A CNN Based Approach for Garments Texture Design Classification

Identifying garments texture design automatically for recommending the fashion trends is important nowadays because of the rapid growth of online shopping. By learning the properties of images efficiently, a machine can give better accuracy of classification. Several Hand-Engineered feature coding exists for identifying garments design classes. Recently, Deep Convolutional Neural Networks (CNNs) have shown better performances for different object recognition. Deep CNN uses multiple levels of representation and abstraction that helps a machine to understand the types of data more accurately. In this paper, a CNN model for identifying garments design classes has been proposed. Experimental results on two different datasets show better results than existing two well-known CNN models (AlexNet and VGGNet) and some state-of-the-art Hand-Engineered feature extraction methods

Enhancing Performance And Reliability of Rule Management Platforms

ABSTRACT RulE Management Platforms (REMPs) enable software engineers to represent programming logic as conditional sentences that relate statements of facts. A key benefit of REMPs is that they make software adaptable by burying the complexity of rule invocation in their engines, so that programmers can concentrate on business aspects of highly modular rules. Naturally, rule-driven applications are expected to have excellent performance, since REMP engines should be able to invoke highly modular rules in parallel in response to asserting different facts. In reality, it is very difficult to parallelize rule executions, since it leads to the loss of reliability and adaptability of rule-driven applications. We created a novel solution that is based on obtaining a rule execution model that is used at different layers of REMPs to enhance the performance of rule-driven applications while maintaining their reliability and adaptability. First, using this model, possible races are detected statically among rules, and we evaluate an implementation of our abstraction of algorithms for automatically preventing races among rules. Next, we use the sensitivity analysis to find better schedules among simultaneously executing rules to improve the overall performance of the application. We implemented our solution for JBoss Drools and we evaluated it on three applications. The results suggest that our solution is effective, since we achieved over 225% speedup on average

Exploration of the drivers influencing the growth of hybrid electric vehicle adoption in the emerging economies: Implications towards sustainability and low-carbon economy

The heavy reliance of the transportation and power generation sector on fossil fuels is seriously impacting the environment. Transitioning towards more sustainable transportation modes is necessary to reduce this dependency on fossil fuels. Even though shifting toward electric vehicles (EVs) can reduce harmful emissions, due to the lack of adequate charging infrastructures, underdeveloped power transmission systems, and increased cost of power generation, it is difficult for a developing country to adopt and rely heavily on EVs. However, developing countries like Bangladesh can adopt a different strategy to address this issue. Harmful emission reduction is also possible by transitioning from conventional internal combustion engine (ICE) vehicles to hybrid electric vehicles (HEVs). The drivers that can promote the expansion of HEV adoption have not been extensively studied to date, which inspired the proposed study. This study explores the drivers for the growth of HEV adoption in emerging economies. First, the study identifies seventeen drivers from the literature review and expert feedback. Then the identified drivers were assessed using the Bayesian Best-Worst method (BWM). The study findings indicate that no requirement for a charging station, incentivizing consumers through policy measures, and enhanced fuel efficiency are the top three drivers influencing the growth of HEV adoption in developing or emerging economies. This study can help the decision-makers and end users in developing counties to gradually shift towards a low-carbon emission-based economy and ensure a greener and more sustainable future

DTCTH: a discriminative local pattern descriptor for image classification

Abstract Despite lots of effort being exerted in designing feature descriptors, it is still challenging to find generalized feature descriptors, with acceptable discrimination ability, which are able to capture prominent features in various image processing applications. To address this issue, we propose a computationally feasible discriminative ternary census transform histogram (DTCTH) for image representation which uses dynamic thresholds to perceive the key properties of a feature descriptor. The code produced by DTCTH is more stable against intensity fluctuation, and it mainly captures the discriminative structural properties of an image by suppressing unnecessary background information. Thus, DTCTH becomes more generalized to be used in different applications with reasonable accuracies. To validate the generalizability of DTCTH, we have conducted rigorous experiments on five different applications considering nine benchmark datasets. The experimental results demonstrate that DTCTH performs as high as 28.08% better than the existing state-of-the-art feature descriptors such as GIST, SIFT, HOG, LBP, CLBP, OC-LBP, LGP, LTP, LAID, and CENTRIST

Nanoparticle Location and Material-Dependent Dose Enhancement in X-ray Radiation Therapy

Nanoparticles of high atomic number (Z) materials can act as radiosensitizers to enhance radiation dose delivered to tumors. An analytical approach is used to calculate dose enhancements to tumor endothelial cells and their nuclei for a series of nanoparticles (bismuth, gold and platinum) located at different locations relative to nuclei by considering contributions from both photoelectrons and Auger electrons. The ratio of the dose delivered to cells with and without the nanoparticles is known as the dose enhancement factor (DEF). DEFs depend on material composition, size and location of nanoparticles with respect to the cell and the nucleus. Energy of irradiating X-ray beam affects X-ray absorption by nanoparticles and plays an important role in dose enhancements. For diagnostic X-ray sources, bismuth nanoparticles provide higher dose enhancements than gold and platinum nanoparticles for a given nanoparticle size, concentration and location. The highest DEFs are achieved for nanoparticles located closest to the nucleus where energy depositions from short range Auger electrons are maximum. With nanoparticles ranging in diameter between 2-400 nm, the dose enhancement increases with decrease in particle size. The results are useful in finding optimized conditions for nanoparticle enhanced X-ray radiation therapy of cancer