Search results for: automatic classification of tremor types

Authors: Younghoon Kim, Seoung Bum Kim

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One-class classification plays an important role in detecting outlier and abnormality from normal observations. In the previous research, several attempts were made to extend the scope of application of the one-class classification techniques to statistical process control problems. For most previous approaches, such as support vector data description (SVDD) control chart, the design of the control limits is commonly based on the assumption that the proportion of abnormal observations is approximately equal to an expected Type I error rate in Phase I process. Because of the limitation of the one-class classification techniques based on convex optimization, we cannot make the proportion of abnormal observations exactly equal to expected Type I error rate: controlling Type I error rate requires to optimize constraints with integer decision variables, but convex optimization cannot satisfy the requirement. This limitation would be undesirable in theoretical and practical perspective to construct effective control charts. In this work, to address the limitation of previous approaches, we propose the one-class classification algorithm based on the mixed integer programming technique, which can solve problems formulated with continuous and integer decision variables. The proposed method minimizes the radius of a spherically shaped boundary subject to the number of normal data to be equal to a constant value specified by users. By modifying this constant value, users can exactly control the proportion of normal data described by the spherically shaped boundary. Thus, the proportion of abnormal observations can be made theoretically equal to an expected Type I error rate in Phase I process. Moreover, analogous to SVDD, the boundary can be made to describe complex structures by using some kernel functions. New multivariate control chart applying the effectiveness of the algorithm is proposed. This chart uses a monitoring statistic to characterize the degree of being an abnormal point as obtained through the proposed one-class classification. The control limit of the proposed chart is established by the radius of the boundary. The usefulness of the proposed method was demonstrated through experiments with simulated and real process data from a thin film transistor-liquid crystal display.

Keywords: control chart, mixed integer programming, one-class classification, support vector data description

Procedia PDF Downloads 157

Authors: A.Boularouk, F. Chouikh, M. Lamri, H. Moualkia, Y. Bouznit

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The principal aim of this study is to considerably reduce the resistivity of the SnO2 thin layers. In this order, we have doped tin oxide with aluminum and antimony incorporation with different atomic percentages (0 and 4%). All the pure and doped SnO2 films were grown by simple, flexible and cost-effective Automatic Spray Pyrolysis Method (ASPM) on glass substrates at a temperature of 350 °C. The microstructural, optical, morphological and electrical properties of the films have been studied. The XRD results demonstrate that all films have polycrystalline nature with a tetragonal rutile structure and exhibit the (200) preferential orientation. It has been observed that all the dopants are soluble in the SnO2 matrix without forming secondary phases. However, dopant introduction does not modify the film growth orientation. The crystallite size of the pure SnO2 film is about 36 nm. The films are highly transparent in the visible region with an average transmittance reaching up to 80% and it slightly reduces with increasing doping concentration (Al and Sb). The optical band gap value was evaluated between 3.60 eV and 3.75 eV as a function of doping. The SEM image reveals that all films are nanostructured, densely continuous, with good adhesion to the substrate. We note again that the surface morphology change with the type and concentration dopant. The minimum resistivity is 0.689*10-4, which is observed for SnO2 film doped 4% Al. This film shows better properties and is considered the best among all films. Finally, we concluded that the physical properties of the pure and doped SnO2 films grown on a glass substrate by ASPM strongly depend on the type and concentration dopant (Al and Sb) and have highly desirable optical and electrical properties and are promising materials for several applications.

Keywords: tin oxide, automatic spray, Al and Sb doped, transmittance, MEB, XRD and UV-VIS

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Authors: Vedat Kurt, Tansel Koyunoglu, Gamze Kurt, Ozgen Aras

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Low back pain is one of the most common health problem in public. Common symptoms that can be associated with low back pain include; pain, functional disability, gait disturbances. The aim of the study was to investigate the differences between disability scores and gait parameters in subjects with low back pain. Sixty participants are included in our study, (35 men, 25 women, mean age: 37.65±10.02 years). Demographic characteristics of participants were recorded. Pain (visual analog scale) and disability level (Oswestry Disability Index(ODI)) were evaluated. Gait parameters were measured with Zebris-FDM-2 platform. Independent samples t-test was used to analyse the differences between subjects with under 40 points (n=31, mean age:35.8±11.3) and above 40 points (n=29, mean age:39.6±8.1) of ODI scores. Significant level in statistical analysis was accepted as 0.05. There was no significant difference between the ODI scores and groups’ ages. Statistically significant differences were found in step width between subjects with under 40 points of ODI and above 40 points of ODI score(p < 0.05). But there were non-significant differences with other gait parameters (p > 0.05). The differences between gait parameters and pain scores were not statistically significant (p > 0.05). Researchers generally agree that individuals with LBP walk slower and take shorter steps and have asymmetric step lengths when compared with than their age-matched pain-free counterparts. Also perceived general disability may have moderate correlation with walking performance. In the current study, the patients classified as minimal/moderate and severe disability level by using ODI scores. As a result, a patient with LBP who have higher disability level tends to increase support surface. On the other hand, we did not find any relation between pain intensity and gait parameters. It may be caused by the classification system of pain scores. Additional research is needed to investigate the effects of functionality level and pain intensity on gait in subjects with low back pain under different classification types.

Keywords: functionality, low back pain, gait, pain

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Authors: Folong Tchoffo Marlyse Fabiola, Anaba Onana Achille Basile

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Located in the western region of Cameroon, in the BAMBOUTOS department, the city of Mbouda belongs to the Pan-African basement. The water resources exploited in this region consist of surface water and groundwater from weathered and fractured aquifers within the same basement. To study the factors determining the productivity of aquifers in the Mbouda area, we adopted a methodology based on collecting data from boreholes drilled in the region, identifying different types of rocks, analyzing structures, and conducting geophysical surveys in the field. The results obtained allowed us to distinguish two main types of rocks: metamorphic rocks composed of amphibolites and migmatitic gneisses and igneous rocks, namely granodiorites and granites. Several types of structures were also observed, including planar structures (foliation and schistosity), folded structures (folds), and brittle structures (fractures and lineaments). A structural synthesis combines all these elements into three major phases of deformation. Phase D1 is characterized by foliation and schistosity, phase D2 is marked by shear planes and phase D3 is characterized by open and sealed fractures. The analysis of structures (fractures in outcrops, Landsat lineaments, subsurface structures) shows a predominance of ENE-WSW and WNW-ESE directions. Through electrical surveys and borehole data, we were able to identify the sequence of different geological formations. Four geo-electric layers were identified, each with a different electrical conductivity: conductive, semi-resistive, or resistive. The last conductive layer is considered a potentially aquiferous zone. The flow rates of the boreholes ranged from 2.6 to 12 m3/h, classified as moderate to high according to the CIEH classification. The boreholes were mainly located in basalts, which are mineralogically rich in ferromagnesian minerals. This mineral composition contributes to their high productivity as they are more likely to be weathered. The boreholes were positioned along linear structures or at their intersections.

Keywords: Mbouda, Pan-African basement, productivity, west-Cameroon

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Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

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Authors: Kaewkarn Phuangsombat, Arthit Phuangsombat, Anupun Terdwongworakul

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Hard seeds will not grow and can cause mold in sprouting process. Thus, the hard seeds need to be separated from the normal seeds. Near infrared hyperspectral imaging in a range of 900 to 1700 nm was implemented to develop a model by partial least squares discriminant analysis to discriminate the hard seeds from the normal seeds. The orientation of the seeds was also studied to compare the performance of the models. The model based on hilum-up orientation achieved the best result giving the coefficient of determination of 0.98, and root mean square error of prediction of 0.07 with classification accuracy was equal to 100%.

Keywords: mung bean, near infrared, germinatability, hard seed

Procedia PDF Downloads 280

Authors: Rana Alaa El-Deen Ahmed, M. Elemam Shehab, Shereen Morsy, Nermeen Mekawie

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With the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the large number of online offers. Thus, techniques for personalization and shopping guides are needed by users. For a pleasant and successful shopping experience, users need to know easily which products to buy with high confidence. Since selling a wide variety of products has become easier due to the popularity of online stores, online retailers are able to sell more products than a physical store. The disadvantage is that the customers might not find products they need. In this research the customer will be able to find the products he is searching for, because recommender systems are used in some ecommerce web sites. Recommender system learns from the information about customers and products and provides appropriate personalized recommendations to customers to find the needed product. In this paper eleven classification algorithms are comparatively tested to find the best classifier fit for consumer online shopping attitudes and behavior in the experimented dataset. The WEKA knowledge analysis tool, which is an open source data mining workbench software used in comparing conventional classifiers to get the best classifier was used in this research. In this research by using the data mining tool (WEKA) with the experimented classifiers the results show that decision table and filtered classifier gives the highest accuracy and the lowest accuracy classification via clustering and simple cart.

Keywords: classification, data mining, machine learning, online shopping, WEKA

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Authors: Mi-Hye Hwang, Young Min Son, Kichan Lee, Bang-Hun Hyun, Byeong Yeal Jung

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Botulism is a paralytic disease of human beings and animals caused by neurotoxin produced by Clostridium botulinum. The neurotoxins are genetically distinguished into 8 types, A to H. Ingestion of performed toxin, usually types B, C, and D, have been shown to produce diseases in most cases of cattle botulism. Vaccination is the best measure to prevent cattle botulism. However, the commercially available toxoid-based vaccines are difficult and hazardous to produce. We produced recombinant protein using gene of heavy chain domain of botulinum toxin B of which binds to cellular receptor of neuron cells and used as immunogen. In this study, we evaluated the safety and efficacy of botulism vaccine composed of recombinant types B. Safety test was done by National Regulation for Veterinary Biologicals. For efficacy test, female ICR mice (5 weeks old) were subcutaneously injected, intraperitoneally challenged, and examined the survival rates compared with vaccination and non-vaccination group. Mouse survival rate of recombinant types B vaccine was above 80%, while one of non-vaccination group was 0%. A vaccine composed of recombinant types B was safe and efficacious in mouse. Our results suggest that recombinant heavy chain receptor binding domain can be used as an effective vaccine candidate for type B botulism.

Keywords: botulism, livestock, vaccine, recombinant protein, toxin

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Authors: Hamidreza Fallah Lajimi , Elham Karami, Fatemeh Ali nasab, Mostafa Mahdavikia

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Being responsive is an increasingly important skill for firms in today’s global economy; thus firms must be agile. Naturally, it follows that an organization’s agility depends on its supply chain being agile. However, achieving supply chain agility is a function of other abilities within the organization. This paper analyses results from a survey of 71 Iran manufacturing companies in order to identify some of the factors for agile organizations in managing their supply chains. Then we classification this company in four cluster with fuzzy c-mean technique and with four validations functional determine automatically the optimal number of clusters.

Keywords: agile supply chain, clustering, fuzzy clustering

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Authors: Bilel Chalghaf, Mathieu Varin

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Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

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Authors: Nikolay P. Brayanov, Anna V. Stoynova

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Model-based development approach is gaining more support and acceptance. Its higher abstraction level brings simplification of systems’ description that allows domain experts to do their best without particular knowledge in programming. The different levels of simulation support the rapid prototyping, verifying and validating the product even before it exists physically. Nowadays model-based approach is beneficial for modelling of complex embedded systems as well as a generation of code for many different hardware platforms. Moreover, it is possible to be applied in safety-relevant industries like automotive, which brings extra automation of the expensive device certification process and especially in the software qualification. Using it, some companies report about cost savings and quality improvements, but there are others claiming no major changes or even about cost increases. This publication demonstrates the level of maturity and autonomy of model-based approach for code generation. It is based on a real live automotive seat heater (ASH) module, developed using The Mathworks, Inc. tools. The model, created with Simulink, Stateflow and Matlab is used for automatic generation of C code with Embedded Coder. To prove the maturity of the process, Code generation advisor is used for automatic configuration. All additional configuration parameters are set to auto, when applicable, leaving the generation process to function autonomously. As a result of the investigation, the publication compares the quality of generated embedded code and a manually developed one. The measurements show that generally, the code generated by automatic approach is not worse than the manual one. A deeper analysis of the technical parameters enumerates the disadvantages, part of them identified as topics for our future work.

Keywords: embedded code generation, embedded C code quality, embedded systems, model-based development

Procedia PDF Downloads 221

Authors: Lamees Nasser, Yago Diez, Robert Martí, Joan Martí, Ibrahim Sadek

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Automated 3D breast ultrasound (ABUS) screening is a novel modality in medical imaging because of its common characteristics shared with other ultrasound modalities in addition to the three orthogonal planes (i.e., axial, sagittal, and coronal) that are useful in analysis of tumors. In the literature, few automatic approaches exist for typical tasks such as segmentation or registration. In this work, we deal with two problems concerning ABUS images: nipple and rib detection. Nipple and ribs are the most visible and salient features in ABUS images. Determining the nipple position plays a key role in some applications for example evaluation of registration results or lesion follow-up. We present a nipple detection algorithm based on color and shape of the nipple, besides an automatic approach to detect the ribs. In point of fact, rib detection is considered as one of the main stages in chest wall segmentation. This approach consists of four steps. First, images are normalized in order to minimize the intensity variability for a given set of regions within the same image or a set of images. Second, the normalized images are smoothed by using anisotropic diffusion filter. Next, the ribs are detected in each slice by analyzing the eigenvalues of the 3D Hessian matrix. Finally, a breast mask and a probability map of regions detected as ribs are used to remove false positives (FP). Qualitative and quantitative evaluation obtained from a total of 22 cases is performed. For all cases, the average and standard deviation of the root mean square error (RMSE) between manually annotated points placed on the rib surface and detected points on rib borders are 15.1188 mm and 14.7184 mm respectively.

Keywords: Automated 3D Breast Ultrasound, Eigenvalues of Hessian matrix, Nipple detection, Rib detection

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Authors: B. G. Ayantunji, B. Musa, H. Mai-Unguwa, L. A. Sunmonu, A. S. Adewumi, L. Sa'ad, A. Kado

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For achieving reliable and efficient communication system, both terrestrial and satellite communication, surface refractivity is critical in planning and design of radio links. This study analyzed the impact of atmospheric parameters on Ultra High Frequency (UHF) signal strength over Gusau, North West, Nigeria. The analysis exploited meteorological data measured simultaneously with UHF signal strength for the month of June 2017 using a Davis Vantage Pro2 automatic weather station and UHF signal strength measuring devices respectively. The instruments were situated at the premise of Federal University, Gusau (6° 78' N, 12° 13' E). The refractivity values were computed using ITU-R model. The result shows that the refractivity value attained the highest value of 366.28 at 2200hr and a minimum value of 350.66 at 2100hr local time. The correlation between signal strength and refractivity is 0.350; Humidity is 0.532 and a negative correlation of -0.515 for temperature.

Keywords: refractivity, UHF (ultra high frequency) signal strength, free space, automatic weather station

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Authors: Jesús Horacio, Alfredo Ollero, Víctor Bouzas-Blanco, Augusto Pérez-Alberti

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Rivers have traditionally been classified, assessed and managed in terms of hydrological, chemical and / or biological criteria. Geomorphological classifications had in the past a secondary role, although proposals like River Styles Framework, Catchment Baseline Survey or Stroud Rural Sustainable Drainage Project did incorporate geomorphology for management decision-making. In recent years many studies have been attracted to the geomorphological component. The geomorphological processes and their associated forms determine the structure of a river system. Understanding these processes and forms is a critical component of the sustainable rehabilitation of aquatic ecosystems. The fluvial auto-classification approach suggests that a river is a self-built natural system, with processes and forms designed to effectively preserve their ecological function (hydrologic, sedimentological and biological regime). Fluvial systems are formed by a wide range of elements with multiple non-linear interactions on different spatial and temporal scales. Besides, the fluvial auto-classification concept is built using data from the river itself, so that each classification developed is peculiar to the river studied. The variables used in the classification are specific stream power and mean grain size. A discriminant analysis showed that these variables are the best characterized processes and forms. The statistical technique applied allows to get an individual discriminant equation for each geomorphological type. The geomorphological classification was developed using sites with high naturalness. Each site is a control point of high ecological and geomorphological quality. The changes in the conditions of the control points will be quickly recognizable, and easy to apply a right management measures to recover the geomorphological type. The study focused on Galicia (NW Spain) and the mapping was made analyzing 122 control points (sites) distributed over eight river basins. In sum, this study provides a method for fluvial geomorphological classification that works as an open and flexible tool underlying the fluvial auto-classification concept. The hydrographic mapping is the visual expression of the results, such that each river has a particular map according to its geomorphological characteristics. Each geomorphological type is represented by a particular type of hydraulic geometry (channel width, width-depth ratio, hydraulic radius, etc.). An alteration of this geometry is indicative of a geomorphological disturbance (whether natural or anthropogenic). Hydrographic mapping is also dynamic because its meaning changes if there is a modification in the specific stream power and/or the mean grain size, that is, in the value of their equations. The researcher has to check annually some of the control points. This procedure allows to monitor the geomorphology quality of the rivers and to see if there are any alterations. The maps are useful to researchers and managers, especially for conservation work and river restoration.

Keywords: fluvial auto-classification concept, mapping, geomorphology, river

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Authors: Khaled Nagaty, Heba Nagaty, Gerard McKee

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A signature is a behavioral biometric that is used for authenticating users in most financial and legal transactions. Signatures can be easily forged by skilled forgers. Therefore, it is essential to verify whether a signature is genuine or forged. The aim of any signature verification algorithm is to accommodate the differences between signatures of the same person and increase the ability to discriminate between signatures of different persons. This work presented in this paper proposes an automatic signature verification system to indicate whether a signature is genuine or not. The system comprises four phases: (1) The pre-processing phase in which image scaling, binarization, image rotation, dilation, thinning, and connecting ridge breaks are applied. (2) The feature extraction phase in which global and local features are extracted. The local features are minutiae points, curvature orientation, and curve plateau. The global features are signature area, signature aspect ratio, and Hu moments. (3) The post-processing phase, in which false minutiae are removed. (4) The classification phase in which features are enhanced before feeding it into the classifier. k-nearest neighbors and support vector machines are used. The classifier was trained on a benchmark dataset to compare the performance of the proposed offline signature verification system against the state-of-the-art. The accuracy of the proposed system is 92.3%.

Keywords: signature, ridge breaks, minutiae, orientation

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Authors: A. Deswal, N. S. Deora, H. N. Mishra

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The aim of the present study was to develop a rapid method for electronic nose for online quality control of oat milk. Analysis by electronic nose and bacteriological measurements were performed to analyse spoilage kinetics of oat milk samples stored at room temperature and refrigerated conditions for up to 15 days. Principal component analysis (PCA), discriminant factorial analysis (DFA) and soft independent modelling by class analogy (SIMCA) classification techniques were used to differentiate the samples of oat milk at different days. The total plate count (bacteriological method) was selected as the reference method to consistently train the electronic nose system. The e-nose was able to differentiate between the oat milk samples of varying microbial load. The results obtained by the bacteria total viable counts showed that the shelf-life of oat milk stored at room temperature and refrigerated conditions were 20 hours and 13 days, respectively. The models built classified oat milk samples based on the total microbial population into “unspoiled” and “spoiled”.

Keywords: electronic-nose, bacteriological, shelf-life, classification

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Authors: Hannele Kuusisto, Paivi Makila, Ursula Hyrkkanen

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Introduction: There have been arguments about the skills university students should have when graduated. Current trends argue that as well as the specific job-related skills the graduated students need problem-solving, interaction and networking skills as well as self-management skills. Skills required in working life are also considered in the Finnish national project called VALTE (short for 'prepared for working life'). The project involves 11 Finnish school organizations. As one result of this project, a five-credit independent online course in study and job engagement as well as in study and job crafting was developed at Turku University of Applied Sciences. The aim of the oral or e-poster presentation is to present the online course developed in the project. The purpose of this abstract is to present the development work of the online course and the feedback received from the pilots. Method: As the University of Turku is the leading partner of the VALTE project, the collaborative education platform ViLLE (https://ville.utu.fi, developed by the University of Turku) was chosen as the online platform for the course. Various exercise types with automatic assessment were used; for example, quizzes, multiple-choice questions, classification exercises, gap filling exercises, model answer questions, self-assessment tasks, case tasks, and collaboration in Padlet. In addition, the free material and free platforms on the Internet were used (Youtube, Padlet, Todaysmeet, and Prezi) as well as the net-based questionnaires about the study engagement and study crafting (made with Webropol). Three teachers with long teaching experience (also with job crafting and online pedagogy) and three students working as trainees in the project developed the content of the course. The online course was piloted twice in 2017 as an elective course for the students at Turku University of Applied Sciences, a higher education institution of about 10 000 students. After both pilots, feedback from the students was gathered and the online course was developed. Results: As the result, the functional five-credit independent online course suitable for students of different educational institutions was developed. The student feedback shows that students themselves think that the developed online course really enhanced their job and study crafting skills. After the course, 91% of the students considered their knowledge in job and study engagement as well as in job and study crafting to be at a good or excellent level. About two-thirds of the students were going to exploit their knowledge significantly in the future. Students appreciated the variability and the game-like feeling of the exercises as well as the opportunity to study online at the time and place they chose themselves. On a five-point scale (1 being poor and 5 being excellent), the students graded the clarity of the ViLLE platform as 4.2, the functionality of the platform as 4.0 and the easiness of operating as 3.9.

Keywords: job crafting, job engagement, online course, study crafting, study engagement

Procedia PDF Downloads 138

Authors: Mariam Matiashvili

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Argumentation is an integral part of our daily communications - formal or informal. Argumentative reasoning, techniques, and language tools are used both in personal conversations and in the business environment. Verbalization of the opinions requires the use of extraordinary syntactic-pragmatic structural quantities - arguments that add credibility to the statement. The study of argumentative structures allows us to identify the linguistic features that make the text argumentative. Knowing what elements make up an argumentative text in a particular language helps the users of that language improve their skills. Also, natural language processing (NLP) has become especially relevant recently. In this context, one of the main emphases is on the computational processing of argumentative texts, which will enable the automatic recognition and analysis of large volumes of textual data. The research deals with the linguistic analysis of the argumentative structures of Georgian political speeches - particularly the linguistic structure, characteristics, and functions of the parts of the argumentative text - claims, support, and attack statements. The research aims to describe the linguistic cues that give the sentence a judgmental/controversial character and helps to identify reasoning parts of the argumentative text. The empirical data comes from the Georgian Political Corpus, particularly TV debates. Consequently, the texts are of a dialogical nature, representing a discussion between two or more people (most often between a journalist and a politician). The research uses the following approaches to identify and analyze the argumentative structures Lexical Classification & Analysis - Identify lexical items that are relevant in argumentative texts creating process - Creating the lexicon of argumentation (presents groups of words gathered from a semantic point of view); Grammatical Analysis and Classification - means grammatical analysis of the words and phrases identified based on the arguing lexicon. Argumentation Schemas - Describe and identify the Argumentation Schemes that are most likely used in Georgian Political Speeches. As a final step, we analyzed the relations between the above mentioned components. For example, If an identified argument scheme is “Argument from Analogy”, identified lexical items semantically express analogy too, and they are most likely adverbs in Georgian. As a result, we created the lexicon with the words that play a significant role in creating Georgian argumentative structures. Linguistic analysis has shown that verbs play a crucial role in creating argumentative structures.

Keywords: georgian, argumentation schemas, argumentation structures, argumentation lexicon

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Authors: Jigg Pelayo, Ricardo Villar, Einstine Opiso

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The rampant and unintended spread of urban areas resulted in increasing artificial component features in the land cover types of the countryside and bringing forth the urban heat island (UHI). This paved the way to wide range of negative influences on the human health and environment which commonly relates to air pollution, drought, higher energy demand, and water shortage. Land cover type also plays a relevant role in the process of understanding the interaction between ground surfaces with the local temperature. At the moment, the depiction of the land surface temperature (LST) at city/municipality scale particularly in certain areas of Misamis Oriental, Philippines is inadequate as support to efficient mitigations and adaptations of the surface urban heat island (SUHI). Thus, this study purposely attempts to provide application on the Landsat 8 satellite data and low density Light Detection and Ranging (LiDAR) products in mapping out quality automated LST model and crop-level land cover classification in a local scale, through theoretical and algorithm based approach utilizing the principle of data analysis subjected to multi-dimensional image object model. The paper also aims to explore the relationship between the derived LST and land cover classification. The results of the presented model showed the ability of comprehensive data analysis and GIS functionalities with the integration of object-based image analysis (OBIA) approach on automating complex maps production processes with considerable efficiency and high accuracy. The findings may potentially lead to expanded investigation of temporal dynamics of land surface UHI. It is worthwhile to note that the environmental significance of these interactions through combined application of remote sensing, geographic information tools, mathematical morphology and data analysis can provide microclimate perception, awareness and improved decision-making for land use planning and characterization at local and neighborhood scale. As a result, it can aid in facilitating problem identification, support mitigations and adaptations more efficiently.

Keywords: LiDAR, OBIA, remote sensing, local scale

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Authors: Reza Shakoori

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This paper presents a structural correction of Arabic and Persian strokes using manipulation of their chain codes in order to improve the rate and performance of Persian and Arabic handwritten word recognition systems. It collects pure and effective features to represent a character with one consolidated feature vector and reduces variations in order to decrease the number of training samples and increase the chance of successful classification. Our results also show that how the proposed approaches can simplify classification and consequently recognition by reducing variations and possible noises on the chain code by keeping orientation of characters and their backbone structures.

Keywords: Arabic, chain code normalization, OCR systems, image processing

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Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

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Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

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Authors: Hai L. Tran

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In the digital age, evidence-based reporting is touted as a best practice for seeking the truth and keeping the public well-informed. Journalists are expected to rely on evidence to demonstrate the validity of a factual statement and lend credence to an individual account. Evidence can be obtained from various sources, and due to a rich supply of evidence types available, the definition of this important concept varies semantically. To promote clarity and understanding, it is necessary to break down the various types of evidence and categorize them in a more coherent, systematic way. There is a wide array of devices that digital journalists deploy as proof to back up or refute a truth claim. Evidence can take various formats, including verbal and visual materials. Verbal evidence encompasses quotes, soundbites, talking heads, testimonies, voice recordings, anecdotes, and statistics communicated through written or spoken language. There are instances where evidence is simply non-verbal, such as when natural sounds are provided without any verbalized words. On the other hand, other language-free items exhibited in photos, video footage, data visualizations, infographics, and illustrations can serve as visual evidence. Moreover, there are different sources from which evidence can be cited. Supporting materials, such as public or leaked records and documents, data, research studies, surveys, polls, or reports compiled by governments, organizations, and other entities, are frequently included as informational evidence. Proof can also come from human sources via interviews, recorded conversations, public and private gatherings, or press conferences. Expert opinions, eye-witness insights, insider observations, and official statements are some of the common examples of testimonial evidence. Digital journalism studies tend to make broad references when comparing qualitative versus quantitative forms of evidence. Meanwhile, limited efforts are being undertaken to distinguish between sister terms, such as “data,” “statistical,” and “base-rate” on one side of the spectrum and “narrative,” “anecdotal,” and “exemplar” on the other. The present study seeks to develop the evidence taxonomy, which classifies evidence through the quantitative-qualitative juxtaposition and in a hierarchical order from broad to specific. According to this scheme, data, statistics, and base rate belong to the quantitative evidence group, whereas narrative, anecdote, and exemplar fall into the qualitative evidence group. Subsequently, the taxonomical classification arranges data versus narrative at the top of the hierarchy of types of evidence, followed by statistics versus anecdote and base rate versus exemplar. This research reiterates the central role of evidence in how journalists describe and explain social phenomena and issues. By defining the various types of evidence and delineating their logical connections it helps remove a significant degree of conceptual inconsistency, ambiguity, and confusion in digital journalism studies.

Keywords: evidence, evidence forms, evidence types, taxonomy

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Authors: Malarvizhi Selvaraj, Paul Fergus, Andy Shaw

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Uterine contractions produced during labour have the potential to damage the foetus by diminishing the maternal blood flow to the placenta. In order to observe this phenomenon labour and delivery are routinely monitored using cardiotocography monitors. An obstetrician usually makes the diagnosis of foetus hypoxia by interpreting cardiotocography recordings. However, cardiotocography capture and interpretation is time-consuming and subjective, often lead to misclassification that causes damage to the foetus and unnecessary caesarean section. Both of these have a high impact on the foetus and the cost to the national healthcare services. Automatic detection of foetal heart rate may be an objective solution to help to reduce unnecessary medical interventions, as reported in several studies. This paper aim is to provide a system for better identification and interpretation of abnormalities of the fetal heart rate using RStudio. An open dataset of 552 Intrapartum recordings has been filtered with 0.034 Hz filters in an attempt to remove noise while keeping as much of the discriminative data as possible. Features were chosen following an extensive literature review, which concluded with FIGO features such as acceleration, deceleration, mean, variance and standard derivation. The five features were extracted from 552 recordings. Using these features, recordings will be classified either normal or abnormal. If the recording is abnormal, it has got more chances of hypoxia.

Keywords: cardiotocography, foetus, intrapartum, hypoxia

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Authors: Haziyah Hussin

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Hazi Attire software is purposely designed to be used for pattern drafting of the Malay Traditional Women Apparel. It is software created using LISP Program that works under AutoCAD engine and able to draft various patterns for Malay women apparels from fitted, semi-fitted and loose silhouettes. It is fully automatic and the user can select styles from the menu on the screen and enter the measurements. Within five seconds patterns are ready to be printed and sewn. Hazi Attire is different from other programmes available in the market since it is fully automatic, user-friendly and able to print selected pattern chosen quickly and accurately. With this software (Hazi Attire), the selected styles can be generated the pattern according to made-to-measure or standard sizes. It would benefit the apparel industries by reducing manufacturing lead time and cycle time.

Keywords: basic pattern, pattern drafting, toile, Malay traditional women apparel, the measurement parameters, fitted, semi-fitted and loose silhouette

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Authors: Asma A. Alsufyani, Maram A. Alharthi, Maha J. Althobaiti, Manal S. Alharthi, Huda Rizq

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Twitter is one of the most popular microblogging sites that allows users to publish short text messages called 'tweets'. Increasing the number of accounts to follow (followings) increases the number of tweets that will be displayed from different topics in an unclassified manner in the timeline of the user. Therefore, it can be a vital solution for many Twitter users to have their tweets in a timeline classified into general categories to save the user’s time and to provide easy and quick access to tweets based on topics. In this paper, we developed a classifier for timeline tweets trained on a dataset consisting of 3600 tweets in total, which were collected from Saudi Twitter and annotated manually. We experimented with the well-known Bag-of-Words approach to text classification, and we used support vector machines (SVM) in the training process. The trained classifier performed well on a test dataset, with an average F1-measure equal to 92.3%. The classifier has been integrated into an application, which practically proved the classifier’s ability to classify timeline tweets of the user.

Keywords: corpus creation, feature extraction, machine learning, short text classification, social media, support vector machine, Twitter

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Authors: Seynabou Toure, Oumar Diop, Kidiyo Kpalma, Amadou S. Maiga

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Color and texture are the two most determinant elements for perception and recognition of the objects in an image. For this reason, color and texture analysis find a large field of application, for example in image classification and segmentation. But, the pioneering work in texture analysis was conducted on grayscale images, thus discarding color information. Many grey-level texture descriptors have been proposed and successfully used in numerous domains for image classification: face recognition, industrial inspections, food science medical imaging among others. Taking into account color in the definition of these descriptors makes it possible to better characterize images. Color texture is thus the subject of recent work, and the analysis of color texture images is increasingly attracting interest in the scientific community. In optical remote sensing systems, sensors measure separately different parts of the electromagnetic spectrum; the visible ones and even those that are invisible to the human eye. The amounts of light reflected by the earth in spectral bands are then transformed into grayscale images. The primary natural colors Red (R) Green (G) and Blue (B) are then used in mixtures of different spectral bands in order to produce RGB images. Thus, good color texture discrimination can be achieved using RGB under controlled illumination conditions. Some previous works investigate the effect of using different color space for color texture classification. However, the selection of the best performing color space in land-sea segmentation is an open question. Its resolution may bring considerable improvements in certain applications like coastline detection, where the detection result is strongly dependent on the performance of the land-sea segmentation. The aim of this paper is to present the results of a study conducted on different color spaces in order to show the best-performing color space for land-sea segmentation. In this sense, an experimental analysis is carried out using five different color spaces (RGB, XYZ, Lab, HSV, YCbCr). For each color space, the Haar wavelet decomposition is used to extract different color texture features. These color texture features are then used for Fusion of Over Segmentation (FOOS) based classification; this allows segmentation of the land part from the sea one. By analyzing the different results of this study, the HSV color space is found as the best classification performance while using color and texture features; which is perfectly coherent with the results presented in the literature.

Keywords: classification, coastline, color, sea-land segmentation

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Authors: Rahil Bahrami, Kaveh Ashenayi

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This paper presents a systematic classification and comparative analysis of non-isolated bi-directional DC-DC converters. The increasing demand for efficient energy conversion in diverse applications has spurred the development of various converter topologies. In this study, we categorize bi-directional converters into three distinct classes: Inverting, Non-Inverting, and Interleaved. Each category is characterized by its unique operational characteristics and benefits. Furthermore, a practical comparison is conducted by evaluating the results of simulation of each bi-directional converter. BDCs can be classified into isolated and non-isolated topologies. Non-isolated converters share a common ground between input and output, making them suitable for applications with minimal voltage change. They are easy to integrate, lightweight, and cost-effective but have limitations like limited voltage gain, switching losses, and no protection against high voltages. Isolated converters use transformers to separate input and output, offering safety benefits, high voltage gain, and noise reduction. They are larger and more costly but are essential for automotive designs where safety is crucial. The paper focuses on non-isolated systems.The paper discusses the classification of non-isolated bidirectional converters based on several criteria. Common factors used for classification include topology, voltage conversion, control strategy, power capacity, voltage range, and application. These factors serve as a foundation for categorizing converters, although the specific scheme might vary depending on contextual, application, or system-specific requirements. The paper presents a three-category classification for non-isolated bi-directional DC-DC converters: inverting, non-inverting, and interleaved. In the inverting category, converters produce an output voltage with reversed polarity compared to the input voltage, achieved through specific circuit configurations and control strategies. This is valuable in applications such as motor control and grid-tied solar systems. The non-inverting category consists of converters maintaining the same voltage polarity, useful in scenarios like battery equalization. Lastly, the interleaved category employs parallel converter stages to enhance power delivery and reduce current ripple. This classification framework enhances comprehension and analysis of non-isolated bi-directional DC-DC converters. The findings contribute to a deeper understanding of the trade-offs and merits associated with different converter types. As a result, this work aids researchers, practitioners, and engineers in selecting appropriate bi-directional converter solutions for specific energy conversion requirements. The proposed classification framework and experimental assessment collectively enhance the comprehension of non-isolated bi-directional DC-DC converters, fostering advancements in efficient power management and utilization.The simulation process involves the utilization of PSIM to model and simulate non-isolated bi-directional converter from both inverted and non-inverted category. The aim is to conduct a comprehensive comparative analysis of these converters, considering key performance indicators such as rise time, efficiency, ripple factor, and maximum error. This systematic evaluation provides valuable insights into the dynamic response, energy efficiency, output stability, and overall precision of the converters. The results of this comparison facilitate informed decision-making and potential optimizations, ensuring that the chosen converter configuration aligns effectively with the designated operational criteria and performance goals.

Keywords: bi-directional, DC-DC converter, non-isolated, energy conversion

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Authors: Wanderson Goncalves e Goncalves, José Alberto Silva de Sá

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Human actions are a threat to the balance and conservation of the Amazon forest. Therefore the environmental monitoring services play an important role as the preservation and maintenance of this environment. This study classified forest types using data from a forest inventory provided by the 'Florestal e da Biodiversidade do Estado do Pará' (IDEFLOR-BIO), located between the municipalities of Santarém, Juruti and Aveiro, in the state of Pará, Brazil, covering an area approximately of 600,000 hectares, Bands 3, 4 and 5 of the TM-Landsat satellite image, and Self - Organizing Maps. The information from the satellite images was extracted using QGIS software 2.8.1 Wien and was used as a database for training the neural network. The midpoints of each sample of forest inventory have been linked to images. Later the Digital Numbers of the pixels have been extracted, composing the database that fed the training process and testing of the classifier. The neural network was trained to classify two forest types: Rain Forest of Lowland Emerging Canopy (Dbe) and Rain Forest of Lowland Emerging Canopy plus Open with palm trees (Dbe + Abp) in the Mamuru Arapiuns glebes of Pará State, and the number of examples in the training data set was 400, 200 examples for each class (Dbe and Dbe + Abp), and the size of the test data set was 100, with 50 examples for each class (Dbe and Dbe + Abp). Therefore, total mass of data consisted of 500 examples. The classifier was compiled in Orange Data Mining 2.7 Software and was evaluated in terms of the confusion matrix indicators. The results of the classifier were considered satisfactory, and being obtained values of the global accuracy equal to 89% and Kappa coefficient equal to 78% and F1 score equal to 0,88. It evaluated also the efficiency of the classifier by the ROC plot (receiver operating characteristics), obtaining results close to ideal ratings, showing it to be a very good classifier, and demonstrating the potential of this methodology to provide ecosystem services, particularly in anthropogenic areas in the Amazon.

Keywords: artificial neural network, computational intelligence, pattern recognition, unsupervised learning

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