Big Data, the new buzz word in the industry, is data that exceeds the processing and analytic capacity of conventional database systems within the time necessary to make them useful. With multiple data stores in abundant formats, billions of rows of data with hundreds of millions of data combinations and the urgent need of making best possible decisions, the challenge is big and the solution bigger, Big Data. Comes with it, new advances in computing technology together with its high performance analytics for simpler and faster processing of only relevant data to enable timely and accurate insights using data mining and predictive analytics, text mining, forecasting and optimization on complex data to continuously drive innovation and make the best possible decisions. While Big Data provides solutions to complex business problems like analyzing larger volumes of data than was previously possible to drive more precise answers, analyzing data in motion to capture opportunities that were previously lost, it poses bigger challenges in testing these scenarios. Testing such highly volatile data, which is more often than not unstructured generated from myriad sources such as web logs, radio frequency Id (RFID), sensors embedded in devices, GPS systems etc. and mostly clustered data for its accuracy, high availability, security requires specialization. One of the most challenging things for a tester is to keep pace with changing dynamics of the industry. While on most aspects of testing, the tester need not know the technical details behind the scene however this is where testing Big Data Technology is so different. A tester not only needs to be strong on testing fundamentals but also has to be equally aware of minute details in the architecture of the database designs to analyze several performance bottlenecks and other issues. Like in the example quoted above on In-Memory databases, a tester would need to know how the operating systems allocate and de-allocate memory and understand how much memory is being used at any given time. So, concluding, as the data-analytics Industry evolves further we would see the IT Testing Services getting closely aligned with the Database Engineering and the industry would need more skilled testing professional in this domain to grab the new opportunities.
In the field of wireless multimedia authentication unimodal biometric model is commonly used but it suffers from spoofing and limited accuracy. The present work proposes the fusion of features of face and fingerprint recognition system as an Improved Biometric Fusion System (IBFS) leads to improvement in performance. Integrating multiple biometric traits recognition performance is improved and thereby reducing fraudulent access.The paper introduces an IBFS comprising of authentication systems that are Improved Fingerprint Recognition System (IFPRS) and Improved Face Recognition System (IFRS) are introduced. Whale optimization algorithm is used with minutiae feature for IFPRS and Maximally Stable External Regions (MSER) for IFRS. To train the designed IBFS, Pattern net model is used as a classification algorithm. Pattern net works based on processed data set along with SVM to train the IBFS model to achieve better classification accuracy. It is observed that the proposed fusion system exhibited average true positive rate and accuracy of 99.8 percentage and 99.6 percentage, respectively.
The most important factor in quality measurement of any technique or system is the efficiency. The hyper spectral images are the complex ones those are taken from the low resolution cameras. These images are often degraded in the acquisition process due to the integrated so many different noises in it. These can include various types of noises as impulse noise, Gaussian noise, deadlines and stripes etc. This paper defines new hybrid image restoration method based on Linear Discriminative Analysis (LDA) and Low Rank Matrix Recovery (LRMR). The proposed approach works in two phase, first it converting an image into number of patches and treating each patch as a separate image, performancecan be measured out and that patches induced in LRMR framework. In second phase, for further improvement in image quality LDA approach is used.
BACKGROUND: The development of innovative approaches in drug analysis is a challenging task for medicine, pharmacy, and engineering sciences. For instance, requirement of proper dosage forms in releasing active ingredients is crucial. It is essential to analyze drugs in biological liquids for early diagnosis and treatment purposes. Drug analysis is also of great importance to control the quality of pharmaceutical products, test their efficacy, and develop novel drug formulation. The present review is aimed to highlight the most recent spectrophotometric approaches applied to analyze various classes of drugs in biological media and/or dosage forms. METHODS: As well as direct and derivative UV/UV–Vis spectrophotometry, combination of various techniques with spectrophotometry, such as injection analysis and chemometrics, has been most widely applied in the analysis of dosage forms. In addition, emerging technologies, such as UV imaging allowing to obtain the distribution of drug concentration in a time-resolved 2D images based on UV light absorption, utilization of nanotechnology, self-assembled nanomaterials, and aptamer-based nanoparticles have been gained interests to investigate drug assays and to quantify the proper drug release. RESULTS: Due to their high versatility, ease of application, low cost, and fast response, spectrophotometric methods are one of the most preferable methods providing high accuracy and precision with a wide linear range in drug analysis. CONCLUSION: Selected examples demonstrating the applicability of spectrophotometric methods in pharmaceutical assays in this review might contribute to the overall importance of the analytical test used in the modern pharmaceutical analysis.
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