Data-driven based Optimal Feature Selection Algorithm using Ensemble Techniques for Classification

The shift in paradigm with advanced Machine Learning algorithms will help to face the challenges such as computational power, training time, and algorithmic stability. The individual feature selection techniques, hardly give the appropriate feature subsets, that might be vulnerable to the variations induced at the input data and thus led to wrong conclusions. An expedient technique should be designed for approximating the feature relevance to improve the performance for the data. Unlike the prevailing techniques, the novelty of the proposed Data-driven based Optimal Feature Selection (DOFS) algorithm is the optimal k-value ‘kf’ determined by the data for effective feature selection that minimizes the computational complexity and expands the prediction power using the gradient descent method. The experimental analysis of proposed algorithm is demonstarted with ensemble techniques for the non-communicable disease such as diabetes mellitus dataset produces an accuracy of 80.80%, whereas comparative performance analysis for benchmark dataset depicts the improved accuracy of 86.03%

Traffic Path Recommendation Model based on a Weighted Sum of Extracted Parameter

A path recommendation for vehicular traffic is important task of traffic analysis. It is a challenging problem for researchers to extract all paths and recommend the shortest path between Origin and Destination (OD) pairs. This paper comes up with a model which is established on the weighted sum of selected link references to recommend a path for OD pairs. First, to maintain spatial dependence between link references, a vehicular traffic network of roads is proposed as a rectangular coordinate system. The algorithm based on K-means and smoothing is introduced to select link references across OD pairs. A distance aggregation algorithm is proposed to evaluate all possible paths across an OD pair. Finally, out of overwhelming paths, the algorithm recommends the shortest distance path across an OD pair. Our proposed model effectively selects the link references and gets an overall shortest path recommendation. The proposed model analyzes the non-Euclidean distance of selected link references. Our experimental analysis shows that on an average, the first four link predictions lead to 77.37% distance coverage for the recommended path

Image Tagging using Modified Association Rule based on Semantic Neighbors

With the rapid development of the internet, mobiles, and social image-sharing websites, a large number of images are generated daily.  The huge repository of the images poses challenges for an image retrieval system. On image-sharing social websites such as Flickr, the users can assign keywords/tags to the images which can describe the content of the images. These tags play important role in an image retrieval system. However, the user-assigned tags are highly personalized which brings many challenges for retrieval of the images.  Thus, it is necessary to suggest appropriate tags to the images. Existing methods for tag recommendation based on nearest neighbors ignore the relationship between tags. In this paper, the method is proposed for tag recommendations for the images based on semantic neighbors using modified association rule. Given an image, the method identifies the semantic neighbors using random forest based on the weight assigned to each category. The tags associated with the semantic neighbors are used as candidate tags. The candidate tags are expanded by mining tags using modified association rules where each semantic neighbor is considered a transaction. In modified association rules, the probability of each tag is calculated using TF-IDF and confidence value. The experimentation is done on Flickr, NUS-WIDE, and Corel-5k datasets. The result obtained using the proposed method gives better performance as compared to the existing tag recommendation methods

Speech Recognition Using Vector Quantization through Modified K-meansLBG Algorithm

In the Vector Quantization, the main task is to generate a good codebook. The distortion measure between the original pattern and the reconstructed pattern should be minimum. In this paper, a proposed algorithm called Modified K-meansLBG algorithm used to obtain a good codebook. The system has shown good performance on limited vocabulary tasks. Keywords: K-means algorithm, LBG algorithm, Vector Quantization, Speech Recognitio

INHIBITION OF α-AMYLASE AND α-GLUCOSIDASE BY (6RS)-22-HYDROXY-23,24,25,26,27-PENTANOR-VITAMIN-D3-6,19-SULFUR DIOXIDE-ADDUCT, MANOALIDE AND 5β-CHOLESTANE-3α,7α,12α,24,25,26-HEXOL ISOLATED FROM ACETONE EXTRACT OF HELIANTHUS ANNUUS L. SEEDS

Objective: This investigation includes characterization of phytochemicals from acetone extract of Helianthus annuus L. seeds responsible for α-amylase and α-glucosidase inhibition revealed from in vitroand in silico approaches. Methods: Seed extract was qualitatively and quantitatively analysed for the presence of bioactive molecules. In vitro α-amylase and α-glucosidase inhibition assays and kinetics studies for α-glucosidase were done. Thin layer chromatography (TLC) autography of extract was done to screen potent inhibitors and characterized by high-resolution liquid chromatography-mass spectrometry (HR LC-MS). Characterized molecules were further used for in silico studies. Results: Qualitative investigation reveals the presence of flavonoids, glycosides, alkaloids, terpenoids, and steroids. Quantitative analysis for total phenolic content and total flavonoid content of the extract was 0.1±0.005 mg/ml GAE and 0.025±0.003 mg/ml QE respectively. Percent inhibition of α-amylase and α-glucosidase ascertained in presence of extract was 60.42±0.6 and 83.22±0.18 at 0.01 mg while 36.24±0.81 and 37.67±0.15 at 0.005 mg of extracts for both enzymes respectively. Kinetics studies of α-glucosidase inhibition illustrated the non-competitive type of inhibition. TLC autography inhibition patterns were characterized by HR LC-MS. Characterized molecules on docking revealed (6RS)-22-hydroxy-23,24,25,26,27-pentanor-vitamin-D3-6,19-sulfurdioxide-adduct, manoalide and 5β-cholestane-3α,7α,12α,24,25,26-hexol as the best docked molecules with lowest binding energies of-12.5,-11 and-10.2 kcal/mol for α-amylase and-14.2,-11 and-11.2 kcal/mol for α-glucosidase respectively. Conclusion: Results clearly suggested that (6RS)-22-hydroxy-23,24,25,26,27-pentanor-vitamin-D3-6,19-sulfurdioxide-adduct, manoalide and 5β-cholestane-3α,7α,12α,24,25,26-hexol could be considered as lead molecules for the discovery of potent antidiabetic agents

FORMULATION AND OPTIMIZATION OF NANOSUSPENSION FOR IMPROVING SOLUBILITY AND DISSOLUTION OF GEMFIBROZIL

Objective: The study aims at the formulation and optimization of gemfibrozil (Gem) nanosuspension (NS) for improving its solubility and dissolution rate.Method: Gem NS was prepared by precipitation-ultrasonication method using ethanol as solvent, water as anti-solvent, and polyvinyl alcohol (PVA) as a stabilizer. A Box–Behnken design was employed to study the effect of the independent variables, Gem concentration in the organic phase (X1), PVA concentration (X2) and sonication time (X3) on the dependent variable, drug release after 90 min (Y). The resulting data were statistically analyzed and subjected to 3D response surface methodology to study the influence of variables on the response. NS was evaluated for particle size, zeta potential, solubility and in vitro drug release and characterized using Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and X-ray diffractometry (XRD).Results: On the basis of the evaluation, NS4 formulation (with 80 mg/ml Gem, 0.5% PVA concentration, and 20 min of sonication time) demonstrated highest drug content with a particle size of 191.0 nm and zeta potential of −12.0 mV. Dissolution profiles of NS indicated 2.5-fold increase in drug release than pure drug. NS demonstrated 5- and 9-fold increase in solubility, in water, and phosphate buffer (pH 7.5), respectively, pure drug. DSC and XRD studies indicated changes in the crystallinity of Gem during NS formulation. No chemical change was evident in NS as indicated by FTIR.Conclusion: Gem NS formulation could serve as a promising approach for improving its solubility and dissolution rate

The radio source in Abell 980: A Detached-Double-Double Radio Galaxy?

It is argued that the new morphological and spectral information gleaned from the recently published LoFAR Two meter Sky Survey data release 2 (LoTSS-2 at 144 MHz) observations of the cluster Abell 980 (A980), in combination with its existing GMRT and VLA observations at higher frequencies, provide the much-needed evidence to strengthen the proposal that the cluster's radio emission comes mainly from two double radio sources, both produced by the brightest cluster galaxy (BCG) in two major episodes of jet activity. The two radio lobes left from the previous activity have become diffuse and developed an ultra-steep radio spectrum while rising buoyantly through the confining hot intra-cluster medium (ICM) and, concomitantly, the host galaxy has drifted to the cluster centre and entered a new active phase manifested by a coinciding younger double radio source. The new observational results and arguments presented here bolster the case that the old and young double radio sources in A980 conjointly represent a `double-double' radio galaxy whose two lobe-pairs have lost colinearity due to the (lateral) drift of their parent galaxy, making this system by far the most plausible case of a `Detached-Double-Double Radio Galaxy' (dDDRG).Comment: Accepted for publication by Publications of the Astronomical Society of Australia (PASA); 10 pages, 6 figure

Content Centric Network: Solution over IP Network Issues

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