A Novel Mapping Technique for an Adaptive Length of Codes in SAC-OCDMA Systems

Wireless Personal Communications - In this paper an adaptive mapping technique (AMT) is proposed for spectral amplitude coding-optical code division multiple access (SAC-OCDMA) system. The main...     

Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model

Machine learning (ML) becomes a familiar topic among decision makers in several domains, particularly healthcare. Effective design of ML models assists to detect and classify the occurrence of diseases using healthcare data. Besides, the parameter tuning of the ML models is also essential to accomplish effective classification results. This article develops a novel red colobuses monkey optimization with kernel extreme learning machine (RCMO-KELM) technique for epileptic seizure detection and classification. The proposed RCMO-KELM technique initially extracts the chaotic, time, and frequency domain features in the actual EEG signals. In addition, the min-max normalization approach is employed for the pre-processing of the EEG signals. Moreover, KELM model is used for the detection and classification of epileptic seizures utilizing EEG signal. Furthermore, the RCMO technique was utilized for the optimal parameter tuning of the KELM technique in such a way that the overall detection outcomes can be considerably enhanced. The experimental result analysis of the RCMO-KELM technique has been examined using benchmark dataset and the results are inspected under several aspects. The comparative result analysis reported the better outcomes of the RCMO-KELM technique over the recent approaches with the of 0.956.     

Transfer Learning for Chest X-rays Diagnosis Using Dipper ThroatedAlgorithm

Most children and elderly people worldwide die from pneumonia, which is a contagious illness that causes lung ulcers. For diagnosing pneumonia from chest X-ray images, many deep learning models have been put forth. The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases. By varying the deep learning approach, three pre-trained models, GoogLeNet, ResNet18, and DenseNet121, are employed in this research to extract the main features of pneumonia and normal cases. In addition, the binary dipper throated optimization (DTO) algorithm is utilized to select the most significant features, which are then fed to the K-nearest neighbor (KNN) classifier for getting the final classification decision. To guarantee the best performance of KNN, its main parameter (K) is optimized using the continuous DTO algorithm. To test the proposed approach, six evaluation metrics were employed namely, positive and negative predictive values, accuracy, specificity, sensitivity, and F1-score. Moreover, the proposed approach is compared with other traditional approaches, and the findings confirmed the superiority of the proposed approach in terms of all the evaluation metrics. The minimum accuracy achieved by the proposed approach is (98.5%), and the maximum accuracy is (99.8%) when different test cases are included in the evaluation experiments.     

Ab initio Insight of the Electronic,Structural, Mechanical and Optical Properties of X $$_3$$ 3 P $$_2$$ 2 (X= Mg,Ca) from GGA and Hybrid Functional (HSE06)

Journal of Superconductivity and Novel Magnetism - In this paper, the structural, mechanical and optoelectronic properties of X $$_3$$ P $$_2$$ (X= Mg, Ca) have been investigated by using the...     

Phytoextract assisted hydrothermal synthesis of ZnO–NiO nanocomposites using neem leaves extract

This work represents the synthesis of zinc oxide nanorods, nickel oxide nanoparticles, and zinc oxide-nickel oxide nanocomposites by combining Neem leave extract with hydrothermal synthesis. Five samples were prepared by green synthesis which is based on Neem leaves aqueous extract followed by hydrothermal treatment at 250 °C for 2.5 h. The five prepared samples are: 100% NiO (N100), 75% NiO: 25% ZnO (Z25-N75), 50% NiO: 50% ZnO (Z50-N50), 25% NiO: 75% ZnO (Z75-N25), and 100% ZnO (Z100). The prepared nanoparticles and nanocomposites were characterized by FTIR, XRD, SEM, and TEM. The XRD results show that the ZnO formed as a hexagonal wurtzite phase and NiO as a cubic phase. The microstructure of the formed samples is versatile with nanorods ZnO and spherical NiO. The nanocomposites microstructure appears as a heterostructure of both oxides with NiO particles on the surface of ZnO rods. The TEM confirms the nanosize and the crystallinity of samples.     

Theoretical Study of the Ozone Production and NOx Molecules Processing in N2/O2 Pulsed Discharge

Environmental decontamination is a major challenge due to the rapid growth of industrial and technological development, requiring an important consumption of fossil energies. Nowadays, a new way to treat polluting molecules based on the use of nonequilibrium reactive plasmas, is under development. These plasmas are generated by electrical discharges at atmospheric pressure, for neutralizing or transforming the toxic oxides. In this respect, the goal of the present work is to analyze the possibilities of treatment of NO_x molecules and ozone generation by pulsed discharge. The study was carried out by using a one-dimensional model based on the parallel resistor network concept, consisting in dividing the discharge volume into plasma elements which are connected in no way but through their contribution to the total resistance of the plasma. The model calculations indicate the effect of gas heating and inhomogeneous preionization on the ozone production and NO_x destruction.     

Two‐dimensional gel electrophoresis of apolipoprotein C‐III and other serum glycoproteins for the combined screening of human congenital disorders of O‐ and N‐glycosylation

Congenital disorders of glycosylation (CDG) are inherited diseases that can affect not only the N‐glycan (e.g. CDG type I and II) but also the O‐glycan biosynthesis pathway. In the absence of specific clinical symptoms, there is a need for a reliable biological screening of these two groups of CDG. Using a few microlitres of human serum, 2‐DE and immunoblotting were applied to the separation and simultaneous detection of the isoforms of the O‐glycosylated protein apolipoprotein C‐III (apoC‐III) and of four N‐glycosylated proteins, namely alpha‐antitrypsin, alpha‐1 acid glycoprotein, haptoglobin and transferrin. For the study of O‐glycosylation, this technique allowed the reliable separation of the three fractions of apoC‐III and the determination of normal percentage values in an adult population. Concerning N‐glycosylation, the study of serum samples from patients with CDG type Ia revealed marked abnormalities systematically affecting the four 2‐DE separated N‐linked glycoproteins. 2‐DE coupled to immunoblotting using a mixture of specific antibodies could be easily and reliably employed for the combined screening of both N‐ and O‐glycosylation disorders in humans.     

A formal approach based on UML and B for the specification and development of database applications

This article describes a formal approach to specify and develop database applications. This approach consists of two complementary phases. In the first phase, B specifications are automatically generated from UML class, state and collaboration diagrams describing the data and the transactions of the system we are developing. In the second phase, these specifications are successively refined until they become close enough to a relational implementation. The tool supporting this approach is implemented as an extension of the Rational Rose tool to develop and visualize graphical (UML) and formal (B) notations in a single environment.