Search results for: sensor node data processing

Authors: Rokhmatul Insani, Hira Laksmiwati Soemitro

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Business Intelligence is a methodology that exploits the data to produce information and knowledge systematically, business intelligence can support the decision-making process. Some methods in business intelligence are data warehouse and data mining. A data warehouse can store historical data from transactional data. For data modelling in data warehouse, we apply dimensional modelling by Kimball. While data mining is used to extracting patterns from the data and get insight from the data. Data mining has many techniques, one of which is segmentation. For profiling of telecommunication customer, we use customer segmentation according to customer’s usage of services, customer invoice and customer payment. Customers can be grouped according to their characteristics and can be identified the profitable customers. We apply K-Means Clustering Algorithm for segmentation. The input variable for that algorithm we use RFM (Recency, Frequency and Monetary) model. All process in data mining, we use tools IBM SPSS modeller.

Keywords: business intelligence, customer segmentation, data warehouse, data mining

Procedia PDF Downloads 483

Authors: A. F. C. Nassar, D. K. Tessler, L. Okuda, C. Del Fava, D. P. Chiebao, A. H. C. N. Romaldini, A. P. Alvim, M. J. Sanchez-Vazquez, M. S. Rosa, J. C. Pompei, R. Harakava, M. C. S. Araujo, G. H. F. Marques, E. M. Pituco

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Glanders is a zoonotic disease of Equidae caused by the bacterium Burkholderia mallei presented in acute or chronic clinical forms with inflammatory nodules in the respiratory tract, lymphangitis and caseous lymph nodes. There is not a treatment with veterinary drugs to this life-threatening disease; thus, its occurrence must be notified to official animal health services and any infected animal must be eliminated. This study aims to detect B. mallei from horses euthanized in outbreaks of glanders in Brazil, providing a better understanding of the bacterial characteristics and determine a proper protocol for isolation. The work was carried out with the collaboration of the Ministry of Agriculture and the Sao Paulo State Animal Health Department, while its procedures were approved by the Committee of Ethics in Animal Experimentation from the Instituto Biologico (CETEA n°156/2017). To the present time, 16 horses from farms with outbreaks of glanders detected by complement fixation test (CFT) serology method were analyzed. During the necropsy, samples of possibly affected organs (lymph nodes, lungs, heart, liver, spleen, kidneys and trachea) were collected for bacterial isolation, molecular tests and pathology. Isolation was performed using two enriched mediums, a potato infusion agar with 5% sheep blood, 4% glycerol and antibiotics (penicilin100U/ mL), and another with the same ingredients except the antibiotic. A PCR protocol was modified for this study using primers design to identify a region of the Flip gen of B. mallei. Thru isolation, 12.5% (2/16) animals were confirmed positive using only the enriched medium with antibiotic and confirmed by PCR: from mediastinal and submandibular lymph nodes and lungs in one animal and from mediastinal lymph node in the other. The detection of the bacterium using PCR showed positivity of 100% (16/16) horses from 144 samples of organs. Pathology macroscopic lesions observed were catarrhal nasal discharge, fetlock ulcers, emaciation, lymphangitis in limbs, suppurative lymphangitis, lymph node enlargement, star shaped liver, and spleen scars, adherence of the renal capsule, pulmonary hemorrhage, and miliary nodules. Microscopic lesions were suppurative bronchopneumonia with microabscesses and Langhans giant cells in lungs; lymph nodes with abscesses and intense lymphoid reaction; hemosiderosis and abscesses in spleen. Positive samples on PCR will be sequenced later and analyzed comparing with previous records in the literature. A throughout description of the recent acute cases of glanders occurring in Brazil and characterization of the bacterium related will contribute to advances in the knowledge of the pathogenicity, clinical symptoms, and epidemiology of this zoonotic disease. Acknowledgment: This project is sponsored by FAPESP.

Keywords: equines, bacterial isolation, zoonosis, PCR, pathology

Procedia PDF Downloads 138

Authors: Abdul Basit Kiani

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Web technologies are growing rapidly in the current era with the increasing development of the web, various novel web technologies emerged to web applications, compared to HTML. JavaScript is the language that provided a dynamic web site which actively interacts with users. The JavaScript language supports the Model View Controller (MVC) architecture that maintains a readable code and clearly separates parts of the program code. Our research is focused on the comparison of the popular JavaScript frameworks; Angular JS, Django, Node JS, Laravel. These frameworks are rely on MVC. In this paper, we will discuss the merits and demerits of each framework, the influence on the application speed, testing methods, for example, JS applications, and methods to advance code security.

Keywords: java script, react, nodejs, htmlcsss

Procedia PDF Downloads 123

Authors: Dong Seop Lee, Byung Sik Kim

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In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. These artificial intelligence technologies are applied in various services, including disaster management. Disaster information management does not just support disaster work, but it is also the foundation of smart disaster management. Furthermore, it gets historical disaster information using artificial intelligence technology. Disaster information is one of important elements of entire disaster cycle. Disaster information management refers to the act of managing and processing electronic data about disaster cycle from its’ occurrence to progress, response, and plan. However, information about status control, response, recovery from natural and social disaster events, etc. is mainly managed in the structured and unstructured form of reports. Those exist as handouts or hard-copies of reports. Such unstructured form of data is often lost or destroyed due to inefficient management. It is necessary to manage unstructured data for disaster information. In this paper, the Optical Character Recognition approach is used to convert handout, hard-copies, images or reports, which is printed or generated by scanners, etc. into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored in the disaster database system. Gathering and creating disaster information based on Optical Character Recognition for unstructured data is important element as realm of the smart disaster management. In this paper, Korean characters were improved to over 90% character recognition rate by using upgraded OCR. In the case of character recognition, the recognition rate depends on the fonts, size, and special symbols of character. We improved it through the machine learning algorithm. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle such as historical disaster progress, damages, response, and recovery. The expected effect of this research will be able to apply it to smart disaster management and decision making by combining artificial intelligence technologies and historical big data.

Keywords: disaster information management, unstructured data, optical character recognition, machine learning

Procedia PDF Downloads 129

Authors: Jimeng Shi, Mahek Jain, Giri Narasimhan

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Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed-length window in the past as an explicit input. In this paper, we study how the performance of predictive models changes as a function of different look-back window sizes and different amounts of time to predict the future. We also consider the performance of the recent attention-based Transformer models, which have had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

Keywords: air quality prediction, deep learning algorithms, time series forecasting, look-back window

Procedia PDF Downloads 154

Authors: Xinxin Ma, Guangze Tang, Shuxin Yang, Jinguang He, Fan Zhang, Peiling Sun, Ming Liu, Minyu Sun, Liqin Wang

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As a traditional surface hardening technique, shot peening is widely used in industry. By using shot peening, a residual compressive stress is formed in the surface which is beneficial for improving the fatigue life of metal materials. At the same time, very fine grains and high density defects are generated in the surface layer which enhances the surface hardness, either. However, most of the processes are carried out at room temperature. For high strength steel, such as M50, the thickness of the strengthen layer is limited. In order to obtain a thick strengthen surface layer, elevated temperature shot peening was carried out in this work by using Φ1mm cast ion balls with a speed of 80m/s. Considering the tempering temperature of M50 steel is about 550 oC, the processing temperature was in the range from 300 to 500 oC. The effect of processing temperature and processing time of shot peening on distribution of residual stress and surface hardness was investigated. As we known, the working temperature of M50 steel can be as high as 315 oC. Because the defects formed by shot peening are unstable when the working temperature goes higher, it is worthy to understand what happens during the shot peening process, and what happens when the strengthen samples were kept at a certain temperature. In our work, the shot peening time was selected from 2 to 10 min. And after the strengthening process, the samples were annealed at various temperatures from 200 to 500 oC up to 60 h. The results show that the maximum residual compressive stress is near 900 MPa. Compared with room temperature shot peening, the strengthening depth of 500 oC shot peening sample is about 2 times deep. The surface hardness increased with the processing temperature, and the saturation peening time decreases. After annealing, the residual compressive stress decreases, however, for 500 oC peening sample, even annealing at 500 oC for 20 h, the residual compressive stress is still over 600 MPa. However, it is clean to see from SEM that the grain size of surface layers is still very small.

Keywords: shot peening, M50 steel, residual compressive stress, elevated temperature

Procedia PDF Downloads 456

Authors: Luiz Daniel Garay Trindade, Antonio De Freitas Valle Neto, Fabio Paulo Basso, Elder De Macedo Rodrigues, Maicon Bernardino, Daniel Welfer, Daniel Muller

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Agriculture plays an important role in society since it is one of the main sources of food in the world. To help the production and yield of crops, precision agriculture makes use of technologies aiming at improving productivity and quality of agricultural commodities. One of the problems hampering quality of agricultural production is the disease affecting crops. Failure in detecting diseases in a short period of time can result in small or big damages to production, causing financial losses to farmers. In order to provide a map of the contributions destined to the early detection of plant diseases and a comparison of the accuracy of the selected studies, a systematic literature review of the literature was performed, showing techniques for digital image processing and neural networks. We found 35 interesting tool support alternatives to detect disease in 19 plants. Our comparison of these studies resulted in an overall average accuracy of 87.45%, with two studies very closer to obtain 100%.

Keywords: pattern recognition, image processing, deep learning, precision agriculture, smart farming, agricultural automation

Procedia PDF Downloads 379

Authors: Ekaterina Tikhonova

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This research is aimed to evaluate the effectiveness of T&D (Training and Development) on the example of two T&D programs for the Executive TOP Management run in 2012, 2015-2016 in Komos Group. This study is commissioned to research the effectiveness of two similar corporate T&D programs (within one company) in two periods of time (2012, 2015-2016) through evaluating the programs’ effectiveness using the four-level Kirkpatrick’s model of evaluating T&D programs and calculating ROI as an instrument for T&D program measuring by Phillips’ formula. The research investigates the correlation of two figures: the ROI calculated and the rating percentage scale per the ROI implementation (Wagle’s scale). The study includes an assessment of feedback 360 (Kirkpatrick's model) and Phillips’ ROI Methodology that provides a step-by-step process for collecting data, summarizing and processing the collected information. The data is collected from the company accounting data, the HR budgets, MCFO and the company annual reports for the research periods. All analyzed data and reports are organized and presented in forms of tables, charts, and graphs. The paper also gives a brief description of some constrains of the research considered. After ROI calculation, the study reveals that ROI ranges between the average implementation (65% to 75%) by Wagle’s scale that can be considered as a positive outcome. The paper also gives some recommendations how to use ROI in practice and describes main benefits of ROI implementation.

Keywords: ROI, organizational performance, efficacy of T&D program, employee performance

Procedia PDF Downloads 250

Authors: Ali Berkan Ural

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This paper presents the analysis, evaluation, and pre-diagnosis of early stage breast based diagnostic problems (breast cancer, nodulesorlumps) by Computer Aided Diagnosing (CAD) system from mammogram radiological images. According to the statistics, the time factor is crucial to discover the disease in the patient (especially in women) as possible as early and fast. In the study, a new algorithm is developed using advanced image processing and deep learning method to detect and classify the problem at earlystagewithmoreaccuracy. This system first works with image processing methods (Image acquisition, Noiseremoval, Region Growing Segmentation, Morphological Operations, Breast BorderExtraction, Advanced Segmentation, ObtainingRegion Of Interests (ROIs), etc.) and segments the area of interest of the breast and then analyzes these partly obtained area for cancer detection/lumps in order to diagnosis the disease. After segmentation, with using the Spectrogramimages, 5 different deep learning based methods (specified Convolutional Neural Network (CNN) basedAlexNet, ResNet50, VGG16, DenseNet, Xception) are applied to classify the breast based problems.

Keywords: computer aided diagnosis, breast cancer, region growing, segmentation, deep learning

Procedia PDF Downloads 95

Authors: M. Omer, D. Wilson

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Creating a database scheme is essentially a manual process. From a requirement specification, the information contained within has to be analyzed and reduced into a set of tables, attributes and relationships. This is a time-consuming process that has to go through several stages before an acceptable database schema is achieved. The purpose of this paper is to implement a Natural Language Processing (NLP) based tool to produce a from a requirement specification. The Stanford CoreNLP version 3.3.1 and the Java programming were used to implement the proposed model. The outcome of this study indicates that the first draft of a relational database schema can be extracted from a requirement specification by using NLP tools and techniques with minimum user intervention. Therefore, this method is a step forward in finding a solution that requires little or no user intervention.

Keywords: information extraction, natural language processing, relation extraction

Procedia PDF Downloads 261

Authors: Stephanie Chen, Mitja Echim, Christof Büskens

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To describe and propagate the behavior of a system mathematical models are formulated. Parameter identification is used to adapt the coefficients of the underlying laws of science. For complex systems this approach can be incomplete and hence imprecise and moreover too slow to be computed efficiently. Therefore, these models might be not applicable for the numerical optimization of real systems, since these techniques require numerous evaluations of the models. Moreover not all quantities necessary for the identification might be available and hence the system must be adapted manually. Therefore, an approach is described that generates models that overcome the before mentioned limitations by not focusing on physical laws, but on measured (sensor) data of real systems. The approach is more general since it generates models for every system detached from the scientific background. Additionally, this approach can be used in a more general sense, since it is able to automatically identify correlations in the data. The method can be classified as a multivariate data regression analysis. In contrast to many other data regression methods this variant is also able to identify correlations of products of variables and not only of single variables. This enables a far more precise and better representation of causal correlations. The basis and the explanation of this method come from an analytical background: the series expansion. Another advantage of this technique is the possibility of real-time adaptation of the generated models during operation. Herewith system changes due to aging, wear or perturbations from the environment can be taken into account, which is indispensable for realistic scenarios. Since these data driven models can be evaluated very efficiently and with high precision, they can be used in mathematical optimization algorithms that minimize a cost function, e.g. time, energy consumption, operational costs or a mixture of them, subject to additional constraints. The proposed method has successfully been tested in several complex applications and with strong industrial requirements. The generated models were able to simulate the given systems with an error in precision less than one percent. Moreover the automatic identification of the correlations was able to discover so far unknown relationships. To summarize the above mentioned approach is able to efficiently compute high precise and real-time-adaptive data-based models in different fields of industry. Combined with an effective mathematical optimization algorithm like WORHP (We Optimize Really Huge Problems) several complex systems can now be represented by a high precision model to be optimized within the user wishes. The proposed methods will be illustrated with different examples.

Keywords: adaptive modeling, automatic identification of correlations, data based modeling, optimization

Procedia PDF Downloads 409

Authors: Valerie Porr, Sydney A. DeCaro

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TARA for borderline personality disorder (BPD), an education and advocacy organization, helps families to compassionately and effectively deal with troubling BPD behaviors. Our psychoeducational programs focus on understanding underlying neurobiological features of BPD and evidence-based methodology integrating dialectical behavior therapy (DBT) and mentalization based therapy (MBT,) clarifying the inherent misunderstanding of BPD behaviors and improving family communication. TARA4BPD conducts online surveys, workshops, and topical webinars. For over 25 years, we have collected data from BPD helpline callers. This data drew our attention to particular childhood idiosyncrasies that seem to characterize many of the children who later met the criteria for BPD. The idiosyncrasies we observed, heightened sensory sensitivity and hypervigilance, were included in Adolf Stern’s 1938 definition of “Borderline.” This aspect of BPD has not been prioritized by personality disorder researchers, presently focused on emotion processing and social cognition in BPD. Parents described sleep reversal problems in infants who, early on, seem to exhibit dysregulation in circadian rhythm. Families describe children as supersensitive to sensory sensations, such as specific sounds, heightened sense of smell, taste, textures of foods, and an inability to tolerate various fabrics textures (i.e., seams in socks). They also exhibit high sensitivity to particular words and voice tones. Many have alexithymia and dyslexia. These children are either hypo- or hypersensitive to sensory sensations, including pain. Many suffer from fibromyalgia. BPD reactions to pain have been studied (C. Schmahl) and confirm the existence of hyper and hypo-reactions to pain stimuli in people with BPD. To date, there is little or no data regarding what comprises a normative range of sensitivity in infants and children. Many parents reported that their children were tested or treated for sensory processing disorder (SPD), learning disorders, and ADHD. SPD is not included in the DSM and is treated by occupational therapists. The overwhelming anecdotal data from thousands of parents of children who later met criteria for BPD led TARA4BPD to develop a sensitivity survey to develop evidence of the possible role of early sensory perception problems as a pre-cursor to BPD, hopefully initiating new directions in BPD research. At present, the research community seems unaware of the role supersensory sensitivity might play as an early indicator of BPD. Parents' observations of childhood sensitivity obtained through family interviews and results of an extensive online survey on sensory responses across various ages of development will be presented. People with BPD suffer from a sense of isolation and otherness that often results in later interpersonal difficulties. Early identification of supersensitive children while brain circuits are developing might decrease the development of social interaction deficits such as rejection sensitivity, self-referential processes, and negative bias, hallmarks of BPD, ultimately minimizing the maladaptive methods of coping with distress that characterizes BPD. Family experiences are an untapped resource for BPD research. It is hoped that this data will give family observations the critical credibility to inform future treatment and research directions.

Keywords: alexithymia, dyslexia, hypersensitivity, sensory processing disorder

Procedia PDF Downloads 201

Authors: Veli Durmus, Mert Uydaci

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Every patient who needs to get a medical treatment should share health-related personal data with healthcare providers. Therefore, personal health data plays an important role to make health decisions and identify health threats during every encounter between a patient and caregivers. In other words, health data can be defined as privacy and sensitive information which is protected by various health laws and regulations. In many cases, the data are an outcome of the confidential relationship between patients and their healthcare providers. Globally, almost all nations have own laws, regulations or rules in order to protect personal data. There is a variety of instruments that allow authorities to use the health data or to set the barriers data sharing across international borders. For instance, Directive 95/46/EC of the European Union (EU) (also known as EU Data Protection Directive) establishes harmonized rules in European borders. In addition, the General Data Protection Regulation (GDPR) will set further common principles in 2018. Because of close policy relationship with EU, this study provides not only information on regulations, directives but also how they play a role during the legislative process in Turkey. Even if the decision is controversial, the Board has recently stated that private or public healthcare institutions are responsible for the patient call system, for doctors to call people waiting outside a consultation room, to prevent unlawful processing of personal data and unlawful access to personal data during the treatment. In Turkey, vast majority private and public health organizations provide a service that ensures personal data (i.e. patient’s name and ID number) to call the patient. According to the Board’s decision, hospital or other healthcare institutions are obliged to take all necessary administrative precautions and provide technical support to protect patient privacy. However, this application does not effectively and efficiently performing in most health services. For this reason, it is important to draw a legal framework of personal health data by stating what is the main purpose of this regulation and how to deal with complicated issues on personal health data in Turkey. The research is descriptive on data protection law for health care setting in Turkey. Primary as well as secondary data has been used for the study. The primary data includes the information collected under current national and international regulations or law. Secondary data include publications, books, journals, empirical legal studies. Consequently, privacy and data protection regimes in health law show there are some obligations, principles and procedures which shall be binding upon natural or legal persons who process health-related personal data. A comparative approach presents there are significant differences in some EU member states due to different legal competencies, policies, and cultural factors. This selected study provides theoretical and practitioner implications by highlighting the need to illustrate the relationship between privacy and confidentiality in Personal Data Protection in Health Law. Furthermore, this paper would help to define the legal framework for the health law case studies on data protection and privacy.

Keywords: data protection, personal data, privacy, healthcare, health law

Procedia PDF Downloads 224

Authors: Berkan Ural, Arif Eser, Sinan Apaydin

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Medical image processing is generally become a challenging task nowadays. Indeed, processing of brain MRI images is one of the difficult parts of this area. This study proposes a hybrid well-defined approach which is consisted from tumor detection, extraction and analyzing steps. This approach is mainly consisted from a computer aided diagnostics system for identifying and detecting the tumor formation in any region of the brain and this system is commonly used for early prediction of brain tumor using advanced image processing and probabilistic neural network methods, respectively. For this approach, generally, some advanced noise removal functions, image processing methods such as automatic segmentation and morphological operations are used to detect the brain tumor boundaries and to obtain the important feature parameters of the tumor region. All stages of the approach are done specifically with using MATLAB software. Generally, for this approach, firstly tumor is successfully detected and the tumor area is contoured with a specific colored circle by the computer aided diagnostics program. Then, the tumor is segmented and some morphological processes are achieved to increase the visibility of the tumor area. Moreover, while this process continues, the tumor area and important shape based features are also calculated. Finally, with using the probabilistic neural network method and with using some advanced classification steps, tumor area and the type of the tumor are clearly obtained. Also, the future aim of this study is to detect the severity of lesions through classes of brain tumor which is achieved through advanced multi classification and neural network stages and creating a user friendly environment using GUI in MATLAB. In the experimental part of the study, generally, 100 images are used to train the diagnostics system and 100 out of sample images are also used to test and to check the whole results. The preliminary results demonstrate the high classification accuracy for the neural network structure. Finally, according to the results, this situation also motivates us to extend this framework to detect and localize the tumors in the other organs.

Keywords: image processing algorithms, magnetic resonance imaging, neural network, pattern recognition

Procedia PDF Downloads 418

Authors: Hannachi Med Tahar, H. Djebaili, B. Daheche

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In this approach, we have tried to describe the flattening of welded tubes, and its experimental application. The test is carried out at the (National product processing company dishes and tubes production). Usually, the final products (tubes) undergo a series of non-destructive inspection online and offline welding, and obviously destructive mechanical testing (bending, flattening, flaring, etc.). For this and for the purpose of implementing the flattening test, which applies to the processing of round tubes in other forms, it took four sections of welded tubes draft (before stretching hot) and welded tubes finished (after drawing hot and annealing), it was also noted the report 'health' flattened tubes must not show or crack or tear. The test is considered poor if it reveals a lack of ductility of the metal.

Keywords: flattening, destructive testing, tube drafts, finished tube, Castem 2001

Procedia PDF Downloads 446

Authors: David Jurado, Carlos Ávila

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Detection of early signs of breast cancer development is crucial to quickly diagnose the disease and to define adequate treatment to increase the survival probability of the patient. Computer Aided Detection systems (CADs), along with modern data techniques such as Machine Learning (ML) and Neural Networks (NN), have shown an overall improvement in digital mammography cancer diagnosis, reducing the false positive and false negative rates becoming important tools for the diagnostic evaluations performed by specialized radiologists. However, ML and NN-based algorithms rely on datasets that might bring issues to the segmentation tasks. In the present work, an automatic segmentation and detection algorithm is described. This algorithm uses image processing techniques along with the Hough transform to automatically identify microcalcifications that are highly correlated with breast cancer development in the early stages. Along with image processing, automatic segmentation of high-contrast objects is done using edge extraction and circle Hough transform. This provides the geometrical features needed for an automatic mask design which extracts statistical features of the regions of interest. The results shown in this study prove the potential of this tool for further diagnostics and classification of mammographic images due to the low sensitivity to noisy images and low contrast mammographies.

Keywords: breast cancer, segmentation, X-ray imaging, hough transform, image analysis

Procedia PDF Downloads 83

Authors: H. Chassaigne, S. Gioria, J. Lobo Vicente, D. Carpi, P. Barboro, G. Tomasi, A. Kinsner-Ovaskainen, F. Rossi

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Emerging approaches in the area of exposure to nanomaterials and assessment of human health effects combine the use of in vitro systems and analytical techniques to study the perturbation of the proteome and/or the metabolome. We investigated the modification in the cytoplasmic compartment of the Balb/3T3 cell line exposed to gold nanoparticles. On one hand, the proteomic approach is quite standardized even if it requires precautions when dealing with in vitro systems. On the other hand, metabolomic analysis is challenging due to the chemical diversity of cellular metabolites that complicate data elaboration and interpretation. Differentially expressed proteins were found to cover a range of functions including stress response, cell metabolism, cell growth and cytoskeleton organization. In addition, de-regulated metabolites were annotated using the HMDB database. The "omics" fields hold huge promises in the interaction of nanoparticles with biological systems. The combination of proteomics and metabolomics data is possible however challenging.

Keywords: data processing, gold nanoparticles, in vitro systems, metabolomics, proteomics

Procedia PDF Downloads 503

Authors: Younies Saeed Hassan Mahmoud, Mai Mabrouk, Elsayed Sallam

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Analysing DNA microarray data sets is a great challenge, which faces the bioinformaticians due to the complication of using statistical and machine learning techniques. The challenge will be doubled if the microarray data sets contain missing data, which happens regularly because these techniques cannot deal with missing data. One of the most important data analysis process on the microarray data set is feature selection. This process finds the most important genes that affect certain disease. In this paper, we introduce a technique for imputing the missing data in microarray data sets while performing feature selection.

Keywords: DNA microarray, feature selection, missing data, bioinformatics

Procedia PDF Downloads 574

Authors: Saziye Ilgaz, Atilla Polat

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There is a need for development of new processing techniques to ensure safety and quality of final product while minimizing the adverse impact of extraction solvents on environment and residue levels of these solvents in final product, decaffeinated black tea. In this study pilot scale supercritical carbon dioxide (SCCO₂) extraction was used to produce decaffeinated black tea in place of solvent extraction. Pressure (250, 375, 500 bar), extraction time (60, 180, 300 min), temperature (55, 62.5, 70 °C), CO₂ flow rate (1, 2 ,3 LPM) and co-solvent quantity (0, 2.5, 5 %mol) were selected as extraction parameters. The five factors BoxBehnken experimental design with three center points was performed to generate 46 different processing conditions for caffeine removal from black tea samples. As a result of these 46 experiments caffeine content of black tea samples were reduced from 2.16 % to 0 – 1.81 %. The experiments showed that extraction time, pressure, CO₂ flow rate and co-solvent quantity had great impact on decaffeination yield. Response surface methodology (RSM) was used to optimize the parameters of the supercritical carbon dioxide extraction. Optimum extraction parameters obtained of decaffeinated black tea were as follows: extraction temperature of 62,5 °C, extraction pressure of 375 bar, CO₂ flow rate of 3 LPM, extraction time of 176.5 min and co-solvent quantity of 5 %mol.

Keywords: supercritical carbon dioxide, decaffeination, black tea, extraction

Procedia PDF Downloads 364

Authors: Javier Comes Fayos, Isabel RodríGuez Moreno, Sara Bressanutti, Marisol Lila, Angel Romero MartíNez, Luis Moya Albiol

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Given the great impact produced by gender-based violence, its comprehensive approach seems essential. Consequently, research has focused on risk factors for violent behaviour, linking various psychosocial variables, as well as cognitive and neuropsychological deficits with the aggressors. However, studies on affective processing are scarce, so the present study investigates possible emotional alterations in men convicted of gender violence. The participants were 51 aggressors, who attended the CONTEXTO program with sentences of less than two years, and 47 men with no history of violence. The sample did not differ in age, socioeconomic level, education, or alcohol and other substances consumption. Anger, alexithymia and facial recognition of other people´s emotions were assessed through the State-Trait Anger Expression Inventory (STAXI-2), the Toronto Alexithymia Scale (TAS-20) and Reading the mind in the eyes (REM), respectively. Men convicted of gender-based violence showed higher scores on the anger trait and temperament dimensions, as well as on the anger expression index. They also scored higher on alexithymia and in the identification and emotional expression subscales. In addition, they showed greater difficulties in the facial recognition of emotions by having a lower score in the REM. These results seem to show difficulties in different affective areas in men condemned for gender violence. The deficits are reflected in greater difficulty in identifying and expressing emotions, in processing anger and in recognizing the emotions of others. All these difficulties have been related to the use of violent behavior. Consequently, it is essential and necessary to include emotional regulation in intervention programs for men who have been convicted of gender-based violence.

Keywords: alexithymia, anger, emotional processing, emotional recognition, empathy, intimate partner violence

Procedia PDF Downloads 199

Authors: M. Ahmadi, M. Kafil, H. Ebrahimi

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Nowadays, induction motors have a significant role in industries. Condition monitoring (CM) of this equipment has gained a remarkable importance during recent years due to huge production losses, substantial imposed costs and increases in vulnerability, risk, and uncertainty levels. Motor current signature analysis (MCSA) is one of the most important techniques in CM. This method can be used for rotor broken bars detection. Signal processing methods such as Fast Fourier transformation (FFT), Wavelet transformation and Empirical Mode Decomposition (EMD) are used for analyzing MCSA output data. In this study, these signal processing methods are used for broken bar problem detection of Mobarakeh steel company induction motors. Based on wavelet transformation method, an index for fault detection, CF, is introduced which is the variation of maximum to the mean of wavelet transformation coefficients. We find that, in the broken bar condition, the amount of CF factor is greater than the healthy condition. Based on EMD method, the energy of intrinsic mode functions (IMF) is calculated and finds that when motor bars become broken the energy of IMFs increases.

Keywords: broken bar, condition monitoring, diagnostics, empirical mode decomposition, fourier transform, wavelet transform

Procedia PDF Downloads 150

Authors: Abdulraaof H. Alqaili, Hamad A. Alsoliman

Abstract:

Through progress in pavement design developments, a pavement design method was developed, which is titled the Mechanistic Empirical Pavement Design Guide (MEPDG). Nowadays, the evolution in roads network and highways is observed in Saudi Arabia as a result of increasing in traffic volume. Therefore, the MEPDG currently is implemented for flexible pavement design by the Saudi Ministry of Transportation. Implementation of MEPDG for local pavement design requires the calibration of distress models under the local conditions (traffic, climate, and materials). This paper aims to prepare data for calibration of MEPDG in Central Saudi Arabia. Thus, the first goal is data collection for the design of flexible pavement from the local conditions of the Riyadh region. Since, the modifying of collected data to input data is needed; the main goal of this paper is the analysis of collected data. The data analysis in this paper includes processing each: Trucks Classification, Traffic Growth Factor, Annual Average Daily Truck Traffic (AADTT), Monthly Adjustment Factors (MAFi), Vehicle Class Distribution (VCD), Truck Hourly Distribution Factors, Axle Load Distribution Factors (ALDF), Number of axle types (single, tandem, and tridem) per truck class, cloud cover percent, and road sections selected for the local calibration. Detailed descriptions of input parameters are explained in this paper, which leads to providing of an approach for successful implementation of MEPDG. Local calibration of MEPDG to the conditions of Riyadh region can be performed based on the findings in this paper.

Keywords: mechanistic-empirical pavement design guide (MEPDG), traffic characteristics, materials properties, climate, Riyadh

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Authors: Sankaran Rajendran

Abstract:

Remote sensing plays a vital role in mapping of resources and monitoring of environments of the earth. In the present research study, mapping and monitoring of clay siltations occurred in the Alkhod Dam of Muscat, Sultanate of Oman are carried out using low-cost multispectral Landsat and ASTER data. The dam is constructed across the Wadi Samail catchment for ground water recharge. The occurrence and spatial distribution of siltations in the dam are studied with five years of interval from the year 1987 of construction to 2014. The deposits are mainly due to the clay, sand, and silt occurrences derived from the weathering rocks of ophiolite sequences occurred in the Wadi Samail catchment. The occurrences of clays are confirmed by minerals identification using ASTER VNIR-SWIR spectral bands and Spectral Angle Mapper supervised image processing method. The presence of clays and their spatial distribution are verified in the field. The study recommends the technique and the low-cost satellite data to similar region of the world.

Keywords: Alkhod Dam, ASTER siltation, Landsat, remote sensing, Oman

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Authors: Adegbite Bashiru Adeniyi, Olaniyi Akeem Olawale, Ayobamidele Sinatu Juliet

Abstract:

The study was carried out in the 6 South Western states of Nigeria to analyze socio-economic characteristic of garri processors and their anthropometric qualities with respect to modern technologies used in garri processing. About 20 respondents were randomly selected from each of the 6 workstations purposively considered for the study due to their daily processing activities already attracted high patronage of customers. These include Oguntolu village (Ogun State), Igoba-Akure (Ondo State), Imo-Ilesa (Osun State), Odo Oba-Ileri (Oyo State), Irasa village (Ekiti State) and Epe in Lagos state. Interview schedule was conducted for 120 respondents to elicit information. Data were analyzed using descriptive statistical tools. It was observed from the findings that respondents were in their most productive age range (36-45 years) except Ogun state where majority (45%) were relatively older than 45 years. A fewer processors were much younger than 26 years old. It furthers revealed that not less than 55% have body weight greater than 50.0 kilogram, also not less than 70% were taller than 1.5 meter. So also, the hand length and hand thickness of the majority were long and bulky which are considered suitable for operating some modern and improved technologies in garri-frying process. This information could be used by various technological developers to enhance production of modern equipment and tools for a greater efficiency.

Keywords: agro-business, anthromorphic, modernization, proficiency

Procedia PDF Downloads 512

Authors: Veronica S. Moertini, Vinsensius Kevin, Gede Karya

Abstract:

Customer reviews have been collected by many kinds of e-commerce websites selling products, services, hotel rooms, tickets and so on. Each website collects its own customer reviews. The reviews can be crawled, collected from those websites and stored as big data. Text analysis techniques can be used to analyze that data to produce summarized information, such as customer opinions. Then, these opinions can be published by independent service provider websites and used to help customers in choosing the most suitable products or services. As the opinions are analyzed from big data of reviews originated from many websites, it is expected that the results are more trusted and accurate. Indonesian customers write reviews in Indonesian language, which comes with its own structures and uniqueness. We found that most of the reviews are expressed with “daily language”, which is informal, do not follow the correct grammar, have many abbreviations and slangs or non-formal words. Hadoop is an emerging platform aimed for storing and analyzing big data in distributed systems. A Hadoop cluster consists of master and slave nodes/computers operated in a network. Hadoop comes with distributed file system (HDFS) and MapReduce framework for supporting parallel computation. However, MapReduce has weakness (i.e. inefficient) for iterative computations, specifically, the cost of reading/writing data (I/O cost) is high. Given this fact, we conclude that MapReduce function is best adapted for “one-pass” computation. In this research, we develop an efficient technique for extracting or mining opinions from big data of Indonesian reviews, which is based on MapReduce with one-pass computation. In designing the algorithm, we avoid iterative computation and instead adopt a “look up table” technique. The stages of the proposed technique are: (1) Crawling the data reviews from websites; (2) cleaning and finding root words from the raw reviews; (3) computing the frequency of the meaningful opinion words; (4) analyzing customers sentiments towards defined objects. The experiments for evaluating the performance of the technique were conducted on a Hadoop cluster with 14 slave nodes. The results show that the proposed technique (stage 2 to 4) discovers useful opinions, is capable of processing big data efficiently and scalable.

Keywords: big data analysis, Hadoop MapReduce, analyzing text data, mining Indonesian reviews

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Authors: Umar Shehu Umar

Abstract:

Contract farming is a viable strategy agribusinesses rely on to strengthen vertical coordination. However, low contract compliance remains a significant setback to agribusinesses' contract performance. The present study aims to understand what drives smallholder farmers’ contract compliance behaviour. Qualitative information was collected through Focus Group Discussions to enrich the design of the survey questionnaire administered on a sample of 300 randomly selected farmers contracted by the Dangote Tomato Processing Plant (DTPP) in four regions of northern Nigeria. Novel transaction level data of tomato sales covering one season were collected in addition to socio-economic information of the sampled farmers. Binary logistic model results revealed that open fresh market tomato prices and payment delays negatively affect farmers' compliance behaviour while quantity harvested, education level and input provision correlated positively with compliance. The study suggests that contract compliance will increase if contracting firms devise a reliable and timely payment plan (e.g., digital payment), continue input and service provisions (e.g., improved seeds, extension services) and incentives (e.g., loyalty rewards, bonuses) in the contract.

Keywords: contract farming, compliance, farmers and processors., smallholder

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Authors: Fanqiang Kong, Jindong Zhang, Daiyin Zhu

Abstract:

Space-time adaptive processing (STAP) techniques have been motivated as a key enabling technology for advanced airborne radar applications. In this paper, the notion of cognitive radar is extended to STAP technique, and cognitive STAP is discussed. The principle for improving signal-to-clutter ratio (SCNR) based on slow-time coding is given, and the corresponding optimization algorithm based on cyclic and power-like algorithms is presented. Numerical examples show the effectiveness of the proposed method.

Keywords: space-time adaptive processing (STAP), airborne radar, signal-to-clutter ratio, slow-time coding

Procedia PDF Downloads 273

Authors: M. Stefanovska, B. Samakoski, S. Risteska, G. Maneski

Abstract:

In this study was performed in situ consolidation of polypropylene matrix/glass reinforced roving by combining heating systems and roll pressing. The commingled roving during hoop winding was winded on a cylindrical mandrel. The work also presents the advances made in the processing of these materials into composites by conventional technique filament winding. Experimental studies were performed with changing parameters – temperature, pressure and speed. Finally, it describes the investigation of the optimal processing conditions that maximize the mechanical properties of the composites. These properties are good enough for composites to be used as engineering materials in many structural applications.

Keywords: commingled fiber, consolidation heat, filament winding, voids

Procedia PDF Downloads 266

Authors: Abdal-Hafeez Alhussein

Abstract:

Artificial Intelligence (AI) has become a transformative force in the field of information technologies, reshaping how data is processed, analyzed, and utilized across various domains. This paper explores the multifaceted applications of AI within information technology, focusing on three key areas: automation, scalability, and data-driven decision-making. We delve into how AI-powered automation is optimizing operational efficiency in IT infrastructures, from automated network management to self-healing systems that reduce downtime and enhance performance. Scalability, another critical aspect, is addressed through AI’s role in cloud computing and distributed systems, enabling the seamless handling of increasing data loads and user demands. Additionally, the paper highlights the use of AI in cybersecurity, where real-time threat detection and adaptive response mechanisms significantly improve resilience against sophisticated cyberattacks. In the realm of data analytics, AI models—especially machine learning and natural language processing—are driving innovation by enabling more precise predictions, automated insights extraction, and enhanced user experiences. The paper concludes with a discussion on the ethical implications of AI in information technologies, underscoring the importance of transparency, fairness, and responsible AI use. It also offers insights into future trends, emphasizing the potential of AI to further revolutionize the IT landscape by integrating with emerging technologies like quantum computing and IoT.

Keywords: artificial intelligence, information technology, automation, scalability

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Authors: Anatoly D. Pluzhnikov, Elena N. Pribludova, Alexander G. Ryndyk

Abstract:

In conjunction with the problem of the target selection on a clutter background, the analysis of the scanning rate influence on the spatial-temporal signal structure, the generalized multivariate correlation function and the quality of the resolution with the increase pulse repetition frequency is made. The possibility of the object space-distance resolution, which is conditioned by the range-to-angle conversion with an increased scanning rate, is substantiated. The calculations for the real cylindrical array at high scanning rate are presented. The high scanning rate let to get the signal to noise improvement of the order of 10 dB for the space-time signal processing.

Keywords: antenna pattern, array, signal processing, spatial resolution

Procedia PDF Downloads 180