Search results for: seismo-tectonic features

Authors: Min-Ha Hong, Kee-Won Kim, Seung-Hoon Kim

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The character in the story-based content, such as novels and movies, is one of the core elements to understand the story. In particular, the character’s tendency is an important factor to analyze the story-based content, because it has a significant influence on the storyline. If readers have the knowledge of the tendency of characters before reading a novel, it will be helpful to understand the structure of conflict, episode and relationship between characters in the novel. It may therefore help readers to select novel that the reader wants to read. In this paper, we propose a method of extracting the tendency of the characters from a novel written in Korean. In advance, we build the dictionary with pairs of the emotional words in Korean and English since the emotion words in the novel’s sentences express character’s feelings. We rate the degree of polarity (positive or negative) of words in our emotional words dictionary based on SenticNet. Then we extract characters and emotion words from sentences in a novel. Since the polarity of a word grows strong or weak due to sentence features such as quotations and modifiers, our proposed method consider them to calculate the polarity of characters. The information of the extracted character’s polarity can be used in the book search service or book recommendation service.

Keywords: character tendency, data mining, emotion word, Korean novel

Procedia PDF Downloads 334

Authors: Meruert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova

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The article highlights and describes the characteristic features of real-time face detection in images and videos using machine learning algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As a result, the advantages and disadvantages of Haar Cascade (Haar Cascade OpenCV), HoG SVM (Histogram of Oriented Gradients, Support Vector Machine), and MMOD CNN Dlib (Max-Margin Object Detection, convolutional neural network) detectors used for face detection were determined. Dlib is a general-purpose cross-platform software library written in the programming language C++. It includes detectors used for determining face detection. The Cascade OpenCV algorithm is efficient for fast face detection. The considered work forms the basis for the development of machine learning methods by future computer science teachers.

Keywords: algorithm, artificial intelligence, education, machine learning

Procedia PDF Downloads 73

Authors: Norhidayah Suleiman, Afza Zulfaka

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Zingiber officinale var. Bentong has been identified as the source of high added value compound specifically gingerol-related compounds. The extraction of the high-value compound using conventional method resulted in low yield and time consumption. Hence, the motivation for this work is to investigate the effect of the extraction technique on the essential oil from Zingiber officinale var. Bentong rhizome for commercialization purpose in many industries namely, functional food, pharmaceutical, and cosmeceutical. The investigation begins with a pre-treatment using ultrasound assisted in order to enhance the recovery of essential oil. It was conducted at a fixed frequency (20 kHz) of ultrasound with various time (10, 20, 40 min). The extraction using supercritical carbon dioxide (scCO2) were carried out afterward at a specific condition of temperature (50 °C) and pressure (30 MPa). scCO2 extraction seems to be a promising sustainable green method for the extraction of essential oil due to the benefits that CO2 possesses. The expected results demonstrated the ultrasound-assisted-scCO2 produces a higher yield of essential oil compared to solely scCO2 extraction. This research will provide important features for its application in food supplements or phytochemical preparations.

Keywords: essential oil, scCO2, ultrasound assisted, Zingiber officinale Var. Bentong

Procedia PDF Downloads 133

Authors: Suzi M. S. Cavalari, Tim Lewis

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Teletandem is a bilingual model of virtual exchange in which two partners from different countries( and speak different languages) meet synchronously and regularly over a period of 8 weeks to learn each other’s mother tongue (or the language of proficiency). At São Paulo State University (UNESP), participants should answer a questionnaire before starting the exchanges in which one of the questions refers to setting a goal to be accomplished with the help of the teletandem partner. In this context, the present presentation aims to examine the goal-setting activity of 79 Brazilians who participated in Portuguese-English teletandem exchanges over a period of four years (2012-2015). The theoretical background is based on goal setting and self-regulated learning theories that propose that appropriate efficient goals are focused on the learning process (not on the product) and are specific, proximal (short-term) and moderately difficult. The data set used was 79 initial questionnaires retrieved from the MulTeC (Multimodal Teletandem Corpus). Results show that only approximately 10% of goals can be considered appropriate. Features of these goals are described in relation to specificities of the teletandem context. Based on the results, three mechanisms that can help learners to set attainable goals are discussed.

Keywords: foreign language learning, goal setting, teletandem, virtual exchange

Procedia PDF Downloads 184

Authors: M. Al-Jepoori, D. Bennett

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Teaching Object-Oriented Programming (OOP) as part of a Computing-related university degree is a very difficult task; the road to ensuring that students are actually learning object oriented concepts is unclear, as students often find it difficult to understand the concept of objects and their behavior. This problem is especially obvious in advanced programming modules where Design Pattern and advanced programming features such as Multi-threading and animated GUI are introduced. Looking at the students’ performance at their final year on a university course, it was obvious that the level of students’ understanding of OOP varies to a high degree from one student to another. Students who aim at the production of Games do very well in the advanced programming module. However, the students’ assessment results of the last few years were relatively low; for example, in 2016-2017, the first quartile of marks were as low as 24.5 and the third quartile was 63.5. It is obvious that many students were not confident or competent enough in their programming skills. In this paper, the reasons behind poor performance in Advanced OOP modules are investigated, and a suggested practice for teaching OOP based on a complex case study is described and evaluated.

Keywords: complex programming case study, design pattern, learning advanced programming, object oriented programming

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Authors: Quynhan Tran, Hong Fan, Quockhanh Pham

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Automatic map generalization is a well-known problem in cartography. The development of map generalization research accompanied the development of cartography. The traditional map is plotted manually by cartographic experts. The paper studies none-scale automation generalization of resident polygons and house marker symbol, proposes methodology to evaluate the result maps based on minimal spanning tree. In this paper, the minimal spanning tree before and after map generalization is compared to evaluate whether the generalization result maintain the geographical distribution of features. The minimal spanning tree in vector format is firstly converted into a raster format and the grid size is 2mm (distance on the map). The statistical number of matching grid before and after map generalization and the ratio of overlapping grid to the total grids is calculated. Evaluation experiments are conduct to verify the results. Experiments show that this methodology can give an objective evaluation for the feature distribution and give specialist an hand while they evaluate result maps of none-scale automation generalization with their eyes.

Keywords: automatic cartography generalization, evaluation model, geographic feature distribution, minimal spanning tree

Procedia PDF Downloads 636

Authors: Santiago Moll Lopez, Erica Vega Fleitas, Dolors Rosello Ferragud, Luis Manuel Sanchez Ruiz, Jose Antonio Moraño Fernandez

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Game-based learning (GBL) is becoming popular in education. Learning through games offers students a motivating experience related to the social aspect of games. Among the significant positive outcomes are promoting positive emotions and collaboration, improving the assimilation of concepts, and creating an attractive and dynamic environment standout. This work presents a study of the design and implementation of games created with RPG Maker MZ software with a Sci-Fi storytelling environment for developing specific and transversal skills in a Mathematics subject at the Beng in Aerospace Engineering. Games were applied during regular classes and as a part of a Flip-Teaching methodology to increase the motivation and the assimilation of mathematical concepts in an engaging way. The key features of the games were the introduction of avatar design and the promotion of collaboration among students. Students' opinions and grades obtained in the activities and exams showed increased motivation and a significant improvement in their performance compared with other groups or past students' performances.

Keywords: game-based learning, rol games, mathematics, science fiction

Procedia PDF Downloads 95

Authors: H. Ahmed, A. Schlenkhoff

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Recently, permeable breakwaters have been suggested to overcome the disadvantages of fully protection breakwaters. These protection structures have minor impacts on the coastal environment and neighboring beaches where they provide a more economical protection from waves and currents. For regular waves, a numerical model is used (FLOW-3D, VOF) to investigate the hydraulic performance of a permeable breakwater. The model of permeable breakwater consists of a pair of identical vertical slotted walls with an impermeable upper and lower part, where the draft is a decimal multiple of the total depth. The middle part is permeable with a porosity of 50%. The second barrier is located at distant of 0.5 and 1.5 of the water depth from the first one. The numerical model is validated by comparisons with previous laboratory data and semi-analytical results of the same model. A good agreement between the numerical results and both laboratory data and semi-analytical results has been shown and the results indicate the applicability of the numerical model to reproduce most of the important features of the interaction. Through the numerical investigation, the friction factor of the model is carefully discussed.

Keywords: coastal structures, permeable breakwater, slotted wall, numerical model, energy dissipation coefficient

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Authors: Migena Ceyhan, Zeynep Orhan, Dimitrios Karras

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People in modern societies are continuously sharing their experiences, emotions, and thoughts in different areas of life. The information reaches almost everyone in real-time and can have an important impact in shaping people’s way of living. This phenomenon is very well recognized and advantageously used by the market representatives, trying to earn the most from this means. Given the abundance of information, people and organizations are looking for efficient tools that filter the countless data into important information, ready to analyze. This paper is a modest contribution in this field, describing the process of automatically classifying social media comments in the Turkish language into positive or negative. Once data is gathered and preprocessed, feature sets of selected single words or groups of words are build according to the characteristics of language used in the texts. These features are used later to train, and test a system according to different machine learning algorithms (Naïve Bayes, Sequential Minimal Optimization, J48, and Bayesian Linear Regression). The resultant high accuracies can be important feedback for decision-makers to improve the business strategies accordingly.

Keywords: feature selection, machine learning, natural language processing, sentiment analysis, social media reviews

Procedia PDF Downloads 146

Authors: A. Fayad, Q. Alqhazaly, T. Cinkler

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In light of the substantial increase in end-user requirements and the incessant need of network operators to upgrade the capabilities of access networks, in this paper, the performance of the different modulation formats on eight-channels Time and Wavelength Division Multiplexing Passive Optical Network (TWDM-PON) transmission system has been examined and compared. Limitations and features of modulation formats have been determined to outline the most suitable design to enhance the data rate and transmission reach to obtain the best performance of the network. The considered modulation formats are On-Off Keying Non-Return-to-Zero (NRZ-OOK), Carrier Suppressed Return to Zero (CSRZ), Duo Binary (DB), Modified Duo Binary (MODB), Quadrature Phase Shift Keying (QPSK), and Differential Quadrature Phase Shift Keying (DQPSK). The performance has been analyzed by varying transmission distances and bit rates under different channel spacing. Furthermore, the system is evaluated in terms of minimum Bit Error Rate (BER) and Quality factor (Qf) without applying any dispersion compensation technique, or any optical amplifier. Optisystem software was used for simulation purposes.

Keywords: BER, DuoBinary, NRZ-OOK, TWDM-PON

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Authors: Jiaqi Huang, Lu Jin

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Pervious concrete features with high water permeability rate. However, due to the lack of fine aggregates, the compressive strength is usually lower than other conventional concrete products. Optimization of pervious concrete mix design has long been recognized as an effective mechanism to achieve high compressive strength while maintaining desired permeability rate. In this paper, a Gibbs Sampling based algorithm is proposed to approximate the optimal mix design to achieve a high compressive strength of pervious concrete. We prove that the proposed algorithm efficiently converges to the set of global optimal solutions. The convergence rate and accuracy depend on a control parameter employed in the proposed algorithm. The simulation results show that, by using the proposed approach, the system converges to the optimal solution quickly and the derived optimal mix design achieves the maximum compressive strength while maintaining the desired permeability rate.

Keywords: convergence, Gibbs Sampling, high compressive strength, optimal mix design, pervious concrete

Procedia PDF Downloads 181

Authors: Robert Höttger, Lukas Krawczyk, Burkhard Igel

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This paper introduces novel approaches to partitioning and mapping in terms of model-based embedded multicore system engineering and further discusses benefits, industrial relevance and features in common with existing approaches. In order to assess and evaluate results, both approaches have been applied to a real industrial application as well as to various prototypical demonstrative applications, that have been developed and implemented for different purposes. Evaluations show, that such applications improve significantly according to performance, energy efficiency, meeting timing constraints and covering maintaining issues by using the AMALTHEA platform and the implemented approaches. Further- more, the model-based design provides an open, expandable, platform independent and scalable exchange format between OEMs, suppliers and developers on different levels. Our proposed mechanisms provide meaningful multicore system utilization since load balancing by means of partitioning and mapping is effectively performed with regard to the modeled systems including hardware, software, operating system, scheduling, constraints, configuration and more data.

Keywords: partitioning, mapping, distributed systems, scheduling, embedded multicore systems, model-based, system analysis

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Authors: Evgenia Dimova-Aleksandrova, Elitsa Deianova

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Dynamic changes in modern life and habitation due to demographic, urban, technology, and ecological factors affect the size of modern homes leading to a trend of decreasing their area. The current paper aims to investigate the differences between “micro” homes and “small” homes. In Bulgaria, these two types are not included in legal regulations, and therefore, a precise definition and special requirements are needed and sought in order to include their characteristic features in contemporary individual habitation. The purpose of the current study is to determine limits in built-up volume for the two types, to create a definition of the terms “micro” and “small” home, and to find methods to distinguish them. A comparative analysis will differentiate these types of habitation units, thus determining the boundaries for the built-up area for both concepts. The analysis is based on a case study from European practices and is focused on defining minimal requirements for “micro” and “small” home in the context of contemporary demands for high quality habitation in limited areas.

Keywords: Bulgaria, differentiation, micro home, requirements, small home

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Authors: Jamshaid ul Rahman, Akhter Ali, Adnan Manzoor

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Learning-based systems have received increasing interest in recent years; recognition structures, including end-to-end speak recognition, are one of the hot topics in this area. A famous work on end-to-end speaker verification by using Angular Softmax Loss gained significant importance and is considered useful to directly trains a discriminative model instead of the traditional adopted i-vector approach. The margin-based strategy in angular softmax is beneficial to learn discriminative speaker embeddings where the random selection of margin values is a big issue in additive angular margin and multiplicative angular margin. As a better solution in this matter, we present an alternative approach by introducing a bit similar form of an additive parameter that was originally introduced for face recognition, and it has a capacity to adjust automatically with the corresponding margin values and is applicable to learn more discriminative features than the Softmax. Experiments are conducted on the part of Fisher dataset, where it observed that the additive parameter with angular softmax to train the front-end and probabilistic linear discriminant analysis (PLDA) in the back-end boosts the performance of the structure.

Keywords: additive parameter, angular softmax, speaker verification, PLDA

Procedia PDF Downloads 103

Authors: Fernanda A. Vechietti, Natália O. B. Muniz, Laura C. Treccani, Kurosch. Rezwan, Luis Alberto dos Santos

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CoCr alloys are widely used in prosthetic implants due to their excellent mechanical properties, such as good tensile strength, elastic modulus and wear resistance. Their biocompatibility and lack of corrosion are also prominent features of this alloy. One of the most effective and simple ways to protect metal’s surfaces are treatments, such as electrochemical oxidation by passivation, which is used as a protect release of metallic ions. Another useful treatment is the electropolishing, which is used to reduce the carbide concentration and protrusion at the implanted surface. Electropolishing is a cheap and effective method for treatment of implants, which generally has complex geometries. The purpose of this study is surface modification of the alloy CoCr(ASTM F90-09) by different methods: polishing, electro polishing, passivation and heat treatment for application as biomaterials. The modification of the surface was studied and characterized by SEM, profilometry, wettability and compared to the surface of the samples untreated. The heat treatment and of passivation increased roughness (0.477 µm and 0.825 µm) the samples in relation the sample electropolished and polished(0.131 µm and 0.274 µm) and were observed the improve wettability’s with the increase the roughness.

Keywords: biomaterial, CoCr, surface treatment, heat treatment, roughness

Procedia PDF Downloads 543

Authors: Renjie Liu, Junshuai Xue, Jiajia Yao, Guanlin Wu, Zumao L, Xueyan Yang, Fang Liu, Zhuang Guo

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Here, we report the study of the performance of AlN/GaN bipolar resonance tunneling diodes (BRTDs) using numerical simulations. The I-V characteristics of BRTDs show double negative differential resistance regions, which exhibit similar peak current density and peak-to-valley current ratio (PVCR). Investigations show that the PVCR can approach 4.6 for the first and 5.75 for the second negative resistance region. The appearance of the two negative differential resistance regions is realized by changing the collector material of conventional GaN RTD to P-doped GaN. As the bias increases, holes in the P-region and electrons in the N-region undergo resonant tunneling, respectively, resulting in two negative resistance regions. The appearance of two negative resistance regions benefits from the high AlN barrier and the precise regulation of the potential well thickness. This result shows the promise of GaN BRTDs in the development of multi-valued logic circuits.

Keywords: GaN bipolar resonant tunneling diode, double negative differential resistance regions, peak to valley current ratio, multi-valued logic

Procedia PDF Downloads 163

Authors: Fabio Salvador Aparecido Santos, Spencer Chainey, Richard Wortley

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Urban morphology can be understood as the study of the physical form of cities through its elements. Crime, at this turn, can be oversimplified as an action that breaks the rules established in a certain society. This study involves these two subjects through the relationship between elements of urban morphology and density of crime occurrences. We consider that there is a research gap about the influence of urban features on crime occurrences using statistic methods and mapping techniques on Geographic Information Systems. The investigation will comprehend three main phases. The first phase involves examining how theoretical principles associated with urban morphology can be viewed in terms of their influence on crime patterns. The second phase involves the development of tools to be used to model elements of urban morphology, and measure the relationship between these urban morphological elements and patterns of crime. The third phase involves determining the extent to which elements of the urban environment can contribute to crime reduction. Understanding the relationship between urban morphology and crime patterns in a Latin American context will help highlight the influence urban planning has on the crime problems that emerge in these settings, and how effectively urban planning can contribute to reducing crime.

Keywords: Agent-based Modelling, Environmental Criminology, Geographic Information System, Urban Morphology

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Authors: Chung-Ming Lo, Kevin Li-Chun Hsieh

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The central nervous system in the World Health Organization defines grade 2, 3, 4 gliomas according to the aggressiveness. For brain tumors, using image examination would have a lower risk than biopsy. Besides, it is a challenge to extract relevant tissues from biopsy operation. Observing the whole tumor structure and composition can provide a more objective assessment. This study further proposed a computer-aided diagnosis (CAD) system based on a convolutional neural network to quantitatively evaluate a tumor's malignancy from brain magnetic resonance imaging. A total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were collected in the experiment. Transferred parameters from AlexNet were fine-tuned to classify the target brain tumors and achieved an accuracy of 98% and an area under the receiver operating characteristics curve (Az) of 0.99. Without pre-trained features, only 61% of accuracy was obtained. The proposed convolutional neural network can accurately and efficiently classify grade 2, 3, and 4 gliomas. The promising accuracy can provide diagnostic suggestions to radiologists in the clinic.

Keywords: convolutional neural network, computer-aided diagnosis, glioblastoma, magnetic resonance imaging

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Authors: Khaled Halimi, Hassina Seridi-Bouchelaghem

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The purpose of this paper is to describe how learning analytics approaches based on social semantic web techniques can be applied to enhance the lifelong learning experiences in a connectivist perspective. For this reason, a prototype of a system called SoLearn (Social Learning Environment) that supports this approach. We observed and studied literature related to lifelong learning systems, social semantic web and ontologies, connectivism theory, learning analytics approaches and reviewed implemented systems based on these fields to extract and draw conclusions about necessary features for enhancing the lifelong learning process. The semantic analytics of learning can be used for viewing, studying and analysing the massive data generated by learners, which helps them to understand through recommendations, charts and figures their learning and behaviour, and to detect where they have weaknesses or limitations. This paper emphasises that implementing a learning analytics approach based on social semantic web representations can enhance the learning process. From one hand, the analysis process leverages the meaning expressed by semantics presented in the ontology (relationships between concepts). From the other hand, the analysis process exploits the discovery of new knowledge by means of inferring mechanism of the semantic web.

Keywords: connectivism, learning analytics, lifelong learning, social semantic web

Procedia PDF Downloads 215

Authors: Emad M.Abdullat, Hasan A. Ader-Rahman, Rayyan Al Ali, Arwa.A.Hudaib

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This retrospective study aims to determine the epidemiological features of such deaths in one of the general teaching hospitals in Jordan with a focus on weaning practices and its relation to sucking as major factors underlying the mechanism of choking in infants and young children. The study utilized a retrospective design to review the records of forensic cases due to foreign body aspiration examined at the forensic department at the Jordan University Hospital. A total of 27 cases of choking in the pediatric age group were retrieved from the reports of the autopsy cases dissected. All cases of children who died due to chocking by foreign bodies were under 11 years old. Choking by food materials constituted (44.4%) of cases under 3 years of age while choking by non-food material were less prevalent under 3 years of age and comprising 18.5% of the cases. Health care personnel and parents need to be aware that introduction of solid food, unlike exclusive breast or formula-milk feeding, can have serious consequences if occurring in inappropriate timing or consistency during early childhood physical and functional development. Parents need to be educated regarding the appropriate timing and process of weaning.

Keywords: chocking, infants, weaning practices, young children

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Authors: Omer Oral, Y. Emre Yilmaz

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Topology optimization is an approach that optimizes material distribution within a given design space for a certain load and boundary conditions by providing performance goals. It uses various restrictions such as boundary conditions, set of loads, and constraints to maximize the performance of the system. It is different than size and shape optimization methods, but it reserves some features of both methods. In this study, interior structures of the parts were optimized by using SIMP (Solid Isotropic Material with Penalization) method. The volume of the part was preassigned parameter and minimum deflection was the objective function. The basic idea behind the theory was considered, and different methods were discussed. Rhinoceros 3D design tool was used with Grasshopper and TopOpt plugins to create and optimize parts. A Grasshopper algorithm was designed and tested for different beams, set of arbitrary located forces and support types such as pinned, fixed, etc. Finally, 2.5D shapes were obtained and verified by observing the changes in density function.

Keywords: Grasshopper, lattice structure, microstructures, Rhinoceros, solid isotropic material with penalization method, TopOpt, topology optimization

Procedia PDF Downloads 136

Authors: Jadisha Cornejo, Helio Pedrini

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Recognition of emotions based on facial expressions has received increasing attention from the scientific community over the last years. Several fields of applications can benefit from facial emotion recognition, such as behavior prediction, interpersonal relations, human-computer interactions, recommendation systems. In this work, we develop and analyze an emotion recognition framework based on facial expressions robust to occlusions through the Weber Local Descriptor (WLD). Initially, the occluded facial expressions are reconstructed following an extension approach of Robust Principal Component Analysis (RPCA). Then, WLD features are extracted from the facial expression representation, as well as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). The feature vector space is reduced using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers are used to recognize the expressions. Experimental results on three public datasets demonstrated that the WLD representation achieved competitive accuracy rates for occluded and non-occluded facial expressions compared to other approaches available in the literature.

Keywords: emotion recognition, facial expression, occlusion, fiducial landmarks

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Authors: Nitika Sharma, Yogesh Jain

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Introduction: An efficient Information System helps in improving the service delivery as well provides the foundation for policy and regulation of other building blocks of Health System. Health care organizations require an integrated working of its various sub-systems. An efficient EMR software boosts the teamwork amongst the various sub-systems thereby resulting in improved service delivery. Although there has been a huge impetus to EMR under the Digital India initiative, it has still not been mandated in India. It is generally implemented in huge funded public or private healthcare organizations only. Objective: The study was conducted to understand the factors that lead to the successful adoption of an affordable EMR in the low level healthcare organization. It intended to understand the design of the EMR and address the solutions to the challenges faced in adoption of the EMR. Methodology: The study was conducted in a non-profit registered Healthcare organization that has been providing healthcare facilities to more than 2500 villages including certain areas that are difficult to access. The data was collected with help of field notes, in-depth interviews and participant observation. A total of 16 participants using the EMR from different departments were enrolled via purposive sampling technique. The participants included in the study were working in the organization before the implementation of the EMR system. The study was conducted in one month period from 25 June-20 July 2018. The Ethical approval was taken from the institute along with prior approval of the participants. Data analysis: A word document of more than 4000 words was obtained after transcribing and translating the answers of respondents. It was further analyzed by focused coding, a line by line review of the transcripts, underlining words, phrases or sentences that might suggest themes to do thematic narrative analysis. Results: Based on the answers the results were thematically grouped under four headings: 1. governance of organization, 2. architecture and design of the software, 3. features of the software, 4. challenges faced in adoption and the solutions to address them. It was inferred that the successful implementation was attributed to the easy and comprehensive design of the system which has facilitated not only easy data storage and retrieval but contributes in constructing a decision support system for the staff. Portability has lead to increased acceptance by physicians. The proper division of labor, increased efficiency of staff, incorporation of auto-correction features and facilitation of task shifting has lead to increased acceptance amongst the users of various departments. Geographical inhibitions, low computer literacy and high patient load were the major challenges faced during its implementation. Despite of dual efforts made both by the architects and administrators to combat these challenges, there are still certain ongoing challenges faced by organization. Conclusion: Whenever any new technology is adopted there are certain innovators, early adopters, late adopters and laggards. The same pattern was followed in adoption of this software. He challenges were overcome with joint efforts of organization administrators and users as well. Thereby this case study provides a framework of implementing similar systems in public sector of countries that are struggling for digitizing the healthcare in presence of crunch of human and financial resources.

Keywords: EMR, healthcare technology, e-health, EHR

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Authors: Maurice Ntahobari, Levin Kuhlmann, Mario Boley, Zhinoos Razavi Hesabi

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For machine learning based epileptic seizure prediction, it is important for the model to be implemented in small implantable or wearable devices that can be used to monitor epilepsy patients; however, current state-of-the-art methods are complex and computationally intensive. We use Shapley Additive Explanation (SHAP) to find relevant intracranial electroencephalogram (iEEG) features and improve the computational efficiency of a state-of-the-art seizure prediction method based on the extra trees classifier while maintaining prediction performance. Results for a small contest dataset and a much larger dataset with continuous recordings of up to 3 years per patient from 15 patients yield better than chance prediction performance (p < 0.004). Moreover, while the performance of the SHAP-based model is comparable to that of the benchmark, the overall training and prediction time of the model has been reduced by a factor of 1.83. It can also be noted that the feature called zero crossing value is the best EEG feature for seizure prediction. These results suggest state-of-the-art seizure prediction performance can be achieved using efficient methods based on optimal feature selection.

Keywords: machine learning, seizure prediction, extra tree classifier, SHAP, epilepsy

Procedia PDF Downloads 113

Authors: Zine Souhila Rached, Mazari Benyounes Bouzid, Mohamed Amine, Allaoui Tayeb

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Wind energy has many advantages, it does not pollute and it is an inexhaustible source. However, its high cost is a major constraint, especially on the less windy sites. The purpose of wind energy systems is to maximize energy efficiency, and extract maximum power from the wind speed. In other words, having a power coefficient is maximum and therefore the maximum power point tracking. In this case, the MPPT control becomes important.To realize this control, strategy conventional proportional and integral (PI) controller is usually used. However, this strategy cannot achieve better performance. This paper proposes a robust control of a turbine which optimizes its production, that is improve the quality and energy efficiency, namely, a strategy of sliding mode control. The proposed sliding mode control strategy presents attractive features such as robustness to parametric uncertainties of the turbine; the proposed sliding mode control approach has been simulated on three-blade wind turbine. The simulation result under Matlab\Simulink has validated the performance of the proposed MPPT strategy.

Keywords: wind turbine, maximum power point tracking, sliding mode, energy conversion systems

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Authors: Imtiaz Ahmed Khan, Sabu John, Sania Waqar, Lijing Wang, Mac Fergusson, Ilija Najdovski

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Evolution of textiles, from its orthodox to more interactive role has stirred the researchers to uncover its application in numerous arenas. The idea of using textile based materials for wearable energy harvesting and storage devices have gained immense popularity. This is mainly due to textile comfort and flexibility features. In this work, nano-carbonous materials were infused on cellulosic fibers using caustic soda treatment. This paper presents the complete procedure of yarn supercapacitors fabrication process through dip coating technique and its characterization method. The main objective is to study, the effect of varying caustic soda concentration on mass loading of activated carbon on yarns and the related capacitance output of the designed yarn supercapacitor. Polyvinyl alcohol and Phosphoric acid were used as electrolyte in a two-electrode cell assembly to measure device electrochemical performance. The results show a promising increase in capacitance value using this technique.

Keywords: yarn supercapacitors, activated carbon, dip coating, caustic soda, electrolyte, electrochemical characterization

Procedia PDF Downloads 463

Authors: Chia-Hao Chang, Geng-Gui Wang, Jee-Cheng Wu

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The prediction of a landslide is a difficult task because it requires a detailed study of past activities using a complete range of investigative methods to determine the changing condition. In this research, first step, LiDAR 1-meter by 1-meter resolution of digital elevation model (DEM) was used to generate six environmental factors of landslide. Then, back-propagation neural networks (BPNN) was adopted to identify scarp, landslide areas and non-landslide areas. The BPNN uses 6 environmental factors in input layer and 1 output layer. Moreover, 6 landslide areas are used as training areas and 4 landslide areas as test areas in the BPNN. The hidden layer is set to be 1 and 2; the hidden layer neurons are set to be 4, 5, 6, 7 and 8; the learning rates are set to be 0.01, 0.1 and 0.5. When using 1 hidden layer with 7 neurons and the learning rate sets to be 0.5, the result of Network training root mean square error is 0.001388. Finally, evaluation of BPNN classification accuracy by the confusion matrix shows that the overall accuracy can reach 94.4%, and the Kappa value is 0.7464.

Keywords: digital elevation model, DEM, environmental factors, back-propagation neural network, BPNN, LiDAR

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Authors: Jameela Ali Alkrimi, Abdul Rahim Ahmad, Azizah Suliman, Loay E. George

Abstract:

Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. The lack of RBCs is a condition characterized by lower than normal hemoglobin level; this condition is referred to as 'anemia'. In this study, a software was developed to isolate RBCs by using a machine learning approach to classify anemic RBCs in microscopic images. Several features of RBCs were extracted using image processing algorithms, including principal component analysis (PCA). With the proposed method, RBCs were isolated in 34 second from an image containing 18 to 27 cells. We also proposed that PCA could be performed to increase the speed and efficiency of classification. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network ANN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained for a short time period with more efficient when PCA was used.

Keywords: red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC

Procedia PDF Downloads 405

Authors: Asmau Mukhtar Ahmed, Olga Duran

Abstract:

Hydrocarbon (HC) spills constitute a significant problem that causes great concern to the environment. With the latest technology (hyperspectral images) and state of the earth techniques (image processing tools), hydrocarbon spills can easily be detected at an early stage to mitigate the effects caused by such menace. In this study; a controlled laboratory experiment was used, and clay soil was mixed and homogenized with different hydrocarbon types (diesel, bio-diesel, and petrol). The different mixtures were scanned with HYSPEX hyperspectral camera under constant illumination to generate the hypersectral datasets used for this experiment. So far, the Short Wave Infrared Region (SWIR) has been exploited in detecting HC spills with excellent accuracy. However, the Near-Infrared Region (NIR) is somewhat unexplored with regards to HC contamination and how it affects the spectrum of soils. In this study, Deep Neural Network (DNN) was applied to the controlled datasets to detect and quantify the amount of HC spills in soils in the Near-Infrared Region. The initial results are extremely encouraging because it indicates that the DNN was able to identify features of HC in the Near-Infrared Region with a good level of accuracy.

Keywords: hydrocarbon, Deep Neural Network, short wave infrared region, near-infrared region, hyperspectral image

Procedia PDF Downloads 113

Authors: Yani Shi, Jiaqi Yan

Abstract:

A new product is important for companies to extend consumers and manifest competitiveness. New product often involves new features that consumers might not be familiar with while it may also have a competitive advantage to attract consumers compared to established products. However, although most online retailers employ recommendation agents (RA) to influence consumers’ product choice decision, recommended new products are not accepted and chosen as expected. We argue that it might also be caused by providing a new product recommendation in the wrong way at the wrong time. This study seeks to discuss how new product evaluations sourced from third parties could be employed in RAs as evidence of the superiority for the new product and how the new product recommendation could be provided to a consumer at the right time so that it can be accepted and finally chosen during the consumer’s decision-making process. A 2*2 controlled laboratory experiment was conducted to understand the selection of new product recommendation sources and recommendation timing. Human subjects were randomly assigned to one of the four treatments to minimize the effects of individual differences on the results. Participants were told to make purchase choices from our product categories. We find that a new product recommended right after a similar existing product and with the source of the expert review will be more likely to be accepted. Based on this study, both theoretical and practical contributions are provided regarding new product recommendation.

Keywords: new product recommendation, recommendation timing, recommendation source, recommendation agents

Procedia PDF Downloads 154