Extraction of chitin from edible crab shells of Callinectes sapidus and comparison with market purchased chitin

Chitin and its derived products have immense economic value due to their vital role in various biological activities as well as biomedical and industrial application. Insects, microorganism and crustaceans are the main supply of chitin but the crustaceans shell like shrimp, krill, lobsters and crabs are the main commercial sources. Chitin content of an individual varies depending on the structures possessing the polymer and the species. In this study edible crabs’ shells (Callinectes sapidus) were demineralized and deproteinized resulting in 13.8% (dry weight) chitin recovery from chitin wastes. FTIR and XRD analyses of the experimental crude as well as purified chitins revealed that both were much comparable to the commercially purchased controls. The acid pretreatment ceded 54g of colloidal chitin that resulted in 1080% of the crude chitin. The colloidal chitin was exploited for isolation of eighty five chitinolytic bacterial isolates from different sources. Zone of clearance was displayed by the thirty five isolates (41.17%) succeeding their growth at pH 7 on colloidal chitin agar medium. Maximum chitinolytic activity i.e. 301.55 U/ml was exhibited by isolate JF70 when cultivated in extracted chitin containing both carbon and nitrogen. The study showed wastes of blue crabs can be utilized for extraction of chitin and isolation of chitinolytic bacteria that can be used to degrade chitin waste, resolve environmental pollution as well as industrial purpose.

Expansion dataset COVID-19 chest X-ray using data augmentation and histogram equalization

The main important factor that plays vital role in success the deep learning is the deep training by many and many images, if neural networks are getting bigger and bigger but the training datasets are not, then it sounds like going to hit an accuracy wall. Briefly, this paper investigates the current state of the art of approaches used for a data augmentation for expansion the corona virus disease 2019 (COVID-19) chest X-ray images using different data augmentation methods (transformation and enhancement) the dataset expansion helps to rise numbers of images from 138 to 5520, the increasing rate is 3,900%, this proposed model can be used to expand any type of image dataset, in addition, the dataset have used with convolutional neural network (CNN) model to make classification if detected infection with COVID-19 in X-ray, the results have gotten high training accuracy=99%

Utilization of Transfer Learning Model in Detecting COVID-19 cases from Chest X-Ray Images

Diagnosis of COVID-19 pneumonia using patients’ chest X-Ray images is new but yet important task in the field of medicine. Researchers from different parts of the globe have developed many deep learning models to classify COVID-19. The performance of feature extraction and classifier plays a vital role in the recognizing the different patterns in the image. The pivotal process is the extraction of optimum features from the chest X-Ray images. The main goal of this study is to design an efficient hybrid algorithm that integrates the robustness of MobileNet (using transfer learning approach) to extract features and Support Vector Machine (SVM) to classify COVID-19. Experiments were conducted to test the proposed algorithm and it was found to have a high classification accuracy of 95%.

A Deep Neural Network for Detecting Coronavirus Disease Using Chest X-Ray Images

The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world and 5 lakh people die as of June 2020 due to Covid-19. Due to the highly contagious nature, early detection of this virus plays a vital role to break Covid chain. Recent studies done by China says that chest CT and X-Ray image may be used as a preliminary test for Covid detection. Deep learning-based CNN model can use to detect Coronavirus automatically from the chest X-rays images. This paper proposed a transfer learning-based approach to detect Covid disease. Due to the less number of Covid chest images, we are using a pre-trained model to classify X-ray images into Covid and Normal class. This paper presents the comparative study of a various pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2. Experiment results show that Inception_ResNet_V2 gives the better result as compare to VGGNet and ResNet model with training and test accuracy of 99.26 and 94, respectively.

Utilization of Transfer Learning Model in Detecting COVID-19 Cases From Chest X-Ray Images

Diagnosis of COVID-19 pneumonia using patients’ chest X-Ray images is new but yet important task in the field of medicine. Researchers from different parts of the globe have developed many deep learning models to classify COVID-19. The performance of feature extraction and classifier plays a vital role in the recognizing the different patterns in the image. The pivotal process is the extraction of optimum features from the chest X-Ray images. The main goal of this study is to design an efficient hybrid algorithm that integrates the robustness of MobileNet (using transfer learning approach) to extract features and Support Vector Machine (SVM) to classify COVID-19. Experiments were conducted to test the proposed algorithm and it was found to have a high classification accuracy of 95%.

Question Tagging via Graph-guided Ranking

With the increasing prevalence of portable devices and the popularity of community Question Answering (cQA) sites, users can seamlessly post and answer many questions. To effectively organize the information for precise recommendation and easy searching, these platforms require users to select topics for their raised questions. However, due to the limited experience, certain users fail to select appropriate topics for their questions. Thereby, automatic question tagging becomes an urgent and vital problem for the cQA sites, yet it is non-trivial due to the following challenges. On the one hand, vast and meaningful topics are available yet not utilized in the cQA sites; how to model and tag them to relevant questions is a highly challenging problem. On the other hand, related topics in the cQA sites may be organized into a directed acyclic graph. In light of this, how to exploit relations among topics to enhance their representations is critical. To settle these challenges, we devise a graph-guided topic ranking model to tag questions in the cQA sites appropriately. In particular, we first design a topic information fusion module to learn the topic representation by jointly considering the name and description of the topic. Afterwards, regarding the special structure of topics, we propose an information propagation module to enhance the topic representation. As the comprehension of questions plays a vital role in question tagging, we design a multi-level context-modeling-based question encoder to obtain the enhanced question representation. Moreover, we introduce an interaction module to extract topic-aware question information and capture the interactive information between questions and topics. Finally, we utilize the interactive information to estimate the ranking scores for topics. Extensive experiments on three Chinese cQA datasets have demonstrated that our proposed model outperforms several state-of-the-art competitors.

Technology readiness and e-service quality – impact on purchase intention and loyalty

PurposeThis paper aims to explore impact of technology readiness (TR) on e-service quality (ESQ) and effect of ESQ and TR on purchase intention (PI) and behavioral loyalty (BL) in the context of online shopping.Design/methodology/approachWith the help of the existing literature, the authors propose a conceptual model. Questionnaire was designed to collect data, and analysis has been done using a final sample of 341 respondents.FindingsThe results show how TR has a significant impact on ESQ, PI and BL. Outcomes also highlight that only three dimensions of ESQ have a positive impact on both PI and BL. System availability dimension of ESQ impacts neither PI nor BL. Therefore, TR and ESQ together play a vital role as enablers in influencing BL and PI in online shopping context.Practical implicationsThe study results will serve as a guide to business-to-consumer e-commerce players and help them to determine how TR and ESQ dimensions will help them to build BL and PI for online shopping.Originality/valueThis is one of the first studies that takes into consideration both TR and ESQ and check how they impact PI and BL. Also, in the Indian context, it is an under-researched area and tries to fulfill this gap.