Search results for: time-lapse imaging data

Authors: Ruchi Yadav, Shivani Pandey, Prachi Srivastava

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Microarrays provide a rich source of data on the molecular working of cells. Each microarray reports on the abundance of tens of thousands of mRNAs. Virtually every human disease is being studied using microarrays with the hope of finding the molecular mechanisms of disease. Bioinformatics analysis plays an important part of processing the information embedded in large-scale expression profiling studies and for laying the foundation for biological interpretation. A basic, yet challenging task in the analysis of microarray gene expression data is the identification of changes in gene expression that are associated with particular biological conditions. Careful statistical design and analysis are essential to improve the efficiency and reliability of microarray experiments throughout the data acquisition and analysis process. One of the most popular platforms for microarray analysis is Bioconductor, an open source and open development software project based on the R programming language. This paper describes specific procedures for conducting quality assessment, visualization and preprocessing of Affymetrix Gene Chip and also details the different bioconductor packages used to analyze affymetrix microarray data and describe the analysis and outcome of each plots.

Keywords: microarray analysis, R language, affymetrix visualization, bioconductor

Procedia PDF Downloads 478

Authors: Najrullah Khan, Athar Ali Khan

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The Topp-Leone distribution was introduced by Topp- Leone in 1955. In this paper, an attempt has been made to fit Topp-Leone Generalized exponential (TPGE) distribution. A real survival data set is used for illustrations. Implementation is done using R and JAGS and appropriate illustrations are made. R and JAGS codes have been provided to implement censoring mechanism using both optimization and simulation tools. The main aim of this paper is to describe and illustrate the Bayesian modelling approach to the analysis of survival data. Emphasis is placed on the modeling of data and the interpretation of the results. Crucial to this is an understanding of the nature of the incomplete or 'censored' data encountered. Analytic approximation and simulation tools are covered here, but most of the emphasis is on Markov chain based Monte Carlo method including independent Metropolis algorithm, which is currently the most popular technique. For analytic approximation, among various optimization algorithms and trust region method is found to be the best. In this paper, TPGE model is also used to analyze the lifetime data in Bayesian paradigm. Results are evaluated from the above mentioned real survival data set. The analytic approximation and simulation methods are implemented using some software packages. It is clear from our findings that simulation tools provide better results as compared to those obtained by asymptotic approximation.

Keywords: Bayesian Inference, JAGS, Laplace Approximation, LaplacesDemon, posterior, R Software, simulation

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Authors: Amir Taghizadeh Vahed, Adithya Thaduri

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Shovels are the main components in the mining transportation system. The productivity of the mines depends on the availability of shovels due to its high capital and operating costs. The unplanned failure/shutdowns of a shovel results in higher repair costs, increase in downtime, as well as increasing indirect cost (i.e. loss of production and company’s reputation). In order to mitigate these failures, predictive maintenance can be useful approach using failure prediction. The modern mining machinery or shovels collect huge datasets automatically; it consists of reliability and maintenance data. However, the gathered datasets are useless until the information and knowledge of data are extracted. Machine learning as well as data mining, which has a major role in recent studies, has been used for the knowledge discovery process. In this study, data mining and machine learning approaches are implemented to detect not only anomalies but also patterns from a dataset and further detection of failures.

Keywords: maintenance, machine learning, shovel, conditional based monitoring

Procedia PDF Downloads 216

Authors: Vladimir Simovic, Matija Varga, Predrag Oreski

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XHTML and XBRL are the standard languages for creating a database for the purpose of displaying financial statements on web applications. Today, XBRL is one of the most popular languages for business reporting. A large number of countries in the world recognize the role of XBRL language for financial reporting and the benefits that the reporting format provides in the collection, analysis, preparation, publication and the exchange of data (information) which is the positive side of this language. Here we present all advantages and opportunities that a company may have by using the XBRL format for business reporting. Also, this paper presents XBRL and other languages that are used for creating the database, such XML, XHTML, etc. The role of the AJAX complex model and technology will be explained in detail, and during the exchange of financial data between the web client and web server. Here will be mentioned basic layers of the network for data exchange via the web.

Keywords: XHTML, XBRL, XML, JavaScript, AJAX technology, data exchange

Procedia PDF Downloads 393

Authors: Aymen Sekhri, Emmanuel Kwabena Frimpong, Bolaji Mubarak Ayeyemi, Aleksi Hirvonen, Matias Hirvonen, Tedros Tesfay Andemichael

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Fixation identification, which involves isolating and identifying fixations and saccades in eye-tracking protocols, is an important aspect of eye-movement data processing that can have a big impact on higher-level analyses. However, fixation identification techniques are frequently discussed informally and rarely compared in any meaningful way. With two state-of-the-art algorithms, we will implement fixation detection and analysis in this work. The velocity threshold fixation algorithm is the first algorithm, and it identifies fixation based on a threshold value. For eye movement detection, the second approach is U'n' Eye, a deep neural network algorithm. The goal of this project is to analyze and visualize eye-tracking data from an eye gaze dataset that has been provided. The data was collected in a scenario in which individuals were shown photos and asked whether or not they recognized them. The results of the two-fixation detection approach are contrasted and visualized in this paper.

Keywords: human-computer interaction, eye-tracking, CNN, fixations, saccades

Procedia PDF Downloads 132

Authors: Farid Risheq, M. Zaid Alrisheq, Shuaa Al-Sadoon, Karim Al-Faqih, Mays Abdulazeez

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Breast cancer is the most frequently diagnosed cancer worldwide, and a common cause of death among women. Various conventional anatomical imaging tools are utilized for diagnosis, histological assessment and TNM (Tumor, Node, Metastases) staging of breast cancer. Biopsy of sentinel lymph node is becoming an alternative to the axillary lymph node dissection. Advances in 18-Fluoro-Deoxi-Glucose Positron Emission Tomography/Computed Tomography (18FDG-PET/CT) imaging have facilitated breast cancer diagnosis utilizing biological trapping of 18FDG inside lesion cells, expressed as Standardized Uptake Value (SUVmax). Objective: To present the utility of 18FDG uptake PET/CT scans in detecting active extra lymph nodes and distant occult metastases for breast cancer staging. Subjects and Methods: Four female patients were presented with initially classified TNM stages of breast cancer based on conventional anatomical diagnostic techniques. 18FDG-PET/CT scans were performed one hour post 18FDG intra-venous injection of (300-370) MBq, and (7-8) bed/130sec. Transverse, sagittal, and coronal views; fused PET/CT and MIP modality were reconstructed for each patient. Results: A total of twenty four lesions in breast, extended lesions to lung, liver, bone and active extra lymph nodes were detected among patients. The initial TNM stage was significantly changed post 18FDG-PET/CT scan for each patient, as follows: Patient-1: Initial TNM-stage: T1N1M0-(stage I). Finding: Two lesions in right breast (3.2cm2, SUVmax=10.2), (1.8cm2, SUVmax=6.7), associated with metastases to two right axillary lymph nodes. Final TNM-stage: T1N2M0-(stage II). Patient-2: Initial TNM-stage: T2N2M0-(stage III). Finding: Right breast lesion (6.1cm2, SUVmax=15.2), associated with metastases to right internal mammary lymph node, two right axillary lymph nodes, and sclerotic lesions in right scapula. Final TNM-stage: T2N3M1-(stage IV). Patient-3: Initial TNM-stage: T2N0M1-(stage III). Finding: Left breast lesion (11.1cm2, SUVmax=18.8), associated with metastases to two lymph nodes in left hilum, and three lesions in both lungs. Final TNM-stage: T2N2M1-(stage IV). Patient-4: Initial TNM-stage: T4N1M1-(stage III). Finding: Four lesions in upper outer quadrant area of right breast (largest: 12.7cm2, SUVmax=18.6), in addition to one lesion in left breast (4.8cm2, SUVmax=7.1), associated with metastases to multiple lesions in liver (largest: 11.4cm2, SUV=8.0), and two bony-lytic lesions in left scapula and cervicle-1. No evidence of regional or distant lymph node involvement. Final TNM-stage: T4N0M2-(stage IV). Conclusions: Our results demonstrated that 18FDG-PET/CT scans had significantly changed the TNM stages of breast cancer patients. While the T factor was unchanged, N and M factors showed significant variations. A single session of PET/CT scan was effective in detecting active extra lymph nodes and distant occult metastases, which were not identified by conventional diagnostic techniques, and might advantageously replace bone scan, and contrast enhanced CT of chest, abdomen and pelvis. Applying 18FDG-PET/CT scan early in the investigation, might shorten diagnosis time, helps deciding adequate treatment protocol, and could improve patients’ quality of life and survival. Trapping of 18FDG in malignant lesion cells, after a PET/CT scan, increases the retention index (RI%) for a considerable time, which might help localize sentinel lymph node for biopsy using a hand held gamma probe detector. Future work is required to demonstrate its utility.

Keywords: axillary lymph nodes, breast cancer staging, fluorodeoxyglucose positron emission tomography/computed tomography, lymph nodes

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Authors: B. Shukir, L. Szivos, D. Kis, P. Barzo

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Glioblastoma is the most malignant brain tumor. In general, the survival rate varies between (14-18) months. Glioblastoma consists a solid and infiltrative part. The standard therapeutic management of glioblastoma is maximum safe resection followed by chemo-radiotherapy. It’s hypothesized that the pretumoral hyperintense region in fluid attenuated inversion recovery (FLAIR) images includes both vasogenic edema and infiltrated tumor cells. In our study, we aimed to define the sensitivity and specificity of hyperintense FLAIR images preoperatively to examine how well it can define the true extent of glioblastoma. (16) glioblastoma patients included in this study. Hyperintense FLAIR region were delineated preoperatively as tumor mask. The infiltrative part of glioblastoma considered the regions where the tumor recurred on the follow up MRI. The recurrence on the CE-T1 images was marked as the recurrence masks. According to (AAL3) and (JHU white matter labels) atlas, the brain divided into cortical and subcortical regions respectively. For calculating specificity and sensitivity, the FLAIR and the recurrence masks overlapped counting how many regions affected by both . The average sensitivity and specificity was 83% and 85% respectively. Individually, the sensitivity and specificity varied between (31-100)%, and (100-58)% respectively. These results suggest that despite FLAIR being as an effective radiologic imaging tool its prognostic value remains controversial and probabilistic tractography remain more reliable available method for identifying the true extent of glioblastoma.

Keywords: brain tumors, glioblastoma, MRI, FLAIR

Procedia PDF Downloads 51

Authors: Anna Citterbergova

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This study explores the safeguards and guarantees to children’s personal data protection under the current EU and US legislative framework, namely the GDPR (2018) and COPPA (2000). Considering that children are online for the majority of their free time, one cannot overlook the negative side effects that may be associated with online participation, which may put children’s wellbeing and their fundamental rights at risk. The question of whether the current relevant legislative framework in relation to the responsibilities of the internet service providers (ISPs) are adequate safeguards and guarantees to children’s personal data protection has been an evolving debate both in the US and in the EU. From a children’s rights perspective, processors of personal data have certain obligations that must meet the international human rights principles (e. g. the CRC, ECHR), which require taking into account the best interest of the child. Accordingly, the need to protect children’s privacy online remains strong and relevant with the expansion of the number and importance of social media platforms to human life. At the same time, the landscape of the internet is rapidly evolving, and commercial interests are taking a more targeted approach in seeking children’s data. Therefore, it is essential to constantly evaluate the ongoing and evolving newly adopted market policies of ISPs that may misuse the gap in the current letter of the law. Previous studies in the field have already pointed out that both GDPR and COPPA may theoretically not be sufficient in protecting children’s personal data. With the focus on social media platforms, this study uses the doctrinal-descriptive method to identifiy the mechanisms enshrined in the GDPR and COPPA designed to protect children’s personal data. In its second part, the study includes a data gathering phase by the national data protection authorities responsible for monitoring and supervision of the GDPR in relation to children’s personal data protection who monitor the enforcement of the data protection rules throughout the European Union an contribute to their consistent application. These gathered primary source of data will later be used to outline the series of benefits and challenges to children’s persona lata protection faced by these institutes and the analysis that aims to suggest if and/or how to hold ISPs accountable while striking a fair balance between the commercial rights and the right to protection of the personal data of children. The preliminary results can be divided into two categories. First, conclusions in the doctrinal-descriptive part of the study. Second, specific cases and situations from the practice of national data protection authorities. While for the first part, concrete conclusions can already be presented, the second part is currently still in the data gathering phase. The result of this research is a comprehensive analysis on the safeguards and guarantees to children’s personal data protection under the current EU and US legislative framework, based on doctrinal-descriptive approach and original empirical data.

Keywords: personal data of children, personal data protection, GDPR, COPPA, ISPs, social media

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Authors: Sourour Ramzi, Claudia Sarrico

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Little has been done on network DEA in education, and nobody has attempted to model the whole education supply chain using network DEA. As such the contribution of the present paper is to propose a model for measuring the efficiency of education supply chains using network DEA. First, we use a general survey of data envelopment analysis (DEA) to establish the emergent themes for research in DEA, and focus on the theme of Network DEA. Second, we use a survey on two-stage DEA models, and Network DEA to write a state of the art on Network DEA, particularly applied to supply chain management. Third, we use a survey on DEA applications to establish the most influential papers on DEA education applications, in order to establish the state of the art on applications of DEA in education, in general, and applications of DEA to education using network DEA, in particular. Finally, we propose a model for measuring the performance of education supply chains of different education systems (countries or states within a country, for instance). We then use this model on some empirical data.

Keywords: supply chain, education, data envelopment analysis, network DEA

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Authors: Bangwon Seo

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In this paper, we consider multicast device-to-device (D2D) direct communication systems in cellular networks. In multicast D2D communications, nearby mobile devices exchanges, their data directly without going through a base station and a D2D transmitter send its data to multiple D2D receivers that compose of D2D multicast group. We consider wiretap channel where there is an eavesdropper that attempts to overhear the transmitted data of the D2D transmitter. In this paper, we propose a secure transmission scheme in D2D multicast communications in cellular networks. In order to prevent the eavesdropper from overhearing the transmitted data of the D2D transmitter, a precoding vector is employed at the D2D transmitter in the proposed scheme. We perform computer simulations to evaluate the performance of the proposed scheme. Through the simulation, we show that the secrecy rate performance can be improved by selecting an appropriate precoding vector.

Keywords: device-to-device communications, wiretap channel, secure transmission, precoding

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Authors: Andrzej Poszewiecki

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The presented paper contributes to the experimental current of research on privacy. The question of privacy is being discussed at length at present, primarily among lawyers and politicians. However, the matter of privacy has been of interest for economists for some time as well. The valuation of privacy by people is of great importance now. This article is about how people valuate their privacy. An experimental method has been utilised in the conducted research – the survey was carried out among customers of an online store, and the studied issue was whether their readiness to sell their data (WTA) was different from the willingness to buy data back (WTP). The basic aim of this article is to analyse whether people shopping on the Internet differentiate their privacy depending on whether they protect or sell it. The achieved results indicate the presence of major differences in this respect, which do not always come up with the original expectations. The obtained results have supported the hypothesis that people are more willing to sell their data than to repurchase them. However, the hypothesis that the value of proposed remuneration affects the willingness to sell/buy back personal data (one’s privacy) has not been supported.

Keywords: privacy, experimental economics, behavioural economics, internet

Procedia PDF Downloads 290

Authors: Karan Vishavjit, Aakash Lakra, Shafaq Khan

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The COVID-19 pandemic has pushed healthcare professionals to use big data analytics as a vital tool for tracking and evaluating the effects of contagious viruses. To effectively analyze huge datasets, efficient NoSQL databases are needed. The analysis of post-COVID-19 health and well-being outcomes and the evaluation of the effectiveness of government efforts during the pandemic is made possible by this research’s integration of several datasets, which cuts down on query processing time and creates predictive visual artifacts. We recommend applying sharding and indexing technologies to improve query effectiveness and scalability as the dataset expands. Effective data retrieval and analysis are made possible by spreading the datasets into a sharded database and doing indexing on individual shards. Analysis of connections between governmental activities, poverty levels, and post-pandemic well being is the key goal. We want to evaluate the effectiveness of governmental initiatives to improve health and lower poverty levels. We will do this by utilising advanced data analysis and visualisations. The findings provide relevant data that supports the advancement of UN sustainable objectives, future pandemic preparation, and evidence-based decision-making. This study shows how Big Data and NoSQL databases may be used to address problems with global health.

Keywords: big data, COVID-19, health, indexing, NoSQL, sharding, scalability, well being

Procedia PDF Downloads 68

Authors: Prachi Abhishek Dave, Abhishek Dave

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Purpose: The purpose of this study is to elucidate the role of intravitreal injection Aflibercept in managing complex cases of Wet Age Related Macular Degeneration (ARMD) and the gratifying visual recovery experienced with a minimally invasive procedure. Methods: A 73-year-old gentleman presented with a drop in vision in the left eye for 25 days. On examination, his best corrected visual acuity (BCVA) in the Right eye (OD) was 6/60, and finger counting close to face in the Left eye (OS). On multimodal imaging, he was diagnosed to have a scarred Wet ARMD in OD and an active Wet ARMD with a large subretinal bleed secondary to Wet ARMD in OS. Treatment management options included monotherapy with an Injection Aflibercept or an intravitreal gas injection with tPA followed by Injection Aflibercept. Considering his one-eyed status, the patient decided to go for Aflibercept monotherapy. Results: After 3 monthly injections of injection Aflibercept, the subretinal bleed reduced, the subretinal fluid resolved, and his vision in OS improved to 6/9. He is on a regular follow-up and has not needed any further injections in OS and he maintains 6/9 vision. Conclusions: Conventional treatment guidelines for a large subretinal bleed dictate the use of gas followed by intravitreal Injection Aflibercept. However, gas has its own limitations of causing a rise in intraocular pressure and a transient loss of vision, which is particularly troublesome in one-eyed patients. Injection Aflibercept offers a much safer, less invasive, and elegant treatment option for such patients with equally good or even better visual outcomes.

Keywords: wet ARMD, subretinal bleed, intravitreal injections, aflibercept, EYELEA, intravitreal gas

Procedia PDF Downloads 41

Authors: Ruchika Goyal, Ashwani Kumar, Sandeep Jain

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The experimental data for anticancer potential of curcumin nanoparticles was calculated by means of eclectic data. The optimal descriptors were examined using Monte Carlo method based CORAL SEA software. The statistical quality of the model is following: n = 14, R² = 0.6809, Q² = 0.5943, s = 0.175, MAE = 0.114, F = 26 (sub-training set), n =5, R²= 0.9529, Q² = 0.7982, s = 0.086, MAE = 0.068, F = 61, Av Rm² = 0.7601, ∆R²m = 0.0840, k = 0.9856 and kk = 1.0146 (test set) and n = 5, R² = 0.6075 (validation set). This data can be used to build predictive QSAR models for anticancer activity.

Keywords: anticancer potential, curcumin, model, nanoparticles, optimal descriptors, QSAR

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Authors: Musaab Riyadh, Norwati Mustapha, Dina Riyadh

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Trajectory similarity can be defined as the cost of transforming one trajectory into another based on certain similarity method. It is the core of numerous mining tasks such as clustering, classification, and indexing. Various approaches have been suggested to measure similarity based on the geometric and dynamic properties of trajectory, the overlapping between trajectory segments, and the confined area between entire trajectories. In this article, an evaluation of these approaches has been done based on computational cost, usage memory, accuracy, and the amount of data which is needed in advance to determine its suitability to stream mining applications. The evaluation results show that the stream mining applications support similarity methods which have low computational cost and memory, single scan on data, and free of mathematical complexity due to the high-speed generation of data.

Keywords: global distance measure, local distance measure, semantic trajectory, spatial dimension, stream data mining

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Authors: Christopher Leung, Tony Kim, Rebecca Lendzion, Scott Mackenzie

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Retroperitoneal abscesses are a rare but serious condition with often delayed diagnosis, non-specific symptoms, multiple causes and high morbidity/mortality. With the advent of more readily available cross-sectional imaging, retroperitoneal abscesses are treated earlier and better outcomes are achieved. Occasionally, a retroperitoneal abscess is present as a huge retroperitoneal abscess, as evident in this 53-year-old male. With a background of chronic renal disease and left partial nephrectomy, this gentleman presented with a one-month history of left flank pain without any other symptoms, including fevers or abdominal pain. CT abdomen and pelvis demonstrated a huge retroperitoneal abscess spanning from the diaphragm, abutting the spleen, down to the iliopsoas muscle and abutting the iliac vessels at the pelvic brim. This large retroperitoneal abscess required open drainage as well as drainage by interventional radiology. A long course of intravenous antibiotics and multiple drainages was required to drain the abscess. His blood culture and fluid culture grew Proteus species suggesting a urinary source, likely from his non-functioning kidney, which had a partial nephrectomy. Such a huge retroperitoneal abscess has rarely been described in the literature. The learning point here is that the basic principle of source control and antibiotics is paramount in treating retroperitoneal abscesses regardless of the size of the abscess.

Keywords: retroperitoneal abscess, retroperitoneal mass, sepsis, genitourinary infection

Procedia PDF Downloads 220

Authors: Sara Bonetti

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The use of quantitative data to support policies and justify investments has become imperative in many fields including the field of education. However, the topic of data literacy has only marginally touched the early care and education (ECE) field. In California, within the ECE workforce, there is a group of professionals working in policy and advocacy that use quantitative data regularly and whose educational and professional experiences have been neglected by existing research. This study aimed at analyzing these experiences in accessing, using, and producing quantitative data. This study utilized semi-structured interviews to capture the differences in educational and professional backgrounds, policy contexts, and power relations. The participants were three key professionals from county-level organizations and one working at a State Department to allow for a broader perspective at systems level. The study followed Núñez’s multilevel model of intersectionality. The key in Núñez’s model is the intersection of multiple levels of analysis and influence, from the individual to the system level, and the identification of institutional power dynamics that perpetuate the marginalization of certain groups within society. In a similar manner, this study looked at the dynamic interaction of different influences at individual, organizational, and system levels that might intersect and affect ECE professionals’ experiences with quantitative data. At the individual level, an important element identified was the participants’ educational background, as it was possible to observe a relationship between that and their positionality, both with respect to working with data and also with respect to their power within an organization and at the policy table. For example, those with a background in child development were aware of how their formal education failed to train them in the skills that are necessary to work in policy and advocacy, and especially to work with quantitative data, compared to those with a background in administration and/or business. At the organizational level, the interviews showed a connection between the participants’ position within the organization and their organization’s position with respect to others and their degree of access to quantitative data. This in turn affected their sense of empowerment and agency in dealing with data, such as shaping what data is collected and available. These differences reflected on the interviewees’ perceptions and expectations for the ECE workforce. For example, one of the interviewees pointed out that many ECE professionals happen to use data out of the necessity of the moment. This lack of intentionality is a cause for, and at the same time translates into missed training opportunities. Another interviewee pointed out issues related to the professionalism of the ECE workforce by remarking the inadequacy of ECE students’ training in working with data. In conclusion, Núñez’s model helped understand the different elements that affect ECE professionals’ experiences with quantitative data. In particular, what was clear is that these professionals are not being provided with the necessary support and that we are not being intentional in creating data literacy skills for them, despite what is asked of them and their work.

Keywords: data literacy, early childhood professionals, intersectionality, quantitative data

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Authors: Alexiou Dimitra, Fragkaki Maria

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The objective of the paper is the study of geographic, economic and educational variables and their contribution to determine the position of each member-state among the EU-28 countries based on the values of seven variables as given by Eurostat. The Data Analysis methods of Multiple Factorial Correspondence Analysis (MFCA) Principal Component Analysis and Factor Analysis have been used. The cross tabulation tables of data consist of the values of seven variables for the 28 countries for 2014. The data are manipulated using the CHIC Analysis V 1.1 software package. The results of this program using MFCA and Ascending Hierarchical Classification are given in arithmetic and graphical form. For comparison reasons with the same data the Factor procedure of Statistical package IBM SPSS 20 has been used. The numerical and graphical results presented with tables and graphs, demonstrate the agreement between the two methods. The most important result is the study of the relation between the 28 countries and the position of each country in groups or clouds, which are formed according to the values of the corresponding variables.

Keywords: Multiple Factorial Correspondence Analysis, Principal Component Analysis, Factor Analysis, E.U.-28 countries, Statistical package IBM SPSS 20, CHIC Analysis V 1.1 Software, Eurostat.eu Statistics

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Authors: Tigabu Dagne Akal

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Electronic health records (EHRs) can help to store, maintain, and make the appropriate handling of patient histories for proper treatment and decision. Merging the EHRs with big data analytics (BDA) capabilities enable healthcare stakeholders to provide effective and efficient treatments for chronic diseases. Though there are huge opportunities and efforts that exist in the deployment of EMRs and the development of BDA, there are challenges in addressing resources and organizational capabilities that are required to achieve the competitive advantage and sustainability of EHRs and BDA. The resource-based view (RBV), information system (IS), and non- IS theories should be extended to examine organizational capabilities and resources which are required for successful data analytics in the healthcare industries. The main purpose of this study is to develop a conceptual framework for the development of healthcare BDA capabilities based on past works so that researchers can extend. The research question was formulated for the search strategy as a research methodology. The study selection was made at the end. Based on the study selection, the conceptual framework for the development of BDA capabilities in the healthcare settings was formulated.

Keywords: EHR, EMR, Big data, Big data analytics, resource-based view

Procedia PDF Downloads 130

Authors: Adekunle Kolawole Ojo, Idowu Peter Adebayo, Egwuche Sylvester O.

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Chronic Kidney Disease (CKD) is a significant cause of morbidity and mortality across developed and developing nations and is associated with increased risk. There are no existing electronic means of capturing and monitoring CKD in Nigeria. The work is aimed at developing a spatial data model that can be used to implement renal registries required for tracking and monitoring the spatial distribution of renal diseases by public health officers and patients. In this study, we have developed a spatial data model for a functional renal registry.

Keywords: renal registry, health informatics, chronic kidney disease, interface

Procedia PDF Downloads 209

Authors: Dudu Ka Ruth Mkhize

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Adolescents are at the front and centre of poor mathematics performance in schools. Literature has concluded in some countries that there is a permanent and perpetual mathematics crisis in schools of the persistent poor performance in mathematics by teens. There is no shortage of interventions and research to solve this problem. However, none has capitalised on the cognitive prowess of adolescents, which was revealed at the turn of the century by the introduction of neuroimaging technologies such as structural and functional magnetic resonance imaging (sMRI and fMRI). This research found that brain growth during adolescence results in enhanced cognitive abilities essential for mathematics learning. This paper is based on the four-year case study of rural high school adolescents who had a negative attitude towards mathematics and hence were failing mathematics. But through a ten-day intervention where teaching revolved around invoking their cognitive ability, their attitude and motivation for mathematics changed for the better. The paper concludes that despite educational psychology being part of teacher education as well as education systems, there are numerous overlooked gems of psychological theories which have the potential to enhance academic achievement for youth in schools. A recommendation is made to take cues from positive psychology, whose establishment was a rejection of the dominance of the disease model in psychology. Similarly, the general perspective of poor mathematics performance can take a u-turn towards the cognitive ability acquired by adolescents because of their developmental stage.

Keywords: adolescence, cognitive growth, mathematics performance

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Authors: H. Aksas, S. Boughrara, K. Louhab

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The goal of this study was the use of life cycle assessment (LCA) methodology to assess the environmental impact of pharmaceutical product (four kinds of syrup form and tree kinds of pomade form), which are produced in one leader manufactory in Algeria town that is SAIDAL Company. The impacts generated have evaluated using SimpaPro7.1 with CML92 Method for syrup form and EPD 2007 for pomade form. All impacts evaluated have compared between them, with determination of the compound contributing to each impacts in each case. Data needed to conduct Life Cycle Inventory (LCI) came from this factory, by the collection of theoretical data near the responsible technicians and engineers of the company, the practical data are resulting from the assay of pharmaceutical liquid, obtained at the laboratories of the university. This data represent different raw material imported from European and Asian country necessarily to formulate the drug. Energy used is coming from Algerian resource for the input. Outputs are the result of effluent analysis of this factory with different form (liquid, solid and gas form). All this data (input and output) represent the ecobalance.

Keywords: pharmaceutical product, drug residues, LCA methodology, environmental impacts

Procedia PDF Downloads 245

Authors: Muhammad Imran Hussain, Ramisha Ibtisam, Tayyaba Fatima, Huba Khalid, Ayesha Aziz, Khansa, Adan Sitara, Anam Shahzad, Aymen Jabeen

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Vitamin D supports the immune system fight TB by inhibiting Interferon-gamma (IFN-γ) and lowering host inflammation. The purpose of the research was to see if giving the vitamin D supplements to TB patients affected their prognosis. A randomized placebo control study of 200 TB patients was performed among which 106 received 400,000 IU of injectable vitamin D3 and 94 received placebo for 2 doses. Assessment was carried out at the end of every month for 3 months. IFN-γ responses to whole blood stimulation generated by the Mycobacterium tuberculosis sonicate (MTBs) antigen and early secreted and T cell activated 6 kDa (ESAT6) were assessed at 0 and 12 weeks. The statistical analysis used descriptive statistics (mean and standard deviation), Friedman's test and Fisher's test. The vitamin D group gained significantly more weight (+3.90 pounds) and had less persistent lung disease on imaging (1.33 zones vs. 1.84 zones). They also had a 50% decrease in cavity size. Additionally, patients with low baseline serum concentrations of 25-(OH)D had a significant increase in MTB-induced IFN-γ production after taking vitamin D supplements. Vitamin D administration in large amounts can hasten the recovery of TB patients. The findings point is a therapeutically useful activity of Vitamin D's in the management for tuberculosis.

Keywords: tuberculosis, vitamin D, interferon gamma, protein, infection

Procedia PDF Downloads 52

Authors: Gajendra Kumar Mourya, Dinesh Bhatia, Akash Handique, Sunita Warjri, Syed Achaab Amir

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Nowadays, Medical imaging has become an integral part of modern healthcare. Abdominal CT images are an invaluable mean for abdominal organ investigation and have been widely studied in the recent years. Diagnosis of liver pathologies is one of the major areas of current interests in the field of medical image processing and is still an open problem. To deeply study and diagnose the liver, segmentation of liver is done to identify which part of the liver is mostly affected. Manual segmentation of the liver in CT images is time-consuming and suffers from inter- and intra-observer differences. However, automatic or semi-automatic computer aided segmentation of the Liver is a challenging task due to inter-patient Liver shape and size variability. In this paper, we present a technique for automatic segmenting the liver from CT images using Random Forest Classifier. Random forests or random decision forests are an ensemble learning method for classification that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes of the individual trees. After comparing with various other techniques, it was found that Random Forest Classifier provide a better segmentation results with respect to accuracy and speed. We have done the validation of our results using various techniques and it shows above 89% accuracy in all the cases.

Keywords: CT images, image validation, random forest, segmentation

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Authors: Rajeev Kumar Bedi, Jaswinder Singh, Sunil Kumar Gupta

Abstract:

Cloud storage is a model of online data storage where data is stored in virtualized pool of servers hosted by third parties (CSPs) and located in different geographical locations. Cloud storage revolutionized the way how users access their data online anywhere, anytime and using any device as a tablet, mobile, laptop, etc. A lot of issues as vendor lock-in, frequent service outage, data loss and performance related issues exist in single cloud storage systems. So to evade these issues, the concept of multi cloud storage introduced. There are a lot of multi cloud storage systems exists in the market for mobile devices. In this article, we are providing comparison of four multi cloud storage systems for mobile devices Otixo, Unclouded, Cloud Fuze, and Clouds and evaluate their performance on the basis of CPU usage, battery consumption, time consumption and data usage parameters on three mobile phones Nexus 5, Moto G and Nexus 7 tablet and using Wi-Fi network. Finally, open research challenges and future scope are discussed.

Keywords: cloud storage, multi cloud storage, vendor lock-in, mobile devices, mobile cloud computing

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Authors: M. Zayoud, S. Oueida, S. Ionescu, P. AbiChar

Abstract:

Background: Wireless sensor network is encompassed of diversified tools of information technology, which is widely applied in a range of domains, including military surveillance, weather forecasting, and earthquake forecasting. Strengthened grounds are always developed for wireless sensor networks, which usually emerges security issues during professional application. Thus, essential technological tools are necessary to be assessed for secure aggregation of data. Moreover, such practices have to be incorporated in the healthcare practices that shall be serving in the best of the mutual interest Objective: Aggregation of encrypted data has been assessed through homomorphic stream cipher to assure its effectiveness along with providing the optimum solutions to the field of healthcare. Methods: An experimental design has been incorporated, which utilized newly developed cipher along with CPU-constrained devices. Modular additions have also been employed to evaluate the nature of aggregated data. The processes of homomorphic stream cipher have been highlighted through different sensors and modular additions. Results: Homomorphic stream cipher has been recognized as simple and secure process, which has allowed efficient aggregation of encrypted data. In addition, the application has led its way to the improvisation of the healthcare practices. Statistical values can be easily computed through the aggregation on the basis of selected cipher. Sensed data in accordance with variance, mean, and standard deviation has also been computed through the selected tool. Conclusion: It can be concluded that homomorphic stream cipher can be an ideal tool for appropriate aggregation of data. Alongside, it shall also provide the best solutions to the healthcare sector.

Keywords: aggregation, cipher, homomorphic stream, encryption

Procedia PDF Downloads 259

Authors: Omar Al Ali

Abstract:

The current study was aimed to explore the relationships between emotional intelligence, cognitive ability, and leader's performance. Data were collected from 260 senior managers from UAE. The results showed that there are significant relationships between emotional intelligence and leadership performance as measured by the annual internal evaluations of each participant (r = .42, p < .01). Data from regression analysis revealed that both variables namely emotional intelligence (beta = .31, p < .01), and cognitive ability (beta = .29, p < .01), predicted leadership competencies, and together explained 26% of its variance. Data suggests that EI and cognitive ability are significantly correlated with leadership performance. In depth implications of the present findings for human resource development theory and practice are discussed.

Keywords: emotional intelligence, cognitive ability, leadership, performance

Procedia PDF Downloads 475

Authors: Tisciane Perpetuo e Oliveira, Dante Inga Narvaez, Marcelo Gradella Villalva

Abstract:

Installations of solar photovoltaic systems have increased considerably in the last decade. Therefore, it has been noticed that monitoring of meteorological data (solar irradiance, air temperature, wind velocity, etc.) is important to predict the potential of a given geographical area in solar energy production. In this sense, the present work compares two computational tools that are capable of estimating the energy generation of a photovoltaic system through correlation analyzes of solar radiation data: PVsyst software and an algorithm based on the PVlib package implemented in MATLAB. In order to achieve the objective, it was necessary to obtain solar radiation data (measured and from a solarimetric database), analyze the decomposition of global solar irradiance in direct normal and horizontal diffuse components, as well as analyze the modeling of the devices of a photovoltaic system (solar modules and inverters) for energy production calculations. Simulated results were compared with experimental data in order to evaluate the performance of the studied methods. Errors in estimation of energy production were less than 30% for the MATLAB algorithm and less than 20% for the PVsyst software.

Keywords: energy production, meteorological data, irradiance decomposition, solar photovoltaic system

Procedia PDF Downloads 140

Authors: Srushti Patil, Preethi Baligar, Gopalkrishna Joshi, Gururaj N. Bhadri

Abstract:

In designing learning environments, the instructional strategies can be tailored to suit the learning style of an individual to ensure effective learning. In this study, the information shared on social media like Facebook is being used to predict learning style of a learner. Previous research studies have shown that Facebook data can be used to predict user personality. Users with a particular personality exhibit an inherent pattern in their digital footprint on Facebook. The proposed work aims to correlate the user's’ personality, predicted from Facebook data to the learning styles, predicted through questionnaires. For Millennial learners, Facebook has become a primary means for information sharing and interaction with peers. Thus, it can serve as a rich bed for research and direct the design of learning environments. The authors have conducted this study in an undergraduate freshman engineering course. Data from 320 freshmen Facebook users was collected. The same users also participated in the learning style and personality prediction survey. The Kolb’s Learning style questionnaires and Big 5 personality Inventory were adopted for the survey. The users have agreed to participate in this research and have signed individual consent forms. A specific page was created on Facebook to collect user data like personal details, status updates, comments, demographic characteristics and egocentric network parameters. This data was captured by an application created using Python program. The data captured from Facebook was subjected to text analysis process using the Linguistic Inquiry and Word Count dictionary. An analysis of the data collected from the questionnaires performed reveals individual student personality and learning style. The results obtained from analysis of Facebook, learning style and personality data were then fed into an automatic classifier that was trained by using the data mining techniques like Rule-based classifiers and Decision trees. This helps to predict the user personality and learning styles by analysing the common patterns. Rule-based classifiers applied for text analysis helps to categorize Facebook data into positive, negative and neutral. There were totally two models trained, one to predict the personality from Facebook data; another one to predict the learning styles from the personalities. The results show that the classifier model has high accuracy which makes the proposed method to be a reliable one for predicting the user personality and learning styles.

Keywords: educational data mining, Facebook, learning styles, personality traits

Procedia PDF Downloads 229

Authors: U. Pruthviraj, Praveen Kumar R. A. K. Athul, K. V. Gangadharan, S. Rao Shrikantha

Abstract:

An open source based autonomous unmanned marine surface vehicle (UMSV) is developed for some of the marine applications such as pollution control, environmental monitoring and thermal imaging. A double rotomoulded hull boat is deployed which is rugged, tough, quick to deploy and moves faster. It is suitable for environmental monitoring, and it is designed for easy maintenance. A 2HP electric outboard marine motor is used which is powered by a lithium-ion battery and can also be charged from a solar charger. All connections are completely waterproof to IP67 ratings. In full throttle speed, the marine motor is capable of up to 7 kmph. The motor is integrated with an open source based controller using cortex M4F for adjusting the direction of the motor. This UMSV can be operated by three modes: semi-autonomous, manual and fully automated. One of the channels of a 2.4GHz radio link 8 channel transmitter is used for toggling between different modes of the USMV. In this electric outboard marine motor an on board GPS system has been fitted to find the range and GPS positioning. The entire system can be assembled in the field in less than 10 minutes. A Flir Lepton thermal camera core, is integrated with a 64-bit quad-core Linux based open source processor, facilitating real-time capturing of thermal images and the results are stored in a micro SD card which is a data storage device for the system. The thermal camera is interfaced to an open source processor through SPI protocol. These thermal images are used for finding oil spills and to look for people who are drowning at low visibility during the night time. A Real Time clock (RTC) module is attached with the battery to provide the date and time of thermal images captured. For the live video feed, a 900MHz long range video transmitter and receiver is setup by which from a higher power output a longer range of 40miles has been achieved. A Multi-parameter probe is used to measure the following parameters: conductivity, salinity, resistivity, density, dissolved oxygen content, ORP (Oxidation-Reduction Potential), pH level, temperature, water level and pressure (absolute).The maximum pressure it can withstand 160 psi, up to 100m. This work represents a field demonstration of an open source based autonomous navigation system for a marine surface vehicle.

Keywords: open source, autonomous navigation, environmental monitoring, UMSV, outboard motor, multi-parameter probe

Procedia PDF Downloads 238