A Word Embedding Based Approach for Focused Web Crawling Using the Recurrent Neural Network

Learning-based focused crawlers download relevant uniform resource locators (URLs) from the web for a specific topic. Several studies have used the term frequency-inverse document frequency (TF-IDF) weighted cosine vector as an input feature vector for learning algorithms. TF-IDF-based crawlers calculate the relevance of a web page only if a topic word co-occurs on the said page, failing which it is considered irrelevant. Similarity is not considered even if a synonym of a term co-occurs on a web page. To resolve this challenge, this paper proposes a new methodology that integrates the Adagrad-optimized Skip Gram Negative Sampling (A-SGNS)-based word embedding and the Recurrent Neural Network (RNN).The cosine similarity is calculated from the word embedding matrix to form a feature vector that is given as an input to the RNN to predict the relevance of the website. The performance of the proposed method is evaluated using the harvest rate (hr) and irrelevance ratio (ir). The proposed methodology outperforms existing methodologies with an average harvest rate of 0.42 and irrelevance ratio of 0.58

ADVENT OF AUGMENTED REALITY EXPERIENCE IN RETAIL AND ONLINE SHOPPING AND ITS INFLUENCING SIGNIFICANCE IN FUTURE

Augmented Reality (AR) is a trending technology that augments or superimposes an image generated by a computer system virtually into the real world environment for the user’s viewpoint using a smart phone or other hand held devices. AR shows recent advancements in the shopping domain with various implementation trails and refinement. The simplicity and flexibility in online shopping where people stay in their own place and do shopping brought a great challenge to retail shopping environment today. Retail stores are now struggling a lot to bring in the customers and the foot traffic has been greatly reduced due to which online sales are boosting and retail sales are stalling. This necessitates to bring new technological innovations to offline shopping to attract people. With the use of AR, it is possible to merge digital component to physical products in the store to stimulate the engagement of the shopping experience with more fun and joy. On the other hand, in the online shopping, though user reviews and product showcase aids the customers to analyze the quality, look and feel of diverse products, the buyer still cannot see how exactly the product fits in a real environment or how it works. Here plays AR a vital role in online shopping where it uses animations and visualization techniques to offer more value to their shoppers virtually aiding to see exactly the look of the product in user environment. This paper explains the advancement of AR in both retail and online shopping of various product domains with an implementation model of ShopAR for Online shopping and AR significance in near future

Nanoparticle delivery systems in the treatment of diabetes complications

Diabetes mellitus, an incurable metabolic disease, is characterized by changes in the homeostasis of blood sugar levels, being the subcutaneous injection of insulin the first line treatment. This administration route is however associated with limited patients compliance, due to the risk of pain, discomfort and local infection. Nanoparticles have been proposed as insulin carriers to make possible the administration of the peptide via friendlier pathways without the need of injection, i.e., via oral or nasal routes. Nanoparticles stand for particles in the nanometer range that can be obtained from different materials (e.g., polysaccharides, synthetic polymers, lipid) and are commonly used with the aim to improve the physicochemical stability of the loaded drug and thereby its bioavailability. This review discusses the use of different types of nanoparticles (e.g., polymeric and lipid nanoparticles, liposomes, dendrimers, niosomes, micelles, nanoemulsions and also drug nanosuspensions) for improved delivery of different oral hypoglycemic agents in comparison to conventional therapies.The authors acknowledge the financial support received from Portuguese Science and Technology Foundation (FCT/MCT) and from European Funds (PRODER/COMPETE) under the project reference M-ERA-NET/0004/2015-PAIRED, co-financed by FEDER, under the Partnership Agreement PT2020. The authors also acknowledge the support of the research project: “Nutraceutica come supporto nutrizionale nel paziente oncologico”, CUP: B83D18000140007.info:eu-repo/semantics/publishedVersio

Engineering nanoparticles for targeting rheumatoid arthritis: Past, present, and future trends

Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by synovial joint inflammation and cartilage and bone tissue destruction. Although there exist some treatment strategies for RA, they are not completely safe and effective. Therefore, it is important to develop and test new drugs for RA that specifically target inflamed/swollen joints and simultaneously attenuate other possible damages to healthy tissues. Nanotechnology can be a good alternative to consider when envisioning precise medication for treating RA. Through the use of nanoparticles, it is possible to increase bioavailability and bioactivity of therapeutics and enable selective targeting to damaged joints. Herein, recent studies using nanoparticles for the treatment of RA, namely with liposomes, polymeric nanoparticles, dendrimers, and metallic nanoparticles, have been reviewed. These therapeutic strategies have shown great promise in improving the treatment over that by traditional drugs. The results of these studies confirm that feasibility of the use of nanoparticles is mainly due to their biocompatibility, low toxicity, controlled release, and selective drug delivery to inflamed tissues in animal RA models. Therefore, it is possible to claim that nanotechnology will, in the near future, play a crucial role in advanced treatments and patient-specific therapies for human diseases such as RA.Financial support under the ARTICULATE project (No. QREN-13/SI/2011-23189). This study was also funded by the Portuguese Foundation for Science and Technology (FCT) project OsteoCart (No. PTDC/CTM-BPC/115977/2009), as well as the European Union’s FP7 Programme under grant agreement no REGPOT-CT2012-316331-POLARIS. The FCT distinction attributed to J. M. O. under the Investigator FCT program (No. IF/00423/2012) is also greatly acknowledged. C. G. also wished to acknowledge FCT for supporting her research (No. SFRH/BPD/94277/2013)info:eu-repo/semantics/publishedVersio

M2F-Net: A Deep Learning-Based Multimodal Classification with High-Throughput Phenotyping for Identification of Overabundance of Fertilizers

Amaranth, a pseudocereal crop which is rich in nutrients and climate resistant, can provide an opportunity to increase food security and nutritional content for the growing population. Farmers rely mainly on synthetic fertilizers to improve the quality and yield of the crop; however, this overuse harms the ecosystem. Understanding the mechanism causing this environmental deterioration is crucial for crop production and ecological sustainability. In recent years, high-throughput phenotyping using Artificial Intelligence (AI) has been thriving and can provide an effective solution for the identification of fertilizer overuse. Influenced by the strength of deep learning paradigms and IoT sensors, a novel multimodal fusion network (M2F-Net) is proposed for high-throughput phenotyping to diagnose overabundance of fertilizers. In this paper, we developed and analyzed three strategies that fuse agrometeorological and image data by assessing fusion at various stages. Initially two unimodal baseline networks were trained: Multi-Layer Perceptron (MLP) on agrometeorological data and a pre-trained Convolutional Neural Network (CNN) model DenseNet-121 on image data. With these baselines, the multimodal fusion network is developed, capable of adeptly learning from image and non-image data and the model’s performance is evaluated in terms of accuracy and Area Under Curve (AUC). Moreover, the fusion approaches that are considered outperformed the unimodal networks remarkably with 91% accuracy. From the experimental result, it is proven that incorporating agrometeorological information and images can substantially boost the classification performance for the overabundance of fertilizer

Dimensionality reduction using Principal Component Analysis for network intrusion detection

Intrusion detection is the identification of malicious activities in a given network by analyzing its traffic. Data mining techniques used for this analysis study the traffic traces and identify hostile flows in the traffic. Dimensionality reduction in data mining focuses on representing data with minimum number of dimensions such that its properties are not lost and hence reducing the underlying complexity in processing the data. Principal Component Analysis (PCA) is one of the prominent dimensionality reduction techniques widely used in network traffic analysis. In this paper, we focus on the efficiency of PCA for intrusion detection and determine its Reduction Ratio (RR), ideal number of Principal Components needed for intrusion detection and the impact of noisy data on PCA. We carried out experiments with PCA using various classifier algorithms on two benchmark datasets namely, KDD CUP and UNB ISCX. Experiments show that the first 10 Principal Components are effective for classification. The classification accuracy for 10 Principal Components is about 99.7% and 98.8%, nearly same as the accuracy obtained using original 41 features for KDD and 28 features for ISCX, respectively

Mammographic mass classification according to Bi‐RADS lexicon

The goal of this study is to propose a computer‐aided diagnosis system to differentiate between four breast imaging reporting and data system (Bi‐RADS) classes in digitised mammograms. This system is inspired by the approach of the doctor during the radiologic examination as it was agreed in BI‐RADS, where masses are described by their form, their boundary and their density. The segmentation of masses in the authors’ approach is manual because it is supposed that the detection is already made. When the segmented region is available, the features extraction process can be carried out. 22 visual characteristics are automatically computed from shape, edge and textural properties; only one human feature is used in this study, which is the patient's age. Classification is finally done using a multi‐layer perceptron according to two separate schemes; the first one consists of classify masses to distinguish between the four BI‐RADS classes (2, 3, 4 and 5). In the second one the authors classify abnormalities on two classes (benign and malign). The proposed approach has been evaluated on 480 mammographic masses extracted from the digital database for screening mammography, and the obtained results are encouraging