Search results for: short-term insurance data

Authors: Iman Shabanzadeh

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Over the last three decades, the main demands of the Iranian workforce have been focused on three areas: "The right to a decent wage", "The right to organize" and "The right to job security". In order to investigate and analyze this situation, the present study focuses on the component of job security. The purpose of the study is to figure out what mechanisms in Iran's Labor Law have led to the destabilization and undermining of workers' job security. The research method is descriptive-analytical. To collect information, library and document sources in the field of laws related to labor rights in Iran and, semi-structured interviews with experts have been used. In the data analysis stage, the qualitative content analysis method was also used. The trend analysis of the statistics related to the labor force situation in Iran in the last three decades shows that the employment structure has been facing an increase in the active population, but in the last decade, a large part of this population has been mainly active in the service sector, and contract-free enterprises, so a smaller share of this employment has insurance coverage and a larger share has underemployment. In this regard, the results of this study show that four contexts have been proposed as the main legal and executive mechanisms of labor instability in Iran, which are: 1) temporaryization of the labor force by providing different interpretations of labor law, 2) adjustment labor in the public sector and the emergence of manpower contracting companies, 3) the cessation of labor law protection of workers in small workshops and 4) the existence of numerous restrictions on the effective organization of workers. The theoretical conclusion of this article is that the main root of the challenges of the labor society and the destabilized workforce in Iran is the existence of structural inequalities in the field of labor security, whose traces can be seen in the legal provisions and executive regulations of this field.

Keywords: inequality, precariat, temporaryization, labor force, labor law

Procedia PDF Downloads 32

Authors: Gus Calderon, Richard McCreight, Tammy Schwartz

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Forensic analysis of community wildfire destruction in California has shown that reducing or removing flammable vegetation in proximity to buildings and structures is one of the most important wildfire defenses available to homeowners. State laws specify the requirements for homeowners to create and maintain defensible space around all structures. Unfortunately, this decades-long effort had limited success due to noncompliance and minimal enforcement. As a result, vulnerable communities continue to experience escalating human and economic costs along the wildland-urban interface (WUI). Quantifying vegetative fuels at both the community and parcel scale requires detailed imaging from an aircraft with remote sensing technology to reduce uncertainty. FireWatch has been delivering high spatial resolution (5” ground sample distance) wildfire hazard maps annually to the community of Rancho Santa Fe, CA, since 2019. FireWatch uses a multispectral imaging system mounted onboard an aircraft to create georeferenced orthomosaics and spectral vegetation index maps. Using proprietary algorithms, the vegetation type, condition, and proximity to structures are determined for 1,851 properties in the community. Secondary data processing combines object-based classification of vegetative fuels, assisted by machine learning, to prioritize mitigation strategies within the community. The remote sensing data for the 10 sq. mi. community is divided into parcels and sent to all homeowners in the form of defensible space maps and reports. Follow-up aerial surveys are performed annually using repeat station imaging of fixed GPS locations to address changes in defensible space, vegetation fuel cover, and condition over time. These maps and reports have increased wildfire awareness and mitigation efforts from 40% to over 85% among homeowners in Rancho Santa Fe. To assist homeowners fighting increasing insurance premiums and non-renewals, FireWatch has partnered with Black Swan Analytics, LLC, to leverage the multispectral imagery and increase homeowners’ understanding of wildfire risk drivers. For this study, a subsample of 100 parcels was selected to gain a comprehensive understanding of wildfire risk and the elements which can be mitigated. Geospatial data from FireWatch’s defensible space maps was combined with Black Swan’s patented approach using 39 other risk characteristics into a 4score Report. The 4score Report helps property owners understand risk sources and potential mitigation opportunities by assessing four categories of risk: Fuel sources, ignition sources, susceptibility to loss, and hazards to fire protection efforts (FISH). This study has shown that susceptibility to loss is the category residents and property owners must focus their efforts. The 4score Report also provides a tool to measure the impact of homeowner actions on risk levels over time. Resiliency is the only solution to breaking the cycle of community wildfire destruction and it starts with high-quality data and education.

Keywords: defensible space, geospatial data, multispectral imaging, Rancho Santa Fe, susceptibility to loss, wildfire risk.

Procedia PDF Downloads 80

Authors: Diego Rodrigues, Marcelo Lisboa, Elismar Batista, Marcos Dias

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Based on data on child labor, this work arises questions about how to understand and locate the factors that make up the child labor rates, and which properties are important to analyze these cases. Using data mining techniques to discover valid patterns on Brazilian social databases were evaluated data of child labor in the State of Tocantins (located north of Brazil with a territory of 277000 km2 and comprises 139 counties). This work aims to detect factors that are deterministic for the practice of child labor and their relationships with financial indicators, educational, regional and social, generating information that is not explicit in the government database, thus enabling better monitoring and updating policies for this purpose.

Keywords: social data, government decision making, association of social data, data mining

Procedia PDF Downloads 344

Authors: Hester Fass, Ofir Pinto

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Research indicates gaps in education, employment rates and wages between people with disabilities and those without disabilities. One of the main tools available to reduce these gaps is vocational rehabilitation. In order to examine the effects of vocational rehabilitation, a follow-up study, based on comprehensive administrative data, was conducted. The study included 88,286 people with disabilities who participated in vocational rehabilitation of the National Insurance Institute of Israel (NII), and completed the process between 1999 and 2012. Research variables included: employment rates, job persistence and wage levels. This research, the first of its kind in Israel, has several unique aspects: a)a long-range follow-up study on people who completed vocational rehabilitation; b) examination of a broad population spectrum, including also people that are not eligible to disability pensions ; c) a comparison among those with work-related injuries, those injured in hostile acts and those injured in other circumstances; and finally d) the identification of the characteristics of those who are entitled to vocational rehabilitation but who do not participate in any vocational rehabilitation plan. The most notable results include: 1. Vocational rehabilitation contributed to employment, job persistence and wage levels. Participation in vocational rehabilitation resulted in an employment rate of 65% within two years after completing the program, and 73% eventually. Participation in a vocational rehabilitation plan also contributed to job persistence and wage levels. 2. Vocational rehabilitation plans aimed at integration in universal frameworks increased the chances of being employed, persisting at the job and receiving a higher wage than did the vocational rehabilitation aimed at selective frameworks (such as sheltered workshops). 3. The type of disability affected the chances of integration in a vocational rehabilitation plan and in the labor market. People with a disability from birth had greater chances of integration in a vocational rehabilitation plan, while the type of disability and its severity affected the chances of the person with disabilities to find employment.

Keywords: vocational rehabilitation, employment, job persistence, wages

Procedia PDF Downloads 431

Authors: Sadam Alwadi

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Outlier values become a problem that frequently occurs in the data observation or recording process. Thus, the need for data imputation has become an essential matter. In this work, it will make use of the methods described in the prior work to detect the outlier values based on a collection of stock market data. In order to implement the detection and find some solutions that maybe helpful for investors, real closed price data were obtained from the Amman Stock Exchange (ASE). Tukey and Maximum Overlapping Discrete Wavelet Transform (MODWT) methods will be used to impute the detect the outlier values.

Keywords: outlier values, imputation, stock market data, detecting, estimation

Procedia PDF Downloads 63

Authors: Amira Kaddour, Hedia Teraoui, Khmayes Bougatef

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The main purpose of this paper is to determine the major causes of the underdevelopment of Islamic finance in Tunisia. Indeed, it’s surprising to note that Zitouna bank established in May 2010 is the first Islamic Tunisian bank although 99% of Tunisians are Muslim and Islam is the religion of the State according to the Constitution. So we rely in our paper on the opinions of number of professors of finance and economics as educated people to prove or reject our hypothesis that the underdevelopment of Islamic finance in Tunisia can be explained by the ignorance of its main principles and advantages. Ours findings reveal that this branch of finance is still largely unknown, not only from public but also from professionals. The results obtained surprisingly show that this insignificance of Islamic banking cannot be explained by the fact that Tunisia has been governed since its independence by a secular left-wing party. Indeed, only 3% of respondents believe that legislation and regulation in Tunisia represent an obstacle to the development of Islamic finance. Moreover, respondents are not very optimistic about the future role of Islamic financing.

Keywords: Islamic banking, Islamic insurance (takaful), Islamic law (shariah), usury (riba)

Procedia PDF Downloads 338

Authors: P. Jayashree, S. Rajkumar

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With social networks and smart devices generating a multitude of data, effective data management is the need of the hour for networks and cloud applications. Some applications need effective storage while some other applications need effective communication over networks and data reduction comes as a handy solution to meet out both requirements. Most of the data compression techniques are based on data statistics and may result in either lossy or lossless data reductions. Though lossy reductions produce better compression ratios compared to lossless methods, many applications require data accuracy and miniature details to be preserved. A variety of data compression algorithms does exist in the literature for different forms of data like text, image, and multimedia data. In the proposed work, a generic progressive compression algorithm, based on probabilistic encoding, called PEINS is projected as an enhancement over irrational number stored coding technique to cater to storage issues of increasing data volumes as a cost effective solution, which also offers data security as a secondary outcome to some extent. The proposed work reveals cost effectiveness in terms of better compression ratio with no deterioration in compression time.

Keywords: compression ratio, generic compression, irrational number storage, probabilistic encoding

Procedia PDF Downloads 263

Authors: Femi Elegbeleye, Omobayo Esan, Muienge Mbodila, Patrick Bowe

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This paper focused on cost effective storage architecture using fog and cloud data storage gateway and presented the design of the framework for the data privacy model and data analytics framework on a real-time analysis when using machine learning method. The paper began with the system analysis, system architecture and its component design, as well as the overall system operations. The several results obtained from this study on data privacy model shows that when two or more data privacy model is combined we tend to have a more stronger privacy to our data, and when fog storage gateway have several advantages over using the traditional cloud storage, from our result shows fog has reduced latency/delay, low bandwidth consumption, and energy usage when been compare with cloud storage, therefore, fog storage will help to lessen excessive cost. This paper dwelt more on the system descriptions, the researchers focused on the research design and framework design for the data privacy model, data storage, and real-time analytics. This paper also shows the major system components and their framework specification. And lastly, the overall research system architecture was shown, its structure, and its interrelationships.

Keywords: IoT, fog, cloud, data analysis, data privacy

Procedia PDF Downloads 71

Authors: Nordila Ahmad, Thamer Mohammad, Bruce W. Melville, Zuliziana Suif

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Current methods for predicting local scour at wide bridge piers, were developed on the basis of laboratory studies and very limited scour prediction were tested with field data. Laboratory wide pier scour equation from previous findings with field data were presented. A wide range of field data were used and it consists of both live-bed and clear-water scour. A method for assessing the quality of the data was developed and applied to the data set. Three other wide pier-scour equations from the literature were used to compare the performance of each predictive method. The best-performing scour equation were analyzed using statistical analysis. Comparisons of computed and observed scour depths indicate that the equation from the previous publication produced the smallest discrepancy ratio and RMSE value when compared with the large amount of laboratory and field data.

Keywords: field data, local scour, scour equation, wide piers

Procedia PDF Downloads 374

Authors: Inkyu Kim, SangMan Moon

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This IEEE 802.11b protocol provides up to 11Mbps data rate, whereas aerospace industry wants to seek higher data rate COTS data link system in the UAV. The Total Maximum Throughput (TMT) and delay time are studied on many researchers in the past years This paper provides theoretical data throughput performance of UAV formation flight data link using the existing 802.11b performance theory. We operate the UAV formation flight with more than 30 quad copters with 802.11b protocol. We may be predicting that UAV formation flight numbers have to bound data link protocol performance limitations.

Keywords: UAV datalink, UAV formation flight datalink, UAV WLAN datalink application, UAV IEEE 802.11b datalink application

Procedia PDF Downloads 361

Authors: Radoslav Židek, Veronika Šidlová, Radovan Kasarda, Birgit Fuerst-Waltl

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Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.

Keywords: genetic data, Pinzgau cattle, supervised learning, machine learning

Procedia PDF Downloads 521

Authors: Nidhi Gopal

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Routing is the process of moving a packet of data from source to destination and enables messages to pass from one computer to another and eventually reach the target machine. A router is a networking device that forwards data packets between computer networks. It is connected to two or more data lines from different networks (as opposed to a network switch, which connects data lines from one single network). This paper mainly emphasizes upon the study of router device, its top level architecture, and how various sub-modules of router i.e. Register, FIFO, FSM and Synchronizer are synthesized, and simulated and finally connected to its top module.

Keywords: data packets, networking, router, routing

Procedia PDF Downloads 769

Authors: Julius Onyancha, Valentina Plekhanova

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One of the significant issues facing web users is the amount of noise in web data which hinders the process of finding useful information in relation to their dynamic interests. Current research works consider noise as any data that does not form part of the main web page and propose noise web data reduction tools which mainly focus on eliminating noise in relation to the content and layout of web data. This paper argues that not all data that form part of the main web page is of a user interest and not all noise data is actually noise to a given user. Therefore, learning of noise web data allocated to the user requests ensures not only reduction of noisiness level in a web user profile, but also a decrease in the loss of useful information hence improves the quality of a web user profile. Noise Web Data Learning (NWDL) tool/algorithm capable of learning noise web data in web user profile is proposed. The proposed work considers elimination of noise data in relation to dynamic user interest. In order to validate the performance of the proposed work, an experimental design setup is presented. The results obtained are compared with the current algorithms applied in noise web data reduction process. The experimental results show that the proposed work considers the dynamic change of user interest prior to elimination of noise data. The proposed work contributes towards improving the quality of a web user profile by reducing the amount of useful information eliminated as noise.

Keywords: web log data, web user profile, user interest, noise web data learning, machine learning

Procedia PDF Downloads 240

Authors: Zeba Mahmood

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The modern business organizations are adopting technological advancement to achieve competitive edge and satisfy their consumer. The development in the field of Information technology systems has changed the way of conducting business today. Business operations today rely more on the data they obtained and this data is continuously increasing in volume. The data stored in different locations is difficult to find and use without the effective implementation of Data mining and Knowledge management techniques. Organizations who smartly identify, obtain and then convert data in useful formats for their decision making and operational improvements create additional value for their customers and enhance their operational capabilities. Marketers and Customer relationship departments of firm use Data mining techniques to make relevant decisions, this paper emphasizes on the identification of different data mining and Knowledge management techniques that are applied to different business industries. The challenges and issues of execution of these techniques are also discussed and critically analyzed in this paper.

Keywords: knowledge, knowledge management, knowledge discovery in databases, business, operational, information, data mining

Procedia PDF Downloads 509

Authors: Adarsh Shroff

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Big data is a collection of dataset so large and complex that it becomes difficult to process using data base management tools. To perform operations like search, analysis, visualization on big data by using data mining; which is the process of extraction of patterns or knowledge from large data set. In recent years, the data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refreshing mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. This project uses i2MapReduce, an incremental processing extension to Map Reduce, the most widely used framework for mining big data. I2MapReduce performs key-value pair level incremental processing rather than task level re-computation, supports not only one-step computation but also more sophisticated iterative computation, which is widely used in data mining applications, and incorporates a set of novel techniques to reduce I/O overhead for accessing preserved fine-grain computation states. To optimize the mining results, evaluate i2MapReduce using a one-step algorithm and three iterative algorithms with diverse computation characteristics for efficient mining.

Keywords: big data, map reduce, incremental processing, iterative computation

Procedia PDF Downloads 322

Authors: Jerry Q. Cheng

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Currently, in analyzing large-scale recurrent event data, there are many challenges such as memory limitations, unscalable computing time, etc. In this research, a divide-and-conquer method is proposed using parametric frailty models. Specifically, the data is randomly divided into many subsets, and the maximum likelihood estimator from each individual data set is obtained. Then a weighted method is proposed to combine these individual estimators as the final estimator. It is shown that this divide-and-conquer estimator is asymptotically equivalent to the estimator based on the full data. Simulation studies are conducted to demonstrate the performance of this proposed method. This approach is applied to a large real dataset of repeated heart failure hospitalizations.

Keywords: big data analytics, divide-and-conquer, recurrent event data, statistical computing

Procedia PDF Downloads 137

Authors: Ashiff Khan, A. Seetharaman, Abhijit Dasgupta

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The new era of big data (BD) is influencing chemical industries tremendously, providing several opportunities to reshape the way they operate and help them shift towards intelligent manufacturing. Given the availability of free software and the large amount of real-time data generated and stored in process plants, chemical industries are still in the early stages of big data adoption. The industry is just starting to realize the importance of the large amount of data it owns to make the right decisions and support its strategies. This article explores the importance of professional competencies and data science that influence BD in chemical industries to help it move towards intelligent manufacturing fast and reliable. This article utilizes a literature review and identifies potential applications in the chemical industry to move from conventional methods to a data-driven approach. The scope of this document is limited to the adoption of BD in chemical industries and the variables identified in this article. To achieve this objective, government, academia, and industry must work together to overcome all present and future challenges.

Keywords: chemical engineering, big data analytics, industrial revolution, professional competence, data science

Procedia PDF Downloads 57

Authors: Maximiliano Guerra, Lexi Frankel, Amalia D. Ardeljan, Sarah Ghali, Diya Kohli, Omar M. Rashid.

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Introduction/Background: Haemophilus influenzae is present as a commensal organism in the nasopharynx of most healthy adults from where it can spread to cause both systemic and respiratory tract infection. Pathogenic properties of this bacterium as well as defects in host defense may result in the spread of these bacteria throughout the body. This can result in a proinflammatory state and colonization particularly in the lungs. Recent studies have failed to determine a link between H. Influenzae colonization and prostate cancer, despite previous research demonstrating the presence of proinflammatory states in preneoplastic and neoplastic prostate lesions. Given these contradictory findings, the primary goal of this study was to evaluate the correlation between H. Influenzae infection and the incidence of prostate cancer. Methods: To evaluate the incidence of Haemophilus influenzae infection and the development of prostate cancer in the future we used data provided by a Health Insurance Portability and Accountability Act (HIPAA) compliant national database. We were afforded access to this database by Holy Cross Health, Fort Lauderdale for the express purpose of academic research. Standard statistical methods were employed in this study including Pearson’s chi-square tests. Results: Between January 2010 and December 2019, the query was analyzed and resulted in 13, 691 patients in both the control and C. difficile infected groups, respectively. The two groups were matched by age range and CCI score. In the Haemophilus influenzae infected group, the incidence of prostate cancer was 1.46%, while the incidence of the prostate cancer control group was 4.56%. The observed difference in cancer incidence was determined to be a statistically significant p-value (< 2.2x10^-16). This suggests that patients with a history of C. difficile have less risk of developing prostate cancer (OR 0.425, 95% CI: 0.382 - 0.472). Treatment bias was considered, the data was analyzed and resulted in two groups matched groups of 3,208 patients in both the infected with H. Influenzae treated group and the control who used the same medications for a different cause. Patients infected with H. Influenzae and treated had an incidence of prostate cancer of 2.49% whereas the control group incidence of prostate cancer was 4.92% with a p-value (< 2.2x10^-16) OR 0.455 CI 95% (0.526 -0.754), proving that the initial results were not due to the use of medications. Conclusion: The findings of our study reveal a statistically significant correlation between H. Influenzae infection and a decreased incidence of prostate cancer. Our findings suggest that prior infection with H. Influenzae may confer some degree of protection to patients and reduce their risk for developing prostate cancer. Future research is recommended to further characterize the potential role of Haemophilus influenzae in the pathogenesis of prostate cancer.

Keywords: Haemophilus Influenzae, incidence, prostate cancer, risk.

Procedia PDF Downloads 174

Authors: M. Sumana, K. S. Hareesha

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Secure computations are essential while performing privacy preserving data mining. Distributed privacy preserving data mining involve two to more sites that cannot pool in their data to a third party due to the violation of law regarding the individual. Hence in order to model the private data without compromising privacy and information loss, secure multiparty computations are used. Secure computations of product, mean, variance, dot product, sigmoid function using the additive and multiplicative homomorphic property is discussed. The computations are performed on vertically partitioned data with a single site holding the class value.

Keywords: homomorphic property, secure product, secure mean and variance, secure dot product, vertically partitioned data

Procedia PDF Downloads 393

Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

Procedia PDF Downloads 314

Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

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The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: emotion recognition, facial recognition, signal processing, machine learning

Procedia PDF Downloads 292

Authors: Sartaj Singh, Amar Singh, Ashok Sharma, Sandeep Kaur

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With upcoming threats in a digital world, we need to work continuously in the area of security in all aspects, from hardware to software as well as data modelling. The rise in social media activities and hunger for data by various entities leads to cybercrime and more attack on the privacy and security of persons. Cryptography has always been employed to avoid access to important data by using many processes. Symmetric key and asymmetric key cryptography have been used for keeping data secrets at rest as well in transmission mode. Various cryptosystems have evolved from time to time to make the data more secure. In this research article, we are studying various cryptosystems in asymmetric cryptography and their application with usefulness, and much emphasis is given to Elliptic curve cryptography involving algebraic mathematics.

Keywords: cryptography, symmetric key cryptography, asymmetric key cryptography

Procedia PDF Downloads 94

Authors: Neda Navidi, Rene Jr. Landry

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Vehicle tracking and accident recognizing are considered by many industries like insurance and vehicle rental companies. The main goal of this paper is to detect the location of a car accident by combining different methods. The methods, which are considered in this paper, are Global Navigation Satellite Systems/Inertial Measurement Units (GNSS/IMU)-based navigation and vehicle accident detection algorithms. They are expressed by a set of raw measurements, which are obtained from a designed integrator black box using GNSS and inertial sensors. Another concern of this paper is the definition of accident detection algorithm based on its jerk to identify the position of that accident. In fact, the results convinced us that, even in GNSS blockage areas, the position of the accident could be detected by GNSS/INS integration with 50% improvement compared to GNSS stand alone.

Keywords: driver behavior monitoring, integration, IMU, GNSS, monitoring, tracking

Procedia PDF Downloads 198

Authors: Rong-Terng Juang

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Autonomous vehicles offer the possibility of significant benefits to social welfare. However, fully automated cars might not be going to happen in the near further. To speed the adoption of the self-driving technologies, many governments worldwide are passing laws requiring data recorders for the testing of autonomous vehicles. Currently, the self-driving vehicle, (e.g., shuttle bus) has to be monitored from a remote control center. When an autonomous vehicle encounters an unexpected driving environment, such as road construction or an obstruction, it should request assistance from a remote operator. Nevertheless, large amounts of data, including images, radar and lidar data, etc., have to be transmitted from the vehicle to the remote center. Therefore, this paper proposes a data compression method of in-vehicle networks for remote monitoring of autonomous vehicles. Firstly, the time-series data are rearranged into a multi-dimensional signal space. Upon the arrival, for controller area networks (CAN), the new data are mapped onto a time-data two-dimensional space associated with the specific CAN identity. Secondly, the data are sampled based on differential sampling. Finally, the whole set of data are encoded using existing algorithms such as Huffman, arithmetic and codebook encoding methods. To evaluate system performance, the proposed method was deployed on an in-house built autonomous vehicle. The testing results show that the amount of data can be reduced as much as 1/7 compared to the raw data.

Keywords: autonomous vehicle, data compression, remote monitoring, controller area networks (CAN), Lidar

Procedia PDF Downloads 135

Authors: Davie Hope Moyo

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The environment is a combination of two things: resources and hazards. One of the hazards that is a result of environmental changes is the occurrence of flooding. Floods are one of the disasters that are highly feared by people because they have a huge impact on the human population and their environment. In recent years, flooding disasters in the Nsanje district are increasing in both frequency and magnitude. This study aims to understand the root causes of this phenomenon. To understand the causes of flooding, this study focused on the case of TA Ndamera in the Nsanje district, southern Malawi. People in the Nsanje district face disruption in their day-to-day life because of floods that affect their communities. When floods happen, people lose their property, land, livestock, and even lives. The study was carried out in order to have a better understanding of the root causes of floods. The findings of this study may help the government and other development agencies to put in place mitigation measures that will make Nsanje District resilient to the occurrence of future flood hazards. Data was collected from the area of TA Ndamera in order to assess the causes of flooding in the district. Interviews, transect walks, and researcher observation was done to appreciate the topography of the district and evaluate other factors that are making the people become vulnerable to the impacts of flooding in the district. It was found that flooding in the district is mainly caused by heavy rainfall in the upper shire, settlements along river banks, deforestation, and the topography of the district in general. The research study ends by providing recommendation strategies that need to be put in place to increase the resilience of the communities to future flood hazards. The research recommends the development of indigenous knowledge systems to alert people of incoming floods, construction of evacuation centers to ease pressure on schools, savings, and insurance schemes, construction of dykes, desilting rivers, and afforestation.

Keywords: disaster causes, mitigation, safety measures, Nsanje Malawi

Procedia PDF Downloads 51

Authors: Samar M. Alqhtani, Suhuai Luo, Brian Regan

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Data fusion technology can be the best way to extract useful information from multiple sources of data. It has been widely applied in various applications. This paper presents a data fusion approach in multimedia data for event detection in twitter by using Dempster-Shafer evidence theory. The methodology applies a mining algorithm to detect the event. There are two types of data in the fusion. The first is features extracted from text by using the bag-ofwords method which is calculated using the term frequency-inverse document frequency (TF-IDF). The second is the visual features extracted by applying scale-invariant feature transform (SIFT). The Dempster - Shafer theory of evidence is applied in order to fuse the information from these two sources. Our experiments have indicated that comparing to the approaches using individual data source, the proposed data fusion approach can increase the prediction accuracy for event detection. The experimental result showed that the proposed method achieved a high accuracy of 0.97, comparing with 0.93 with texts only, and 0.86 with images only.

Keywords: data fusion, Dempster-Shafer theory, data mining, event detection

Procedia PDF Downloads 380

Authors: Ioannis Iglezakis, Theodoros D. Trokanas, Panagiota Kiortsi

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This paper aims to explore major legal issues arising from the collection and processing of Health Big Data in the light of the new European secondary legislation for the protection of personal data of natural persons, placing emphasis on the General Data Protection Regulation 679/2016. Whether Big Health Data can be characterised as ‘personal data’ or not is really the crux of the matter. The legal ambiguity is compounded by the fact that, even though the processing of Big Health Data is premised on the de-identification of the data subject, the possibility of a combination of Big Health Data with other data circulating freely on the web or from other data files cannot be excluded. Another key point is that the application of some provisions of GPDR to Big Health Data may both absolve the data controller of his legal obligations and deprive the data subject of his rights (e.g., the right to be informed), ultimately undermining the fundamental right to the protection of personal data of natural persons. Moreover, data subject’s rights (e.g., the right not to be subject to a decision based solely on automated processing) are heavily impacted by the use of AI, algorithms, and technologies that reclaim health data for further use, resulting in sometimes ambiguous results that have a substantial impact on individuals. On the other hand, as the COVID-19 pandemic has revealed, Big Data analytics can offer crucial sources of information. In this respect, this paper identifies and systematises the legal provisions concerned, offering interpretative solutions that tackle dangers concerning data subject’s rights while embracing the opportunities that Big Health Data has to offer. In addition, particular attention is attached to the scope of ‘consent’ as a legal basis in the collection and processing of Big Health Data, as the application of data analytics in Big Health Data signals the construction of new data and subject’s profiles. Finally, the paper addresses the knotty problem of role assignment (i.e., distinguishing between controller and processor/joint controllers and joint processors) in an era of extensive Big Health data sharing. The findings are the fruit of a current research project conducted by a three-member research team at the Faculty of Law of the Aristotle University of Thessaloniki and funded by the Greek Ministry of Education and Religious Affairs.

Keywords: big health data, data subject rights, GDPR, pandemic

Procedia PDF Downloads 107

Authors: Yong-Kyu Jung

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The fast growth in information technology has led to de-mands to access/process data. CPSs heavily depend on the time of hardware/software operations and communication over the network (i.e., real-time/parallel operations in CPSs (e.g., autonomous vehicles). Since data processing is an im-portant means to overcome the issue confronting data management, reducing the gap between the technological-growth and the data-complexity and channel-bandwidth. An adaptive perpetual data approximation method is intro-duced to manage the actual entropy of the digital spectrum. An ADAC implemented as an accelerator and/or apps for servers/smart-connected devices adaptively rescales digital contents (avg.62.8%), data processing/access time/energy, encryption/decryption overheads in AI/ML applications (facial ID/recognition).

Keywords: adaptive codec, AI, ML, HPC, cyber-physical, cybersecurity

Procedia PDF Downloads 54

Authors: Andreea Molnar, Amalia Ardeljan, Lexi Frankel, Marissa Dallara, Brittany Nagel, Omar Rashid

Abstract:

Background: Escherichia coli is a gram-negative, facultative anaerobic bacteria that belongs to the family Enterobacteriaceae and resides in the intestinal tracts of individuals. E.Coli has numerous strains grouped into serogroups and serotypes based on differences in antigens in their cell walls (somatic, or “O” antigens) and flagella (“H” antigens). More than 700 serotypes of E. coli have been identified. Although most strains of E. coli are harmless, a few strains, such as E. coli O157:H7 which produces Shiga toxin, can cause intestinal infection with symptoms of severe abdominal cramps, bloody diarrhea, and vomiting. Infection with E. Coli can lead to the development of systemic inflammation as the toxin exerts its effects. Chronic inflammation is now known to contribute to cancer development in several organs, including the prostate. The purpose of this study was to evaluate the correlation between E. Coli and the incidence of prostate cancer. Methods: Data collected in this cohort study was provided by a Health Insurance Portability and Accountability Act (HIPAA) compliant national database to evaluate patients infected with E.Coli infection and prostate cancer using the International Classification of Disease (ICD-10 and ICD-9 codes). Permission to use the database was granted by Holy Cross Health, Fort Lauderdale for the purpose of academic research. Data analysis was conducted through the use of standard statistical methods. Results: Between January 2010 and December 2019, the query was analyzed and resulted in 81, 037 patients after matching in both infected and control groups, respectively. The two groups were matched by Age Range and CCI score. The incidence of prostate cancer was 2.07% and 1,680 patients in the E. Coli group compared to 5.19% and 4,206 patients in the control group. The difference was statistically significant by a p-value p<2.2x10-16 with an Odds Ratio of 0.53 and a 95% CI. Based on the specific treatment for E.Coli, the infected group vs control group were matched again with a result of 31,696 patients in each group. 827 out of 31,696 (2.60%) patients with a prior E.coli infection and treated with antibiotics were compared to 1634 out of 31,696 (5.15%) patients with no history of E.coli infection (control) and received antibiotic treatment. Both populations subsequently developed prostate carcinoma. Results remained statistically significant (p<2.2x10-16), Odds Ratio=0.55 (95% CI 0.51-0.59). Conclusion: This retrospective study shows a statistically significant correlation between E.Coli infection and a decreased incidence of prostate cancer. Further evaluation is needed in order to identify the impact of E.Coli infection and prostate cancer development.

Keywords: E. Coli, prostate cancer, protective, microbiology

Procedia PDF Downloads 192

Authors: Sašo Pečnik, Borut Žalik

Abstract:

This paper presents a real-time visualization technique and filtering of classified LiDAR point clouds. The visualization is capable of displaying filtered information organized in layers by the classification attribute saved within LiDAR data sets. We explain the used data structure and data management, which enables real-time presentation of layered LiDAR data. Real-time visualization is achieved with LOD optimization based on the distance from the observer without loss of quality. The filtering process is done in two steps and is entirely executed on the GPU and implemented using programmable shaders.

Keywords: filtering, graphics, level-of-details, LiDAR, real-time visualization

Procedia PDF Downloads 277