Memory based cuckoo search algorithm for feature selection of gene expression dataset

Cancer prediction has been shown to be important in the cancer research area. This importance has prompted many researchers to review machine learning-approaches to predict cancer outcome using gene expression dataset. This dataset consists of many genes (features) which can mislead the prediction ability of the machine learning methods, as some features may lead to confusion or inaccurate classification. Since finding the most informative genes for cancer prediction is challenging, feature selection techniques are recommended to pick important and relevant features out of large and complex datasets. In this research, we propose the Cuckoo search method as a feature selection algorithm, guided by the memory-based mechanism to save the most informative features that are identified by the best solutions. The purpose of the memory is to keep track of the selected features at every iteration and find the features that enhance classification accuracy. The suggested algorithm has been contrasted with the original algorithm using microarray datasets and the proposed algorithm has been shown to produce good results as compared to original and contemporary algorithms

Insight into the Structural, Mechanical and Optoelectronic Properties of Ternary Cubic Barium-Based BaMCl₃ (M = Ag, Cu) Chloroperovskites Compounds

Prediction of new materials is crucial for the advancement of technology. Here, in this research work, the first-principle computation has been conducted utilizing the WIEN2K package to probe the structural, electronic, mechanical, and optical properties of barium-based chloroperovskites BaMCl3 (M = Ag, Cu) compounds. The optimized lattice constants are calculated for both compounds which are 9.90 Bohr for BaAgCl3 and 9.38 Bohr for BaCuCl3. To obtain better and more precise results for the electronic band’s structure, TDOS and PDOS (total and partial density of states), and the TB-mBJ potential approximation are employed. The indirect band gap (R–Γ) is found for both compounds having values of 1.173 eV and 2.30 eV for BaCuCl3 and BaAgCl3, respectively, which depicts its semiconducting nature. The calculation of elastic properties is conducted with IRelast code. The Cauchy pressure, Bulk modulus, Young’s modulus, Shear modulus, anisotropic ratio, Kleinman parameters, and Poisson’s ratio are calculated from the obtained elastic constants. The computation of elastic parameters indicates that the interested chloroperovskites are anisotropic, mechanically stable, hard to scratch, and ductile. From 0 eV to 40 eV incident photon energy ranges, the various optical parameter such as refractive index, absorption coefficient, dielectric function, reflectivity, extinction coefficient, and optical conductivity are analyzed. These compounds absorb maximum light within 5 to 25 eV incident photon energy. Hence, these materials are good light absorbers, therefore, they can be used in optoelectronic devices for high-frequency applications