This effectively can work with features (like computational features and statistical features) that cannot be obtained through visual analysis or through intuitive examinations. In this work we have proposed the new algorithm called “Optimized Weighted KNNI algorithm” (OWKNNI). In this research paper we have sorted out the problem of missing value handling is used to detect the Thyroid Disease my enhancing the data mining algorithms to detect the type of thyroid diseases.
Representation of any network graphically has vast applications and used for knowledge extraction efficiently. Due to the increase in applications of a graph, the size of the graph becomes larger as well as its comple...
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Diabetes Mellitus (DM) plays a significant role in increasing the associated health problems worldwide by acting as a Comorbid condition. Moreover, it is a progressive illness without severe external symptoms leading ...
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Multimodal manipulations (also known as audiovisual deepfakes) make it difficult for unimodal deepfake detectors to detect forgeries in multimedia content. To avoid the spread of false propaganda and fake news, timely...
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To solve real-world global optimization problem differential evolution algorithm is used as one of the best nature influenced algorithm. The use of different effective mutation strategies and proper selection of effec...
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ISBN:
(数字)9781728154329
ISBN:
(纸本)9781728154336
To solve real-world global optimization problem differential evolution algorithm is used as one of the best nature influenced algorithm. The use of different effective mutation strategies and proper selection of effective control parameters directly affect the performance and convergent rate of differential evolution method. Although its performance is very good but suffers from population diversity and *** this paper, new self-adaptive mutation strategies with booster vector to improve global optimization of DE/rand/1/bin and DE/best/1/bin is proposed. Elite archive strategies with dynamic adjustment of control parameter with booster vector added to afford more bandwidth for electing an effective mutant solution. The proposed algorithm is compared with five DE and six non-DE algorithms by using a set of twenty benchmark functions on COCO (comparing Continuous Optimizers) framework. The experimental result verifies that proposed self-adaptive strategies outperformed the competitors.
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