Search results for: cloud data privacy and integrity
23868 Channels Splitting Strategy for Optical Local Area Networks of Passive Star Topology
Authors: Peristera Baziana
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In this paper, we present a network configuration for a WDM LANs of passive star topology that assume that the set of data WDM channels is split into two separate sets of channels, with different access rights over them. Especially, a synchronous transmission WDMA access algorithm is adopted in order to increase the probability of successful transmission over the data channels and consequently to reduce the probability of data packets transmission cancellation in order to avoid the data channels collisions. Thus, a control pre-transmission access scheme is followed over a separate control channel. An analytical Markovian model is studied and the average throughput is mathematically derived. The performance is studied for several numbers of data channels and various values of control phase duration.Keywords: access algorithm, channels division, collisions avoidance, wavelength division multiplexing
Procedia PDF Downloads 28223867 Analyzing Tools and Techniques for Classification In Educational Data Mining: A Survey
Authors: D. I. George Amalarethinam, A. Emima
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Educational Data Mining (EDM) is one of the newest topics to emerge in recent years, and it is concerned with developing methods for analyzing various types of data gathered from the educational circle. EDM methods and techniques with machine learning algorithms are used to extract meaningful and usable information from huge databases. For scientists and researchers, realistic applications of Machine Learning in the EDM sectors offer new frontiers and present new problems. One of the most important research areas in EDM is predicting student success. The prediction algorithms and techniques must be developed to forecast students' performance, which aids the tutor, institution to boost the level of student’s performance. This paper examines various classification techniques in prediction methods and data mining tools used in EDM.Keywords: classification technique, data mining, EDM methods, prediction methods
Procedia PDF Downloads 10923866 Digital Transformation and Digitalization of Public Administration
Authors: Govind Kumar
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The concept of ‘e-governance’ that was brought about by the new wave of reforms, namely ‘LPG’ in the early 1990s, has been enabling governments across the globe to digitally transform themselves. Digital transformation is leading the governments with qualitative decisions, optimization in rational use of resources, facilitation of cost-benefit analyses, and elimination of redundancy and corruption with the help of ICT-based applications interface. ICT-based applications/technologies have enormous potential for impacting positive change in the social lives of the global citizenry. Supercomputers test and analyze millions of drug molecules for developing candidate vaccines to combat the global pandemic. Further, e-commerce portals help distribute and supply household items and medicines, while videoconferencing tools provide a visual interface between the clients and hosts. Besides, crop yields are being maximized with the help of drones and machine learning, whereas satellite data, artificial intelligence, and cloud computing help governments with the detection of illegal mining, tackling deforestation, and managing freshwater resources. Such e-applications have the potential to take governance an extra mile by achieving 5 Es (effective, efficient, easy, empower, and equity) of e-governance and six Rs (reduce, reuse, recycle, recover, redesign and remanufacture) of sustainable development. If such digital transformation gains traction within the government framework, it will replace the traditional administration with the digitalization of public administration. On the other hand, it has brought in a new set of challenges, like the digital divide, e-illiteracy, technological divide, etc., and problems like handling e-waste, technological obsolescence, cyber terrorism, e-fraud, hacking, phishing, etc. before the governments. Therefore, it would be essential to bring in a rightful mixture of technological and humanistic interventions for addressing the above issues. This is on account of the reason that technology lacks an emotional quotient, and the administration does not work like technology. Both are self-effacing unless a blend of technology and a humane face are brought in into the administration. The paper will empirically analyze the significance of the technological framework of digital transformation within the government set up for the digitalization of public administration on the basis of the synthesis of two case studies undertaken from two diverse fields of administration and present a future framework of the study.Keywords: digital transformation, electronic governance, public administration, knowledge framework
Procedia PDF Downloads 8623865 Assessing Sexual and Reproductive Health Literacy and Engagement Among Refugee and Immigrant Women in Massachusetts: A Qualitative Community-Based Study
Authors: Leen Al Kassab, Sarah Johns, Helen Noble, Nawal Nour, Elizabeth Janiak, Sarrah Shahawy
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Introduction: Immigrant and refugee women experience disparities in sexual and reproductive health (SRH) outcomes, partially as a result of barriers to SRH literacy and to regular healthcare access and engagement. Despite the existing data highlighting growing needs for culturally relevant and structurally competent care, interventions are scarce and not well-documented. Methods: In this IRB-approved study, we used a community-based participatory research approach, with the assistance of a community advisory board, to conduct a qualitative needs assessment of SRH knowledge and service engagement with immigrant and refugee women from Africa or the Middle East and currently residing in Boston. We conducted a total of nine focus group discussions (FGDs) in partnership with medical, community, and religious centers, in six languages: Arabic, English, French, Somali, Pashtu, and Dari. A total of 44 individuals participated. We explored migrant and refugee women’s current and evolving SRH care needs and gaps, specifically related to the development of interventions and clinical best practices targeting SRH literacy, healthcare engagement, and informed decision-making. Recordings of the FGDs were transcribed verbatim and translated by interpreter services. We used open coding with multiple coders who resolved discrepancies through consensus and iteratively refined our codebook while coding data in batches using Dedoose software. Results: Participants reported immigrant adaptation experiences, discrimination, and feelings of trust, autonomy, privacy, and connectedness to family, community, and the healthcare system as factors surrounding SRH knowledge and needs. The context of previously learned SRH knowledge was commonly noted to be in schools, at menstruation, before marriage, from family members, partners, friends, and online search engines. Common themes included empowering strength drawn from religious and cultural communities, difficulties bridging educational gaps with their US- born daughters, and a desire for more SRH education from multiple sources, including family, health care providers, and religious experts & communities. Regarding further SRH education, participants’ preferences varied regarding ideal platform (virtual vs. in-person), location (in religious and community centers or not), smaller group sizes, and the involvement of men. Conclusions: Based on these results, empowering SRH initiatives should include both community and religious center-based, as well as clinic-based, interventions. Interventions should be composed of frequent educational workshops in small groups involving age-grouped women, daughters, and (sometimes) men, tailored SRH messaging, and the promotion of culturally, religiously, and linguistically competent care.Keywords: community, immigrant, religion, sexual & reproductive health, women's health
Procedia PDF Downloads 11523864 A Concept of Data Mining with XML Document
Authors: Akshay Agrawal, Anand K. Srivastava
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The increasing amount of XML datasets available to casual users increases the necessity of investigating techniques to extract knowledge from these data. Data mining is widely applied in the database research area in order to extract frequent correlations of values from both structured and semi-structured datasets. The increasing availability of heterogeneous XML sources has raised a number of issues concerning how to represent and manage these semi structured data. In recent years due to the importance of managing these resources and extracting knowledge from them, lots of methods have been proposed in order to represent and cluster them in different ways.Keywords: XML, similarity measure, clustering, cluster quality, semantic clustering
Procedia PDF Downloads 36623863 Speed-Up Data Transmission by Using Bluetooth Module on Gas Sensor Node of Arduino Board
Authors: Hiesik Kim, YongBeum Kim
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Internet of Things (IoT) applications are widely serviced and spread worldwide. Local wireless data transmission technique must be developed to speed up with some technique. Bluetooth wireless data communication is wireless technique is technique made by Special Inter Group(SIG) using the frequency range 2.4 GHz, and it is exploiting Frequency Hopping to avoid collision with different device. To implement experiment, equipment for experiment transmitting measured data is made by using Arduino as Open source hardware, Gas sensor, and Bluetooth Module and algorithm controlling transmission speed is demonstrated. Experiment controlling transmission speed also is progressed by developing Android Application receiving measured data, and controlling this speed is available at the experiment result. it is important that in the future, improvement for communication algorithm be needed because few error occurs when data is transferred or received.Keywords: Arduino, Bluetooth, gas sensor, internet of things, transmission Speed
Procedia PDF Downloads 47523862 Evaluating the Total Costs of a Ransomware-Resilient Architecture for Healthcare Systems
Authors: Sreejith Gopinath, Aspen Olmsted
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This paper is based on our previous work that proposed a risk-transference-based architecture for healthcare systems to store sensitive data outside the system boundary, rendering the system unattractive to would-be bad actors. This architecture also allows a compromised system to be abandoned and a new system instance spun up in place to ensure business continuity without paying a ransom or engaging with a bad actor. This paper delves into the details of various attacks we simulated against the prototype system. In the paper, we discuss at length the time and computational costs associated with storing and retrieving data in the prototype system, abandoning a compromised system, and setting up a new instance with existing data. Lastly, we simulate some analytical workloads over the data stored in our specialized data storage system and discuss the time and computational costs associated with running analytics over data in a specialized storage system outside the system boundary. In summary, this paper discusses the total costs of data storage, access, and analytics incurred with the proposed architecture.Keywords: cybersecurity, healthcare, ransomware, resilience, risk transference
Procedia PDF Downloads 12723861 System for the Detecting of Fake Profiles on Online Social Networks Using Machine Learning and the Bio-Inspired Algorithms
Authors: Sekkal Nawel, Mahammed Nadir
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The proliferation of online activities on Online Social Networks (OSNs) has captured significant user attention. However, this growth has been hindered by the emergence of fraudulent accounts that do not represent real individuals and violate privacy regulations within social network communities. Consequently, it is imperative to identify and remove these profiles to enhance the security of OSN users. In recent years, researchers have turned to machine learning (ML) to develop strategies and methods to tackle this issue. Numerous studies have been conducted in this field to compare various ML-based techniques. However, the existing literature still lacks a comprehensive examination, especially considering different OSN platforms. Additionally, the utilization of bio-inspired algorithms has been largely overlooked. Our study conducts an extensive comparison analysis of various fake profile detection techniques in online social networks. The results of our study indicate that supervised models, along with other machine learning techniques, as well as unsupervised models, are effective for detecting false profiles in social media. To achieve optimal results, we have incorporated six bio-inspired algorithms to enhance the performance of fake profile identification results.Keywords: machine learning, bio-inspired algorithm, detection, fake profile, system, social network
Procedia PDF Downloads 5723860 Exploring the Capabilities of Sentinel-1A and Sentinel-2A Data for Landslide Mapping
Authors: Ismayanti Magfirah, Sartohadi Junun, Samodra Guruh
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Landslides are one of the most frequent and devastating natural disasters in Indonesia. Many studies have been conducted regarding this phenomenon. However, there is a lack of attention in the landslide inventory mapping. The natural condition (dense forest area) and the limited human and economic resources are some of the major problems in building landslide inventory in Indonesia. Considering the importance of landslide inventory data in susceptibility, hazard, and risk analysis, it is essential to generate landslide inventory based on available resources. In order to achieve this, the first thing we have to do is identify the landslides' location. The presence of Sentinel-1A and Sentinel-2A data gives new insights into land monitoring investigation. The free access, high spatial resolution, and short revisit time, make the data become one of the most trending open sources data used in landslide mapping. Sentinel-1A and Sentinel-2A data have been used broadly for landslide detection and landuse/landcover mapping. This study aims to generate landslide map by integrating Sentinel-1A and Sentinel-2A data use change detection method. The result will be validated by field investigation to make preliminary landslide inventory in the study area.Keywords: change detection method, landslide inventory mapping, Sentinel-1A, Sentinel-2A
Procedia PDF Downloads 15623859 A DEA Model in a Multi-Objective Optimization with Fuzzy Environment
Authors: Michael Gidey Gebru
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Most DEA models operate in a static environment with input and output parameters that are chosen by deterministic data. However, due to ambiguity brought on shifting market conditions, input and output data are not always precisely gathered in real-world scenarios. Fuzzy numbers can be used to address this kind of ambiguity in input and output data. Therefore, this work aims to expand crisp DEA into DEA with fuzzy environment. In this study, the input and output data are regarded as fuzzy triangular numbers. Then, the DEA model with fuzzy environment is solved using a multi-objective method to gauge the Decision Making Units’ efficiency. Finally, the developed DEA model is illustrated with an application on real data 50 educational institutions.Keywords: efficiency, DEA, fuzzy, decision making units, higher education institutions
Procedia PDF Downloads 3323858 Culture Dimensions of Information Systems Security in Saudi Arabia National Health Services
Authors: Saleh Alumaran, Giampaolo Bella, Feng Chen
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The study of organisations’ information security cultures has attracted scholars as well as healthcare services industry to research the topic and find appropriate tools and approaches to develop a positive culture. The vast majority of studies in Saudi national health services are on the use of technology to protect and secure health services information. On the other hand, there is a lack of research on the role and impact of an organisation’s cultural dimensions on information security. This research investigated and analysed the role and impact of cultural dimensions on information security in Saudi Arabia health service. Hypotheses were tested and two surveys were carried out in order to collect data and information from three major hospitals in Saudi Arabia (SA). The first survey identified the main cultural-dimension problems in SA health services and developed an initial information security culture framework model. The second survey evaluated and tested the developed framework model to test its usefulness, reliability and applicability. The model is based on human behaviour theory, where the individual’s attitude is the key element of the individual’s intention to behave as well as of his or her actual behaviour. The research identified six cultural dimensions: Saudi national culture, Saudi health service leadership, employees’ trust, technology, multicultural interactions and employees’ job roles. The research also identified a set of cultural sub-dimensions. These include working values and norms, tribe values and norms, attitudes towards women, power sharing, vision, social interaction, respect and understanding, hospital intra-net, hospital employees’ language(s) used, multi-national culture, communication system, employees’ job satisfaction and job security. The research identified that (a) the human behaviour towards medical information in SA is one of the main threats to information security and one of the main challenges to SA health authority, (b) The current situation of SA hospitals’ IS cultures is falling short in protecting medical information due to the current value and norms towards information security, (c) Saudi national culture and employees’ job role are the main dimensions playing major roles in the employees’ attitude, and technology is the least important dimension playing a role in the employees’ attitudes.Keywords: cultural dimension, electronic health record, information security, privacy
Procedia PDF Downloads 34223857 Modeling the Elastic Mean Free Path of Electron Collision with Pyrimidine: The Screen Corrected Additivity Rule Method
Authors: Aouina Nabila Yasmina, Chaoui Zine El Abiddine
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This study presents a comprehensive investigation into the elastic mean free path (EMFP) of electrons colliding with pyrimidine, a precursor to the pyrimidine bases in DNA, employing the Screen Corrected Additivity Rule (SCAR) method. The SCAR method is introduced as a novel approach that combines classical and quantum mechanical principles to elucidate the interaction of electrons with pyrimidine. One of the most fundamental properties characterizing the propagation of a particle in the nuclear medium is its mean free path. Knowledge of the elastic mean free path is essential to accurately predict the effects of radiation on biological matter, as it contributes to the distances between collisions. Additionally, the mean free path plays a role in the interpretation of almost all experiments in which an excited electron moves through a solid. Pyrimidine, the precursor of the pyrimidine bases of DNA, has interesting physicochemical properties, which make it an interesting molecule to study from a fundamental point of view. These include a relatively large dipole polarizability and dipole moment and an electronic charge cloud with a significant spatial extension, which justifies its choice in this present study.Keywords: elastic mean free path, elastic collision, pyrimidine, SCAR
Procedia PDF Downloads 5623856 The Perceptions of Patients with Osteoarthritis at a Public Community Rehabilitation Centre in the Cape Metropole for Using Digital Technology in Rehabilitation
Authors: Gabriela Prins, Quinette Louw, Dawn Ernstzen
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Background: Access to rehabilitation services is a major challenge globally, especially in low-and-middle income countries (LMICs) where resources and infrastructure are extremely limited. Telerehabilitation (TR) has emerged in recent decades as a highly promising method to dramatically expand accessibility to rehabilitation services globally. TR provides rehabilitation care remotely using communication technologies such as video conferencing, smartphones, and internet-connected devices. This boosts accessibility to underprivileged regions and allows for greater flexibility for patients. Despite this, TR is hindered by several factors, including limited technological resources, high costs, lack of digital access, and the unavailability of healthcare systems, which are major barriers to widespread adoption among LMIC patients. These barriers have collectively hindered the potential implementation and adoption of TR services in LMICs healthcare settings. Adoption of TR will also require the buy-in of end users and limited information is known on the perspectives of the SA population. Aim: The study aimed to understand patients' perspectives regarding the use of digital technology as part of their OA rehabilitation at a public community healthcare centre in the Cape Metropole Area. Methods: A qualitative descriptive study design was used on 10 OA patients from a public community rehabilitation centre in South Africa. Data collection included semi-structured interviews and patient-reported outcome measures (PSFS, ASES-8, and EuroQol EQ-5D-5L) on functioning and quality of life. Transcribed interview data were coded in Atlas.ti. 22.2 and analyzed using thematic analysis. The results were narratively documented. Results: Four themes arose from the interviews. The themes were Telerehabilitation awareness (Use of Digital Technology Information Sources and Prior Experience with Technology /TR), Telerehabilitation Benefits (Access to healthcare providers, Access to educational information, Convenience, Time and Resource Efficiency and Facilitating Family Involvement), Telerehabilitation Implementation Considerations (Openness towards TR Implementation, Learning about TR and Technology, Therapeutic relationship, and Privacy) and Future use of Telerehabilitation (Personal Preference and TR for the next generation). The ten participants demonstrated limited awareness and exposure to TR, as well as minimal digital literacy and skills. Skepticism was shown when comparing the effectiveness of TR to in-person rehabilitation and valued physical interactions with health professionals. However, some recognized potential benefits of TR for accessibility, convenience, family involvement and improving community health in the long term. Willingness existed to try TR with sufficient training. Conclusion: With targeted efforts addressing identified barriers around awareness, technological literacy, clinician readiness and resource availability, perspectives on TR may shift positively from uncertainty towards endorsement of this expanding approach for simpler rehabilitation access in LMICs.Keywords: digital technology, osteoarthritis, primary health care, telerehabilitation
Procedia PDF Downloads 6123855 Internal Product Management: The Key to Achieving Digital Maturity and Business Agility for Manufacturing IT Organizations
Authors: Frederick Johnson
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Product management has a long and well-established history within the consumer goods industry, despite being one of the most obscure aspects of brand management. Many global manufacturing organizations are now opting for external cloud-based Manufacturing Execution Systems (MES) to replace costly and outdated monolithic MES solutions. Other global manufacturing leaders are restructuring their organizations to support human-centered values, agile methodologies, and fluid operating principles. Still, industry-leading organizations struggle to apply the appropriate framework for managing evolving external MES solutions as internal "digital products." Product management complements these current trends in technology and philosophical thinking in the market. This paper discusses the central problems associated with adopting product management processes by analyzing its traditional theories and characteristics. Considering these ideas, the article then constructs a translated internal digital product management framework by combining new and existing approaches and principles. The report concludes by demonstrating the framework's capabilities and potential effectiveness in achieving digital maturity and business agility within a manufacturing environment.Keywords: internal product management, digital transformation, manufacturing information technology, manufacturing execution systems
Procedia PDF Downloads 12423854 Data-Driven Decision Making: Justification of Not Leaving Class without It
Authors: Denise Hexom, Judith Menoher
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Teachers and administrators across America are being asked to use data and hard evidence to inform practice as they begin the task of implementing Common Core State Standards. Yet, the courses they are taking in schools of education are not preparing teachers or principals to understand the data-driven decision making (DDDM) process nor to utilize data in a much more sophisticated fashion. DDDM has been around for quite some time, however, it has only recently become systematically and consistently applied in the field of education. This paper discusses the theoretical framework of DDDM; empirical evidence supporting the effectiveness of DDDM; a process a department in a school of education has utilized to implement DDDM; and recommendations to other schools of education who attempt to implement DDDM in their decision-making processes and in their students’ coursework.Keywords: data-driven decision making, institute of higher education, special education, continuous improvement
Procedia PDF Downloads 37623853 Quantile Coherence Analysis: Application to Precipitation Data
Authors: Yaeji Lim, Hee-Seok Oh
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The coherence analysis measures the linear time-invariant relationship between two data sets and has been studied various fields such as signal processing, engineering, and medical science. However classical coherence analysis tends to be sensitive to outliers and focuses only on mean relationship. In this paper, we generalized cross periodogram to quantile cross periodogram and provide richer inter-relationship between two data sets. This is a general version of Laplace cross periodogram. We prove its asymptotic distribution under the long range process and compare them with ordinary coherence through numerical examples. We also present real data example to confirm the usefulness of quantile coherence analysis.Keywords: coherence, cross periodogram, spectrum, quantile
Procedia PDF Downloads 37923852 Conception of a Predictive Maintenance System for Forest Harvesters from Multiple Data Sources
Authors: Lazlo Fauth, Andreas Ligocki
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For cost-effective use of harvesters, expensive repairs and unplanned downtimes must be reduced as far as possible. The predictive detection of failing systems and the calculation of intelligent service intervals, necessary to avoid these factors, require in-depth knowledge of the machines' behavior. Such know-how needs permanent monitoring of the machine state from different technical perspectives. In this paper, three approaches will be presented as they are currently pursued in the publicly funded project PreForst at Ostfalia University of Applied Sciences. These include the intelligent linking of workshop and service data, sensors on the harvester, and a special online hydraulic oil condition monitoring system. Furthermore the paper shows potentials as well as challenges for the use of these data in the conception of a predictive maintenance system.Keywords: predictive maintenance, condition monitoring, forest harvesting, forest engineering, oil data, hydraulic data
Procedia PDF Downloads 12623851 Assessment of Causes of Building Collapse in Nigeria
Authors: Olufemi Oyedele
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Building collapse (BC) in Nigeria is becoming a regular occurrence, each recording great casualties in the number of lives and materials lost. Building collapse is a situation where building which has been completed and occupied, completed but not occupied or under construction, collapses on its own due to action or inaction of man or due to natural event like earthquake, storm, flooding, tsunami or wildfire. It is different from building demolition. There are various causes of building collapse and each case requires expert judgment to decide the cause of its collapse. Rate of building collapse is a reflection of the level of organization and control of building activities and degree of sophistication of the construction professionals in a country. This study explored the use of case study by examining the causes of six (6) collapsed buildings (CB) across Nigeria. Samples of materials from the sites of the collapsed buildings were taken for testing and analysis, while critical observations were made at the sites to note the conditions of the ground (building base). The study found out that majority of the building collapses in Nigeria were due to poor workmanship, sub-standard building materials, followed by bad building base and poor design. The National Building Code 2006 is not effective due to lack of enforcement and the Physical Development Departments of states and Federal Capital Territory are just mere agents of corruption allowing all types of construction without building approvals.Keywords: building collapse, concrete tests, differential settlement, integrity test, quality control
Procedia PDF Downloads 51723850 Sampled-Data Control for Fuel Cell Systems
Authors: H. Y. Jung, Ju H. Park, S. M. Lee
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A sampled-data controller is presented for solid oxide fuel cell systems which is expressed by a sector bounded nonlinear model. The sector bounded nonlinear systems, which have a feedback connection with a linear dynamical system and nonlinearity satisfying certain sector type constraints. Also, the sampled-data control scheme is very useful since it is possible to handle digital controller and increasing research efforts have been devoted to sampled-data control systems with the development of modern high-speed computers. The proposed control law is obtained by solving a convex problem satisfying several linear matrix inequalities. Simulation results are given to show the effectiveness of the proposed design method.Keywords: sampled-data control, fuel cell, linear matrix inequalities, nonlinear control
Procedia PDF Downloads 56023849 Technology for Enhancing the Learning and Teaching Experience in Higher Education
Authors: Sara M. Ismael, Ali H. Al-Badi
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The rapid development and growth of technology has changed the method of obtaining information for educators and learners. Technology has created a new world of collaboration and communication among people. Incorporating new technology into the teaching process can enhance learning outcomes. Billions of individuals across the world are now connected together, and are cooperating and contributing their knowledge and intelligence. Time is no longer wasted in waiting until the teacher is ready to share information as learners can go online and get it immediately. The objectives of this paper are to understand the reasons why changes in teaching and learning methods are necessary, to find ways of improving them, and to investigate the challenges that present themselves in the adoption of new ICT tools in higher education institutes. To achieve these objectives two primary research methods were used: questionnaires, which were distributed among students at higher educational institutes and multiple interviews with faculty members (teachers) from different colleges and universities, which were conducted to find out why teaching and learning methodology should change. The findings show that both learners and educators agree that educational technology plays a significant role in enhancing instructors’ teaching style and students’ overall learning experience; however, time constraints, privacy issues, and not being provided with enough up-to-date technology do create some challenges.Keywords: e-books, educational technology, educators, e-learning, learners, social media, Web 2.0, LMS
Procedia PDF Downloads 25823848 Treating Voxels as Words: Word-to-Vector Methods for fMRI Meta-Analyses
Authors: Matthew Baucum
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With the increasing popularity of fMRI as an experimental method, psychology and neuroscience can greatly benefit from advanced techniques for summarizing and synthesizing large amounts of data from brain imaging studies. One promising avenue is automated meta-analyses, in which natural language processing methods are used to identify the brain regions consistently associated with certain semantic concepts (e.g. “social”, “reward’) across large corpora of studies. This study builds on this approach by demonstrating how, in fMRI meta-analyses, individual voxels can be treated as vectors in a semantic space and evaluated for their “proximity” to terms of interest. In this technique, a low-dimensional semantic space is built from brain imaging study texts, allowing words in each text to be represented as vectors (where words that frequently appear together are near each other in the semantic space). Consequently, each voxel in a brain mask can be represented as a normalized vector sum of all of the words in the studies that showed activation in that voxel. The entire brain mask can then be visualized in terms of each voxel’s proximity to a given term of interest (e.g., “vision”, “decision making”) or collection of terms (e.g., “theory of mind”, “social”, “agent”), as measured by the cosine similarity between the voxel’s vector and the term vector (or the average of multiple term vectors). Analysis can also proceed in the opposite direction, allowing word cloud visualizations of the nearest semantic neighbors for a given brain region. This approach allows for continuous, fine-grained metrics of voxel-term associations, and relies on state-of-the-art “open vocabulary” methods that go beyond mere word-counts. An analysis of over 11,000 neuroimaging studies from an existing meta-analytic fMRI database demonstrates that this technique can be used to recover known neural bases for multiple psychological functions, suggesting this method’s utility for efficient, high-level meta-analyses of localized brain function. While automated text analytic methods are no replacement for deliberate, manual meta-analyses, they seem to show promise for the efficient aggregation of large bodies of scientific knowledge, at least on a relatively general level.Keywords: FMRI, machine learning, meta-analysis, text analysis
Procedia PDF Downloads 43623847 Detection of Powdery Mildew Disease in Strawberry Using Image Texture and Supervised Classifiers
Authors: Sultan Mahmud, Qamar Zaman, Travis Esau, Young Chang
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Strawberry powdery mildew (PM) is a serious disease that has a significant impact on strawberry production. Field scouting is still a major way to find PM disease, which is not only labor intensive but also almost impossible to monitor disease severity. To reduce the loss caused by PM disease and achieve faster automatic detection of the disease, this paper proposes an approach for detection of the disease, based on image texture and classified with support vector machines (SVMs) and k-nearest neighbors (kNNs). The methodology of the proposed study is based on image processing which is composed of five main steps including image acquisition, pre-processing, segmentation, features extraction and classification. Two strawberry fields were used in this study. Images of healthy leaves and leaves infected with PM (Sphaerotheca macularis) disease under artificial cloud lighting condition. Colour thresholding was utilized to segment all images before textural analysis. Colour co-occurrence matrix (CCM) was introduced for extraction of textural features. Forty textural features, related to a physiological parameter of leaves were extracted from CCM of National television system committee (NTSC) luminance, hue, saturation and intensity (HSI) images. The normalized feature data were utilized for training and validation, respectively, using developed classifiers. The classifiers have experimented with internal, external and cross-validations. The best classifier was selected based on their performance and accuracy. Experimental results suggested that SVMs classifier showed 98.33%, 85.33%, 87.33%, 93.33% and 95.0% of accuracy on internal, external-I, external-II, 4-fold cross and 5-fold cross-validation, respectively. Whereas, kNNs results represented 90.0%, 72.00%, 74.66%, 89.33% and 90.3% of classification accuracy, respectively. The outcome of this study demonstrated that SVMs classified PM disease with a highest overall accuracy of 91.86% and 1.1211 seconds of processing time. Therefore, overall results concluded that the proposed study can significantly support an accurate and automatic identification and recognition of strawberry PM disease with SVMs classifier.Keywords: powdery mildew, image processing, textural analysis, color co-occurrence matrix, support vector machines, k-nearest neighbors
Procedia PDF Downloads 11123846 How Western Donors Allocate Official Development Assistance: New Evidence From a Natural Language Processing Approach
Authors: Daniel Benson, Yundan Gong, Hannah Kirk
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Advancement in national language processing techniques has led to increased data processing speeds, and reduced the need for cumbersome, manual data processing that is often required when processing data from multilateral organizations for specific purposes. As such, using named entity recognition (NER) modeling and the Organisation of Economically Developed Countries (OECD) Creditor Reporting System database, we present the first geotagged dataset of OECD donor Official Development Assistance (ODA) projects on a global, subnational basis. Our resulting data contains 52,086 ODA projects geocoded to subnational locations across 115 countries, worth a combined $87.9bn. This represents the first global, OECD donor ODA project database with geocoded projects. We use this new data to revisit old questions of how ‘well’ donors allocate ODA to the developing world. This understanding is imperative for policymakers seeking to improve ODA effectiveness.Keywords: international aid, geocoding, subnational data, natural language processing, machine learning
Procedia PDF Downloads 5923845 Fish Oil and Its Methyl Ester as an Alternate Fuel in the Direct Injection Diesel Engine
Authors: Pavan Pujar
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Mackerel Fish oil was used as the raw material to produce the biodiesel in this study. The raw oil (RO) was collected from discarded fish products. This oil was filtered and heated to 110oC and made it moisture free. The filtered and moisture free RO was transesterified to produce biodiesel. The experimental results showed that oleic acid and lauric acid were the two major components of the fish oil biodiesel (FOB). Palmitic acid and linoleic acid were found approximately same in the quantity. The fuel properties kinematic viscosity, flash point, fire point, specific gravity, calorific value, cetane number, density, acid value, saponification value, iodine value, cloud point, pour point, ash content, Cu strip corrosion, carbon residue, API gravity were determined for FOB. A comparative study of the properties was carried out with RO and Neat diesel (ND). It was found that Cetane number was 59 for FOB which was more than RO, which showed 57. Blends (B20, B40, B60, B80: example: B20: 20% FOB + 80% ND) of FOB and ND were prepared on volume basis and comparative study was carried out with ND and FOB. Performance parameters BSFE, BSEC, A:F Ratio, Break thermal efficiency were analyzed and it was found that complete replacement of neat diesel (ND) is possible without any engine modifications.Keywords: fish oil biodiesel, raw oil, blends, performance parameters
Procedia PDF Downloads 40623844 Compressed Suffix Arrays to Self-Indexes Based on Partitioned Elias-Fano
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A practical and simple self-indexing data structure, Partitioned Elias-Fano (PEF) - Compressed Suffix Arrays (CSA), is built in linear time for the CSA based on PEF indexes. Moreover, the PEF-CSA is compared with two classical compressed indexing methods, Ferragina and Manzini implementation (FMI) and Sad-CSA on different type and size files in Pizza & Chili. The PEF-CSA performs better on the existing data in terms of the compression ratio, count, and locates time except for the evenly distributed data such as proteins data. The observations of the experiments are that the distribution of the φ is more important than the alphabet size on the compression ratio. Unevenly distributed data φ makes better compression effect, and the larger the size of the hit counts, the longer the count and locate time.Keywords: compressed suffix array, self-indexing, partitioned Elias-Fano, PEF-CSA
Procedia PDF Downloads 24023843 Medical Ethics in the Hospital: Towards Quality Ethics Consultation
Authors: Dina Siniora, Jasia Baig
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During the past few decades, the healthcare system has undergone profound changes in their healthcare decision-making competencies and moral aptitudes due to the vast advancement in technology, clinical skills, and scientific knowledge. Healthcare decision-making deals with morally contentious dilemmas ranging from illness, life and death judgments that require sensitivity and awareness towards the patient’s preferences while taking into consideration medicine’s abilities and boundaries. As the ever-evolving field of medicine continues to become more scientifically and morally multifarious; physicians and the hospital administrators increasingly rely on ethics committees to resolve problems that arise in everyday patient care. The role and latitude of responsibilities of ethics committees which includes being dispute intermediaries, moral analysts, policy educators, counselors, advocates, and reviewers; suggest the importance and effectiveness of a fully integrated committee. Despite achievements on Integrated Ethics and progress in standards and competencies, there is an imminent necessity for further improvement in quality within ethics consultation services in areas of credentialing, professionalism and standards of quality, as well as the quality of healthcare throughout the system. These concerns can be resolved first by collecting data about particular quality gaps and comprehend the level to which ethics committees are consistent with newly published ASBH quality standards. Policymakers should pursue improvement strategies that target both academic bioethics community and major stakeholders at hospitals, who directly influence ethics committees. This broader approach oriented towards education and intervention outcome in conjunction with preventive ethics to address disparities in quality on a systematic level. Adopting tools for improving competencies and processes within ethics consultation by implementing a credentialing process, upholding normative significance for the ASBH core competencies, advocating for professional Code of Ethics, and further clarifying the internal structures will improve productivity, patient satisfaction, and institutional integrity. This cannot be systemically achieved without a written certification exam for HCEC practitioners, credentialing and privileging HCEC practitioners at the hospital level, and accrediting HCEC services at the institutional level.Keywords: ethics consultation, hospital, medical ethics, quality
Procedia PDF Downloads 17823842 Recommendations for Data Quality Filtering of Opportunistic Species Occurrence Data
Authors: Camille Van Eupen, Dirk Maes, Marc Herremans, Kristijn R. R. Swinnen, Ben Somers, Stijn Luca
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In ecology, species distribution models are commonly implemented to study species-environment relationships. These models increasingly rely on opportunistic citizen science data when high-quality species records collected through standardized recording protocols are unavailable. While these opportunistic data are abundant, uncertainty is usually high, e.g., due to observer effects or a lack of metadata. Data quality filtering is often used to reduce these types of uncertainty in an attempt to increase the value of studies relying on opportunistic data. However, filtering should not be performed blindly. In this study, recommendations are built for data quality filtering of opportunistic species occurrence data that are used as input for species distribution models. Using an extensive database of 5.7 million citizen science records from 255 species in Flanders, the impact on model performance was quantified by applying three data quality filters, and these results were linked to species traits. More specifically, presence records were filtered based on record attributes that provide information on the observation process or post-entry data validation, and changes in the area under the receiver operating characteristic (AUC), sensitivity, and specificity were analyzed using the Maxent algorithm with and without filtering. Controlling for sample size enabled us to study the combined impact of data quality filtering, i.e., the simultaneous impact of an increase in data quality and a decrease in sample size. Further, the variation among species in their response to data quality filtering was explored by clustering species based on four traits often related to data quality: commonness, popularity, difficulty, and body size. Findings show that model performance is affected by i) the quality of the filtered data, ii) the proportional reduction in sample size caused by filtering and the remaining absolute sample size, and iii) a species ‘quality profile’, resulting from a species classification based on the four traits related to data quality. The findings resulted in recommendations on when and how to filter volunteer generated and opportunistically collected data. This study confirms that correctly processed citizen science data can make a valuable contribution to ecological research and species conservation.Keywords: citizen science, data quality filtering, species distribution models, trait profiles
Procedia PDF Downloads 18723841 Data Quality Enhancement with String Length Distribution
Authors: Qi Xiu, Hiromu Hota, Yohsuke Ishii, Takuya Oda
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Recently, collectable manufacturing data are rapidly increasing. On the other hand, mega recall is getting serious as a social problem. Under such circumstances, there are increasing needs for preventing mega recalls by defect analysis such as root cause analysis and abnormal detection utilizing manufacturing data. However, the time to classify strings in manufacturing data by traditional method is too long to meet requirement of quick defect analysis. Therefore, we present String Length Distribution Classification method (SLDC) to correctly classify strings in a short time. This method learns character features, especially string length distribution from Product ID, Machine ID in BOM and asset list. By applying the proposal to strings in actual manufacturing data, we verified that the classification time of strings can be reduced by 80%. As a result, it can be estimated that the requirement of quick defect analysis can be fulfilled.Keywords: string classification, data quality, feature selection, probability distribution, string length
Procedia PDF Downloads 31023840 Determining Abnomal Behaviors in UAV Robots for Trajectory Control in Teleoperation
Authors: Kiwon Yeom
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Change points are abrupt variations in a data sequence. Detection of change points is useful in modeling, analyzing, and predicting time series in application areas such as robotics and teleoperation. In this paper, a change point is defined to be a discontinuity in one of its derivatives. This paper presents a reliable method for detecting discontinuities within a three-dimensional trajectory data. The problem of determining one or more discontinuities is considered in regular and irregular trajectory data from teleoperation. We examine the geometric detection algorithm and illustrate the use of the method on real data examples.Keywords: change point, discontinuity, teleoperation, abrupt variation
Procedia PDF Downloads 15523839 Multidimensional Item Response Theory Models for Practical Application in Large Tests Designed to Measure Multiple Constructs
Authors: Maria Fernanda Ordoñez Martinez, Alvaro Mauricio Montenegro
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This work presents a statistical methodology for measuring and founding constructs in Latent Semantic Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations present on Item Response Theory. More precisely, we propose initially reducing dimensionality with specific use of Principal Component Analysis for the linguistic data and then, producing axes of groups made from a clustering analysis of the semantic data. This approach allows the user to give meaning to previous clusters and found the real latent structure presented by data. The methodology is applied in a set of real semantic data presenting impressive results for the coherence, speed and precision.Keywords: semantic analysis, factorial analysis, dimension reduction, penalized logistic regression
Procedia PDF Downloads 428