Natural language problems have already been investigated for around five years. Recent progress in artificial intelligence (AI) has greatly improved the performance of models. However, the results are still not sufficiently satisfying. Machines cannot imitate human brains and the way they communicate, so it remains an ongoing task. Due to the increasing amount of information on this topic, it is very difficult to keep on track with the newest researches and results achieved in the image captioning field. In this study a comprehensive Systematic Literature Review (SLR) provides a brief overview of improvements in image captioning over the last four years. The main focus of the paper is to explain the most common techniques and the biggest challenges in image captioning and to summarize the results from the newest papers. Inconsistent comparison of results achieved in image captioning was noticed during this study and hence the awareness of incomplete data collection is raised in this paper. Therefore, it is very important to compare results of a newly created model produced with the newest information and not only with the state of the art methods. This SLR is a source of such information for researchers in order for them to be precisely correct on result comparison before publishing new achievements in the image caption generation field.

A Systematic Literature Review on Image Captioning

Human eye is affected by the different eye diseases including choroidal neovascularization (CNV), diabetic macular edema (DME) and age-related macular degeneration (AMD) This work aims to design an artificial intelligence (AI) based clinical decision support system for eye disease detection and classification to assist the ophthalmologists more effectively detecting and classifying CNV, DME and drusen by using the Optical Coherence Tomography (OCT) images depicting different tissues The methodology used for designing this system involves different deep learning convolutional neural network (CNN) models and long short-term memory networks (LSTM) The best image captioning model is selected after performance analysis by comparing nine different image captioning systems for assisting ophthalmologists to detect and classify eye diseases The quantitative data analysis results obtained for the image captioning models designed using DenseNet201 with LSTM have superior performance in terms of overall accuracy of 0969, positive predictive value of 0972 and true-positive rate of 0969using OCT images enhanced by the generative adversarial network (GAN) The corresponding performance values for the Xception with LSTM image captioning models are 0969, 0969 and 0938, respectively Thus, these two models yield superior performance and have potential to assist ophthalmologists in making optimal diagnostic decision

An enhanced OCT image captioning system to assist ophthalmologists in detecting and classifying eye diseases.

Although the image captioning methods based on RNN has made great progress in recent years, these are often lacking in variability and ignore some minor information. In this paper, a novel image captioning method based on Generative Adversarial Networks is proposed, which improve the naturalness and diversity of image description. In the method, matcher is added to the generator to get the feature of the image that does not appear in the standard description, then to produce descriptions conditioned on image, and discriminator to access how well a description fits the visual content. It is noteworthy that training a sequence generator is nontrivial. Experiments on MSCOCO and Flickr30k show that it performed competitively against real people in our user study and outperformed other methods on various tasks.

A Novel Image Captioning Method Based on Generative Adversarial Networks

It has received increasing attention to exploiting temporal visual features and corresponding descriptions in video captioning. Most of the existing models generate the captioning words merely depend on video temporal structure, ignoring fine-grained complete scene information. And the traditional long-short term memory (LSTM) in recent models is used as decoder to generate sentences, the last generated states in previous hidden ones are used to do work directly to predict the current word. This may lead to the predicted word highly related to the last generated hidden state other than the overall context information. To model the temporal aspects of activities typically shown in the video and better capture long-range context information, we propose a novel video captioning framework via context-guided semantic features model (CSF). Specifically, to maximum information flow, several previous and future information are aggregated to guide the current token by the semantic loss in the encoding and decoding phase respectively. The visual and linguistic information are corrected by fusing the surrounding information. Extensive experiments conducted on MSVD and MSR-VTT video captioning datasets demonstrate the effectiveness of our method compared with state-of-the-art approaches.

Modeling Context-Guided Visual and Linguistic Semantic Feature for Video Captioning

A plethora of therapeutic properties can be attributed to phytocompounds, and increasing new methodologies can be employed to optimise their utilisation In cancer research, variations of molecular docking have been employed for mapping protein-phytochemical interactions, seeking alternative approaches to chemotherapy, radiotherapy, and surgery In neurodegenerative pathologies, progressing research on phytotherapies may lead to effective substitutes for palliative treatments In Parkinson’s disease, for instance, temporary relief results from usage of dopamine agonists, mimicking wild-field dopamine action, in the brain, and stimulating dopamine receptors On the contrary, plant secondary metabolites are promising, offering long-term modulation of biomarkers for neuro-dysfunction For nearly a decade, the World Health Organization (WHO) has been reporting on critical trends in cancer, cardiovascular and neurological disorders According to WHO, in 2005, neuropathologies resulted in 92 million DALYs (disability-adjusted life years), a measure of disease burden, in terms of reducing life expectancy The figures are projected to peak at 103 million in 2030, representing 12% increase The predictions for Alzheimer and other dementias indicate 66% increase, between 2005 and 2030 Therefore, it urges finding effective measures to mitigate these trends In 2017, regenerative medicine saw 5H-purin-6-amine from Sedum sarmentosum extracts, modulating signalling pathways, contributing to increase of spermatogonial stem cell (SSC) activity This is key in fertility studies, because 5H-purin-6-amine seems to induce SSC concentration within germ cells However, the benefits of increasing phytochemical extracts-driven stem cell activity go beyond that, with the high-mobility group box 1 (HMGB1) acting as a key upstream mediator in tissue regeneration Computing is fast becoming a key instrument in drug discovery Big data analytics and process automation are the domain knowledge revolutionising the field On one hand, governing large volumes of heterogeneous information challenges drug development On the other, it brings about rich sources of insights employed in unveiling new medicinal phenotypes and repurposing existing ones One singular example derives from ontologies used to characterise molecular compounds, forming the basis of large annotated standardised information systems where medicinal compounds are assembled, in dictionaries (eg SNOMED) and semantic networks (eg Unified Medical Language System) Undoubtedly, the ability to correlate this data is fundamental, endowing conventional systems with cognitive power (eg biotechnology empowered by cognitive analytics), to analyse genotype-phenotype pairs, target compounds’ interactions, and track diseases’ biomarkers, to both elucidate and designate potential ligand-receptor binding prone to modulate dysfunction in diseases’ signalling cascades Within this ambit, this chapter discusses how technology has revolutionised pharmacology Special attention is given to phytocompound interactions and their role in drug development

Computational Approaches in Drug Development and Phytocompound Analysis

Language models based on Recurrent Neural Networks, e.g. Long Short Term Memory Network (LSTM), have shown strong ability in generating captions from image. However, in previous LSTM-based image captioning models, the image information is input to LSTM at 0th time step, and the network gradually forgets the image information, and only uses the language model to generate a simple description, leaving the potential in generating a better description. To address this challenge, in this paper, a Long-term Recurrent Merge Network (LRMN) model is proposed to merge the image feature at each step via a language model, which not only can improve the accuracy of image captioning, but also can describe the image better. Experimental results show that the proposed LRMN model has a promising improvement in image captioning.

Long-Term Recurrent Merge Network Model for Image Captioning

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Long-Term Recurrent Merge Network Model for Image Captioning

A Novel Image Captioning Method Based on Generative Adversarial Networks