Search results for: electronic data interchange

Authors: Rabbia Idrees, Ananda Maiti, Saurabh Garg, Muhammad Bilal Amin

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IoT devices are the basic building blocks of IoT network that generate enormous volume of real-time and high-speed data to help organizations and companies to take intelligent decisions. To integrate this enormous data from multisource and transfer it to the appropriate client is the fundamental of IoT development. The handling of this huge quantity of devices along with the huge volume of data is very challenging. The IoT devices are battery-powered and resource-constrained and to provide energy efficient communication, these IoT devices go sleep or online/wakeup periodically and a-periodically depending on the traffic loads to reduce energy consumption. Sometime these devices get disconnected due to device battery depletion. If the node is not available in the network, then the IoT network provides incomplete, missing, and inaccurate data. Moreover, many IoT applications, like vehicle tracking and patient tracking require the IoT devices to be mobile. Due to this mobility, If the distance of the device from the sink node become greater than required, the connection is lost. Due to this disconnection other devices join the network for replacing the broken-down and left devices. This make IoT devices dynamic in nature which brings uncertainty and unreliability in the IoT network and hence produce bad quality of data. Due to this dynamic nature of IoT devices we do not know the actual reason of abnormal data. If data are of poor-quality decisions are likely to be unsound. It is highly important to process data and estimate data quality before bringing it to use in IoT applications. In the past many researchers tried to estimate data quality and provided several Machine Learning (ML), stochastic and statistical methods to perform analysis on stored data in the data processing layer, without focusing the challenges and issues arises from the dynamic nature of IoT devices and how it is impacting data quality. A comprehensive review on determining the impact of dynamic nature of IoT devices on data quality is done in this research and presented a data quality model that can deal with this challenge and produce good quality of data. This research presents the data quality model for the sensors monitoring water quality. DBSCAN clustering and weather sensors are used in this research to make data quality model for the sensors monitoring water quality. An extensive study has been done in this research on finding the relationship between the data of weather sensors and sensors monitoring water quality of the lakes and beaches. The detailed theoretical analysis has been presented in this research mentioning correlation between independent data streams of the two sets of sensors. With the help of the analysis and DBSCAN, a data quality model is prepared. This model encompasses five dimensions of data quality: outliers’ detection and removal, completeness, patterns of missing values and checks the accuracy of the data with the help of cluster’s position. At the end, the statistical analysis has been done on the clusters formed as the result of DBSCAN, and consistency is evaluated through Coefficient of Variation (CoV).

Keywords: clustering, data quality, DBSCAN, and Internet of things (IoT)

Procedia PDF Downloads 139

Authors: Haythem Yahyaoui, Samir Moalla, Mounir Bouden, Skander ghorbel

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Nowadays, Cloud environments are becoming a need for companies, this new technology gives the opportunities to access to the data anywhere and anytime, also an optimized and secured access to the resources and gives more security for the data which stored in the platform, however, some companies do not trust Cloud providers, in their point of view, providers can access and modify some confidential data such as bank accounts, many works have been done in this context, they conclude that encryption methods realized by providers ensure the confidentiality, although, they forgot that Cloud providers can decrypt the confidential resources. The best solution here is to apply some modifications on the data before sending them to the Cloud in the objective to make them unreadable. This work aims on enhancing the quality of service of providers and improving the trust of the customers.

Keywords: cloud, confidentiality, cryptography, security issues, trust issues

Procedia PDF Downloads 378

Authors: Ilker Ali Ozkan

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In this study, an artificial neural network model has been developed to estimate chronic kidney failure which is a common disease. The patients’ age, their blood and biochemical values, and 24 input data which consists of various chronic diseases are used for the estimation process. The input data have been subjected to preprocessing because they contain both missing values and nominal values. 147 patient data which was obtained from the preprocessing have been divided into as 70% training and 30% testing data. As a result of the study, artificial neural network model with 25 neurons in the hidden layer has been found as the model with the lowest error value. Chronic kidney failure disease has been able to be estimated accurately at the rate of 99.3% using this artificial neural network model. The developed artificial neural network has been found successful for the estimation of chronic kidney failure disease using clinical data.

Keywords: estimation, artificial neural network, chronic kidney failure disease, disease diagnosis

Procedia PDF Downloads 447

Authors: Hassan Noun, Christian Urban-Seelmann, Mohamed Abdelfattah, Guillaume Zeller, Rajesh G., Iryna Mozgova, Roland Lachmayer

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Several modules in automotive are usually modified and adapted for various project-specific applications. Due to a standardized safety concept, high reusability is accessible. A safety element out of context (SEooC) according to ISO 26262 can be a suitable approach. Based on the same safety concept and analysis, common modules can reach high usability. For developing according to a module out of context, an appropriate and detailed development approach is required. This paper shows how to derive these development processes for platform modules. Therefore, the detailed approach to the safety element out of context is derived. The aim is to create a detailed workflow for all phases of the development and integration of any kind of system modules. As an application example, an automotive project for an actuator control module is considered.

Keywords: functional safety, engineering processes, system engineering, electronic engineering

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Authors: G. N. Srinivas, K. Raju

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This paper presents Markov processes for determining the reliability indices of distribution system. The continuous Markov modeling is applied to a complex radial distribution system and electrical equivalent circuits are developed for the modeling. In general PSPICE is being used for electrical and electronic circuits and various applications of power system like fault analysis, transient analysis etc. In this paper, the SPICE modeling equivalent circuits which are developed are applied in a novel way to Distribution System reliability analysis. These circuits are simulated using PSPICE software to obtain the state probabilities, the basic and performance indices. Thus the basic indices and the performance indices obtained by this method are compared with those obtained by FMEA technique. The application of the concepts presented in this paper are illustrated and analyzed for IEEE-Roy Billinton Test System (RBTS).

Keywords: distribution system, Markov Model, reliability indices, spice simulation

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Authors: Lin Li, P. G. R. N. I. Pussella

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When representing spatial data and their attributes on different types of maps, the scale plays a key role in the process of map generalization. The process is consisted with two main operators such as selection and omission. Once some data were selected, they would undergo of several geometrical changing processes such as elimination, simplification, smoothing, exaggeration, displacement, aggregation and size reduction. As a result of these operations at different levels of data, the geometry of the spatial features such as length, sinuosity, orientation, perimeter and area would be altered. This would be worst in the case of preparation of small scale maps, since the cartographer has not enough space to represent all the features on the map. What the GIS users do is when they wanted to analyze a set of spatial data; they retrieve a data set and does the analysis part without considering very important characteristics such as the scale, the purpose of the map and the degree of generalization. Further, the GIS users use and compare different maps with different degrees of generalization. Sometimes, GIS users are going beyond the scale of the source map using zoom in facility and violate the basic cartographic rule 'it is not suitable to create a larger scale map using a smaller scale map'. In the study, the effect of map generalization for GIS analysis would be discussed as the main objective. It was used three digital maps with different scales such as 1:10000, 1:50000 and 1:250000 which were prepared by the Survey Department of Sri Lanka, the National Mapping Agency of Sri Lanka. It was used common features which were on above three maps and an overlay analysis was done by repeating the data with different combinations. Road data, River data and Land use data sets were used for the study. A simple model, to find the best place for a wild life park, was used to identify the effects. The results show remarkable effects on different degrees of generalization processes. It can see that different locations with different geometries were received as the outputs from this analysis. The study suggests that there should be reasonable methods to overcome this effect. It can be recommended that, as a solution, it would be very reasonable to take all the data sets into a common scale and do the analysis part.

Keywords: generalization, GIS, scales, spatial analysis

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Authors: LuoJiaoyang, Yu Hongyang

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In this paper, we experimented with a new approach to multimodal identification using RGB, RGB-D and voice data. The multimodal combination of RGB and voice data has been applied in tasks such as emotion recognition and has shown good results and stability, and it is also the same in identity recognition tasks. We believe that the data of different modalities can enhance the effect of the model through mutual reinforcement. We try to increase the three modalities on the basis of the dual modalities and try to improve the effectiveness of the network by increasing the number of modalities. We also implemented the single-modal identification system separately, tested the data of these different modalities under clean and noisy conditions, and compared the performance with the multimodal model. In the process of designing the multimodal model, we tried a variety of different fusion strategies and finally chose the fusion method with the best performance. The experimental results show that the performance of the multimodal system is better than that of the single modality, especially in dealing with noise, and the multimodal system can achieve an average improvement of 5%.

Keywords: multimodal, three modalities, RGB-D, identity verification

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Authors: A. Jomartov, A. Tuleshov, B. Tultaev

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One of the main tasks in the development of textile machinery is to increase the rapidity of automatic looms, and consequently, their productivity. With increasing automatic loom speeds, the dynamic loads on their separate mechanisms and moving joints sharply increase. Dynamic research allows us to determine the weakest mechanisms of the automatic loom. The modern automatic loom consists of a large number of structurally different mechanisms. These are cam, lever, gear, friction and combined cyclic mechanisms. The modern automatic loom contains various mechatronic devices: A device for the automatic removal of faulty weft, electromechanical drive warp yarns, electronic controllers, servos, etc. In the paper, we consider the multibody dynamic model of the automatic loom on the software complex SimulationX. SimulationX is multidisciplinary software for modeling complex physical and technical facilities and systems. The multibody dynamic model of the automatic loom allows consideration of: The transition processes, backlash at the joints and nodes, the force of resistance and electric motor performance.

Keywords: automatic loom, dynamics, model, multibody, SimulationX

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Authors: Flora Babongo, Valerie Chavez

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Inferring causality from observational data is one of the fundamental subjects, especially in quantitative finance. So far most of the papers analyze additive noise models with either linearity, nonlinearity or Gaussian noise. We fill in the gap by providing a nonlinear and non-gaussian causal multiplicative noise model that aims to distinguish the cause from the effect using a two steps method based on Bayesian additive models for location, scale and shape (BAMLSS) and on causal additive models (CAM). We have tested our method on simulated and real data and we reached an accuracy of 0.86 on average. As real data, we considered the causality between financial indices such as S&P 500, Nasdaq, CAC 40 and Nikkei, and companies' log-returns. Our results can be useful in inferring causality when the data is heteroskedastic or non-injective.

Keywords: causal inference, DAGs, BAMLSS, financial index

Procedia PDF Downloads 151

Authors: Madiha Liaqat, Rehan Ahmed Khan, Shahid Kamal

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Multiple imputation with delta adjustment is a versatile and transparent technique for addressing univariate missing data in the presence of various missing mechanisms. This approach allows for the exploration of sensitivity to the missing-at-random (MAR) assumption. In this review, we outline the delta-adjustment procedure and illustrate its application for assessing the sensitivity to deviations from the MAR assumption. By examining diverse missingness scenarios and conducting sensitivity analyses, we gain valuable insights into the implications of missing data on our analyses, enhancing the reliability of our study's conclusions. In our study, we focused on assessing logPSA, a continuous biomarker in incomplete prostate cancer data, to examine the robustness of conclusions against plausible departures from the MAR assumption. We introduced several approaches for conducting sensitivity analyses, illustrating their application within the pattern mixture model (PMM) under the delta adjustment framework. This proposed approach effectively handles missing data, particularly loss to follow-up.

Keywords: loss to follow-up, incomplete response, multiple imputation, sensitivity analysis, prostate cancer

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Authors: Guru Prakash, Revanth Dugalam

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A road’s condition often deteriorates due to harsh loading such as overload due to trucks, and severe environmental conditions such as heavy rain, snow load, and cyclic loading. In absence of proper maintenance planning, this results in potholes, wide cracks, bumps, and increased roughness of roads. In this paper, a data-driven model will be developed to detect these damages using vibration and image signals. The key idea of the proposed methodology is that the road anomaly manifests in these signals, which can be detected by training a machine learning algorithm. The use of various machine learning techniques such as the support vector machine and Radom Forest method will be investigated. The proposed model will first be trained and tested with artificially simulated data, and the model architecture will be finalized by comparing the accuracies of various models. Once a model is fixed, the field study will be performed, and data will be collected. The field data will be used to validate the proposed model and to predict the future road’s health condition. The proposed will help to automate the road condition monitoring process, repair cost estimation, and maintenance planning process.

Keywords: SVM, data-driven, road health monitoring, pot-hole

Procedia PDF Downloads 86

Authors: U. Borasi, Amit Kr. Jain, Rakesh, Piyush Jain

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Data collection, data preparation, model training, model evaluation, and deployment are all processes in a typical machine learning workflow. Training data needs to be gathered and organised. This often entails collecting a sizable dataset and cleaning it to remove or correct any inaccurate or missing information. Preparing the data for use in the machine learning model requires pre-processing it after it has been acquired. This often entails actions like scaling or normalising the data, handling outliers, selecting appropriate features, reducing dimensionality, etc. This pre-processed data is then used to train a model on some machine learning algorithm. After the model has been trained, it needs to be assessed by determining metrics like accuracy, precision, and recall, utilising a test dataset. Every time a new model is built, both data pre-processing and model training—two crucial processes in the Machine learning (ML) workflow—must be carried out. Thus, there are various Machine Learning algorithms that can be employed for every single approach to data pre-processing, generating a large set of combinations to choose from. Example: for every method to handle missing values (dropping records, replacing with mean, etc.), for every scaling technique, and for every combination of features selected, a different algorithm can be used. As a result, in order to get the optimum outcomes, these tasks are frequently repeated in different combinations. This paper suggests a simple architecture for organizing this largely produced “combination set of pre-processing steps and algorithms” into an automated workflow which simplifies the task of carrying out all possibilities.

Keywords: machine learning, automation, AUTOML, architecture, operator pool, configuration, scheduler

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Authors: Shilan Rahmani Azr, Siavash Emtiyaz

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Recent development in computing and communicative technologies leads to much easier mobile accessibility to the information. Users can access to the information in different places using various deceives in which the care variety of abilities. Meanwhile, the format and details of electronic documents are changing each day. In these cases, a mismatch is created between content and client’s abilities. Recently the service-oriented content adaption has been developed which the adapting tasks are dedicated to some extended services. In this method, the main problem is to choose the best appropriate service among accessible and distributed services. In this paper, a method for determining the optimal path to the best services, based on the quality control parameters and user preferences, is proposed using max heap tree. The efficiency of this method in contrast to the other previous methods of the content adaptation is related to the determining the optimal path of the best services which are measured. The results show the advantages and progresses of this method in compare of the others.

Keywords: service-oriented content adaption, QoS, max heap tree, web services

Procedia PDF Downloads 259

Authors: Ratna Wulandari, Shahidin

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This article aims to see how the Game Based Learning learning model with the Roaming The Stairs game makes a tajribi method can make PAI lessons active and interactive learning. This research uses a qualitative approach with a case study type of research. Data collection methods were carried out using interviews, observation, and documentation. Data analysis was carried out through the stages of data reduction, data display, and verification and drawing conclusions. The data validity test was carried out using the triangulation method. and drawing conclusions. The results of the research show that (1) children in grades 9A, 9B, and 9C like learning PAI using the Roaming The Stairs game (2) children in grades 9A, 9B, and 9C are active and can work in groups to solve problems in the Roaming The Stairs game (3) the class atmosphere becomes fun with learning method, namely learning while playing.

Keywords: game based learning, Roaming The Stairs, Tajribi PAI

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Authors: Roghaye Behroozi

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The development of functionalized metal complexes and nanomaterials has gained significant attention due to their potential in catalyzing selective oxidation and coupling reactions. These catalysts play a crucial role in various industrial and pharmaceutical processes, enhancing the efficiency, selectivity, and sustainability of chemical reactions. This research aims to design and synthesize new functionalized metal complexes and nanomaterials to explore their catalytic applications in the selective oxidation of alcohols and coupling reactions, focusing on improving yield, selectivity, and catalyst reusability. The study involves the synthesis of a nickel Schiff base complex stabilized within 41-MCM as a heterogeneous catalyst. A Schiff base ligand derived from glycine was used to create a tin (IV) metal complex characterized through spectroscopic techniques and computational analysis. Additionally, iron-based magnetic nanoparticles functionalized with melamine were synthesized for catalytic evaluation. Lastly, a palladium (IV) complex was prepared, and its oxidative stability was analyzed. The nickel Schiff base catalyst showed high selectivity in converting primary and secondary alcohols to aldehydes and ketones, with yields ranging from 73% to 90%. The tin (IV) complex demonstrated accurate structural and electronic properties, with consistent results between experimental and computational data. The melamine-functionalized iron nanoparticles exhibited efficient catalytic activity in producing triazoles, with enhanced reaction speed and reusability. The palladium (IV) complex displayed remarkable stability and low reactivity towards C–C bond formation due to its symmetrical structure. The synthesized metal complexes and nanomaterials demonstrated significant potential as efficient, selective, and reusable catalysts for oxidation and coupling reactions. These findings pave the way for developing environmentally friendly and cost-effective catalytic systems for industrial applications.

Keywords: catalysts, Schiff base complexes, metal-organic frameworks, oxidation reactions, nanoparticles, reusability

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Authors: Sanjay Upadhyay, Om Prakash Pandey

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Hydrogen has been promoted as an alternative source of energy, which is renewable, cost-effective, and nature-friendly. Hydrogen evolution reaction (HER) can be used for mass production of hydrogen at a very low cost through electrochemical water splitting. An active and efficient electrocatalyst is required to perform this reaction. Till date, platinum (Pt) is a stable and efficient electrocatalyst towards HER. But its high cost and low abundance hiders its large scale uses. Molybdenum carbide having a similar electronic structure to platinum can be a great alternative to costly platinum. In this study, pure phase molybdenum carbide (Mo₂C) has been synthesized in a single step. Synthesis temperature and holding time have been optimized to obtain pure phases of Mo₂C. The surface, structural and morphological properties of as-synthesized compounds have been studied. The HER activity of as-synthesized compounds has been explored in detail.

Keywords: capacitance, hydrogen fuel, molybdenum carbide, nanoparticles

Procedia PDF Downloads 204

Authors: Lorenzo Bonoldi, Gianluca Memoli, Abdelhalim Azbaid El Ouahabi

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In this paper, a series of ring-shaped attenuators utilizing Helmholtz and quarter wavelength resonators in variable, fixed, and combined configurations have been manufactured using a 3D printer. We illustrate possible uses by incorporating such devices into musical instruments (e.g. in acoustic guitar sound holes) and audio speakers with a view to controlling such devices tonal emissions without electronic equalization systems. Numerical investigations into the transmission loss values of these ring-shaped attenuators using finite element method simulations (COMSOL Multiphysics) have been presented in the frequency range of 100– 1000 Hz. We compare such results for each attenuator model with experimental measurements using different driving sources such as white noise, a maximum-length sequence (MLS), square and sine sweep pulses, and point scans in the frequency domain. Finally, we present a preliminary discussion on the comparison of numerical and experimental results.

Keywords: equaliser, metamaterials, musical, instruments

Procedia PDF Downloads 174

Authors: Rifky Cahya Putra

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At this time of corona makes some countries affected difficult. As a result, many traders or companies are difficult to work in this pandemic era. So human activities in some fields must implement a new lifestyle or known as new normal. The transition from the one activity to another certainly requires high adaptation. So that almost in all sectors experience the impact of this phase, on of which is the wedding organizer. This research aims to find out what strategies are used so that the company can run in this pandemic. Techniques in data collection in the form interview to the owner of the wedding organizer and his team. Data analysis qualitative descriptive use interactive model analysis consisting of three main things, namely data reduction, data presentaion, and conclusion. For the result of the interview, the conclusion is that there are three strategies consisting of social media, sponsorship, and promotion.

Keywords: strategy, wedding organizer, pandemic, indonesia

Procedia PDF Downloads 135

Authors: Zhou Mo, Dennis Chow

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In the dynamic positioning system (DPS) for vessels, the reliable information transmission between each note basically relies on the wireless protocols. From the perspective of cluster-based routing pro-tocols for wireless sensor networks, the data fusion technology based on the sleep scheduling mechanism and remaining energy in network layer is proposed, which applies the sleep scheduling mechanism to the routing protocols, considering the remaining energy of node and location information when selecting cluster-head. The problem of uneven distribution of nodes in each cluster is solved by the Equilibrium. At the same time, Classified Forwarding Mechanism as well as Redelivery Policy strategy is adopted to avoid congestion in the transmission of huge amount of data, reduce the delay in data delivery and enhance the real-time response. In this paper, a simulation test is conducted to improve the routing protocols, which turns out to reduce the energy consumption of nodes and increase the efficiency of data delivery.

Keywords: DPS for vessel, wireless sensor network, data fusion, routing protocols

Procedia PDF Downloads 467

Authors: Raya Hamed Hilal Al Maamari

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The Gulf societies have been undergoing radical changes due to the technology transfer. It altered the humanities attitudes, especially, youth habits as they have become an addicted to using social media. This study aimed to find out the ratio of social media in guiding youth to participate with government’s institutions in decision-making and developing their societies. The study considered a descriptive study, social survey method was used on a sample of 100 young from different gulf countries, using an electronic questionnaire, as well as, some interviews with famous leaders of youth groups. Finally, the researcher suggested some effective ways activate youth efforts using social media in an effective manner to plan for the development policy in the community. The findings illustrated that social media plays a vital role in encouraging youth to participate enthusiastically in providing services. Noticeably, social media contains large numbers of youth. Therefore, the influences will be widely and feasible. Moreover, the study indicated the fact that most of the youth teamwork started in social media. Then, it has been growing in the real society.

Keywords: community, participation, social media, youth

Procedia PDF Downloads 375

Authors: Ahmed Fradi

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In a digital world in perpetual evolution and acceleration, data more and more voluminous, rich and varied, the new software solutions emerged with the Big Data phenomenon offer new opportunities to the company enabling it not only to optimize its business and to evolve its production model, but also to reorganize itself to increase competitiveness and to identify new strategic axes. Design and manufacturing industrial companies, like the others, face these challenges, data represent a major asset, provided that they know how to capture, refine, combine and analyze them. The objective of our paper is to propose a solution allowing geometric and topological information extraction from 3D CAD model (precisely STEP files) databases, with specific algorithm based on the programming paradigm MapReduce. Our proposal is the first step of our future approach to 3D CAD object retrieval.

Keywords: Big Data, MapReduce, 3D object retrieval, CAD, STEP format

Procedia PDF Downloads 541

Authors: Phuoc Si Nguyen

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Frequency transformation with Pascal matrix equations is a method for transforming an electronic filter (analogue or digital) into another filter. The technique is based on frequency transformation in the s-domain, bilinear z-transform with pre-warping frequency, inverse bilinear transformation and a very useful application of the Pascal’s triangle that simplifies computing and enables calculation by hand when transforming from one filter to another. This paper will introduce two methods to transform a filter into a digital filter: frequency transformation from the s-domain into the z-domain; and frequency transformation in the z-domain. Further, two Pascal matrix equations are derived: an analogue to digital filter Pascal matrix equation and a digital to digital filter Pascal matrix equation. These are used to design a desired digital filter from a given filter.

Keywords: frequency transformation, bilinear z-transformation, pre-warping frequency, digital filters, analog filters, pascal’s triangle

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Authors: R. K. Pateriya, Jyoti Bharti

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This paper presents an approach for data hiding methods which provides a secret communication between sender and receiver. The data is hidden in gray-scale images and the boundary of gray-scale image is used to store the mapping information. In this an approach data is in ASCII format and the mapping is in between ASCII value of hidden message and pixel value of cover image, since pixel value of an image as well as ASCII value is in range of 0 to 255 and this mapping information is occupying only 1 bit per character of hidden message as compared to 8 bit per character thus maintaining good quality of stego image.

Keywords: ASCII value, cover image, PSNR, pixel value, stego image, secret message

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Authors: Gunasekaran Raja, Kottilingam Kottursamy, Rajakumar Arul, Ramkumar Jayaraman, Krithika Sairam, Lakshmi Ravi

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The synchronization server maintains a dynamically changing cache, which contains the data items which were requested and collected by the mobile node from the server. The order and presence of tuples in the cache changes dynamically according to the frequency of updates performed on the data, by the server and client. To synchronize, the data which has been modified by client and the server at an instant are collected, batched together by the type of modification (insert/ update/ delete), and sorted according to their update frequencies. This ensures that the DCASH (Dynamic Cache Synchronization Algorithm for Heterogeneous Reverse Y synchronizing Mobile Database Systems) gives priority to the frequently accessed data with high usage. The optimal memory management algorithm is proposed to manage data items according to their frequency, theorems were written to show the current mobile data activity is reverse Y in nature and the experiments were tested with 2g and 3g networks for various mobile devices to show the reduced response time and energy consumption.

Keywords: mobile databases, synchronization, cache, response time

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Authors: Supunmali Ahangama, Danny Chiang Choon Poo

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Health analytics (HA) is used in healthcare systems for effective decision-making, management, and planning of healthcare and related activities. However, user resistance, the unique position of medical data content, and structure (including heterogeneous and unstructured data) and impromptu HA projects have held up the progress in HA applications. Notably, the accuracy of outcomes depends on the skills and the domain knowledge of the data analyst working on the healthcare data. The success of HA depends on having a sound process model, effective project management and availability of supporting tools. Thus, to overcome these challenges through an effective process model, we propose an HA process model with features from the rational unified process (RUP) model and agile methodology.

Keywords: agile methodology, health analytics, unified process model, UML

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Authors: Maximilian Winkens, Matthias Goerke

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The state-oriented maintenance enables the preventive intervention before the failure of a component and guarantees avoidance of expensive breakdowns. Because the timing of the maintenance is defined by the component’s state, the remaining service life can be exhausted to the limit. The basic requirement for the state-oriented maintenance is the ability to define the component’s state. New potential for this is offered by gentelligent components. They are developed at the Corporative Research Centre 653 of the German Research Foundation (DFG). Because of their sensory ability they enable the registration of stresses during the component’s use. The data is gathered and evaluated. The methodology developed determines the current state of the gentelligent component based on the gathered data. This article presents this methodology as well as current research. The main focus of the current scientific work is to improve the quality of the state determination based on the life-cycle data analysis. The methodology developed until now evaluates the data of the usage phase and based on it predicts the timing of the gentelligent component’s failure. The real failure timing though, deviate from the predicted one because the effects from the production phase aren’t considered. The goal of the current research is to develop a methodology for state determination which considers both production and usage data.

Keywords: state-oriented maintenance, life-cycle data, gentelligent component, preventive intervention

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Authors: Ali Ulvi Uzer

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Data reduction is an important topic in the field of pattern recognition applications. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. The Principal Component Analysis (PCA) method is frequently used for data reduction. The Support Vector Machine (SVM) method is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, two boreholes taken from the soil sample was used. The classification accuracies for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising.

Keywords: feature selection, sequential forward selection, support vector machines, soil sample

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Authors: Sabri Serkan Güllüoğlu

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Analysis can be defined as the process of gathering, recording and researching data related to products and services, in order to learn something. But for marketers, analyses are not only used for learning but also an essential and critical part of the business, because this allows companies to offer products or services which are focused and well targeted. Market analysis also identify market trends, demographics, customer’s buying habits and important information on the competition. Data mining is used instead of traditional research, because it extracts predictive information about customer and sales from large databases. In contrast to traditional research, data mining relies on information that is already available. Simply the goal is to improve the efficiency of supermarkets. In this study, the purpose is to find dependency on products. For instance, which items are bought together, using association rules in data mining. Moreover, this information will be used for improving the profitability of customers such as increasing shopping time and sales of fewer sold items.

Keywords: data mining, association rule mining, market basket analysis, purchasing

Procedia PDF Downloads 483

Authors: Rizka Aisha Rahmi Hariadi, Chao Ou-Yang, Han-Cheng Wang, Rajesri Govindaraju

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As the increasing of medical check-up popularity, there are a huge number of medical check-up data stored in database and have not been useful. These data actually can be very useful for future strategic planning if we mine it correctly. In other side, a lot of patients come with unpredictable coming and also limited available facilities make medical check-up service offered by hospital not maximal. To solve that problem, this study used those medical check-up data to predict patient re-coming. Sequential pattern mining (SPM) and association rules method were chosen because these methods are suitable for predicting patient re-coming using sequential data. First, based on patient personal information the data was grouped into … groups then discriminant analysis was done to check significant of the grouping. Second, for each group some frequent patterns were generated using SPM method. Third, based on frequent patterns of each group, pairs of variable can be extracted using association rules to get general pattern of re-coming patient. Last, discussion and conclusion was done to give some implications of the results.

Keywords: patient re-coming, medical check-up, health examination, data mining, sequential pattern mining, association rules, discriminant analysis

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Authors: Samiah Alammari, Nassim Ammour

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When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on HSI dataset Indian Pines. The results confirm the capability of the proposed method.

Keywords: continual learning, data reconstruction, remote sensing, hyperspectral image segmentation

Procedia PDF Downloads 266