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

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

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

Leveraging Readability and Sentiment in Spam Review Filtering Using Transformer Models

 Leveraging Readability and Sentiment in Spam Review Filtering Using Transformer Models </p

A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories

 A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories </p

QUMA: Quantum Unified Medical Architecture Using Blockchain

A significant increase in the demand for quality healthcare has resulted from people becoming more aware of health issues. With blockchain, healthcare providers may safely share patient information electronically, which is especially important given the sensitive nature of the data contained inside them. However, flaws in the current blockchain design have surfaced since the dawn of quantum computing systems. The study proposes a novel quantum-inspired blockchain system (Qchain) and constructs a unique entangled quantum medical record (EQMR) system with an emphasis on privacy and security. This Qchain relies on entangled states to connect its blocks. The automated production of the chronology indicator reduces storage capacity requirements by connecting entangled BloQ (blocks with quantum properties) to controlled activities. We use one qubit to store the hash value of each block. A lot of information regarding the quantum internet is included in the protocol for the entangled quantum medical record (EQMR). The EQMR can be accessed in Medical Internet of Things (M-IoT) systems that are kept private and secure, and their whereabouts can be monitored in the event of an emergency. The protocol also uses quantum authentication in place of more conventional methods like encryption and digital signatures. Mathematical research shows that the quantum converged blockchain (QCB) is highly safe against attacks such as external attacks, intercept measure -repeat attacks, and entanglement measure attacks. We present the reliability and auditability evaluations of the entangled BloQ, along with the quantum circuit design for computing the hash value. There is also a comparison between the suggested approach and several other quantum blockchain designs

Nature-inspired classification for mining social space information: National security intelligence and big data perspective

 Nature-inspired classification for mining social space information: National security intelligence and big data perspective </p

Mammogram mass classification using various geometric shape and margin features for early detection of breast cancer

 Mammogram mass classification using various geometric shape and margin features for early detection of breast cancer </p