Online shopping is increasingly becoming people's first choice when shopping, as it is very convenient to choose products based on their reviews. Even for moderately popular products, there are ...thousands of reviews constantly being posted on e-commerce sites. Such a large volume of data constantly being generated can be considered as a big data challenge for both online businesses and consumers. That makes it difficult for buyers to go through all the reviews to make purchase decisions. In this research, we have developed models based on machine learning that can predict the helpfulness of the consumer reviews using several textual features such as polarity, subjectivity, entropy, and reading ease. The model will automatically assign helpfulness values to an initial review as soon as it is posted on the website so that the review gets a fair chance of being viewed by other buyers. The results of this study will help buyers to write better reviews and thereby assist other buyers in making their purchase decisions, as well as help businesses to improve their websites.
Automatic Sign Language Recognition (SLR) systems are usually designed by means of recognizing hand and finger gestures. However, facial expressions play an important role to represent the emotional ...states during sign language communication, has not yet been analyzed to its fullest potential in SLR systems. A SLR system is incomplete without the signer’s facial expressions corresponding to the sign gesture. In this paper, we present a novel multimodal framework for SLR system by incorporating facial expression with sign gesture using two different sensors, namely Leap motion and Kinect. Sign gestures are recorded using Leap motion and simultaneously a Kinect is used to capture the facial data of the signer. We have collected a dataset of 51 dynamic sign word gestures. The recognition is performed using Hidden Markov Model (HMM). Next, we have applied Independent Bayesian Classification Combination (IBCC) approach to combine the decision of different modalities for improving recognition performance. Our analysis shows promising results with recognition rates of 96.05% and 94.27% for single and double hand gestures, respectively. The proposed multimodal framework achieves 1.84% and 2.60% gains as compared to uni-modal framework on single and double hand gestures, respectively.
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•The DLPNO-CCSD(T) method is assessed against the gold standard CCSD(T) method in determining the barrier heights and reaction energetics for hydrogen atom transfer reactions.•The ...dataset consists of both closed as well as open shell systems.•The standard deviation of the barrier height between DLPNO-CCSD(T) and CCSD(T) method was found to be less than 0.2 kcal mol−1.•The multireference systems required a high value of TcutPNO to achieve a chemical accuracy.
We have assessed the accuracy of the DLPNO-CCSD(T) method against the CCSD(T) method in determining the barrier heights and reaction energetics for a series of hydrogen atom transfer reactions. Our reaction list consists of both closed shell and open shell prototype systems. For closed shell systems, the difference in calculated barrier heights at DLPNO-CCSD(T)/aug-cc-pVnZ and CCSD(T)/aug-cc-pVnZ (n = D, T and Q) level of theory were found to be always less than ∼0.8 kcal mol−1 with a standard deviation of 0.23, 0.18 and 0.16 kcal mol−1 at aug-cc-pVDZ, aug-cc-pVTZ, and aug-cc-pVQZ basis sets, respectively. The standard deviation of the same for the open shell systems were found to be 0.43, 0.79 and 0.91 kcal mol−1 at aug-cc-pVDZ, aug-cc-pVTZ, and aug-cc-pVQZ basis sets, respectively. Furthermore, the study reveals that a modification is needed in the recommended value of the parameter “TcutPNO” while using the DLPNO-CCSD(T) method for reactions exhibiting multireference character.
Health experts use advanced technological equipment to find complex diseases and diagnose them. Medical imaging nowadays is popular for detecting abnormalities in human bodies. This research ...discusses using the Internet of Medical Things in the COVID-19 crisis perspective. COVID-19 disease created an unforgettable remark on human memory. It is something like never happened before, and people do not expect it in the future. Medical experts are continuously working on getting a solution for this deadly disease. This pandemic warns the healthcare system to find an alternative solution to monitor the infected person remotely. Internet of Medical Things can be helpful in a pandemic scenario. This paper suggested a ensemble transfer learning framework predict COVID-19 infection. The model used the weighted transfer learning concept and predicted the COVID- 19 infected people with an F1-score of 0.997 for the best case on the test dataset. KEYWORDS Convolutional Neural Network, COVID-19, Deep Learning, Ensemble Learning, Healthcare,Transfer Learning.
Recognition of 3-D texts drawn by fingers using Leap motion sensor can be challenging for existing text recognition frameworks. The texts sensed by Leap motion device are different from traditional ...offline and on-line writing systems. This is because of frequent jitters and non-uniform character sizes while writing using Leap motion interface. Moreover, because of air writing, characters, words, and lines are usually connected by continuous stroke that makes it difficult to recognize. In this paper, we present a study of segmentation and recognition of text recorded using Leap motion sensor. The segmentation task of continuous text into words is performed using a heuristic analysis of stroke length between two successive words. Next, the recognition of each segmented word is performed using sequential classifiers. In this paper, we have performed 3-D text recognition using hidden Markov model (HMM) and bidirectional long short-term memory neural networks (BLSTM-NNs). We have created a data set consisting of 560 Latin sentences drawn by ten participants using Leap motion sensor for experiments. An accuracy of 78.2% has been obtained in word segmentation, whereas 86.88% and 81.25% accuracies have been recorded in word recognition using BLSTM-NN and HMM classifiers, respectively.
The rapid growth of Internet users led to unwanted cyber issues, including cyberbullying, hate speech, and many more. This article deals with the problems of hate speech on Twitter. Hate speech ...appears to be an inflammatory kind of interaction process that uses misconceptions to express a hate ideology. The hate speech focuses on various protected aspects, including gender, religion, race, and disability. Owing to hate speech, sometimes unwanted crimes are going to happen as someone or a group of people get disheartened. Hence, it is essential to monitor user's posts and filter the hate speech related post before it is spread. However, Twitter receives more than six hundred tweets per second and about 500 million tweets per day. Manually filtering any information from such a huge incoming traffic is almost impossible. Concerning to this aspect, an automated system is developed using the Deep Convolutional Neural Network (DCNN). The proposed DCNN model utilises the tweet text with GloVe embedding vector to capture the tweets' semantics with the help of convolution operation and achieved the precision, recall and F1-score value as 0.97, 0.88, 0.92 respectively for the best case and outperformed the existing models.
Given the spasmodic increment in antimicrobial resistance (AMR), world is on the verge of "post-antibiotic era". It is anticipated that current SARS-CoV2 pandemic would worsen the situation in ...future, mainly due to the lack of new/next generation of antimicrobials. In this context, nanoscale materials with antimicrobial potential have a great promise to treat deadly pathogens. These functional materials are uniquely positioned to effectively interfere with the bacterial systems and augment biofilm penetration. Most importantly, the core substance, surface chemistry, shape, and size of nanomaterials define their efficacy while avoiding the development of AMR. Here, we review the mechanisms of AMR and emerging applications of nanoscale functional materials as an excellent substitute for conventional antibiotics. We discuss the potential, promises, challenges and prospects of nanobiotics to combat AMR.
Over the last couple of decades, community question‐answering sites (CQAs) have been a topic of much academic interest. Scholars have often leveraged traditional machine learning (ML) and deep ...learning (DL) to explore the ever‐growing volume of content that CQAs engender. To clarify the current state of the CQA literature that has used ML and DL, this paper reports a systematic literature review. The goal is to summarise and synthesise the major themes of CQA research related to (i) questions, (ii) answers and (iii) users. The final review included 133 articles. Dominant research themes include question quality, answer quality, and expert identification. In terms of dataset, some of the most widely studied platforms include Yahoo! Answers, Stack Exchange and Stack Overflow. The scope of most articles was confined to just one platform with few cross‐platform investigations. Articles with ML outnumber those with DL. Nonetheless, the use of DL in CQA research is on an upward trajectory. A number of research directions are proposed.
Vertebrate glycoproteins and glycolipids are synthesized in complex biosynthetic pathways localized predominantly within membrane compartments of the secretory pathway. The enzymes that catalyze ...these reactions are exquisitely specific, yet few have been extensively characterized because of challenges associated with their recombinant expression as functional products. We used a modular approach to create an expression vector library encoding all known human glycosyltransferases, glycoside hydrolases, and sulfotransferases, as well as other glycan-modifying enzymes. We then expressed the enzymes as secreted catalytic domain fusion proteins in mammalian and insect cell hosts, purified and characterized a subset of the enzymes, and determined the structure of one enzyme, the sialyltransferase ST6GalNAcII. Many enzymes were produced at high yields and at similar levels in both hosts, but individual protein expression levels varied widely. This expression vector library will be a transformative resource for recombinant enzyme production, broadly enabling structure-function studies and expanding applications of these enzymes in glycochemistry and glycobiology.
This work is mainly based on the optimal design of a standalone Hybrid Renewable Energy System (HRES) consisting of PV/diesel/battery systems, implemented in an academic building. Different hybrid ...system configurations such as PV-diesel generator-battery, diesel generator-battery, and PV-diesel generator are compared based on Net Present Cost (NPC) and Cost Of Energy (COE) to find out the best economically viable and environmentally friendly solution. Li-ion and lead-acid batteries were taken into consideration, and the optimization was done in HOMER PRO software. The PV-DG-Li-ion battery configuration emits approximately 2825387kg/year CO2 whereas the conventional DG system emits 4565074kg/year. It is concluded that the PV-DG-Li-ion battery configuration provides the cleanest and most environment-friendly and techno-economically feasible solution.
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