Skewed Evolving Data Streams Classification with Actionable Knowledge Extraction using Data Approximation and Adaptive Classification Framework

Skewed evolving data stream (SEDS) classification is a challenging research problem for online streaming data applications. The fundamental challenges in streaming data classification are class imbalance and concept drift. However, recently, either independently or together, the two topics have received enough attention; the data redundancy while performing stream data mining and classification remains unexplored. Moreover, the existing solutions for the classification of SEDSs have focused on solving concept drift and/or class imbalance problems using the sliding window mechanism, which leads to higher computational complexity and data redundancy problems. To end this, we propose a novel Adaptive Data Stream Classification (ADSC) framework for solving the concept drift, class imbalance, and data redundancy problems with higher computational and classification efficiency. Data approximation, adaptive clustering, classification, and actionable knowledge extraction are the major phases of ADSC. For the purpose of approximating unique items in the data stream with data pre-processing during the data approximation phase, we develop the Flajolet Martin (FM) algorithm. The periodically approximated tuples are grouped into distinct classes using an adaptive clustering algorithm to address the problem of concept drift and class imbalance. In the classification phase, the supervised classifiers are employed to classify the unknown incoming data streams into either of the classes discovered by the adaptive clustering algorithm. We then extract the actionable knowledge using classified skewed evolved data stream information for the end user decision-making process. The ADSC framework is empirically assessed utilizing two streaming datasets regarding classification and computing efficiency factors. The experimental results shows the better efficiency of the proposed ADSC framework as compared with existing classification methods

Context-Aware Clustering and the Optimized Whale Optimization Algorithm: An Effective Predictive Model for the Smart Grid

For customers to participate in key peak pricing, period-of-use fees, and individualized responsiveness to demand programmes taken from multi-dimensional data flows, energy use projection and analysis must be done well. However, it is a difficult study topic to ascertain the knowledge of use of electricity as recorded in the electricity records' Multi-Dimensional Data Streams (MDDS). Context-Aware Clustering (CAC) and the Optimized Whale Optimization Algorithm were suggested by researchers as a fresh power usage knowledge finding model from the multi-dimensional data streams (MDDS) to resolve issue (OWOA). The proposed CAC-OWOA framework first performs the data cleaning to handle the noisy and null elements. The predictive features are extracted from the novel context-aware group formation algorithm using the statistical context parameters from the pre-processed MDDS electricity logs. To perform the energy consumption prediction, researchers have proposed the novel Artificial Neural Network (ANN) predictive algorithm using the bio-inspired optimization algorithm called OWOA. The OWOA is the modified algorithm of the existing WOA to overcome the problems of slow convergence speed and easily falling into the local optimal solutions. The ANN training method is used in conjunction with the suggested bio-inspired OWOA algorithm to lower error rates and boost overall prediction accuracy. The efficiency of the CAC-OWOA framework is evaluated using the publicly available smart grid electricity consumption logs. The experimental results demonstrate the effectiveness of the CAC-OWOA framework in terms of forecasting accuracy, precision, recall, and duration when compared to underlying approaches

Machine Learning Approach for Comparative Analysis of De-Noising Techniques in Ultrasound Images of Ovarian Tumors

Ovarian abnormalities such ovarian cysts, tumors, and polycystic ovaries are one of the serious disorders affecting women's health. In ultrasound imaging of ovarian abnormalities, noise during capturing of the image and its transmission process frequently corrupts the image. In order to make the best judgments possible at the appropriate moment, ovarian cysts in females must be accurately detected.  In computer aided diagnosis of ovarian tumors, preprocessing is a very important step. In preprocessing, de-noising of medical images is a particularly a difficult task since it must be done while maintaining image features that are essential for diagnosis. In this research work we are using various denoising filters on ultrasound images of ovarian tumors. For different noise denoising techniques, performance measures like MSE, PSNR, SSIM, and UQI etc. are calculated. According to experimental findings, Block matching 3-D filter outperforms all other methods. Radiologists can better diagnose the condition with the use of this computer-assisted system

Prescription pattern of antimicrobial agents prescribed in outpatient department of dermatology in a tertiary care hospital in India

Background: Skin diseases contribute largely to global disease burden. Antimicrobial agents are used for treatment of various skin diseases of microbial aetiology caused by fungi, bacteria, viruses and ectoparasites. The primary objective of this study was to study the prescription pattern of antimicrobial agents in dermatology, to provide insights into the disease patterns, profile of the drugs used and their rationality. Methods: Cross-sectional observational study was conducted in dermatology outpatient department of T. N. M. C. and B. Y. L. Nair Charitable Hospital, Mumbai for period of 6 months. 372 prescriptions containing an antimicrobial agent (AMA) were analysed. Demographic data, disease pattern, associated comorbidities and prescription details were recorded after taking written informed consent. Results: Fungal infections were the most common (48%) followed by bacterial infections (31%). The most encountered condition was dermatophytosis. Average number of AMA per prescription was 2.33±0.73. Percentage of AMA prescribed by generic name was 48%. Percentage of AMA prescribed from National list of essential medicines 2015 (NLEM) was 32.60%. 87.9% of AMA were prescribed as combination therapy and 12.10% were prescribed as monotherapy. The commonest prescribed drugs were antifungals followed by antibiotics. Topical creams were the commonest prescribed dosage form. Conclusions: The most common class of antimicrobial agents prescribed was antifungal agents. Prescribing combination of oral antimicrobials with topical antimicrobials was found to be high. This study provides a framework for continuous prescription audit of antimicrobials in an outpatient setting and thus can help in rational use of antimicrobials in dermatological prescribing

Evaluation of the Antiasthmatic Activity of Methanolic Extract of Trigonella Foenum Graecum on Experimental Models of Bronchial Asthma

The present study deals with the phytochemical screening and evaluation of antiasthmatic activity of methanolic extract of Trigonella foenumgraecum on experimental models of bronchial asthma and anaphylaxis. The antiasthmatic activity was studied on histamine-induced bronchospasm in guinea pig (Dunkey-Hartley) for respiratory parameters such as maximum airflow, minimum airflow, tidal volume, respiratory rate, minute volume, specific airway resistance determination on double chambered whole body plethysmography on un-anesthetized guinea pigs, for mast cell degranulation by compound 48/80 (in vitro) was done using rat (Albino Wistar) peritoneal fluid. Trigonella foenum graecum treated result indicated significant protection against histamine-induced bronchospasm in guinea pigs at highest dose i.e. 400mg/kg. The bronchodilatory effect of Trigonella foenum graecum was found comparable to the protection offered by the standard drug Salbutamol on respiratory parameters in double chambered whole body plethysmography, Treatment with Trigonella foenum graecum at a dose of 400mg/kg showed a significant decrease in degranulation rate of actively and passively sensitized mast cells of sensitized rats when challenged with antigen. Trigonella foenum graecum. Possess significant anti-asthmatic activity due to its potential anti inflammatory, antioxidant and the antihistaminic activity, which reflects as anti-degranulating effect on mast cells and on respiratory parameters. Keywords: Trigonella foenum graecum; asthma; mast cell; compound 48/80; histamin

Effect of Conductive Filament Temperature on ZrO2 based Resistive Random Access Memory Devices

In the present work, the effect of reset voltage, filament radius, filament resistivity, and oxide membrane thickness on the nanoscale ZrO2 RRAM devices was reported. The present investigation is based on the thermal reaction model of RRAM. The outcomes show a decline in saturated temperature with a rise in the radius and resistivity of filament. Furthermore, increases in saturated temperature with an increase in oxide membrane thickness were observed for the ZrO2 based RRAM device. The saturated temperature of the device was mainly influenced by reset voltage, oxide layer thickness, filament size, and filament resistivity. The simulation results of the present investigation can be beneficial for the optimization of RRAM devices

An efficient naphthalimide based receptor for selective detection of Hg2+and Pb2+ions

Naphthalimide based receptor 1 with N-substituted benzothiazole and pyrrolidine subunit is designed, synthesized, and characterized using FT-IR,1H and 13C NMR spectroscopy and mass spectrometry techniques. The receptor 1 exhibits prominent optical response for Hg2+and Pb2+ions allowing the detection of these ions in acetonitrile (ACN). The formation of the receptor 1:cation complexes have been investigated using UV-Vis and fluorescence emission titration. Further, the selectivity of the receptor 1towards Hg2+and Pb2+ ions on the presence of various interfering cations such as Mg2+, Ba2+, Ni2+, Co2+, Cu2+, Ag2+, Fe2+, Fe3+and Cr3+ has been confirmed by UV-Vis and fluorescence spectroscopy. The binding constant between receptor 1 and Hg2+ and Pb2+ was estimated by Benesi-Hildebrand plot and equations. The binding constants have been found to be Ka= 3.43286 ´ 10−6 and Ka= 2.84079 ´ 10−6 M for Hg2+ and Pb2+, respectively. The limit of detection (LOD) for Hg2+and Pb2+by receptor 1are down to 7.44 ´ 10−10 M and 1.26 ´ 10−9 M, respectively. In addition, Job’s plot analysis reveals 1:2 binding stoichiometry between the receptor 1 and Pb2+ and Hg2+ cations.

Continued self-similar breakup of drops in viscous continuous phase in agitated vessels

Drop breakup inviscous liquids in agitated vessels occurs in elongational flow around impeller blade edges. The drop size distributions measured over extended periods for impellers of different sizes show that breakup process continues up to 15-20 h, before a steady state is reached. The size distributions evolve in a self-similar way till the steady state is reached. The scaled size distributions vary with impeller size and impeller speed, in contrast with the near universal scaling known for drop breakup in turbulent flows. The steady state size of the largest drop follows inverse scaling with impeller tip velocity. The breadth of the scaled size distributions also shows a monotonic relationship with impeller tip velocity only. (C) 2011 Elsevier Ltd. All rights reserved