Search results for: artificial intelligence and office

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

Procedia PDF Downloads 325

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

Procedia PDF Downloads 215

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

Procedia PDF Downloads 483

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

Procedia PDF Downloads 86

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

Procedia PDF Downloads 251

Authors: Fatemeh Deldarabdolmaleki, Nur Dalilah Dahlan, Farzad Hejazi

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Human individual physiological and psycho-physical responses widely affect thermal comfort and preferences. They should be carefully researched to help improve the design and comfort of indoor environments. This paper aims to explore and test the degree and importance of individual physiological and psycho-physical differences, reviewing the most preferred, neutral, and comfortable temperature in previous studies conducted across the world. Basic individual physiological differences like gender, age, BMI and etc., have been the focus of this research. There is no unique consensus in the literature to date in regard to providing a universal thermal comfort formula that meets all individual physiological and psycho-physical needs. In order to achieve a balanced, thermally comfortable indoor environment, studying and evaluating individual needs in different parts of the world could be helpful. Even though personalized comfort systems in indoor environments sound promising, they might not be easily achieved in bigger office interiors, considering the cost and current open-plan office trends.

Keywords: thermal comfort, indoor environments, occupants' physiological response, occupants psycho-physical response

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Authors: Joseph Ignagni, Rebecca Deen

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This paper analyzes the extent to which the U.S. Supreme Court votes to support the position of the United States in cases where the government is a party to the litigation. This study considers the relationship between the Solicitor General’s Office and the U.S. Supreme Court. The Solicitor General has the unique position of being the representative of the Executive Branch and the U.S. government before the Supreme Court. While a great deal of research has looked at the Solicitor General’s success as a “friend of the court,” far less has considered this relationship when the U.S. is a direct party in the litigation. This paper investigates the success rate of the Solicitor General’s Office in these cases. We find that there is considerable variation in the U.S. government’s success rate before the Court depending on the issue, Supreme Court leadership, the ideological direction of the Court and whether the U.S. approached the Court as a petitioner or respondent. We conduct our analysis on the Court’s decisions from 1953-2009. This study adds to our understanding of checks and balances, separation of powers, and inter-institutional relationships between the branches of the federal government of the United States.

Keywords: U.S. president, solicitor general, U.S. Supreme Court, separation of power, checks and balances

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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

Procedia PDF Downloads 141

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: Nhi Phuoc Thuc Le

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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

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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: Chingwen Yeh, I. Chen Huang

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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: 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: Carlotta D’Alessandro, Giuseppe Ioppolo, Katarzyna Szopik-Depczyńska

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— The growing scarcity of resources and the mounting pressures of climate change, water pollution, and chemical contamination have prompted societies, governments, and businesses to seek ways to minimize their environmental impact. To combat climate change, and foster sustainability, Industrial Symbiosis (IS) offers a powerful approach, facilitating the shift toward a circular economic model. IS has gained prominence in the European Union's policy framework as crucial enabler of resource efficiency and circular economy practices. The essence of IS lies in the collaborative sharing of resources such as energy, material by-products, waste, and water, thanks to geographic proximity. It can be exemplified by eco-industrial parks (EIPs), which are natural environments for boosting cooperation and resource sharing between businesses. EIPs are characterized by group of businesses situated in proximity, connected by a network of both cooperative and competitive interactions. They represent a sustainable industrial model aimed at reducing resource use, waste, and environmental impact while fostering economic and social wellbeing. IS, combined with Artificial Intelligence (AI)-driven technologies, can further optimize resource sharing and efficiency within EIPs. This research, supported by the “CE_IPs” project, aims to analyze the potential for IS and AI, in advancing circularity and sustainability at Fosso Imperatore. The Fosso Imperatore Industrial Park in Nocera Inferiore, Italy, specializes in agriculture and the industrial transformation of agricultural products, particularly tomatoes, tobacco, and textile fibers. This unique industrial cluster, centered around tomato cultivation and processing, also includes mechanical engineering enterprises and agricultural packaging firms. To stimulate the shift from a traditional to a circular economic model, an AI-powered Local Development Plan (LDP) is developed for Fosso Imperatore. It can leverage data analytics, predictive modeling, and stakeholder engagement to optimize resource utilization, reduce waste, and promote sustainable industrial practices. A comprehensive SWOT analysis of the AI-powered LDP revealed several key factors influencing its potential success and challenges. Among the notable strengths and opportunities arising from AI implementation are reduced processing times, fewer human errors, and increased revenue generation. Furthermore, predictive analytics minimize downtime, bolster productivity, and elevate quality while mitigating workplace hazards. However, the integration of AI also presents potential weaknesses and threats, including significant financial investment, since implementing and maintaining AI systems can be costly. The widespread adoption of AI could lead to job losses in certain sectors. Lastly, AI systems are susceptible to cyberattacks, posing risks to data security and operational continuity. Moreover, an Analytic Hierarchy Process (AHP) analysis was employed to yield a prioritized ranking of the outlined AI-driven LDP practices based on the stakeholder input, ensuring a more comprehensive and representative understanding of their relative significance for achieving sustainability in Fosso Imperatore Industrial Park. While this study provides valuable insights into the potential of AIpowered LDP at the Fosso Imperatore, it is important to note that the findings may not be directly applicable to all industrial parks, particularly those with different sizes, geographic locations, or industry compositions. Additional study is necessary to scrutinize the generalizability of these results and to identify best practices for implementing AI-driven LDP in diverse contexts.

Keywords: artificial intelligence, climate change, Fosso Imperatore, industrial park, industrial symbiosis

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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: Felib Ayman Shawky Salem

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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.

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Authors: Ogunremi Beatrice, Oluwole David Adebayo

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Public secondary school students are face with some challenges from the parents, government and teachers in school. Some of the challenges that arises from the parents are lack of attention and adequate communication. From the government are unavailability of useful instructional materials, competent and professionally trained teachers for each subject the students do in school. The challenges that arise from the teachers most often are mismanagement of time, inability to understand the capacity of the student and lack class management and follow up. This study investigated self-regulation, emotional intelligence, self-efficacy and goal orientation as predictors of school engagement among public secondary school students in Ibadan. A structured questionnaire was administered on 258 students from six mixed secondary schools in Ibadan. Pearson Product Moment Correlation method was used for data analysis. Four hypothesis were raised and answered, the results showed there is positive and significant relationships between school engagement among public secondary school students and each of the independent variable: Self-regulation, Emotional intelligence, Self-efficacy, Goal orientation. On the basis of these findings, it was recommended that the parents have to encourage their children on how to be goal oriented ,build their self-efficacy skill, to be self-regulated and emotionally intelligent in order to be effective in school and be able to increase their intellectual ability.

Keywords: emotional intelligence, self-efficacy, goal orientation, school engagement, self-regulation

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Authors: Behdoukht Mall Amiri, Zohreh Gheydar

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The thrust of the current study was to investigate the relationship among EFL learners' creativity (CR), emotional intelligence (EI), and self-efficacy (SE). To this end, a group of 120 male and female learners, between the ages of 19 and 35 studying BA in English Translation and MA in Teaching English at Islamic Azad University, Central Tehran were selected using convenient sampling and were given three questionnaires: Bar-On’s EQ-I questionnaire by Bar-On (1997), the General Self-Efficacy Scale questionnaire (SGSES) by Sherer et al. (1982), and a questionnaire of creativity (CR) by O'Neil, Abedi, and Spielberger (1992). Analysis of the results through Pearson Moment Correlation Coefficient showed that there was not a significant relationship between students’ CR and EI, and EI and SE. In addition, CR and SE were correlated significantly but negatively. Multiple regressions revealed that CR could significantly predict SE. Regarding the findings of the study, the obtained results may help EFL teachers, teacher trainers, materials developers, and educational policy makers to possess a broader perspective and heightened degree knowledge toward the TEFL practice and to take practical steps toward the attainments of the desired objectives of the profession.

Keywords: creativity, emotional intelligence, self-efficacy, learning

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Authors: Bouchra Nadji, Elaid Bouchetob

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PV systems are common in residential and industrial settings because of their low, upfront costs and operating costs throughout their lifetimes. Buck or boost converters are used in photovoltaic systems, regardless of whether the system is autonomous or connected to the grid. These converters became less appealing because of their low efficiency, inadequate power density, and use of silicon for their power components. Traditional devices based on Si are getting close to reaching their theoretical performance limits, which makes it more challenging to improve the performance and efficiency of these devices. GaN and SiC are the two types of WBG semiconductors with the most recent technological advancements and are available. Tolerance to high temperatures and switching frequencies can reduce active and passive component size. Utilizing high-efficiency dc-dc boost converters is the primary emphasis of this work. These converters are for photovoltaic systems that use wave energy.

Keywords: component, Artificial intelligence, PV System, ANN MPPT, DC-DC converter

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Authors: Ahmed Kayad Abdourazak, Abderafi Souad, Zejli Driss, Idriss Abdoulkader Ibrahim

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In this paper Artificial Neural Network (ANN) is used to predict the solar irradiation in Djibouti for the first Time that is useful to the integration of Concentrating Solar Power (CSP) and sites selections for new or future solar plants as part of solar energy development. An ANN algorithm was developed to establish a forward/reverse correspondence between the latitude, longitude, altitude and monthly solar irradiation. For this purpose the German Aerospace Centre (DLR) data of eight Djibouti sites were used as training and testing in a standard three layers network with the back propagation algorithm of Lavenber-Marquardt. Results have shown a very good agreement for the solar irradiation prediction in Djibouti and proves that the proposed approach can be well used as an efficient tool for prediction of solar irradiation by providing so helpful information concerning sites selection, design and planning of solar plants.

Keywords: artificial neural network, solar irradiation, concentrated solar power, Lavenberg-Marquardt

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: Zongping Wang, Chengjun Cui, Jiacun Wang

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The concept of intelligence has been widely used to access an individual's cognitive abilities to learn, form concepts, understand, apply logic, and reason. According to the multiple intelligence theory, there are eight distinguished types of intelligence. One of them is the bodily-kinaesthetic intelligence that links to the capacity of an individual controlling his body and working with objects. Motor intelligence, on the other hand, reflects the capacity to understand, perceive and solve functional problems by motor behavior. Both bodily-kinaesthetic intelligence and motor intelligence refer directly or indirectly to bodily capacity. Inspired by these two intelligence concepts, this paper introduces motional intelligence (MI). MI is two-fold. (1) Body strength, which is the capacity of various organ functions manifested by muscle activity under the control of the central nervous system during physical exercises. It can be measured by the magnitude of muscle contraction force, the frequency of repeating a movement, the time to finish a movement of body position, the duration to maintain muscles in a working status, etc. Body strength reflects the objective of MI. (2) Level of psychiatric willingness to physical events. It is a subjective thing and determined by an individual’s self-consciousness to physical events and resistance to fatigue. As such, we call it subjective MI. Subjective MI can be improved through education and proper social events. The improvement of subjective MI can lead to that of objective MI. A quantitative score of an individual’s MI is motional quotient (MQ). MQ is affected by several factors, including genetics, physical training, diet and lifestyle, family and social environment, and personal awareness of the importance of physical exercise. Genes determine one’s body strength potential. Physical training, in general, makes people stronger, faster and swifter. Diet and lifestyle have a direct impact on health. Family and social environment largely affect one’s passion for physical activities, so does personal awareness of the importance of physical exercise. The key to the success of the MQ study is developing an acceptable and efficient system that can be used to assess MQ objectively and quantitatively. We should apply different accessing systems to different groups of people according to their ages and genders. Field test, laboratory test and questionnaire are among essential components of MQ assessment. A scientific interpretation of MQ score is part of an MQ assessment system as it will help an individual to improve his MQ. IQ (intelligence quotient) and EQ (emotional quotient) and their test have been studied intensively. We argue that IQ and EQ study alone is not sufficient for an individual’s all round development. The significance of MQ study is that it offsets IQ and EQ study. MQ reflects an individual’s mental level as well as bodily level of intelligence in physical activities. It is well-known that the American Springfield College seal includes the Luther Gulick triangle with the words “spirit,” “mind,” and “body” written within it. MQ, together with IQ and EQ, echoes this education philosophy. Since its inception in 2012, the MQ research has spread rapidly in China. By now, six prestigious universities in China have established research centers on MQ and its assessment.

Keywords: motional Intelligence, motional quotient, multiple intelligence, motor intelligence, all round development

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Authors: Dimitrios Triantakonstantis, Demetris Stathakis

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Urban areas have been expanded throughout the globe. Monitoring and modeling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modeling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: artificial neural networks, CORINE, urban atlas, urban growth prediction

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Authors: David Ofosu-Hamilton, John K. E. Edumadze

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Universities have come to recognize the role Information and Communication Technology (ICT) skills plays in the daily activities of tertiary students. The ability to use ICT – essentially, computers and their diverse applications – are important resources that influence an individual’s economic and social participation and human capital development. Our society now increasingly relies on the Internet, and the Cloud as a means to communicate and disseminate information. The educated individual should, therefore, be able to use ICT to create and share knowledge that will improve society. It is, therefore, important that universities require incoming students to demonstrate a level of computer proficiency or trained to do so at a minimal cost by deploying advanced educational technologies. The training and standardized assessment of all in-coming first-year students of the University of Cape Coast in Information Technology Skills (ITS) have become a necessity as students’ most often than not highly overestimate their digital skill and digital ignorance is costly to any economy. The one-semester course is targeted at fresh students and aimed at enhancing the productivity and software skills of students. In this respect, emphasis is placed on skills that will enable students to be proficient in using Microsoft Office and Google Apps for Education for their academic work and future professional work whiles using emerging digital multimedia technologies in a safe, ethical, responsible, and legal manner. The course is delivered in blended mode - online and self-paced (student centered) using Alison’s free cloud-based tutorial (Moodle) of Microsoft Office videos. Online support is provided via discussion forums on the University’s Moodle platform and tutor-directed and assisted at the ICT Centre and Google E-learning laboratory. All students are required to register for the ITS course during either the first or second semester of the first year and must participate and complete it within a semester. Assessment focuses on Alison online assessment on Microsoft Office, Alison online assessment on ALISON ABC IT, Peer assessment on e-portfolio created using Google Apps/Office 365 and an End of Semester’s online assessment at the ICT Centre whenever the student was ready in the cause of the semester. This paper, therefore, focuses on the digital culture approach of hybrid teaching, learning and paperless examinations and the possible adoption by other courses or programs at the University of Cape Coast.

Keywords: assessment, blended, cloud, paperless

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Authors: Raja Omman Zafar, Yves Rybarczyk, Johan Borg

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This article proposes a conceptual framework for the application of digital twin technology in home care. The main goal is to bridge the gap between advanced digital twin concepts and their practical implementation in home care. This study uses a literature review and thematic analysis approach to synthesize existing knowledge and proposes a structured framework suitable for homecare applications. The proposed framework integrates key components such as IoT sensors, data-driven models, cloud computing, and user interface design, highlighting the importance of personalized and predictive homecare solutions. This framework can significantly improve the efficiency, accuracy, and reliability of homecare services. It paves the way for the implementation of digital twins in home care, promoting real-time monitoring, early intervention, and better outcomes.

Keywords: digital twin, homecare, older adults, healthcare, IoT, artificial intelligence

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Authors: Nadia Chagtmi, Nejla Rejab, Noureddine Boulejfen

Abstract:

This paper presents a calibration approach based on artificial neural networks (ANN) to determine the envelop signal (I+jQ) of a six-port based receiver (SPR). The memory effects called also dynamic behavior and the nonlinearity brought by diode based power detector have been taken into consideration by the ANN. Experimental set-up has been performed to validate the efficiency of this method. The efficiency of this approach has been confirmed by the obtained results in terms of waveforms. Moreover, the obtained error vector magnitude (EVM) and the mean absolute error (MAE) have been calculated in order to confirm and to test the ANN’s performance to achieve I/Q recovery using the output voltage detected by the power based detector. The baseband signal has been recovered using ANN with EVMs no higher than 1 % and an MAE no higher than 17, 26 for the SPR excited different type of signals such QAM (quadrature amplitude modulation) and LTE (Long Term Evolution).

Keywords: six-port based receiver; calibration, nonlinearity, memory effect, artificial neural network

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Authors: Laidi Maamar, Hanini Salah

Abstract:

The present work investigates the potential of artificial neural network (ANN) model to predict the horizontal global solar radiation (HGSR). The ANN is developed and optimized using three years meteorological database from 2011 to 2013 available at the meteorological station of Blida (Blida 1 university, Algeria, Latitude 36.5°, Longitude 2.81° and 163 m above mean sea level). Optimal configuration of the ANN model has been determined by minimizing the Root Means Square Error (RMSE) and maximizing the correlation coefficient (R2) between observed and predicted data with the ANN model. To select the best ANN architecture, we have conducted several tests by using different combinations of parameters. A two-layer ANN model with six hidden neurons has been found as an optimal topology with (RMSE=4.036 W/m²) and (R²=0.999). A graphical user interface (GUI), was designed based on the best network structure and training algorithm, to enhance the users’ friendliness application of the model.

Keywords: artificial neural network, global solar radiation, solar energy, prediction, Algeria

Procedia PDF Downloads 493

Authors: M. Azugama, P. Okoro Ugo Chigozie, A. O. Nnamocha

Abstract:

Emotional Intelligence constitutes powerful psychological forces that can strongly influence performance in behaviour, interaction and relationship management. Surprisingly how emotions are interpreted and employed in marketing of hospitality experience have had limited comprehension. Marketing of hospitality experiences have important emotional dimensions which the traditional marketing techniques tend to underplay. Guest and host relationship are challenged by mutual hospitableness obligations; suggesting that the commercial practice of delivering satisfactory guest experience has much to gain from traditional understanding of hospitality. By understanding the emotion-based hospitality transaction between guests and hosts, customers’ experiences can be delivered over and against competitor pressure. In this paper, marketing strategies and tactics in hospitality and tourism are principally concerned with adjusting each of the 6P & T elements (i.e. product, place, price and promotion; and adding people, processes and Time in service contexts), to provide a competitive offer (experience) to customers.

Keywords: Emotional intelligence, hospitality and tourism, relationship management, marketing

Procedia PDF Downloads 468

Authors: Sabir Messalti, Bilal Boudjellal, Azouz Said

Abstract:

In this paper, power system Voltage improvement using wind turbine is presented. Two controllers are used: a PI controller and Artificial Neural Networks (ANN) controllers are studied to control of the power flow exchanged between the wind turbine and the power system in order to improve the bus voltage. The wind turbine is based on a doubly-fed induction generator (DFIG) controlled by field-oriented control. Indirect control is used to control of the reactive power flow exchanged between the DFIG and the power system. The proposed controllers are tested on power system for large voltage disturbances.

Keywords: artificial neural networks controller, DFIG, field-oriented control, PI controller, power system voltage improvement

Procedia PDF Downloads 457

Authors: Shola Abidemi Olabode

Abstract:

The technological revolution over the years has impacted journalistic practice. As a matter of fact, journalistic practice has evolved alongside technologies of every generation transforming news and reporting, entertainment, and politics. Alongside these developments, the emergence of new kinds of risks and harms associated with generative AI has become rife with implications for media and journalism. Despite their numerous benefits for research and development, generative AI technologies like ChatGPT introduce new practical, ethical, and regulatory complexities in the practice of media and journalism. This paper presents a preliminary overview of the new kinds of challenges and issues for journalism and media practice in the era of generative AI, the implications for Nigeria, and invites a consideration of methods to mitigate the evolving complexity. It draws mainly on desk-based research underscoring the literature in both developed and developing non-western contexts as a contribution to knowledge.

Keywords: AI, journalism, media, online harms

Procedia PDF Downloads 75