Showing papers by "Cochin University of Science and Technology published in 2020"
Harvard University1, University of Washington2, Humboldt University of Berlin3, Imperial College London4, University of Belgrade5, Istituto Nazionale di Fisica Nucleare6, Technical University of Berlin7, University of Bordeaux8, University of Oxford9, University of Valencia10, Rutherford Appleton Laboratory11, University of Strathclyde12, King's College London13, Foundation for Research & Technology – Hellas14, University of Birmingham15, University College London16, University of Liverpool17, National Physical Laboratory18, University of Nottingham19, University of Sussex20, Northern Illinois University21, Fermilab22, Peking University23, University of Pisa24, University of California, Riverside25, University of Nevada, Reno26, CERN27, University of Niš28, National Institute of Chemical Physics and Biophysics29, Beni-Suef University30, British University in Egypt31, Leibniz University of Hanover32, Paul Sabatier University33, University of Paris34, University of Cambridge35, Wayne State University36, Stanford University37, University of Bergen38, University of Amsterdam39, Northwestern University40, University of Bristol41, University of Warsaw42, University of Illinois at Urbana–Champaign43, Fayoum University44, University of Crete45, Queen's University Belfast46, Brandeis University47, University of Bologna48, Cochin University of Science and Technology49, German Aerospace Center50, University of Manchester51, University of Copenhagen52, University of Düsseldorf53, University of Vienna54, Florida State University55, University of Florence56, University of Illinois at Chicago57, University of Bremen58, University of Mainz59, Chinese Academy of Sciences60, University of Cincinnati61
TL;DR: The Atomic Experiment for Dark Matter and Gravity Exploration (AEDGE) as mentioned in this paper is a space experiment using cold atoms to search for ultra-light dark matter, and to detect gravitational waves in the frequency range between the most sensitive ranges of LISA and the terrestrial LIGO/Virgo/KAGRA/INDIGO experiments.
Abstract: We propose in this White Paper a concept for a space experiment using cold atoms to search for ultra-light dark matter, and to detect gravitational waves in the frequency range between the most sensitive ranges of LISA and the terrestrial LIGO/Virgo/KAGRA/INDIGO experiments. This interdisciplinary experiment, called Atomic Experiment for Dark Matter and Gravity Exploration (AEDGE), will also complement other planned searches for dark matter, and exploit synergies with other gravitational wave detectors. We give examples of the extended range of sensitivity to ultra-light dark matter offered by AEDGE, and how its gravitational-wave measurements could explore the assembly of super-massive black holes, first-order phase transitions in the early universe and cosmic strings. AEDGE will be based upon technologies now being developed for terrestrial experiments using cold atoms, and will benefit from the space experience obtained with, e.g., LISA and cold atom experiments in microgravity.
259 citations
TL;DR: It is shown that blended learning demonstrated consistently better effects on knowledge outcomes when compared with traditional learning in health education, and the utility of different design variants of blended learning is explored.
Abstract: Background: Blended learning, which combines face-to-face learning and e-learning, has grown rapidly to be commonly used in education. Nevertheless, the effectiveness of this learning approach has not been completely quantitatively synthesized and evaluated using knowledge outcomes in health education.
Objective: The aim of this study was to assess the effectiveness of blended learning compared to that of traditional learning in health education.
Methods: We performed a systematic review of blended learning in health education in MEDLINE from January 1990 to July 2019. We independently selected studies, extracted data, assessed risk of bias, and compared overall blended learning versus traditional learning, offline blended learning versus traditional learning, online blended learning versus traditional learning, digital blended learning versus traditional learning, computer-aided instruction blended learning versus traditional learning, and virtual patient blended learning versus traditional learning. All pooled analyses were based on random-effect models, and the I2 statistic was used to quantify heterogeneity across studies.
Results: A total of 56 studies (N=9943 participants) assessing several types of learning support in blended learning met our inclusion criteria; 3 studies investigated offline support, 7 studies investigated digital support, 34 studies investigated online support, 8 studies investigated computer-assisted instruction support, and 5 studies used virtual patient support for blended learning. The pooled analysis comparing all blended learning to traditional learning showed significantly better knowledge outcomes for blended learning (standardized mean difference 1.07, 95% CI 0.85 to 1.28, I2=94.3%). Similar results were observed for online (standardized mean difference 0.73, 95% CI 0.60 to 0.86, I2=94.9%), computer-assisted instruction (standardized mean difference 1.13, 95% CI 0.47 to 1.79, I2=78.0%), and virtual patient (standardized mean difference 0.62, 95% CI 0.18 to 1.06, I2=78.4%) learning support, but results for offline learning support (standardized mean difference 0.08, 95% CI –0.63 to 0.79, I2=87.9%) and digital learning support (standardized mean difference 0.04, 95% CI –0.45 to 0.52, I2=93.4%) were not significant.
Conclusions: From this review, blended learning demonstrated consistently better effects on knowledge outcomes when compared with traditional learning in health education. Further studies are needed to confirm these results and to explore the utility of different design variants of blended learning.
197 citations
TL;DR: A rapid, cost-effective, environmentally-friendly method for ZnO-NPs synthesis was demonstrated, which can be used as a potential antimicrobial agent against different microbial species.
189 citations
TL;DR: The experiments performed in this study proved the effectiveness of pre-trained multi-CNN over single CNN in the detection of COVID-19, a pandemic caused by novel coronavirus, from X-ray images.
121 citations
TL;DR: In this article, a review of different chemical strategies adopted for grafting polymers onto the carbon nanotubes (CNTs) leads to better polymer-filler interaction and optimum filler dispersion for the development of high-performance polymer nanocomposites.
Abstract: Carbon-based nanomaterials such as carbon nanotubes (CNTs) have become the most promising materials in biomedical, electronic and aerospace applications. When added to polymers, they can enhance the properties and the utility of the polymers to a large extent. This is because of their superior thermo-mechanical and electrical properties which can be effectively transferred to the resulting composites with their proper dispersion in the polymer matrix. But the uniform dispersion of CNTs in various polymer matrices is the major challenge faced by scientists. This paper critically reviews the different chemical strategies adopted for grafting polymers onto the CNTs which ultimately leads to better polymer-filler interaction and optimum filler dispersion for the development of high-performance polymer nanocomposites. This review also discusses the synthesis, properties, and applications of polymer grafted CNTs and their composites.
113 citations
TL;DR: The present review summarises the antimicrobial use and AMR in cultured fishes, genetic mechanisms involved in the development of resistance, and the management strategies to restrict the spread ofAMR in aquaculture.
Abstract: Emergence of antimicrobial resistance (AMR) in cultured fishes is one of the major challenges faced in aquaculture. The high prevalence of bacterial infections in fishes leads to frequent use of antibiotics and thus their persistence in the aquatic environment, which in turn results in the proliferation of antibiotic resistant bacteria. The AMR in aquaculture can be transferred to clinically important strains of natural environment through horizontal gene transfer, thereby affecting the whole ecosystem. Most of the cultured fishes, including ornamental possess diverse pathogens exhibiting multiple antibiotic resistance. A thorough understanding of the gene transfer systems such as plasmids, transposons, integrons and gene cassettes can unravel the complexity of antimicrobial resistance in aquaculture. Continuous monitoring programmes, timely detections of the resistant bacteria and implementation of proper regulations are necessary to curb the dissemination of AMR in aquaculture. The present review summarises the antimicrobial use and AMR in cultured fishes, genetic mechanisms involved in the development of resistance, and the management strategies to restrict the spread of AMR in aquaculture.
98 citations
TL;DR: Results suggest that consumption of peeled but undeveined or whole dried white shrimps can be one of the ways of the human uptake of microplastics, especially during the monsoon season.
95 citations
TL;DR: Microplastics ingested by two bivalve species Perna viridis and Meretrix meretrix collected from three estuaries viz.
93 citations
TL;DR: In this article, a review explores the innovations in electrochemical biosensing based on the various electroanalytical techniques including voltammetry, impedance, amperometry and potentiometry and discusses their potential in diagnosis of emerging and re-emerging infectious diseases (Re-EIDs), which are potential pandemic threats.
92 citations
TL;DR: The study conducted on the evolution of travel recommender systems, their features and current set of limitations is described and the key algorithms being used for classification and recommendation processes and metrics that can be used to evaluate the performance of the algorithms and thereby the recommenders are discussed.
Abstract: Ever since the beginning of civilization, travel for various causes exists as an essential part of human life so as travel recommendations, though the early form of recommendations were the accrued experiences shared by the community Modern recommender systems evolved along with the growth of Information Technology and are contributing to all industry and service segments inclusive of travel and tourism The journey started with generic recommender engines which gave way to personalized recommender systems and further advanced to contextualized personalization with advent of artificial intelligence Current era is also witnessing a boom in social media usage and the social media big data is acting as a critical input for various analytics with no exception for recommender systems This paper details about the study conducted on the evolution of travel recommender systems, their features and current set of limitations We also discuss on the key algorithms being used for classification and recommendation processes and metrics that can be used to evaluate the performance of the algorithms and thereby the recommenders
90 citations
TL;DR: The effect of replacing carbon black by rice husk derived type-I nanocellulose (RHNC) in natural rubber vulcanization is presented and shows that RHNC can impart low rolling resistance, which is a crucial parameter for green tire applications.
06 Mar 2020
TL;DR: This work is to create a Deep Convolutional Neural Network model that classifies 5 different human facial emotions using the manually collected image dataset.
Abstract: The rapid growth of artificial intelligence has contributed a lot to the technology world. As the traditional algorithms failed to meet the human needs in real time, Machine learning and deep learning algorithms have gained great success in different applications such as classification systems, recommendation systems, pattern recognition etc. Emotion plays a vital role in determining the thoughts, behaviour and feeling of a human. An emotion recognition system can be built by utilizing the benefits of deep learning and different applications such as feedback analysis, face unlocking etc. can be implemented with good accuracy. The main focus of this work is to create a Deep Convolutional Neural Network (DCNN) model that classifies 5 different human facial emotions. The model is trained, tested and validated using the manually collected image dataset.
TL;DR: Spatial and temporal variation in microplastic abundance was observed with higher abundance in surface water indicating threats to pelagic ecosystem, and Raman spectroscopy indicated that Polyethylene (PE) and Polypropylene (PP) were the polymer types of microplastics from the fish gut.
TL;DR: The results suggest the possibility of human intake of microplastics by the consumption of pelagic fishes from this region, albeit in small quantities.
French Institute of Health and Medical Research1, Ghent University Hospital2, John Radcliffe Hospital3, Garvan Institute of Medical Research4, University of Paris5, University of Oxford6, Ghent University7, University of New South Wales8, University of California, Los Angeles9, Boston Children's Hospital10, Harvard University11, Cochin University of Science and Technology12, Rockefeller University13, Baylor College of Medicine14, University of North Carolina at Chapel Hill15, Ankara University16, Comenius University in Bratislava17, University of Freiburg18
TL;DR: Patients with HIES patients with dominant-negative variations of IL6ST, encoding the GP130 receptor, which normally recruits STAT3 upon stimulation with IL-6 and related cytokines, are reported.
Abstract: Autosomal dominant hyper-IgE syndrome (AD-HIES) is typically caused by dominant-negative (DN) STAT3 mutations. Patients suffer from cold staphylococcal lesions and mucocutaneous candidiasis, severe allergy, and skeletal abnormalities. We report 12 patients from 8 unrelated kindreds with AD-HIES due to DN IL6ST mutations. We identified seven different truncating mutations, one of which was recurrent. The mutant alleles encode GP130 receptors bearing the transmembrane domain but lacking both the recycling motif and all four STAT3-recruiting tyrosine residues. Upon overexpression, the mutant proteins accumulate at the cell surface and are loss of function and DN for cellular responses to IL-6, IL-11, LIF, and OSM. Moreover, the patients' heterozygous leukocytes and fibroblasts respond poorly to IL-6 and IL-11. Consistently, patients with STAT3 and IL6ST mutations display infectious and allergic manifestations of IL-6R deficiency, and some of the skeletal abnormalities of IL-11R deficiency. DN STAT3 and IL6ST mutations thus appear to underlie clinical phenocopies through impairment of the IL-6 and IL-11 response pathways.
TL;DR: The average microplastic abundance of the eight stations covered along the South Andaman beach was found to be 414.35 ± 87.4 particles per kilogram of beach sediment.
TL;DR: The recent outbreaks of Nipah virus in Malaysia, Bangladesh and India are discussed, the routes of transmission, prevention and control measures employed along with possible reasons behind the outbreaks, and the precautionary measures to be ensured by private–public undertakings to contain and ensure a lower incidence in the future are discussed.
Abstract: Viral outbreaks of varying frequencies and severities have caused panic and havoc across the globe throughout history. Influenza, small pox, measles, and yellow fever reverberated for centuries, causing huge burden for economies. The twenty-first century witnessed the most pathogenic and contagious virus outbreaks of zoonotic origin including severe acute respiratory syndrome coronavirus (SARS-CoV), Ebola virus, Middle East respiratory syndrome coronavirus (MERS-CoV) and Nipah virus. Nipah is considered one of the world's deadliest viruses with the heaviest mortality rates in some instances. It is known to cause encephalitis, with cases of acute respiratory distress turning fatal. Various factors contribute to the onset and spread of the virus. All through the infected zone, various strategies to tackle and enhance the surveillance and awareness with greater emphasis on personal hygiene has been formulated. This review discusses the recent outbreaks of Nipah virus in Malaysia, Bangladesh and India, the routes of transmission, prevention and control measures employed along with possible reasons behind the outbreaks, and the precautionary measures to be ensured by private-public undertakings to contain and ensure a lower incidence in the future.
University of Lausanne1, National Institute for Health Research2, University of Paris3, University of Barcelona4, Mario Negri Institute for Pharmacological Research5, Mercy Hospital for Women6, Newcastle upon Tyne Hospitals NHS Foundation Trust7, Newcastle University8, Cochin University of Science and Technology9, Centre national de la recherche scientifique10, Complutense University of Madrid11, University College Hospital12, Ohio State University13
TL;DR: An international multidisciplinary working group summarizes the current knowledge of these potentially severe disorders and proposes a practical clinical approach to diagnose and manage an episode of pregnancy-associated TMA.
TL;DR: In this paper, the mechanism of photocatalytic self-cleaning and super-hydrophilicity of nano TiO2 based systems is carefully investigated, and various techniques adopted for the synthesis and fabrication of superhydrophilic selfcleaning materials and coatings are discussed.
Abstract: The bio-inspired nanotechnology of self-cleaning surfaces and coatings are nowadays being much attracted in the field of energy and environment because of the ever-increasing demand of uncontaminated, self-disinfected and hygienic surfaces. Such surfaces/coatings can be adopted on glass windows, automobile windshields, anti-fouling membranes, textiles, building construction materials, paints; on optoelectronic devices like solar panels, photochromic glasses, etc; in the food industry and for medical aids. This review gives a special emphasis to superhydrophilic self-cleaning surfaces. The mechanism of photocatalytic self-cleaning and superhydrophilicity of nano TiO2 based systems is carefully investigated. The photogenerated reactive oxygen species generated during photoexcitation of electrons from the valence band to the conduction band of TiO2 is responsible for its photocatalytic behaviour, anti-bacterial property and superhydrophilicity. Emphasis is given on various systems and methods that could enhance the self-cleaning behaviour of TiO2 in the solar spectrum since pristine TiO2 is only ultraviolet responsive. TiO2-semiconductor heterojunctions, hybrids of TiO2 with graphene and graphitic monolayers, tailoring the exposed crystal facets in TiO2, metal and non-metal doping and dye sensitization, attributing to visible light photocatalytic self-cleaning activity are focused in detail. Various techniques adopted for the synthesis and fabrication of superhydrophilic self-cleaning materials and coatings are discussed.
TL;DR: Sentiment Analysis of Malayalam Tweets using Machine Learning techniques is done and the Random Forest classifier shows higher accuracy while considering Unigram with Sentiwordnet including negation words as a feature.
01 Jan 2020
TL;DR: This work is intended to suggest a novel encryption scheme for medical images which is strong, secure and efficient, and can be applied to any kind of the medical images, irrespective of its storage format.
Abstract: Medical imaging describes the noninvasive methods that enable medical practitioners to look inside the body, which are critical in modern clinical diagnosis. The diagnosed medical images may have confidential information related to patient’s privacy. Privacy and security must be guaranteed for the digital images over Internet, to ensure its confidentiality. In this work, a novel encryption method is suggested for medical images. The work is intended to suggest a novel encryption scheme for medical images which is strong, secure and efficient. Also, it can be applied to any kind of the medical images, irrespective of its storage format. In the proposed method, the pixels of the image are shuffled using a pseudorandom number generator based on two-dimensional Zaslavski map. The permuted image is encrypted by DNA encryption. Various visual analysis, correlation analysis, quality and security analysis techniques are applied to verify the performance of the method.
TL;DR: In this article, a hybrid clay- graphene oxide nanocomposite catalysts were successfully used for the first time in the multicomponent one pot organic synthesis, which was achieved by a cost-effective method without the use of any surfactants.
TL;DR: The various algorithms applied for the nuclear pleomorphism scoring of breast cancer are discussed, the challenges to be dealt with, and the importance of benchmark datasets are outlined.
Abstract: Breast cancer is the most common type of malignancy diagnosed in women. Through early detection and diagnosis, there is a great chance of recovery and thereby reduce the mortality rate. Many preliminary tests like non-invasive radiological diagnosis using ultrasound, mammography, and MRI are widely used for the diagnosis of breast cancer. However, histopathological analysis of breast biopsy specimen is inevitable and is considered to be the golden standard for the affirmation of cancer. With the advancements in the digital computing capabilities, memory capacity, and imaging modalities, the development of computer-aided powerful analytical techniques for histopathological data has increased dramatically. These automated techniques help to alleviate the laborious work of the pathologist and to improve the reproducibility and reliability of the interpretation. This paper reviews and summarizes digital image computational algorithms applied on histopathological breast cancer images for nuclear atypia scoring and explores the future possibilities. The algorithms for nuclear pleomorphism scoring of breast cancer can be widely grouped into two categories: handcrafted feature-based and learned feature-based. Handcrafted feature-based algorithms mainly include the computational steps like pre-processing the images, segmenting the nuclei, extracting unique features, feature selection, and machine learning–based classification. However, most of the recent algorithms are based on learned features, that extract high-level abstractions directly from the histopathological images utilizing deep learning techniques. In this paper, we discuss the various algorithms applied for the nuclear pleomorphism scoring of breast cancer, discourse the challenges to be dealt with, and outline the importance of benchmark datasets. A comparative analysis of some prominent works on breast cancer nuclear atypia scoring is done using a benchmark dataset which enables to quantitatively measure and compare the different features and algorithms used for breast cancer grading. Results show that improvements are still required, to have an automated cancer grading system suitable for clinical applications.
TL;DR: This review article introduces the very recent progress and novel paradigms on the aspects of both borophene derivatives and boron fullerene-based systems reported for hydrogen storage, focused on the synthesis, physiochemical properties, hydrogen storage mechanism and practical applications.
Abstract: Two-dimensional materials have led to a leap forward in materials science research, especially in the fields of energy conversion and storage. Borophene and its spherical counterpart boron fullerene represent emerging materials that have attracted much attention in the whole area of advanced energy materials and technologies. Owing to their prominent features, such as electronic environment and geometry, borophene and boron fullerene have been used in versatile applications, such as supercapacitors, superconductors, anode materials for photochemical water splitting, and biosensors. Herein, one of the most promising applications/areas of hydrogen storage is discussed. Boron fullerenes have been considered and discussed for hydrogen-storage applications, and recently borophene was also included in the list of materials with promising hydrogen-storage properties. Studies focus mainly on doped borophene systems over pristine borophene due to enhanced features available upon decoration with metal atoms. This Review introduces very recent progress and novel paradigms with respect to both borophene derivatives and boron fullerene based systems reported for hydrogen storage, with a focus on the synthesis, physiochemical properties, hydrogen-storage mechanism, and practical applications.
TL;DR: This mini review summarizes the works on MXenes reported for cancer theranostic applications on various in vitro and in vivo models and includes various types of MXene systems as Ti3C2, Nb2C, Ti2C in conjugation with drug molecules, metallic nanoparticles and other macromolecules.
Abstract: MXenes have been emerging as one of the most versatile types of nanomaterials over the last decade due to their exciting physical and chemical properties. Thanks to their exciting biocompatibility and tunable electronic and optical properties, the search of promising biomedical applications of MXenes has reached on cancer theranostics. Literature shows valuable results where MXenes have been employed in vitro and in vivo cancer models. This involves drug delivery systems, sensoring probes, auxiliary agents for strategies as photothermal therapy and hyperthermia. This mini review summarizes the works on MXenes reported for cancer theranostic applications on various in vitro and in vivo models. It includes various types of MXene systems as Ti3C2, Nb2C, Ti2C in conjugation with drug molecules, metallic nanoparticles and other macromolecules. Finally, the future possibilities of the scenario were also discussed in detail.
TL;DR: It is shown that dMMR/MSI mCRC patients experienced short PFS with first‐line chemotherapy with or without targeted therapy, and a trend to better OS was observed with anti‐VEGF.
Abstract: Mismatch repair-deficient (dMMR) and/or microsatellite instability-high (MSI) colorectal cancers (CRC) represent about 5% of metastatic CRC (mCRC). Prognosis and chemosensitivity of dMMR/MSI mCRC remain unclear. This multicenter study included consecutive patients with dMMR/MSI mCRC from 2007 to 2017. The primary endpoint was the progression-free survival (PFS) in a population receiving first-line chemotherapy. Associations between chemotherapy regimen and survival were evaluated using a Cox regression model and inverse of probability of treatment weighting (IPTW) methodology in order to limit potential biases. Overall, 342 patients with dMMR/MSI mCRC were included. Median PFS and overall survival (OS) on first-line chemotherapy were 6.0 and 26.3 months, respectively. For second-line chemotherapy, median PFS and OS were 4.4 and 21.6 months. Longer PFS (8.1 vs. 5.4 months, p = 0.0405) and OS (35.1 vs. 24.4 months, p = 0.0747) were observed for irinotecan-based chemotherapy compared to oxaliplatin-based chemotherapy. The association was no longer statistically significant using IPTW methodology. In multivariable analysis, anti-VEGF as compared to anti-EGFR was associated with a trend to longer OS (HR = 1.78, 95% CI 1.00-3.19, p = 0.0518), whatever the backbone chemotherapy used. Our study shows that dMMR/MSI mCRC patients experienced short PFS with first-line chemotherapy with or without targeted therapy. OS was not different according to the chemotherapy regimen used, but a trend to better OS was observed with anti-VEGF. Our study provides some historical results concerning chemotherapy in dMMR/MSI mCRC in light of the recent nonrandomized trials with immune checkpoint inhibitors.
TL;DR: An attempt has been made to review the available works in the area of medical image processing of blood smear images, highlighting automated detection of leukemia.
TL;DR: Comparison with previous cruise studies conducted nearly two decades ago shows a more than two-fold increase in the concentration of nss-SO42-, over the continental outflow region in Arabian Sea.
TL;DR: Dynamic mechanical analysis, thermogravimetric analysis, and morphological studies of tensile fractured samples also confirm that CNF isolated from Cuscuta reflexa plant can be considered as a promising green reinforcement for rubbers.
Abstract: In the present work, we used the steam explosion method for the isolation of cellulose nanofiber (CNF) from Cuscuta reflexa, a parasitic plant commonly seen in Kerala and we evaluated its reinforcing efficiency in natural rubber (NR). Fourier Transform Infrared Spectroscopy (FTIR), X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), and Thermogravimetric analysis (TGA) techniques indicated that type I cellulose nanofibers, with diameter: 10–30 nm and a 67% crystallinity index were obtained by the proposed method. The results showed that application of CNF in NR based nanocomposites resulted in significant improvement of their processing and performance properties. It was observed that the tensile strength and tear strength of NR/CNF nanocomposites are found to be a maximum at 2 phr CNF loading, which corresponds with the studies of equilibrium swelling behavior. Dynamic mechanical analysis, thermogravimetric analysis, and morphological studies of tensile fractured samples also confirm that CNF isolated from Cuscuta reflexa plant can be considered as a promising green reinforcement for rubbers.
TL;DR: This review extensively and critically examines the current status of VQA research in terms of step by step solution methodologies, datasets and evaluation metrics and discusses future research directions for all the above-mentioned aspects of V QA separately.
Abstract: Visual question answering (VQA) is a task that has received immense consideration from two major research communities: computer vision and natural language processing. Recently it has been widely accepted as an AI-complete task which can be used as an alternative to visual turing test. In its most common form, it is a multi-modal challenging task where a computer is required to provide the correct answer for a natural language question asked about an input image. It attracts many deep learning researchers after their remarkable achievements in text, voice and vision technologies. This review extensively and critically examines the current status of VQA research in terms of step by step solution methodologies, datasets and evaluation metrics. Finally, this paper also discusses future research directions for all the above-mentioned aspects of VQA separately.