World-Health-Organization (WHO) has listed Tuberculosis (TB) as one among the top 10 reasons for death and an early diagnosis will help to cure the patient by giving suitable treatment. TB usually affects the lungs and an accurate bio-imaging scheme will be apt to diagnose the infection. This research aims to implement an automated scheme to detect TB infection in chest radiographs (X-ray) using a chosen Deep-Learning (DL) approach. The primary objective of the proposed scheme is to attain better classification accuracy while detecting TB in X-ray images. The proposed scheme consists of the following phases namely, (1) image collection and pre-processing, (2) feature extraction with pre-trained VGG16 and VGG19, (3) Mayfly-algorithm (MA) based optimal feature selection, (4) serial feature concatenation and (5) binary classification with a 5-fold cross validation. In this work, the performance of the proposed DL scheme is separately validated for (1) VGG16 with conventional features, (2) VGG19 with conventional features, (3) VGG16 with optimal features, (4) VGG19 with optimal features and (5) concatenated dual-deep-features (DDF). All experimental investigations are conducted and achieved using MATLAB® program. Experimental outcome confirms that the proposed system with DDF yields a classification accuracy of 97.8%using a K Nearest-Neighbor (KNN) classifier.
Recently, a significant way to diagnose the disease is using the model of medical data mining. The most challenging task in the healthcare field is to face a large amount of data during disease analyzes and prediction. Once the data are transformed into valuable data by means of data mining models then the actual prediction and decision making is easier. The existing studies met few shortcomings because of higher execution time, more computational complexities, less scalability, slow convergence, and lack of providing the solution. In this article, we have proposed an ensemble SVM‐based sample weighted random forests (eSVM‐swRF) with novel improved colliding body optimization (NICBO) algorithm to predict liver diseases. The extraction, loading, transformation, and analysis (ELTA) are used to pre‐process the patient data. The significant feature with a suitable model is generated depending upon the filter‐based method. Based on eSVM‐swRF, the parameter values such as penalty parameter (P), threshold (T), and mTry are optimized via a novel improved colliding boding optimization (NICBO) algorithm. The UCI dataset provides liver disease data for this study. The implementation platform of RapidMiner Studio version 7.6 with different evaluation measures is used to validate the performance of eSVM‐swRF with the NICBO method. Anyway, the proposed method yields outstanding performance than other existing methods such as Particle Swarm Optimization‐based Support Vector Machine (PSO‐SVM), fuzzy adaptive, and neighbor weighted k‐NN (FuzzyANWKNN), Naïve Bayes‐based Support Vector Machine (NB‐SVM), and Neural network.
Attack detection is the major issue in healthcare‐based wireless sensor networks (H‐WSNs). Due to their low processing speed, very low storage space, poor attack detection rate, longer deployment time, poor communication range, and reduced energy, H‐WSNs are subjected to difficult implementation and have their own limitations. To tackle these issues, we have presented a hybrid deep learning model using convolutional neural network and long short term memory (HDMCL) for attack detection in H‐WSN. This research is divided into three steps: preprocessing, dimensionality reduction, and classification (attack detection). At first, the raw input data (patient's health data) is preprocessed using the one‐hot encoding method. Next, the modified Huber independent component analysis based squirrel search algorithm (MHICA‐SSA) effectively reduces the data dimensionality in which the MHICA‐SSA is the amalgamation of both modified Huber independent component analysis and squirrel search algorithm. The novel algorithm designed overcomes the complexities associated with existing techniques such as the curse of dimensionality problem, improves the parameter interpretation, minimizes the time and storage space, minimizes space complexity, and enhances the attack detection accuracy of the convolutional neural network‐based long short‐term memory (CNN‐LSTM). After that, the deep learning CNN‐LSTM model is utilized for normal, black hole, and gray hole attack detection. The NS2.34 network simulator implements the proposed work thereby the proposed work efficiency is validated using different performance measures such as packet delivery ratio, false alarm rate, network lifetime, energy consumption, throughput, attack detection time, and rate. The proposed work performance is evaluated using different existing methods such as fish swarm optimization based particle swarm optimization, intelligent opportunistic routing algorithm, Jensen‐Shannon divergence‐based independent component analysis, and radio frequency identification based WSN. The throughput of the proposed work is increased up to 3.2 times and the network lifetime is increased up to 4 times when compared to the existing techniques.
A post column derivatization method for the estimation of fat soluble vitamins K 2 -4 and K 2 -7 was developed by reverse phase HPLC and validated as per ICH guidelines. The compounds were extracted by solvent extraction with acetone followed by evaporation in a rotary evaporator. The residue was dissolved in ethanol and injected in to a chromatograph consisting of C18 column (Waters symmetry, 150×4.6, 3.5 μ) and fluorescence detector. The derivatization reagent was prepared by dissolving 136 mg of zinc chloride, 40 mg of sodium acetate and 0.1 ml of glacial acetic acid in methanol. The mobile phase comprises of methanol:isopropyl alcohol:acetonitrile:zinc chloride buffer solution 850:90:50:10 with a flow rate of 1 ml/min. The overall percentage recoveries of five different levels were found to be 99.85 and 100.5%, respectively. The linearity of the analytical method was determined from 10% to 120% level and the linear regression coefficients were 0.9991 and 0.9995 which is well within the acceptance criteria of 0.999. The limits of detection and limits of quantification were determined based on signal to noise ratio. The established values were 0.050, 0.50 μg/ml and 0.005, 0.047 μg/ml which is much lesser than available literatue limits. The developed method can be more suitable for the estimation of K 2 -4 and K 2 -7 present in drug substances as well as drug product formulations.Key words: Post column derivatization, nutraceutical solid dosage forms, estimation of K 2 -4 and K 2 -7 by HPLC, zinc chloride buffer solution, analytical estimation Vitamin K 2 includes several subtypes; the two subtypes most studied are menaquinone-4 (menatetrenone, MK-4) and menaquinone-7 (MK-7). Vitamin K 2 -4 ( fig. 1a) is yellow coloured powder, molecular formula C 31 H 40 O 2, molecular weight 444.65 g/mol, soluble in acetone and ethyl alcohol [1] . Vitamin K 2 -7 ( fig. 1b) is light yellow microcrystalline powder, molecular formula C 46 H 64 O 2, molecular weight 648.99 g/mol, soluble in petroleum ether, acetone, ethyl alcohol and insoluble in water [1] . Vitamin K is used for the treatment of anticoagulantinduced prothrombin deficiency caused by warfarin [2][3][4] . The American Academy of Paediatrics recommends that vitamin K should be given to all new born babies as a single intramuscular dose to prevent vitamin K deficiency bleeding. In Japan, vitamin K 2 is used for the management of osteoporosis. The fermented soya product nattō is rich in menaquinone-7 [2][3][4] .Derivatization reaction with the reduction of zinc metal ions to isolate compounds of closely eluting peaks by chromatography is fast emerging tool for analytical separation. The dosage form formulated with two active ingredients such as K 2 -4 and K 2 -7 along with excipients is very challenging effort to separate from its moiety. In order to separate the K series vitamins with almost same structural properties, the post column derivatization technique with fluorescence detection was used. The reduction mechanism of components with zinc chloride derivatizatio...
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