Deep Learning for User Behaviour Prediction Using Streaming Analytics

Streams of web user interactions reflect behaviour of customers or users of a web application through which a company is being operated online. The interactions may be in the form of visits to web components and even purchases made by users in case of e-Commerce applications. Modelling user behaviour can help the organizations to ascertain patterns of user behaviours and improve their products and services to meet their needs besides making promotional schemes. There are many existing methods for modelling user behaviour. However, of late, deep learning models are found to be more accurate and useful. In this paper a deep learning based framework is proposed for predicting web user behaviour from streams of user interactions. The framework is based on the mechanisms that exploit Recurrent Neural Network (RNN), one of the deep learning approaches, to learn from low-level features of sequential and streaming data. The mechanisms are used to model user interactions and predict the user behaviour with respect to purchasing items in future. In presence of plenty of items, item embeddings is explored for better results. In addition to this, attention mechanisms are employed to achieve RNN model interoperability. The empirical study revealed that the proposed framework is useful besides helping to evaluate different variants of attention mechanisms and item embeddings

Sensors Based Trash Can Using IOT

The main theme of our project is to develop a sensor based dustbin for a proper garbage management. This project depicts a worthy elucidation for maintaining green environment. The disposal of waste can be done efficiently by segregating between dry waste and wet waste. This system reduces the maintenance stress. A smart handling technology is used to avoid all such hazardous scenario and maintain cleanliness. The whole process is upheld by an embedded system integrated with microcontroller and sensors. The dustbin lid will automatically open when something is sensed within its boundary or limit. This project works on the basis of colour sensor. The sensor distinguishes the colour for efficient disposal of garbage. Thus the system is solution for environmental maintenance and reduces the work of human intervention in garbage maintenance

Genetic Variability for Quantitative Traits in China Aster [Callistephus chinensis (L.) Nees]

A field study was conducted to estimate genetic variability, heritability and genetic advance in 20 genotypes of China aster for 15 traits during the year 2012-13 in Randomized Complete Block Design, with three replications. Results revealed that the magnitude of phenotypic co-efficient of variation (PCV) was higher than genotypic co-efficient of variation (GCV) for all the traits studied. Narrow differences between GCV and PCV were recorded in all the characters except flowering duration, vase-life and shelf-life, indicating little environmental influence on expression of these characters. High (>20%) GCV and PCV were recorded for plant height, number of branches and leaves per plant, flower diameter, number of ray and disc florets/flower head, stalk length, and, number and weight of flowers/plant. Heritability estimates ranged from 28.30% (flowering duration) to 99.54% (flower diameter). High heritability (<60%) was observed for all the traits except flowering duration. High heritability, coupled with high genetic advance as per cent mean, was recorded for flower diameter, stalk-length, number of branches/plant, weight of flowers/plant, days to first flower opening, days to 50 per cent flowering, plant height, number of leaves/plant, number of ray and disc florets/flower head, number of flowers/plant, indicating a possible role of additive gene action. Thus, these traits can be improved through selection and breeding

Heavy metals concentration in soils under rainfed agro-ecosystems and their relationship with soil properties and management practices

Heavy metals are governed by parent material of soils and influenced by the soil physicochemical properties and soil and crop management practices. This paper evaluates total heavy metal concentrations in rainfed soils under diverse management practices of tropical India. Vertisols (clayey soils with high shrink/swell capacity) had the highest concentrations of heavy metals. However, chromium (Cr) content was above the threshold value in Aridisol [calcium carbonate (CaCO3)]-containing soils of the arid environments with subsurface horizon development. Concentration increased at lower depths (>30 cm). Basaltic soils showed higher concentrations of nickel (Ni), copper (Cu) and manganese (Mn). Cadmium (Cd), cobalt (Co), Cu and Mn concentrations were higher in soils cultivated to cotton, whereas Cr concentration was above the threshold level of 110 mg kg−1 in food crop cultivated soils. As the specific soil surface is closely related to clay content and clay type, soil’s ability to retain heavy metals is more closely tied to the specific surface than to the soil cation exchange capacity. Higher positive correlations were found between heavy metal concentrations and clay content [Cd(r = 0.85; p ≤ 0.01); Co (r = 0.88; p ≤ 0.05); Ni (r = 0.87; p ≤ 0.01); Co (r = 0.81; p ≤ 0.05); Zn (r = 0.49; p ≤ 0.01); Cr (r = 0.80; p ≤ 0.05); Mn (r = 0.79; p ≤ 0.01)]. The amounts of nitrogen–phosphorus–potassium applied showed a positive correlation with Co and Ni (r = 0.62; p ≤ 0.05). As several soils used for growing food crops are high in Ni, Cr and Mn, the flow of these metals in soil–plant–livestock/human chain needs further attention

How 'universal' is the United Nations' Universal Periodic Review process? An examination of the discussions held on polygamy

In 2006, United Nations Human Rights Council was tasked to establish a new human rights monitoring mechanism: Universal Periodic Review process. The primary aim of this process is to promote and protect the universality of all human rights issues and concerns via a dialogical peer review process. The aim of this investigation isto ask the following question: has this claim of promoting and protecting the universality of the human rights been met, or challenged, during state reviews in the UPR process? The issue of polygamy has been selected as the focus for this investigation to be used, primarily, as a tool to undertake an in-depth analysis of the discussions held during state reviews in the review process. In addition, this paper will employ scholarly debates between universalism and cultural relativism, as well as the sophisticated and nuanced approaches that fall in between the polarised opposites, to analyse the discussions held on human rights during state reviews. Ultimately, the findings and discussion of this investigation will provide a unique and valuable insight to the work and operation of the UPR process, so far

Collagen based magnetic nanocomposites for oil removal applications

A stable magnetic nanocomposite of collagen and superparamagnetic iron oxide nanoparticles (SPIONs) is prepared by a simple process utilizing protein wastes from leather industry. Molecular interaction between helical collagen fibers and spherical SPIONs is proven through calorimetric, microscopic and spectroscopic techniques. This nanocomposite exhibited selective oil absorption and magnetic tracking ability, allowing it to be used in oil removal applications. The environmental sustainability of the oil adsorbed nanobiocomposite is also demonstrated here through its conversion into a bi-functional graphitic nanocarbon material via heat treatment. The approach highlights new avenues for converting bio-wastes into useful nanomaterials in scalable and inexpensive ways

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species

Metabolites of Purine Nucleoside Phosphorylase (NP) in Serum Have the Potential to Delineate Pancreatic Adenocarcinoma

Pancreatic Adenocarcinoma (PDAC), the fourth highest cause of cancer related deaths in the United States, has the most aggressive presentation resulting in a very short median survival time for the affected patients. Early detection of PDAC is confounded by lack of specific markers that has motivated the use of high throughput molecular approaches to delineate potential biomarkers. To pursue identification of a distinct marker, this study profiled the secretory proteome in 16 PDAC, 2 carcinoma in situ (CIS) and 7 benign patients using label-free mass spectrometry coupled to 1D-SDS-PAGE and Strong Cation-Exchange Chromatography (SCX). A total of 431 proteins were detected of which 56 were found to be significantly elevated in PDAC. Included in this differential set were Parkinson disease autosomal recessive, early onset 7 (PARK 7) and Alpha Synuclein (aSyn), both of which are known to be pathognomonic to Parkinson's disease as well as metabolic enzymes like Purine Nucleoside Phosphorylase (NP) which has been exploited as therapeutic target in cancers. Tissue Microarray analysis confirmed higher expression of aSyn and NP in ductal epithelia of pancreatic tumors compared to benign ducts. Furthermore, extent of both aSyn and NP staining positively correlated with tumor stage and perineural invasion while their intensity of staining correlated with the existence of metastatic lesions in the PDAC tissues. From the biomarker perspective, NP protein levels were higher in PDAC sera and furthermore serum levels of its downstream metabolites guanosine and adenosine were able to distinguish PDAC from benign in an unsupervised hierarchical classification model. Overall, this study for the first time describes elevated levels of aSyn in PDAC as well as highlights the potential of evaluating NP protein expression and levels of its downstream metabolites to develop a multiplex panel for non-invasive detection of PDAC

8th European Conference on Rare Diseases & Orphan Products (ECRD 2016)

MEETING ABSTRACTS: Open Access 8th European Conference on Rare Diseases & Orphan Products (ECRD 2016), Edinburgh, UK. 26-28 May 201