Search results for: healthcare data

Authors: M. Abdullah, Al-Wadud

Abstract:

Sensor-based activity recognition systems usually accounts which sensors have been activated to perform an activity. The system then combines the conditional probabilities of those sensors to represent different activities and takes the decision based on that. However, the information about the sensors which are not activated may also be of great help in deciding which activity has been performed. This paper proposes an approach where the sensory data related to both usage and non-usage of objects are utilized to make the classification of activities. Experimental results also show the promising performance of the proposed method.

Keywords: Naïve Bayesian, based classification, activity recognition, sensor data, object-usage model

Procedia PDF Downloads 322

Authors: Nastaran Moosavi, Mohammad Mokhtari

Abstract:

Seismic inversion is a technique which has been in use for years and its main goal is to estimate and to model physical characteristics of rocks and fluids. Generally, it is a combination of seismic and well-log data. Seismic inversion can be carried out through different methods; we have conducted and compared post-stack and pre- stack seismic inversion methods on real data in one of the fields in the Persian Gulf. Pre-stack seismic inversion can transform seismic data to rock physics such as P-impedance, S-impedance and density. While post- stack seismic inversion can just estimate P-impedance. Then these parameters can be used in reservoir identification. Based on the results of inverting seismic data, a gas reservoir was detected in one of Hydrocarbon oil fields in south of Iran (Persian Gulf). By comparing post stack and pre-stack seismic inversion it can be concluded that the pre-stack seismic inversion provides a more reliable and detailed information for identification and prediction of hydrocarbon reservoirs.

Keywords: density, p-impedance, s-impedance, post-stack seismic inversion, pre-stack seismic inversion

Procedia PDF Downloads 324

Authors: Fouzi Harrou, Ying Sun, Sofiane Khadraoui

Abstract:

Anomaly detection based on Principal Component Analysis (PCA) was studied intensively and largely applied to multivariate processes with highly cross-correlated process variables. Monitoring metrics such as the Hotelling's T2 and the Q statistics are usually used in PCA-based monitoring to elucidate the pattern variations in the principal and residual subspaces, respectively. However, these metrics are ill suited to detect small faults. In this paper, the Exponentially Weighted Moving Average (EWMA) based on the Q and T statistics, T2-EWMA and Q-EWMA, were developed for detecting faults in the process mean. The performance of the proposed methods was compared with that of the conventional PCA-based fault detection method using synthetic data. The results clearly show the benefit and the effectiveness of the proposed methods over the conventional PCA method, especially for detecting small faults in highly correlated multivariate data.

Keywords: data-driven method, process control, anomaly detection, dimensionality reduction

Procedia PDF Downloads 299

Authors: Elaheh Talebi, Fariba Yavari, Lucy Philip, Lesley Town

Abstract:

The mining industry generates vast amounts of data, necessitating robust data management systems and advanced analytics tools to achieve better decision-making processes in the development of mining production and maintaining safety. This paper highlights the advantages of Power BI, a powerful intelligence tool, over traditional Excel-based approaches for effectively managing and harnessing mining data. Power BI enables professionals to connect and integrate multiple data sources, ensuring real-time access to up-to-date information. Its interactive visualizations and dashboards offer an intuitive interface for exploring and analyzing geotechnical data. Advanced analytics is a collection of data analysis techniques to improve decision-making. Leveraging some of the most complex techniques in data science, advanced analytics is used to do everything from detecting data errors and ensuring data accuracy to directing the development of future project phases. However, while Power BI is a robust tool, specific visualizations required by geotechnical engineers may have limitations. This paper studies the capability to use Python or R programming within the Power BI dashboard to enable advanced analytics, additional functionalities, and customized visualizations. This dashboard provides comprehensive tools for analyzing and visualizing key geotechnical data metrics, including spatial representation on maps, field and lab test results, and subsurface rock and soil characteristics. Advanced visualizations like borehole logs and Stereonet were implemented using Python programming within the Power BI dashboard, enhancing the understanding and communication of geotechnical information. Moreover, the dashboard's flexibility allows for the incorporation of additional data and visualizations based on the project scope and available data, such as pit design, rock fall analyses, rock mass characterization, and drone data. This further enhances the dashboard's usefulness in future projects, including operation, development, closure, and rehabilitation phases. Additionally, this helps in minimizing the necessity of utilizing multiple software programs in projects. This geotechnical dashboard in Power BI serves as a user-friendly solution for analyzing, visualizing, and communicating both new and historical geotechnical data, aiding in informed decision-making and efficient project management throughout various project stages. Its ability to generate dynamic reports and share them with clients in a collaborative manner further enhances decision-making processes and facilitates effective communication within geotechnical projects in the mining industry.

Keywords: geotechnical data analysis, power BI, visualization, decision-making, mining industry

Procedia PDF Downloads 92

Authors: Bency Ann Massinello, Nancy Day, Janet Fellini

Abstract:

Background: The use of an appropriate mode of communication among the multidisciplinary care team members regarding coordination of care is an extremely complicated yet important patient safety initiative. Effective communication among the team members(nursing staff, medical staff, respiratory therapists, rehabilitation therapists, patient-family services team…) become essential to develop a culture of trust and collaboration to deliver the highest quality care to patients are their families. The inpatient post-acute pediatrics, where children and their caregivers come for continuity of care, is no exceptions to the increasing use of text messages as a means to communication among clinicians. One such platform is the Vocera Communications (Vocera Smart Mobile App called Vocera Edge) allows the teams to use the application and share sensitive patient information through an encrypted platform using IOS company provided shared and assigned mobile devices. Objective: This paper discusses the quality initiative of implementing the transition from Vocera Smartbage to Vocera Edge Mobile App, technology advantage, use case expansion, and lessons learned about a secure alternative modality that allows sending and receiving secure text messages in a pediatric post-acute setting using an IOS device. This implementation process included all direct care staff, ancillary teams, and administrative teams on the clinical units. Methods: Our institution launched this transition from voice prompted hands-free Vocera Smartbage to Vocera Edge mobile based app for secure care team texting using a big bang approach during the first PDSA cycle. The pre and post implementation data was gathered using a qualitative survey of about 500 multidisciplinary team members to determine the ease of use of the application and its efficiency in care coordination. The technology was further expanded in its use by implementing clinical alerts and alarms notification using middleware integration with patient monitoring (Masimo) and life safety (Nurse call) systems. Additional use of the smart mobile iPhone use include pushing out apps like Lexicomp and Up to Date to have it readily available for users for evident-based practice in medication and disease management. Results: Successful implementation of the communication system in a shared and assigned model with all of the multidisciplinary teams in our pediatric post-acute setting. In just a 3-monthperiod post implementation, we noticed a 14% increase from 7,993 messages in 6 days in December 2020 to 9,116messages in March 2021. This confirmed that all clinical and non-clinical teams were using this mode of communication for coordinating the care for their patients. System generated data analytics used in addition to the pre and post implementation staff survey for process evaluation. Conclusion: A secure texting option using a mobile device is a safe and efficient mode for care team communication and collaboration using technology in real time. This allows for the settings like post-acute pediatric care areas to be in line with the widespread use of mobile apps and technology in our mainstream healthcare.

Keywords: nursing informatics, mobile secure texting, multidisciplinary communication, pediatrics post acute care

Procedia PDF Downloads 196

Authors: Ahmed M. Jamel

Abstract:

Electronic government initiatives and public participation to them are among the indicators of today's development criteria of the countries. After consequent two wars, Iraq's current position in, for example, UN's e-government ranking is quite concerning and did not improve in recent years, either. In the preparation of this work, we are motivated with the fact that handling geographic data of the public facilities and resources are needed in most of the e-government projects. Geographical information systems (GIS) provide most common tools not only to manage spatial data but also to integrate such type of data with nonspatial attributes of the features. With this background, this paper proposes that establishing a working GIS in the health sector of Iraq would improve e-government applications. As the case study, investigating hospital locations in Erbil is chosen.

Keywords: e-government, GIS, Iraq, Erbil

Procedia PDF Downloads 389

Authors: V. Ayishwarya Bharathi, S. M. Hasker, J. Indhu, M. Mohamed Azarudeen, G. Gowthami, R. Vinoth Rajan, N. Vijayarangan

Abstract:

Identification of water quality conditions in a river system based on limited observations is an essential task for meeting the goals of environmental management. The traditional method of water quality testing is to collect samples manually and then send to laboratory for analysis. However, it has been unable to meet the demands of water quality monitoring today. So a set of automatic measurement and reporting system of water quality has been developed. In this project specifies Water quality parameters collected by multi-parameter water quality probe are transmitted to data processing and monitoring center through GPRS wireless communication network of mobile. The multi parameter sensor is directly placed above the water level. The monitoring center consists of GPRS and micro-controller which monitor the data. The collected data can be monitor at any instant of time. In the pollution control board they will monitor the water quality sensor data in computer using Visual Basic Software. The system collects, transmits and processes water quality parameters automatically, so production efficiency and economy benefit are improved greatly. GPRS technology can achieve well within the complex environment of poor water quality non-monitored, and more specifically applicable to the collection point, data transmission automatically generate the field of water analysis equipment data transmission and monitoring.

Keywords: multiparameter sensor, GPRS, visual basic software, RS232

Procedia PDF Downloads 412

Authors: E. Fathallahi Aghdam, V. Hosseini

Abstract:

Environmental pollution is not limited to a specific region or country; that is why sustainable development, as a necessary process for improvement, pays attention to issues such as destruction of natural resources, degradation of biological system, global pollution, and climate change in the world, especially in the developing countries. According to the World Health Organization, as a developing city, Tehran (capital of Iran) is one of the most polluted cities in the world in terms of air pollution. In this study, three pollutants including particulate matter less than 10 microns, nitrogen oxides, and sulfur dioxide were evaluated in Tehran using data mining techniques and through Crisp approach. The data from 21 air pollution measuring stations in different areas of Tehran were collected from 1999 to 2013. Commercial softwares Clementine was selected for this study. Tehran was divided into distinct clusters in terms of the mentioned pollutants using the software. As a data mining technique, clustering is usually used as a prologue for other analyses, therefore, the similarity of clusters was evaluated in this study through analyzing local conditions, traffic behavior, and industrial activities. In fact, the results of this research can support decision-making system, help managers improve the performance and decision making, and assist in urban studies.

Keywords: data mining, clustering, air pollution, crisp approach

Procedia PDF Downloads 427

Authors: Anuradha Chug, Sunali Gandhi

Abstract:

Regression Testing is a very expensive and time-consuming process carried out to ensure the validity of modified software. Due to the availability of insufficient resources to re-execute all the test cases in time constrained environment, efforts are going on to generate test data automatically without human efforts. Many search based techniques have been proposed to generate efficient, effective as well as optimized test data, so that the overall cost of the software testing can be minimized. The generated test data should be able to uncover all potential lapses that exist in the software or product. Inspired from the natural behavior of bat for searching her food sources, current study employed a meta-heuristic, search-based bat algorithm for optimizing the test data on the basis certain parameters without compromising their effectiveness. Mathematical functions are also applied that can effectively filter out the redundant test data. As many as 50 Java programs are used to check the effectiveness of proposed test data generation and it has been found that 86% saving in testing efforts can be achieved using bat algorithm while covering 100% of the software code for testing. Bat algorithm was found to be more efficient in terms of simplicity and flexibility when the results were compared with another nature inspired algorithms such as Firefly Algorithm (FA), Hill Climbing Algorithm (HC) and Ant Colony Optimization (ACO). The output of this study would be useful to testers as they can achieve 100% path coverage for testing with minimum number of test cases.

Keywords: regression testing, test case selection, test case prioritization, genetic algorithm, bat algorithm

Procedia PDF Downloads 381

Authors: Agria Rhamdhan

Abstract:

WhatsApp as the most popular mobile messaging app has been used as evidence in many criminal cases. As the use of mobile messages generates large amounts of data, forensic investigation faces the challenge of large data problems. The hardest part of finding this important evidence is because current practice utilizes tools and technique that require manual analysis to check all messages. That way, analyze large sets of mobile messaging data will take a lot of time and effort. Our work offers methodologies based on forensic triage to reduce large data to manageable sets resulting easier to do detailed reviews, then show the results through interactive visualization to show important term, entities and relationship through intelligent ranking using Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) Model. By implementing this methodology, investigators can improve investigation processing time and result's accuracy.

Keywords: forensics, triage, visualization, WhatsApp

Procedia PDF Downloads 168

Authors: Mohkammad Nur Cahyadi, Hepi Hapsari Handayani, Agus Budi Raharjo, Ronny Mardianto, Daud Wahyu Imani, Arizal Bawazir, Luki Adi Triawan

Abstract:

PDAM (local water company) determines customer charges by considering the customer's building or house. Charges determination significantly affects PDAM income and customer costs because the PDAM applies a subsidy policy for customers classified as small households. Periodic updates are needed so that pricing is in line with the target. A thorough customer survey in Surabaya is needed to update customer building data. However, the survey that has been carried out so far has been by deploying officers to conduct one-by-one surveys for each PDAM customer. Surveys with this method require a lot of effort and cost. For this reason, this research offers a technology called moblie mapping, a mapping method that is more efficient in terms of time and cost. The use of this tool is also quite simple, where the device will be installed in the car so that it can record the surrounding buildings while the car is running. Mobile mapping technology generally uses lidar sensors equipped with GNSS, but this technology requires high costs. In overcoming this problem, this research develops low-cost mobile mapping technology using a webcam camera sensor added to the GNSS and IMU sensors. The camera used has specifications of 3MP with a resolution of 720 and a diagonal field of view of 78⁰. The principle of this invention is to integrate four camera sensors, a GNSS webcam, and GPS to acquire photo data, which is equipped with location data (latitude, longitude) and IMU (roll, pitch, yaw). This device is also equipped with a tripod and a vacuum cleaner to attach to the car's roof so it doesn't fall off while running. The output data from this technology will be analyzed with artificial intelligence to reduce similar data (Cosine Similarity) and then classify building types. Data reduction is used to eliminate similar data and maintain the image that displays the complete house so that it can be processed for later classification of buildings. The AI method used is transfer learning by utilizing a trained model named VGG-16. From the analysis of similarity data, it was found that the data reduction reached 50%. Then georeferencing is done using the Google Maps API to get address information according to the coordinates in the data. After that, geographic join is done to link survey data with customer data already owned by PDAM Surya Sembada Surabaya.

Keywords: mobile mapping, GNSS, IMU, similarity, classification

Procedia PDF Downloads 84

Authors: Shokoufeh Abrisham, Seyed Mahmoud Hossieni, Elham Pishbin

Abstract:

In this paper, a hybrid method based on the fault tree analysis (FTA) and Bayesian networks (BNs) are employed to evaluate the team performance quality of advanced cardiac life support (ACLS) procedures in emergency department. According to American Heart Association (AHA) guidelines, a category relying on staff action leading to clinical incidents and also some discussions with emergency medicine experts, a fault tree model for ACLS procedure is obtained based on the human performance. The obtained FTA model is converted into BNs, and some different scenarios are defined to demonstrate the efficiency and flexibility of the presented model of BNs. Also, a sensitivity analysis is conducted to indicate the effects of team leader presence and uncertainty knowledge of experts on the quality of ACLS. The proposed model based on BNs shows that how the results of risk analysis can be closed to reality comparing to the obtained results based on only FTA in medical procedures.

Keywords: advanced cardiac life support, fault tree analysis, Bayesian belief networks, numan performance, healthcare systems

Procedia PDF Downloads 147

Authors: Abraham Motlhabane

Abstract:

This research analysed the written views of science education students regarding the teaching of laboratory work using the online mode. The research adopted the qualitative methodology. The qualitative research was aimed at investigating small and distinct groups normally regarded as a single-site study. Qualitative research was used to describe and analyze the phenomena from the student’s perspective. This means the research began with assumptions of the world view that use theoretical lenses of research problems inquiring into the meaning of individual students. The research was conducted with three groups of students studying for Postgraduate Certificate in Education, Bachelor of Education and honors Bachelor of Education respectively. In each of the study programmes, the science education module is compulsory. Five science education students from each study programme were purposively selected to participate in this research. Therefore, 15 students participated in the research. In order to analysis the data, the data were first printed and hard copies were used in the analysis. The data was read several times and key concepts and ideas were highlighted. Themes and patterns were identified to describe the data. Coding as a process of organising and sorting data was used. The findings of the study are very diverse; some students are in favour of online laboratory whereas other students argue that science can only be learnt through hands-on experimentation.

Keywords: online learning, laboratory work, views, perceptions

Procedia PDF Downloads 145

Authors: Y. Bai, M. Bodlak, V. Frolov, S. Huber, V. Jary, I. Konorov, D. Levit, J. Novy, D. Steffen, O. Subrt, M. Virius

Abstract:

Modern experiments in high energy physics impose great demands on the reliability, the efficiency, and the data rate of Data Acquisition Systems (DAQ). This contribution focuses on the development and deployment of the new communication library DIALOG for the intelligent, FPGA-based Data Acquisition System (iFDAQ) of the COMPASS experiment at CERN. The iFDAQ utilizing a hardware event builder is designed to be able to readout data at the maximum rate of the experiment. The DIALOG library is a communication system both for distributed and mixed environments, it provides a network transparent inter-process communication layer. Using the high-performance and modern C++ framework Qt and its Qt Network API, the DIALOG library presents an alternative to the previously used DIM library. The DIALOG library was fully incorporated to all processes in the iFDAQ during the run 2016. From the software point of view, it might be considered as a significant improvement of iFDAQ in comparison with the previous run. To extend the possibilities of debugging, the online monitoring of communication among processes via DIALOG GUI is a desirable feature. In the paper, we present the DIALOG library from several insights and discuss it in a detailed way. Moreover, the efficiency measurement and comparison with the DIM library with respect to the iFDAQ requirements is provided.

Keywords: data acquisition system, DIALOG library, DIM library, FPGA, Qt framework, TCP/IP

Procedia PDF Downloads 316

Authors: J. K. Alhassan, S. A. Lawal

Abstract:

This work is on decision tree-based classification for the disbursement of scholarship. Tree-based data mining classification technique is used in other to determine the generic rule to be used to disburse the scholarship. The system based on the defined rules from the tree is able to determine the class (status) to which an applicant shall belong whether Granted or Not Granted. The applicants that fall to the class of granted denote a successful acquirement of scholarship while those in not granted class are unsuccessful in the scheme. An algorithm that can be used to classify the applicants based on the rules from tree-based classification was also developed. The tree-based classification is adopted because of its efficiency, effectiveness, and easy to comprehend features. The system was tested with the data of National Information Technology Development Agency (NITDA) Abuja, a Parastatal of Federal Ministry of Communication Technology that is mandated to develop and regulate information technology in Nigeria. The system was found working according to the specification. It is therefore recommended for all scholarship disbursement organizations.

Keywords: classification, data mining, decision tree, scholarship

Procedia PDF Downloads 376

Authors: K. M. Saif Ur Rahman, Razib Mamun, Himica Arjuman, Fida Al Shams

Abstract:

Introduction: Autism spectrum disorder (ASD) has become an emerging neurodevelopmental disorder with increasing prevalence. It has become an important public health issue globally. Many approaches including speech and language therapy (SLT), occupational therapy, behavioral therapy etc. are being applied for the betterment of the ASD patients. This study aims to describe the characteristics of ASD patients and perception of caregiver regarding SLT in Bangladesh. Methods: This cross-sectional study was conducted in a therapy and rehabilitation center at Dhaka city. Caregivers of 48 ASD patients responded regarding their perception of SLT and characteristics of patients. Results: Among 48 ASD patients, 56.3% were between 3 to 5 years age group with a male predominance (87.5%). More than half of the participants (56.3%) initiated SLT at the age of 1-3 years and the majority (43.8%) were taking SLT for less than 1 year. Majority of the patients (64.6%) were taken to a physician for healthcare as a first contact of which 29.2% were referred to SLT by physicians. More than half (56.3%) of the caregivers were moderately satisfied with SLT and most of them (62.5%) mentioned moderate improvement through SLT. Improvement rate was 10-15% in specific symptoms such as eye contact, complex mannerism, pointing, imitation etc. Conclusion: This study reveals the self-reported perception of caregivers on SLT. Despite reported improvements, more exploration of different approaches and intervention for management of ASD is recommended.

Keywords: ASD, characteristics, SLT, Bangladesh

Procedia PDF Downloads 181

Authors: Bharatendra Rai

Abstract:

Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: housing data, feature selection, random forest, Boruta algorithm, root mean square error

Procedia PDF Downloads 323

Authors: M. M. Ali, Ahmed Al- Ani, Derek Eamus, Daniel K. Y. Tan

Abstract:

In this glasshouse study, we developed the new image-based non-destructive technique for detecting leaf P status of different crops such as cotton, tomato and lettuce. Plants were allowed to grow on nutrient media containing different P concentrations, i.e. 0%, 50% and 100% of recommended P concentration (P0 = no P, L; P1 = 2.5 mL 10 L-1 of P and P2 = 5 mL 10 L-1 of P as NaH2PO4). After 10 weeks of growth, plants were harvested and data on leaf P contents were collected using the standard destructive laboratory method and at the same time leaf images were collected by a handheld crop image sensor. We calculated leaf area, leaf perimeter and RGB (red, green and blue) values of these images. This data was further used in the linear discriminant analysis (LDA) to estimate leaf P contents, which successfully classified these plants on the basis of leaf P contents. The data indicated that P deficiency in crop plants can be predicted using the image and morphological data. Our proposed non-destructive imaging method is precise in estimating P requirements of different crop species.

Keywords: image-based techniques, leaf area, leaf P contents, linear discriminant analysis

Procedia PDF Downloads 382

Authors: Azizah Nurdin, Trika Irianta, Mardiah Tahir, Maisuri T. Chalid

Abstract:

Background: Studies of anal incontinence in the general population are rare however its financial healthcare cost is significant. Women attended Hasanuddin University Teaching Hospital and its networking in Makassar, Indonesia was surveyed between February to April 2015 about their obstetrical and gynecological history. Aims: To establish obstetrical risk factor of anal incontinence among women in Makassar. Methods: In a cross sectional face to face interview study, 135 women aged 30 years or more were selected randomly. Participants were asked to complete an anal incontinence questionnaire. Results: From a total sample of 135 respondents, 42,2 % reported has flatulence incontinence. Parity, history of anal sphincter laceration, history of having large baby, history of assisted vaginal delivery were shown have no significant association with anal incontinence, while history of episiotomy was shown have a significant association with anal incontinence (p value < 0.05). The risk of flatulence incontinence was higher among women with history of episiotomy (OR : 2,85, 95 % CI = 1,58- 5,13) Conclusions: This study has confirmed that fecal incontinence is a fairly common symptom. Flatulence incontinence is the most frequent even. An obstetrical factor like episiotomy is one of risk factor that could be avoided in order to prevent anal incontinence.

Keywords: anal incontinence, flatulence incontinence, obstetrical risk factor, women

Procedia PDF Downloads 318

Authors: Rushiraj Heshi, Smriti Bhandari

Abstract:

Master Data Management requires creation of Central repository, applying constraints on Repository and designing processes to manage data. Designing of Repository, constraints on repository and business processes is very tedious and time consuming task for large Enterprise. Hence Visual Repository, constraints and Process (Workflow) modeling is the most critical step in Master Data Management.In this paper, we realize a Visual Modeling tool for implementing Repositories, Constraints and Processes based on Eclipse Plugin using GMF/EMF which follows principles of Model Driven Engineering (MDE).

Keywords: EMF, GMF, GEF, repository, constraint, process

Procedia PDF Downloads 497

Authors: Lily Ingsrisawang, Tasanee Nacharoen

Abstract:

Introduction: The problems of unbalanced data sets generally appear in real world applications. Due to unequal class distribution, many research papers found that the performance of existing classifier tends to be biased towards the majority class. The k -nearest neighbors’ nonparametric discriminant analysis is one method that was proposed for classifying unbalanced classes with good performance. Hence, the methods of discriminant analysis are of interest to us in investigating misclassification error rates for class-imbalanced data of three diabetes risk groups. Objective: The purpose of this study was to compare the classification performance between parametric discriminant analysis and nonparametric discriminant analysis in a three-class classification application of class-imbalanced data of diabetes risk groups. Methods: Data from a healthy project for 599 staffs in a government hospital in Bangkok were obtained for the classification problem. The staffs were diagnosed into one of three diabetes risk groups: non-risk (90%), risk (5%), and diabetic (5%). The original data along with the variables; diabetes risk group, age, gender, cholesterol, and BMI was analyzed and bootstrapped up to 50 and 100 samples, 599 observations per sample, for additional estimation of misclassification error rate. Each data set was explored for the departure of multivariate normality and the equality of covariance matrices of the three risk groups. Both the original data and the bootstrap samples show non-normality and unequal covariance matrices. The parametric linear discriminant function, quadratic discriminant function, and the nonparametric k-nearest neighbors’ discriminant function were performed over 50 and 100 bootstrap samples and applied to the original data. In finding the optimal classification rule, the choices of prior probabilities were set up for both equal proportions (0.33: 0.33: 0.33) and unequal proportions with three choices of (0.90:0.05:0.05), (0.80: 0.10: 0.10) or (0.70, 0.15, 0.15). Results: The results from 50 and 100 bootstrap samples indicated that the k-nearest neighbors approach when k = 3 or k = 4 and the prior probabilities of {non-risk:risk:diabetic} as {0.90:0.05:0.05} or {0.80:0.10:0.10} gave the smallest error rate of misclassification. Conclusion: The k-nearest neighbors approach would be suggested for classifying a three-class-imbalanced data of diabetes risk groups.

Keywords: error rate, bootstrap, diabetes risk groups, k-nearest neighbors

Procedia PDF Downloads 435

Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

Abstract:

Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

Procedia PDF Downloads 169

Authors: Rahmad Wisnu Wardana, Eakachai Warinsiriruk, Sutep Joy-A-Ka

Abstract:

Selecting the most suitable welding process usually depends on experiences or common application in similar companies. However, this approach generally ignores many criteria that can be affecting the suitable welding process selection. Therefore, knowledge automation through knowledge-based systems will significantly improve the decision-making process. The aims of this research propose integrated data envelopment analysis (DEA) and fuzzy credibility constrained programming approach for identifying the best welding process for stainless steel storage tank in the food and beverage industry. The proposed approach uses fuzzy concept and credibility measure to deal with uncertain data from experts' judgment. Furthermore, 12 parameters are used to determine the most appropriate welding processes among six competitive welding processes.

Keywords: welding process selection, data envelopment analysis, fuzzy credibility constrained programming, storage tank

Procedia PDF Downloads 167

Authors: Kayode Balogun, Femi Ayoola

Abstract:

Crime is at alarming rate in this part of world and there are many factors that are contributing to this antisocietal behaviour both among the youths and old. In this work, principal component analysis (PCA) was used as a tool to reduce the dimensionality and to really know those variables that were crime prone in the study region. Data were collected on twenty-eight crime variables from National Bureau of Statistics (NBS) databank for a period of fifteen years, while retaining as much of the information as possible. We use PCA in this study to know the number of major variables and contributors to the crime in the Southwest Nigeria. The results of our analysis revealed that there were eight principal variables have been retained using the Scree plot and Loading plot which implies an eight-equation solution will be appropriate for the data. The eight components explained 93.81% of the total variation in the data set. We also found that the highest and commonly committed crimes in the Southwestern Nigeria were: Assault, Grievous Harm and Wounding, theft/stealing, burglary, house breaking, false pretence, unlawful arms possession and breach of public peace.

Keywords: crime rates, data, Southwest Nigeria, principal component analysis, variables

Procedia PDF Downloads 444

Authors: Hyun-Woo Cho

Abstract:

Detection of incipient abnormal events in production processes is important to improve safety and reliability of manufacturing operations and reduce losses caused by failures. The construction of calibration models for predicting faulty conditions is quite essential in making decisions on when to perform preventive maintenance. This paper presents a multivariate calibration monitoring approach based on the statistical analysis of process measurement data. The calibration model is used to predict faulty conditions from historical reference data. This approach utilizes variable selection techniques, and the predictive performance of several prediction methods are evaluated using real data. The results shows that the calibration model based on supervised probabilistic model yielded best performance in this work. By adopting a proper variable selection scheme in calibration models, the prediction performance can be improved by excluding non-informative variables from their model building steps.

Keywords: calibration model, monitoring, quality improvement, feature selection

Procedia PDF Downloads 356

Authors: Rupali Bhardwaj

Abstract:

Cryptography is the science of using mathematics to encrypt and decrypt data; the data are converted into some other gibberish form, and then the encrypted data are transmitted. The primary purpose of this paper is to provide two levels of security through a two-step process, rather than transmitted the message bits directly, first encrypted it using 2D cellular automata and then scrambled with Arnold Cat Map transformation; it provides an additional layer of protection and reduces the chance of the transmitted message being detected. A comparative analysis on effectiveness of scrambling technique is provided by scrambling degree measurement parameters i.e. Gray Difference Degree (GDD) and Correlation Coefficient.

Keywords: scrambling, cellular automata, Arnold cat map, game of life, gray difference degree, correlation coefficient

Procedia PDF Downloads 378

Authors: Naveed Ghani, Samreen Javed

Abstract:

In today’s heterogeneous network environment, there is a growing demand for distrust clients to jointly execute secure network to prevent from malicious attacks as the defining task of propagating malicious code is to locate new targets to attack. Residual risk is always there no matter what solutions are implemented or whet so ever security methodology or standards being adapted. Security is the first and crucial phase in the field of Computer Science. The main aim of the Computer Security is gathering of information with secure network. No one need wonder what all that malware is trying to do: It's trying to steal money through data theft, bank transfers, stolen passwords, or swiped identities. From there, with the help of our survey we learn about the importance of white listing, antimalware programs, security patches, log files, honey pots, and more used in banks for financial data protection but there’s also a need of implementing the IPV6 tunneling with Crypto data transformation according to the requirements of new technology to prevent the organization from new Malware attacks and crafting of its own messages and sending them to the target. In this paper the writer has given the idea of implementing IPV6 Tunneling Secessions on private data transmission from financial organizations whose secrecy needed to be safeguarded.

Keywords: network worms, malware infection propagating malicious code, virus, security, VPN

Procedia PDF Downloads 358

Authors: Abdallah Al-ZoubI, Mamoun Dmour

Abstract:

The spectrum of IoT devices is becoming widely diversified, entering almost all possible fields and finding applications in industry, health, finance, logistics, education, to name a few. The IoT active endpoint sensors and devices exceeded the 12 billion mark in 2021 and are expected to reach 27 billion in 2025, with over $34 billion in total market value. This sheer rise in numbers and use of IoT devices bring with it considerable concerns regarding data storage, analysis, manipulation and protection. IoT Blockchain-based systems have recently been proposed as a decentralized solution for large-scale data storage and protection. COVID-19 has actually accelerated the desire to utilize IoT devices as it impacted both demand and supply and significantly affected several regions due to logistic reasons such as supply chain interruptions, shortage of shipping containers and port congestion. An IoT-blockchain system is proposed to handle big data generated by a distributed network of sensors and controllers in an interactive manner. The system is designed using the Ethereum platform, which utilizes smart contracts, programmed in solidity to execute and manage data generated by IoT sensors and devices. such as Raspberry Pi 4, Rasbpian, and add-on hardware security modules. The proposed system will run a number of applications hosted by a local machine used to validate transactions. It then sends data to the rest of the network through InterPlanetary File System (IPFS) and Ethereum Swarm, forming a closed IoT ecosystem run by blockchain where a number of distributed IoT devices can communicate and interact, thus forming a closed, controlled environment. A prototype has been deployed with three IoT handling units distributed over a wide geographical space in order to examine its feasibility, performance and costs. Initial results indicated that big IoT data retrieval and storage is feasible and interactivity is possible, provided that certain conditions of cost, speed and thorough put are met.

Keywords: IoT devices, blockchain, Ethereum, big data

Procedia PDF Downloads 150

Authors: Abdul-Rahman Al-Ali

Abstract:

As the number of mobile devices is growing exponentially, it is estimated to connect about 50 million devices to the Internet by the year 2020. At the end of this decade, it is expected that an average of eight connected devices per person worldwide. The 50 billion devices are not mobile phones and data browsing gadgets only, but machine-to-machine and man-to-machine devices. With such growing numbers of devices the Internet of Things (I.o.T) concept is one of the emerging technologies as of recently. Within the smart grid technologies, smart home appliances, Intelligent Electronic Devices (IED) and Distributed Energy Resources (DER) are major I.o.T objects that can be addressable using the IPV6. These objects are called the smart grid internet of things (SG-I.o.T). The SG-I.o.T generates big data that requires high-speed computing infrastructure, widespread computer networks, big data storage, software, and platforms services. A company’s utility control and data centers cannot handle such a large number of devices, high-speed processing, and massive data storage. Building large data center’s infrastructure takes a long time, it also requires widespread communication networks and huge capital investment. To maintain and upgrade control and data centers’ infrastructure and communication networks as well as updating and renewing software licenses which collectively, requires additional cost. This can be overcome by utilizing the emerging computing paradigms such as cloud computing. This can be used as a smart grid enabler to replace the legacy of utilities data centers. The talk will highlight the role of I.o.T, cloud computing services and their development models within the smart grid technologies.

Keywords: intelligent electronic devices (IED), distributed energy resources (DER), internet, smart home appliances

Procedia PDF Downloads 324

Authors: Shishen Xie, Yingda L. Xie

Abstract:

Tuberculosis (TB) is a potentially serious infectious disease that remains a health concern. The Interferon Gamma Release Assay (IGRA) is a blood test to find out if an individual is tuberculous positive or negative. This study applies statistical analysis to the clinical data of interferon-gamma levels of seventy-three subjects who diagnosed pulmonary TB in an anti-tuberculous treatment. Data analysis is performed to determine if there is a significant decline in interferon-gamma levels for the subjects during a period of six months, and to infer if the anti-tuberculous treatment is effective.

Keywords: data analysis, interferon gamma release assay, statistical methods, tuberculosis infection

Procedia PDF Downloads 306