Search results for: neural perception.

Authors: K. Y. Shih, H. M. Lin, S. Y. Lee, T. L. Hong

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Recently a number of food safety scandals have been on the news. In view of the fact that in Taiwan the majority of undergraduate college students reside in the dorms and dine out, the problem of restaurant sanitation is of utmost importance in their lives. The purpose of this study is to analyze students' dining habit and their perception of food safety. Four universities in the city of Tainan were randomly selected, and from each selected university a class was then chosen to receive 50 questionnaires. The total of 200 questionnaires yielded 144 usable returns. Students were asked to respond to questions, and each question was graded on a scale from 1 to 5 according to the importance. There were 32 questions ranging over various aspects: cleanliness of surroundings, washroom, food sanitation, serving temperature, kitchen sanitation, and service personnel cleanliness. It is found that the food sanitation received the highest score, while the service personnel ranked the lowest. An incidental finding is that the students tend to dine out in groups and as such their choice of restaurants are mostly dictated by consensus.

Keywords: food safety, restaurant, risk perception, sanitation

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Authors: Safayat Ali Shaikh

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Software has been developed for optimal cropping pattern in an irrigation project considering land constraint, water availability constraint and pick up flow constraint using modified Simplex Algorithm. Artificial Neural Network Models (ANN) have been developed to predict rainfall. AR (1) model used to generate 1000 years rainfall data to train the ANN. Simulation has been done with expected rainfall data. Eight number crops and three types of soil class have been considered for optimization model. Area under each crop and each soil class have been quantified using Modified Simplex Algorithm to get optimum net return. Efficacy of the software has been tested using data of large irrigation project in India.

Keywords: artificial neural network, large irrigation project, modified simplex algorithm, optimal cropping pattern

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Authors: Harshit Mittal, Neeraj Garg

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Hand symbol recognition is a pivotal component in the domain of computer vision, with far-reaching applications spanning sign language interpretation, human-computer interaction, and accessibility. This research paper discusses the approach with the integration of the Canny Edge algorithm and convolutional neural network. The significance of this study lies in its potential to enhance communication and accessibility for individuals with hearing impairments or those engaged in gesture-based interactions with technology. In the experiment mentioned, the data is manually collected by the authors from the webcam using Python codes, to increase the dataset augmentation, is applied to original images, which makes the model more compatible and advanced. Further, the dataset of about 6000 coloured images distributed equally in 5 classes (i.e., 1, 2, 3, 4, 5) are pre-processed first to gray images and then by the Canny Edge algorithm with threshold 1 and 2 as 150 each. After successful data building, this data is trained on the Convolutional Neural Network model, giving accuracy: 0.97834, precision: 0.97841, recall: 0.9783, and F1 score: 0.97832. For user purposes, a block of codes is built in Python to enable a window for hand symbol recognition. This research, at its core, seeks to advance the field of computer vision by providing an advanced perspective on hand sign recognition. By leveraging the capabilities of the Canny Edge algorithm and convolutional neural network, this study contributes to the ongoing efforts to create more accurate, efficient, and accessible solutions for individuals with diverse communication needs.

Keywords: hand symbol recognition, computer vision, Canny edge algorithm, convolutional neural network

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Authors: Krista Mae F. Ramos, Lance Micaiah C. Dauz, Gylza Nicole D. Bautista, Rua R. Galang, Jeric Xyrus G. Karganilla

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With the recent proclamation of the possible reinforcement of Compulsory Military Service in the Philippines, debates and societal talks rose and circulated as opinions and perceptions regarding the topic continue to clash. This study aims to determine the perception of the youth on its reimplementation and identify various advantages and disadvantages based on their perspective. The responses were gathered through a virtual call interview, underwent the process of thematization, and were categorized into different themes. Results reflect that the students perceive compulsory military service as a necessity for national defense but requires a long time that can hinder their education and needs a strong foundation to be implemented and sustained. The participants acknowledged that the practice would instill discipline, patriotism, and nationalism, develop an individual’s physical abilities, provide skills and knowledge and improve a person’s self-defense. However, there are also concerns regarding the prominent military shaping and abuse, their loss of freedom of choice, and the chances of health deterioration.

Keywords: compulsory, military, service, reinforcement, perception

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Authors: O. B. Enemuo, A. A. Onubuogu

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This work evaluated the influence of interior of decoration on customer perception of hotels in Uyo Akwa Ibom State. Specifically the study identified the various interior decoration used in hotels in the study area, determined the interior decoration used in hotels that appeal to the customer more, ascertained the influence of interior decoration on the level of patronage in the hotel in the study area and suggested ways of improving the interior decoration of hotels in the study area for sustainability. The study was guided by four research questions and two hypotheses. It adopted survey research design; structured questionnaire was used for data collection. The samples for the study were four hundred (400) staff and managers from the various hotels in the study area. Data generated were analyzed using mean and standard deviation analyses of variance (ANOVA) derived from regression analyses to test the hypotheses. The result of the finding showed that satisfactory interior decoration has a positive influence on the sustainability of the hospitality establishments in Uyo. The hypothesis showed that there was a significant relationship between the gender perception on the influence of interior decoration in the hotel and significant relationship between the gender perceptions on the influence of interior decoration in the hotels. From the finding, it was recommended that the hotels should design interior decorative service delivery system which has an impact on customer satisfaction in the hospitality industry and practiced healthy decorative environment and increased customer satisfaction.

Keywords: influence, interior decoration, customer’s perception, hotels

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Authors: Salim Ouchtati, Aissa Belmeguenai, Mouldi Bedda

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In this study, we propose an offline system for the recognition of the fragmented handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part; the study is based mainly on the evaluation of neural network performances, trained with the gradient backpropagation algorithm. The used parameters to form the input vector of the neural network are extracted from the binary images of the isolated handwritten digit by several methods: the distribution sequence, sondes application, the Barr features, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the fragmented handwritten numeric chains constituted of a variable number of digits. The vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits).

Keywords: features extraction, handwritten numeric chains, image processing, neural networks

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Authors: Teja Sai Puligadda

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Satellite images in detail can serve an important role in the geographic study. Quantitative and qualitative information provided by the satellite and remote sensing images minimizes the complexity of work and time. Data/images are captured at regular intervals by satellite remote sensing systems, and the amount of data collected is often enormous, and it expands rapidly as technology develops. Interpreting remote sensing images, geographic data mining, and researching distinct vegetation types such as agricultural and forests are all part of satellite image categorization. One of the biggest challenge data scientists faces while classifying satellite images is finding the best suitable classification algorithms based on the available that could able to classify images with utmost accuracy. In order to categorize satellite images, which is difficult due to the sheer volume of data, many academics are turning to deep learning machine algorithms. As, the CNN algorithm gives high accuracy in image recognition problems and automatically detects the important features without any human supervision and the ANN algorithm stores information on the entire network (Abhishek Gupta., 2020), these two deep learning algorithms have been used for satellite image classification. This project focuses on remote sensing through Deep Neural Networks i.e., ANN and CNN with Deep Sat (SAT-4) Airborne dataset for classifying images. Thus, in this project of classifying satellite images, the algorithms ANN and CNN are implemented, evaluated & compared and the performance is analyzed through evaluation metrics such as Accuracy and Loss. Additionally, the Neural Network algorithm which gives the lowest bias and lowest variance in solving multi-class satellite image classification is analyzed.

Keywords: artificial neural network, convolutional neural network, remote sensing, accuracy, loss

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Authors: Pingping Lin, Xudong Luo, Yifan Fan

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Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: document analysis, deep learning, multimodal sentiment analysis, natural language processing

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Authors: Nihal Murali, Kunal Gupta, Surekha Bhanot

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Training of artificial neural networks (ANNs) using reinforcement learning (RL) techniques is being widely discussed in the robot learning literature. The high model complexity of ANNs along with the model-free nature of RL algorithms provides a desirable combination for many robotics applications. There is a huge need for algorithms that generalize using raw sensory inputs, such as vision, without any hand-engineered features or domain heuristics. In this paper, the standard control problem of line following robot was used as a test-bed, and an ANN controller for the robot was trained on images from a live video feed using Q-learning. A virtual agent was first trained in simulation environment and then deployed onto a robot’s hardware. The robot successfully learns to traverse a wide range of curves and displays excellent generalization ability. Qualitative analysis of the evolution of policies, performance and weights of the network provide insights into the nature and convergence of the learning algorithm.

Keywords: artificial neural networks, q-learning, reinforcement learning, robot learning

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Authors: Saima Shafiq, Najma Iqbal Malik

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This study was aimed to explore the relationship of perceived stigma, perception of burden and psychological distress among parents of intellectually disabled children. The study also aimed to explore the moderating role of perceived social support on all the variables of the study. The sample of the study comprised of (N = 250) parents of intellectually disabled children. The present study utilized the co-relational research design. It consists of two phases. Phase-I consisted of two steps which contained the translation of two scales that were used in the present study and tried out on the sample of parents (N = 70). The Affiliated Stigma Scale and Care Giver Burden Inventory were translated into Urdu for the present study. Phase-1 revealed that translated scaled entailed satisfactory psychometric properties. Phase -II of the study was carried out in order to test the hypothesis. Correlation, linear regression analysis, and t-test were computed for hypothesis testing. Hierarchical regression analysis was applied to study the moderating effect of perceived social support. Findings revealed that there was a positive relationship between perceived stigma and psychological distress, perception of burden and psychological distress. Linear regression analysis showed that perceived stigma and perception of burden were positive predictors of psychological distress. The study did not show the moderating role of perceived social support among variables of the present study. The major limitation of the study is the sample size and the major implication is awareness regarding problems of parents of intellectually disabled children.

Keywords: perceived stigma, perception of burden, psychological distress, perceived social support

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Authors: Xie Weifan, Wang Zhongde

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Everyday landscape in the countryside has long played an important role as a cultural representation of the countryside and a link between the countryside and social relations. In the transformation of modernization, the daily landscape in the countryside needs to change with the transformation of daily life in countryside therefore, interpreting the daily landscape in the countryside and understanding the basic characteristics and value perception of the daily landscape from the villagers' perspective can help to understand the daily landscape in the countryside and its conservation and development. Taking Lizi Village in Qianjiang District, Chongqing Municipality, China, as a case study, we collected important daily landscapes in villagers' perceptions through in-depth interviews, categorized them into personal living space, public affairs space, and public activity space, and analyzed the characteristics of the spatial distribution of daily landscapes. The perceptual characteristics of the villagers' perceptions are analyzed and divided into four major types, namely, physical environment perception, atmosphere and culture perception, emotional feelings, and behavioral preferences, and their perceptual characteristics are analyzed respectively to understand the important characteristics of the villagers' perceptions of the daily landscapes. Finally, it is proposed that the protection and development of daily landscape in villages need to improve the mechanism of discovering and evaluating daily landscape, encourage residents to participate in the construction of daily landscape, protect the high-value daily landscape, and promote the innovative development of daily landscape.

Keywords: rural landscape, everyday landscape, landscape perception, conservation and development

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Authors: Dragan Zivotic, Mirjana Ilic, Aleksandra Perovic, Predrag Gavrilovic

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As with the provision of other services, the service quality perception is one of the key factors that the modern manager must pay attention to. Countries in which the state regulation is in transition also have specific features in providing fitness services. Identification of the dimensions in which the most significant different service quality perception between different types of fitness centers, enables managers to profile the offer according to the wishes and expectations of users. The aim of the paper was the comparison of the quality of services perception in the field of fitness in Serbia between three categories of fitness centers: the privately owned centers, the publicly owned centers, and the Public-private partnership centers. For this research 350 respondents of both genders (174 men and 176 women) were interviewed, aged between 18 and 68 years, being beneficiaries of fitness services for at least 1 year. Administered questionnaire with 100 items provided information about the 15 basic areas in which they expressed the service quality perception in the gym. The core sample was composed of 212 service users in private fitness centers, 69 service users in public fitness centers and 69 service users in the public-private partnership. Sub-samples were equal in representation of women and men, as well as by age and length of use of fitness services. The obtained results were subject of univariate analysis with the Kruskal-Wallis non-parametric analysis of variance. Significant differences between the analyzed sub-samples were not found solely in the areas of rapid response and quality outcomes. In the multivariate model, the results were processed by backward stepwise discriminant analysis that extracted 3 areas that maximize the differences between sub-samples: material and technical basis, secondary facilities and coaches. By applying the classification function 93.87% of private centers services users, 62.32% of public centers services users and 85.51% of the public-private partnership centers users of services were correctly classified (total 86.00%). These results allow optimizing the allocation of the necessary resources in profiling offers of a fitness center in order to optimally adjust it to the user’s needs and expectations.

Keywords: fitness, quality perception, management, public ownership, private ownership, public-private partnership, discriminative analysis

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Authors: Makenzy Lee Gilbert

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This literature review explores the role of attractor neural networks (ANNs) in modeling psychological processes in artificial and biological systems. By synthesizing research from dynamical systems theory, psychology, and computational neuroscience, the review provides an overview of the current understanding of ANN function in memory formation, reinforcement, retrieval, and forgetting. Key mathematical foundations, including dynamical systems theory and energy functions, are discussed to explain the behavior and stability of these networks. The review also examines empirical applications of ANNs in cognitive processes such as semantic memory and episodic recall, as well as highlighting the hippocampus's role in pattern separation and completion. The review addresses challenges like catastrophic forgetting and noise effects on memory retrieval. By identifying gaps between theoretical models and empirical findings, it highlights the interdisciplinary nature of ANN research and suggests future exploration areas.

Keywords: attractor neural networks, connectionism, computational modeling, cognitive neuroscience

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Authors: Hsu-Yung Cheng, Kuo-Chang Hsu, Chi-Chang Chan, Mei-Hui Tseng, Chih-Chang Yu, Ya-Sheng Liu

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Photovoltaics modules are usually not installed horizontally to avoid water or dust accumulation. However, the measured irradiance data on tilted surfaces are rarely available since installing pyranometers with various tilt angles induces high costs. Therefore, estimating solar irradiance on tilted surfaces is an important research topic. In this work, artificial neural networks (ANN) are utilized to construct the transfer model to estimate solar irradiance on tilted surfaces. Instead of predicting tilted irradiance directly, the proposed method estimates the differences between the horizontal irradiance and the irradiance on a tilted surface. The outputs of the ANNs in the proposed design are differential values. The experimental results have shown that the proposed ANNs with differential outputs can substantially improve the estimation accuracy compared to ANNs that estimate the titled irradiance directly.

Keywords: photovoltaics, artificial neural networks, tilted irradiance, solar energy

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Authors: Hassam Muazzam

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This paper devises an autonomous self-driven vehicle that is capable of taking a disabled person to his/her desired location using three different power sources (gasoline, solar, electric) without any control from the user, avoiding the obstacles in the way. The GPS co-ordinates of the desired location are sent to the main processing board via a GSM module. After the GPS co-ordinates are sent, the path to be followed by the vehicle is devised by Pythagoras theorem. The distance and angle between the present location and the desired location is calculated and then the vehicle starts moving in the desired direction. Meanwhile real-time data from ultrasonic sensors is fed to the board for obstacle avoidance mechanism. Ultrasonic sensors are used to quantify the distance of the vehicle from the object. The distance and position of the object is then used to make decisions regarding the direction of vehicle in order to avoid the obstacles using artificial neural network which is implemented using ATmega1280. Also the vehicle provides the feedback location at remote location.

Keywords: autonomous self-driven vehicle, obstacle avoidance, desired location, pythagoras theorem, neural network, remote location

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Authors: Oyebade K. Oyedotun

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The cardiotocogram is a technical recording of the heartbeat rate and uterine contractions of a fetus during pregnancy. During pregnancy, several complications can occur to both the mother and the fetus; hence it is very crucial that medical experts are able to find technical means to check the healthiness of the mother and especially the fetus. It is very important that the fetus develops as expected in stages during the pregnancy period; however, the task of monitoring the health status of the fetus is not that which is easily achieved as the fetus is not wholly physically available to medical experts for inspection. Hence, doctors have to resort to some other tests that can give an indication of the status of the fetus. One of such diagnostic test is to obtain cardiotocograms of the fetus. From the analysis of the cardiotocograms, medical experts can determine the status of the fetus, and therefore necessary medical interventions. Generally, medical experts classify examined cardiotocograms into ‘normal’, ‘suspect’, or ‘pathological’. This work presents an artificial neural network based decision support system which can filter cardiotocograms data, producing the corresponding statuses of the fetuses. The capability of artificial neural network to explore the cardiotocogram data and learn features that distinguish one class from the others has been exploited in this research. In this research, feedforward and radial basis neural networks were trained on a publicly available database to classify the processed cardiotocogram data into one of the three classes: ‘normal’, ‘suspect’, or ‘pathological’. Classification accuracies of 87.8% and 89.2% were achieved during the test phase of the trained network for the feedforward and radial basis neural networks respectively. It is the hope that while the system described in this work may not be a complete replacement for a medical expert in fetus status evaluation, it can significantly reinforce the confidence in medical diagnosis reached by experts.

Keywords: decision support, cardiotocogram, classification, neural networks

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Authors: Fadwa Alaskar, Fang-Chieh Chou, Carlos Flores, Xiao-Yun Lu, Alexandre M. Bayen

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This article proposes a perception model for enhancing the accuracy and stability of car-following control of a longitudinally automated truck. We applied a fusion-based tracking algorithm on measurements of a single preceding vehicle needed for car-following control. This algorithm fuses two types of data, radar and LiDAR data, to obtain more accurate and robust longitudinal perception of the subject vehicle in various weather conditions. The filter’s resulting signals are fed to the gap control algorithm at every tracking loop composed by a high-level gap control and lower acceleration tracking system. Several highway tests have been performed with two trucks. The tests show accurate and fast tracking of the target, which impacts on the gap control loop positively. The experiments also show the fulfilment of control design requirements, such as fast speed variations tracking and robust time gap following.

Keywords: object tracking, perception, sensor fusion, adaptive cruise control, cooperative adaptive cruise control

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Authors: Rodrigo S. Fonseca, Antônio C. P. Veiga

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Aiming at helping people with some movement limitation that makes typing and communication difficult, there is a need to customize an assistive tool with a learning environment that helps the user in order to optimize text input, identifying the error and providing the correction and possibilities of choice in the Portuguese language. The work presents an Orthographic and Grammatical System that can be incorporated into writing environments, improving and facilitating the use of an alphanumeric keyboard, using a prototype built using a genetic algorithm in addition to carrying out the prediction, which can occur based on the quantity and position of the inserted letters and even placement in the sentence, ensuring the sequence of ideas using a Long Short Term Memory (LSTM) neural network. The prototype optimizes data entry, being a component of assistive technology for the textual formulation, detecting errors, seeking solutions and informing the user of accurate predictions quickly and effectively through machine learning.

Keywords: genetic algorithm, neural networks, word prediction, machine learning

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Authors: Gaurav Sancheti

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This study presents an impacting approach of Artificial Neural Networks (ANNs) in determining the quantity of High Tensile Steel (HTS) strands required in post-tensioned (PT) slabs. Various PT slab configurations were generated by varying the span and depth of the slab. For each of these slab configurations, quantity of required HTS strands were recorded. ANNs with backpropagation algorithm and varying architectures were developed and their performance was evaluated in terms of Mean Square Error (MSE). The recorded data for the quantity of HTS strands was used as a feeder database for training the developed ANNs. The networks were validated using various validation techniques. The results show that the proposed ANNs have a great potential with good prediction and generalization capability.

Keywords: artificial neural networks, back propagation, conceptual design, high tensile steel strands, post tensioned slabs, validation techniques

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Authors: Dhriti Kirpalani

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The survey aims to explore the impact of Instagram on the perception of lifestyle aspirations (such as social life, fitness, trends followed in fashion, etc.) and perception of self in relation to an idealized lifestyle: Amidst today's media-saturated environment, university students are constantly exposed to idealized portrayals of lifestyles, often leading to unrealistic expectations and dissatisfaction with their own lives. This study investigates the impact of media on university students' perceptions of their own lifestyle, the discrepancy between their self-perception and idealized lifestyle, and their mental health. Employing a mixed-methods approach, the study combines quantitative and qualitative data collection methods to understand the issue comprehensively. A literature review was conducted in order to determine the effects of idealized lifestyle portrayal on Instagram; however, less attention has been received in the Indian setting. The researchers wish to employ a convenience sampling method among undergraduate students from India. The surveys that would be employed for quantitative analysis are Negative Social Media Comparison (NSMCS), Lifestyle Satisfaction Scale (LSS), Psychological Well-being Scale (PWB), and Self-Perception Profile for Adolescents (SPPA). The qualitative aspect would include in-depth interviews to provide deeper insights into participants' experiences and the mechanisms by which media influences their lifestyle aspirations and mental health. With the aim of being an exploratory study, the basis of the idea is found in the social comparison theory described by Leon Festinger. The findings aim to inform interventions to promote realistic expectations about lifestyle, reduce the negative effects of media on university students, and improve their mental health and well-being.

Keywords: declined self-perception, idealized lifestyle, Instagram, Indian university students, social comparison

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Authors: Madjid Nancy, Rakinaung Natalia, Lumowa Fresy

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Background: Bullying becomes one of the problems that concern the world of education, especially in adolescents, which has a negative impact on learning achievement, psychology, and physical health. The psychological impact is shame, depression, distress, fear, sadness, and anxiety, so that if prolonged leave can lead to depression in the victim. While the impact on physical health in the form of bruises on the hit area, blisters, swelling and in more severe cases will lead to death. Objectives: This study aims to explore the perception of bullying in adolescent students Sekolah Menengah Kejuruan (SMK) Negeri 1 Manado and the people associated with that adolescent students. Methods: This research uses descriptive qualitative research design and using thematic analysis, and supported by Urie Bronfenbrenner Ecological Framework. The data collection that will be used is by in-depth interview. Sampling using purposive sampling and snowball techniques. This research was conducted at SMK Negeri 1 Manado. Result: From the analysis obtained three themes with the categories: 1) the perception of bullying with categories are: Understanding of Bullying and The Impact of Bullying, 2) the originator of bullying with categories are: Fulfillment of Youth Development Tasks and Needs, Peers Influence, and Family Communication; 3) the effort to handle bullying with categories are: the Individual Coping and Teacher Role. Conclusion: This research get three themes, those are perception of bullying, bullying’s originator and the effort of handling bullying.

Keywords: adolscent, students, bullying, perception

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Authors: Ashish James, Anusha James

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The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its widespread use. Estimating the confidence of these predictions is paramount for improving the safety and reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to be overconfident and unreasonable. Ensemble of NNs typically produce good predictions but uncertainty estimates tend to be inconsistent. Inspired by these, this paper presents a framework that can quantitatively estimate the uncertainties by leveraging the advances in transfer learning through slight modification to the existing training pipelines. This promising algorithm is developed with an intention of deployment in real world problems which already boast a good predictive performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of experiments with known and unknown distributions show that the proposed approach produces well calibrated uncertainty estimates with high quality predictions.

Keywords: uncertainty estimation, neural networks, transfer learning, regression

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Authors: Eunice Tang

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The main focus of this study is investigating introvert and extrovert university students’ perception of the use of interactive digital tools (such as Padlet and Mentimeter) in a face-to-face English for Specific Purposes (ESP) class after all classes in the university had been switched to online mode for three semesters. The subjects of the study were business students from three ESP classes at The Hong Kong University of Science and Technology. The basic tool for data collection was an anonymous online survey, which included 3 required multiple-choice questions and 3 open questions (2 required; 1 optional) about the effects of interactive digital tools on their amount of contribution to the class discussions, their perception of the role of interactive digital tools to the sharing of ideas and whether the students considered themselves introvert or extrovert. The online survey will be emailed to all 54 students in the three ESP classes and subjected to a three-week data collection period. The survey results will then be analyzed qualitatively, particularly on the effect the use of interactive digital tools had on the amount of contribution to the class among introvert and extrovert students, their perception of a language class with and without digital tools and most importantly, the implication to educators about how interactive digital tools can be used (or not) to cater for the needs of the introvert and extrovert students. The pandemic has given educators various opportunities to use interactive digital tools in class, especially in an online environment. It is interesting for educators to explore the potential of such tools when classes are back face-to-face. This research thus offers the students’ perspective on using interactive digital tools in a face-to-face classroom. While a lot has been said about introverted students responding positively to digital learning online, the student's perception of their own personality collected in the survey and the digital impact tools have on their contribution to class may shed some light on the potential of interactive digital tools in a post-pandemic era.

Keywords: psychology for language learning, interactive digital tools, personality-based investigation, ESP

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Authors: Mostafa Mjahed

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Tilt Rotor UAVs (Unmanned Aerial Vehicles) are naturally unstable and difficult to maneuver. The purpose of this paper is to design controllers for the stabilization and trajectory tracking of this type of UAV. To this end, artificial intelligence methods have been exploited. First, the dynamics of this UAV was modeled using the Lagrange-Euler method. The conventional method based on Proportional, Integral and Derivative (PID) control was applied by decoupling the different flight modes. To improve stability and trajectory tracking of the Tilt Rotor, the fuzzy approach and the technique of multilayer neural networks (NN) has been used. Thus, Fuzzy Proportional Integral and Derivative (FPID) and Neural Network-based Proportional Integral and Derivative controllers (NNPID) have been developed. The meta-heuristic approach based on Particle Swarm Optimization (PSO) method allowed adjusting the setting parameters of NNPID controller, giving us an improved NNPID-PSO controller. Simulation results under the Matlab environment show the efficiency of the approaches adopted. Besides, the Tilt Rotor UAV has become stable and follows different types of trajectories with acceptable precision. The Fuzzy, NN and NN-PSO-based approaches demonstrated their robustness because the presence of the disturbances did not alter the stability or the trajectory tracking of the Tilt Rotor UAV.

Keywords: neural network, fuzzy logic, PSO, PID, trajectory tracking, tilt-rotor UAV

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Authors: Philip Symonds, Jon Taylor, Zaid Chalabi, Michael Davies

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With the world climate projected to warm and major cities in developing countries becoming increasingly populated and polluted, governments are tasked with the problem of overheating and air quality in residential buildings. This paper presents the development of an adaptable model of these risks. Simulations are performed using the EnergyPlus building physics software. An accurate metamodel is formed by randomly sampling building input parameters and training on the outputs of EnergyPlus simulations. Metamodels are used to vastly reduce the amount of computation time required when performing optimisation and sensitivity analyses. Neural Networks (NNs) are compared to a Radial Basis Function (RBF) algorithm when forming a metamodel. These techniques were implemented using the PyBrain and scikit-learn python libraries, respectively. NNs are shown to perform around 15% better than RBFs when estimating overheating and air pollution metrics modelled by EnergyPlus.

Keywords: neural networks, radial basis functions, metamodelling, python machine learning libraries

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Authors: Păcurar Diana Istina

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The indissoluble link between aesthetics and semiotics, the harmonization and semiotic understanding of the interactions between the viewer and the object being looked at, are the basis of the practical demonstration of the importance of aesthetic perception within the theater performance. The design of a theater performance includes several structures, some considered from the beginning, art forms (i.e., the text), others being represented by simple, common objects (e.g., scenographic elements), which, if reunited, can trigger a certain aesthetic perception. The audience is delivered, by the team involved in the performance, a series of auditory and visual signs with which they interact. It is necessary to explain some notions about the physiological support of the transformation of different types of stimuli at the level of the cerebral hemispheres. The cortex considered the superior integration center of extransecal and entanged stimuli, permanently processes the information received, but even if it is delivered at a constant rate, the generated response is individualized and is conditioned by a number of factors. Each changing situation represents a new opportunity for the viewer to cope with, developing feelings of different intensities that influence the generation of meanings and, therefore, the management of interactions. In this sense, aesthetic perception depends on the detection of the “correctness” of signs, the forms of which are associated with an aesthetic property. Fairness and aesthetic properties can have positive or negative values. Evaluating the emotions that generate judgment and implicitly aesthetic perception, whether we refer to visual emotions or auditory emotions, involves the integration of three areas of interest: Valence, arousal and context control. In this context, superior human cognitive processes, memory, interpretation, learning, attribution of meanings, etc., help trigger the mechanism of anticipation and, no less important, the identification of error. This ability to locate a short circuit produced in a series of successive events is fundamental in the process of forming an aesthetic perception. Our main purpose in this research is to investigate the possible conditions under which aesthetic perception and its minimum content are generated by all these structures and, in particular, by interactions with forms that are not commonly considered aesthetic forms. In order to demonstrate the quantitative and qualitative importance of the categories of signs used to construct a code for reading a certain message, but also to emphasize the importance of the order of using these indices, we have structured a mathematical analysis that has at its core the analysis of the percentage of signs used in a theater performance.

Keywords: semiology, aesthetics, theatre semiotics, theatre performance, structure, aesthetic perception

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Authors: William Kwabena Nantwi

Abstract:

This quantitative study investigates Creative Arts teachers' perceptions regarding the implementation of an Inclusive Creative Arts curriculum. The research employs a descriptive approach utilizing a 5-point Likert scale questionnaire comprising 15 items to gather data from Creative Arts educators. The Census with a disproportionate stratified sampling method was utilized to select 226 teachers from five educational circuits (Circuit A, B, C, D & E) within Offinso Municipality, Ghana. The findings indicate that most Creative Arts teachers hold a positive perception towards implementing an inclusive Creative Arts curriculum. Positive perceptions and attitudes among teachers are correlated with increased student engagement and participation in class activities. This study recommends organizing workshops and in-service training sessions focused on inclusive Creative Arts education for Creative Arts teachers. Additionally, it suggests that Colleges of Education and Universities responsible for teacher training integrate foundational courses in Creative Arts and Special Education into their primary education teacher preparation programs.

Keywords: perception, attitude, inclusive education, creative art, creative arts teachers, Ghana

Procedia PDF Downloads 29

Authors: Ng Ziyi Zoe, Yow Wei Quin

Abstract:

Humor plays a pivotal role in our interaction with people. According to the age-related positivity effect, older adults (OA) demonstrate more positive emotions and are better able to modulate negative emotional states than younger adults (YA), suggesting an increase in humor appreciation with age. However, different types of humor might show different patterns of change in appreciation with age (e.g., incongruity-resolution humor, aggressive humor, self-vs.-other-deprecating humor). Thus, we aim to explore age-related effects in the perception of different types of humor in a single study, including the impact of local slang in humor appreciation. Twenty OA aged 60-and-above and 24 YA aged 13-20 were watched four short videos (i.e., benign, violent, satire+local slang, and others-deprecating humor) and rated how funny the videos were (from a scale of 1-not funny-at-all to 5-very funny). Participants were also asked to rank the videos in the order of most- to least-entertaining. Repeated measures of ANOVA found significant main effects of age, F(3,39)=12,88, p < .001, where OA gave higher ratings than YA (M=3.20 vs. 2.63), and humor type, F(3,123)=19.66, p < .001. Post-hoc analyses revealed a significant linear contrast where benign and violent humor had the lowest ratings while others-deprecating humor had the highest ratings. No significant interaction effect was found. The distribution of ranking ratings also differed between OA and YA (e.g., preferred satire+local slang and others-deprecating humor vs. overwhelmingly preferred other-deprecating humor, respectively). Overall, OA displayed a greater appreciation across various types of humor than YA. Humor perception will be discussed in the larger context of cognitive, societal, and cultural implications.

Keywords: humor, older adults, perception, age differences

Procedia PDF Downloads 174

Authors: F. Allag, S. Bouharati, M. Belmahdi, R. Zegadi

Abstract:

The rapid expansion of deserts in recent decades as a result of human actions combined with climatic changes has highlighted the necessity to understand biological processes in arid environments. Whereas physical processes and the biology of flora and fauna have been relatively well studied in marginally used arid areas, knowledge of desert soil micro-organisms remains fragmentary. The objective of this study is to conduct a diversity analysis of bacterial communities in unvegetated arid soils. Several biological phenomena in hot deserts related to microbial populations and the potential use of micro-organisms for restoring hot desert environments. Dry land ecosystems have a highly heterogeneous distribution of resources, with greater nutrient concentrations and microbial densities occurring in vegetated than in bare soils. In this work, we found it useful to use techniques of artificial intelligence in their treatment especially artificial neural networks (ANN). The use of the ANN model, demonstrate his capability for addressing the complex problems of uncertainty data.

Keywords: desert soil, climatic changes, bacteria, vegetation, artificial neural networks

Procedia PDF Downloads 395

Authors: Rania Jradi, Christophe Marvillet, Mohamed Razak Jeday

Abstract:

One of the most frequently encountered problems in industrial heat exchangers is fouling, which degrades the thermal and hydraulic performances of these types of equipment, leading thus to failure if undetected. And it occurs due to the accumulation of undesired material on the heat transfer surface. So, it is necessary to know about the heat exchanger fouling dynamics to plan mitigation strategies, ensuring a sustainable and safe operation. This paper proposes an Artificial Neural Network (ANN) approach to estimate the fouling resistance in a cross-flow heat exchanger by the collection of the operating data of the phosphoric acid concentration loop. The operating data of 361 was used to validate the proposed model. The ANN attains AARD= 0.048%, MSE= 1.811x10⁻¹¹, RMSE= 4.256x 10⁻⁶ and r²=99.5 % of accuracy which confirms that it is a credible and valuable approach for industrialists and technologists who are faced with the drawbacks of fouling in heat exchangers.

Keywords: cross-flow heat exchanger, fouling, estimation, phosphoric acid concentration loop, artificial neural network approach

Procedia PDF Downloads 198