Search results for: R data science

Authors: Ruomeng Xiao, Zhulin Zong, Longfa Yang

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Aiming at the problem that the target signal is difficult to detect under the strong ground clutter environment, this paper proposes a clutter suppression algorithm based on the combination of singular value decomposition and the Mallat fast wavelet algorithm. The method first carries out singular value decomposition on the radar echo data matrix, realizes the initial separation of target and clutter through the threshold processing of singular value, and then carries out wavelet decomposition on the echo data to find out the target location, and adopts the discard method to select the appropriate decomposition layer to reconstruct the target signal, which ensures the minimum loss of target information while suppressing the clutter. After the verification of the measured data, the method has a significant effect on the target extraction under low SCR, and the target reconstruction can be realized without the prior position information of the target and the method also has a certain enhancement on the output SCR compared with the traditional single wavelet processing method.

Keywords: clutter suppression, singular value decomposition, wavelet transform, Mallat algorithm, low SCR

Procedia PDF Downloads 102

Authors: Jane O`Halloran

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Japanese university students have traditionally shown antipathy towards English due to a generally unsatisfactory language-learning experience at the secondary level with a focus on grammar and translation rather than communication. The situation has become urgent, however, due to the rapid decline in the Japanese population, which will present both difficulties and opportunities as employees will increasingly be forced to use English in the workplace. For university lecturers, the challenge is to overcome the students` apathy and convince them of the need for English in the increasingly diverse workplaces they will be entering. This article will illustrate how English teachers and content teachers at a private science university came together to address this quandary.

Keywords: student motivation, CLIL, globalization, demographics

Procedia PDF Downloads 90

Authors: Chunhua Liao, Jinfei Wang, Bo Shan, Yang Song, Yongjun He, Taifeng Dong

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Winter wheat is one of the main crops in Canada. Forecasting of within-field variability of yield in winter wheat at the early stages is essential for precision farming. However, the crop yield modelling based on high spatial resolution satellite data is generally affected by the lack of continuous satellite observations, resulting in reducing the generalization ability of the models and increasing the difficulty of crop yield forecasting at the early stages. In this study, the correlations between Sentinel-2 data (vegetation indices and reflectance) and yield data collected by combine harvester were investigated and a generalized multivariate linear regression (MLR) model was built and tested with data acquired in different years. It was found that the four-band reflectance (blue, green, red, near-infrared) performed better than their vegetation indices (NDVI, EVI, WDRVI and OSAVI) in wheat yield prediction. The optimum phenological stage for wheat yield prediction with highest accuracy was at the growing stages from the end of the flowering to the beginning of the filling stage. The best MLR model was therefore built to predict wheat yield before harvest using Sentinel-2 data acquired at the end of the flowering stage. Further, to improve the ability of the yield prediction at the early stages, three simple unsupervised domain adaptation (DA) methods were adopted to transform the reflectance data at the early stages to the optimum phenological stage. The winter wheat yield prediction using multiple vegetation indices showed higher accuracy than using single vegetation index. The optimum stage for winter wheat yield forecasting varied with different fields when using vegetation indices, while it was consistent when using multispectral reflectance and the optimum stage for winter wheat yield prediction was at the end of flowering stage. The average testing RMSE of the MLR model at the end of the flowering stage was 604.48 kg/ha. Near the booting stage, the average testing RMSE of yield prediction using the best MLR was reduced to 799.18 kg/ha when applying the mean matching domain adaptation approach to transform the data to the target domain (at the end of the flowering) compared to that using the original data based on the models developed at the booting stage directly (“MLR at the early stage”) (RMSE =1140.64 kg/ha). This study demonstrated that the simple mean matching (MM) performed better than other DA methods and it was found that “DA then MLR at the optimum stage” performed better than “MLR directly at the early stages” for winter wheat yield forecasting at the early stages. The results indicated that the DA had a great potential in near real-time crop yield forecasting at the early stages. This study indicated that the simple domain adaptation methods had a great potential in crop yield prediction at the early stages using remote sensing data.

Keywords: wheat yield prediction, domain adaptation, Sentinel-2, within-field scale

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Authors: Venkateswar Pujari

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This study tries to investigate the complex interplay between control, awareness, and attention in human cognitive processes. The fundamental elements of cognitive functioning that play a significant role in influencing perception, decision-making, and behavior are attention, awareness, and control. Understanding how they interact can help us better understand how our minds work and may even increase our understanding of cognitive science and its therapeutic applications. The study uses an empirical methodology to examine the relationships between attention, awareness, and control by integrating different experimental paradigms and neuropsychological tests. To ensure the generalizability of findings, a wide sample of participants is chosen, including people with various cognitive profiles and ages. The study is structured into four primary parts, each of which focuses on one component of how attention, awareness, and control interact: 1. Evaluation of Attentional Capacity and Selectivity: In this stage, participants complete established attention tests, including the Stroop task and visual search tasks. 2. Evaluation of Awareness Degrees: In the second stage, participants' degrees of conscious and unconscious awareness are assessed using perceptual awareness tasks such as masked priming and binocular rivalry tasks. 3. Investigation of Cognitive Control Mechanisms: In the third phase, reaction inhibition, cognitive flexibility, and working memory capacity are investigated using exercises like the Wisconsin Card Sorting Test and the Go/No-Go paradigm. 4. Results Integration and Analysis: Data from all phases are integrated and analyzed in the final phase. To investigate potential links and prediction correlations between attention, awareness, and control, correlational and regression analyses are carried out. The study's conclusions shed light on the intricate relationships that exist between control, awareness, and attention throughout cognitive function. The findings may have consequences for cognitive psychology, neuroscience, and clinical psychology by providing new understandings of cognitive dysfunctions linked to deficiencies in attention, awareness, and control systems.

Keywords: attention, awareness, control, cognitive functioning, neuropsychological assessment

Procedia PDF Downloads 78

Authors: Dan Kanmegne

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Africa will become the most populated continent by the end of the century, with around 4 billion inhabitants. Food security and climate changes will become continental issues since agricultural practices depend on climate but also contribute to global emissions and land degradation. Agroforestry has been identified as a cost-efficient and reliable strategy to address these two issues. It is defined as the integrated management of trees and crops/animals in the same land unit. Agroforestry provides benefits in terms of goods (fruits, medicine, wood, etc.) and services (windbreaks, fertility, etc.), and is acknowledged to have a great potential for carbon sequestration; therefore it can be integrated into reduction mechanisms of carbon emissions. Particularly in sub-Saharan Africa, the constraint stands in the lack of information about both areas under agroforestry and the characterization (composition, structure, and management) of each agroforestry system at the country level. This study describes and quantifies “what is where?”, earliest to the quantification of carbon stock in different systems. Remote sensing (RS) is the most efficient approach to map such a dynamic technology as agroforestry since it gives relatively adequate and consistent information over a large area at nearly no cost. RS data fulfill the good practice guidelines of the Intergovernmental Panel On Climate Change (IPCC) that is to be used in carbon estimation. Satellite data are getting more and more accessible, and the archives are growing exponentially. To retrieve useful information to support decision-making out of this large amount of data, satellite data needs to be organized so to ensure fast processing, quick accessibility, and ease of use. A new solution is a data cube, which can be understood as a multi-dimensional stack (space, time, data type) of spatially aligned pixels and used for efficient access and analysis. A data cube for Burkina Faso has been set up from the cooperation project between the international service provider WASCAL and Germany, which provides an accessible exploitation architecture of multi-temporal satellite data. The aim of this study is to map and characterize agroforestry systems using the Burkina Faso earth observation data cube. The approach in its initial stage is based on an unsupervised image classification of a normalized difference vegetation index (NDVI) time series from 2010 to 2018, to stratify the country based on the vegetation. Fifteen strata were identified, and four samples per location were randomly assigned to define the sampling units. For safety reasons, the northern part will not be part of the fieldwork. A total of 52 locations will be visited by the end of the dry season in February-March 2020. The field campaigns will consist of identifying and describing different agroforestry systems and qualitative interviews. A multi-temporal supervised image classification will be done with a random forest algorithm, and the field data will be used for both training the algorithm and accuracy assessment. The expected outputs are (i) map(s) of agroforestry dynamics, (ii) characteristics of different systems (main species, management, area, etc.); (iii) assessment report of Burkina Faso data cube.

Keywords: agroforestry systems, Burkina Faso, earth observation data cube, multi-temporal image classification

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Authors: Reggie Chandra

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Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

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Authors: Juliana A. Knocikova

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Methods of nonlinear signal analysis are based on finding that random behavior can arise in deterministic nonlinear systems with a few degrees of freedom. Considering the dynamical systems, entropy is usually understood as a rate of information production. Changes in temporal dynamics of physiological data are indicating evolving of system in time, thus a level of new signal pattern generation. During last decades, many algorithms were introduced to assess some patterns of physiological responses to external stimulus. However, the reflex responses are usually characterized by short periods of time. This characteristic represents a great limitation for usual methods of nonlinear analysis. To solve the problems of short recordings, parameter of approximate entropy has been introduced as a measure of system complexity. Low value of this parameter is reflecting regularity and predictability in analyzed time series. On the other side, increasing of this parameter means unpredictability and a random behavior, hence a higher system complexity. Reduced neurophysiological data complexity has been observed repeatedly when analyzing electroneurogram and electromyogram activities during defence reflex responses. Quantitative phrenic neurogram changes are also obvious during severe hypoxia, as well as during airway reflex episodes. Concluding, the approximate entropy parameter serves as a convenient tool for analysis of reflex behavior characterized by short lasting time series.

Keywords: approximate entropy, neurophysiological data, nonlinear dynamics, reflex

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Authors: Pan Long, Bi Dongjie, Li Xifeng, Xie Yongle

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The near-field synthetic aperture radar (SAR) imaging is an advanced nondestructive testing and evaluation (NDT&E) technique. This paper investigates the complex-valued signal processing related to the near-field SAR imaging system, where the measurement data turns out to be noncircular and improper, meaning that the complex-valued data is correlated to its complex conjugate. Furthermore, we discover that the degree of impropriety of the measurement data and that of the target image can be highly correlated in near-field SAR imaging. Based on these observations, A modified generalized sparse Bayesian learning algorithm is proposed, taking impropriety and noncircularity into account. Numerical results show that the proposed algorithm provides performance gain, with the help of noncircular assumption on the signals.

Keywords: complex-valued signal processing, synthetic aperture radar, 2-D radar imaging, compressive sensing, sparse Bayesian learning

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Authors: Kung-Jen Tu, Danny Vernatha

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To assist individual departments within universities in their energy management tasks, this study explores the application of Building Information Modeling in establishing the ‘BIM based Energy Management Support System’ (BIM-EMSS). The BIM-EMSS consists of six components: (1) sensors installed for each occupant and each equipment, (2) electricity sub-meters (constantly logging lighting, HVAC, and socket electricity consumptions of each room), (3) BIM models of all rooms within individual departments’ facilities, (4) data warehouse (for storing occupancy status and logged electricity consumption data), (5) building energy management system that provides energy managers with various energy management functions, and (6) energy simulation tool (such as eQuest) that generates real time 'standard energy consumptions' data against which 'actual energy consumptions' data are compared and energy efficiency evaluated. Through the building energy management system, the energy manager is able to (a) have 3D visualization (BIM model) of each room, in which the occupancy and equipment status detected by the sensors and the electricity consumptions data logged are displayed constantly; (b) perform real time energy consumption analysis to compare the actual and standard energy consumption profiles of a space; (c) obtain energy consumption anomaly detection warnings on certain rooms so that energy management corrective actions can be further taken (data mining technique is employed to analyze the relation between space occupancy pattern with current space equipment setting to indicate an anomaly, such as when appliances turn on without occupancy); and (d) perform historical energy consumption analysis to review monthly and annually energy consumption profiles and compare them against historical energy profiles. The BIM-EMSS was further implemented in a research lab in the Department of Architecture of NTUST in Taiwan and implementation results presented to illustrate how it can be used to assist individual departments within universities in their energy management tasks.

Keywords: database, electricity sub-meters, energy anomaly detection, sensor

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Authors: Mohamed Ahmed Madkour, Haitham Magdy Hamad

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This research aims to study of the surgery science and imaging in ancient Egypt, and how to diagnose the surgical cases, whether due to injuries or disease that requires surgical intervention, Medical diagnosis and how to treat it. The ancient Egyptian physician tried to change over from magic and theological thinking to become a stand-alone experimental science, they were able to distinguish between diseases and they divide them into internal and external diseases even this division exists to date in modern medicine. There is no evidence to recognize the amount of human knowledge in the prehistoric knowledge of medicine and surgery except skeleton. It is not far from the human being in those times familiar with some means of treatment, Surgery in the Stone age was rudimentary, Flint stone was used after trimming in a certain way as a lancet to slit and open the skin. Wooden tree branches were used to make splints to treat bone fractures. Surgery developed further when copper was discovered, it led to the advancement of Egyptian civilization, then modern and advanced tools appeared in the operating theater like a knife or a scalpel. The climate and environmental conditions have preserved medical papyri and human remains that have confirmed their knowledge of surgical methods including sedation. The ancient Egyptians reached a great importance in surgery, evidenced by the scenes that depict the pathological image and the surgical process, but the image alone is not sufficient to prove the pathology, its presence in ancient Egypt and its treatment method. As there are a number of medical papyri, especially Edwin Smith and Ebris, which prove the ancient Egyptian surgeon's knowledge of the pathological condition that It requires a surgical intervention, otherwise its diagnosis and the method of treatment will not be described with such accuracy through these texts. Some surgeries are described in the department of surgery at Ebris papyrus. The level of surgery in ancient Egypt was high, and they performed surgery such as hernias and Aneurysm, however we have not received a lengthy explanation of the various surgeries and the surgeon has usually only said “treated surgically”. It is evident in the Ebris papyrus that they used sharp surgical tools and cautery in operations where bleeding is expected, such as hernias, arterial sacs and tumors.

Keywords: ancient Egypt, archaeology, Egyptian history, ancient asurgical imaging, Egyptian civilization, civilization

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Authors: H. J. Joshi, Satyajeet Patil, Parth Dandavate, Mihir Kulkarni, Harshita Agrawal

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As the world looks towards a sustainable future, electric vehicles have become increasingly popular. Millions worldwide are looking to switch to Electric cars over the previously favored combustion engine-powered cars. This demand has seen an increase in Electric Vehicle Charging Stations. The big challenge is that the randomness of electrical energy makes it tough for these charging stations to provide an adequate amount of energy over a specific amount of time. Thus, it has become increasingly crucial to model these patterns and forecast the energy needs of power stations. This paper aims to analyze how different machine learning models perform on Electric Vehicle charging time-series data. The data set consists of authentic Electric Vehicle Data from the Netherlands. It has an overview of ten thousand transactions from public stations operated by EVnetNL.

Keywords: forecasting, smart grid, electric vehicle load forecasting, machine learning, time series forecasting

Procedia PDF Downloads 90

Authors: Sreejith Jayachandran, Mojtaba Ghods, Morteza Mohammadzaheri

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The modern busy world is running behind new embedded technologies based on computers and software; meanwhile, some people forget to do their health condition and regular medical check-ups. Some of them postpone medical check-ups due to a lack of time and convenience, while others skip these regular evaluations and medical examinations due to huge medical bills and hospital expenses. Engineers and medical experts have come together to give birth to a new device in the telemonitoring system capable of monitoring, checking, and evaluating the health status of the human body remotely through the internet for the needs of all kinds of people. The remote health monitoring device is a microcontroller-based embedded unit. Various types of sensors in this device are connected to the human body, and with the help of an Arduino UNO board, the required analogue data is collected from the sensors. The microcontroller on the Arduino board processes the analogue data collected in this way into digital data and transfers that information to the cloud, and stores it there, and the processed digital data is instantly displayed through the LCD attached to the machine. By accessing the cloud storage with a username and password, the concerned person’s health care teams/doctors and other health staff can collect this data for the assessment and follow-up of that patient. Besides that, the family members/guardians can use and evaluate this data for awareness of the patient's current health status. Moreover, the system is connected to a Global Positioning System (GPS) module. In emergencies, the concerned team can position the patient or the person with this device. The setup continuously evaluates and transfers the data to the cloud, and also the user can prefix a normal value range for the evaluation. For example, the blood pressure normal value is universally prefixed between 80/120 mmHg. Similarly, the RHMS is also allowed to fix the range of values referred to as normal coefficients. This IoT-based miniature system (11×10×10) cm³ with a low weight of 500 gr only consumes 10 mW. This smart monitoring system is manufactured with 100 GBP, which can be used not only for health systems, it can be used for numerous other uses including aerospace and transportation sections.

Keywords: embedded technology, telemonitoring system, microcontroller, Arduino UNO, cloud storage, global positioning system, remote health monitoring system, alert system

Procedia PDF Downloads 71

Authors: Weera Weerasophon

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The purpose of this research was to study Tourists’s satisfaction strategies case of Tourists who attended and shopped in Chatuchak weekend market (Bangkok) in order to improve service operation of Chatuchak weekend market to serve tourists’ need to impress them. The researcher used the marketing mix as a main factor that affect to tourist satisfaction. This research was emphasized as quantitative research as 400 of questionnaires were used for collecting the data from international tourists around Chatuchak weekend market that questionnaires divided in to 3 parts as a personal information part, satisfaction of marketing/services and facilities and suggestion part. After collecting all the data that would be processed in statistic program of SPSS to use for analyze the data later on. The result is described that most of international tourists satisfied Chatuchak weekend market in the level of 4 as more satisfaction for example friendly staff, Chatuchak information, price of product, facilities and service by the way, the environment of Chatuchak weekend market is the most satisfaction level.

Keywords: Chatuchak, satisfaction, Thailand tourism, marketing mix, tourists

Procedia PDF Downloads 348

Authors: Sekkal Nawel

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The Web of Things (WoT), integration of smart technologies from the Internet or network to Web architecture or application, is becoming more complex, larger, and dynamic. The WoT is associated with various elements such as sensors, devices, networks, protocols, data, functionalities, and architectures to perform services for stakeholders. These services operate in the context of the interaction of stakeholders and the WoT elements. Such context is becoming a key information source from which data are of various nature and uncertain, thus leading to complex situations. In this paper, we take interest in the development of intelligent Web services. The key ingredients of this “intelligent” notion are the context diversity, the necessity of a semantic representation to manage complex situations and the capacity to reason with uncertain data. In this perspective, we introduce a multi-layered architecture based on a generic intelligent Web service model dealing with various contexts, which proactively predict future situations and reactively respond to real-time situations in order to support decision-making. For semantic context data representation, we use PR-OWL, which is a probabilistic ontology based on Multi-Entity Bayesian Networks (MEBN). PR-OWL is flexible enough to represent complex, dynamic, and uncertain contexts, the key requirements of the development for the intelligent Web services. A case study was carried out using the proposed architecture for intelligent plant watering to show the role of proactive and reactive contextual reasoning in terms of WoT.

Keywords: smart web service, the web of things, context reasoning, proactive, reactive, multi-entity bayesian networks, PR-OWL

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Authors: Mohammad Hadi Khorashadi Zadeh ‎

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An entrepreneur is a manager or the owner of the commercial company that creates resources and money by risking and initiative. The Netpreneur is the capability to run an online business. It needs only the Connectivity. An Entrepreneur, as long as he has a service which the market demands can set up a feasible and viable trade with his Intellectual Capital as the principle input and the Connectivity Infrastructure as the only physical input. The internet is possibly the most significant revolution in science and technology that our generation could fantasize or imagine. It has introduced in various benefits to the society, culture, economics and politics. The entrepreneur is a premium member in the community. She/he provides services to the society and community including employment.

Keywords: entrepreneur, Netpreneur, intellectual capital, infrastructure

Procedia PDF Downloads 307

Authors: Tijjani Bashir Musa

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The recent global crisis which suddenly results to Nigerian stock market crash revealed some peculiarities of Nigerian firms. Some firms in Nigeria are performing but their stock prices are not increasing while some firms are at the brink of collapse but their stock prices are increasing. Thus, this study examines the relationship between firm performance and stock price in Nigeria. The study covered the period of 2005 to 2009. This period is the period of stock boom and also marked the period of stock market crash as a result of global financial meltdown. The study is a panel study. A total of 140 firms were sampled from 216 firms listed on the Nigerian Stock Exchange (NSE). Data were collected from secondary source. These data were divided into four strata comprising the most performing stock, the least performing stock, most performing firms and the least performing firms. Each stratum contains 35 firms with characteristic of most performing stock, most performing firms, least performing stock and least performing firms. Multiple linear regression models were used to analyse the data while statistical/econometrics package of Stata 11.0 version was used to run the data. The study found that, relationship exists between selected firm performance parameters (operating efficiency, firm profit, earning per share and working capital) and stock price. As such firm performance gave sufficient information or has predictive power on stock prices movements in Nigeria for all the years under study.. The study recommends among others that Managers of firms in Nigeria should formulate policies and exert effort geared towards improving firm performance that will enhance stock prices movements.

Keywords: firm, Nigeria, performance, stock price

Procedia PDF Downloads 461

Authors: Thi Thanh Nga Pham, Damien Philippon, Alexis Drogoul, Thi Thu Thuy Nguyen, Tien Cong Nguyen

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Mekong River Delta region of Vietnam is recognized as one of the most vulnerable to climate change due to flooding and seawater rise and therefore an increased burden of climate change-related diseases. Changes in temperature and precipitation are likely to alter the incidence and distribution of vector-borne diseases such as dengue fever. In this region, the peak of the dengue epidemic period is around July to September during the rainy season. It is believed that climate is an important factor for dengue transmission. This study aims to enhance the capacity of dengue prediction by the relationship of dengue incidences with climate and environmental variables for Mekong River Delta of Vietnam during 2005-2015. Mathematical models for vector-host infectious disease, including larva, mosquito, and human being were used to calculate the impacts of climate to the dengue transmission with incorporating geospatial data for model input. Monthly dengue incidence data were collected at provincial level. Precipitation data were extracted from satellite observations of GSMaP (Global Satellite Mapping of Precipitation), land surface temperature and land cover data were from MODIS. The value of seasonal reproduction number was estimated to evaluate the potential, severity and persistence of dengue infection, while the final infected number was derived to check the outbreak of dengue. The result shows that the dengue infection depends on the seasonal variation of climate variables with the peak during the rainy season and predicted dengue incidence follows well with this dynamic for the whole studied region. However, the highest outbreak of 2007 dengue was not captured by the model reflecting nonlinear dependences of transmission on climate. Other possible effects will be discussed to address the limitation of the model. This suggested the need of considering of both climate variables and another variability across temporal and spatial scales.

Keywords: infectious disease, dengue, geospatial data, climate

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Authors: Loai AbdAllah, Mahmoud Kaiyal

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Missing values in real-world datasets are a common problem. Many algorithms were developed to deal with this problem, most of them replace the missing values with a fixed value that was computed based on the observed values. In our work, we used a distance function based on Bhattacharyya distance to measure the distance between objects with missing values. Bhattacharyya distance, which measures the similarity of two probability distributions. The proposed distance distinguishes between known and unknown values. Where the distance between two known values is the Mahalanobis distance. When, on the other hand, one of them is missing the distance is computed based on the distribution of the known values, for the coordinate that contains the missing value. This method was integrated with Wikaya, a digital health company developing a platform that helps to improve prevention of chronic diseases such as diabetes and cancer. In order for Wikaya’s recommendation system to work distance between users need to be measured. Since there are missing values in the collected data, there is a need to develop a distance function distances between incomplete users profiles. To evaluate the accuracy of the proposed distance function in reflecting the actual similarity between different objects, when some of them contain missing values, we integrated it within the framework of k nearest neighbors (kNN) classifier, since its computation is based only on the similarity between objects. To validate this, we ran the algorithm over diabetes and breast cancer datasets, standard benchmark datasets from the UCI repository. Our experiments show that kNN classifier using our proposed distance function outperforms the kNN using other existing methods.

Keywords: missing values, incomplete data, distance, incomplete diabetes data

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Authors: Vijaya K. Narne, Gerard Loquet, Tobias Piechowiak, Dorte Hammershoi, Jesper H. Schmidt

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BEAR, which stands for better hearing rehabilitation is a large-scale project in Denmark designed and executed by three national universities, three hospitals, and the hearing aid industry with the aim to improve hearing aid fitting. A total of 1963 hearing impaired people were included and were segmented into subgroups based on hearing-loss, demographics, audiological and questionnaires data (i.e., the speech, spatial and qualities of hearing scale [SSQ-12] and the International Outcome Inventory for Hearing-Aids [IOI-HA]). With the aim to provide a better hearing-aid fit to individual patients, we applied modern machine learning techniques with traditional audiograms rule-based systems. Results show that age, speech discrimination scores, and audiogram configurations were evolved as important parameters in characterizing sub-population from the data-set. The attempt to characterize sub-population reveal a clearer picture about the individual hearing difficulties encountered and the benefits derived from more individualized hearing aids.

Keywords: hearing loss, audiological data, machine learning, hearing aids

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Authors: Ebobo Urowoli Christiana

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The Nigerian Prison Service statistics of 2007; 2009 revealed that though crime in the past was ascribed to men, but today there is a steady increase in the population of women involved in crime. This study focused on the investigation of female criminality in Lagos State: A case of Armed Robbery. Its major objective was to find out if there is an increase or decrease in female involvement in armed robbery and its growth rate. The major research question is 'Is there an increase in the perpetration of armed robbery by females in Lagos State?' the null hypotheses is 'There is no significant increase in the perpetration of armed robbery by females in Lagos State.' As a result, this study adopted the survey design, purposive sampling method and a sample size of 120 respondents. The rational choice theory was used to explain the reason for female involvement in armed robbery. Both primary and secondary data was generated for this study; the primary data was collected from the criminal records in Lagos State Police Command, Panti while the Quantitative data was collected using the questionnaire from 120 female detainees and inmates. The data collected was analyzed using the simple frequency tables and percentages and chi square was used to test for relationships. The study revealed a persistent rise in the prevalence of female armed robbery and recommended that youths should be equipped with educational/vocational skills in order to lead responsible lives.

Keywords: criminality, armed robbery, female, police commands, panti, nature

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Authors: Nahla H. Alaswadko, Rayya A. Hassan, Bayar N. Mohammed

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Empirical deterministic models have been developed to predict roughness progression of heavy duty spray sealed pavements for a dataset representing rural arterial roads. The dataset provides a good representation of the relevant network and covers a wide range of operating and environmental conditions. A sample with a large size of historical time series data for many pavement sections has been collected and prepared for use in multilevel regression analysis. The modelling parameters include road roughness as performance parameter and traffic loading, time, initial pavement strength, reactivity level of subgrade soil, climate condition, and condition of drainage system as predictor parameters. The purpose of this paper is to report the approaches adopted for models development and validation. The study presents multilevel models that can account for the correlation among time series data of the same section and to capture the effect of unobserved variables. Study results show that the models fit the data very well. The contribution and significance of relevant influencing factors in predicting roughness progression are presented and explained. The paper concludes that the analysis approach used for developing the models confirmed their accuracy and reliability by well-fitting to the validation data.

Keywords: roughness progression, empirical model, pavement performance, heavy duty pavement

Procedia PDF Downloads 159

Authors: Panupong Makvichian

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Global Navigation Satellite System (GNSS) is nowadays a common technology that improves navigation functions in our life. Additionally, GNSS is also being employed on behalf of an accurate atmospheric sensor these times. Meteorology is a practical application of GNSS, which is unnoticeable in the background of people’s life. GNSS Precise Point Positioning (PPP) is a positioning method that requires data from a single dual-frequency receiver and precise information about satellite positions and satellite clocks. In addition, careful attention to mitigate various error sources is required. All the above data are combined in a sophisticated mathematical algorithm. At this point, the research is going to demonstrate how GNSS and PPP method is capable to provide high-precision estimates, such as 3D positions or Zenith tropospheric delays (ZTDs). ZTDs combined with pressure and temperature information allows us to estimate the water vapor in the atmosphere as precipitable water vapor (PWV). If the process is replicated for a network of GNSS sensors, we can create thematic maps that allow extract water content information in any location within the network area. All of the above are possible thanks to the advances in GNSS data processing. Therefore, we are able to use GNSS data for climatic trend analysis and acquisition of the further knowledge about the atmospheric water content.

Keywords: GNSS, precise point positioning, Zenith tropospheric delays, precipitable water vapor

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Authors: Sherly M. A., Varsha Vijaykumar, Subhankar Karmakar, Terence Chan, Christian Rau

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This study aims at proposing an indicator-based framework for assessing social vulnerability of any coastal megacity to floods. The final set of indicators of social vulnerability are chosen from a set of feasible and available indicators which are prepared using a Geographic Information System (GIS) framework on a smaller scale considering 1-km grid cell to provide an insight into the spatial variability of vulnerability. The optimal weight for each individual indicator is assigned using data envelopment analysis (DEA) as it avoids subjective weights and improves the confidence on the results obtained. In order to de-correlate and reduce the dimension of multivariate data, principal component analysis (PCA) has been applied. The proposed methodology is demonstrated on twenty four wards of Mumbai under the jurisdiction of Municipal Corporation of Greater Mumbai (MCGM). This framework of vulnerability assessment is not limited to the present study area, and may be applied to other urban damage centers.

Keywords: urban floods, vulnerability, data envelopment analysis, principal component analysis

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Authors: Alida Sandison, Louise A. Stroud

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Narcissism is a personality configuration which has both normal and pathological personality expressions. Narcissism is highly complex, and is linked to a broad field of research. There are both dimensional and categorical conceptualisations of narcissism, and a variety of theoretical formulations that have been put forward to understand the narcissistic personality configuration. Currently, Kernberg’s Object Relations theory is well supported for this purpose. The complexity and particular defense mechanisms at play in the narcissistic personality make it a difficult personality configuration worth further research. Psychobiography as a methodology allows for the exploration of the lived life, and is thus a useful methodology to surmount these inherent challenges. Narcissism has been a focus of academic interest for a long time, and although there is a lot of research done in this area, to the researchers' knowledge, narcissistic dynamics have never been explored within a psychobiographical format. Thus, the primary aim of the research was to explore and describe narcissism in the life of Howard Hughes, with the objective of gaining further insight into narcissism through the use of this unconventional research approach. Hughes was chosen as subject for the study as he is renowned as an eccentric billionaire who had his revolutionary effect on the world, but was concurrently disturbed within his personal pathologies. Hughes was dynamic in three different sectors, namely motion pictures, aviation and gambling. He became more and more reclusive as he entered into middle age. From his early fifties he was agoraphobic, and the social network of connectivity that could reasonably be expected from someone in the top of their field was notably distorted. Due to his strong narcissistic personality configuration, and the interpersonal difficulties he experienced, Hughes represents an ideal figure to explore narcissism. The study used a single case study design, and purposive sampling to select Hughes. Qualitative data was sampled, using secondary data sources. Given that Hughes was a famous figure, there is a plethora of information on his life, which is primarily autobiographical. This includes books written about his life, and archival material in the form of newspaper articles, interviews and movies. Gathered data were triangulated to avoid the effect of author bias, and increase the credibility of the data used. It was collected using Yin’s guidelines for data collection. Data was analysed using Miles and Huberman strategy of data analysis, which consists of three steps, namely, data reduction, data display, and conclusion drawing and verification. Patterns which emerged in the data highlighted the defense mechanisms used by Hughes, in particular that of splitting and projection, in defending his sense of self. These defense mechanisms help us to understand the high levels of entitlement and paranoia experienced by Hughes. Findings provide further insight into his sense of isolation and difference, and the consequent difficulty he experienced in maintaining connections with others. Findings furthermore confirm the effectiveness of Kernberg’s theory in understanding narcissism observing an individual life.

Keywords: Howard Hughes, narcissism, narcissistic defenses, object relations

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Authors: Breno M. Castilho

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The Iron Ore Quadrangle, located in the state of Minas Gerais, Brazil is responsible for most of the country’s iron ore production. As a result, some of the biggest tailings dams in the country are located in this area. In recent years, several major failure events have happened in Tailings Storage Facilities (TSF) located in the Iron Ore Quadrangle. Some of these failures were found to be caused by liquefaction flowslides. This paper presents Piezocone Penetration Test (CPTu) data that was used, by applying Olson and Peterson methods, for the liquefaction susceptibility assessment of the iron ore tailings that are typically found in most TSF in the area. Piezocone data was also used to determine the steady-state strength of the tailings so as to allow for comparison with its drained strength. Results have shown great susceptibility for liquefaction to occur in the studied tailings and, more importantly, a large reduction in its strength. These results are key to understanding the failures that took place over the last few years.

Keywords: Piezocone Penetration Test CPTu, iron ore tailings, mining, liquefaction susceptibility assessment

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Authors: Jiao Sun, Li Pan, Shijun Liu

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Recommender systems have been widely used in contemporary industry, and plenty of work has been done in this field to help users to identify items of interest. Collaborative Filtering (CF, for short) algorithm is an important technology in recommender systems. However, less work has been done in automobile recommendation system with the sharp increase of the amount of automobiles. What’s more, the computational speed is a major weakness for collaborative filtering technology. Therefore, using MapReduce framework to optimize the CF algorithm is a vital solution to this performance problem. In this paper, we present a recommendation of the users’ comment on industrial automobiles with various properties based on real world industrial datasets of user-automobile comment data collection, and provide recommendation for automobile providers and help them predict users’ comment on automobiles with new-coming property. Firstly, we solve the sparseness of matrix using previous construction of score matrix. Secondly, we solve the data normalization problem by removing dimensional effects from the raw data of automobiles, where different dimensions of automobile properties bring great error to the calculation of CF. Finally, we use the MapReduce framework to optimize the CF algorithm, and the computational speed has been improved times. UV decomposition used in this paper is an often used matrix factorization technology in CF algorithm, without calculating the interpolation weight of neighbors, which will be more convenient in industry.

Keywords: collaborative filtering, recommendation, data normalization, mapreduce

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Richard Grünwald, Wenling Wang, Yan Feng

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The presented paper explores the changing dynamics of regional water security in the Mekong River Basin and examines the contemporary water-related challenges from a critical hydropolitical perspective. By drawing on the Lancang-Mekong Cooperation and Conflict Database (LMCCD) recording more than 3000 water-related events within the basin in the last 30 years, we identified several trends changing the dynamics of the regional water security in the Mekong River Basin. Firstly, there is growing politicization of water that is no longer interpreted as abundant. While some scientists blame the rapid basin development, particularly in upstream countries, other researchers consider climate change and cumulative environmental impacts of various water projects as the main culprit for changing the water flow. Secondly, there is an increasing securitization of large-scale hydropower dams with questionable outcomes. Despite hydropower dams raise many controversies, many riparian states push the development at all cost. Such water security dilemma can be especially traced to Laos and Cambodia, which highly invest in the hydropower sector even at the expense of the local environment and good relations with neighbouring countries situated lower on the river. Thirdly, there is a lack of accountable transboundary water governance that will effectively face a looming water crisis. To date, most of the existing cooperation mechanisms are undermined by the geopolitical interests of foreign donors and increasing mistrust to scientific approaches dealing with water insecurity. Our findings are beneficial for the policy-makers and other water experts who want to grasp the broader hydropolitical context in the Mekong River Basin and better understand the new water security threats, including misinterpretation of the hydrological data and legitimization of the pro-development narratives.

Keywords: critical hydropolitics, mekong river, politicization of science, water governance, water security

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Authors: Ashiq Hussain, Mahwash Hussain, Zeenat Parveen

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Now a days the demand for broadband service has increased. Due to which the researchers are trying to find a solution to provide a large amount of service. There is a shortage of bandwidth because of the use of downloading video, voice and data. One of the solutions to overcome this shortage of bandwidth is to provide the communication system with passive optical components. We have increased the data rate in this system. From experimental results we have concluded that the quality factor has increased by adding passive optical networks.

Keywords: WDM-PON, optical fiber, BER, Q-factor, eye diagram

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Authors: Jack L. Harris, Marya L. Doerfel, Hyunsook Youn, Minkyung Kim, Kautuki Sunil Jariwala

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Organizational resilience refers to the ability to sustain business or general work functioning despite wide-scale interruptions. We focus on organization and businesses as a pillar of their communities and how they attempt to sustain work when a natural disaster impacts their surrounding regions and economies. While it may be more common to think of resilience as a trait possessed by an organization, an emerging area of research recognizes that for organizations and businesses, resilience is a set of processes that are constituted through communication, social networks, and organizing. Indeed, five processes, robustness, rapidity, resourcefulness, redundancy, and external availability through social media have been identified as critical to organizational resilience. These organizing mechanisms involve multi-level coordination, where individuals intersect with groups, organizations, and communities. Because the nature of such interactions are often networks of people and organizations coordinating material resources, information, and support, they necessarily require some way to coordinate despite being displaced. Little is known, however, if physical and digital spaces can substitute one for the other. We thus are guided by the question, is digital space sufficient when disaster creates a scarcity of physical space? This study presents a cross-case comparison based on field research from four different regions of the United States that were impacted by Hurricanes Katrina (2005), Sandy (2012), Maria (2017), and Harvey (2017). These four cases are used to extend the science of resilience by examining multi-level processes enacted by individuals, communities, and organizations that together, contribute to the resilience of disaster-struck organizations, businesses, and their communities. Using field research about organizations and businesses impacted by the four hurricanes, we code data from interviews, participant observations, field notes, and document analysis drawn from New Orleans (post-Katrina), coastal New Jersey (post-Sandy), Houston Texas (post-Harvey), and the lower keys of Florida (post-Maria). This paper identifies an additional organizing mechanism, networked gathering spaces, where citizens and organizations, alike, coordinate and facilitate information sharing, material resource distribution, and social support. Findings show that digital space, alone, is not a sufficient substitute to effectively sustain organizational resilience during a disaster. Because the data are qualitative, we expand on this finding with specific ways in which organizations and the people who lead them worked around the problem of scarce space. We propose that gatherings after disaster are a sixth mechanism that contributes to organizational resilience.

Keywords: communication, coordination, disaster management, information and communication technologies, interorganizational relationships, resilience, work

Procedia PDF Downloads 158