Search results for: neural fingerprint

Authors: Yeliz Ekinci, Melis Koyuncu

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Successful planning of solid waste management systems requires successful prediction of the amount of solid waste generated in an area. Waste management planning can protect the environment and human health, hence it is tremendously important for countries. The lack of information in waste generation can cause many environmental and health problems. Turkey is a country that plans to join European Union, hence, solid waste management is one of the most significant criteria that should be handled in order to be a part of this community. Solid waste management system requires a good forecast of solid waste generation. Thus, this study aims to forecast solid waste generation in Turkey. Artificial Neural Network and Linear Regression models will be used for this aim. Many models will be run and the best one will be selected based on some predetermined performance measures.

Keywords: forecast, solid waste generation, solid waste management, Turkey

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

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Authors: Beata Lachtara, Renata Szewczyk, Katarzyna Bielinska, Kinga Wieczorek, Jacek Osek

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Listeria monocytogenes is an important human and animal pathogen that causes foodborne outbreaks. The bacteria may be present in different types of food: cheese, raw vegetables, sliced meat products and vacuum-packed sausages, poultry, meat, fish. The most common method, which has been used for the investigation of genetic diversity of L. monocytogenes, is PFGE. This technique is reliable and reproducible and established as gold standard for typing of L. monocytogenes. The aim of the study was characterization by molecular serotyping and PFGE analysis of L. monocytogenes strains isolated from fresh fish and fish products in Poland. A total of 301 samples, including fresh fish (n = 129) and fish products (n = 172) were, collected between January 2014 and March 2016. The bacteria were detected using the ISO 11290-1 standard method. Molecular serotyping was performed with PCR. The isolates were tested with the PFGE method according to the protocol developed by the European Union Reference Laboratory for L. monocytogenes with some modifications. Based on the PFGE profiles, two dendrograms were generated for strains digested separately with two restriction enzymes: AscI and ApaI. Analysis of the fingerprint profiles was performed using Bionumerics software version 6.6 (Applied Maths, Belgium). The 95% of similarity was applied to differentiate the PFGE pulsotypes. The study revealed that 57 of 301 (18.9%) samples were positive for L. monocytogenes. The bacteria were identified in 29 (50.9%) ready-to-eat fish products and in 28 (49.1%) fresh fish. It was found that 40 (70.2%) strains were of serotype 1/2a, 14 (24.6%) 1/2b, two (4.3%) 4b and one (1.8%) 1/2c. Serotypes 1/2a, 1/2b, and 4b were presented with the same frequency in both categories of food, whereas serotype 1/2c was detected only in fresh fish. The PFGE analysis with AscI demonstrated 43 different pulsotypes; among them 33 (76.7%) were represented by only one strain. The remaining 10 profiles contained more than one isolate. Among them 8 pulsotypes comprised of two L. monocytogenes isolates, one profile of three isolates and one restriction type of 5 strains. In case of ApaI typing, the PFGE analysis showed 27 different pulsotypes including 17 (63.0%) types represented by only one strain. Ten (37.0%) clusters contained more than one strain among which four profiles covered two strains; three had three isolates, one with five strains, one with eight strains and one with ten isolates. It was observed that the isolates assigned to the same PFGE type were usually of the same serotype (1/2a or 1/2b). The majority of the clusters had strains of both sources (fresh fish and fish products) isolated at different time. Most of the strains grouped in one cluster of the AscI restriction was assigned to the same groups in ApaI investigation. In conclusion, PFGE used in the study showed a high genetic diversity among L. monocytogenes. The strains were grouped into varied clonal clusters, which may suggest different sources of contamination. The results demonstrated that 1/2a serotype was the most common among isolates from fresh fish and fish products in Poland.

Keywords: Listeria monocytogenes, molecular characteristic, PFGE, serotyping

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Authors: Edgar Santoyo, Daniel Perez-Zarate, Agustin Acevedo, Lorena Diaz-Gonzalez, Mirna Guevara

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Four new gas geothermometers have been derived from a multivariate geo chemometric analysis of a geothermal fluid chemistry database, two of which use the natural logarithm of CO₂ and H2S concentrations (mmol/mol), respectively, and the other two use the natural logarithm of the H₂S/H₂ and CO₂/H₂ ratios. As a strict compilation criterion, the database was created with gas-phase composition of fluids and bottomhole temperatures (BHTM) measured in producing wells. The calibration of the geothermometers was based on the geochemical relationship existing between the gas-phase composition of well discharges and the equilibrium temperatures measured at bottomhole conditions. Multivariate statistical analysis together with the use of artificial neural networks (ANN) was successfully applied for correlating the gas-phase compositions and the BHTM. The predicted or simulated bottomhole temperatures (BHTANN), defined as output neurons or simulation targets, were statistically compared with measured temperatures (BHTM). The coefficients of the new geothermometers were obtained from an optimized self-adjusting training algorithm applied to approximately 2,080 ANN architectures with 15,000 simulation iterations each one. The self-adjusting training algorithm used the well-known Levenberg-Marquardt model, which was used to calculate: (i) the number of neurons of the hidden layer; (ii) the training factor and the training patterns of the ANN; (iii) the linear correlation coefficient, R; (iv) the synaptic weighting coefficients; and (v) the statistical parameter, Root Mean Squared Error (RMSE) to evaluate the prediction performance between the BHTM and the simulated BHTANN. The prediction performance of the new gas geothermometers together with those predictions inferred from sixteen well-known gas geothermometers (previously developed) was statistically evaluated by using an external database for avoiding a bias problem. Statistical evaluation was performed through the analysis of the lowest RMSE values computed among the predictions of all the gas geothermometers. The new gas geothermometers developed in this work have been successfully used for predicting subsurface temperatures in high-temperature geothermal systems of Mexico (e.g., Los Azufres, Mich., Los Humeros, Pue., and Cerro Prieto, B.C.) as well as in a blind geothermal system (known as Acoculco, Puebla). The last results of the gas geothermometers (inferred from gas-phase compositions of soil-gas bubble emissions) compare well with the temperature measured in two wells of the blind geothermal system of Acoculco, Puebla (México). Details of this new development are outlined in the present research work. Acknowledgements: The authors acknowledge the funding received from CeMIE-Geo P09 project (SENER-CONACyT).

Keywords: artificial intelligence, gas geochemistry, geochemometrics, geothermal energy

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Authors: Szymon Rutkowski, Tomasz Korbak

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We present a reinforcement-learning-based method of training neural machine translation models without parallel corpora. The standard encoder-decoder approach to machine translation suffers from two problems we aim to address. First, it needs parallel corpora, which are scarce, especially for low-resource languages. Second, it lacks psychological plausibility of learning procedure: learning a foreign language is about learning to communicate useful information, not merely learning to transduce from one language’s 'encoding' to another. We instead pose the problem of learning to translate as learning a policy in a communication game between two agents: the translator and the classifier. The classifier is trained beforehand on a natural language inference task (determining the entailment relation between a premise and a hypothesis) in the target language. The translator produces a sequence of actions that correspond to generating translations of both the hypothesis and premise, which are then passed to the classifier. The translator is rewarded for classifier’s performance on determining entailment between sentences translated by the translator to disciple’s native language. Translator’s performance thus reflects its ability to communicate useful information to the classifier. In effect, we train a machine translation model without the need for parallel corpora altogether. While similar reinforcement learning formulations for zero-shot translation were proposed before, there is a number of improvements we introduce. While prior research aimed at grounding the translation task in the physical world by evaluating agents on an image captioning task, we found that using a linguistic task is more sample-efficient. Natural language inference (also known as recognizing textual entailment) captures semantic properties of sentence pairs that are poorly correlated with semantic similarity, thus enforcing basic understanding of the role played by compositionality. It has been shown that models trained recognizing textual entailment produce high-quality general-purpose sentence embeddings transferrable to other tasks. We use stanford natural language inference (SNLI) dataset as well as its analogous datasets for French (XNLI) and Polish (CDSCorpus). Textual entailment corpora can be obtained relatively easily for any language, which makes our approach more extensible to low-resource languages than traditional approaches based on parallel corpora. We evaluated a number of reinforcement learning algorithms (including policy gradients and actor-critic) to solve the problem of translator’s policy optimization and found that our attempts yield some promising improvements over previous approaches to reinforcement-learning based zero-shot machine translation.

Keywords: agent-based language learning, low-resource translation, natural language inference, neural machine translation, reinforcement learning

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Authors: Panda Krouse

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This work is a marriage of the science of Applied Behavioral Analysis and an educator’s look at Neuroscience. The focus is integrating what we know about the anatomy of the brain in autism and evidence-based practices in education. It is a bold attempt to present links between neurological research and the application of evidence-based practices in education. In researching for this work, no discovery of articles making these connections was made. Consideration of the areas of structural differences in the brain are aligned with evidence-based strategies. A brief literary review identifies how identified areas affect overt behavior, which is what, as educators, is what we can see and measure. Giving further justification and validation of our practices in education from a second scientific field is significant for continued improvement in intervention for students on the autism spectrum.

Keywords: autism, evidence based practices, neurological differences, education intervention

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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: Hee Sun (Sunny) Choi, Gerhard Bruyns, Wang Zhang, Sky Cheng, Saijal Sharma

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The main focus of this paper is to propose a comprehensive framework for the cognitive measurement and modelling of the built environment. This will involve exploring and measuring neural mechanisms. The aim is to create a foundation for further studies in this field that are consistent and rigorous. Additionally, this framework will facilitate collaboration with cognitive neuroscientists by establishing a shared conceptual basis. The goal of this research is to develop a human-centric approach for urban design that is scientific and measurable, producing a set of urban design guidelines that incorporate cognitive measurement and modelling. By doing so, the broader intention is to design urban spaces that prioritize human needs and well-being, making them more liveable.

Keywords: vertical urbanism, human centric design, spatial cognition and psychology, vertical urban design guidelines

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Authors: P. Shrivastava, A. Shukla, K. Verma, S. Rungta

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Parkinson's disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity and tremors. Gait serve as a primary outcome measure for studies aiming at early recognition of disease. Using gait techniques, this paper implements efficient binary bat algorithm for an early detection of Parkinson's disease by selecting optimal features required for classification of affected patients from others. The data of 166 people, both fit and affected is collected and optimal feature selection is done using PSO and Bat algorithm. The reduced dataset is then classified using neural network. The experiments indicate that binary bat algorithm outperforms traditional PSO and genetic algorithm and gives a fairly good recognition rate even with the reduced dataset.

Keywords: parkinson, gait, feature selection, bat algorithm

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Authors: Thor Alexis Sazon, Alexander Guzman-Urbina, Yasuhiro Fukushima

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This study outlines the method of how to develop a surrogate life cycle model based on fuzzy logic using three fuzzy inference methods: (1) the conventional Fuzzy Inference System (FIS), (2) the hybrid system of Data Analytics and Fuzzy Inference (DAFIS), which uses data clustering for defining the membership functions, and (3) the Adaptive-Neuro Fuzzy Inference System (ANFIS), a combination of fuzzy inference and artificial neural network. These methods were demonstrated with a case study where the Global Warming Potential (GWP) and the Levelized Cost of Energy (LCOE) of solar photovoltaic (PV) were estimated using Solar Irradiation, Module Efficiency, and Performance Ratio as inputs. The effects of using different fuzzy inference types, either Sugeno- or Mamdani-type, and of changing the number of input membership functions to the error between the calibration data and the model-generated outputs were also illustrated. The solution spaces of the three methods were consequently examined with a sensitivity analysis. ANFIS exhibited the lowest error while DAFIS gave slightly lower errors compared to FIS. Increasing the number of input membership functions helped with error reduction in some cases but, at times, resulted in the opposite. Sugeno-type models gave errors that are slightly lower than those of the Mamdani-type. While ANFIS is superior in terms of error minimization, it could generate solutions that are questionable, i.e. the negative GWP values of the Solar PV system when the inputs were all at the upper end of their range. This shows that the applicability of the ANFIS models highly depends on the range of cases at which it was calibrated. FIS and DAFIS generated more intuitive trends in the sensitivity runs. DAFIS demonstrated an optimal design point wherein increasing the input values does not improve the GWP and LCOE anymore. In the absence of data that could be used for calibration, conventional FIS presents a knowledge-based model that could be used for prediction. In the PV case study, conventional FIS generated errors that are just slightly higher than those of DAFIS. The inherent complexity of a Life Cycle study often hinders its widespread use in the industry and policy-making sectors. While the methodology does not guarantee a more accurate result compared to those generated by the Life Cycle Methodology, it does provide a relatively simpler way of generating knowledge- and data-based estimates that could be used during the initial design of a system.

Keywords: solar photovoltaic, fuzzy logic, inference system, artificial neural networks

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Authors: M. Pilgun

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The report presents the results of a study of the specifics of speech perception by actors of video hosting services on the material of urban planning conflicts. To analyze the content, the multimodal approach using neural network technologies is employed. Analysis of word associations and associative networks of relevant stimulus revealed the evaluative reactions of the actors. Analysis of the data identified key topics that generated negative and positive perceptions from the participants. The calculation of social stress and social well-being indices based on user-generated content made it possible to build a rating of road transport construction objects according to the degree of negative and positive perception by actors.

Keywords: social media, speech perception, video hosting, networks

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Authors: Reza Bazargan lari, Mohammad H. Fattahi

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Natural resources management including water resources requires reliable estimations of time variant environmental parameters. Small improvements in the estimation of environmental parameters would result in grate effects on managing decisions. Noise reduction using wavelet techniques is an effective approach for pre-processing of practical data sets. Predictability enhancement of the river flow time series are assessed using fractal approaches before and after applying wavelet based pre-processing. Time series correlation and persistency, the minimum sufficient length for training the predicting model and the maximum valid length of predictions were also investigated through a fractal assessment.

Keywords: wavelet, de-noising, predictability, time series fractal analysis, valid length, ANN

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Authors: Su Yin Chiam, Zhipeng Ding, Guang Yang, Danny Jian Hang Tng, Peiyi Song, Geok Ing Ng, Ken-Tye Yong, Qing Xin Zhang

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Brain-machine interfaces (BMI) is a ground rich of exploration opportunities where manipulation of neural activity are used for interconnect with myriad form of external devices. These research and intensive development were evolved into various areas from medical field, gaming and entertainment industry till safety and security field. The technology were extended for neurological disorders therapy such as obsessive compulsive disorder and Parkinson’s disease by introducing current pulses to specific region of the brain. Nonetheless, the work to develop a real-time observing, recording and altering of neural signal brain-machine interfaces system will require a significant amount of effort to overcome the obstacles in improving this system without delay in response. To date, feature size of interface devices and the density of the electrode population remain as a limitation in achieving seamless performance on BMI. Currently, the size of the BMI devices is ranging from 10 to 100 microns in terms of electrodes’ diameters. Henceforth, to accommodate the single cell level precise monitoring, smaller and denser Nano-scaled nanowire electrode arrays are vital in fabrication. In this paper, we would like to showcase the fabrication of high aspect ratio of vertical silicon nanowire electrodes arrays using microelectromechanical system (MEMS) method. Nanofabrication of the nanowire electrodes involves in deep reactive ion etching, thermal oxide thinning, electron-beam lithography patterning, sputtering of metal targets and bottom anti-reflection coating (BARC) etch. Metallization on the nanowire electrode tip is a prominent process to optimize the nanowire electrical conductivity and this step remains a challenge during fabrication. Metal electrodes were lithographically defined and yet these metal contacts outline a size scale that is larger than nanometer-scale building blocks hence further limiting potential advantages. Therefore, we present an integrated contact solution that overcomes this size constraint through self-aligned Nickel silicidation process on the tip of vertical silicon nanowire electrodes. A 4 x 4 array of vertical silicon nanowires electrodes with the diameter of 290nm and height of 3µm has been successfully fabricated.

Keywords: brain-machine interfaces, microelectromechanical systems (MEMS), nanowire, nickel silicide

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Authors: Gaurav Shinde, Sai Charan Gongiguntla, Prajwal Shirur, Ahmed Hambaba

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Health issues are significantly increasing, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly. We introduce DeClEx, a pipeline that ensures that data mirrors real-world settings by incorporating Gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state-of-the-art pre-trained models while achieving a threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification, and explainability in a single pipeline called DeClEx.

Keywords: machine learning, healthcare, classification, explainability

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Authors: Oubelkacem Yacine, El Bast Hassan, El Bakkali Abdelmajid, Lamhasni Taibi, Ettakni Mahmoud, Ait Lyazidi Saadia, Haddad Mustapha, Ben-Ncer Abdelouahed, El Ferrane Mohammed, Boufarra Abdelkrim

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The present work relates to an on-site and completely non-invasive investigation of one of the most famous west Mediterranean Torah Scroll housed at the National Library of the Kingdom of Morocco. The scroll is 26 m long and consists of 143 parchment sheets of 59 cm x 19 cm, exhibiting only black writings; it is of unknown age. The artifact has been restored by the curator staff of the library. The investigation exploring separately the parchment support and the writing black ink aims at: i) the examination of the parchment conservation/degradation state, ii) the identification of the black ink and iii) the identification of the parchment handcrafting materials. For this purpose, the analyses have been based on combining all of elemental XRF and structural Raman, ATR-FT Infrared Red and Fiber Optical Reflectance spectroscopies, in addition to chroma-metric and pH measurements. pH measurements showing values around 6.5 are in concordance with the absence of any visual corrosion related to the parchment acidity. However, on the basis of the relative intensities and frequency shift of amid I (AI) and amid II (AII) vibrational bands of the collagen, ATR-FTIR spectra revealed diffuse hydrolysis and gelatinization of the parchment writing support; diffuse and non-homogeny degradation by gelatinization has been also confirmed by the IG gelatinization index deduced from the NIR bands on the FOR spectra. This IG index, defined as the ratio I (6860 cm-1) / I (6685 cm-1), ranges in the interval 0.98 – 1 and highlights collagen degradation at the molecular level. Sequentially Shifted Excitation Raman measurements (SSERS) crossed to X-ray fluorescence (XRF) ones on the black writings revealed that the black ink used is an iron-copper gall one, while FOR spectra are typical of pure metal gall inks. These later reflectance measurements exclude, thus, any intentional addition of carbon black to the ink recipe. Moreover, no lead white had been used while pre-drawing the writing lines. On another side, ATR-FTIR measurements highlighted the presence of oxalates as ink degradation products. Considering the parchment handcrafting, the combination of XRF and ATR-FTIR measurements led to the assumption that this writing support had been prepared according to ancient Middle East practices; the parchment infrared fingerprint seems identical to that of the Dead Sea scroll. The present multi-technical analyses are the first ones performed on an ancient Judaic written parchment of Morocco; it is under furthering. The investigation will be extended to other parchments belonging to the Jewish Cultural Heritage Museum of Morocco in Casablanca.

Keywords: torah scroll, parchment, black ink, non-invasive analyses, XRF/ATR-FTIR/RAMAN/FORS

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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: Tim Farrelly

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In recent years, AI-powered object detection systems have opened the doors for innovative new applications and products, especially those operating in the real world or ‘on edge’ – namely, in sport. This paper investigates the viability of an ultra-low parameter convolutional neural network specially designed for the detection of footballs on ‘on the edge’ devices. The main contribution of this paper is the exploration of integrating new design features (depth-wise separable convolutional blocks and squeezed and excitation modules) into an ultra-low parameter network and demonstrating subsequent improvements in performance. The results show that tracking the ball from Full HD images with negligibly high accu-racy is possible in real-time.

Keywords: deep learning, object detection, machine vision applications, sport, network design

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Authors: Feng Guo

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Human brain organoids, 3D brain tissue cultures derived from human pluripotent stem cells, hold promising potential in modeling neuroinflammation for a variety of neurological diseases. However, challenges remain in generating standardized human brain organoids that can recapitulate key physiological features of a human brain. Here, this study presents a series of organoids-on-a-chip systems to generate better human brain organoids and model neuroinflammation. By employing 3D printing and microfluidic 3D cell culture technologies, the study’s systems enable the reliable, scalable, and reproducible generation of human brain organoids. Compared with conventional protocols, this study’s method increased neural progenitor proliferation and reduced heterogeneity of human brain organoids. As a proof-of-concept application, the study applied this method to model substance use disorders.

Keywords: human brain organoids, microfluidics, organ-on-a-chip, neuroinflammation

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Authors: Motonobu Hattori

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In neural networks, when new patters are learned by a network, they radically interfere with previously stored patterns. This drawback is called catastrophic forgetting. We have already proposed a biologically inspired dual-network memory model which can much reduce this forgetting for static patterns. In this model, information is first stored in the hippocampal network, and thereafter, it is transferred to the neocortical network using pseudo patterns. Because, temporal sequence learning is more important than static pattern learning in the real world, in this study, we improve our conventional dual-network memory model so that it can deal with temporal sequences without catastrophic forgetting. The computer simulation results show the effectiveness of the proposed dual-network memory model.

Keywords: catastrophic forgetting, dual-network, temporal sequences, hippocampal

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Authors: David Edgar

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To understand how the brain’s subconscious and conscious functions, we must conquer the physics of Unity, which leads to duality’s algorithm. Where the subconscious (bottom-up) and conscious (top-down) processes function together to produce and consume intelligence, we use terms like ‘time is relative,’ but we really do understand the meaning. In the brain, there are different processes and, therefore, different observers. These different processes experience time at different rates. A sensory system such as the eyes cycles measurement around 33 milliseconds, the conscious process of the frontal lobe cycles at 300 milliseconds, and the subconscious process of the thalamus cycle at 5 milliseconds. Three different observers experience time differently. To bridge observers, the thalamus, which is the fastest of the processes, maintains a synchronous state and entangles the different components of the brain’s physical process. The entanglements form a synchronous cohesion between the brain components allowing them to share the same state and execute in the same measurement cycle. The thalamus uses the shared state to control the firing sequence of the brain’s linear subconscious process. Sharing state also allows the brain to cheat on the amount of sensory data that must be exchanged between components. Only unpredictable motion is transferred through the synchronous state because predictable motion already exists in the shared framework. The brain’s synchronous subconscious process is entirely based on energy conservation, where prediction regulates energy usage. So, the eyes every 33 milliseconds dump their sensory data into the thalamus every day. The thalamus is going to perform a motion measurement to identify the unpredictable motion in the sensory data. Here is the trick. The thalamus conducts its measurement based on the original observation time of the sensory system (33 ms), not its own process time (5 ms). This creates a data payload of synchronous motion that preserves the original sensory observation. Basically, a frozen moment in time (Flat 4D). The single moment in time can then be processed through the single state maintained by the synchronous process. Other processes, such as consciousness (300 ms), can interface with the synchronous state to generate awareness of that moment. Now, synchronous data traveling through a separate faster synchronous process creates a theoretical time tunnel where observation time is tunneled through the synchronous process and is reproduced on the other side in the original time-relativity. The synchronous process eliminates time dilation by simply removing itself from the equation so that its own process time does not alter the experience. To the original observer, the measurement appears to be instantaneous, but in the thalamus, a linear subconscious process generating sensory perception and thought production is being executed. It is all just occurring in the time available because other observation times are slower than thalamic measurement time. For life to exist in the physical universe requires a linear measurement process, it just hides by operating at a faster time relativity. What’s interesting is time dilation is not the problem; it’s the solution. Einstein said there was no universal time.

Keywords: neural synchronization, natural intelligence, 99.95% IoT data transmission savings, artificial subconscious intelligence (ASI)

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Authors: Nidhal K. Azawi, John M. Gauch

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Colorectal cancer is one of the leading causes of cancer death in the US and the world, which is why millions of colonoscopy examinations are performed annually. Unfortunately, noise, specular highlights, and motion artifacts corrupt many images in a typical colonoscopy exam. The goal of our research is to produce automated techniques to detect and correct or remove these noninformative images from colonoscopy videos, so physicians can focus their attention on informative images. In this research, we first automatically extract features from images. Then we use machine learning and deep neural network to classify colonoscopy images as either informative or noninformative. Our results show that we achieve image classification accuracy between 92-98%. We also show how the removal of noninformative images together with image alignment can aid in the creation of image panoramas and other visualizations of colonoscopy images.

Keywords: colonoscopy classification, feature extraction, image alignment, machine learning

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Authors: Hans Schumann, Colin Domnauer, Louis Rosenberg

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In this study, machine learning techniques were deployed on real-time human swarm data to forecast the likelihood of outcomes for English Premier League matches in the 2020/21 season. These techniques included ensemble models in combination with neural networks and were tested against an industry standard of Vegas Oddsmakers. Predictions made from the collective intelligence of human swarm participants managed to achieve a positive return on investment over a full season on matches, empirically proving the usefulness of a new artificial intelligence valuing human instinct and intelligence.

Keywords: artificial intelligence, data science, English Premier League, human swarming, machine learning, sports betting, swarm intelligence

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Authors: Hossein Havaeji, Tony Wong, Thien-My Dao

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1. Research Problems and Research Objectives: Blockchain Technology-enabled Supply Chain System (BT-enabled SCS) is the system using BT to drive SCS transparency, security, durability, and process integrity as SCS data is not always visible, available, or trusted. The costs of operating BT in the SCS are a common problem in several organizations. The costs must be estimated as they can impact existing cost control strategies. To account for system and deployment costs, it is necessary to overcome the following hurdle. The problem is that the costs of developing and running a BT in SCS are not yet clear in most cases. Many industries aiming to use BT have special attention to the importance of BT installation cost which has a direct impact on the total costs of SCS. Predicting BT installation cost in SCS may help managers decide whether BT is to be an economic advantage. The purpose of the research is to identify some main BT installation cost components in SCS needed for deeper cost analysis. We then identify and categorize the main groups of cost components in more detail to utilize them in the prediction process. The second objective is to determine the suitable Supervised Learning technique in order to predict the costs of developing and running BT in SCS in a particular case study. The last aim is to investigate how the running BT cost can be involved in the total cost of SCS. 2. Work Performed: Applied successfully in various fields, Supervised Learning is a method to set the data frame, treat the data, and train/practice the method sort. It is a learning model directed to make predictions of an outcome measurement based on a set of unforeseen input data. The following steps must be conducted to search for the objectives of our subject. The first step is to make a literature review to identify the different cost components of BT installation in SCS. Based on the literature review, we should choose some Supervised Learning methods which are suitable for BT installation cost prediction in SCS. According to the literature review, some Supervised Learning algorithms which provide us with a powerful tool to classify BT installation components and predict BT installation cost are the Support Vector Regression (SVR) algorithm, Back Propagation (BP) neural network, and Artificial Neural Network (ANN). Choosing a case study to feed data into the models comes into the third step. Finally, we will propose the best predictive performance to find the minimum BT installation costs in SCS. 3. Expected Results and Conclusion: This study tends to propose a cost prediction of BT installation in SCS with the help of Supervised Learning algorithms. At first attempt, we will select a case study in the field of BT-enabled SCS, and then use some Supervised Learning algorithms to predict BT installation cost in SCS. We continue to find the best predictive performance for developing and running BT in SCS. Finally, the paper will be presented at the conference.

Keywords: blockchain technology, blockchain technology-enabled supply chain system, installation cost, supervised learning

Procedia PDF Downloads 116

Authors: Ragaa T. Darwish, Maha A. Demellawy, Haidy M. Megahed, Doreen N. Younan, Wael S. Kholeif

Abstract:

The testing of drug abuse is authentic in order to affirm the misuse of drugs. Several analytical approaches have been developed for the detection of drugs of abuse in pharmaceutical and common biological samples, but few methodologies have been created to identify them from fingerprints. Liquid Chromatography-Mass Spectrometry (LC-MS) plays a major role in this field. The current study aimed at assessing the possibility of detection of some drugs of abuse (tramadol, clonazepam, and phenobarbital) from fingerprints using LC-MS in drug abusers. The aim was extended in order to assess the possibility of detection of the above-mentioned drugs in fingerprints of drug handlers till three days of handling the drugs. The study was conducted on randomly selected adult individuals who were either drug abusers seeking treatment at centers of drug dependence in Alexandria, Egypt or normal volunteers who were asked to handle the different studied drugs (drug handlers). An informed consent was obtained from all individuals. Participants were classified into 3 groups; control group that consisted of 50 normal individuals (neither abusing nor handling drugs), drug abuser group that consisted of 30 individuals who abused tramadol, clonazepam or phenobarbital (10 individuals for each drug) and drug handler group that consisted of 50 individuals who were touching either the powder of drugs of abuse: tramadol, clonazepam or phenobarbital (10 individuals for each drug) or the powder of the control substances which were of similar appearance (white powder) and that might be used in the adulteration of drugs of abuse: acetyl salicylic acid and acetaminophen (10 individuals for each drug). Samples were taken from the handler individuals for three consecutive days for the same individual. The diagnosis of drug abusers was based on the current Diagnostic and Statistical Manual of Mental disorders (DSM-V) and urine screening tests using immunoassay technique. Preliminary drug screening tests of urine samples were also done for drug handlers and the control groups to indicate the presence or absence of the studied drugs of abuse. Fingerprints of all participants were then taken on a filter paper previously soaked with methanol to be analyzed by LC-MS using SCIEX Triple Quad or QTRAP 5500 System. The concentration of drugs in each sample was calculated using the regression equations between concentration in ng/ml and peak area of each reference standard. All fingerprint samples from drug abusers showed positive results with LC-MS for the tested drugs, while all samples from the control individuals showed negative results. A significant difference was noted between the concentration of the drugs and the duration of abuse. Tramadol, clonazepam, and phenobarbital were also successfully detected from fingerprints of drug handlers till 3 days of handling the drugs. The mean concentration of the chosen drugs of abuse among the handlers group decreased when the days of samples intake increased.

Keywords: drugs of abuse, fingerprints, liquid chromatography–mass spectrometry, tramadol

Procedia PDF Downloads 115

Authors: J. Y. Tan, L. C. Lum, M. G. Lee, S. Mansouri, K. Hapgood, X. D. Chen, M. W. Woo

Abstract:

Folic acid (FA) is known to be an important supplement to prevent neural tube defect (NTD) in pregnant women. Similar to some commercial formulations, sodium bicarbonate solution is used as a solvent for FA. This work uses the antisolvent vapor precipitation (AVP), incorporating ethanol vapor as the convective drying medium in place of air to produce branch-like micro-structure FA particles. Interestingly, the dissolution rate of the resultant particle is 2-3 times better than the particle produce from conventional air drying due to the higher surface area of particles produced. The higher dissolution rate could possibly improve the delivery and absorption of FA in human body. This application could potentially be extended to other commercial products, particularly in less soluble drugs to improve its solubility.

Keywords: absorption, antisolvent vapor precipitation, dissolution rate, folic acid

Procedia PDF Downloads 441

Authors: Sumit Sen, Sohan Khedekar, Umang Shinde, Shivam Bhargava

Abstract:

This study has been undertaken to investigate whether the deep learning models are able to predict the future stock prices by training the model with the historical stock price data. Since this work required time series analysis, various models are present today to perform time series analysis such as Recurrent Neural Network LSTM, ARIMA and Facebook Prophet. Applying these models the movement of stock price of stocks are predicted and also tried to provide the future prediction of the stock price of a stock. Final product will be a stock price prediction web application that is developed for providing the user the ease of analysis of the stocks and will also provide the predicted stock price for the next seven days.

Keywords: Autoregressive Integrated Moving Average, Deep Learning, Long Short Term Memory, Time-series

Procedia PDF Downloads 136

Authors: K. E. Ch. Vidyasagar, M. Moghavvemi, T. S. S. T. Prabhat

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Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy.

Keywords: classification, seizure, KNN, SVM, LDA, ANN, epilepsy

Procedia PDF Downloads 517

Authors: Pierre Tize Mha, Mohammad Jahazi, Amèvi Togne, Olivier Pantalé

Abstract:

Large-size forged blocks made of medium carbon high-strength steels are extensively used in the automotive industry as dies for the production of bumpers and dashboards through the plastic injection process. The manufacturing process of the large blocks starts with ingot casting, followed by open die forging and a quench and temper heat treatment process to achieve the desired mechanical properties and numerical simulation is widely used nowadays to predict these properties before the experiment. But the temperature gradient inside the specimen remains challenging in the sense that the temperature before loading inside the material is not the same, but during the simulation, constant temperature is used to simulate the experiment because it is assumed that temperature is homogenized after some holding time. Therefore to be close to the experiment, real distribution of the temperature through the specimen is needed before the mechanical loading. Thus, We present here a robust algorithm that allows the calculation of the temperature gradient within the specimen, thus representing a real temperature distribution within the specimen before deformation. Indeed, most numerical simulations consider a uniform temperature gradient which is not really the case because the surface and core temperatures of the specimen are not identical. Another feature that influences the mechanical properties of the specimen is recrystallization which strongly depends on the deformation conditions and the type of deformation like Upsetting, Cogging...etc. Indeed, Upsetting and Cogging are the stages where the greatest deformations are observed, and a lot of microstructural phenomena can be observed, like recrystallization, which requires in-depth characterization. Complete dynamic recrystallization plays an important role in the final grain size during the process and therefore helps to increase the mechanical properties of the final product. Thus, the identification of the conditions for the initiation of dynamic recrystallization is still relevant. Also, the temperature distribution within the sample and strain rate influence the recrystallization initiation. So the development of a technique allowing to predict the initiation of this recrystallization remains challenging. In this perspective, we propose here, in addition to the algorithm allowing to get the temperature distribution before the loading stage, an analytical model leading to determine the initiation of this recrystallization. These two techniques are implemented into the Abaqus finite element software via the UAMP and VUHARD subroutines for comparison with a simulation where an isothermal temperature is imposed. The Artificial Neural Network (ANN) model to describe the plastic behavior of the material is also implemented via the VUHARD subroutine. From the simulation, the temperature distribution inside the material and recrystallization initiation is properly predicted and compared to the literature models.

Keywords: dynamic recrystallization, finite element modeling, artificial neural network, numerical implementation

Procedia PDF Downloads 79

Authors: Aishrith Rao

Abstract:

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 135

Authors: Tetyana Baydyk, Ernst Kussul, Sandra Bonilla Meza

Abstract:

There are many articles that attempt to establish the role of different facial fragments in face recognition. Various approaches are used to estimate this role. Frequently, authors calculate the entropy corresponding to the fragment. This approach can only give approximate estimation. In this paper, we propose to use a more direct measure of the importance of different fragments for face recognition. We propose to select a recognition method and a face database and experimentally investigate the recognition rate using different fragments of faces. We present two such experiments in the paper. We selected the PCNC neural classifier as a method for face recognition and parts of the LFW (Labeled Faces in the Wild) face database as training and testing sets. The recognition rate of the best experiment is comparable with the recognition rate obtained using the whole face.

Keywords: face recognition, labeled faces in the wild (LFW) database, random local descriptor (RLD), random features

Procedia PDF Downloads 355