Search results for: distributed artificial intelligence

Authors: Sawsan Eissa, Momen Gharib, Omnia Kabbany

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A flushing numerical study has been performed for intended artificial islands on the Persian Gulf coast in Abu Dhabi, UAE. The island masterplan was tested for flushing using the DELFT 3D hydrodynamic model, and it was found that its residence time exceeds the acceptable PIANC flushing Criteria. Therefore, a number of mitigation measures were applied and tested one by one using the flushing model. Namely, changing the location of the entrance opening, dredging, removing part of the mangrove existing in the near vicinity to create a channel, removing the mangrove altogether, using culverts of different numbers and locations, and pumping at selected points. The pumping option gave the best solution, but it was disregarded due to high capital and running costs. Therefore, it opted for a combination of other solutions, including removing mangroves, introducing culverts, and adjusting island boundaries and types of protection.

Keywords: hydrodynamics, flushing, delft 3d, Persian Gulf, artificial islands.

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Authors: Yingxu Wang, Jianhua Lu, Jun Peng, Jiawei Zhang

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The fundamental demand in contemporary intelligent science towards Autonomous AI (AI*) is the creation of unprecedented formal means of Intelligent Mathematics (IM). It is discovered that natural intelligence is inductively created rather than exhaustively trained. Therefore, IM is a family of algebraic and denotational mathematics encompassing Inference Algebra, Real-Time Process Algebra, Concept Algebra, Semantic Algebra, Visual Frame Algebra, etc., developed in our labs. IM plays indispensable roles in training-free AI* theories and systems beyond traditional empirical data-driven technologies. A set of applications of IM-driven AI* systems will be demonstrated in contemporary intelligence science, AI*, and cognitive computers.

Keywords: intelligence mathematics, foundations of intelligent science, autonomous AI, cognitive computers, inference algebra, real-time process algebra, concept algebra, semantic algebra, applications

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Authors: E. Vazquez-Santacruz, M. Gamboa-Zuniga

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In this paper, we show a methodology for bodies classification in lying state using HOG descriptors and pressures sensors positioned in a matrix form (14 x 32 sensors) on the surface where bodies lie down. it will be done in real time. Our system is embedded in a care robot that can assist the elderly patient and medical staff around to get a better quality of life in and out of hospitals. Due to current technology a limited number of sensors is used, wich results in low-resolution data array, that will be used as image of 14 x 32 pixels. Our work considers the problem of human posture classification with few information (sensors), applying digital process to expand the original data of the sensors and so get more significant data for the classification, however, this is done with low-cost algorithms to ensure the real-time execution.

Keywords: real-time classification, sensors, robots, health care, elderly patients, artificial intelligence

Procedia PDF Downloads 857

Authors: Anwesha Pathak

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With the increasing number of satellites and space debris, space traffic management (STM) becomes crucial. AI can aid in STM by predicting and preventing potential collisions, optimizing satellite trajectories, and managing orbital slots. Governance frameworks need to address the integration of AI algorithms in STM to ensure safe and sustainable satellite activities. AI and governance play significant roles in the context of satellite activities in space. Artificial intelligence (AI) technologies, such as machine learning and computer vision, can be utilized to process vast amounts of data received from satellites. AI algorithms can analyse satellite imagery, detect patterns, and extract valuable information for applications like weather forecasting, urban planning, agriculture, disaster management, and environmental monitoring. AI can assist in automating and optimizing satellite operations. Autonomous decision-making systems can be developed using AI to handle routine tasks like orbit control, collision avoidance, and antenna pointing. These systems can improve efficiency, reduce human error, and enable real-time responsiveness in satellite operations. AI technologies can be leveraged to enhance the security of satellite systems. AI algorithms can analyze satellite telemetry data to detect anomalies, identify potential cyber threats, and mitigate vulnerabilities. Governance frameworks should encompass regulations and standards for securing satellite systems against cyberattacks and ensuring data privacy. AI can optimize resource allocation and utilization in satellite constellations. By analyzing user demands, traffic patterns, and satellite performance data, AI algorithms can dynamically adjust the deployment and routing of satellites to maximize coverage and minimize latency. Governance frameworks need to address fair and efficient resource allocation among satellite operators to avoid monopolistic practices. Satellite activities involve multiple countries and organizations. Governance frameworks should encourage international cooperation, information sharing, and standardization to address common challenges, ensure interoperability, and prevent conflicts. AI can facilitate cross-border collaborations by providing data analytics and decision support tools for shared satellite missions and data sharing initiatives. AI and governance are critical aspects of satellite activities in space. They enable efficient and secure operations, ensure responsible and ethical use of AI technologies, and promote international cooperation for the benefit of all stakeholders involved in the satellite industry.

Keywords: satellite, space debris, traffic, threats, cyber security.

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Authors: Adewale O. Ogunde, Olusegun Folorunso, Adesina S. Sodiya

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Most existing Distributed Association Rule Mining (DARM) systems are still facing several challenges. One of such challenges that have not received the attention of many researchers is the inability of existing systems to adapt to constantly changing databases and mining environments. In this work, an Adaptive Incremental Mining Algorithm (AIMA) is therefore proposed to address these problems. AIMA employed multiple mobile agents for the entire mining process. AIMA was designed to adapt to changes in the distributed databases by mining only the incremental database updates and using this to update the existing rules in order to improve the overall response time of the DARM system. In AIMA, global association rules were integrated incrementally from one data site to another through Results Integration Coordinating Agents. The mining agents in AIMA were made adaptive by defining mining goals with reasoning and behavioral capabilities and protocols that enabled them to either maintain or change their goals. AIMA employed Java Agent Development Environment Extension for designing the internal agents’ architecture. Results from experiments conducted on real datasets showed that the adaptive system, AIMA performed better than the non-adaptive systems with lower communication costs and higher task completion rates.

Keywords: adaptivity, data mining, distributed association rule mining, incremental mining, mobile agents

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Authors: Ana-Marija Stjepić, Luka Sušac, Dalia Suša Vugec

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In the last few years, examples from scientific literature and business practices show that the adoption of technological innovations increases enterprises' performance. Recently, when it comes to the field of information technology innovation, business intelligence systems (BISs) have drawn a significant amount of attention of the scientific circles. BISs can be understood as a form of technological innovation which can bring certain benefits to the organizations that are adopting it. Therefore, the aim of this paper is twofold: (1) to define determinants of successful BISs adoption in small and medium enterprises and thus contribute to this neglected research area and (2) to present the current state of BISs adoption in small and medium-sized companies. In order to do so, determinants are defined and classified into three dimensions, according to the Technology – Organization – Environment (TOE) theoretical framework that describes the impact of each dimension on technological innovations adoption. Moreover, paper brings a case study presenting the adoption of BISs in practice within an organization from tertiary (service) industry sector. Based on the results of the study, guidelines for more efficient, faster and easier BISs adoption are presented.

Keywords: adoption, business intelligence, business intelligence systems, case study, TOE framework

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Authors: Gadah Alkeniah

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Leadership is acknowledged internationally as fundamental to school efficiency and school enhancement nevertheless there are various understandings of what leadership is and how it is realised in practice. There are a number of educational leadership models that are considered important. However, the present study uses a systematic review method to examine and compare five models of the most well-known contemporary models of leadership as well as introduces the dimension of each model. Our results reveal that recently the distributed leadership has grown in popularity within the field of education. The study concludes by suggesting future directions in leadership development and education research.

Keywords: distributed leadership, instructional leadership, leadership models, moral leadership, strategic leadership, transformational leadership

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Authors: Rachid Dehini, Brahim Berbaoui

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The main objectives of shunt active power filter (SAPF) is to preserve the power system from unwanted harmonic currents produced by nonlinear loads, as well as to compensate the reactive power. The aim of this paper is to present a (PAPF) supplied by the Photovoltaic cells ,in such a way that the (PAPF) feeds the linear and nonlinear loads by harmonics currents and the excess of the energy is injected into the power system. In order to improve the performances of conventional (PAPF) This paper also proposes artificial neural networks (ANN) for harmonics identification and DC link voltage control. The simulation study results of the new (SAPF) identification technique are found quite satisfactory by assuring good filtering characteristics and high system stability.

Keywords: SAPF, harmonics current, photovoltaic cells, MPPT, artificial neural networks (ANN)

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Authors: Guangyuan Zhao, Nan Huang, Xuesong Han, Xu Huang

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In order to solve the problem of sample dilution in the traditional particle filter algorithm and achieve accurate state estimation in a nonlinear system, a particle filter method based on an improved artificial bee colony (ABC) algorithm was proposed. The algorithm simulated the process of bee foraging and optimization and made the high likelihood region of the backward probability of particles moving to improve the rationality of particle distribution. The opposition-based learning (OBL) strategy is introduced to optimize the initial population of the artificial bee colony algorithm. The convergence factor is introduced into the neighborhood search strategy to limit the search range and improve the convergence speed. Finally, the crossover and mutation operations of the genetic algorithm are introduced into the search mechanism of the following bee, which makes the algorithm jump out of the local extreme value quickly and continue to search the global extreme value to improve its optimization ability. The simulation results show that the improved method can improve the estimation accuracy of particle filters, ensure the diversity of particles, and improve the rationality of particle distribution.

Keywords: particle filter, impoverishment, state estimation, artificial bee colony algorithm

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Authors: Judit Vilarmau

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Generative AI is emerging rapidly and transforming higher education in many ways, occasioning new challenges and disrupting traditional models and methods. The studies and authors explored remark on the impact on the ethics, curriculum, and pedagogical methods. Students are increasingly using generative AI for study, as a virtual tutor, and as a resource for generating works and doing assignments. This point is crucial for educators to make sure that students are using generative AI with ethical considerations. Generative AI also has relevant benefits for educators and can help them personalize learning experiences and promote self-regulation. Educators must seek and explore tools like ChatGPT to innovate without forgetting an ethical and pedagogical perspective. Eighteen studies were systematically reviewed, and the findings provide implementation guidelines with pedagogical and ethical considerations.

Keywords: ethics, generative artificial intelligence, guidelines, higher education, pedagogy

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Authors: Amir Lotfi, Seyed Hamid Hosseini

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During the recent past, due to the increase of electrical energy demand and governmental resources constraints in creating additional capacity in the generation, transmission, and distribution, privatization, and restructuring in electrical industry have been considered. So, in most of the countries, different parts of electrical industry like generation, transmission, and distribution have been separated in order to create competition. Considering these changes, environmental issues, energy growth, investment of private equity in energy generation units and difficulties of transmission lines expansion, distributed generation (DG) units have been used in power systems. Moreover, reduction in the need for transmission and distribution, the increase of reliability, improvement of power quality, and reduction of power loss have caused DG to be placed in power systems. On the other hand, considering low liquidity need, private investors tend to spend their money for DGs. In this project, the main goal is to offer an algorithm for planning and placing DGs in order to reduce the need for transmission and distribution network.

Keywords: planning, transmission, distributed generation, power security, power systems

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Authors: Seham Elia Moussa Shenouda

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The relationship between development and human rights has long been the subject of academic debate. To understand the dynamics between these two concepts, various principles are adopted, from the right to development to development-based human rights. Despite the initiatives taken, the relationship between development and human rights remains unclear. However, the overlap between these two views and the idea that efforts should be made in the field of human rights have increased in recent years. It is then evaluated whether the right to sustainable development is acceptable or not. The article concludes that the principles of sustainable development are directly or indirectly recognized in various human rights instruments, which is a good answer to the question posed above. This book therefore cites regional and international human rights agreements such as , as well as the jurisprudence and interpretative guidelines of human rights institutions, to prove this hypothesis.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security

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Authors: M. H. Hussain, I. Musirin, A. F. Abidin, S. R. A. Rahim

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This paper presents the implementation of Artificial Bees Colony (ABC) algorithm in solving Directional OverCurrent Relays (DOCRs) coordination problem for near-end faults occurring in fixed network topology. The coordination optimization of DOCRs is formulated as linear programming (LP) problem. The objective function is introduced to minimize the operating time of the associated relay which depends on the time multiplier setting. The proposed technique is to taken as a technique for comparison purpose in order to highlight its superiority. The proposed algorithms have been tested successfully on 8 bus test system. The simulation results demonstrated that the ABC algorithm which has been proved to have good search ability is capable in dealing with constraint optimization problems.

Keywords: artificial bees colony, directional overcurrent relay coordination problem, relay settings, time multiplier setting

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Authors: Tugce Talay, Kadir Erkan

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In this study, the necessary steps for the design of axial flow permanent magnet motors are shown. The design and analysis of the engine were carried out based on ANSYS Maxwell program. The design parameters of the ANSYS Maxwell program and the artificial neural network system were established in MATLAB and the most efficient design parameters were found with the trained neural network. The results of the Maxwell program and the results of the artificial neural networks are compared and optimal working design parameters are found. The most efficient design parameters were submitted to the ANSYS Maxwell 3D design and the cogging torque was examined and design studies were carried out to reduce the cogging torque.

Keywords: AFPM, ANSYS Maxwell, cogging torque, design optimisation, efficiency, NNTOOL

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Authors: Mohammed A. M. Ibrahim

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This paper investigate the utilization of wire and wireless networks to be platform for distributed educational monitoring system. Universities in developing countries suffer from a lot of shortages(staff, equipment, and finical budget) and optimal utilization of the wire and wireless network, so universities can mitigate some of the mentioned problems and avoid the problems that maybe humble the education processes in many universities by using our implementation of the examinations system as a test-bed to utilize the network as a solution to the shortages for academic staff in Taiz University. This paper selects a two areas first one quizzes activities is only a test bed application for wireless network learning environment system to be distributed among students. Second area is the features and the security of wireless, our tested application implemented in a promising area which is the use of WLAN in higher education for leering environment.

Keywords: networking wire and wireless technology, wireless network security, distributed computing, algorithm, encryption and decryption

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Authors: Wisdom Dordudnu, George M. Y. Owusu, Samuel N. Y. Simpson

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The recent failures of many corporate giants have generated intense research interest in the factors that influence accountants’ job performance. Against the backdrop that these factors also create an enabling environment for success at the work place, this study contributes to literature on job performance of accountants by exploring the impact of two psycho-spiritual factors: religiosity and spiritual intelligence on job performance of accountants in Ghana. The study employs a survey approach using questionnaires as the principal means of data collection to elicit responses from accountants working in the 222 certified firms of Institute of Chartered Accountants Ghana (ICAG). A structural equation modeling-based approach is employed to examine the relationship among the study constructs. Results of this study indicate that there is a positive relationship between these factors and accountants’ performance. It is expected that this study provides strong evidence and highlight the need for specific action from managers to look critically at the non-material aspect of accountants in accounting firms.

Keywords: job performance, psycho-spiritual, religiosity, spiritual intelligence

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Authors: Naser Zourikalatehsamad, Seyed Abdorreza Payambarpour, Ibrahim Alwashali, Zahra Abdolkarimi

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The paper aims to evaluate the effect of online advertising on consumer purchase behavior in Malaysian organizations. The paper has potential to extend and refine theory. A survey was distributed among Students of UTM university during the winter 2014 and 160 responses were collected. Regression analysis was used to test the hypothesized relationships of the model. Result shows that the predictors (cost saving factor, convenience factor and customized product or services) have positive impact on intention to continue seeking online advertising.

Keywords: consumer purchase, convenience, customized product, cost saving, customization, flow theory, mass communication, online advertising ads, online advertising measurement, online advertising mechanism, online intelligence system, self-confidence, willingness to purchase

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Authors: Badr M. Alshammari, T. Guesmi

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This study presents a modified version of the artificial bee colony (ABC) algorithm by including a local search technique for solving the non-convex economic power dispatch problem. The local search step is incorporated at the end of each iteration. Total system losses, valve-point loading effects and prohibited operating zones have been incorporated in the problem formulation. Thus, the problem becomes highly nonlinear and with discontinuous objective function. The proposed technique is validated using an IEEE benchmark system with ten thermal units. Simulation results demonstrate that the proposed optimization algorithm has better convergence characteristics in comparison with the original ABC algorithm.

Keywords: economic power dispatch, artificial bee colony, valve-point loading effects, prohibited operating zones

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Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

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Authors: Razak Rahmat, Azizah Ahmad, Rafidah Razak, Roshidi Din, Azizi Abas

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Telecenter is a place where communities can access computers, the Internet, and other digital technologies to enable them to gather information, create, learn, and communicate with others. However, previous studies found that sustainability issues related to economic, political and institutional, social and technology is one of the major problem faced by the telecenter. Based on that problem, this research is planning to design a possible solution on rural telecenters sustainability with the support of business intelligence (BI). The empirical study will be conducted through the qualitative and quantitative method including interviews and observations with a range of stakeholders including ministry officers, telecenters managers and operators. Result from the data collection will be analyze using the causal modeling approach of SEM SmartPLS for the validity. The expected finding from this research is the Business Intelligent Requirement Model as a guild for sustainability of the rural telecenters.

Keywords: Rural ICT Telecenter(RICTT), business intelligence, sustainability, requirement analysis modal

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Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

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Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

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Authors: Ashirgul Kozhagulova, Ainash Shabdirova, Galym Tokazhanov, Minh Nguyen

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The synthetic rocks have been advantageous over the natural rocks in terms of availability and the consistent studying the impact of a particular parameter. The artificial rocks can be fabricated using variety of techniques such as mixing sand and Portland cement or gypsum, firing the mixture of sand and fine powder of borosilicate glass or by in-situ precipitation of calcite solution. In this study, sodium silicate solution has been used as the cementing agent for the quartz sand. The molded soft cylindrical sandstone samples are placed in the gas-tight pressure vessel, where the hardening of the material takes place as the chemical reaction between carbon dioxide and the silicate solution progresses. The vessel allows uniform disperse of carbon dioxide and control over the ambient gas pressure. Current paper shows how the bonding material is initially distributed in the intergranular space and the surface of the sand particles by the usage of Electron Microscopy and the Energy Dispersive Spectroscopy. During the study, the strength of the cement bond as a function of temperature is observed. The impact of cementing agent dosage on the micro and macro characteristics of the sandstone is investigated. The analysis of the cement bond at micro level helps to trace the changes to particles bonding damage after a potential yielding. Shearing behavior and compressional response have been examined resulting in the estimation of the shearing resistance and cohesion force of the sandstone. These are considered to be main input values to the mathematical prediction models of sand production from weak clastic oil reservoir formations.

Keywords: artificial sanstone, cement bond, microstructure, SEM, triaxial shearing

Procedia PDF Downloads 163

Authors: Bola George Asaad Bekledas

Abstract:

Relationship between development and human rights has long been the subject of academic debate. To understand the dynamics between these two concepts, various principles are adopted, from the right to development to development-based human rights. Despite the initiatives taken, the relationship between development and human rights remains unclear. However, the compatibility between these two concepts and the idea that these efforts should be made to respect human rights guarantees have gained momentum in recent years. It is then evaluated whether the right to sustainable development is acceptable or not. The article concludes that the principles of sustainable development are directly or indirectly recognized in various human rights instruments, which is a good answer to the question posed above. This study, therefore, cites regional and international human rights agreements such as, as well as the jurisprudence and interpretative guidelines of human rights institutions, to prove this hypothesis.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security.

Procedia PDF Downloads 41

Authors: Alexandros Lioulemes, Michail Theofanidis, Varun Kanal, Konstantinos Tsiakas, Maher Abujelala, Chris Collander, William B. Townsend, Angie Boisselle, Fillia Makedon

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This paper presents a home-based robot-rehabilitation instrument, called ”MAGNI Dynamics”, that utilized a vision-based kinematic/dynamic module and an adaptive haptic feedback controller. The system is expected to provide personalized rehabilitation by adjusting its resistive and supportive behavior according to a fuzzy intelligence controller that acts as an inference system, which correlates the user’s performance to different stiffness factors. The vision module uses the Kinect’s skeletal tracking to monitor the user’s effort in an unobtrusive and safe way, by estimating the torque that affects the user’s arm. The system’s torque estimations are justified by capturing electromyographic data from primitive hand motions (Shoulder Abduction and Shoulder Forward Flexion). Moreover, we present and analyze how the Barrett WAM generates a force-field with a haptic controller to support or challenge the users. Experiments show that by shifting the proportional value, that corresponds to different stiffness factors of the haptic path, can potentially help the user to improve his/her motor skills. Finally, potential areas for future research are discussed, that address how a rehabilitation robotic framework may include multisensing data, to improve the user’s recovery process.

Keywords: human-robot interaction, kinect, kinematics, dynamics, haptic control, rehabilitation robotics, artificial intelligence

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Authors: Michael R. Phangtriastu, Herriyandi Herriyandi, Diaz D. Santika

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This paper presents a comparison of the implementation of metaheuristic algorithms to train the antecedent parameters and consequence parameters in the adaptive network-based fuzzy inference system (ANFIS). The algorithms compared are genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The objective of this paper is to benchmark well-known metaheuristic algorithms. The algorithms are applied to several data set with different nature. The combinations of the algorithms' parameters are tested. In all algorithms, a different number of populations are tested. In PSO, combinations of velocity are tested. In ABC, a different number of limit abandonment are tested. Experiments find out that ABC is more reliable than other algorithms, ABC manages to get better mean square error (MSE) than other algorithms in all data set.

Keywords: ANFIS, artificial bee colony, genetic algorithm, metaheuristic algorithm, particle swarm optimization

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Authors: Nhi Phuoc Thuc Le

Abstract:

Cross-cultural adaptability —one’s readiness to interact with people who are different from oneself or to adapt to living in another culture— is essential to the well-being and experience of international students. This research was set out to find the correlation between certain personality traits of international students and their likelihood to adapt to the U.S., the host culture. The study used Qualtrics, an online survey, to investigate the relationships between international students’ social self-efficacy, ego-resiliency, cultural intelligence, Big Five personality traits and cross-cultural adaptability (sociocultural and psychological adaptability). The data were analysed with the software SPSS. The findings of this quantitative study show that high scores in ego-resiliency, social self-efficacy, cultural intelligence and personality traits (including extraversion, agreeableness, intellect and conscientiousness) are correlated with better cross-cultural adaptation. Meanwhile, the Big-Five trait neuroticism is correlated with lower cross-cultural adaptability. Such insight is suggested to help international students be better prepared for an immersion into the US culture.

Keywords: Big Five, cross-cultural adaptability, cultural intelligence, ego-resiliency, international students, personality, self-efficacy

Procedia PDF Downloads 187

Authors: Ji Eun Shin, Dong Hoon Lim

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Data clustering is a common technique used in data analysis and is used in many applications, such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing. K-means clustering is a well-known clustering algorithm aiming to cluster a set of data points to a predefined number of clusters. In this paper, we implement K-means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. The main idea is to introduce a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. The experimental results demonstrated that K-means algorithm using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also showed that our K-means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases.

Keywords: big data, combiner, K-means clustering, RHadoop

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Authors: Bensaid A., Mostephaoui T., Nedjai R.

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Nowadays, wetlands are the subject of contradictory debates opposing scientific, political and administrative meanings. Indeed, given their multiple services (drinking water, irrigation, hydrological regulation, mineral, plant and animal resources...), wetlands concentrate many socio-economic and biodiversity issues. In some regions, they can cover vast areas (>100 thousand ha) of the landscape, such as the Camargue area in the south of France, inside the Rhone delta. The high biological productivity of wetlands, the strong natural selection pressures and the diversity of aquatic environments have produced many species of plants and animals that are found nowhere else. These environments are tremendous carbon sinks and biodiversity reserves depending on their age, composition and surrounding environmental conditions, wetlands play an important role in global climate projections. Covering more than 3% of the earth's surface, wetlands have experienced since the beginning of the 1990s a tremendous revival of interest, which has resulted in the multiplication of inventories, scientific studies and management experiments. The geographical and physical characteristics of the wetlands of the central region conceal a large number of natural habitats that harbour a great biological diversity. These wetlands, one of the natural habitats, are still influenced by human activities, especially agriculture, which affects its layout and functioning. In this perspective, decision-makers need to delimit spatial objects (natural habitats) in a certain way to be able to take action. Thus, wetlands are no exception to this rule even if it seems to be a difficult exercise to delimit a type of environment as whose main characteristic is often to occupy the transition between aquatic and terrestrial environment. However, it is possible to map wetlands with databases, derived from the interpretation of photos and satellite images, such as the European database Corine Land cover, which allows quantifying and characterizing for each place the characteristic wetland types. Scientific studies have shown limitations when using high spatial resolution images (SPOT, Landsat, ASTER) for the identification and characterization of small wetlands (1 hectare). To address this limitation, it is important to note that these wetlands generally represent spatially complex features. Indeed, the use of very high spatial resolution images (>3m) is necessary to map small and large areas. However, with the recent evolution of artificial intelligence (AI) and deep learning methods for satellite image processing have shown a much better performance compared to traditional processing based only on pixel structures. Our research work is also based on spectral and textural analysis on THR images (Spot and IRC orthoimage) using two object-oriented approaches, the nearest neighbour approach (k-NN) and the Super Vector Machine approach (SVM). The k-NN approach gave good results for the delineation of wetlands (wet marshes and moors, ponds, artificial wetlands water body edges, ponds, mountain wetlands, river edges and brackish marshes) with a kappa index higher than 85%.

Keywords: land development, GIS, sand dunes, segmentation, remote sensing

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Authors: Chingwen Yeh, I. Chen Huang

Abstract:

This paper aims to conduct a case study on kinesthetic intelligence development intervention with a child who has Asperger symptom identified by physician. First, the characteristics of Asperger were defined based on the related literature. Some Asperger's people are born with outstanding insight and are good at solving complex and difficult problems. In contrast to high-functioning autistic, Asperger children do not lose their ability to express themselves verbally. However in the cognitive function, they focus mainly on the things they are interested in instead of paying attention to the whole surrounding situation. Thus it is difficult for them not only to focus on things that they are not interesting in, but also to interact with people. Secondly, 8-weeks of kinesthetic intelligence development courses were designed within a series of physical action that including the following sections: limbs coordination, various parts of body rhythm changes, strength and space awareness and breathing practice. In classroom observations were recorded both on words and with video as the qualitative research data. Finally, in-depth interview with the case child’s teachers, parents and other in class observers were documented on a weekly base in order to examine the effectiveness of before and after the kinesthetic intelligence development course and to testify the usefulness of the lesson plan. This research found that the case child has improved significantly in terms of attention span and body movement creativity. In the beginning of intervention, the case child made less eyes contact with others. The instructor needed to face the case child to confirm the eyes contact. And the instructor also used various adjective words as guiding language for all kinds of movement sequence practice. The result can cause the case child’s attention and learning motivation. And the case child understand what to do to enhance kinesthetic intelligence. These authors hope findings of this study can contribute as reference for the further research on the related topic.

Keywords: asperger symptom, body rhythm, kinesthetic intelligence, space awareness

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Authors: Felib Ayman Shawky Salem

Abstract:

Experiential marketing means an unforgettable experience that remains deeply anchored in the customer's memory. Furthermore, customer satisfaction is defined as the emotional response to the experiences provided that relate to specific products or services purchased. Therefore, experiential marketing activities can influence the level of customer satisfaction and loyalty. In this context, the study aims to examine the relationship between experiential marketing, customer satisfaction and loyalty of beauty products in Konya. The results of this study showed that experiential marketing is an important indicator of customer satisfaction and loyalty and that experiential marketing has a significant positive impact on customer satisfaction and loyalty.

Keywords: sponsorship, marketing communication theories, marketing communication tools internet, marketing, tourism, tourism management corporate responsibility, employee organizational performance, internal marketing, internal customer experiential marketing, customer satisfaction, customer loyalty, social sciences.

Procedia PDF Downloads 60