Generalized differentiability and integrability for fuzzy set-valued functions on time scales

This paper deals with the fuzzy set-valued functions of real variables on time scale whose values are normal, convex, upper semicontinuous and compactly supported fuzzy sets in \(\mathbb {R}^{n}\). We introduce and study the fundamental properties of new class of derivative called generalized delta derivative (\(\Delta _{g}\)-derivative) and generalized delta integral (\(\Delta _{g}\)-integral) for such fuzzy functions.     

Association Rule Mining Frequent-Pattern-Based Intrusion Detection in Network

In the network security system, intrusion detection plays a significant role. The network security system detects the malicious actions in the network and also conforms the availability, integrity and confidentiality of data information resources. Intrusion identification system can easily detect the false positive alerts. If large number of false positive alerts are created then it makes intrusion detection system as difficult to differentiate the false positive alerts from genuine attacks. Many research works have been done. The issues in the existing algorithms are more memory space and need more time to execute the transactions of records. This paper proposes a novel framework of network security Intrusion Detection System (IDS) using Modified Frequent Pattern (MFP-Tree) via K-means algorithm. The accuracy rate of Modified Frequent Pattern Tree (MFPT)-K means method in finding the various attacks are Normal 94.89%, for DoS based attack 98.34%, for User to Root (U2R) attacks got 96.73%, Remote to Local (R2L) got 95.89% and Probe attack got 92.67% and is optimal when it is compared with other existing algorithms of K-Means and APRIORI.     

Product beamforming and nested array in tandem for enhanced sonar performance

Multidimensional Systems and Signal Processing - High-performance sonar systems mostly combine a large sensor array and an efficient beamformer for enhancing the overall detection capabilities and...     

Design and analysis of text document clustering using salp swarm algorithm

The Journal of Supercomputing - In the technological era, exponential increase of unorganized text documents offers increased difficulties retrieving the most relevant data. The document clustering...     

A new method using feature extraction for identifying paddy rice species for quality seed selection

The purpose of this work was to explore a new feature extraction method for classifying paddy seeds using a feature extraction algorithm to achieve the area ratio, horizontal–slant and front–rear angles and find whether the proposed features have high discriminating power. Another objective was to find the smallest feature set that can ensure highly accurate recognition of seeds. A total of a 100 image features were extracted, and features having significant discriminating power were identified based on the analysis of variance (ANOVA). From the 100 features, 14 features were found to have high discriminating power and from these features, six were selected as the proposed features. Experimental results show that the proposed features and removal of redundant features enhanced the discriminating power of the feature set, and that the proposed features have an excellent discriminating property for seeds. The presented features resulted in the highest classification accuracy (98.8%) when compared to other methods.     

Mixed-Mode Stress Intensity Factor Determination of Riveted Lap Joints Used in Aircraft Fuselage Structures

Journal of Failure Analysis and Prevention - In the present work, mixed-mode stress intensity factor (SIF) of multiple cracks in a riveted lap joint has been determined, with and without the...     

Myopic vs. far-sighted behaviours in a revenue-sharing supply chain with reference quality effects

In this paper, we consider a dynamic supply chain where a manufacturer produces and distributes a featured product through an exclusive retailer to end consumers. The manufacturer decides the product quality and wholesale price, while the retailer sets the retail price in the presence of a revenue-sharing contract and consumers’ reference quality effects. The objective of this study is to investigate the impacts of myopic behaviour regarding the reference quality on the product quality and pricing strategies and profits of both members. Our results suggest that the manufacturer’s myopia leads to a higher quality, higher price strategy and a higher quality–price ratio which benefits consumers. Meanwhile, relative to the far-sighted behaviour, myopia results in a more quality-sensitive but less price-sensitive market demand. What’s more, we find that the manufacturer is apt to act in a far-sighted way, but the retailer isn’t always willing to cooperate with a far-sighted manufacturer. Taking myopic strategies for both members is likely to gain a high profit of the whole supply chain for a relatively high marginal contribution of product quality on demand and a relatively low revenue-sharing proportion.     

ACO–EEOLSR: enhanced energy model based link stability routing protocol in mobile ad hoc networks

A mobile ad hoc network (MANET) has abundant mobile nodes that are free to communicate independently in many locations. Many existing energy models address the inadequacy of resources based on Ad hoc On-demand Multipath Distance Vector and Optimized Link State Protocol (OLSP) routing protocols for MANET along with various parameters. The architecture of energy-efficient routing mechanisms is a challenging problem in a MANET. In this work, a novel energy-aware routing model is introduced for MANET comprising an ant colony optimization (ACO) enhanced approach to energy-efficient-optimized link state routing (named ACO–EEOLSR). Initially, the route discovery is progressed by means of neighbor estimation and also with the authentication of link stability. Parameters such as energy, distance, and hop count are employed as willingness nodes, where both the energy and distance are entrenched through the OLSP. Consequently, the hop count is applied via the ACO system that is beneficial for link stability. After the acceptance of an acknowledgement, the hop count is authorized for further performance analysis. This approach increases the Quality of Service and also uses less energy compared to other energy models. The accomplished simulation upshot depicts that the ACO–EEOLSR outperforms the EEOLSR scheme with respect to the performance metrics of energy consumption, packet delivery ratio, total remaining time, average network lifetime, and a variance of energy.     

Performance comparison of texture feature analysis methods using PNN classifier for segmentation and classification of brain CT images

A computer software system is designed for the segmentation and classification of benign and malignant tumor slices in brain computed tomography images. In this paper, we present a texture analysis methods to find and select the texture features of the tumor region of each slice to be segmented by support vector machine (SVM). The images considered for this study belongs to 208 benign and malignant tumor slices. The features are extracted and selected using Student's t‐test. The reduced optimal features are used to model and train the probabilistic neural network (PNN) classifier and the classification accuracy is evaluated using k fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. Quantitative analysis between ground truth and segmented tumor is presented in terms of quantitative measure of segmentation accuracy and the overlap similarity measure of Jaccard index. The proposed system provides some newly found texture features have important contribution in segmenting and classifying benign and malignant tumor slices efficiently and accurately. The experimental results show that the proposed hybrid texture feature analysis method using Probabilistic Neural Network (PNN) based classifier is able to achieve high segmentation and classification accuracy effectiveness as measured by Jaccard index, sensitivity, and specificity.