Analytical framework for optimized feature extraction for upgrading occupancy sensing performance

The adoption of the occupancy sensors has become an inevitable in commercial and non-commercial security devices, owing to their proficiency in the energy management. It has been found that the usages of conventional sensors is shrouded with operational problems, hence the use of the Doppler radar offers better mitigation of such problems. However, the usage of Doppler radar towards occupancy sensing in existing system is found to be very much in infancy stage. Moreover, the performance of monitoring using Doppler radar is yet to be improved more. Therefore, this paper introduces a simplified framework for enriching the event sensing performance by efficient selection of minimal robust attributes using Doppler radar. Adoption of analytical methodology has been carried out to find that different machine learning approaches could be further used for improving the accuracy performance for the feature that has been extracted in the proposed system of occuancy system

Granular Mobility-Factor Analysis Framework for enriching Occupancy Sensing with Doppler Radar

With the growing need for adoption of smarter resource control system in existing infrastructure, the proliferation of occupancy sensing is slowly increasing its pace. After reviewing an existing system, we find that utilization of Doppler radar is less progressive in enhancing the accuracy of occupancy sensing operation. Therefore, we introduce a novel analytical model that is meant for incorporating granularity in tracing the psychological periodic characteristic of an object by emphasizing on the mobility and uncertainty movement of an object in the monitoring area. Hence, the model is more emphasized on identifying the rate of change in any periodic physiological characteristic of an object with the aid of mathematical modelling. At the same time, the model extracts certain traits of frequency shift and directionality for better tracking of the unidentified object behavior where its applicabilibility can be generalized in majority of the fields related to object detection

Evaluation of Fungicides against Leaf Blotch of Turmeric Caused by Taphrina maculans Butler

A field experiment was conducted in the first fortnight of July 2008, 2009 and 2010 at Horticultural Research Station, Jagtial, and in 2010-2011 at Turmeric Research Station, Kammarpally, to evaluate various fungicides against leaf blotch of turmeric. Treatments included the fungicides Propiconazole (0.1%), Hexaconazole (0.1%), Tricyclazole (0.1%) and Carbendazim + Mancozeb (0.1%) for rhizome treatment (dipping) and for foliar spray at 45 and 90 days after planting (DAP); and foliar application alone at 45 and 90 DAP. Among the treatments, rhizome treatment with Carbendazim + Mancozeb (0.1%) gave the best germination (90.52%); Rhizome treatment followed by foliar application of Carbendazim + Mancozeb (0.1%) at 45 and 90 DAP significantly reduced disease incidence of turmeric leaf blotch (16.13%) and enhanced fresh-rhizome yield (18.30t ha-1) compared to other fungicide applications. High cost-benefit ratio was achieved with rhizome treatment, followed by foliar application of Carbendazim + Mancozeb at 45 and 90 DAP (1:1.92)

Multi-dimensional parametric assessment with IoT in acquaintance of digital pipeline

In IoT researches are found millions of devices are connected in global digital infrastructure framed in network fashion. Most of the things on the network use cloud or central database to share their data and control signals to control and manage the devices on the IoT network. Devices on the network need fast traversing paths, to take correct decision in right time. The data synchronization between source and destination is very important in real time parameter maintenance. The multidimensional parametric evaluation of the environment or the surrounding area is facing problems with data synchronization and congestion in the sensor network. This will lead arrival of fault data at the destination. The fault data also leads a misconception at user end. In the present work pipeline integration is proposed to overcome the misconception and synchronization failures. The pipeline and Arduino UNO processor are integrated on Proteus simulation tool. Transducer nodes and the functionalities are analysed with pipeline

Novel Approach for Control Data Theft Attack in Cloud Computing

Information security is a major problem faced by cloud computing around the world. Because of their adverse effects on organizational information systems, viruses, hackers, and attackers insiders can jeopardize organizations capabilities to pursue their undertaken effectively. Although technology based solutions help to mitigate some of the many problems of information security, even the preeminent technology can’t work successfully unless effective human computer communication occurs.IT experts, users and administrators all play crucial role to determine the behavior that occurs as people interact with information technology will support the maintenance of effective security or threaten it. In the present paper we try to apply behavioral science concepts and techniques to understanding problems of information security in organizations

Comparison of performance parameters of poly(3,4 ethylenedioxythiophene) (PEDOT) based electrochromic device on glass with and without counter electrode

Conjugated polymers are promising materials for electrochromic device technology. Aqueous dispersions of poly(3,4-ethylenedioxythiophene)-(PEDOT) were spin coated onto transparent conducting oxide (TCO) coated glass substrates. A seven-layer electrochromic device was fabricated with the following configuration: glass/transparent conducting oxide (TCO)/PEDOT (main electrochromic layer)/gel electrolyte/prussian blue (counter electrode)/TCO/glass. The device fabricated with counter electrode (Prussian blue) showed a contrast of 18% and without counter electrode showed visible contrast of 5% at 632 nm at a voltage of 1·9 V. The comparison of the device is done in terms of the colouration efficiency of the devices with and without counter electrode

METHOD DEVELOPMENT AND VALIDATION STUDY FOR QUANTITATIVE DETERMINATION OF GENOTOXIC IMPURITY AND ITS PRECURSOR IN FLUCONAZOLE SAMPLE BY LIQUID CHROMATOGRAPHY–TANDEM MASS SPECTROMETRY

Objective: The objective of this work is method development and validation study for quantitative determination of 1-[2-(2,4-difluorophenyl)-2,3-epoxypropyl]-1H-1,2,4-triazole, a genotoxic impurity and its precursor in a fluconazole drug sample by liquid chromatography–tandem mass spectrometry.Methods: LC-MS/MS analysis of these impurities was performed on Hypersil BDS C18 (100 mm x 4.0 mm, 3 µm) column. 5 mmol ammonium acetate and acetonitrile in the ratio of 65:35 (v/v) was used as the mobile phase with a flow rate of 0.4 ml/min. The developed method was accomplished with a short run time of 10 min. Triple quadrupole mass detector coupled with positive electrospray ionization was used for the quantification of genotoxic impurities in multiple reaction monitoring (MRM).Results: The method was validated as per International Conference on Harmonization (ICH) guidelines. The method was linear in the range of 0.30 µg/g to 11.37 µg/g for impurity A and 0.30 µg/g to 11.34 µg/g for impurity B with a correlation coefficient of 0.999. The accuracy of the method was in the range of 98.25 % to 100.53 % for both impurities.Conclusion: A specific, selective, highly sensitive and more accurate analytical method using LC-MS/MS coupled with positive electrospray ionization has been developed for the quantification of genotoxic impurity (1-[2-(2,4-difluorophenyl)-2,3-epoxypropyl]-1H-1,2,4-triazole) and its precursor (1-(2,4-difluorophenyl)-2-[1,2,4]triazol-1-yl-ethanone) at 0.3 µg/g with respect to the 5.0 mg/ml of fluconazole

DESIGN, DEVELOPMENT AND EVALUATION OF DILTIAZEM HYDROCHLORIDE LOADED NANOSPONGES FOR ORAL DELIVERY

Objective: In the current investigation,nanosponges were set up by emulsion solvent diffusion technique utilizing ethyl cellulose and β-cyclodextrin as polymers. Methods: Diltiazem hydrochloride is taken as model medication for considering different nanosponge formulations. The similarity of different formulation segments was set up by Fourier Transform Infra-Red (FTIR) spectroscopy. Molecular size, surface morphology, entrapment efficiency and drug content of nanosponges were analyzed. Shape and surface morphology of the nanosponges were inspected utilizing scanning electron microscopy. Results: Molecule size of formulated nanosponges was seen in the scope of 186 to 476 nm. Scanning electron microscopy uncovered the permeable, round nature of the nanosponges. The drug content of nanosponges for ethyl cellulose containing formulations was seen as in the scope of 62.25 to 85.11% and for the β-cyclodextrin containing details were seen as in the scope of 65.18-89.67%. The percentage entrapment effectiveness of nanosponges for ethyl cellulose containing formulations were seen as in the scope of 54.18 to 79.49% and for the β-cyclodextrin containing details were seen as in the scope of 58.21-83.45%. In vitro drugreleasefindings demonstrated that at 12 h ethyl cellulose containing formulations discharged the drug in the scope of 57.27-89.09% and for the β-cyclodextrin containing formulations discharged in the scope of 73.94-93.26%. Conclusion: Sustained drugreleasefrom formulations is supported if there is an occurrence of ethyl cellulose in the formulations rather with plans containing β-cyclodextrin

Application and Analysis of Machine Learning Algorithms on Pima and Early Diabetes Datasets for Diabetes Prediction

Diabetes is a chronic condition that strike how your body burns food for energy. Much of the food you consume is converted by your body into sugar (glucose), which is then released into your bloodstream. Your pancreas releases insulin when your blood sugar levels rise. Over the years, several scholars have sought to create reliable diabetes prediction models. Due to a lack of adequate data sets and prediction techniques, this discipline still faces many unsolved research issues, which forces researchers to apply big data analytics and ML-based methodology. Four distinct machine learning algorithms are used in the study to analyze healthcare prediction analytics and solve the issues. In this investigation, the Pima and Early detection datasets were employed. We applied the Decision Tree, MLP, Naive Bayes, and Random Forest algorithms to these datasets and evaluated the accuracy and F-Measure. The goal of this research is to develop a system that could more precisely predict a patient's risk of developing diabetes