Multi-objective optimization of feature selection using hybrid cat swarm optimization

With the pervasive generation of information from a wide range of sensors and devices, there always exist a large number of input features in databases, thus complicating machine learning problem formulation. However, certain features are relatively impertinent to specific problems, which may degrade the performances of classifiers in terms of prediction accuracy, sensitivity,specificity, and recall rate. The main goal of a multi-objective optimization problem is to identify the subsets of the given features. To this end, a hybrid cat swarm optimization(HCSO) algorithm is proposed in our paper for performance improvement of the basic cat swarm optimization(CSO) that incorporates guided and competitive inherent characteristics into the original CSO. The performance of HCSO has been tested by finding the optimal feature subset for 15 benchmark datasets. The number of class labels for these datasets varies between 2 and 40. The time complexity analysis of both CSO and HCSO has also been evaluated. Moreover, the performance of the proposed algorithm has been compared with that of simple CSO and other state-ofthe-art techniques. The performances obtained by HCSO have an average 2.68% improvement with a standard deviation of 2.91.The maximum performance improvement is up to 10.09% in prediction accuracy. Tested on the same datasets, CSO has yielded improvements within the range of-7.27% to 8.51% with an average improvement 0.9% and standard deviation 3.96. The statistical tests carried out in the experiments prove that HCSO manifests a moderately better feature selection capacity than that of its counterparts.     

Firework inspired load balancing approach for wireless sensor networks

Wireless Networks - In Wireless Sensor Networks (WSNs), where power consumption is a huge concern, the improvement of the network’s lifetime is an area of constant study and innovation. The...     

Length–weight relationships of five catfish species (Siluriformes) from the Ganga River,India

Knowledge of the length–weight relationships (LWRs) of fish is an important tool to understand fish body form, growth pattern, stock management and their conservation. The present study focused on investigating the length–weight relationships for five catfish species, Pachypterus atherinoides (Bloch, 1794), belonging to family Horabagridae; Batasio batasio (Hamilton, 1822) family Bagridae; Bagarius yarrelli (Sykes, 1839), family Gogangra viridescens (Hamilton, 1822); and Sisor rhabdophorus (Hamilton, 1822) belonging to family Sisoridae. Specimens were collected from the middle stretch of the Ganga River in India from November 2016 to May 2018. A total of 174 specimens of five fish species were collected, and their total lengths were measured to the nearest centimetre and the body weight to the nearest gram. The value of the parameter slope (b) of LWRs of the five species ranged from 2.86 (B. yarrelli) to 3.16 (G. viridescens), with a mean value of 2.99. The results of the present study documented the new maximum total length (TL) for P. atherinoides and S. rhabdophorus. The present study also provides the first reference regarding LWRs for S. rhabdophorus.     

Hydrothermal synthesis and characterization of CsNiP and 2(LiZnHP2O7) crystals

CsNiP and 2(LiZnHP₂O₇) crystals were synthesized by hydrothermal technique at moderate P-T conditions. Solubility results of both the compound shown positive thermal coefficient and single crystal X-ray studies revealed, CsNiP crystallized in hexagonal system with cell parameters; a = 7.173(2), c = 5.944(9) Å, V = 264.87(7) ų and space group P63/mmc and 2(LiZnHP₂O₇) crystallized in orthorhombic system with cell parameters; a = 12.3636 Å, b = 27.5330 Å, c = 6.8647 Å and space group, Pca21 exhibiting ring type of cavities with open aperture in the structure. CsNiP is a frequency dependent paramagnetic and 2(LiZnHP₂O₇) is a diamagnetic.     

Two‐Dimensional Molybdenum Trioxide and Dichalcogenides

In the quest to discover the properties of planar semiconductors, two‐dimensional molybdenum trioxide and dichalcogenides have recently attracted a large amount of interest. This family, which includes molybdenum trioxide (MoO₃), disulphide (MoS₂), diselenide (MoSe₂) and ditelluride (MoTe₂), possesses many unique properties that make its compounds appealing for a wide range of applications. These properties can be thickness dependent and may be manipulated via a large number of physical and chemical processes. In this Feature Article, a comprehensive review is delivered of the fundamental properties, synthesis techniques and applications of layered and planar MoO₃, MoS₂, MoSe₂, and MoTe₂ along with their future prospects.     

太阳能电池生产线中新型等离子体刻蚀机刻蚀工艺探讨

介绍了一种新型等离子体刻蚀机的使用条件,通过对各种参数变化的组合实验,得到了最佳工艺数据.利用该刻蚀机生产的电池片刻蚀效果好,刻蚀后经检测,电池片漏电流小,并联电阻大,提高了电池片的综合性能指标.     

Difference expansion based reversible data embedding and edge detection

Multimedia Tools and Applications - Difference Expansion (DE) based techniques are evolving in the last decade. The DE based techniques aims at reversibly embedding the data into cover content....     

Additive noise removal using a novel fuzzy-based filter

The paper proposes a new fuzzy-based two-step filter for restoring images corrupted with additive noise. The goal of the first step is to compute the difference between the central pixel and its neighborhood in a selected window and to compute a fuzzy membership degree for each difference value using a Gaussian membership function. Computed fuzzy membership values are appropriately utilized as weights for each pixel and then computes the weighted average representing the modified value for the current central pixel. The second step is used as an augmented step to the first one and its goal is to improve the result obtained in the first step by reducing the noise in the color component differences without destroying the fine details of the image. The experimental analysis shows that the proposed method gives better results compared to existing advanced filters for additive noise reduction. Both visual, quantitative and qualitative analysis have been done to prove the efficiency and effectiveness of the proposed method.