Carrageenan-Based Hybrids with Biopolymers and Nano-Structured Materials for Biomimetic Applications

Carrageenan is an algal-originated group of polysaccharides with unusual structural and functional capabilities, desired for different biomimetic applications due to their renewable, biocompatible, and biodegradable nature. Carrageenan-based hybrids (nano-/biocomposites) with different biopolymers and nano-structured materials have been widely reported as potential candidates for bone/cartilage tissue engineering, delivery of drugs/bioactive ingredients, wound healing, and 3D bioprinting applications. Owning to the broad-scale biomimetic applications of carrageenan-based materials, this review aims to summarize carrageenan chemistry and distinct physicochemical features of biopolymeric and/or nanostructured materials-based on carrageenans in a detailed manner. Herein, different biopolymers (such as chitosan, cellulose, starch, and alginates), and nano-structured materials (such as silica nanoparticles, magnetic/non-magnetic nanocarriers, graphene oxide nanoparticles, carbon nanotubes/nanorods, metal oxide nanoparticles) are comprehensively described in combination with carrageenan. However, carrageenan toxicity studies have presented major challenges that need to be addressed when using carrageenan-based materials for biomedical and therapeutic purposes. Several existing challenges, prospects, and research recommendations are described at the end of this review.     

Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

Natural Language Processing (NLP) for the Arabic language has gained much significance in recent years. The most commonly-utilized NLP task is the ‘Text Classification’ process. Its main intention is to apply the Machine Learning (ML) approaches for automatically classifying the textual files into one or more pre-defined categories. In ML approaches, the first and foremost crucial step is identifying an appropriate large dataset to test and train the method. One of the trending ML techniques, i.e., Deep Learning (DL) technique needs huge volumes of different types of datasets for training to yield the best outcomes. The current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machine-based Arabic Corpus Classification (DODHBM-ACC) model in this background. The presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding process. For Arabic text classification, the DHBM technique is utilized. This technique is a hybrid version of the Deep Boltzmann Machine (DBM) and Deep Belief Network (DBN). It has the advantage of learning the decisive intention of the classification process. To adjust the hyperparameters of the DHBM technique, the Dice Optimization Algorithm (DOA) is exploited in this study. The experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC model. The outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.     

Entrapped Molecule-Like Europium-Oxide Clusters in Zinc Oxide with Nearly Unaffected Host Structure

Nanocrystalline ZnO sponges doped with 5 mol% EuO_1.5 are obtained by heating metal–salt complex based precursor pastes at 200–900 °C for 3 min. X-ray diffraction, transmission electron microscopy, and extended X-ray absorption fine structure (EXAFS) show that phase separation into ZnO:Eu and c-Eu₂O₃ takes place upon heating at 700 °C or higher. The unit cell of the clean oxide made at 600 °C shows only ≈0.4% volume increase versus undoped ZnO, and EXAFS shows a ZnO local structure that is little affected by the Eu-doping and an average Eu³⁺ ion coordination number of ≈5.2. Comparisons of 23 density functional theory-generated structures having differently sized Eu-oxide clusters embedded in ZnO identify three structures with four or eight Eu atoms as the most energetically favorable. These clusters exhibit the smallest volume increase compared to undoped ZnO and Eu coordination numbers of 5.2–5.5, all in excellent agreement with experimental data. ZnO defect states are crucial for efficient Eu³⁺ excitation, while c-Eu₂O₃ phase separation results in loss of the characteristic Eu³⁺ photoluminescence. The formation of molecule-like Eu-oxide clusters, entrapped in ZnO, proposed here, may help in understanding the nature of the unexpected high doping levels of lanthanide ions in ZnO that occur virtually without significant change in ZnO unit cell dimensions.     

Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

The term ‘corpus’ refers to a huge volume of structured datasets containing machine-readable texts. Such texts are generated in a natural communicative setting. The explosion of social media permitted individuals to spread data with minimal examination and filters freely. Due to this, the old problem of fake news has resurfaced. It has become an important concern due to its negative impact on the community. To manage the spread of fake news, automatic recognition approaches have been investigated earlier using Artificial Intelligence (AI) and Machine Learning (ML) techniques. To perform the medicinal text classification tasks, the ML approaches were applied, and they performed quite effectively. Still, a huge effort is required from the human side to generate the labelled training data. The recent progress of the Deep Learning (DL) methods seems to be a promising solution to tackle difficult types of Natural Language Processing (NLP) tasks, especially fake news detection. To unlock social media data, an automatic text classifier is highly helpful in the domain of NLP. The current research article focuses on the design of the Optimal Quad Channel Hybrid Long Short-Term Memory-based Fake News Classification (QCLSTM-FNC) approach. The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news. To attain this, the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glove-based word embedding process. Besides, the QCLSTM model is utilized for classification. To boost the classification results of the QCLSTM model, a Quasi-Oppositional Sandpiper Optimization (QOSPO) algorithm is utilized to fine-tune the hyperparameters. The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset. The QCLSTM-FNC approach successfully outperformed all other existing DL models under different measures.     

Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

Applied linguistics is an interdisciplinary domain which identifies, investigates, and offers solutions to language-related real-life problems. The new coronavirus disease, otherwise known as Coronavirus disease (COVID-19), has severely affected the everyday life of people all over the world. Specifically, since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection, the country has initiated the appropriate preventive measures (like lockdown, physical separation, and masking) for combating this extremely transmittable disease. So, individuals spent more time on online social media platforms (i.e., Twitter, Facebook, Instagram, LinkedIn, and Reddit) and expressed their thoughts and feelings about coronavirus infection. Twitter has become one of the popular social media platforms and allows anyone to post tweets. This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based sentiment analysis (SCOBGRU-SA) on COVID-19 tweets. The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic. The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this. Moreover, the BGRU model is utilized to recognise and classify sentiments present in the tweets. Furthermore, the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter, which helps attain improved classification performance. The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset, and the results signify its promising performance compared to other DL models.     

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images. Hyperspectral remote sensing contains acquisition of digital images from several narrow, contiguous spectral bands throughout the visible, Thermal Infrared (TIR), Near Infrared (NIR), and Mid-Infrared (MIR) regions of the electromagnetic spectrum. In order to the application of agricultural regions, remote sensing approaches are studied and executed to their benefit of continuous and quantitative monitoring. Particularly, hyperspectral images (HSI) are considered the precise for agriculture as they can offer chemical and physical data on vegetation. With this motivation, this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification (HOADTL-CC) model on Hyperspectral Remote Sensing Images. The presented HOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images. To accomplish this, the presented HOADTL-CC model involves the design of HOA with capsule network (CapsNet) model for generating a set of useful feature vectors. Besides, Elman neural network (ENN) model is applied to allot proper class labels into the input HSI. Finally, glowworm swarm optimization (GSO) algorithm is exploited to fine tune the ENN parameters involved in this article. The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects. Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.     

Investigation of dimensional deviation in biodegradable dielectric-based powder-mixed electric discharge machining of Ni-based superalloy

Journal of Mechanical Science and Technology - Innovations in the manufacturing industries assist to improve machinability of difficult to cut materials, such as nickel superalloys. Critical...