Search results for: convolutional coding

Authors: Ashutosh Mishra, Nikhil Goyal

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A 3D facial emotion recognition model based on deep learning is proposed in this paper. Two convolution layers and a pooling layer are employed in the deep learning architecture. After the convolution process, the pooling is finished. The probabilities for various classes of human faces are calculated using the sigmoid activation function. To verify the efficiency of deep learning-based systems, a set of faces. The Kaggle dataset is used to verify the accuracy of a deep learning-based face recognition model. The model's accuracy is about 65 percent, which is lower than that of other facial expression recognition techniques. Despite significant gains in representation precision due to the nonlinearity of profound image representations.

Keywords: facial recognition, computational intelligence, convolutional neural network, depth map

Procedia PDF Downloads 198

Authors: Huayuan Lu, Chunfang Yang, Ma Zhu, Baojun Qi, Yaqiong Qiao, Jiangqian Xu

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Image geolocalization has great application prospects in fields such as autonomous driving and virtual/augmented reality. In practical application scenarios, the size of the image to be located is not fixed; it is impractical to train different networks for all possible sizes. When its size does not match the size of the input of the descriptor extraction model, existing image geolocalization methods usually directly scale or crop the image in some common ways. This will result in the loss of some information important to the geolocalization task, thus affecting the performance of the image geolocalization method. For example, excessive down-sampling can lead to blurred building contour, and inappropriate cropping can lead to the loss of key semantic elements, resulting in incorrect geolocation results. To address this problem, this paper designs a learnable image resizer and proposes an arbitrary-sized image geolocation method. (1) The designed learnable image resizer employs the self-attention mechanism to enhance the geometric features of the resized image. Firstly, it applies bilinear interpolation to the input image and its feature maps to obtain the initial resized image and the resized feature maps. Then, SKNet (selective kernel net) is used to approximate the best receptive field, thus keeping the geometric shapes as the original image. And SENet (squeeze and extraction net) is used to automatically select the feature maps with strong contour information, enhancing the geometric features. Finally, the enhanced geometric features are fused with the initial resized image, to obtain the final resized images. (2) The proposed image geolocalization method embeds the above image resizer as a fronting layer of the descriptor extraction network. It not only enables the network to be compatible with arbitrary-sized input images but also enhances the geometric features that are crucial to the image geolocalization task. Moreover, the triplet attention mechanism is added after the first convolutional layer of the backbone network to optimize the utilization of geometric elements extracted by the first convolutional layer. Finally, the local features extracted by the backbone network are aggregated to form image descriptors for image geolocalization. The proposed method was evaluated on several mainstream datasets, such as Pittsburgh30K, Tokyo24/7, and Places365. The results show that the proposed method has excellent size compatibility and compares favorably to recently mainstream geolocalization methods.

Keywords: image geolocalization, self-attention mechanism, image resizer, geometric feature

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Authors: Tyler T. Procko, Steve Collins

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New features in .NET (6 and above) permit streamlined access to information residing in JSON-capable relational databases, such as SQL Server (2016 and above). Traditional methods of data access now comparatively involve unnecessary steps which compromise system performance. This work posits that the established ORM (Object Relational Mapping) based methods of data access in applications and APIs result in common issues, e.g., object-relational impedance mismatch. Recent developments in C# and .NET Core combined with a framework of modern SQL Server coding conventions have allowed better technical solutions to the problem. As an amelioration, this work details the language features and coding conventions which enable this streamlined approach, resulting in an open-source .NET library implementation called Codeless Data Access (CODA). Canonical approaches rely on ad-hoc mapping code to perform type conversions between the client and back-end database; with CODA, no mapping code is needed, as JSON is freely mapped to SQL and vice versa. CODA streamlines API data access by improving on three aspects of immediate concern to web developers, database engineers and cybersecurity professionals: Simplicity, Speed and Security. Simplicity is engendered by cutting out the “middleman” steps, effectively making API data access a whitebox, whereas traditional methods are blackbox. Speed is improved because of the fewer translational steps taken, and security is improved as attack surfaces are minimized. An empirical evaluation of the speed of the CODA approach in comparison to ORM approaches ] is provided and demonstrates that the CODA approach is significantly faster. CODA presents substantial benefits for API developer workflows by simplifying data access, resulting in better speed and security and allowing developers to focus on productive development rather than being mired in data access code. Future considerations include a generalization of the CODA method and extension outside of the .NET ecosystem to other programming languages.

Keywords: API data access, database, JSON, .NET core, SQL server

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Authors: Vidhi Jain

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Communication between speakers and non-speakers of American Sign Language (ASL) can be problematic, inconvenient, and expensive. This project attempts to bridge the communication gap by designing a portable glove that captures the user’s ASL gestures and outputs the translated text on a smartphone. The glove is equipped with flex sensors, contact sensors, and a gyroscope to measure the flexion of the fingers, the contact between fingers, and the rotation of the hand. The glove’s Arduino UNO microcontroller analyzes the sensor readings to identify the gesture from a library of learned gestures. The Bluetooth module transmits the gesture to a smartphone. Using this device, one day speakers of ASL may be able to communicate with others in an affordable and convenient way.

Keywords: sign language, morse code, convolutional neural network, American sign language, gesture recognition

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Authors: C. Mayr, J. Periya, A. Kariminezhad

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In this paper, we developed a learning framework for the classification of vulnerable road users (VRU) by their range-Doppler signatures. The frequency-modulated continuous-wave (FMCW) radar raw data is first pre-processed to obtain robust object range-Doppler maps per coherent time interval. The complex-valued range-Doppler maps captured from our outdoor measurements are further fed into a convolutional neural network (CNN) to learn the classification. This CNN has gone through a hyperparameter optimization process for improved learning. By learning VRU range-Doppler signatures, the three classes 'pedestrian', 'dog', and 'noise' are classified with an average accuracy of almost 95%. Interestingly, this classification accuracy holds for a combined longitudinal and lateral object trajectories.

Keywords: machine learning, radar, signal processing, autonomous driving

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Authors: Salman Akrokayan

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Synthetic cDNA (ScDNA) of granulocyte colony-stimulating factor (G-CSF) was constructed using a DNA synthesizer with the aim to increase its expression level. 5' end of the ScDNA of G-CSF coding region was modified by decreasing the GC content without altering the predicted amino acids sequence. The identity of the resulting protein from ScDNA was confirmed by the highly specific enzyme-linked immunosorbent assay. In conclusion, a synthetic G-CSF cDNA in combination with the recombinant DNA protocol offers a rapid and reliable strategy for synthesizing the target protein. However, the commercial utilization of this methodology requires rigorous validation and quality control.

Keywords: synthetic cDNA, recombinant G-CSF, cloning, gene expression

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Authors: Lin Cheng, Zijiang Yang

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Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specification. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and node are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program.

Keywords: program synthesis, flow chart, specification, graph recognition, CNN

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Authors: Hamza Zidoum

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This paper investigates the impact of human genetic variation on the function of human proteins using machine-learning algorithms. Single-Nucleotide Polymorphism represents the most common form of human genome variation. We focus on the single amino-acid polymorphism located in the coding region as they can affect the protein function leading to pathologic phenotypic change. We use several supervised Machine Learning methods to identify structural properties correlated with increased risk of the missense mutation being damaging. SVM associated with Principal Component Analysis give the best performance.

Keywords: single-nucleotide polymorphism, machine learning, feature selection, SVM

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Authors: Netalie Shloim, Brian Brown, Siobhan Hugh-Jones, Jane Plastow, Diana Setiyawati, Anna Madill

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In June 2021, the World Health Organization launched its guidance and technical packages on community mental health services, stressing a human rights-based approach to care. This initiative stems from an increasing acknowledgment of the role mental health plays in achieving the Sustainable Development Goals. Nevertheless, mental health remains a relatively neglected research area and the estimates for untreated mental disorders in low-and-middle-income countries (LMICs) are as high as 78% for adults. Moreover, the development sector and research programs too often side-line mental health as a privilege in the face of often immediate threats to life and livelihood. As a way of addressing this problem, this study aimed to examine past or ongoing GCRF projects to see if there were opportunities where mental health impact could have been achieved without compromising a study's main aim and without overburdening a project. Projects funded by the UKRI Global Challenges Research Fund (GCRF) were analyzed. This program was initiated in 2015 to support cutting-edge research that addresses the challenges faced by developing countries. By the end of May 2020, a total of 15,279 projects were funded of which only 3% had an explicit mental health focus. A sample of 36 non-mental-health-focused projects was then sampled for diversity across research council, challenge portfolio and world region. Each of these 36 projects was coded by two coders for opportunities to embed mental health impact. To facilitate coding, the literature was inspected for dimensions relevant to LMIC settings. Three main psychological and three main social dimensions were identified: promote a positive sense of self; promote positive emotions, safe expression and regulation of challenging emotions, coping strategies, and help-seeking; facilitate skills development; and facilitate community-building; preserve sociocultural identity; support community mobilization. Coding agreement was strong on missed opportunities for mental health impact on the three social dimensions: support community mobilization (92%), facilitate community building (83%), preserve socio-cultural identity (70%). Coding agreement was reasonably strong on missed opportunities for mental health impact on the three psychological dimensions: promote positive emotions (67%), facilitate skills development (61%), positive sense of self (58%). In order of frequency, the agreed perceived opportunities from the highest to lowest are: support community mobilization, facilitate community building, facilitate skills development, promote a positive sense of self, promote positive emotions, preserve sociocultural identity. All projects were considered to have an opportunity to support community mobilization and to facilitate skills development by at least one coder. Findings provided support that there were opportunities to embed mental health impact in research across the range of development sectors and identifies what kind of missed opportunities are most frequent. Hence, mainstreaming mental health has huge potential to tackle the lack of priority and funding it has attracted traditionally. The next steps are to understand the barriers to mainstreaming mental health and to work together to overcome them.

Keywords: GCRF, mental health, psychosocial wellbeing, LMIC

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Authors: Deeksha Karunakar

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The gaming industry is one of the biggest industries in the world. Video games are interactive works of authorship that require the execution of a computer programme on specialized hardware but which also incorporate a wide variety of other artistic mediums, such as music, scripts, stories, video, paintings, and characters, into which the player takes an active role. Therefore, video games are not made as singular, simple works but rather as a collection of elements that, if they reach a certain level of originality and creativity, can each be copyrighted on their own. A video game is made up of a wide variety of parts, all of which combine to form the overall sensation that we, the players, have while playing. The entirety of the components is implemented in the form of software code, which is then translated into the game's user interface. Even while copyright protection is already in place for the coding of software, the work that is produced because of that coding can also be protected by copyright. This includes the game's storyline or narrative, its characters, and even elements of the code on their own. In each sector, there is a potential legal framework required, and the gaming industry also requires legal frameworks. This represents the importance of intellectual property laws in each sector. This paper will explore the beginnings of video games, the various aspects of game copyrights, and the approach of the courts, including examples of a few different instances. Although the creative arts have always been known to draw inspiration from and build upon the works of others, it has not always been simple to evaluate whether a game has been cloned. The video game business is experiencing growth as it has never seen before today. The majority of today's video games are both pieces of software and works of audio-visual art. Even though the existing legal framework does not have a clause specifically addressing video games, it is clear that there is a great many alternative means by which this protection can be granted. This paper will represent the importance of copyright and trademark laws in the gaming industry and its regulations with the help of relevant case laws via utilizing doctrinal methodology to support its findings. The aim of the paper is to make aware of the applicability of intellectual property laws in the gaming industry and how the justice system is evolving to adapt to such new industries. Furthermore, it will provide in-depth knowledge of their relationship with each other.

Keywords: copyright, DMCA, gaming industry, trademark, WIPO

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Authors: Prachi Desai, Ankita Gandhi, Mitali Acharya

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Rainfall forecast is mainly used for predictions of rainfall in a specified area and determining their future rainfall conditions. Rainfall is always a global issue as it affects all major aspects of one's life. Agricultural, fisheries, forestry, tourism industry and other industries are widely affected by these conditions. The studies have resulted in insufficient availability of water resources and an increase in water demand in the near future. We already have a new forecast system that uses the deep Convolutional Neural Network (CNN) to forecast monthly rainfall and climate changes. We have also compared CNN against Artificial Neural Networks (ANN). Machine Learning techniques that are used in rainfall predictions include ARIMA Model, ANN, LR, SVM etc. The dataset on which we are experimenting is gathered online over the year 1901 to 20118. Test results have suggested more realistic improvements than conventional rainfall forecasts.

Keywords: ANN, CNN, supervised learning, machine learning, deep learning

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Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

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Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: Anup Anand Deshmukh, Catherine Soladie, Renaud Seguier

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Emotion plays a key role in many applications like healthcare, to gather patients’ emotional behavior. Unlike typical ASR (Automated Speech Recognition) problems which focus on 'what was said', it is equally important to understand 'how it was said.' There are certain emotions which are given more importance due to their effectiveness in understanding human feelings. In this paper, we propose an approach that models human stress from audio signals. The research challenge in speech emotion detection is finding the appropriate set of acoustic features corresponding to an emotion. Another difficulty lies in defining the very meaning of emotion and being able to categorize it in a precise manner. Supervised Machine Learning models, including state of the art Deep Learning classification methods, rely on the availability of clean and labelled data. One of the problems in affective computation is the limited amount of annotated data. The existing labelled emotions datasets are highly subjective to the perception of the annotator. We address the first issue of feature selection by exploiting the use of traditional MFCC (Mel-Frequency Cepstral Coefficients) features in Convolutional Neural Network. Our proposed Emo-CNN (Emotion-CNN) architecture treats speech representations in a manner similar to how CNN’s treat images in a vision problem. Our experiments show that Emo-CNN consistently and significantly outperforms the popular existing methods over multiple datasets. It achieves 90.2% categorical accuracy on the Emo-DB dataset. We claim that Emo-CNN is robust to speaker variations and environmental distortions. The proposed approach achieves 85.5% speaker-dependant categorical accuracy for SAVEE (Surrey Audio-Visual Expressed Emotion) dataset, beating the existing CNN based approach by 10.2%. To tackle the second problem of subjectivity in stress labels, we use Lovheim’s cube, which is a 3-dimensional projection of emotions. Monoamine neurotransmitters are a type of chemical messengers in the brain that transmits signals on perceiving emotions. The cube aims at explaining the relationship between these neurotransmitters and the positions of emotions in 3D space. The learnt emotion representations from the Emo-CNN are mapped to the cube using three component PCA (Principal Component Analysis) which is then used to model human stress. This proposed approach not only circumvents the need for labelled stress data but also complies with the psychological theory of emotions given by Lovheim’s cube. We believe that this work is the first step towards creating a connection between Artificial Intelligence and the chemistry of human emotions.

Keywords: deep learning, brain chemistry, emotion perception, Lovheim's cube

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Authors: Rashedul Islam

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A firmly-validated aim of teaching science is to support student enthusiasm for science learning with an outspread interest in scientific issues in future life. This is in keeping with the recent development in Gifted and Talented Education statement which instructs that gifted students have a renewed interest and natural aptitude in science. Yet, the practice of science teaching leaves many students with the feeling that science is difficult and compared to other school subjects, students interest in science is declining at the final years of the primary school. As a curriculum guides the teaching-learning activities in school, where significant consequences may result from the context of the curricula and syllabi, are a major feature of certain educational jurisdictions in NSW, Australia. The purpose of this study was an exploration of the curriculum sets the context to identify how science education is practiced through primary schools in Sydney, Australia. This phenomenon was explored through document review from two publicly available documents namely: the NSW Science Syllabus K-6, and Australian Curriculum: Foundation - 10 Science. To analyse the data, this qualitative study applied themed content analysis at three different levels, i.e., first cycle coding, second cycle coding- pattern codes, and thematic analysis. Preliminary analysis revealed the phenomenon of teaching-learning practices drawn from eight themes under three phenomena aligned with teachers’ practices and gifted student’s learning characteristics based on Gagné’s Differentiated Model of Gifted and Talent (DMGT). From the results, it appears that, overall, the two documents are relatively well-placed in terms of identifying the context of teaching and learning primary science to gifted students. However, educators need to make themselves aware of the ways in which the curriculum needs to be adapted to meet gifted students learning needs in science. It explores the important phenomena of teaching-learning context to provide gifted students with optimal educational practices including inquiry-based learning, problem-solving, open-ended tasks, creativity in science, higher order thinking, integration, and challenges. The significance of such a study lies in its potential to schools and further research in the field of gifted education.

Keywords: teaching primary science, gifted student learning, curriculum context, science syllabi, Australia

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Authors: Sayor Ajfar Aaron, Mushfiqur Rahman, Sajjat Hossain Abir, Ashif Newaz

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This study delves into the development of an artificial intelligence application designed for real-time emotion recognition from human vocal patterns. Utilizing advanced machine learning algorithms, including deep learning and neural networks, the paper highlights both the technical challenges and potential opportunities in accurately interpreting emotional cues from speech. Key findings demonstrate the critical role of diverse training datasets and the impact of ambient noise on recognition accuracy, offering insights into future directions for improving robustness and applicability in real-world scenarios.

Keywords: artificial intelligence, convolutional neural network, emotion recognition, vocal patterns

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Authors: Farahnaz Sadat Golestan Hashemi, Mohd Razi Ismail, Mohd Y. Rafii

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A huge revolution has emerged in genome engineering by the discovery of CRISPR (clustered regularly interspaced palindromic repeats) and CRISPR-associated system genes (Cas) in bacteria. The function of type II Streptococcus pyogenes (Sp) CRISPR/Cas9 system has been confirmed in various species. Other S. thermophilus (St) CRISPR-Cas systems, CRISPR1-Cas and CRISPR3-Cas, have been also reported for preventing phage infection. The CRISPR1-Cas system interferes by cleaving foreign dsDNA entering the cell in a length-specific and orientation-dependant manner. The S. thermophilus CRISPR3-Cas system also acts by cleaving phage dsDNA genomes at the same specific position inside the targeted protospacer as observed in the CRISPR1-Cas system. It is worth mentioning, for the effective DNA cleavage activity, RNA-guided Cas9 orthologs require their own specific PAM (protospacer adjacent motif) sequences. Activity levels are based on the sequence of the protospacer and specific combinations of favorable PAM bases. Therefore, based on the specific length and sequence of PAM followed by a constant length of target site for the three orthogonals of Cas9 protein, a well-organized procedure will be required for high-throughput and accurate mining of possible target sites in a large genomic dataset. Consequently, we created a reliable procedure to explore potential gRNA sequences for type I (Streptococcus thermophiles), II (Streptococcus pyogenes), and III (Streptococcus thermophiles) CRISPR/Cas systems. To mine CRISPR target sites, four different searching modes of sgRNA binding to target DNA strand were applied. These searching modes are as follows: i) coding strand searching, ii) anti-coding strand searching, iii) both strand searching, and iv) paired-gRNA searching. The output of such procedure highlights the power of comparative genome mining for different CRISPR/Cas systems. This could yield a repertoire of Cas9 variants with expanded capabilities of gRNA design, and will pave the way for further advance genome and epigenome engineering.

Keywords: CRISPR/Cas systems, gRNA mining, Streptococcus pyogenes, Streptococcus thermophiles

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Authors: Amdy Moustapha Drame, Ilya V. Germashev, E. A. Markushevskaya

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Among the main problems of medicine is breast cancer, from which a significant number of women around the world are constantly dying. Therefore, the detection of malignant breast tumors is an urgent task. For many years, various technologies for detecting these tumors have been used, in particular, in thermal imaging in order to determine different levels of breast cancer development. These periodic screening methods are a diagnostic tool for women and may have become an alternative to older methods such as mammography. This article proposes a model for the identification of malignant neoplasms of the mammary glands by the asymmetry of internal and external thermal imaging fields.

Keywords: asymmetry, breast cancer, tumors, deep learning, thermogram, convolutional transformation, classification

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Authors: Abhishek Oswal

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Background: Inconsistencies between the guidelines for childhood asthma can pose a diagnostic challenge to clinicians. NICE guidelines are the most commonly followed guidelines in primary care in the UK; they state that to be diagnosed with asthma, a child must be more than 5 years old and must have objective evidence of the disease. When diagnoses are coded in general practice (GP), these guidelines may be superseded by communications from secondary care. Hence it is imperative that diagnoses are correct, as per up to date guidelines and evidence, as this affects follow up and management both in primary and secondary care. Methods: A snapshot audit at a general practice surgery was undertaken of children (less than 16 years old) with a coded diagnosis of 'asthma', to review the age at diagnosis and whether any objective evidence of asthma was documented at diagnosis. 50 cases of asthma in children presenting to the emergency department (ED) were then audited to review the age at presentation, whether there was evidence of previous asthma diagnosis and whether the patient was discharged from ED. A repeat audit is planned in ED this winter. Results: In a GP surgery, there were 83 coded cases of asthma in children. 51 children (61%) were diagnosed under 5, with 9 children (11%) who had objective evidence of asthma documented at diagnosis. In ED, 50 cases were collected, of which 4 were excluded as they were referred to the other services, or for incorrect coding. Of the 46 remaining, 27 diagnoses confirmed to NICE guidelines (59%). 33 children (72%) were discharged from ED. Discussion: The most likely reason for the apparent low rate of a correct diagnosis is the significant challenge of obtaining objective evidence of asthma in children. There were a number of patients who were diagnosed from secondary care services and then coded as 'asthma' in GP, without having objective documented evidence. The electronic patient record (EPR) system used in our emergency department (ED) did not allow coding of 'suspected diagnosis' or of 'viral induced wheeze'. This may have led to incorrect diagnoses coded in primary care, of children who had no confirmed diagnosis of asthma. We look forward to the re-audit, as the EPR system has been updated to allow suspected diagnoses. In contrast to the NICE guidelines used here, British Thoracic Society (BTS) guidelines allow for a trial of treatment and subsequent confirmation of diagnosis without objective evidence. It is possible that some of the cases which have been classified as incorrect in this audit may still meet other guidelines. Conclusion: The diagnosis of asthma in children is challenging. Incorrect diagnoses may be related to clinical pressures and the provision of services to allow compliance with NICE guidelines. Consensus statements between the various groups would also aid the decision-making process and diagnostic dilemmas that clinicians face, to allow more consistent care of the patient.

Keywords: asthma, diagnosis, primary care, emergency department, guidelines, audit

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Authors: Divvela Vishnu Sai Kumar, Deepak Arora, Sheenu Rizvi

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Forecasting a cyclone through satellite images consists of the estimation of the intensity of the cyclone and predicting it before a cyclone comes. This research work can help people to take safety measures before the cyclone comes. The prediction of the intensity of a cyclone is very important to save lives and minimize the damage caused by cyclones. These cyclones are very costliest natural disasters that cause a lot of damage globally due to a lot of hazards. Authors have proposed five different CNN (Convolutional Neural Network) models that estimate the intensity of cyclones through INSAT-3D IR images. There are a lot of techniques that are used to estimate the intensity; the best model proposed by authors estimates intensity with a root mean squared error (RMSE) of 10.02 kts.

Keywords: estimating cyclone intensity, deep learning, convolution neural network, prediction models

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Authors: Maria Lopez-Ibarra, Ana Velazquez-Wong, Lucelli Yañez-Gutierrez, Maria Araujo-Solis, Fabio Salamanca-Gomez, Alfonso Mendez-Tenorio, Haydeé Rosas-Vargas

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Congenital heart diseases (CHD) are the most common congenital abnormalities. These conditions can occur as both an element of distinct chromosomal malformation syndromes or as non-syndromic forms. Their etiology is not fully understood. Genetic variants such copy number variants have been associated with CHD. The aim of our study was to analyze these genomic variants in peripheral blood from Mexican children diagnosed with non-syndromic CHD. We included 16 children with atrial and ventricular septal defects and 5 healthy subjects without heart malformations as controls. To exclude the most common heart disease-associated syndrome alteration, we performed a fluorescence in situ hybridization test to identify the 22q11.2, responsible for congenital heart abnormalities associated with Di-George Syndrome. Then, a microarray based comparative genomic hybridization was used to identify global copy number variants. The identification of copy number variants resulted from the comparison and analysis between our results and data from main genetic variation databases. We identified copy number variants gain in three chromosomes regions from pediatric patients, 4q13.2 (31.25%), 9q34.3 (25%) and 20q13.33 (50%), where several genes associated with cellular, biosynthetic, and metabolic processes are located, UGT2B15, UGT2B17, SNAPC4, SDCCAG3, PMPCA, INPP6E, C9orf163, NOTCH1, C20orf166, and SLCO4A1. In addition, after a hierarchical cluster analysis based on the fluorescence intensity ratios from the comparative genomic hybridization, two congenital heart disease groups were generated corresponding to children with atrial or ventricular septal defects. Further analysis with a larger sample size is needed to corroborate these copy number variants as possible biomarkers to differentiate between heart abnormalities. Interestingly, the 20q13.33 gain was present in 50% of children with these CHD which could suggest that alterations in both coding and non-coding elements within this chromosomal region may play an important role in distinct heart conditions.

Keywords: aCGH, bioinformatics, congenital heart diseases, copy number variants, fluorescence in situ hybridization

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Authors: Mohd Ayyub Khan, Nadeem Akhtar

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Due to recent advances in wireless communication technologies and hand-held devices, there is a huge demand for video-based applications such as video surveillance, video conferencing, remote surgery, Digital Video Broadcast (DVB), IPTV, online learning courses, YouTube, WhatsApp, Instagram, Facebook, Interactive Video Games. However, the raw videos posses very high bandwidth which makes the compression a must before its transmission over the wireless channels. The High Efficiency Video Codec (HEVC) (also called H.265) is latest state-of-the-art video coding standard developed by the Joint effort of ITU-T and ISO/IEC teams. HEVC is targeted for high resolution videos such as 4K or 8K resolutions that can fulfil the recent demands for video services. The compression ratio achieved by the HEVC is twice as compared to its predecessor H.264/AVC for same quality level. The compression efficiency is generally increased by removing more correlation between the frames/pixels using complex techniques such as extensive intra and inter prediction techniques. As more correlation is removed, the chances of interdependency among coded bits increases. Thus, bit errors may have large effect on the reconstructed video. Sometimes even single bit error can lead to catastrophic failure of the reconstructed video. In this paper, we study the performance of HEVC bitstream over additive white Gaussian noise (AWGN) channel. Moreover, HEVC over Quadrature Amplitude Modulation (QAM) combined with forward error correction (FEC) schemes are also explored over the noisy channel. The video will be encoded using HEVC, and the coded bitstream is channel coded to provide some redundancies. The channel coded bitstream is then modulated using QAM and transmitted over AWGN channel. At the receiver, the symbols are demodulated and channel decoded to obtain the video bitstream. The bitstream is then used to reconstruct the video using HEVC decoder. It is observed that as the signal to noise ratio of channel is decreased the quality of the reconstructed video decreases drastically. Using proper FEC codes, the quality of the video can be restored up to certain extent. Thus, the performance analysis of HEVC presented in this paper may assist in designing the optimized code rate of FEC such that the quality of the reconstructed video is maximized over wireless channels.

Keywords: AWGN, forward error correction, HEVC, video coding, QAM

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Authors: J. Casteleiro- Pitrez

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The interest in Virtual Reality (VR) keeps increasing among the community of designers. To develop this type of immersive experience, the understanding of new processes and methodologies is as fundamental as its complex implementation which usually implies hiring a specialized team. In this paper, we introduce a case study, a platform that allows designers to easily create complex VR experiences, present its features, and its development process. We conclude that this platform provides a complete solution for the design and development of VR experiences, no-code needed.

Keywords: creatives, designers, virtual reality, virtual reality design platform, virtual reality system, no-coding

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Authors: Lin Huo, Xianwei Liu, Jingxiong Zhou

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Recently, due to the existence of semantic gap between image visual features and human concepts, the semantic of image auto-annotation has become an important topic. Firstly, by extract low-level visual features of the image, and the corresponding Hash method, mapping the feature into the corresponding Hash coding, eventually, transformed that into a group of binary string and store it, image auto-annotation by search is a popular method, we can use it to design and implement a method of image semantic auto-annotation. Finally, Through the test based on the Corel image set, and the results show that, this method is effective.

Keywords: image auto-annotation, color correlograms, Hash code, image retrieval

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Authors: Don Anushka Sandaruwan Elvitigala, P. D. S. U. Wickramasinghe, Jehee Lee

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Cystatins are a large superfamily of proteins which act as reversible inhibitors of cysteine proteases. Papain proteases and cysteine cathepsins are predominant substrates of cystatins. Cystatin superfamily can be further clustered into three groups as Stefins, Cystatins, and Kininogens. Among them, stefines are also known as family 1 cystatins which harbors cystatin Bs and cystatin As. In this study, a homologue of family one cystatins more close to cystatin Bs was identified from Korean black rockfish (Sebastes schlegeli) using a prior constructed cDNA (complementary deoxyribonucleic acid) database and designated as RfCyt1. The full-length cDNA of RfCyt1 consisted of 573 bp, with a coding region of 294 bp. It comprised a 5´-untranslated region (UTR) of 55 bp, and 3´-UTR of 263 bp. The coding sequence encodes a polypeptide consisting of 97 amino acids with a predicted molecular weight of 11kDa and theoretical isoelectric point of 6.3. The RfCyt1 shared homology with other teleosts and vertebrate species and consisted conserved features of cystatin family signature including single cystatin-like domain, cysteine protease inhibitory signature of pentapeptide (QXVXG) consensus sequence and N-terminal two conserved neighboring glycine (⁸GG⁹) residues. As expected, phylogenetic reconstruction developed using the neighbor-joining method showed that RfCyt1 is clustered with the cystatin family 1 members, in which more closely with its teleostan orthologues. An SYBR Green qPCR (quantitative polymerase chain reaction) assay was performed to quantify the RfCytB transcripts in different tissues in healthy and immune stimulated fish. RfCyt1 was ubiquitously expressed in all tissue types of healthy animals with gill and spleen being the highest. Temporal expression of RfCyt1 displayed significant up-regulation upon infection with Aeromonas salmonicida. Recombinantly expressed RfCyt1 showed concentration-dependent papain inhibitory activity. Collectively these findings evidence for detectable protease inhibitory and immunity relevant roles of RfCyt1 in Sebastes schlegeli.

Keywords: Sebastes schlegeli, family 1 cystatin, immune stimulation, expressional modulation

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Authors: Somaya A. Abd El Mottaleb, Ahmed Abd El Aziz, Heba A. Fayed, Moustafa H. Aly

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In this paper, we present the performance of spectral amplitude coding optical code division multiple access using different detectors at different transmission distances using single photodiode detection technique. Modified double weight codes are used as signature codes. Simulation results show that the system using avalanche photo detector can move distance longer than that using positive intrinsic negative photo detector.

Keywords: avalanche photodiode, modified double weight, multiple access technique, single photodiode.

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Authors: Laura Mclean

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A recurring point of interest in computational phylogenetic analysis of Indo-European family trees is the inference of a Germano-Italo-Celtic clade in some versions of the trees produced. The presence of this clade in the models is intriguing as there is little evidence for innovations shared among Germanic, Italic, and Celtic, the evidence generally used in the traditional method to construct a subgroup. One source of this unexpected outcome could be the input to the models. The datasets in the various models used so far, for the most part, take as their basis the Swadesh list, a list compiled by Morris Swadesh and then revised several times, containing up to 207 words that he believed were resistant to change among languages. The judgments made by Swadesh for this list, however, were subjective and based on his intuition rather than rigorous analysis. Some scholars used the Swadesh 200 list as the basis for their Indo-European dataset and made cognacy judgements for each of the words on the list. Another dataset is largely based on the Swadesh 207 list as well although the authors include additional lexical and non-lexical data, and they implement ‘split coding’ to deal with cases of polymorphic characters. A different team of scholars uses a different dataset, IECoR, which combines several different lists, one of which is the Swadesh 200 list. In fact, the Swadesh list is used in some form in every study surveyed and each dataset has three words that, when they are coded as cognates, seemingly contribute to the inference of a Germano-Italo-Celtic clade which could happen due to these clades sharing three words among only themselves. These three words are ‘fish’, ‘flower’, and ‘man’ (in the case of ‘man’, one dataset includes Lithuanian in the cognacy coding and removes the word ‘man’ from the screened data). This collection of cognates shared among Germanic, Italic, and Celtic that were deemed important enough to be included on the Swadesh list, without the ability to account for possible reasons for shared cognates that are not shared innovations, gives an impression of affinity between the Germanic, Celtic, and Italic branches without adequate methodological support. However, by changing how cognacy is defined (ie. root cognates, borrowings vs inherited cognates etc.), we will be able to identify whether these three cognates are significant enough to infer a clade for Germanic, Celtic, and Italic. This paper examines the question of what definition of cognacy should be used for phylogenetic datasets by examining the Germano-Italo-Celtic clade as a case study and offers insights into the reconstruction of a Germano-Italo-Celtic clade.

Keywords: historical, computational, Italo-Celtic, Germanic

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Authors: Laura Struik, Sarah Dow-Fleisner, Robert Janke

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Introduction: The use of e-cigarettes, also known as vaping, has risen exponentially among North American youth and young adults (YYA) in recent years and has become a critical public health concern. The marketing strategies used by e-cigarette companies have been associated with the uptick in use among YYA, with video advertisements on TV and other electronic platforms being the most pervasive strategy. It is unknown if or how these advertisements capitalize on the recently documented multi-faceted influences that contribute to the initiation of vaping among this demographic (e.g., stress, anxiety, gender, peers, etc.), which is examined in this study. Methods: This content analysis is phase one of a two-phased research project that aims to inform meaningful approaches to anti-vaping messaging and campaigns. As part of this first phase, a scoping review has been conducted to identify various influences (environmental, cognitive, contextual, social, and emotional) on e-cigarette uptake among YYA. The results of this scoping review will inform the development of a coding framework to analyze the multiple influences present in vaping advertisements, as seen on two popular television channels (Discovery and AMC). In addition, advertisement characteristics will be incorporated into the coding framework (e.g., the number of people present, demographic details, context, and setting, etc.), and analyzed. Findings: Findings will reveal the types of influences being leveraged in vaping advertisements, and identify the underlying messages that may be particularly attractive to YYA. This will contribute to a more nuanced understanding of how e-cigarette companies market their products and to whom. The results will also inform the next phase of this research project, which will encompass an analysis of anti-vaping advertisements and how the underpinning strategies align with those of the pro-vaping advertisements. Conclusions: Findings of this will study bring forward important implications for developing effective anti-vaping messages, and assist public health professionals in providing more comprehensive prevention and cessation support as it relates to e-cigarette use. Understanding which marketing strategies e-cigarette companies use is vital to our understanding of how to combat them. Findings will inform recommendations for public health efforts aimed at curbing e-cigarette use among YYA, and ultimately contribute to the health and well-being of YYA.

Keywords: e-cigarettes, youth and young adults, advertisements, public health

Procedia PDF Downloads 100

Authors: Aishrith Rao

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Human activity recognition is an important aspect of video analytics, and many approaches have been recommended to enable action recognition. In this approach, the model is used to identify the action of the multiple people in the frame and classify them accordingly. A few approaches use RNNs and 3D CNNs, which are computationally expensive and cannot be trained with the small datasets which are currently available. Multi-person action recognition has been performed in order to understand the positions and action of people present in the video frame. The size of the video frame can be adjusted as a hyper-parameter depending on the hardware resources available. OpenPose has been used to calculate pose estimate using CNN to produce heap-maps, one of which provides skeleton features, which are basically joint features. The features are then extracted, and a classification algorithm can be applied to classify the action.

Keywords: human activity recognition, computer vision, pose estimates, convolutional neural networks

Procedia PDF Downloads 113

Authors: Goutam Kumar Ghorai, Sandip Sadhukhan, Arpita Sarkar, Debprasad Sinha, G. Sarkar, Ashis K. Dhara

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Diabetes mellitus is one of the most common chronic diseases in all countries and continues to increase in numbers significantly. Diabetic retinopathy (DR) is damage to the retina that occurs with long-term diabetes. DR is a major cause of blindness in the Indian population. Therefore, its early diagnosis is of utmost importance towards preventing progression towards imminent irreversible loss of vision, particularly in the huge population across rural India. The barriers to eye examination of all diabetic patients are socioeconomic factors, lack of referrals, poor access to the healthcare system, lack of knowledge, insufficient number of ophthalmologists, and lack of networking between physicians, diabetologists and ophthalmologists. A few diabetic patients often visit a healthcare facility for their general checkup, but their eye condition remains largely undetected until the patient is symptomatic. This work aims to focus on the design and development of a fully automated intelligent decision system for screening retinal fundus images towards detection of the pathophysiology caused by microaneurysm in the early stage of the diseases. Automated detection of microaneurysm is a challenging problem due to the variation in color and the variation introduced by the field of view, inhomogeneous illumination, and pathological abnormalities. We have developed aconvolutional neural network for efficient detection of microaneurysm. A loss function is also developed to handle severe class imbalance due to very small size of microaneurysms compared to background. The network is able to locate the salient region containing microaneurysms in case of noisy images captured by non-mydriatic cameras. The ground truth of microaneurysms is created by expert ophthalmologists for MESSIDOR database as well as private database, collected from Indian patients. The network is trained from scratch using the fundus images of MESSIDOR database. The proposed method is evaluated on DIARETDB1 and the private database. The method is successful in detection of microaneurysms for dilated and non-dilated types of fundus images acquired from different medical centres. The proposed algorithm could be used for development of AI based affordable and accessible system, to provide service at grass root-level primary healthcare units spread across the country to cater to the need of the rural people unaware of the severe impact of DR.

Keywords: retinal fundus image, deep convolutional neural network, early detection of microaneurysms, screening of diabetic retinopathy

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Authors: Wael Ali Mohammed Hadi

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Pseudomonas fluorescens B29B, which has asparaginase enzymatic activity, was isolated from the surface coastal seawater of Trivandrum, India. We report the complete Pseudomonas fluorescens B29B genome sequenced, identified, and annotated from a marine source. We find the genome at most minuscule a 7,331,508 bp single circular chromosome with a GC content of 62.19% and 6883 protein-coding genes. Three hundred forty subsystems were identified, including two predicted asparaginases from the genome analysis of P. fluorescens B29B for further investigation. This genome data will help further industrial biotechnology applications of proteins in general and asparaginase as a target.

Keywords: pseudomonas, marine, asparaginases, Kerala, whole-genome

Procedia PDF Downloads 180