Search results for: traditional models
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 10912

Search results for: traditional models

9112 Phytoestrogen Content of Fermented Lupin Tempeh and Natto

Authors: Niranjani Wickramsinghe, Mario Soares, Stuart Johnson, Ranil Cooray, Vijay Jayasena

Abstract:

Tempeh is a traditional fermented soya bean food in Indonesia which is produced from de-hulled soya fermented with Rhizopusoligosporus. Natto is a traditional Japanese food made from whole soya bean seed fermentation with the bacteriaBacillus subtilis natto. Lupin is a grain legume with a low content of the phytoestrogenic isoflavones genistein and daidzein compared to soya. However due a comparable nutrition profile and increased cost effectiveness relative to soy, lupin has been substituted into various oriental fermented foods such as tempe and natto. Lupin tempeh and lupin natto were prepared using either WS or DHS. Analysis for genistein and daidzein content was conducted using HPLC for time points zero, 12h, 24h, 36h, 48h and 72h after fermentation. Results revealed that the amount of genistein and daidzein significantly increased with time in both tempeh and natto. Both isoflavones peaked at 48h in lupin tempeh and earlier at 36h in lupin natto. WS tempeh and WS natto had significantly more genistein than WHS tempe and WHS natto. Diadzeincontent of WHS tended to be higher than WS across both products. It is concluded that, fermentation time increased the amount of genistein and daidzein content in both lupin tempeh and natto and the form of lupin raw material used affected the genistein level and to some extent the daidzein content of fermented products.

Keywords: lupin, natto, soya, tempeh

Procedia PDF Downloads 375
9111 A Study on the Impact of Perceived Benefits and Switching Costs of Consumers When Shifting from Brick and Mortar Store to Online Shopping of Apparels

Authors: Havisha Banda

Abstract:

Recent advancements in technology have facilitated commerce around the globe. The online medium of commerce has provided and will continue to provide great opportunities for consumers and businesses. Advancements in technology enable apparel stores, for instance, to improve their online services by using personalized virtual models allowing consumers to visualize the product on the model to determine correct sizing and fit. In addition to many advantages in online shopping the consumers will also have to undergo many types of switching costs in this process of buying apparel online. This study is to identify such switching costs and switching benefits from traditional shopping to online shopping and to understand what the consumers value the most. The scope of this study is to understand the types of switching costs and the factors that actually allow the consumers to shift from brick and mortar to online shopping and also to understand why a certain set of customers consider to purchase offline. Hence this study helps to understand the perceived cost and perceived benefit relation that the consumer draws in purchasing the garments online. This will help the upcoming e-commerce sites and brick and mortar store to understand the various factors and formulate new policies and implement strategies in their own ways to attract the customers and to retain them. A sample of 35 is considered for the process of laddered interviews. In the era of e-commerce there are people who feel comfortable to shop in a retail store rather than online purchase. Few respondents who shop online do not prefer to shop apparel online. Few respondents said that they shop online only for apparels. Most of the variables match in terms of switching costs and also in regard to benefits.

Keywords: e-commerce, switching costs, switching benefits, apparel shopping

Procedia PDF Downloads 310
9110 3D Design of Orthotic Braces and Casts in Medical Applications Using Microsoft Kinect Sensor

Authors: Sanjana S. Mallya, Roshan Arvind Sivakumar

Abstract:

Orthotics is the branch of medicine that deals with the provision and use of artificial casts or braces to alter the biomechanical structure of the limb and provide support for the limb. Custom-made orthoses provide more comfort and can correct issues better than those available over-the-counter. However, they are expensive and require intricate modelling of the limb. Traditional methods of modelling involve creating a plaster of Paris mould of the limb. Lately, CAD/CAM and 3D printing processes have improved the accuracy and reduced the production time. Ordinarily, digital cameras are used to capture the features of the limb from different views to create a 3D model. We propose a system to model the limb using Microsoft Kinect2 sensor. The Kinect can capture RGB and depth frames simultaneously up to 30 fps with sufficient accuracy. The region of interest is captured from three views, each shifted by 90 degrees. The RGB and depth data are fused into a single RGB-D frame. The resolution of the RGB frame is 1920px x 1080px while the resolution of the Depth frame is 512px x 424px. As the resolution of the frames is not equal, RGB pixels are mapped onto the Depth pixels to make sure data is not lost even if the resolution is lower. The resulting RGB-D frames are collected and using the depth coordinates, a three dimensional point cloud is generated for each view of the Kinect sensor. A common reference system was developed to merge the individual point clouds from the Kinect sensors. The reference system consisted of 8 coloured cubes, connected by rods to form a skeleton-cube with the coloured cubes at the corners. For each Kinect, the region of interest is the square formed by the centres of the four cubes facing the Kinect. The point clouds are merged by considering one of the cubes as the origin of a reference system. Depending on the relative distance from each cube, the three dimensional coordinate points from each point cloud is aligned to the reference frame to give a complete point cloud. The RGB data is used to correct for any errors in depth data for the point cloud. A triangular mesh is generated from the point cloud by applying Delaunay triangulation which generates the rough surface of the limb. This technique forms an approximation of the surface of the limb. The mesh is smoothened to obtain a smooth outer layer to give an accurate model of the limb. The model of the limb is used as a base for designing the custom orthotic brace or cast. It is transferred to a CAD/CAM design file to design of the brace above the surface of the limb. The proposed system would be more cost effective than current systems that use MRI or CT scans for generating 3D models and would be quicker than using traditional plaster of Paris cast modelling and the overall setup time is also low. Preliminary results indicate that the accuracy of the Kinect2 is satisfactory to perform modelling.

Keywords: 3d scanning, mesh generation, Microsoft kinect, orthotics, registration

Procedia PDF Downloads 185
9109 Tracking Filtering Algorithm Based on ConvLSTM

Authors: Ailing Yang, Penghan Song, Aihua Cai

Abstract:

The nonlinear maneuvering target tracking problem is mainly a state estimation problem when the target motion model is uncertain. Traditional solutions include Kalman filtering based on Bayesian filtering framework and extended Kalman filtering. However, these methods need prior knowledge such as kinematics model and state system distribution, and their performance is poor in state estimation of nonprior complex dynamic systems. Therefore, in view of the problems existing in traditional algorithms, a convolution LSTM target state estimation (SAConvLSTM-SE) algorithm based on Self-Attention memory (SAM) is proposed to learn the historical motion state of the target and the error distribution information measured at the current time. The measured track point data of airborne radar are processed into data sets. After supervised training, the data-driven deep neural network based on SAConvLSTM can directly obtain the target state at the next moment. Through experiments on two different maneuvering targets, we find that the network has stronger robustness and better tracking accuracy than the existing tracking methods.

Keywords: maneuvering target, state estimation, Kalman filter, LSTM, self-attention

Procedia PDF Downloads 156
9108 A Critical Discourse Analysis of Jamaican and Trinidadian News Articles about D/Deafness

Authors: Melissa Angus Baboun

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Utilizing a Critical Discourse Analysis (CDA) methodology and a theoretical framework based on disability studies, how Jamaican and Trinidadian newspapers discussed issues relating to the Deaf community were examined. The term deaf was inputted into the search engine tool of the online website for the Jamaica Observer and the Trinidad & Tobago Guardian. All 27 articles that contained the term deaf in its content and were written between August 1, 2017 and November 15, 2017 were chosen for the study. The data analysis was divided into three steps: (1) listing and analysis instances of metaphorical deafness (e.g. fall on deaf ears), (2) categorization of the content of the articles into the models of disability discourse (the medical, socio-cultural, and superscrip models of disability narratives), and (3) the analysis of any additional data found. A total of 42% of the articles pulled for this study did not deal with the Deaf community in any capacity, but rather instances of the use of idiomatic expressions that use deafness as a metaphor for a non-physical, undesirable trait. The most common idiomatic expression found was fall on deaf ears. Regarding the models of disability discourse, eight articles were found to follow the socio-cultural model, two were found to follow the medical model, and two were found to follow the superscrip model. The additional data found in these articles include two instances of the term deaf and mute, an overwhelming use of lower case d for the term deaf, and the misuse of the term translator (to mean interpreter).

Keywords: deafness, disability, news coverage, Caribbean newspapers

Procedia PDF Downloads 227
9107 Direct Displacement-Based Design Procedure for Performance-Based Seismic Design of Structures

Authors: Haleh Hamidpour

Abstract:

Since the seismic damageability of structures is controlled by the inelastic deformation capacities of structural elements, seismic design of structure based on force analogy methods is not appropriate. In recent year, the basic approach of design codes have been changed from force-based approach to displacement-based. In this regard, a Direct Displacement-Based Design (DDBD) and a Performance-Based Plastic Design (PBPD) method are proposed. In this study, the efficiency of these two methods on seismic performance of structures is evaluated through a sample 12-story reinforced concrete moment frame. The building is designed separately based on the DDBD and the PBPD methods. Once again the structure is designed by the traditional force analogy method according to the FEMA P695 regulation. Different design method results in different structural elements. Seismic performance of these three structures is evaluated through nonlinear static and nonlinear dynamic analysis. The results show that the displacement-based design methods accommodate the intended performance objectives better than the traditional force analogy method.

Keywords: direct performance-based design, ductility demands, inelastic seismic performance, yield mechanism

Procedia PDF Downloads 324
9106 Effect of Roasting Treatment on Milling Quality, Physicochemical, and Bioactive Compounds of Dough Stage Rice Grains

Authors: Chularat Leewuttanakul, Khanitta Ruttarattanamongkol, Sasivimon Chittrakorn

Abstract:

Rice during grain development stage is a rich source of many bioactive compounds. Dough stage rice contains high amounts of photochemical and can be used for rice milling industries. However, rice grain at dough stage had low milling quality due to high moisture content. Thermal processing can be applied to rice grain for improving milled rice yield. This experiment was conducted to study the chemical and physic properties of dough stage rice grain after roasting treatment. Rice were roasted with two different methods including traditional pan roasting at 140 °C for 60 minutes and using the electrical roasting machine at 140 °C for 30, 40, and 50 minutes. The chemical, physical properties, and bioactive compounds of brown rice and milled rice were evaluated. The result of this experiment showed that moisture content of brown and milled rice was less than 10 % and amylose contents were in the range of 26-28 %. Rice grains roasting for 30 min using electrical roasting machine had high head rice yield and length and breadth of grain after milling were close to traditional pan roasting (p > 0.05). The lightness (L*) of rice did not affect by roasting treatment (p > 0.05) and the a* indicated the yellowness of milled rice was lower than brown rice. The bioactive compounds of brown and milled rice significantly decreased with increasing of drying time. Brown rice roasted for 30 minutes had the highest of total phenolic content, antioxidant activity, α-tocopherol, and ɤ-oryzanol content. Volume expansion and elongation of cooked rice decreased as roasting time increased and quality of cooked rice roasted for 30 min was comparable to traditional pan roasting. Hardness of cooked rice as measured by texture analyzer increased with increasing roasting time. The results indicated that rice grains at dough stage, containing a high amount of bioactive compounds, have a great potential for rice milling industries and the electrical roasting machine can be used as an alternative to pan roasting which decreases processing time and labor costs.

Keywords: bioactive compounds, cooked rice, dough stage rice grain, grain development, roasting

Procedia PDF Downloads 155
9105 LACGC: Business Sustainability Research Model for Generations Consumption, Creation, and Implementation of Knowledge: Academic and Non-Academic

Authors: Satpreet Singh

Abstract:

This paper introduces the new LACGC model to sustain the academic and non-academic business to future educational and organizational generations. The consumption of knowledge and the creation of new knowledge is a strength and focal interest of all academics and Non-academic organizations. Implementing newly created knowledge sustains the businesses to the next generation with growth without detriment. Existing models like the Scholar-practitioner model and Organization knowledge creation models focus specifically on academic or non-academic, not both. LACGC model can be used for both Academic and Non-academic at the domestic or international level. Researchers and scholars play a substantial role in finding literature and practice gaps in academic and non-academic disciplines. LACGC model has unrestricted the number of recurrences because the Consumption, Creation, and implementation of new ideas, disciplines, systems, and knowledge is a never-ending process and must continue from one generation to the next.

Keywords: academics, consumption, creation, generations, non-academics, research, sustainability

Procedia PDF Downloads 186
9104 Soap Film Enneper Minimal Surface Model

Authors: Yee Hooi Min, Mohdnasir Abdul Hadi

Abstract:

Tensioned membrane structure in the form of Enneper minimal surface can be considered as a sustainable development for the green environment and technology, it also can be used to support the effectiveness used of energy and the structure. Soap film in the form of Enneper minimal surface model has been studied. The combination of shape and internal forces for the purpose of stiffness and strength is an important feature of membrane surface. For this purpose, form-finding using soap film model has been carried out for Enneper minimal surface models with variables u=v=0.6 and u=v=1.0. Enneper soap film models with variables u=v=0.6 and u=v=1.0 provides an alternative choice for structural engineers to consider the tensioned membrane structure in the form of Enneper minimal surface applied in the building industry. It is expected to become an alternative building material to be considered by the designer.

Keywords: Enneper, minimal surface, soap film, tensioned membrane structure

Procedia PDF Downloads 539
9103 Utilization of Nipa Palm Fibers (Nypa fruticans) and Asian Green Mussels Shells (Perna viridis) as an Additive Material in Making a Fiber-Reinforced Concrete

Authors: Billy Angel B. Bayot, Hubert Clyde Z. Guillermo, Daniela Eve Margaret S. Olano, Lian Angeli Kaye E. Suarez

Abstract:

A utilization of Nipa palm fibers (Nypa fruticans) and Asian green mussel shells (Perna viridis) as additive materials in making fiber-reinforced concrete was carried out. The researchers collected Asian green mussel shells and Nipa palm fibers as additive materials in the production of fiber-reinforced concrete and were used to make 3 Setups containing 20g, 15g, and 10g of Nipa palm fiber varying to 10g, 20g, 30g of Asian green mussel shell powder and a traditional concrete with respect to curing period 7, 14, and 28 days. The concrete blocks were delivered to the UP Institute of Building Materials and Structures Laboratory (CoMSLab) following each curing test in order to evaluate their compressive strength. Researchers employed a Two-Way Analysis of Variance (ANOVA) and determined that curing days, concrete mixture, and the combined curing days with concrete have an effect on the compressive strength of concrete. ANOVA results indicating significant differences had been subjected to post hoc analysis using Tukey's HSD. These results then yielded the comparison of each curing time and different concrete mixtures with traditional concrete, which comes to the conclusion that a longer curing period leads to a higher compressive strength and Setup 3 (30g Asian green mussel shell with 10g Nipa palm fiber) has the larger mean compressive strength, making it the best proportion among the fiber-reinforced concrete mixtures and the only proportion that has significant effect to traditional one. As a result, the study concludes that certain curing times and concrete mix proportions of Asian green mussel shell and Nipa palm fiber are critical determinants in determining concrete compressive strength.

Keywords: Asian green mussel shells (Perna viridis), Nipa palm fibers (Nypa fruticans), additives, fiber-reinforced concrete

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9102 Local Female Dresses of Yuruk Community in Günaydin Village of Balikesir Region

Authors: Melek Tufan, Filiz Erden, E. Elhan Özus

Abstract:

Apparel is a fact that has assigned wide cultural functions in development process even if it basically aims at protection during mankind's cultural development and course of live. It is an important cultural element that has been shaped by ecological conditions, social and personal values, traditions, cultural and economic conditions, at the same time it is a bearer of culture. Customs and traditions that maintain culture create differences in dressing styles of the region. These differences create traditional clothing forms specific to each region, which are different from each other or show close similarities. Differences which have dominant features create sense of dress specific to community owned. Samples of a kind of dress worn over salwar, long shirt, jacket, salwar and underpants that are types of local female dresses available in houses of yuruk community in Günaydın village of Balıkesir region have been found. By examining local dresses in terms of material, color, cutting, sewing, ornamentation technique and ornamentation subject and it has been aimed to record them with observation forms and transfer them to the next generations.

Keywords: women, traditional, Turkish Culture, art, fashion

Procedia PDF Downloads 335
9101 Naturally Occurring Chemicals in Biopesticides' Resistance Control through Molecular Topology

Authors: Riccardo Zanni, Maria Galvez-Llompart, Ramon Garcia-Domenech, Jorge Galvez

Abstract:

Biopesticides, such as naturally occurring chemicals, pheromones, fungi, bacteria and insect predators are often a winning choice in crop protection because of their environmental friendly profile. They are considered to have lower toxicity than traditional pesticides. After almost a century of pesticides use, resistances to traditional insecticides are wide spread, while those to bioinsecticides have raised less attention, and resistance management is frequently neglected. This seems to be a crucial mistake since resistances have already occurred for many marketed biopesticides. With an eye to the future, we present here a selection of new natural occurring chemicals as potential bioinsecticides. The molecules were selected using a consolidated mathematical paradigm called molecular topology. Several QSAR equations were depicted and subsequently applied for the virtual screening of hundred thousands molecules of natural origin, which resulted in the selection of new potential bioinsecticides. The most innovative aspect of this work does not only reside in the importance of the identification of new molecules overcoming biopesticides’ resistances, but on the possibility to promote shared knowledge in the field of green chemistry through this unique in silico discipline named molecular topology.

Keywords: green chemistry, QSAR, molecular topology, biopesticide

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9100 AI-Powered Models for Real-Time Fraud Detection in Financial Transactions to Improve Financial Security

Authors: Shanshan Zhu, Mohammad Nasim

Abstract:

Financial fraud continues to be a major threat to financial institutions across the world, causing colossal money losses and undermining public trust. Fraud prevention techniques, based on hard rules, have become ineffective due to evolving patterns of fraud in recent times. Against such a background, the present study probes into distinct methodologies that exploit emergent AI-driven techniques to further strengthen fraud detection. We would like to compare the performance of generative adversarial networks and graph neural networks with other popular techniques, like gradient boosting, random forests, and neural networks. To this end, we would recommend integrating all these state-of-the-art models into one robust, flexible, and smart system for real-time anomaly and fraud detection. To overcome the challenge, we designed synthetic data and then conducted pattern recognition and unsupervised and supervised learning analyses on the transaction data to identify which activities were fishy. With the use of actual financial statistics, we compare the performance of our model in accuracy, speed, and adaptability versus conventional models. The results of this study illustrate a strong signal and need to integrate state-of-the-art, AI-driven fraud detection solutions into frameworks that are highly relevant to the financial domain. It alerts one to the great urgency that banks and related financial institutions must rapidly implement these most advanced technologies to continue to have a high level of security.

Keywords: AI-driven fraud detection, financial security, machine learning, anomaly detection, real-time fraud detection

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9099 Modeling Biomass and Biodiversity across Environmental and Management Gradients in Temperate Grasslands with Deep Learning and Sentinel-1 and -2

Authors: Javier Muro, Anja Linstadter, Florian Manner, Lisa Schwarz, Stephan Wollauer, Paul Magdon, Gohar Ghazaryan, Olena Dubovyk

Abstract:

Monitoring the trade-off between biomass production and biodiversity in grasslands is critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can model grasslands’ characteristics with varying accuracies. However, studies often fail to cover a sufficiently broad range of environmental conditions, and evidence suggests that prediction models might be case specific. In this study, biomass production and biodiversity indices (species richness and Fishers’ α) are modeled in 150 grassland plots for three sites across Germany. These sites represent a North-South gradient and are characterized by distinct soil types, topographic properties, climatic conditions, and management intensities. Predictors used are derived from Sentinel-1 & 2 and a set of topoedaphic variables. The transferability of the models is tested by training and validating at different sites. The performance of feed-forward deep neural networks (DNN) is compared to a random forest algorithm. While biomass predictions across gradients and sites were acceptable (r2 0.5), predictions of biodiversity indices were poor (r2 0.14). DNN showed higher generalization capacity than random forest when predicting biomass across gradients and sites (relative root mean squared error of 0.5 for DNN vs. 0.85 for random forest). DNN also achieved high performance when using the Sentinel-2 surface reflectance data rather than different combinations of spectral indices, Sentinel-1 data, or topoedaphic variables, simplifying dimensionality. This study demonstrates the necessity of training biomass and biodiversity models using a broad range of environmental conditions and ensuring spatial independence to have realistic and transferable models where plot level information can be upscaled to landscape scale.

Keywords: ecosystem services, grassland management, machine learning, remote sensing

Procedia PDF Downloads 211
9098 Analysis of Public Space Usage Characteristics Based on Computer Vision Technology - Taking Shaping Park as an Example

Authors: Guantao Bai

Abstract:

Public space is an indispensable and important component of the urban built environment. How to more accurately evaluate the usage characteristics of public space can help improve its spatial quality. Compared to traditional survey methods, computer vision technology based on deep learning has advantages such as dynamic observation and low cost. This study takes the public space of Shaping Park as an example and, based on deep learning computer vision technology, processes and analyzes the image data of the public space to obtain the spatial usage characteristics and spatiotemporal characteristics of the public space. Research has found that the spontaneous activity time in public spaces is relatively random with a relatively short average activity time, while social activities have a relatively stable activity time with a longer average activity time. Computer vision technology based on deep learning can effectively describe the spatial usage characteristics of the research area, making up for the shortcomings of traditional research methods and providing relevant support for creating a good public space.

Keywords: computer vision, deep learning, public spaces, using features

Procedia PDF Downloads 62
9097 Microbiological Study of Two Spontaneous Plants of Algerian Sahara Septentrional: Cotula cinerea and Chamomilla recutita

Authors: Mehani Mouna, Boukhari Nadjet, Ladjal Segni

Abstract:

The aim of our study is to determine the antimicrobial effect of essential oils of two plants Cotula cinerea and Chamomilla recutita on some pathogenic bacteria. It is a medicinal plant used in traditional therapy. Essential oils have many therapeutic properties. In herbal medicine, they are used for their antiseptic properties against infectious diseases of fungal origin, against dermatophytes, those of bacterial origin. Essential oils have many therapeutic properties. In herbal medicine, they are used for their antiseptic properties against infectious diseases of fungal origin, against dermatophytes, those of bacterial origin. Humans use plants for thousands of years to treat various ailments, in many developing countries; much of the population relies on traditional doctors and their collections of medicinal plants to cure them. The test adopted is based on the diffusion method on solid medium (Antibiogram), this method allows to determine the susceptibility or resistance of an organism according to the sample studied. Our study reveals that the essential oil of the plants Cotula cinerea and Chamomilla recutita have a different effect on the resistance of germs.

Keywords: antibiogram, Chamomilla recutita, Cotula cinerea, essential oil, microorganism

Procedia PDF Downloads 306
9096 Learning Dynamic Representations of Nodes in Temporally Variant Graphs

Authors: Sandra Mitrovic, Gaurav Singh

Abstract:

In many industries, including telecommunications, churn prediction has been a topic of active research. A lot of attention has been drawn on devising the most informative features, and this area of research has gained even more focus with spread of (social) network analytics. The call detail records (CDRs) have been used to construct customer networks and extract potentially useful features. However, to the best of our knowledge, no studies including network features have yet proposed a generic way of representing network information. Instead, ad-hoc and dataset dependent solutions have been suggested. In this work, we build upon a recently presented method (node2vec) to obtain representations for nodes in observed network. The proposed approach is generic and applicable to any network and domain. Unlike node2vec, which assumes a static network, we consider a dynamic and time-evolving network. To account for this, we propose an approach that constructs the feature representation of each node by generating its node2vec representations at different timestamps, concatenating them and finally compressing using an auto-encoder-like method in order to retain reasonably long and informative feature vectors. We test the proposed method on churn prediction task in telco domain. To predict churners at timestamp ts+1, we construct training and testing datasets consisting of feature vectors from time intervals [t1, ts-1] and [t2, ts] respectively, and use traditional supervised classification models like SVM and Logistic Regression. Observed results show the effectiveness of proposed approach as compared to ad-hoc feature selection based approaches and static node2vec.

Keywords: churn prediction, dynamic networks, node2vec, auto-encoders

Procedia PDF Downloads 306
9095 Statistical Comparison of Ensemble Based Storm Surge Forecasting Models

Authors: Amin Salighehdar, Ziwen Ye, Mingzhe Liu, Ionut Florescu, Alan F. Blumberg

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Storm surge is an abnormal water level caused by a storm. Accurate prediction of a storm surge is a challenging problem. Researchers developed various ensemble modeling techniques to combine several individual forecasts to produce an overall presumably better forecast. There exist some simple ensemble modeling techniques in literature. For instance, Model Output Statistics (MOS), and running mean-bias removal are widely used techniques in storm surge prediction domain. However, these methods have some drawbacks. For instance, MOS is based on multiple linear regression and it needs a long period of training data. To overcome the shortcomings of these simple methods, researchers propose some advanced methods. For instance, ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast. This application creates a better forecast of sea level using a combination of several instances of the Bayesian Model Averaging (BMA). An ensemble dressing method is based on identifying best member forecast and using it for prediction. Our contribution in this paper can be summarized as follows. First, we investigate whether the ensemble models perform better than any single forecast. Therefore, we need to identify the single best forecast. We present a methodology based on a simple Bayesian selection method to select the best single forecast. Second, we present several new and simple ways to construct ensemble models. We use correlation and standard deviation as weights in combining different forecast models. Third, we use these ensembles and compare with several existing models in literature to forecast storm surge level. We then investigate whether developing a complex ensemble model is indeed needed. To achieve this goal, we use a simple average (one of the simplest and widely used ensemble model) as benchmark. Predicting the peak level of Surge during a storm as well as the precise time at which this peak level takes place is crucial, thus we develop a statistical platform to compare the performance of various ensemble methods. This statistical analysis is based on root mean square error of the ensemble forecast during the testing period and on the magnitude and timing of the forecasted peak surge compared to the actual time and peak. In this work, we analyze four hurricanes: hurricanes Irene and Lee in 2011, hurricane Sandy in 2012, and hurricane Joaquin in 2015. Since hurricane Irene developed at the end of August 2011 and hurricane Lee started just after Irene at the beginning of September 2011, in this study we consider them as a single contiguous hurricane event. The data set used for this study is generated by the New York Harbor Observing and Prediction System (NYHOPS). We find that even the simplest possible way of creating an ensemble produces results superior to any single forecast. We also show that the ensemble models we propose generally have better performance compared to the simple average ensemble technique.

Keywords: Bayesian learning, ensemble model, statistical analysis, storm surge prediction

Procedia PDF Downloads 305
9094 Utilizing Federated Learning for Accurate Prediction of COVID-19 from CT Scan Images

Authors: Jinil Patel, Sarthak Patel, Sarthak Thakkar, Deepti Saraswat

Abstract:

Recently, the COVID-19 outbreak has spread across the world, leading the World Health Organization to classify it as a global pandemic. To save the patient’s life, the COVID-19 symptoms have to be identified. But using an AI (Artificial Intelligence) model to identify COVID-19 symptoms within the allotted time was challenging. The RT-PCR test was found to be inadequate in determining the COVID status of a patient. To determine if the patient has COVID-19 or not, a Computed Tomography Scan (CT scan) of patient is a better alternative. It will be challenging to compile and store all the data from various hospitals on the server, though. Federated learning, therefore, aids in resolving this problem. Certain deep learning models help to classify Covid-19. This paper will have detailed work of certain deep learning models like VGG19, ResNet50, MobileNEtv2, and Deep Learning Aggregation (DLA) along with maintaining privacy with encryption.

Keywords: federated learning, COVID-19, CT-scan, homomorphic encryption, ResNet50, VGG-19, MobileNetv2, DLA

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9093 Passport Bros: Exploring Neocolonial Masculinity and Sex Tourism as a Response to Shifting Gender Dynamics

Authors: Kellen Sharp

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This study explores the phenomenon of ‘Passport Bros’, a subset within the manosphere responding to perceived crises in masculinity amidst changing gender dynamics. Focusing on a computational analysis of the passport bro community, the research addresses normative beliefs, deviations from MGTOW ideology, and discussions on nationality, race, and gender. Originating from the MGTOW movement, passport bros engage in a neocolonial approach by seeking traditional, non-Western women, attributing this pursuit to dissatisfaction with modern Western women. The paper examines how hetero pessimism within MGTOW shapes the emergence of passport bros, leading to the adoption of red pill ideologies and ultimately manifesting in the form of sex tourism. Analyzing data collected from passport bro forums through computer-assisted content analysis, the study identifies key discourses such as questions and answers, money, attitudes towards Western and traditional women, and discussions about the movement itself. The findings highlight the nuanced intersection of gender, race, and global power dynamics within the passport bro community, shedding light on their motivations and impact on neocolonial legacies.

Keywords: toxic online community, manosphere, gender and media, neocolonialism

Procedia PDF Downloads 54
9092 The Development of Iranian Theatrical Performance through the Integration of Narrative Elements from Western Drama

Authors: Azadeh Abbasikangevari

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Background and Objectives: Theatre and performance are two separate themes. What is presented in Iran as a performance is the species and ritual and traditional forms of the play. The Iranian performance has its roots in myth and ritual. Drama is essentially a Western phenomenon that has gradually entered Iran and influenced Iranian performance. A theatre is based on antagonism (axis) and protagonism (anti-axis), while performance has a monotonous and steady motion. The elements of Iranian performance include field, performance on the stage, and magnification in performance, all of which are based on narration. This type of narration has been present in Iranian modern drama. The objective of this study was to analyze the drama structure according to narration elements by a comparison between the Western theater and the Iranian performance and determining the structural differences in the type of narrative. Materials and Methods: In this study, the elements of the drama were analyzed using the library method among the available library resources. The review of the literature included research articles and textbooks which focused on Iranian plays, as well as books and articles which encompassed narrative and drama element. Data were analyzed in the comparative-descriptive method. Results: Examining and studying different kinds of Iranian performances, showed that the narrative has always been a characteristic feature of Iranian plays. Iranians have narrated the stories and myths and have had a particular skill of oral literature. Over time, they slowly introduced narrative culture into their art, where this element is the most important structural element in Iran's dramatic art. Considering the fact that narration in Iranian traditional play, such as Ta'ziyeh and Naghali, was oral and consequently, it was slowly forgotten and excluded from written theatrical texts. Since the drama has entered in its western form in Iran, the plays written by the authors were influenced by narrative elements existing in western plays. Conclusions: The narrative’s element has undoubtedly had an impact on modern Iranian drama and Iranian contemporary drama. Therefore, the element of narration is an integral part of the Iranian traditional play structure.

Keywords: drama methodology, Iranian performance, Iranian modern drama, narration

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9091 Generation of High-Quality Synthetic CT Images from Cone Beam CT Images Using A.I. Based Generative Networks

Authors: Heeba A. Gurku

Abstract:

Introduction: Cone Beam CT(CBCT) images play an integral part in proper patient positioning in cancer patients undergoing radiation therapy treatment. But these images are low in quality. The purpose of this study is to generate high-quality synthetic CT images from CBCT using generative models. Material and Methods: This study utilized two datasets from The Cancer Imaging Archive (TCIA) 1) Lung cancer dataset of 20 patients (with full view CBCT images) and 2) Pancreatic cancer dataset of 40 patients (only 27 patients having limited view images were included in the study). Cycle Generative Adversarial Networks (GAN) and its variant Attention Guided Generative Adversarial Networks (AGGAN) models were used to generate the synthetic CTs. Models were evaluated by visual evaluation and on four metrics, Structural Similarity Index Measure (SSIM), Peak Signal Noise Ratio (PSNR) Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), to compare the synthetic CT and original CT images. Results: For pancreatic dataset with limited view CBCT images, our study showed that in Cycle GAN model, MAE, RMSE, PSNR improved from 12.57to 8.49, 20.94 to 15.29 and 21.85 to 24.63, respectively but structural similarity only marginally increased from 0.78 to 0.79. Similar, results were achieved with AGGAN with no improvement over Cycle GAN. However, for lung dataset with full view CBCT images Cycle GAN was able to reduce MAE significantly from 89.44 to 15.11 and AGGAN was able to reduce it to 19.77. Similarly, RMSE was also decreased from 92.68 to 23.50 in Cycle GAN and to 29.02 in AGGAN. SSIM and PSNR also improved significantly from 0.17 to 0.59 and from 8.81 to 21.06 in Cycle GAN respectively while in AGGAN SSIM increased to 0.52 and PSNR increased to 19.31. In both datasets, GAN models were able to reduce artifacts, reduce noise, have better resolution, and better contrast enhancement. Conclusion and Recommendation: Both Cycle GAN and AGGAN were significantly able to reduce MAE, RMSE and PSNR in both datasets. However, full view lung dataset showed more improvement in SSIM and image quality than limited view pancreatic dataset.

Keywords: CT images, CBCT images, cycle GAN, AGGAN

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9090 Information Construction of Higher Education in Teaching Practice

Authors: Yang Meng, James L. Patnao

Abstract:

With the rapid development of information technology and the impact of the epidemic environment, the traditional teaching model can’t longer meet the requirements of the development of the times. The development of teaching mechanism is the inevitable trend of the future development of higher education. We must further promote the informatization of higher education in teaching practice, let modern information technology penetrate and practice in classroom teaching, and provide promising opportunities for the high-quality development of higher education. This article mainly through the distribution of questionnaires to teachers of colleges and universities, so as to understand the degree of informatization in the teaching of colleges and universities. And on the basis of domestic and foreign scholars' research on higher education informatization, it analyzes the existing problems, and finds the optimal solution based on the needs of education and teaching development. According to the survey results, most college teachers will use information technology in teaching practice, but the information technology teaching tools used by teachers are relatively simple, and most of them only use slides. In addition, backward informatization infrastructure and less informatization training are the main challenges facing the current teaching informatization construction. If colleges and universities can make good use of information technology and multimedia technology and combine it with traditional teaching, it will definitely promote the development of college education and further promote the modernization and informatization of higher education.

Keywords: higher education, teaching practice, informatization construction, e-education

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9089 Statistical Models and Time Series Forecasting on Crime Data in Nepal

Authors: Dila Ram Bhandari

Abstract:

Throughout the 20th century, new governments were created where identities such as ethnic, religious, linguistic, caste, communal, tribal, and others played a part in the development of constitutions and the legal system of victim and criminal justice. Acute issues with extremism, poverty, environmental degradation, cybercrimes, human rights violations, crime against, and victimization of both individuals and groups have recently plagued South Asian nations. Everyday massive number of crimes are steadfast, these frequent crimes have made the lives of common citizens restless. Crimes are one of the major threats to society and also for civilization. Crime is a bone of contention that can create a societal disturbance. The old-style crime solving practices are unable to live up to the requirement of existing crime situations. Crime analysis is one of the most important activities of the majority of intelligent and law enforcement organizations all over the world. The South Asia region lacks such a regional coordination mechanism, unlike central Asia of Asia Pacific regions, to facilitate criminal intelligence sharing and operational coordination related to organized crime, including illicit drug trafficking and money laundering. There have been numerous conversations in recent years about using data mining technology to combat crime and terrorism. The Data Detective program from Sentient as a software company, uses data mining techniques to support the police (Sentient, 2017). The goals of this internship are to test out several predictive model solutions and choose the most effective and promising one. First, extensive literature reviews on data mining, crime analysis, and crime data mining were conducted. Sentient offered a 7-year archive of crime statistics that were daily aggregated to produce a univariate dataset. Moreover, a daily incidence type aggregation was performed to produce a multivariate dataset. Each solution's forecast period lasted seven days. Statistical models and neural network models were the two main groups into which the experiments were split. For the crime data, neural networks fared better than statistical models. This study gives a general review of the applied statistics and neural network models. A detailed image of each model's performance on the available data and generalizability is provided by a comparative analysis of all the models on a comparable dataset. Obviously, the studies demonstrated that, in comparison to other models, Gated Recurrent Units (GRU) produced greater prediction. The crime records of 2005-2019 which was collected from Nepal Police headquarter and analysed by R programming. In conclusion, gated recurrent unit implementation could give benefit to police in predicting crime. Hence, time series analysis using GRU could be a prospective additional feature in Data Detective.

Keywords: time series analysis, forecasting, ARIMA, machine learning

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9088 Focus-Latent Dirichlet Allocation for Aspect-Level Opinion Mining

Authors: Mohsen Farhadloo, Majid Farhadloo

Abstract:

Aspect-level opinion mining that aims at discovering aspects (aspect identification) and their corresponding ratings (sentiment identification) from customer reviews have increasingly attracted attention of researchers and practitioners as it provides valuable insights about products/services from customer's points of view. Instead of addressing aspect identification and sentiment identification in two separate steps, it is possible to simultaneously identify both aspects and sentiments. In recent years many graphical models based on Latent Dirichlet Allocation (LDA) have been proposed to solve both aspect and sentiment identifications in a single step. Although LDA models have been effective tools for the statistical analysis of document collections, they also have shortcomings in addressing some unique characteristics of opinion mining. Our goal in this paper is to address one of the limitations of topic models to date; that is, they fail to directly model the associations among topics. Indeed in many text corpora, it is natural to expect that subsets of the latent topics have higher probabilities. We propose a probabilistic graphical model called focus-LDA, to better capture the associations among topics when applied to aspect-level opinion mining. Our experiments on real-life data sets demonstrate the improved effectiveness of the focus-LDA model in terms of the accuracy of the predictive distributions over held out documents. Furthermore, we demonstrate qualitatively that the focus-LDA topic model provides a natural way of visualizing and exploring unstructured collection of textual data.

Keywords: aspect-level opinion mining, document modeling, Latent Dirichlet Allocation, LDA, sentiment analysis

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9087 A Research Using Remote Monitoring Technology for Pump Output Monitoring in Distributed Fuel Stations in Nigeria

Authors: Ofoegbu Ositadinma Edward

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This research paper discusses a web based monitoring system that enables effective monitoring of fuel pump output and sales volume from distributed fuel stations under the domain of a single company/organization. The traditional method of operation by these organizations in Nigeria is non-automated and accounting for dispensed product is usually approximated and manual as there is little or no technology implemented to presently provide information relating to the state of affairs in the station both to on-ground staff and to supervisory staff that are not physically present in the station. This results in unaccountable losses in product and revenue as well as slow decision making. Remote monitoring technology as a vast research field with numerous application areas incorporating various data collation techniques and sensor networks can be applied to provide information relating to fuel pump status in distributed fuel stations reliably. Thus, the proposed system relies upon a microcontroller, keypad and pump to demonstrate the traditional fuel dispenser. A web-enabled PC with an accompanying graphic user interface (GUI) was designed using virtual basic which is connected to the microcontroller via the serial port which is to provide the web implementation.

Keywords: fuel pump, microcontroller, GUI, web

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9086 Predictive Analytics Algorithms: Mitigating Elementary School Drop Out Rates

Authors: Bongs Lainjo

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Educational institutions and authorities that are mandated to run education systems in various countries need to implement a curriculum that considers the possibility and existence of elementary school dropouts. This research focuses on elementary school dropout rates and the ability to replicate various predictive models carried out globally on selected Elementary Schools. The study was carried out by comparing the classical case studies in Africa, North America, South America, Asia and Europe. Some of the reasons put forward for children dropping out include the notion of being successful in life without necessarily going through the education process. Such mentality is coupled with a tough curriculum that does not take care of all students. The system has completely led to poor school attendance - truancy which continuously leads to dropouts. In this study, the focus is on developing a model that can systematically be implemented by school administrations to prevent possible dropout scenarios. At the elementary level, especially the lower grades, a child's perception of education can be easily changed so that they focus on the better future that their parents desire. To deal effectively with the elementary school dropout problem, strategies that are put in place need to be studied and predictive models are installed in every educational system with a view to helping prevent an imminent school dropout just before it happens. In a competency-based curriculum that most advanced nations are trying to implement, the education systems have wholesome ideas of learning that reduce the rate of dropout.

Keywords: elementary school, predictive models, machine learning, risk factors, data mining, classifiers, dropout rates, education system, competency-based curriculum

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9085 Patient Care Needs Assessment: An Evidence-Based Process to Inform Quality Care and Decision Making

Authors: Wynne De Jong, Robert Miller, Ross Riggs

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Beyond the number of nurses providing care for patients, having nurses with the right skills, experience and education is essential to ensure the best possible outcomes for patients. Research studies continue to link nurse staffing and skill mix with nurse-sensitive patient outcomes; numerous studies clearly show that superior patient outcomes are associated with higher levels of regulated staff. Due to the limited number of tools and processes available to assist nurse leaders with staffing models of care, nurse leaders are constantly faced with the ongoing challenge to ensure their staffing models of care best suit their patient population. In 2009, several hospitals in Ontario, Canada participated in a research study to develop and evaluate an RN/RPN utilization toolkit. The purpose of this study was to develop and evaluate a toolkit for Registered Nurses/Registered Practical Nurses Staff mix decision-making based on the College of Nurses of Ontario, Canada practice standards for the utilization of RNs and RPNs. This paper will highlight how an organization has further developed the Patient Care Needs Assessment (PCNA) questionnaire, a major component of the toolkit. Moreover, it will demonstrate how it has utilized the information from PCNA to clearly identify patient and family care needs, thus providing evidence-based results to assist leaders with matching the best staffing skill mix to their patients.

Keywords: nurse staffing models of care, skill mix, nursing health human resources, patient safety

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9084 The Role of Uzbek Music Culture in Tourism

Authors: Odina Omonjonova

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The Uzbek people have a rich history and a rapidly developing music culture for several centuries. Monuments, shrines, places of culture and spirituality, which are the most beautiful proofs of history, show that this country has been a center of wisdom since ancient times. Nowadays, Uzbekistan is opening its face to the world with its unique spiritual heritage, historical monuments, peaceful corners and beautiful landscapes. Tourists from many countries visit and get acquainted with Uzbek culture and history and acknowledge it with great respect. The place of traditional music in describing the national color on the world scale is incomparable. Oral folk works that have reached this period, lapar, yalla, songs and ‘Shashmaqom’ are the intangible spiritual wealth of the Uzbek people. They embody the ancient and great history, spiritual world, artistic philosophy, spirit and values of our nation. National music is the main part of the culture of the nation, and here it is worth emphasizing the importance of music in the tourism of Uzbekistan. Foreign guests can enjoy our national music in various ways: (1) Concerts: There are many concert halls and cultural centers in the cities of Uzbekistan, where many concerts and events are held. Well-known musicians, singers and ensembles add more beauty to the beauty of these places, performing musical samples in Shashmaqom and other traditional styles. In these concert programs, tourists will have the opportunity to listen to works of art in an attractive live performance. (2) Festivals: Many music festivals are held in Uzbekistan throughout the year. The ‘Sharq Taronalari’ international music festival is a unique holiday where musicians from all over the world gather to celebrate the diversity of musical traditions. In recent years, traditional music has been played regularly in a number of festivals such as the ‘International Maqom Festival’, ‘International Craft Festival’ and ‘Boysun Bahari’ held in our country, which has increased the attention of travelers to our music culture. (3) Cultural seminars. Tourists interested in hands-on musical experience can participate in musical workshops. These classes allow tourists to learn to play traditional musical instruments and even participate in group activities. (4) Street musicians: In the central places and ancient streets of Uzbekistan's cities, we can meet street musicians playing soulful tunes. Performing and singing folklore samples on modern instruments directly attracts foreign guests. In Uzbekistan, national music and tourism have a direct and indirect connection. Music serves as a bridge between the country's history and its modern identity and enriches the travel experience. The impact of national music on tourism goes beyond mere statistics. Although tourist arrivals have increased significantly due to music-related attractions, the real impact lies in the stories and live testimonies of visitors. Travelers often say that the rhythms of Uzbekistan touched their hearts and broadened their worldview. In addition, music tourism strengthens the country's economy, provides employment, supports local artisans and performers, and provides an opportunity to showcase their talents to a global audience. In short, Uzbekistan is not only a place of interest, but it is among the countries that attract travelers with its unique national music. Uzbek music, folklore, songs, from the wonderful melodies of ‘Shashmaqom’ to the attractive sounds of traditional musical instruments, give aesthetic and spiritual pleasure and are important in organizing a large-scale trip for tourists visiting the country.

Keywords: traditional music, folklore, shashmaqom, tourism, festivals, street musicians, traditional musical instruments

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9083 Revisionism in Literature: Deconstructing Patriarchal Ideals in Margaret Atwood's The Penelopiad

Authors: Essam Abdelhamid Hegazy

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This paper aims to read Margaret Atwood's The Penelopiad (2005) via a revisionist and deconstructive approach. This novel is a postmodernist exploration of the grand-narrative myth The Odyssey (800 BC) by Homer, who portrayed the heroic warrior and the faithful wife as the epitome of perfect male and female models _examples whom all must follow and mimic. In Atwood's narrative, the same two hero models are the two great tricksters who are willing to perform any sort of obnoxious act for achieving their goals. This research tries to examine how Atwood tried to synthesize the change in character’s narratives leading to the humanization of the perfect hero and the ideal wife. The researcher has used a multidisciplinary approach where the feminist, revisionist and deconstructive theories were implemented to identify and find out the new interpretations of the myths that center the experiences and perspectives of women. Research findings are that revisionist approach was applied through giving an opportunity to the victimized and the voiceless to speak out and retaliate against their prosecutions.

Keywords: margret atwood, patriarchal, penelopiad, revisionism

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