New Hybrid Deep Learning Method to Recognize Human Action from Video

There has been a tremendous increase in internet users and enough bandwidth in recent years. Because Internet connectivity is so inexpensive, information sharing (text, audio, and video) has become more popular and faster. This video content must be examined in order to classify it for different purposes for users. Several machine learning approaches for video classification have been developed to save users time and energy. The use of deep neural networks to recognize human behavior has become a popular issue in recent years. Although significant progress has been made in the field of video recognition, there are still numerous challenges in the realm of video to be overcome. Convolutional neural networks (CNNs) are well-known for requiring a fixed-size image input, which limits the network topology and reduces identification accuracy. Despite the fact that this problem has been solved in the world of photos, it has yet to be solved in the area of video. We present a ten stacked three-dimensional (3D) convolutional network based on the spatial pyramid-based pooling to handle the input problem of fixed size video frames in video recognition. The network structure is made up of three sections, as the name suggests: a ten-layer stacked 3DCNN, DenseNet, and SPPNet. A KTH dataset was used to test our algorithms. The experimental findings showed that our model outperformed existing models in the area of video-based behavior identification by 2% margin accuracy

Ownership and attitudes towards technology use in physiotherapy students from seven countries

PURPOSE: To assess differences in prerequisites to blended learning such as technology use and Internet access in an international sample of physiotherapy students from Bangladesh, Belgium, Brazil, Luxembourg, Sudan, Switzerland and South Africa. RESULTS: Students' digital technology experiences were generally low. They primarily used a smartphone and a laptop to connect to the Internet. However, there was a significant difference between institutions in owning a laptop and access to Internet. Most students preferred learning in environments that included some online components but had never used Twitter or written a blog post and wanted less social media in their learning environments. CONCLUSION: Physiotherapy students would prefer an increase in the use of digital tools in their learning. However, differences in technology use and access highlight the challenges inherent to offering international online courses. Therefore decisions around online and blended course design in health professions education must be made with caution.Michael Rowe receives funding from the South African National Research Foundation

New hybrid deep learning method to recognize human action from video

There has been a tremendous increase in internet users and enough bandwidth in recent years. Because Internet connectivity is so inexpensive, information sharing (text, audio, and video) has become more popular and faster. This video content must be examined in order to classify it for different purposes for users. Several machine learning approaches for video classification have been developed to save users time and energy. The use of deep neural networks to recognize human behavior has become a popular issue in recent years. Although significant progress has been made in the field of video recognition, there are still numerous challenges in the realm of video to be overcome. Convolutional neural networks (CNNs) are well-known for requiring a fixedsize image input, which limits the network topology and reduces identification accuracy. Despite the fact that this problem has been solved in the world of photos, it has yet to be solved in the area of video. We present a ten stacked three-dimensional (3D) convolutional network based on the spatial pyramidbased pooling to handle the input problem of fixed size video frames in video recognition. The network structure is made up of three sections, as the name suggests: a ten-layer stacked 3DCNN, DenseNet, and SPPNet. A KTH dataset was used to test our algorithms. The experimental findings showed that our model outperformed existing models in the area of video-based behavior identification by 2% margin accuracy

Dig Up Tall Fescue Plastid Genomes For The Identification of Morphotype-Specific DNA Variants

Background Tall fescue (Festuca arundinacea Schreb.) is an important cool-season perennial grass species. Hexaploid tall fescue has three distinct morphotypes used either as forage or turf purposes. Its chloroplast genome is conserved due to it being maternally inherited to the next generation progenies. To identify morphotype-specific DNA markers and the genetic variations, plastid genomes of all three tall fescue morphotypes, i.e., Continental cv. Texoma MaxQ II, Rhizomatous cv. Torpedo, and Mediterranean cv. Resolute, have been sequenced using Illumina MiSeq sequencing platform. Results The plastid genomes of Continental-, Rhizomatous-, and Mediterranean tall fescue were assembled into circular master molecules of 135,283 bp, 135,336 bp, and 135,324 bp, respectively. The tall fescue plastid genome of all morphotypes contained 77 protein-coding, 20 tRNAs, four rRNAs, two pseudo protein-coding, and three hypothetical protein-coding genes. We identified 630 SNPs and 124 InDels between Continental and Mediterranean, 62 SNPs and 20 InDels between Continental and Rhizomatous, and 635 SNPs and 123 InDels between Rhizomatous and Mediterranean tall fescue. Only four InDels in four genes (ccsA, rps18, accD, and ndhH-p) were identified, which discriminated Continental and Rhizomatous plastid genomes from the Mediterranean plastid genome. Here, we identified and reported eight InDel markers (NRITCHL18, NRITCHL35, NRITCHL43, NRITCHL65, NRITCHL72, NRITCHL101, NRITCHL104, and NRITCHL110) from the intergenic regions that can successfully discriminate tall fescue morphotypes. Divergence time estimation revealed that Mediterranean tall fescue evolved approximately 7.09 Mya, whereas the divergence between Continental- and Rhizomatous tall fescue occurred about 0.6 Mya. Conclusions To our knowledge, this is the first report of the assembled plastid genomes of Rhizomatous and Mediterranean tall fescue. Our results will help to identify tall fescue morphotypes at the time of pre-breeding and will contribute to the development of lawn and forage types of commercial varieties

Non performing loans - its causes, consequences and some learning

Investment in productive sector is the precondition for achieving the economic growth from a country perspective. Capital formation positively supports this investment function. Once a satisfactory level of capital is formed, the option of sound investment comes that ultimately leads to flow additional capital in future. The financial institutions, mainly banks, do these functions. In countries like ours, investment leakage in the form of non-functionalities poses a great threat on the sound running of this ‘capital formation – investment – capital formation’ process. This paper deals with non performing loan situations, basically the causes and consequences of this economic devil that is very much embedded in current economic structure. The possible steps are also pointed out to handle such situation.capital formation, workout, LRA, recovery agency

A review on Video Classification with Methods, Findings, Performance, Challenges, Limitations and Future Work

In recent years, there has been a rapid development in web users and sufficient bandwidth. Internet connectivity, which is so low cost, makes the sharing of information (text, audio, and videos) more common and faster. This video content needs to be analyzed for prediction it classes in different purpose for the users. Many machines learning approach has been developed for the classification of video to save people time and energy. There are a lot of existing review papers on video classification, but they have some limitations such as limitation of the analysis, badly structured, not mention research gaps or findings, not clearly describe advantages, disadvantages, and future work. But our review paper almost overcomes these limitations. This study attempts to review existing video-classification procedures and to examine the existing methods of video-classification comparatively and critically and to recommend the most effective and productive process. First of all, our analysis examines the classification of videos with taxonomical details, the latest application, process, and datasets information. Secondly, overall inconvenience, difficulties, shortcomings and potential work, data, performance measurements with the related recent relation in science, deep learning, and the model of machine learning. Study on video classification systems using their tools, benefits, drawbacks, as well as other features to compare the techniques they have used also constitutes a key task of this review. Lastly, we also present a quick summary table based on selected features. In terms of precision and independence extraction functions, the RNN (Recurrent Neural Network), CNN (Convolutional Neural Network) and combination approach performs better than the CNN dependent method

HARC-New Hybrid Method with Hierarchical Attention Based Bidirectional Recurrent Neural Network with Dilated Convolutional Neural Network to Recognize Multilabel Emotions from Text

We present a modern hybrid paradigm for managing tacit semantic awareness and qualitative meaning in short texts. The main goals of this proposed technique are to use deep learning approaches to identify multilevel textual sentiment with far less time and more accurate and simple network structure training for better performance. In this analysis, the proposed new hybrid deep learning HARC model architecture for the recognition of multilevel textual sentiment that combines hierarchical attention with Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Bidirectional Long Short-Term Memory (BiLSTM) outperforms other compared approaches. BiGRU and BiLSTM were used in this model to eliminate individual context functions and to adequately manage long-range features. Dilated CNN was used to replicate the retrieved feature by forwarding vector instances for better support in the hierarchical attention layer, and it was used to eliminate better text information using higher coupling correlations. Our method handles the most important features to recover the limitations of handling context and semantics sufficiently. On a variety of datasets, our proposed HARC algorithm solution outperformed traditional machine learning approaches as well as comparable deep learning models by a margin of 1%. The accuracy of the proposed HARC method was 82.50 percent IMDB, 98.00 percent for toxic data, 92.31 percent for Cornflower, and 94.60 percent for Emotion recognition data. Our method works better than other basic and CNN and RNN based hybrid models. In the future, we will work for more levels of text emotions from long and more complex text

Gorlin Goltz Syndrome- A Rare Disease Reported In Bangladesh

Gorlin-Goltz syndrome is an infrequent multisystemic disease with an autosomal dominant trait with complete penetrance and various expressivity. Gorlin Goltz Syndrome is a rare autosomal characterized by an increased predisposition to basal cell carcinoma and associated with multiorgan anomalies having a high level of penetrance. We report here a 60-year-old patient with positive findings of Gorlin-Goltz Syndrome.The following report emphasizes the identification of all the essential clinical diagnostic criteria ,radiological manifestations, and possible genetic tests to be performed for setting up an adequate treatment plan

Isolation of intact chloroplast for sequencing plastid genomes of five festuca species

Isolation of good quality chloroplast DNA (cpDNA) is a challenge in different plant species, although several methods for isolation are known. Attempts were undertaken to isolate cpDNA from Festuca grass species by using available standard protocols; however, they failed due to difficulties separating intact chloroplasts from the polysaccharides, oleoresin, and contaminated nuclear DNA that are present in the crude homogenate. In this study, we present a quick and inexpensive protocol for isolating intact chloroplasts from seven grass varieties/accessions of five Festuca species using a single layer of 30% Percoll solution. This protocol was successful in isolating high quality cpDNA with the least amount of contamination of other DNA. We performed Illumina MiSeq paired-end sequencing (2 × 300 bp) using 200 ng of cpDNA of each variety/accession. Chloroplast genome mapping showed that 0.28%–11.37% were chloroplast reads, which covered 94%–96% of the reference plastid genomes of the closely related grass species. This improved method delivered high quality cpDNA from seven grass varieties/accessions of five Festuca species and could be useful for other grass species with similar genome complexity