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

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

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

Fertilizer Optimization through Machine Learning-driven Models: An Empirical Investigation on Smart Farming of Amaranth

Amaranth is a highly nutritious leafy vegetable cum pseudo-cereal crop known for its adaptability to various climatic conditions, making it a promising crop for addressing the food security and nutritional needs of a growing population. To enhance its quality and boost yields, farmers mainly depend on synthetic fertilizers. However, the excessive use of inorganic fertilizers to maximize crop yields poses significant ecological risks. This study aimed to investigate the impact of excessive inorganic fertilizer on the growth, yield, and physiological attributes of Amaranthus with the aid of advanced machine learning paradigm. An experimental pot trial was conducted using different NPK fertilizer dosage regimes, and agronomic parameters such as moisture level, crop yield, plant height, leaf length, leaf width etc. were measured and analyzed using statistical methods. The results demonstrated that the application of excessive inorganic fertilizer initially promoted plant growth but surpassed optimal levels resulting in negative effects, including stunted growth and reduced vigor. By identifying the Amaranthus's productivity and adaptability in different chemically treated soil conditions and automatically phenotyping its traits using image-based machine learning models, this study aims to determine the overuse of synthetic fertilizers. A comparative evaluation of different learning algorithms was carried out and the experimental result proves that SVM classifier could be a more appropriate learning algorithm for the proposed system with 80% accuracy. These findings highlight the importance of adopting sustainable fertilizer practices for the cultivation of Amaranthus and emphasize the need for ecological balance in crop production systems