Search results for: random subspace-based feature evaluation

Authors: Valerie Habbel, Magdalena Orth, Johanna Richter, Steffen Schimko

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Cooperation between development actors and the private sector is becoming increasingly important, as it is expected to mobilize additional resources to achieve the Sustainable Development Goals (SDGs), among other things. However, whether the goals of cooperation are achieved has so far only been explored in evaluations and studies of individual projects and instruments. The evaluation synthesis attempts to close this gap by systematically analyzing existing evidence (evaluations and academic studies) from national and international development cooperation on private sector engagement. Overall, the evaluations and studies considered report mainly positive effects on investors and donors, intermediaries, partner countries, and target groups. However, various analyses, including on the quality of the evaluations, point to a positive bias in the results. The evaluation synthesis makes recommendations on the definition of indicators, the measurement and evaluation of impacts and additionality, knowledge management, and the consideration of transaction costs in cooperation with private actors.

Keywords: evaluation synthesis, private sector engagement, international development, sustainable development

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Authors: Chin-Chih Chang

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Many manufacturing systems suffer failures due to complex degradation processes and various environment conditions such as random shocks. Consider an operating system is subject to random shocks and works at random times for successive jobs. When successive jobs often result in production losses and performance deterioration, it would be better to do maintenance or replacement at a planned time. A preventive replacement (PR) policy is presented to replace the system before a failure occurs at a continuous time T. In such a policy, the failure characteristics of the system are designed as follows. Each job would cause a random amount of additive damage to the system, and the system fails when the cumulative damage has exceeded a failure threshold. Suppose that the deteriorating system suffers one of the two types of shocks with age-dependent probabilities: type-I (minor) shock is rectified by a minimal repair, or type-II (catastrophic) shock causes the system to fail. A corrective replacement (CR) is performed immediately when the system fails. In summary, a generalized maintenance model to scheduling replacement plan for an operating system is presented below. PR is carried out at time T, whereas CR is carried out when any type-II shock occurs and the total damage exceeded a failure level. The main objective is to determine the optimal continuous schedule time of preventive replacement through minimizing the mean cost rate function. The existence and uniqueness of optimal replacement policy are derived analytically. It can be seen that the present model is a generalization of the previous models, and the policy with preventive replacement outperforms the one without preventive replacement.

Keywords: preventive replacement, working time, cumulative damage model, minimal repair, imperfect maintenance, optimization

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Authors: Tamara Matevosyan

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In this globalized world, intercultural communication is becoming essential for understanding communication among people, for developing understanding of cultures, to appreciate the opportunities and challenges that each culture presents to people. Moreover, it plays an important role in developing an ideal personification to understand different behaviors in different cultures. Native speakers assimilate sociolinguistic knowledge in natural conditions, while it is a great problem for language learners, and in this context feature films reveal cultural peculiarities and involve students in real communication. As we know nowadays the key role of language learning is the development of intercultural competence as communicating with someone from a different cultural background can be exciting and scary, frustrating and enlightening. Intercultural competence is important in FL learning classroom and here feature films can perform as essential tools to develop this competence and overcome the intercultural gap that foreign students face. Current proposal attempts to reveal the correlation of the given culture and language through feature films. To ensure qualified, well-organized and practical classes on Intercultural Communication for language learners a number of methods connected with movie watching have been implemented. All the pre-watching, while watching and post-watching methods and techniques are aimed at developing students’ communicative competence. The application of such activities as Climax, Role-play, Interactive Language, Daily Life helps to reveal and overcome mistakes of cultural and pragmatic character. All the above-mentioned activities are directed at the assimilation of the language vocabulary with special reference to the given culture. The study dwells into the essence of culture as one of the core concepts of intercultural communication. Sometimes culture is not a priority in the process of language learning which leads to further misunderstandings in real life communication. The application of various methods and techniques with feature films aims at developing students’ cultural competence, their understanding of norms and values of individual cultures. Thus, feature film activities will enable learners to enlarge their knowledge of the particular culture and develop a fundamental insight into intercultural communication.

Keywords: climax, intercultural competence, interactive language, role-play

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Authors: Sumitta Sawangtook, Parichart Thano

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The purposes of research were to study the relationship between the awareness of sexual risk behaviors and sexual risk behaviors of adolescent students, and to compare the sexual risk behaviors of adolescent students by gender, education level, sweetheart’s number, achievement, sexual value, and the influence of the friendship group. The research sample of 344 sevenths through twelfth grade students in secondary school for the academic year 2014, Dindang district Bangkok was selected by simple random sampling. The research instruments are: 1) demographic questionnaire 2) evaluation form of the awareness of sexual risk behaviors 3) questionnaire about sexual value 4) questionnaire about the influence of the friendship group and 5) evaluation form of sexual risk behaviors. They were used for data collections which are subsequently analyzed by percentage, mean, standard deviation, t-test, One-way Analysis of Variances. The results of this study were presented as follow: 1) The awareness of sexual risk behaviors was negatively correlated with sexual risk behaviors of adolescent students (r=-.27, p=.000). 2) There was significant difference at .05 level in sexual risk behaviors among adolescent students who had gender difference (t=5.90, p=.000). 3) There was no significant difference at .05 level in sexual risk behaviors among adolescent students who had the different level of education (t=1.41, p=.16). 4) There was significant difference at .05 level in sexual risk behaviors among adolescent students who had the different level of sweetheart’s number (F=13.03, p=.000). 5) There was significant difference at .05 level in sexual risk behaviors among adolescent students who had the different level of achievement (F=4.77, p=.009). 6) There were significant difference at .05 level in sexual risk behaviors among adolescent students who had different level of sexual value (F=50.91, p=.000) 7) There were significant difference at .05 level in sexual risk behaviors among adolescent students who had different level of the influence of the friendship group (F=98.41, p=.000).

Keywords: the awareness of sexual risk behaviors, sexual risk behaviors, adolescent students

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Authors: Linhang Zhu, Hongyu Zhu, Jiahe Liu

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This paper investigated a method of indoor real-time positioning and mapping based on the Manhattan world assumption. In indoor environments, relying solely on feature matching techniques or other geometric algorithms for sensor pose estimation inevitably resulted in cumulative errors, posing a significant challenge to indoor positioning. To address this issue, we adopt the Manhattan world hypothesis to optimize the camera pose algorithm based on feature matching, which improves the accuracy of camera pose estimation. A special processing method was applied to image data frames that conformed to the Manhattan world assumption. When similar data frames appeared subsequently, this could be used to eliminate drift in sensor pose estimation, thereby reducing cumulative errors in estimation and optimizing mapping and positioning. Through experimental verification, it is found that our method achieves high-precision real-time positioning in indoor environments and successfully generates maps of indoor environments. This provides effective technical support for applications such as indoor navigation and robot control.

Keywords: Manhattan world hypothesis, real-time positioning and mapping, feature matching, loopback detection

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Authors: Valeria Bondarenko

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We proposed algorithms for: estimation of parameters fBm (volatility and Hurst exponent) and for the approximation of random time series by functional of fBm. We proved the consistency of the estimators, which constitute the above algorithms, and proved the optimal forecast of approximated time series. The adequacy of estimation algorithms, approximation, and forecasting is proved by numerical experiment. During the process of creating software, the system has been created, which is displayed by the hierarchical structure. The comparative analysis of proposed algorithms with the other methods gives evidence of the advantage of approximation method. The results can be used to develop methods for the analysis and modeling of time series describing the economic, physical, biological and other processes.

Keywords: mathematical model, random process, Wiener process, fractional Brownian motion

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Authors: Tareq Aljaber

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The number of mobile health apps is growing at a fast frequency as it's nearly doubled in a year between 2015 and 2016. Though, there is a lack of an effective evaluation framework to verify the usability and reliability of mobile phone health education applications which would help saving time and effort for the numerous user groups. This abstract describing a framework for evaluating mobile applications in specifically mobile health education applications, along with a guidance select tool to assist different users to select the most suitable mobile health education apps. The effective framework outcome is intended to meet the requirements and needs of the different stakeholder groups additionally to enhancing the development of mobile health education applications with software engineering approaches, by producing new and more effective techniques to evaluate such software. This abstract highlights the significance and consequences of mobile health education apps, before focusing the light on the required to create an effective evaluation framework for these apps. An explanation of the effective evaluation framework is going to be delivered in the abstract, beside with some specific evaluation metrics: an efficient hybrid of selected heuristic evaluation (HE) and usability evaluation (UE) metrics to enable the determination of the usefulness and usability of health education mobile apps. Moreover, an explanation of the qualitative and quantitative outcomes for the effective evaluation framework was accomplished using Epocrates mobile phone app in addition to some other mobile phone apps. This proposed framework-An Evaluation Framework for Mobile Health Education Apps-consists of a hybrid of 5 metrics designated from a larger set in usability evaluation and heuristic evaluation, illuminated grounded on 15 unstructured interviews from software developers (SD), health professionals (HP) and patients (P). These five metrics corresponding to explicit facets of usability recognised through a requirements analysis of typical stakeholders of mobile health apps. These five hybrid selected metrics were scattered across 24 specific questionnaire questions, which are available on request from first author. This questionnaire has been sent to 81 participants distributed in three sets of stakeholders from software developers (SD), health professionals (HP) and patients/general users (P/GU) on the purpose of ranking three sets of mobile health education applications. Finally, the outcomes from the questionnaire data helped us to approach our aims which are finding the profile for different stakeholders, finding the profile for different mobile health educations application packages, ranking different mobile health education application and guide us to build the select guidance too which is apart from the Evaluation Framework for Mobile Health Education Apps.

Keywords: evaluation framework, heuristic evaluation, usability evaluation, metrics

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Authors: Münevver Başman, Ezel Tavşancıl

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Making the right decisions about students depends on teachers’ use of the assessment and evaluation techniques effectively. In order to do that, teachers should have positive attitudes and adequate self-efficacy perception towards assessment and evaluation. The purpose of this study is to investigate relationship between self-efficacy perception and the attitudes of prospective teachers towards assessment and evaluation and what kind of differences these issues have in terms of a variety of demographic variables. The study group consisted of 277 prospective teachers who have been studying in different departments of Marmara University, Faculty of Education. In this study, ‘Personal Information Form’, ‘A Perceptual Scale for Measurement and Evaluation of Prospective Teachers Self-Efficacy in Education’ and ‘Attitudes toward Educational Measurement Inventory’ are applied. As a result, positive correlation was found between self-efficacy perceptions and the attitudes of prospective teachers towards assessment and evaluation. Considering different departments, there is a significant difference between the mean score of attitudes of prospective teachers and between the mean score of self-efficacy perceptions of them. However, considering variables of attending statistics class and the class types at the graduated high school, there is no significant difference between the mean score of attitudes of prospective teachers and between the mean score of self-efficacy perceptions of them.

Keywords: attitude, perception, prospective teacher, self-efficacy

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Authors: Juan Manuel Real-Espinosa

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This paper addresses the first step to evaluate language learning apps: what theoretical framework to adopt when designing the app evaluation framework. The answer is not just one since there are several options that could be proposed. However, the question to be clarified is to what extent the learning design of apps is based on a specific learning approach, or on the contrary, on a fusion of elements from several theoretical proposals and paradigms, such as m-learning, mobile assisted language learning, and a number of theories about language acquisition. The present study suggests that the reality is closer to the second assumption. This implies that the theoretical framework against which the learning design of the apps should be evaluated must also be a hybrid theoretical framework, which integrates evaluation criteria from the different theories involved in language learning through mobile applications.

Keywords: mobile-assisted language learning, action-oriented approach, apps evaluation, post-method pedagogy, second language acquisition

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Authors: Ágoston Nagy

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This paper introduces a method of using unsupervised machine learning to visualize the feature space of a dataset in 2D, in order to find most characteristic segments in the set. After dimension reduction, users can select clusters by manual drawing. Selected clusters are recorded into a data model that is used for later predictions, based on realtime data. Predictions are made with supervised learning, using Gesture Recognition Toolkit. The paper introduces two example applications: a semantic audio organizer for analyzing incoming sounds, and a gesture database organizer where gestural data (recorded by a Leap motion) is visualized for further manipulation.

Keywords: gesture recognition, machine learning, real-time interaction, visualization

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Authors: Samir Bentata, Fatima Bendahma

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We have numerically investigated the effect of Aluminium concentration on the the laser wavelength of random trimer barrier AlxGa1-xAs superlattices (RTBSL). Such systems consist of two different structures randomly distributed along the growth direction, with the additional constraint that the barriers of one kind appear in triply. An explicit formula is given for evaluating the transmission coefficient of superlattices (SL's) with intentional correlated disorder. The method is based on Airy function formalism and the transfer-matrix technique. We discuss the impact of the Aluminium concentration associate to the structure profile on the laser wavelengths.

Keywords: superlattices, correlated disorder, transmission coefficient, laser wavelength

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Authors: Sarah Beaver, Renee Bryce

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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125Hz to 8000Hz. The data contains patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R\textsuperscript{2} values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R\textsuperscript{2} values for the best models for KNN ranges from .89 to .95. The best imputation models received R\textsuperscript{2} between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our best imputation models versus constant imputations by a two percent increase.

Keywords: machine learning, audiograms, data imputations, single imputations

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Authors: Dezhi Li, Peng Cui, Bo Zhang, Tengyuan Chang

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In order to solve the housing problem for the low-income families, the construction of affordable housing is booming in China. However, due to various reasons, the service facilities and systems in the indemnificatory residential community meet many problems. This article established a Satisfaction Evaluation System of the Fundamental Public Services for Large-scale Indemnificatory Residential Community based on the national standards and local criteria and developed evaluation methods and processes. At last, in the case of Huagang project in Nanjing, the satisfaction of basic public service is calculated according to a survey of local residents.

Keywords: indemnificatory residential community, public services, satisfaction evaluation, structural equation modeling

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Authors: Fawaz Abdulmalek

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The two-stage flowshop assembly scheduling problem is considered in this paper. There are more than one parallel machines at stage one and an assembly machine at stage two. The jobs will be processed into the flowshop based on Johnson rule and two extensions of Johnson rule. A simulation model of the two-stage flowshop is constructed where both machines at stage one are subject to random failures. Three simulation experiments will be conducted to test the effect of the three job ranking rules on the makespan. Johnson Largest heuristic outperformed both Johnson rule and Johnson Smallest heuristic for two performed experiments for all scenarios where each experiments having five scenarios.

Keywords: flowshop scheduling, random failures, johnson rule, simulation

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Authors: Lule Basha, Eralda Gjika

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This study is focused on analyzing climate change in Albania and its potential effects on cereal yields. Initially, monthly temperature and rainfalls in Albania were studied for the period 1960-2021. Climacteric variables are important variables when trying to model cereal yield behavior, especially when significant changes in weather conditions are observed. For this purpose, in the second part of the study, linear and nonlinear models explaining cereal yield are constructed for the same period, 1960-2021. The multiple linear regression analysis and lasso regression method are applied to the data between cereal yield and each independent variable: average temperature, average rainfall, fertilizer consumption, arable land, land under cereal production, and nitrous oxide emissions. In our regression model, heteroscedasticity is not observed, data follow a normal distribution, and there is a low correlation between factors, so we do not have the problem of multicollinearity. Machine-learning methods, such as random forest, are used to predict cereal yield responses to climacteric and other variables. Random Forest showed high accuracy compared to the other statistical models in the prediction of cereal yield. We found that changes in average temperature negatively affect cereal yield. The coefficients of fertilizer consumption, arable land, and land under cereal production are positively affecting production. Our results show that the Random Forest method is an effective and versatile machine-learning method for cereal yield prediction compared to the other two methods.

Keywords: cereal yield, climate change, machine learning, multiple regression model, random forest

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Authors: Benjamin Beltzung, Marie Pelé, Julien P. Renoult, Cédric Sueur

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To this day, it is not known if drawing is specifically human behavior or if this behavior finds its origins in ancestor species. An interesting window to enlighten this question is to analyze the drawing behavior in genetically close to human species, such as non-human primate species. A good candidate for this approach is the orangutan, who shares 97% of our genes and exhibits multiple human-like behaviors. Focusing on figurative aspects may not be suitable for orangutans’ drawings, which may appear as scribbles but may have meaning. A manual feature selection would lead to an anthropocentric bias, as the features selected by humans may not match with those relevant for orangutans. In the present study, we used deep learning to analyze the drawings of a female orangutan named Molly († in 2011), who has produced 1,299 drawings in her last five years as part of a behavioral enrichment program at the Tama Zoo in Japan. We investigate multiple ways to decipher Molly’s drawings. First, we demonstrate the existence of differences between seasons by training a deep learning model to classify Molly’s drawings according to the seasons. Then, to understand and interpret these seasonal differences, we analyze how the information spreads within the network, from shallow to deep layers, where early layers encode simple local features and deep layers encode more complex and global information. More precisely, we investigate the impact of feature complexity on classification accuracy through features extraction fed to a Support Vector Machine. Last, we leverage style transfer to dissociate features associated with drawing style from those describing the representational content and analyze the relative importance of these two types of features in explaining seasonal variation. Content features were relevant for the classification, showing the presence of meaning in these non-figurative drawings and the ability of deep learning to decipher these differences. The style of the drawings was also relevant, as style features encoded enough information to have a classification better than random. The accuracy of style features was higher for deeper layers, demonstrating and highlighting the variation of style between seasons in Molly’s drawings. Through this study, we demonstrate how deep learning can help at finding meanings in non-figurative drawings and interpret these differences.

Keywords: cognition, deep learning, drawing behavior, interpretability

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Authors: Steffy Maria Joseph, Abdu Rahiman V, Abdul Hameed K. M.

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Online handwritten character recognition is a challenging field in Artificial Intelligence. The classification success rate of current techniques decreases when the dataset involves similarity and complexity in stroke styles, number of strokes and stroke characteristics variations. Malayalam is a complex south indian language spoken by about 35 million people especially in Kerala and Lakshadweep islands. In this paper, we consider the significant feature extraction for the similar stroke styles of Malayalam. This extracted feature set are suitable for the recognition of other handwritten south indian languages like Tamil, Telugu and Kannada. A classification scheme based on support vector machines (SVM) is proposed to improve the accuracy in classification and recognition of online malayalam handwritten characters. SVM Classifiers are the best for real world applications. The contribution of various features towards the accuracy in recognition is analysed. Performance for different kernels of SVM are also studied. A graphical user interface has developed for reading and displaying the character. Different writing styles are taken for each of the 44 alphabets. Various features are extracted and used for classification after the preprocessing of input data samples. Highest recognition accuracy of 97% is obtained experimentally at the best feature combination with polynomial kernel in SVM.

Keywords: SVM, matlab, malayalam, South Indian scripts, onlinehandwritten character recognition

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Authors: K. S. Parlak, E. Turan

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The separation of marbles according to the pattern quality is a process made according to expert decision. The classification phase is the most critical part in terms of economic value. In this study, a self-learning system is proposed which performs the classification of marbles quickly and with high success. This system performs ten feature extraction by taking ten marble images from the camera. The marbles are classified by decision tree method using the obtained properties. The user forms the training set by training the system at the marble classification stage. The system evolves itself in every marble image that is classified. The aim of the proposed system is to minimize the error caused by the person performing the classification and achieve it quickly.

Keywords: decision tree, feature extraction, k-means clustering, marble classification

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Authors: Bella Llego

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The purpose of this study was to explain and empirically test the influence of attitude towards work, job satisfaction and organizational commitment of SSRU employee’s evaluation and performance. Data used in this study was primary data which were collected through Organizational Commitment Questionnaire with 1-5 Likert Scale. The respondent of this study was 200 managerial and non-managerial staff of SSRU. The statistics to analyze the data provide the descriptive by the mean, standard deviation and test hypothesis by the use of multiple regression. The result of this study is showed that attitude towards work have positive but not significant effect to job satisfaction and employees evaluation and performance. Different with attitude towards work, the organizations commitment has positive and significant influence on job satisfaction and employee performance at SSRU. It means every improvement in organization’s commitment has a positive effect toward job satisfaction and employee evaluation and performance at SSRU.

Keywords: attitude towards work, employee’s evaluation and performance, jobs satisfaction, organization commitment

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Authors: Rosa Figueroa, Christopher Flores

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Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%).

Keywords: comorbidities, machine learning, obesity, Smith-Waterman algorithm

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Authors: Aminu Mubarak Sadis

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This study appraised the maintenance management practice in Federal University Dutse and Jigawa State Polytechnic Dutse, in Nigeria. The Physical Planning, Works and Maintenance Departments of the two Higher Institutions (Federal University Dutse and Jigawa State Polytechnic) are responsible for production and maintenance management of their physical assets. Over–enrollment problem has been a common feature in the higher institutions in Nigeria, Data were collected by the administered questionnaires and subsequent oral interview to authenticate the completed questionnaires. Random sampling techniques was used in selecting 150 respondents across the various institutions (Federal University Dutse and Jigawa State Polytechnic Dutse). Data collected was analyzed using Statistical Package for Social Science (SPSS) and t-test statistical techniques The conclusion was that maintenance management activities are yet to be given their appropriate attention on functions of the university and polytechnic which are crucial to improving teaching, learning and research. The unit responsible for maintenance and managing facilities should focus on their stated functions and effect changes were possible.

Keywords: appraisal, maintenance management, university, Polytechnic, practices

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Authors: Lee Jeong Min, Lee Mi Hee, Eo Yang Dam

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Geospatial feature extraction is a very important issue in the remote sensing research. In the meantime, the image classification based on statistical techniques, but, in recent years, data mining and machine learning techniques for automated image processing technology is being applied to remote sensing it has focused on improved results generated possibility. In this study, artificial neural network and decision tree technique is applied to classify the high-resolution satellite images, as compared to the MLC processing result is a statistical technique and an analysis of the pros and cons between each of the techniques.

Keywords: remote sensing, artificial neural network, decision tree, maximum likelihood classification

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Authors: A. Ramalho, L. Romeiro, R. Ventura, A. Suleman

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A real-time path planning framework for Unmanned Air Vehicles, and in particular multi-rotors is proposed. The framework is designed to provide feasible trajectories from the current UAV position to a goal state, taking into account constraints such as obstacle avoidance, problem kinematics, and vehicle limitations such as maximum speed and maximum acceleration. The framework computes feasible paths online, allowing to avoid new, unknown, dynamic obstacles without fully re-computing the trajectory. These features are achieved using an iterative process in which the robot computes and optimizes the trajectory while performing the mission objectives. A first trajectory is computed using a modified Rapidly-Exploring Random Tree (RRT) algorithm, that provides trajectories that respect a maximum curvature constraint. The trajectory optimization is accomplished using the Interior Point Optimizer (IPOPT) as a solver. The framework has proven to be able to compute a trajectory and optimize to a locally optimal with computational efficiency making it feasible for real-time operations.

Keywords: interior point optimization, multi-rotors, online path planning, rapidly exploring random trees, trajectory optimization

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Authors: Annalakshmi G., Sakthivel Murugan S.

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This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods.

Keywords: feature extraction, local directional pattern, ELM classifier, GWO optimization

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Authors: Boukerch Haroun, Luo Qing Sheng, Li Hua Shi, Boukraa Sebti

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In this paper we present the design and the implementation of a target tracking system where the target is set to be a moving person in a video sequence. The system can be applied easily as a vision system for mobile robot. The system is composed of two major parts the first is the detection of the person in the video frame using the SVM learning machine based on the “HOG” descriptors. The second part is the tracking of a moving person it’s done by using a combination of the Kalman filter and a modified version of the Camshift tracking algorithm by adding the target motion feature to the color feature, the experimental results had shown that the new algorithm had overcame the traditional Camshift algorithm in robustness and in case of occlusion.

Keywords: camshift algorithm, computer vision, Kalman filter, object tracking

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Authors: Numtana Ladplee, Cherif Haberih

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The value of service evaluation depends critically on guests’ understanding of the evaluation objectives and their roles. The present research presents a three-phase investigation of the impact of evaluating participants’ theories about their roles: (a) identifying the theories, (b) testing the process consequences of participants’ role theories, and (c) gaining insights into the impact of participants’ role theories by testing key moderator/s. The findings of this study will hopefully indicate that (a) when forewarned of an upcoming evaluation task, consumers tend to believe that the evaluation objective is to identify aspects that need improvement, (b) this expectation produces a conscious attempt to identify negative aspects, although the encoding of attribute information is not affected, and (c) cognitive load during the evaluation experience greatly decreases the negativity of expected evaluations. The present study can be applied to other market research techniques and thereby improve our understanding of consumer inputs derived from market research. Such insights can help diminish biases produced by participants’ correct or incorrect theories regarding their roles.

Keywords: fulfillment, hotel guests, service, Thailand

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Authors: R. R. Ramsheeja, R. Sreeraj

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For view the internal structures of the human body such as liver, brain, kidney etc have a wide range of different modalities for medical images are provided nowadays. Computer Tomography is one of the most significant medical image modalities. In this paper use CT liver images for study the use of automatic computer aided techniques to calculate the volume of the liver tumor. Segmentation method is used for the detection of tumor from the CT scan is proposed. Gaussian filter is used for denoising the liver image and Adaptive Thresholding algorithm is used for segmentation. Multiple Region Of Interest(ROI) based method that may help to characteristic the feature different. It provides a significant impact on classification performance. Due to the characteristic of liver tumor lesion, inherent difficulties appear selective. For a better performance, a novel proposed system is introduced. Multiple ROI based feature selection and classification are performed. In order to obtain of relevant features for Support Vector Machine(SVM) classifier is important for better generalization performance. The proposed system helps to improve the better classification performance, reason in which we can see a significant reduction of features is used. The diagnosis of liver cancer from the computer tomography images is very difficult in nature. Early detection of liver tumor is very helpful to save the human life.

Keywords: computed tomography (CT), multiple region of interest(ROI), feature values, segmentation, SVM classification

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Authors: Chamtouri I., Kechida M.

Abstract:

Background: Evaluation has a prominent role in medical education and graduation. This evaluation has usually done in face-to-face, by written or oral questions. Simulation is increasingly taking a part as a method of evaluation. Due to the Covid-19 pandemic, which disrupted face-to-face evaluation, simulation using serious games (SG) is emerging in the field of training and assessment of medical students. The aim of our study is to compare the results of the evaluation of medical students by virtual simulation by online serious games versus clinical case-multiple choice questions (MCQ) and to assess the degree of satisfaction from these two evaluation methods. Methods: Medical students from the same study level were voluntarily participated in this study. Groupe 1 had an evaluation by SG dealing with “diagnosis and management of ST-segment elevationmyocardialinfarction (STEMI)alreadyprepared on the website www.Mediactiv.com. Groupe 2 were evaluated by clinical case-MCQ having thes same topic as SG. Results of the two groups were compared. Satisfaction questionnaire was filled by the two groups. Satisfaction degree was compared between the two groups. Results. In this study, 64 medical students (G1:31 and G2: 33) were enrolled. Obtaining complete notes in the "questioning" and "clinical examination" parts is significantly more important in-group 1 compared to group 2. No significant difference detected between the two groups in terms of “ECG interpretation” and “diagnosis of STEMI” parts. A greater number of students of group 1 obtained the full note compared to group 2 in “the initial treatment part” (54.8% vs. 39.4%; p = 0.04). Thirty learners (96.8%) in-group 1 obtained a total score ≥ 50% versus 69.7% in-group 2 (p = 0.004). The full score of 100% was obtained in three learners in-group1, while no student scored 100% in-group2 (p = 0.027). Medical evaluation using SG was reported as more innovative, fun, and realistic compared to evaluation by clinical case-MCQ. No significant difference detected between the two methods in terms of stress. Conclusion: Simulation by SG can be considered as an innovative and effective method in evaluating medical students with a higher degree of satisfaction.

Keywords: evaluation, serious games, medical students, satisfaction

Procedia PDF Downloads 130

Authors: Seun Mayowa Sunday

Abstract:

Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

Procedia PDF Downloads 109

Authors: J. Fryt, K. Pietras, T. Smolen

Abstract:

Intergenerational similarities in values may be effect of value transmission within families or socio-cultural trends prevailing at a specific point in time. According to salience hypothesis, salient family values may be transmitted more frequently. On the other hand, many value studies reveal that generational shift from social values (conservation and self-transcendence) to more individualistic values (openness to change and self-enhancement) suggest that value transmission and value change are two different processes. The first aim of our study was to describe similarities and differences in values of young women and their parents. The second aim was to determine which value similarities may be due to transmission within families. Ninety seven Polish women aged 19-25 and both their mothers and fathers filled in the Portrait Value Questionaire. Intergenerational similarities in values between women were found in strong preference for benevolence, universalism and self-direction as well as low preference for power. Similarities between younger women and older men were found in strong preference for universalism and hedonism as well as lower preference for security and tradition. Young women differed from older generation in strong preference for stimulation and achievement as well as low preference for conformity. To identify the origin of intergenerational similarities (whether they are the effect of value transmission within families or not), we used the comparison between correlations of values in family dyads (mother-daughter, father-daughter) and distribution of correlations in random intergenerational dyads (random mother-daughter, random father-daughter) as well as peer dyads (random daughter-daughter). Values representing conservation (security, tradition and conformity) as well as benevolence and power were transmitted in families between women. Achievement, power and security were transmitted between fathers and daughters. Similarities in openness to change (self-direction, stimulation and hedonism) and universalism were not stronger within families than in random intergenerational and peer dyads. Taken together, our findings suggest that despite noticeable generation shift from social to more individualistic values, we can observe transmission of parents’ salient values such as security, tradition, benevolence and achievement.

Keywords: value transmission, value change, intergenerational similarities, differences in values

Procedia PDF Downloads 414