Multiband orange-red photoluminescence of Eu3+ ions in new "114" LnBaZn(3)GaO(7) and LnBaZn(3)AlO(7) oxides

A new series of gallozincates LnBaZn(3)GaO(7) (Ln=La, Nd, Sm, Eu, Gd, Dy, Y) and new aluminozincates LnBaZn(3)AlO(7) (Ln=Y, Eu, Dy) have been synthesized. Their structure refinements show that these phases belong to the "114" series, with hexagonal P6(3)mc space group previously described for SmBaZn3AlO7. The photoluminescence study of these oxides shows that the Eu3+ activated LnBaZn(3)MO(7) oxides with Ln=Y, La, Gd; and M=Al, Ga exhibit strong magnetic and electric dipole transitions (multiband emission) which is of interest for white light production. These results also confirm that the site occupied by Eu3+ is not strictly centrosymmetric. The electric dipole transition intensity is the highest in GdBaZn3MO7 [M=Al, Ga]: 0.05 Eu3+ as compared with other Eu3+ activated compositions. This is due to the layer distortion around GdO6 octahedra when compared with YO6 and LaO6 octahedra

Prediction of Alzheimer Disease using LeNet-CNN model with Optimal Adaptive Bilateral Filtering

Alzheimer's disease is a kind of degenerative dementia that causes progressively worsening memory loss and other cognitive and physical impairments over time. Mini-Mental State Examinations and other screening tools are helpful for early detection, but diagnostic MRI brain analysis is required. When Alzheimer's disease (AD) is detected in its earliest stages, patients may begin protective treatments before permanent brain damage has occurred. The characteristics of the lesion sites in AD affected role, as identified by MRI, exhibit great variety and are dispersed across the image space, as demonstrated in cross-sectional imaging investigations of the disease. Optimized Adaptive Bilateral filtering using a deep learning model was suggested as part of this study's approach toward this end. Denoising the pictures with the help of the suggested adaptive bilateral filter is the first stage (ABF). The ABF improves denoising in edge, detail, and homogenous areas separately. After then, the ABF is given a weight, and the Adaptive Equilibrium Optimizer is used to determine the best possible value for that weight (AEO). LeNet, a CNN model, is then used to complete the AD organization. The first step in using the LeNet-5 network model to identify AD is to study the model's structure and parameters. The ADNI experimental dataset was used to verify the suggested technique and compare it to other models. The experimental findings prove that the suggested method can achieve a classification accuracy of 97.43%, 98.09% specificity, 97.12% sensitivity, and 89.67% Kappa index. When compared against competing algorithms, the suggested model emerges victorious

Tom and Jerry Based Multipath Routing with Optimal K-medoids for choosing Best Clusterhead in MANET

Given the unpredictable nature of a MANET, routing has emerged as a major challenge in recent years. For effective routing in a MANET, it is necessary to establish both the route discovery and the best route selection from among many routes. The primary focus of this investigation is on finding the best path for data transmission in MANETs. In this research, we provide an efficient routing technique for minimising the time spent passing data between routers. Here, we employ a routing strategy based on Tom and Jerry Optimization (TJO) to find the best path via the MANET's routers, called Ad Hoc On-Demand Distance Vector (AODV). The AODV-TJO acronym stands for the suggested approach. This routing technique takes into account not just one but three goal functions: total number of hops. When a node or connection fails in a network, rerouting must be done. In order to prevent packet loss, the MANET employs this rerouting technique. Analyses of AODV-efficacy TJO's are conducted, and results are presented in terms of energy use, end-to-end latency, and bandwidth, as well as the proportion of living and dead nodes. Vortex Search Algorithm (VSO) and cuckoo search are compared to the AODV-TJO approach in terms of performance. Based on the findings, the AODV-TJO approach uses 580 J less energy than the Cuckoo search algorithm when used with 500 nodes

Essential Requirements for Data Security in the Context of Software Metrics

Abstract By first raising and then dispelling seven common rules about metrics, this paper discusses the requirements and design constraints for a practical system to measure, report and improve data security. Data security will become business-as-usual after the implementation program is completed, but the need for measurement and continuous improvement will persist indefinitely. In other words, we needed more than conventional program or project management metrics. The need for data security metrics was much more pragmatic. Furthermore, intended to embed data security deeper into the academic/corporate culture, meaning that security awareness is an important component. We propose seven rules for data security in the context of Software metrics

Development of Deep Learning based Intelligent Approach for Credit Card Fraud Detection

Credit card fraud (CCF) has long been a major concern of institutions of financial groups and business partners, and it is also a global interest to researchers due to its growing popularity. In order to predict and detect the CCF, machine learning (ML) has proven to be one of the most promising techniques. But, class inequality is one of the main and recurring challenges when dealing with CCF tasks that hinder model performance. To overcome this challenges, a Deep Learning (DL) techniques are used by the researchers. In this research work, an efficient CCF detection (CCFD) system is developed by proposing a hybrid model called Convolutional Neural Network with Recurrent Neural Network (CNN-RNN). In this model, CNN acts as feature extraction for extracting the valuable information of CCF data and long-term dependency features are studied by RNN model. An imbalance problem is solved by Synthetic Minority Over Sampling Technique (SMOTE) technique. An experiment is conducted on European Dataset to validate the performance of CNN-RNN model with existing CNN and RNN model in terms of major parameters. The results proved that CNN-RNN model achieved 95.83% of precision, where CNN achieved 93.63% of precision and RNN achieved 88.50% of precision

NETWORK ARCHITECTURE TO IDENTIFY SPATIAL KNOWLEDGE FOR DENGUE

Recent developments in informationtechnology have enabled collection and processing of vast amounts of personaldata, business data and spatial data. It has been widely recognized thatspatial data analysis capabilities have not kept up with the need for analyzingthe increasingly large volumes of geographic data of various themes that arecurrently being collected and archived. Our study is carried out on the way toprovide the mission-goal strategy (requirements) to predict the disaster. Theco-location rules of spatial data mining are proved to be appropriate to designnuggets for disaster identification and the state-of-the-art and emergingscientific applications require fast access of large quantities of data. Hereboth resources and data are often distributed in a wide area networks withcomponents administrated locally and independently, a framework has beensuggested for the above. Our contribution in this paper is to design networkarchitecture for disaster identification

SPATIAL MINING SYSTEM FOR DISASTER MANAGEMENT

Information Systems enable us to capture up to date effects due to disaster .It has been widely recognized that spatial data analysis capabilities have not kept up with the need for analyzing the increasingly large volumes of geographic data of various themes that are currently being collected and archived. Our analysis is on disaster management through  spatial Maps. Intelligent application algorithms ideal for finding the rules and unknown information from the vast quantities of computer data. The Intelligence system is to obtain and process the data, to interpret the data, and to design the algorithms for decision makers (Health Companion) as a basis for action. Spatial Map for disaster identification is designed. The Intelligence in each of these algorithms are provided the point and multi-point decision making system to capacitive for evaluation of spreading the dengue. Our contribution in this paper is to design Spatial Maps for Dengue

DEVELOPMENT AND VALIDATION OF SEVEN PHENYL HYDRAZINE CHLORO ESTER ISOMERS (PGIs) BY RP-HPLC-UV METHOD IN ANTICOAGULANT DRUG SUBSTANCE; APIXABAN

Objective: The objective of this work was to develop and validate a simple and sensitive reverse-phase high-pressure liquid chromatography method for the determination of seven potential genotoxic impurities in Apixaban drug substance. Methods: The optimized separation was achieved by using ACE 3 C18 PFP (150 mm×4.6 mm, 3 µm) HPLC column. The mobile phase-A was a degassed mixture of 0.01M Ammonium acetate buffer(PH adjusted 4.9±0.05 with diluted glacial acetic acid) and mobile phase-B was a degassed mixture of Acetonitrile, Isopropyl alcohol and Buffer PH 4.9 in the ratio of 60:20:20 v/v/v. The gradient program was operated at a flow rate of 1.0 ml/min and UV detection was at 330 nm. Results: The method was superior at linearity for seven impurities and correlation coefficient values were larger than 0.999, moreover, in the separation point of view, this method further achieved no matrix interference through chromatography by better resolution of the other impurities from the Apixaban drug substance and its related impurities for the accurate analysis of seven potential genotoxic impurities. The established limits of detection (LOD), limits of quantification (LOQ) values for the seven mutagenic impurities were each of 5 ppm (0.015µg/ml) and15 ppm (0.045µg/ml) respectively. The developed method was validated as per ICH guidelines and applied as a generic method to determine these seven potential genotoxic impurities for the pharmaceutical process control and drug material release. Conclusion: Validation of this analytical method was carried out including stability, selectivity, linearity, accuracy, system precision, method precision and intermediate precision thus proving that the described RP-HPLC method could be employed for fast and simple analysis of sevenphenyl hydrazine chloro ester isomers in Apixaban drug substance