Search results for: Kazakh speech dataset

Authors: Sara El Mansouria Beghdadi

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Quantitative structure–activity relationship (QSAR) modelling is one of the main computer tools used in medicinal chemistry. Over the past two decades, the incidence of fungal infections has increased due to the development of resistance. In this study, the QSAR was performed on a series of esters of 2-carboxamido-3-(1H-imidazole-1-yl) propanoic acid derivatives. These compounds have showed moderate and very good antifungal activity. The multiple linear regression (MLR) was used to generate the linear 2d-QSAR models. The dataset consists of 115 compounds with their antifungal activity (log MIC) against «Candida albicans» (ATCC SC5314). Descriptors were calculated, and different models were generated using Chemoffice, Avogadro, GaussView software. The selected model was validated. The study suggests that the increase in lipophilicity and the reduction in the electronic character of the substituent in R1, as well as the reduction in the steric hindrance of the substituent in R2 and its aromatic character, supporting the potentiation of the antifungal effect. The results of QSAR could help scientists to propose new compounds with higher antifungal activities intended for immunocompromised patients susceptible to multi-resistant nosocomial infections.

Keywords: quantitative structure–activity relationship, imidazole, antifungal, candida albicans (ATCC SC5314)

Procedia PDF Downloads 64

Authors: S. S. Dol, M. S. Chan, S. F. Wong, J. S. Lim

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Emulsion formation is unavoidable and can be detrimental to an oil field production. The presence of stable emulsions also reduces the quality of crude oil and causes more problems in the downstream refinery operations, such as corrosion and pipeline pressure drop. Hence, it is important to know the effects of emulsions in the pipeline. Light crude oil was used for the continuous phase in the W/O emulsions where the emulsions pass through a flow loop to test the pressure drop across the pipeline. The results obtained shows that pressure drop increases as water cut is increased until it peaks at the phase inversion of the W/O emulsion between 30% to 40% water cut. Emulsions produced by gradual constrictions show a lower stability as compared to sudden constrictions. Lower stability of emulsions in gradual constriction has the higher influence of pressure drop compared to a sudden sharp decrease in diameter in sudden constriction. Generally, sudden constriction experiences pressure drop of 0.013% to 0.067% higher than gradual constriction of the same ratio. Lower constriction ratio cases cause larger pressure drop ranging from 0.061% to 0.241%. Considering the higher profitability in lower emulsion stability and lower pressure drop at the developed flow region of different constrictions, an optimum design of constriction is found to be gradual constriction with a ratio of 0.5.

Keywords: constriction, pressure drop, turbulence, water-in-oil emulsions

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Authors: Alireza Keshmiri, Shahriar Bagheri, Nan Wu

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This research presents an analytical model for the development of an energy harvester with piezoelectric rings stacked at the boundary of the structure based on the Adomian decomposition method. The model is applied to geometrically non-uniform beams to derive the steady-state dynamic response of the structure subjected to base motion excitation and efficiently harvest the subsequent vibrational energy. The in-plane polarization of the piezoelectric rings is employed to enhance the electrical power output. A parametric study for the proposed energy harvester with various design parameters is done to prepare the dataset required for optimization. Finally, simulation-based optimization technique helps to find the optimum structural design with maximum efficiency. To solve the optimization problem, an artificial neural network is first trained to replace the simulation model, and then, a genetic algorithm is employed to find the optimized design variables. Higher geometrical non-uniformity and length of the beam lowers the structure natural frequency and generates a larger power output.

Keywords: piezoelectricity, energy harvesting, simulation-based optimization, artificial neural network, genetic algorithm

Procedia PDF Downloads 107

Authors: Amir Zand-Moghadam, Mahnaz Alizadeh

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The aim of the present study was to investigate the effect of teacher-scaffolding versus peer-scaffolding on EFL learners’ metapragmatic awareness in the paradigm of task-based language teaching (TBLT). To this end, a number of dialogic information-gap tasks requiring two-way interactant relationship were designed for the five speech acts of request, refusal, apology, suggestion, and compliment following Ellis’s (2003) model. Then, 48 intermediate EFL learners were randomly selected, homogenized, and assigned to two groups: 26 participants in the teacher-scaffolding group (Group One) and 22 in the peer-scaffolding group (Group Two). While going through the three phases of pre-task, while-task, and post-task, the participants in the first group completed the designed tasks by the teacher’s interaction, scaffolding, and feedback. On the other hand, the participants in the second group were required to complete the tasks in expert-novice pairs through peer scaffolding in all the three phases of a task-based syllabus. The findings revealed that the participants in the teacher-scaffolding group developed their L2 metapragmatic awareness more than the peer-scaffolding group. Thus, it can be concluded that teacher-scaffolding is more effective than peer scaffolding in developing metapragmatic awareness among EFL learners. It can also be claimed that the use of tasks can be more influential when they are accompanied by teacher-scaffolding. The findings of the present study have implications for language teachers and researchers.

Keywords: ILP, metapragmatic awareness, scaffolding, task-based instruction

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Authors: Md. Nakib Ul Hasan, Md. Ariful Islam, Md. Sumon Miah, Misbah Ul Hoque, Bulbul Ahmed

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The variability of cotton fiber characteristics has always been influenced by origin, weather conditions, method of culturing, and harvesting. Spinners work tirelessly to ensure consistent yarn quality by using the different origins of fibers to maximizes the profit margin. Spinners often fail to select desired raw materials of various origins to achieve an appropriate mixing plan due to the lack of knowledge on the interrelationship among fiber properties. The purpose of this research is to investigate the correlations among dominating fiber properties such as micronaire, strength, breaking elongation, upper half mean length, length uniformity index, short fiber index, maturity, reflectance, and yellowness. For this purpose, fiber samples from 500 Indian cotton bales and 350 Greek cotton bales were collected and tested using the high volume instrument (HVI). The fiber properties dataset was then compiled and analyzed using python 3.7 to determine the correlations matrix. Results show that Indian cotton fiber have highest correlation between strength-mat = 0.84, followed by SFI-Unf =-0.83, and Neps-Unf = -0.72. Greek cotton fiber, in contrast, have highest correlation between SFI-Unf =-0.98, followed by SFI-Mat = 0.89, +b-Len = 0.84, and Str-Mat = 0.74. Overall, the Greek cotton fiber showed a higher correlational matrix than compared to that of Indian cotton fiber.

Keywords: cotton fiber, fiber properties correlation, Greek cotton, HVI, Indian cotton, spinning

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Authors: Mark Pizzato

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How do ideas, images, and emotions in stage-plays and videos affect us? Do they evoke a greater awareness (or cognitive reappraisal of emotions) through possible shifts between left-cortical, right-cortical, and subcortical networks? To address these questions, this presentation summarizes the research of various neuroscientists, especially Bernard Baars and others involved in Global Workspace Theory, Matthew Lieberman in social neuroscience, Iain McGilchrist on left and right cortical functions, and Jaak Panksepp on the subcortical circuits of primal emotions. Through such research, this presentation offers an ‘inner theatre’ model of the brain, regarding major hubs of neural networks and our animal ancestry. It also considers recent experiments, by Mario Beauregard, on the cognitive reappraisal of sad, erotic, and aversive film clips. Finally, it applies the inner-theatre model and related research to survey results of theatre students who read and then watched the ‘To be or not to be’ speech in 8 different video versions (from stage and screen productions) of William Shakespeare’s Hamlet. Findings show that students become aware of left-cortical, right-cortical, and subcortical brain functions—and shifts between them—through staging and movie-making choices in each of the different videos.

Keywords: cognitive reappraisal, Hamlet, neuroscience, Shakespeare, theatre

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Authors: Hafiz Sajjad Hussain, Asad Razzaq

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The study aims to explore the relationship between application of speech and discourse used by the political workers and their leaders for maintaining authoritative approach and dialog power. The representation of these relationships between ideology and language in the analysis of discourse and spoken text following Van Dijk Socio-Cognitive model. Media and political leaders are two pillars of a state and their role is so important for development and effects on the society. Media has become an industry in the recent years in the globe, and especially, the private sector developed a lot in the last decade in Pakistan. Media is the easiest way of communication with the large community in a short time and used discourse independently. The prime time of the news channels in Pakistan presents the political programs on most favorite story or incident of the day. The current program broadcasted by a private channel ARY News July 6, 2014 covered the most top story of the day. The son of Ex. CJ Arslan Iftikhar moves an application to Election Commission of Pakistan about the daughter of the most popular political leader and chairman PTI Imran Khan. This movement turns the whole scenario of the political parties and media got a hot issue form discussion. This study also shows that the ideology and meanings which are presented by the TV channels not always obvious for readers.

Keywords: electronic media, political discourse, ideology of media, power, authoritative approach

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Authors: Nazmul Shahadat, Anthony S. Maida

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In recent years, hypercomplex inspired neural networks improved deep CNN architectures due to their ability to share weights across input channels and thus improve cohesiveness of representations within the layers. The work described herein studies the effect of replacing existing layers in an Axial Attention ResNet with their quaternion variants that use cross-channel weight sharing to assess the effect on image classification. We expect the quaternion enhancements to produce improved feature maps with more interlinked representations. We experiment with the stem of the network, the bottleneck layer, and the fully connected backend by replacing them with quaternion versions. These modifications lead to novel architectures which yield improved accuracy performance on the ImageNet300k classification dataset. Our baseline networks for comparison were the original real-valued ResNet, the original quaternion-valued ResNet, and the Axial Attention ResNet. Since improvement was observed regardless of which part of the network was modified, there is a promise that this technique may be generally useful in improving classification accuracy for a large class of networks.

Keywords: axial attention, representational networks, weight sharing, cross-channel correlations, quaternion-enhanced axial attention, deep networks

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Authors: Yang Yang, Dan Liu

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Anomaly detection models for network flow based on machine learning have poor detection performance under extremely unbalanced training data conditions and also have slow detection speed and large resource consumption when deploying on network edge devices. Embedding multi-teacher knowledge distillation (mKD) in anomaly detection can transfer knowledge from multiple teacher models to a single model. Inspired by this, we proposed a state-of-the-art model, mKDNAD, to improve detection performance. mKDNAD mine and integrate the knowledge of one-dimensional sequence and two-dimensional image implicit in network flow to improve the detection accuracy of small sample classes. The multi-teacher knowledge distillation method guides the train of the student model, thus speeding up the model's detection speed and reducing the number of model parameters. Experiments in the CICIDS2017 dataset verify the improvements of our method in the detection speed and the detection accuracy in dealing with the small sample classes.

Keywords: network flow anomaly detection (NAD), multi-teacher knowledge distillation, machine learning, deep learning

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Authors: Neetu Manocha

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Image segmentation is a technique where a picture is parted into distinct parts having similar features which have a place with similar items. Various segmentation strategies have been proposed as of late by prominent analysts. But, after ultimate thorough research, the novelists have analyzed that generally, the old methods do not decrease the segmentation error rate. Then author finds the technique HIST-SI to decrease the segmentation error rates. In this technique, cluster-based and threshold-based segmentation techniques are merged together. After then, to improve the result of HIST-SI, the authors added the method of filtering and linking in this technique named Imp_HIST-SI to decrease the segmentation error rates. The goal of this research is to find a new technique to decrease the segmentation error rates and produce much better results than the HIST-SI technique. For testing the proposed technique, a dataset of Bhuvan – a National Geoportal developed and hosted by ISRO (Indian Space Research Organisation) is used. Experiments are conducted using Scikit-image & OpenCV tools of Python, and performance is evaluated and compared over various existing image segmentation techniques for several matrices, i.e., Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR).

Keywords: satellite image, image segmentation, edge detection, error rate, MSE, PSNR, HIST-SI, linking, filtering, imp_HIST-SI

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Authors: Dina H. Gabr, Mona A. El Bannan

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With growing worldwide concern for global warming, green finance has become the fuel that pushes the world to act in combating and mitigating climate change. Coupled with adopting the Paris Agreement and the United Nations Sustainable Development Goals, Green finance became a vital tool in creating a pathway to sustainable development, as it connects the financial world with environmental and societal benefits. This paper provides a comprehensive review of the concepts and definitions of green finance and the importance of 'green' impact investments today. The core challenge in combating climate change is reducing and controlling Greenhouse gas emissions; therefore, this study explores the solutions green finance provides putting emphasis on the use of renewable energy, which is necessary for enhancing the transition to the green economy. With increasing attention to the concept of green finance, multiple forms of green investments and financial tools have come to fruition; the most prominent are green bonds. The rise of green bonds, a debt market to finance climate solutions, provide a promising mechanism for sustainable finance. Following the review, this paper compiles a comprehensive green bond dataset, presenting a statistical study of the evolution of the green bonds market from its first appearance in 2006 until 2021.

Keywords: climate change, GHG emissions, green bonds, green finance, sustainable finance

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Authors: Philipp Ruf, Massiwa Chabbi, Christoph Reich, Djaffar Ould-Abdeslam

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In recent years, convolutional neural networks (CNN) have demonstrated high performance in image analysis, but oftentimes, there is only structured data available regarding a specific problem. By interpreting structured data as images, CNNs can effectively learn and extract valuable insights from tabular data, leading to improved predictive accuracy and uncovering hidden patterns that may not be apparent in traditional structured data analysis. In applying a single neural network for analyzing multimodal data, e.g., both structured and unstructured information, significant advantages in terms of time complexity and energy efficiency can be achieved. Converting structured data into images and merging them with existing visual material offers a promising solution for applying CNN in multimodal datasets, as they often occur in a medical context. By employing suitable preprocessing techniques, structured data is transformed into image representations, where the respective features are expressed as different formations of colors and shapes. In an additional step, these representations are fused with existing images to incorporate both types of information. This final image is finally analyzed using a CNN.

Keywords: CNN, image processing, tabular data, mixed dataset, data transformation, multimodal fusion

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Authors: Mahnaz Poorshahidi

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This article is a psychological reading of Shakespeare’s Hamlet applying Lacan’s ideas to work with a new look. Lacan entitled Hamlet ‘tragedy of desire’. He believes that Hamlet is caught up in the desire of his mother. So he is the universal symbol of all human beings, regardless of their sex, who desire their mother, but based on the rules of Nature and Father, this unity is impossible. Hamlet hesitates in fulfilling the task of revenge and the text says nothing about the reasons and motives behind it. However, this essay tries to answer the question and justify Hamlet’s hesitation. There is one question for the readers, which is why Hamlet appears to delay in killing his uncle, despite the fact that this is precisely what he seems to want to do. In 1958-59 Lacan delivered a series of lectures on Hamlet entitled ‘Desire and Its Interpretations’ and called it ‘tragedy of desire’. However, this article will have a new representation of Hamlet’s decision not to take revenge. The research demonstrates that Hamlet has passed through imaginary, symbolic and real stages, which are the natural process of life. Eliminating father means disturbing this natural process. This essay is going to conclude that killing Claudius can break the natural order of life. On the other hand, Claudius has also disturbed nature and is regretful about his deed. Hamlet’s ever-present speech ‘To be or not to be’ reflects his mental turmoil and disturbance of the natural life cycle: Nature.

Keywords: desire, father figure, lacan, nature

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Authors: S. Bader, N. Essoukri Ben Amara

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With the technological development and rise of virtual worlds, these spaces are becoming more and more attractive for cybercriminals, hidden behind avatars and fictitious identities. Since access to these spaces is not restricted or controlled, some impostors take advantage of gaining unauthorized access and practicing cyber criminality. This paper proposes an identity management approach for securing access to virtual worlds. The major purpose of the suggested solution is to install a strong security mechanism to protect virtual identities represented by avatars. Thus, only legitimate users, through their corresponding avatars, are allowed to access the platform resources. Access is controlled by integrating an authentication process based on biometrics. In the request process for registration, a user fingerprint is enrolled and then encrypted into a watermark utilizing a cancelable and non-invertible algorithm for its protection. After a user personalizes their representative character, the biometric mark is embedded into the avatar through a watermarking procedure. The authenticity of the avatar identity is verified when it requests authorization for access. We have evaluated the proposed approach on a dataset of avatars from various virtual worlds, and we have registered promising performance results in terms of authentication accuracy, acceptation and rejection rates.

Keywords: identity management, security, biometrics authentication and authorization, avatar, virtual world

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Authors: Trupti Chavan, Ramachandra Guda, Kameshwar Rao

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The lack of data is a pain problem in medical image analysis using a convolutional neural network (CNN). This work uses various spatial transformation techniques to address the medical image augmentation issue for knee detection and localization using an enhanced single shot detector (SSD) network. The spatial transforms like a negative, histogram equalization, power law, sharpening, averaging, gaussian blurring, etc. help to generate more samples, serve as pre-processing methods, and highlight the features of interest. The experimentation is done on the OpenKnee dataset which is a collection of knee images from the openly available online sources. The CNN called enhanced single shot detector (SSD) is utilized for the detection and localization of the knee joint from a given X-ray image. It is an enhanced version of the famous SSD network and is modified in such a way that it will reduce the number of prediction boxes at the output side. It consists of a classification network (VGGNET) and an auxiliary detection network. The performance is measured in mean average precision (mAP), and 99.96% mAP is achieved using the proposed enhanced SSD with spatial transformations. It is also seen that the localization boundary is comparatively more refined and closer to the ground truth in spatial augmentation and gives better detection and localization of knee joints.

Keywords: data augmentation, enhanced SSD, knee detection and localization, medical image analysis, openKnee, Spatial transformations

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Authors: Abe Degale D., Cheng Jian

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When creating automated video surveillance systems, violent action recognition is crucial. In recent years, hand-crafted feature detectors have been the primary method for achieving violence detection, such as the recognition of fighting activity. Researchers have also looked into learning-based representational models. On benchmark datasets created especially for the detection of violent sequences in sports and movies, these methods produced good accuracy results. The Hockey dataset's videos with surveillance camera motion present challenges for these algorithms for learning discriminating features. Image recognition and human activity detection challenges have shown success with deep representation-based methods. For the purpose of detecting violent images and identifying aggressive human behaviours, this research suggested a deep representation-based model using the transfer learning idea. The results show that the suggested approach outperforms state-of-the-art accuracy levels by learning the most discriminating features, attaining 99.34% and 99.98% accuracy levels on the Hockey and Movies datasets, respectively.

Keywords: violence detection, faster RCNN, transfer learning and, surveillance video

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Authors: Kay Thinzar Phu, Lwin Lwin Oo

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In an intelligent transport system and advanced driver assistance system, the developing of real-time traffic sign detection and recognition (TSDR) system plays an important part in recent research field. There are many challenges for developing real-time TSDR system due to motion artifacts, variable lighting and weather conditions and situations of traffic signs. Researchers have already proposed various methods to minimize the challenges problem. The aim of the proposed research is to develop an efficient and effective TSDR in real time. This system proposes an adaptive thresholding method based on RGB color for traffic signs detection and new features for traffic signs recognition. In this system, the RGB color thresholding is used to detect the blue and yellow color traffic signs regions. The system performs the shape identify to decide whether the output candidate region is traffic sign or not. Lastly, new features such as termination points, bifurcation points, and 90’ angles are extracted from validated image. This system uses Myanmar Traffic Sign dataset.

Keywords: adaptive thresholding based on RGB color, blue color detection, feature extraction, yellow color detection

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Authors: Adriana De Serio, Adrian Korek

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Aims: This experiential research work presents a Group-MusicTherapy-Theatre-Plan (MusThePlan) the authors have carried out to support the actors. The MusicTherapy gives rise to individual psychophysical feedback and influences the emotional centres of the brain and the subconsciousness. Therefore, the authors underline the effectiveness of the preventive, educational, and training goals of the MusThePlan to lead theatre students and actors to deal with anxiety and to overcome psychophysical weaknesses, shyness, emotional stress in stage performances, to increase flexibility, awareness of one's identity and resources for a positive self-development and psychophysical health, to develop and strengthen social bonds, increasing a network of subjects working for social inclusion and reduction of stigma. Materials-Methods: Thirty students from the University Theatre Faculty participated in weekly music therapy sessions for two months; each session lasted 120 minutes. MusThePlan: Each session began with a free group rhythmic-sonorous-musical-production by body-percussion, voice-canto, instruments, to stimulate communication. Then, a synchronized-structured bodily-rhythmic-sonorous-musical production also involved acting, dances, movements of hands and arms, hearing, and more sensorial perceptions and speech to balance motor skills and the muscular tone. Each student could be the director-leader of the group indicating a story to inspire the group's musical production. The third step involved the students in rhythmic speech and singing drills and in vocal exercises focusing on the musical pitch to improve the intonation and on the diction to improve the articulation and lead up it to an increased intelligibility. At the end of each musictherapy session and of the two months, the Musictherapy Assessment Document was drawn up by analysis of observation protocols and two Indices by the authors: Patient-Environment-Music-Index (time to - tn) to estimate the behavior evolution, Somatic Pattern Index to monitor subject’s eye and mouth and limb motility, perspiration, before, during and after musictherapy sessions. Results: After the first month, the students (non musicians) learned to play percussion instruments and formed a musical band that played classical/modern music on the percussion instruments with the musictherapist/pianist/conductor in a public concert. At the end of the second month, the students performed a public musical theatre show, acting, dancing, singing, and playing percussion instruments. The students highlighted the importance of the playful aspects of the group musical production in order to achieve emotional contact and harmony within the group. The students said they had improved kinetic and vocal and all the skills useful for acting activity and the nourishment of the bodily and emotional balance. Conclusions: The MusThePlan makes use of some specific MusicTherapy methodological models, techniques, and strategies useful for the actors. The MusThePlan can destroy the individual "mask" and can be useful when the verbal language is unable to undermine the defense mechanisms of the subject. The MusThePlan improves actor’s psychophysical activation, motivation, gratification, knowledge of one's own possibilities, and the quality of life. Therefore, the MusThePlan could be useful to carry out targeted interventions for the actors with characteristics of repeatability, objectivity, and predictability of results. Furthermore, it would be useful to plan a University course/master in “MusicTherapy for the Theatre”.

Keywords: musictherapy, sonorous-musical energy, quality of life, theatre

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Authors: Lianzhong Zhang, Chao Huang

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Sea-land segmentation is a basic step in many tasks such as sea surface monitoring and ship detection. The existing sea-land segmentation algorithms have poor segmentation accuracy, and the parameter adjustments are cumbersome and difficult to meet actual needs. Also, the current sea-land segmentation adopts traditional deep learning models that use Convolutional Neural Networks (CNN). At present, the transformer architecture has achieved great success in the field of natural images, but its application in the field of radar images is less studied. Therefore, this paper proposes a sea-land segmentation method based on the transformer architecture to strengthen edge supervision. It uses a self-attention mechanism with a gating strategy to better learn relative position bias. Meanwhile, an additional edge supervision branch is introduced. The decoder stage allows the feature information of the two branches to interact, thereby improving the edge precision of the sea-land segmentation. Based on the Gaofen-3 satellite image dataset, the experimental results show that the method proposed in this paper can effectively improve the accuracy of sea-land segmentation, especially the accuracy of sea-land edges. The mean IoU (Intersection over Union), edge precision, overall precision, and F1 scores respectively reach 96.36%, 84.54%, 99.74%, and 98.05%, which are superior to those of the mainstream segmentation models and have high practical application values.

Keywords: SAR, sea-land segmentation, deep learning, transformer

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

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In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods.

Keywords: human action recognition, Bayesian HMM, Dirichlet process mixture model, Gaussian-Wishart emission model, Variational Bayesian inference, prior distribution and approximate posterior distribution, KTH dataset

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Authors: Sonia Perez-Gamboa, Qingquan Sun, Yan Zhang

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Non-intrusive sensor-based human activity recognition (HAR) is utilized in a spectrum of applications, including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short term memory (LSTM) recurrent neural networks (RNNs) provide a way to achieve HAR accurately and effectively. In this paper, we design a multi-layer hybrid architecture with CNN and LSTM and explore a variety of multi-layer combinations. Based on the exploration, we present a lightweight, hybrid, and multi-layer model, which can improve the recognition performance by integrating local features and scale-invariant with dependencies of activities. The experimental results demonstrate the efficacy of the proposed model, which can achieve a 94.7% activity recognition rate on a benchmark human activity dataset. This model outperforms traditional machine learning and other deep learning methods. Additionally, our implementation achieves a balance between recognition rate and training time consumption.

Keywords: deep learning, LSTM, CNN, human activity recognition, inertial sensor

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Authors: Yong Zhao, Jian He, Cheng Zhang

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Cardiovascular diseases caused by hypertension are extremely threatening to human health, and early diagnosis of hypertension can save a large number of lives. Traditional hypertension detection methods require special equipment and are difficult to detect continuous blood pressure changes. In this regard, this paper first analyzes the principle of heart rate variability (HRV) and introduces sliding window and power spectral density (PSD) to analyze the time domain features and frequency domain features of HRV, and secondly, designs an HRV-based hypertension prediction network by combining Resnet, attention mechanism, and multilayer perceptron, which extracts the frequency domain through the improved ResNet18 features through a modified ResNet18, its fusion with time-domain features through an attention mechanism, and the auxiliary prediction of hypertension through a multilayer perceptron. Finally, the network was trained and tested using the publicly available SHAREE dataset on PhysioNet, and the test results showed that this network achieved 92.06% prediction accuracy for hypertension and outperformed K Near Neighbor(KNN), Bayes, Logistic, and traditional Convolutional Neural Network(CNN) models in prediction performance.

Keywords: feature extraction, heart rate variability, hypertension, residual networks

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Authors: Shahadat Uddin, Md Ekramul Hossain, Arif Khan

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The collaboration among physicians during episodes of care for a hospitalised patient has a significant contribution towards effective health outcome. This research aims at improving this health outcome by analysing the attributes of patient-sharing physician collaboration network (PCN) on hospital data. To accomplish this goal, we present a research framework that explores the impact of several types of attributes (such as clique and clan) of PCN on hospitalisation cost and hospital length of stay. We use electronic health insurance claim dataset to construct and explore PCNs. Each PCN is categorised as ‘low’ and ‘high’ in terms of hospitalisation cost and length of stay. The results from the proposed model show that the clique and clan of PCNs affect the hospitalisation cost and length of stay. The clique and clan of PCNs show the difference between ‘low’ and ‘high’ PCNs in terms of hospitalisation cost and length of stay. The findings and insights from this research can potentially help the healthcare stakeholders to better formulate the policy in order to improve quality of care while reducing cost.

Keywords: clique, clan, electronic health records, physician collaboration

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Authors: Kamyar Fakhr, Roozbeh Salmani

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Alphonse Bertillon developed the first biometric security system in the 1800s. Today, many governments and giant companies are considering or have procured biometrically enabled security schemes. Iris is a kaleidoscope of patterns and colors. Each individual holds a set of irises more unique than their thumbprint. Every single day, giant companies like Google and Apple are experimenting with reliable biometric systems. Now, after almost 200 years of improvements, face ID does not work with masks, it gives access to fake 3D images, and there is no global usage of biometric recognition systems as national identity (ID) card. The goal of this paper is to demonstrate the advantages of iris recognition overall biometric recognition systems. It make two extensions: first, we illustrate how a very large amount of internet fraud and cyber abuse is happening due to bugs in face recognition systems and in a very large dataset of 3.4M people; second, we discuss how establishing a secure global network of iris recognition devices connected to authoritative convolutional neural networks could be the safest solution to this dilemma. Another aim of this study is to provide a system that will prevent system infiltration caused by cyber-attacks and will block all wireframes to the data until the main user ceases the procedure.

Keywords: biometric system, convolutional neural network, cyber-attack, secure

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Authors: Haroon Mahmood, Christopher Gan, Cuong Nguyen

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Maturity transformation risk is highlighted as one of the major causes of recent global financial crisis. Basel III has proposed new liquidity regulations for transformation function of banks and hence to monitor this risk. Specifically, net stable funding ratio (NSFR) is introduced to enhance medium- and long-term resilience against liquidity shocks. Islamic banking is widely accepted in many parts of the world and contributes to a significant portion of the financial sector in many countries. Using a dataset of 68 fully fledged Islamic banks from 11 different countries, over a period from 2005 – 2014, this study has attempted to analyze various factors that may significantly affect the maturity transformation risk in these banks. We utilize 2-step system GMM estimation technique on unbalanced panel and find bank capital, credit risk, financing, size and market power are most significant among the bank specific factors. Also, gross domestic product and inflation are the significant macro-economic factors influencing this risk. However, bank profitability, asset efficiency, and income diversity are found insignificant in determining the maturity transformation risk in Islamic banking model.

Keywords: Basel III, Islamic banking, maturity transformation risk, net stable funding ratio

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Authors: Khaled Nagaty, Heba Nagaty, Gerard McKee

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A signature is a behavioral biometric that is used for authenticating users in most financial and legal transactions. Signatures can be easily forged by skilled forgers. Therefore, it is essential to verify whether a signature is genuine or forged. The aim of any signature verification algorithm is to accommodate the differences between signatures of the same person and increase the ability to discriminate between signatures of different persons. This work presented in this paper proposes an automatic signature verification system to indicate whether a signature is genuine or not. The system comprises four phases: (1) The pre-processing phase in which image scaling, binarization, image rotation, dilation, thinning, and connecting ridge breaks are applied. (2) The feature extraction phase in which global and local features are extracted. The local features are minutiae points, curvature orientation, and curve plateau. The global features are signature area, signature aspect ratio, and Hu moments. (3) The post-processing phase, in which false minutiae are removed. (4) The classification phase in which features are enhanced before feeding it into the classifier. k-nearest neighbors and support vector machines are used. The classifier was trained on a benchmark dataset to compare the performance of the proposed offline signature verification system against the state-of-the-art. The accuracy of the proposed system is 92.3%.

Keywords: signature, ridge breaks, minutiae, orientation

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Authors: A. Nguyen, K. Nguyen, J. Jackson, E. Manlapig

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With the recent advancement in core imaging systems, a large volume of high resolution drill core images can now be collected rapidly. This paper presents a method for rapid prediction of ore-specific breakage parameters from high resolution mineral classified core images. The aim is to allow for a rapid assessment of the variability in ore hardness within a mineral deposit with reduced amount of physical breakage tests. This method sees its application primarily in project evaluation phase, where proper evaluation of the variability in ore hardness of the orebody normally requires prolong and costly metallurgical test work program. Applying this image-based texture analysis method on mineral classified core images, the ores are classified according to their textural characteristics. A small number of physical tests are performed to produce a dataset used for developing the relationship between texture classes and measured ore hardness. The paper also presents a case study in which this method has been applied on core samples from a copper porphyry deposit to predict the ore-specific breakage A*b parameter, obtained from JKRBT tests.

Keywords: geometallurgy, hyperspectral drill core imaging, process simulation, texture analysis

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Authors: Ebru Turgal, Beyza Doganay Erdogan

Abstract:

Machine learning aims to model the relationship between the response and features. Medical decision-making researchers would like to make decisions about patients’ course and treatment, by examining the repeated measurements over time. Boosting approach is now being used in machine learning area for these aims as an influential tool. The aim of this study is to show the usage of multivariate tree boosting in this field. The main reason for utilizing this approach in the field of decision-making is the ease solutions of complex relationships. To show how multivariate tree boosting method can be used to identify important features and feature-time interaction, we used the data, which was collected retrospectively from Ankara University Chest Diseases Department records. Dataset includes repeated PF ratio measurements. The follow-up time is planned for 120 hours. A set of different models is tested. In conclusion, main idea of classification with weighed combination of classifiers is a reliable method which was shown with simulations several times. Furthermore, time varying variables will be taken into consideration within this concept and it could be possible to make accurate decisions about regression and survival problems.

Keywords: boosted multivariate trees, longitudinal data, multivariate regression tree, panel data

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Authors: Grienggrai Rajchakit

Abstract:

As we all know that coronavirus is announced as a pandemic in the world by WHO. It is speeded all over the world with few days of time. To control this spreading, every citizen maintains social distance and self-preventive measures are the best strategies. As of now, many researchers and scientists are continuing their research in finding out the exact vaccine. The machine learning model finds that the coronavirus disease behaves in an exponential manner. To abolish the consequence of this pandemic, an efficient step should be taken to analyze this disease. In this paper, a recurrent neural network model is chosen to predict the number of active cases in a particular state. To make this prediction of active cases, we need a database. The database of COVID-19 is downloaded from the KAGGLE website and is analyzed by applying a recurrent LSTM neural network with univariant features to predict the number of active cases of patients suffering from the corona virus. The downloaded database is divided into training and testing the chosen neural network model. The model is trained with the training data set and tested with a testing dataset to predict the number of active cases in a particular state; here, we have concentrated on Andhra Pradesh state.

Keywords: COVID-19, coronavirus, KAGGLE, LSTM neural network, machine learning

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Authors: Chris Adetuyi, Adeola Adeniran

Abstract:

The concept of meaning is a complex one in language study when cultural features are added. This is mandatory because language cannot be completely separated from the culture in which case language and culture complement each other. When there are two varieties of a language in a society, i.e. two varieties functioning side by side in a speech community, there is a tendency to view one of the varieties with each other. There is, therefore, the need to make a linguistic comparative study of varieties of such languages. In this paper, a semantic contrastive study is made between Standard British English (SBE) and Nigerian English (NB). The semantic study is limited to aspects of semantics: semantic extension (Kinship terms, metaphors), semantic shift (lexical items considered are ‘drop’ ‘befriend’ ‘dowry’ and escort) acronyms (NEPA, JAMB, NTA) linguistic borrowing or loan words (Seriki, Agbada, Eba, Dodo, Iroko) coinages (long leg, bush meat; bottom power and juju). In the study of these aspects of semantics of SBE and NE lexical terms, conservative statements are made, problems areas and hierarchy of difficulties are highlighted with a view to bringing out areas of differences are highlighted in this paper are concerned. The study will also serve as a guide in further contrastive studies in some other area of languages.

Keywords: aspect, British, English, Nigeria, semantics

Procedia PDF Downloads 330