Search results for: extra tree classifier

Authors: Farshideh Einsele, Hassan Foroosh

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In this paper, we present a robust algorithm to recognize extracted text from grocery product images captured by mobile phone cameras. Recognition of such text is challenging since text in grocery product images varies in its size, orientation, style, illumination, and can suffer from perspective distortion. Pre-processing is performed to make the characters scale and rotation invariant. Since text degradations can not be appropriately defined using wellknown geometric transformations such as translation, rotation, affine transformation and shearing, we use the whole character black pixels as our feature vector. Classification is performed with minimum distance classifier using the maximum likelihood criterion, which delivers very promising Character Recognition Rate (CRR) of 89%. We achieve considerably higher Word Recognition Rate (WRR) of 99% when using lower level linguistic knowledge about product words during the recognition process.

Keywords: camera-based OCR, feature extraction, document, image processing, grocery products

Procedia PDF Downloads 394

Authors: Sonia Rebai, Mohamed Naceur Azaiez, Dhafer Saidane

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In this study, we develop a performance evaluation model based on a multi-attribute utility approach aiming at reaching the sustainable banking (SB) status. This model is built accounting for various banks’ stakeholders in a win-win paradigm. In addition, it offers the opportunity for adopting a global measure of performance as an indication of a bank’s sustainability degree. This measure is referred to as banking sustainability performance index (BSPI). This index may constitute a basis for ranking banks. Moreover, it may constitute a bridge between the assessment types of financial and extra-financial rating agencies. A real application is performed on three French banks.

Keywords: multi-attribute utility theory, performance, sustainable banking, financial rating

Procedia PDF Downloads 448

Authors: Rose Mini Agoes Salim, Shahnaz Safitri

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The aim of this study is to test the effectiveness of teaching kindness in preschool children by using several techniques. Kindness is a physical act or emotional support aimed to build or maintain relationships with others. Kindness is known to be essential in the development of moral reasoning to distinguish between the good and bad things. In this study, kindness is operationalized as several acts including helping friends, comforting sad friends, inviting friends to play, protecting others, sharing, saying hello, saying thank you, encouraging others, and apologizing. It is mentioned that kindness is crucial to be developed in preschool children because this is the time the children begin to interact with their social environment through play. Furthermore, preschool children's cognitive development makes them begin to represent the world with words, which then allows them to interact with others. On the other hand, preschool children egocentric thinking makes them still need to learn to consider another person's perspective. In relation to social interaction, preschool children need to be stimulated and assisted by adult to be able to pay attention to other and act with kindness toward them. On teaching kindness to children, the quality of interaction between children and their significant others is the key factor. It is known that preschool children learn about kindness by imitating adults on their two way interaction. Specifically, this study examines two types of teaching techniques that can be done by parents as a way to teach kindness, namely the picture-storybook reading and hand-puppet storytelling. These techniques were examined because both activities are easy to do and both also provide a model of behavior for the child based on the character in the story. To specifically examine those techniques effectiveness in teaching kindness, two studies were conducted. Study I involves 31 children aged 5-6 years old with picture-storybook reading technique, where the intervention is done by reading 8 picture books for 8 days. In study II, hand-puppet storytelling technique is examined to 32 children aged 3-5 years old. The treatments effectiveness are measured using an instrument in the form of nine colored cards that describe the behavior of kindness. Data analysis using Wilcoxon Signed-rank test shows a significant difference on the average score of kindness (p < 0.05) before and after the intervention has been held. For daily observation, a ‘kindness tree’ and observation sheets are used which are filled out by the teacher. Two weeks after interventions, an improvement on all kindness behaviors measured is intact. The same result is also gained from both ‘kindness tree’ and observational sheets.

Keywords: kindness, moral teaching, storytelling, hand puppet

Procedia PDF Downloads 238

Authors: H. Aoudia, Y. Touati, E. H. Teguig, A. Ali Cherif

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Conceiving and developing routing protocols for wireless sensor networks requires considerations on constraints such as network lifetime and energy consumption. In this paper, we propose a hybrid hierarchical routing protocol named HHRP combining both clustering mechanism and multipath optimization taking into account residual energy and RSSI measures. HHRP consists of classifying dynamically nodes into clusters where coordinators nodes with extra privileges are able to manipulate messages, aggregate data and ensure transmission between nodes according to TDMA and CDMA schedules. The reconfiguration of the network is carried out dynamically based on a threshold value which is associated with the number of nodes belonging to the smallest cluster. To show the effectiveness of the proposed approach HHRP, a comparative study with LEACH protocol is illustrated in simulations.

Keywords: routing protocol, optimization, clustering, WSN

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Authors: Seon-Hack Hong, Tae-Il Choi

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Interoperability is defined as systems, components, and devices developed by different entities smoothly exchanging information and functioning organically without mutual consultation, being able to communicate with each other and computer systems of the same type or different types, and exchanging information or the ability of two or more systems to exchange information and use the information exchanged without extra effort. Insufficiencies such as duplication of functions when developing systems and applications due to lack of interoperability in the electric power system and low efficiency due to a lack of mutual information transmission system between the inside of the application program and the design is improved, and the seamless linkage of newly developed systems is improved. Since it is necessary to secure interoperability for this purpose, we designed the smart grid-based interoperability standard model in this paper.

Keywords: interoperability, power system, common information model, SCADA, IEEE2030, Zephyr

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Authors: Shao-Di Feng, Von-Wun Soo

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To generate symbolic music with specific emotions is a challenging task due to symbolic music datasets that have emotion labels are scarce and incomplete. This research aims to generate more refined emotions based on the training datasets that are only labeled with four quadrants in Russel’s 2D emotion model. We focus on the theory of Music Fadernet and map arousal and valence to the low-level attributes, and build a symbolic music generation model by combining transformer and GM-VAE. We adopt an in-attention mechanism for the model and improve it by allowing modulation by conditional information. And we show the music generation model could control the generation of music according to the emotions specified by users in terms of high-level linguistic expression and by manipulating their corresponding low-level musical attributes. Finally, we evaluate the model performance using a pre-trained emotion classifier against a pop piano midi dataset called EMOPIA, and by subjective listening evaluation, we demonstrate that the model could generate music with more refined emotions correctly.

Keywords: music generation, music emotion controlling, deep learning, semi-supervised learning

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Authors: Rosa L. Figueroa, Christopher A. Flores, Qing Zeng-Treitler

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A regular expression consists of sequence characters which allow describing a text path. Usually, in clinical research, regular expressions are manually created by programmers together with domain experts. Lately, there have been several efforts to investigate how to generate them automatically. This article presents a text classification algorithm based on regexes. The algorithm named REX was designed, and then, implemented as a simplified method to create regexes to classify Spanish text automatically. In order to classify ambiguous cases, such as, when multiple labels are assigned to a testing example, REX includes an information gain method Two sets of data were used to evaluate the algorithm’s effectiveness in clinical text classification tasks. The results indicate that the regular expression based classifier proposed in this work performs statically better regarding accuracy and F-measure than Support Vector Machine and Naïve Bayes for both datasets.

Keywords: information gain, regular expressions, smith-waterman algorithm, text classification

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Authors: Ali Seddiki, Mohamed Djebbouri, Driss Guerchi

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Medical imaging produces human body pictures in digital form. Since these imaging techniques produce prohibitive amounts of data, compression is necessary for storage and communication purposes. In some areas in medicine, it may be sufficient to maintain high image quality only in region of interest (ROI). This paper discusses a contribution to quality purpose compression in the region of interest of scintigraphic images based on dual tree complex wavelet transform (DT-CWT) using Run-Length coding (RLE) and Huffman coding (HC).

Keywords: DT-CWT, region of interest, run length coding, Scintigraphic images

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Authors: Nilda Tri Putri, Muhammad Taufik

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Tools and agricultural machinery (Alsintan) is a tool used in agribusiness activities. Alsintan used to change the traditional farming systems generally use manual equipment into modern agriculture with mechanization. CV Nugraha Chakti Consultant make an action plan for industrial development Alsintan West Sumatra in 2012 to develop medium industries of Alsintan become a major industry of Alsintan, one of efforts made is increase the production capacity of the industry Alsintan. Production capacity for superior products as hydrotiller and threshers set each for 2.000 units per year. CV Citra Dragon as one of the medium industry alsintan in West Sumatra has a plan to relocate the existing plant to meet growing consumer demand each year. Increased production capacity and plant relocation plan has led to a change in the layout; therefore need to design the layout of the plant facility CV Citra Dragon. First step the to design of plant layout is design the layout of the production floor. The design of the production floor layout is done by applying group technology layout. The initial step is to do a machine grouping and part family using the Average Linkage Clustering (ALC) and Rank Order Clustering (ROC). Furthermore done independent work station design and layout design using the Modified Spanning Tree (MST). Alternative selection layout is done to select the best production floor layout between ALC and ROC cell grouping. Furthermore, to design the layout of warehouses, offices and other production support facilities. Activity Relationship Chart methods used to organize the placement of factory facilities has been designed. After structuring plan facilities, calculated cost manufacturing facility plant establishment. Type of layout is used on the production floor layout technology group. The production floor is composed of four cell machinery, assembly area and painting area. The total distance of the displacement of material in a single production amounted to 1120.16 m which means need 18,7minutes of transportation time for one time production. Alsintan Factory has designed a circular flow pattern with 11 facilities. The facilities were designed consisting of 10 rooms and 1 parking space. The measure of factory building is 84 m x 52 m.

Keywords: Average Linkage Clustering (ALC), Rank Order Clustering (ROC), Modified Spanning Tree (MST), Activity Relationship Chart (ARC)

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Authors: Mekkaoui Soumiya, Sadki Assia, Ait Heda Abdellatif

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Territorial marketing is a crucial element in the improvement of the attractiveness and competitiveness of a region, city or country. It is important to identify the different tools that distinguish the area from the other places and use them in order to achieve territorial marketing objectives. This paper tries to present the assets of Argan oil, the endemic terroir product, in the development of the tourism sector in Souss Massa. Starting from the Argan tree forests to the final use of the oil, every stage of the process can be developed as a tourism product in order to make the region more appealing to tourists.

Keywords: territorial marketing, terroir product, rural tourism, ecotourism, Argan oil

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Authors: Shelby Simpson, William Stanley, Namir Naba, Xiaodi Wang

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Both t-SNE and UMAP are brand new state of art tools to predominantly preserve the local structure that is to group neighboring data points together, which indeed provides a very informative visualization of heterogeneity in our data. In this research, we develop a t-SNE and UMAP base neural network image classification algorithm to embed the original dataset to a corresponding low dimensional dataset as a preprocessing step, then use this embedded database as input to our specially designed neural network classifier for image classification. We use the fashion MNIST data set, which is a labeled data set of images of clothing objects in our experiments. t-SNE and UMAP are used for dimensionality reduction of the data set and thus produce low dimensional embeddings. Furthermore, we use the embeddings from t-SNE and UMAP to feed into two neural networks. The accuracy of the models from the two neural networks is then compared to a dense neural network that does not use embedding as an input to show which model can classify the images of clothing objects more accurately.

Keywords: t-SNE, UMAP, fashion MNIST, neural networks

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Authors: Ailene B. Anonuevo

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The aim of the study was to evaluate the quality of life presently available for the IP students in selected schools in the Division of Panabo City. This further explores their future dreams and current status in classes and examines some implications relative to their studies. The study adopted the mixed methodology and used a survey research design as the operational framework for data gathering. Data were collected by self-administered questionnaires and interviews with sixty students from three schools in Panabo City. In addition, this study describes the learners’ background and school climate as variables that might influence their performance in school. The study revealed that an IP student needs extra attention due to their unfavorable learning environment. The study also found out that like any other students, IP learners yearns for a brighter future with the support of our government.

Keywords: IP learners, learning landscape, school climate, quality of life

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Authors: Abrehet Kahsay Mehari

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Aquatic plants provide the physical structure of wetlands and increase their habitat complexity and heterogeneity, and as such, have a profound influence on other biotas. In this study, we investigated how human disturbance activities influenced the species richness and community composition of aquatic plants in the wetlands of Lake Tana, Ethiopia. Twelve wetlands were selected: four lacustrine, four river mouths, and four riverine papyrus swamps. Data on aquatic plants, environmental variables, and human activities were collected during the dry and wet seasons of 2018. A linear mixed effect model and a distance-based Redundancy Analysis (db-RDA) were used to relate aquatic plant species richness and community composition, respectively, to human activities and environmental variables. A total of 113 aquatic plant species, belonging to 38 families, were identified across all wetlands during the dry and wet seasons. Emergent species had the maximum area covered at 73.45 % and attained the highest relative abundance, followed by amphibious and other forms. The mean taxonomic richness of aquatic plants was significantly lower in wetlands with high overall human disturbance scores compared to wetlands with low overall human disturbance scores. Moreover, taxonomic richness showed a negative correlation with livestock grazing, tree plantation, and sand mining. The community composition also varied across wetlands with varying levels of human disturbance and was primarily driven by turnover (i.e., replacement of species) rather than nestedness resultant(i.e., loss of species). Distance-based redundancy analysis revealed that livestock grazing, tree plantation, sand mining, waste dumping, and crop cultivation were significant predictors of variation in aquatic plant communities’ composition in the wetlands. Linear mixed effect models and distance-based redundancy analysis also revealed that water depth, turbidity, conductivity, pH, sediment depth, and temperature were important drivers of variations in aquatic plant species richness and community composition. Papyrus swamps had the highest species richness and supported different plant communities. Conservation efforts should therefore focus on these habitats and measures should be taken to restore the highly disturbed and species poor wetlands near the river mouths.

Keywords: species richness, community composition, aquatic plants, wetlands, Lake Tana, human disturbance activities

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Authors: Mohammad H. Yarmohammadian, Fatemeh Rezaei

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Introduction: Improving patient safety in health systems is one of the main priorities in healthcare systems, so clinical risk management in organizations has become increasingly significant. Although several tools have been developed for clinical risk management, each has its own limitations. Aims: This study aims to develop a comprehensive tool that can complete the limitations of each risk assessment and management tools with the advantage of other tools. Methods: Procedure was determined in two main stages included development of an initial model during meetings with the professors and literature review, then implementation and verification of final model. Subjects and Methods: This study is a quantitative − qualitative research. In terms of qualitative dimension, method of focus groups with inductive approach is used. To evaluate the results of the qualitative study, quantitative assessment of the two parts of the fourth phase and seven phases of the research was conducted. Purposive and stratification sampling of various responsible teams for the selected process was conducted in the operating room. Final model verified in eight phases through application of activity breakdown structure, failure mode and effects analysis (FMEA), healthcare risk priority number (RPN), root cause analysis (RCA), FT, and Eindhoven Classification model (ECM) tools. This model has been conducted typically on patients admitted in a day-clinic ward of a public hospital for surgery in October 2012 to June. Statistical Analysis Used: Qualitative data analysis was done through content analysis and quantitative analysis done through checklist and edited RPN tables. Results: After verification the final model in eight-step, patient's admission process for surgery was developed by focus discussion group (FDG) members in five main phases. Then with adopted methodology of FMEA, 85 failure modes along with its causes, effects, and preventive capabilities was set in the tables. Developed tables to calculate RPN index contain three criteria for severity, two criteria for probability, and two criteria for preventability. Tree failure modes were above determined significant risk limitation (RPN > 250). After a 3-month period, patient's misidentification incidents were the most frequent reported events. Each RPN criterion of misidentification events compared and found that various RPN number for tree misidentification reported events could be determine against predicted score in previous phase. Identified root causes through fault tree categorized with ECM. Wrong side surgery event was selected by focus discussion group to purpose improvement action. The most important causes were lack of planning for number and priority of surgical procedures. After prioritization of the suggested interventions, computerized registration system in health information system (HIS) was adopted to prepare the action plan in the final phase. Conclusion: Complexity of health care industry requires risk managers to have a multifaceted vision. Therefore, applying only one of retrospective or prospective tools for risk management does not work and each organization must provide conditions for potential application of these methods in its organization. The results of this study showed that the integrated clinical risk management model can be used in hospitals as an efficient tool in order to improve clinical governance.

Keywords: failure modes and effective analysis, risk management, root cause analysis, model

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Authors: Shyh-Ming Chern, H. Y. Cheng

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Biomass is renewable and sustainable. As an energy source, it will not release extra carbon dioxide into the atmosphere. Hence, tremendous efforts have been made to develop technologies capable of transforming biomass into suitable forms of bio-fuel. One of the viable technologies is gasifying biomass in supercritical water (SCW), a green medium for reactions. While previous studies overwhelmingly selected glucose as a model compound for biomass, the present study adopted fructose for the sake of comparison. The gasification of fructose in SCW was investigated experimentally to evaluate the applicability of supercritical water processes to biomass gasification. Experiments were conducted with an autoclave reactor. Gaseous product mainly consists of H2, CO, CO2, CH4 and C2H6. The effect of two major operating parameters, the reaction temperature (673-873 K) and the dosage of oxidizing agent (0-0.5 stoichiometric oxygen), on the product gas composition, yield and heating value was also examined, with the reaction pressure fixed at 25 MPa.

Keywords: biomass, fructose, gasification, supercritical water

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Authors: Samina Khalid, Shamila Nasreen

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Dimensionality reduction as a preprocessing step to machine learning is effective in removing irrelevant and redundant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection and feature extraction methods with respect to efficiency and effectiveness. In the field of machine learning and pattern recognition, dimensionality reduction is important area, where many approaches have been proposed. In this paper, some widely used feature selection and feature extraction techniques have analyzed with the purpose of how effectively these techniques can be used to achieve high performance of learning algorithms that ultimately improves predictive accuracy of classifier. An endeavor to analyze dimensionality reduction techniques briefly with the purpose to investigate strengths and weaknesses of some widely used dimensionality reduction methods is presented.

Keywords: age related macular degeneration, feature selection feature subset selection feature extraction/transformation, FSA’s, relief, correlation based method, PCA, ICA

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Authors: Jiwan Kumar, Pooja, Sandeep Negi, Anjum Rouf, Amit Kumar, Naveen Lakra

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In modern times where, health issues are increasing day by day, breast cancer is also one of them, which is very crucial and really important to find in the early stages. Doctors can use this model in order to tell their patients whether a cancer is not harmful (benign) or harmful (malignant). We have used the knowledge of machine learning in order to produce the model. we have used algorithms like Logistic Regression, Random forest, support Vector Classifier, Bayesian Network and Radial Basis Function. We tried to use the data of crucial parts and show them the results in pictures in order to make it easier for doctors. By doing this, we're making ML better at finding breast cancer, which can lead to saving more lives and better health care.

Keywords: Bayesian network, radial basis function, ensemble learning, understandable, data making better, random forest, logistic regression, breast cancer

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Authors: Maryam Kiani

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Non-precious electrocatalysts play a crucial role in the oxygen reduction reaction (ORR) for regenerative fuel cells and rechargeable metal-air batteries. To enhance ORR activity, La (a less active element) is added to modify the activity of Ni. This addition increases the surface contents of Ni2+, N, and S species in LaNi/N-S-C, while still maintaining a substantial specific surface area and hierarchical porosity. Therefore, the additional La is essential for the successful ORR process.In addition, the presence of extra La in the LaNi/N-S-C electrocatalyst enhances the efficiency of charge transfer and improves the surface acid-base characteristics, facilitating the adsorption of oxygen molecules during the ORR process. As a result, this superior and desirable electrocatalyst exhibits significantly enhanced ORR bifunctional activity. In fact, its ORR activity is comparable to that of the 20 wt% Pt/C.

Keywords: fuel cells, batteries, dual-doped carbon black, ORR

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Authors: Deeksha Chauhan, Kamal Jain

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Pollutant concentration is high in near-road environments, and vegetation is an effective measure to mitigate urban air quality problems. This paper presents the influence of roadside green infrastructure in dispersion and Deposition of Particulate matter (PM) by the ENVI-met Simulations. Six green infrastructure configurations were specified (i) hedges only, (ii) trees only, (iii) a mix of trees and shrubs (iv) green barrier (v) green wall, and (vi) no tree buffer were placed on both sides of the road. The changes in concentrations at all six scenarios were estimated to identify the best barrier to reduce the dispersion and deposition of PM10 and PM2.5 in an urban environment.

Keywords: barrier, concentration, dispersion, deposition, Particulate matter, pollutant

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Authors: Sergey Ermolin, Olga Ermolin

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A Deep Convolution Neural Network with Triplet Loss is used to identify duplicate images in real-estate advertisements in the presence of image artifacts such as watermarking, cropping, hue/brightness adjustment, and others. The effects of batch normalization, spatial dropout, and various convergence methodologies on the resulting detection accuracy are discussed. For comparative Return-on-Investment study (per industry request), end-2-end performance is benchmarked on both Nvidia Titan GPUs and Intel’s Xeon CPUs. A new real-estate dataset from San Francisco Bay Area is used for this work. Sufficient duplicate detection accuracy is achieved to supplement other database-grounded methods of duplicate removal. The implemented method is used in a Proof-of-Concept project in the real-estate industry.

Keywords: visual recognition, convolutional neural networks, triplet loss, spatial batch normalization with dropout, duplicate removal, advertisement technologies, performance benchmarking

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Authors: Mohsine Khazrouni

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The present study is an attempt to highlight the importance of adaptation in translation. To convey the message, the translator needs to take into account not only the text but also extra-linguistic factors such as the target audience. The present paper claims that adaptation is an unavoidable translation strategy when dealing with texts that are heavy with religious and cultural themes. The translation task becomes even more challenging when dealing with children’s literature as the audience are children whose comprehension, experience and world knowledge are limited. The study uses the Arabic translation of the short story ‘Christmas Every Day’ as a case study. The short story will be translated, and the pragmatic problems involved will be discussed. The focus will be on the issue of adaptation. i.e., the source text should be adapted to the target language audience`s social and cultural environment.

Keywords: pragmatic adaptation, Arabic translation, children's literature, equivalence

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Authors: Anum Ali, Kashaf ad Dooja, Asif Saleem

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The future smart cities trend will be towards Internet of Things (IoT); IoT creates dynamic connections in a ubiquitous manner. Smart cities offer ease and flexibility for daily life matters. By using small devices that are connected to cloud servers based on IoT, network traffic between these devices is growing exponentially, whose security is a concerned issue, since ratio of cyber attack may make the network traffic vulnerable. This paper discusses the latest machine learning approaches in related work further to tackle the increasing rate of cyber attacks, machine learning algorithm is applied to IoT-based network traffic data. The proposed algorithm train itself on data and identify different sections of devices interaction by using supervised learning which is considered as a classifier related to a specific IoT device class. The simulation results clearly identify the attacks and produce fewer false detections.

Keywords: IoT, traffic security, deep learning, classification

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Authors: Gani Pllana, Sadete Pllana, Albulena Pllana Breznica

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The history of appearance and development of technical fields in our country sheds light on the relationships they have entered into with social factors indicating what kinds of factors have prevailed in their appearance and development. Thus, for instance, at the end of the 19th century, a number of knowledge fields were stipulated by political factors, cultural and linguistic factors that are inextricably linked to our nation's efforts to arouse national consciousness through the growth of educational and cultural level of the people. Some sciences, through their fundamental special fields probably would be one of those factors that would accomplish this objective. Other factors were the opening of schools and the drafting of relevant textbooks thereby their accomplishment is to be achieved by means of written language. Therefore the first fundamental knowledge fields were embodied with them, such as mathematics, linguistics, geography.

Keywords: Albanian language, development of terminology, standardization of terminology, technical fields

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Authors: Yulong Wang, Yuan Yan Tang, Cuiming Zou, Lina Yang

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This paper presents a novel sparse representation method for robust pattern classification. Generalized orthogonal matching pursuit (GOMP) is a recently proposed efficient sparse representation technique. However, GOMP adopts the mean square error (MSE) criterion and assign the same weights to all measurements, including both severely and slightly corrupted ones. To reduce the limitation, we propose an information-theoretic GOMP (ITGOMP) method by exploiting the correntropy induced metric. The results show that ITGOMP can adaptively assign small weights on severely contaminated measurements and large weights on clean ones, respectively. An ITGOMP based classifier is further developed for robust pattern classification. The experiments on public real datasets demonstrate the efficacy of the proposed approach.

Keywords: correntropy induced metric, matching pursuit, pattern classification, sparse representation

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Authors: Ali Ulvi Uzer

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

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

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Authors: Veronique Gregorec, Prakash Vel, Collins A. Brobbey

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Service delivery process in the face of ever changing customer expectations could not have been more important in glamorous Dubai luxury hotel service sector. Based on in-depth discussions with Dubai luxury hotel service pioneers, customer expectations, service processes, customer complaining behavior, and service recovery strategies in the luxury hotel industry are evaluated from the perspectives of service providers. Findings are in agreement with the statement that in the service industry the customer is not always right, and that hotel service providers have acknowledged the need to take extra measures towards individualized and personal service experience delivery. Ultimately, hoteliers set highest standards at all stages of the service delivery process in order to achieve positive and high customer ratings in all customer evaluation areas.

Keywords: luxury hotels, Dubai hotels, Dubai hospitality industry, guest service process

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Authors: A. Biran, P. Sobhe Bidari, A. Almazroe, V. Lakshminarayanan, K. Raahemifar

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Diabetic Retinopathy (DR) is a severe retinal disease which is caused by diabetes mellitus. It leads to blindness when it progress to proliferative level. Early indications of DR are the appearance of microaneurysms, hemorrhages and hard exudates. In this paper, an automatic algorithm for detection of DR has been proposed. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. Also, Support Vector Machine (SVM) Classifier is used to classify retinal images to normal or abnormal cases including non-proliferative or proliferative DR. The proposed method has been tested on images selected from Structured Analysis of the Retinal (STARE) database using MATLAB code. The method is perfectly able to detect DR. The sensitivity specificity and accuracy of this approach are 90%, 87.5%, and 91.4% respectively.

Keywords: diabetic retinopathy, fundus images, STARE, Gabor filter, support vector machine

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Authors: Sandeep Singh

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In this article, two classes of iterative schemes are proposed for approximating solutions of nonlinear systems of equations whose orders of convergence are six and eight respectively. Sixth order scheme requires the evaluation of two vector-functions, two first Fr'echet derivatives and three matrices inversion per iteration. This three-step sixth-order method is further extended to eighth-order method which requires one more step and the evaluation of one extra vector-function. Moreover, computational efficiency is compared with some other recently published methods in which we found, our methods are more efficient than existing numerical methods for higher and medium size nonlinear system of equations. Numerical tests are performed to validate the proposed schemes.

Keywords: Nonlinear systems, Computational complexity, order of convergence, Jarratt-type scheme

Procedia PDF Downloads 118

Authors: M. D. D. Chathurangi, M. G. K. Nayanathara, K. M. H. M. M. Gunapala, G. M. R. G. Dayananda, Kavinga Yapa Abeywardena, Deemantha Siriwardana

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Due to the growing popularity of social media platforms at present, there are various concerns, mostly cyberbullying, spam, bot accounts, and the spread of incorrect information. To develop a risk score calculation system as a thorough method for deciphering and exposing unethical social media profiles, this research explores the most suitable algorithms to our best knowledge in detecting the mentioned concerns. Various multiple models, such as Naïve Bayes, CNN, KNN, Stochastic Gradient Descent, Gradient Boosting Classifier, etc., were examined, and the best results were taken into the development of the risk score system. For cyberbullying, the Logistic Regression algorithm achieved an accuracy of 84.9%, while the spam-detecting MLP model gained 98.02% accuracy. The bot accounts identifying the Random Forest algorithm obtained 91.06% accuracy, and 84% accuracy was acquired for fake news detection using SVM.

Keywords: cyberbullying, spam behavior, bot accounts, fake news, machine learning

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Authors: Arjun, A. D. Singh

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Concrete the basic material using in construction across the global these days. The purpose of this special concrete is to provide extra strength and stability for irregular structure where the center of gravity is disturbed. In this paper an effort has been made to use different type of material aggregates has been discussed. We named As "STAR Aggregates" which has qualities to resist Shear, tension and compression forces. We have been divided into coarse aggregates and fine aggregates according to their sizes. Star Aggregates has interlocking behavior and cutting edge technology. Star aggregates had been draft and deign in Auto CAD and then analysis in ANSYS software. by using special aggregates we deign concrete grade of M40 for mega structures and irregular structure. This special concrete with STAR aggregates use in construction for irregular structure like Bridges, Skyscrapers or in deigned buildings.

Keywords: star aggregates, high performance concrete, material aggregates, interlocking

Procedia PDF Downloads 548