A new algorithm of modified binary particle swarm optimization based on the Gustafson-Kessel for credit risk assessment

Neural Computing and Applications - To increase the quality of loans provision and reduce the risk involved in this process, several credit scoring models have been developed and utilized to...     

Formation of As–As Interlayer Bonding in the cT Phase of EuFe<Subscript>2</Subscript>As<Subscript>2</Subscript> and CeFeAsO Under Pressure from First Principle

The electronic band structure and structural phase stability of EuFe₂As₂ and CeFeAsO compounds were studied using the full-potential linearized augmented plane wave (FP-LAPW) method implemented using WIEN2k. To calculate the structural stability and phase transition of these compounds, the total energies have been computed as a function of reduced volumes and fitted with the Birch–Murnaghan equation. The calculated lattice parameters are found to be in agreement with the available experimental data. The present results show that EuFe₂As₂ and CeFeAsO compounds undergo structural phase transition from body-centered tetragonal (BCT) into collapsed tetragonal (cT) and tetragonal (T) into cT phase under pressure. The calculated phase transition pressures are in agreement with recent experimental data. The calculated valence charge density of collapsed tetragonal phase reveals that As–As interactions found to be stronger under pressure.     

A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification

Data fusion is one of the challenging issues, the healthcare sector is facing in the recent years. Proper diagnosis from digital imagery and treatment are deemed to be the right solution. Intracerebral Haemorrhage (ICH), a condition characterized by injury of blood vessels in brain tissues, is one of the important reasons for stroke. Images generated by X-rays and Computed Tomography (CT) are widely used for estimating the size and location of hemorrhages. Radiologists use manual planimetry, a time-consuming process for segmenting CT scan images. Deep Learning (DL) is the most preferred method to increase the efficiency of diagnosing ICH. In this paper, the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning (DL) model, abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification, also known as FFEDL-ICH. The proposed FFEDL-ICH model has four stages namely, preprocessing, image segmentation, feature extraction, and classification. The input image is first preprocessed using the Gaussian Filtering (GF) technique to remove noise. Secondly, the Density-based Fuzzy C-Means (DFCM) algorithm is used to segment the images. Furthermore, the Fusion-based Feature Extraction model is implemented with handcrafted feature (Local Binary Patterns) and deep features (Residual Network-152) to extract useful features. Finally, Deep Neural Network (DNN) is implemented as a classification technique to differentiate multiple classes of ICH. The researchers, in the current study, used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic performance. The findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its performance. For future researches, the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.     

Acoustics and rheological studies of aqueous ethylene glycol blend copper oxide nanofluids

Experimental investigations have been performed to synthesize copper oxide nanoparticles by conventional chemical precipitation method and nanofluids were prepared by two-step method using CuO nanoparticles in different proportions of ethylene glycol–water mixtures (EG–water). Powder X-ray diffraction (PXRD), energy dispersive X-ray (EDX), scanning electron microscope (SEM), particle size, and zeta potential analysis have been studied to characterize both solid and fluid samples for their sizes, shapes, stability, and arrangement. Besides, acoustics and rheological properties such as ultrasonic velocity, density, and viscosity have been measured for all fluid samples at three different temperatures. Interpretations of all these parameters have been made on the basis of particle stability and dispersion capacity of nanoparticles in different proportions of base fluids. The variation of dynamic viscosity with shear rate shows the nanofluids to be behaved like non-Newtonian fluids at very less shear rate but shows Newtonian behavior as the shear rate increases.     

Compression Testing of Ti-6Al-4V in the Temperature Range of 303-873K

Compression testing of Ti-6Al-4V alloy has been carried out at temperatures between 303 K to 873 K. To prevent embrittlement due to atmospheric oxygen and nitrogen, the samples were given a glass coating, which also acts as a lubricant simultaneously. Dynamic Strain Aging was observed to occur in the temperature range of 600 K to 800 K. Below 600 K stresses were high. Warm working has to be done above 800 K but below 1163 K (0.6 T_m where T_m = 1940 K) which is the recrystallization temperature. Based on these conclusions, warm extrusion has been successfully carried out in the Materials Forming Laboratory of I.I.T., Madras, Chennai, India.