Texture Image Classification Using Deep Neural Network and Binary Dragon Fly Optimization with a Novel Fitness Function

Wireless Personal Communications - Texture is one of the most significant characteristics of an image for retrieving visually similar patterns. So far, researchers utilize large number of gray...     

M-Curves path planning model for mobile anchor node and localization of sensor nodes using Dolphin Swarm Algorithm

Wireless Networks - Location information of a sensor node is the primary concern to process the sensed data in Wireless Sensor Networks (WSNs). The location of the sensor node is used in other...     

Energy-Efficient Load Balancing Strategy for Wireless Sensor Networks using Quasi-oppositional based Jaya Optimization

Wireless Personal Communications - The area of wireless sensor networks (WSNs) has gained significant attention from researchers due to its expansive range of applications, such as industrial...     

Binary BAT algorithm and RBFN based hybrid credit scoring model

Credit scoring is a process of calculating the risk associated with an applicant on the basis of applicant’s credentials such as social status, financial status, etc. and it plays a vital role to improve cash flow for financial industry. However, the credit scoring dataset may have a large number of irrelevant or redundant features which leads to poorer classification performances and higher complexity. So, by removing redundant and irrelevant features may overcome the problem with huge number of features. This work emphasized on the role of feature selection and proposed a hybrid model by combining feature selection by utilizing Binary BAT optimization technique with a novel fitness function and aggregated with for Radial Basis Function Neural Network (RBFN) for credit score classification. Further, proposed feature selection approach is aggregated with Support Vector Machine (SVM) & Random Forest (RF), and other optimization approaches namely: Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), Hybrid Particle Swarm Optimization and Genetic Algorithm (PSOGA), Improved Krill Herd (IKH), Improved Cuckoo Search (ICS), Firefly Algorithm (FF) and Differential Evolution (DE) are also applied for comparative analysis.

     

Credit score classification using spiking extreme learning machine

Credit score classification is a prominent research problem in the banking or financial industry, and its predictive performance is responsible for the profitability of financial industry. This paper addresses how Spiking Extreme Learning Machine (SELM) can be effectively used for credit score classification. A novel spike-generating function is proposed in Leaky Nonlinear Integrate and Fire Model (LNIF). Its interspike period is computed and utilized in the extreme learning machine (ELM) for credit score classification. The proposed model is named as SELM and is validated on five real-world credit scoring datasets namely: Australian, German-categorical, German-numerical, Japanese, and Bankruptcy. Further, results obtained by SELM are compared with back propagation, probabilistic neural network, ELM, voting-based Q-generalized extreme learning machine, Radial basis neural network and ELM with some existing spiking neuron models in terms of classification accuracy, Area under curve (AUC), H-measure and computational time. From the experimental results, it has been noticed that improvement in accuracy and execution time for the proposed SELM is highly statistically important for all aforementioned credit scoring datasets. Thus, integrating a biological spiking function with ELM makes it more efficient for categorization.     

A Hybrid Approach for Extracting EMG signals by Filtering EEG Data for IoT Applications for Immobile Persons

Wireless Personal Communications - Brain Computer interface (BCI) is an emerging technology which empowers human to regulate the computer or other electronic gadgets with brain signals. This paper...     

Multilevel Automated Security System for Prevention of Accidents at Unmanned Railway Level Crossings

Wireless Personal Communications - Developing technology to come up with viable, economic and efficient solutions for prevention of accidents at unmanned railway level crossings is an active...     

A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification

Credit scoring focuses on the development of empirical models to support the financial decision‐making processes of financial institutions and credit industries. It makes use of applicants' historical data and statistical or machine learning techniques to assess the risk associated with an applicant. However, the historical data may consist of redundant and noisy features that affect the performance of credit scoring models. The main focus of this paper is to develop a hybrid model, combining feature selection and a multilayer ensemble classifier framework, to improve the predictive performance of credit scoring. The proposed hybrid credit scoring model is modeled in three phases. The initial phase constitutes preprocessing and assigns ranks and weights to classifiers. In the next phase, the ensemble feature selection approach is applied to the preprocessed dataset. Finally, in the last phase, the dataset with the selected features is used in a multilayer ensemble classifier framework. In addition, a classifier placement algorithm based on the Choquet integral value is designed, as the classifier placement affects the predictive performance of the ensemble framework. The proposed hybrid credit scoring model is validated on real‐world credit scoring datasets, namely, Australian, Japanese, German‐categorical, and German‐numerical datasets.