Search results for: artificial intelligence

Authors: Sonia Arradaza-Pajaron

Abstract:

College students are expected to engage in an on-the-job training or internship for completion of a course requirement prior to graduation. In this scenario, they are exposed to the real world of work outside their training institution. To find out their readiness both emotionally and academically, this study has been conducted. A descriptive-correlational research design was employed and random sampling technique method was utilized among 265 randomly selected third year college students of PIT, SY 2014-15. A questionnaire on Emotional Intelligence (bearing the four components namely; emotional literacy, emotional quotient competence, values and beliefs and emotional quotient outcomes) was fielded to the respondents and GWA was extracted from the school automate. Data collected were statistically treated using percentage, weighted mean and Pearson-r for correlation. Results revealed that respondents’ emotional intelligence level is moderately high while their academic performance is good. A high significant relationship was found between the EI component; Emotional Literacy and their academic performance while only significant relationship was found between Emotional Quotient Outcomes and their academic performance. Therefore, if EI influences academic performance significantly when correlated, a possibility that their OJT performance can also be affected either positively or negatively. Thus, EI can be considered predictor of their academic and academic-related performance. Based on the result, it is then recommended that the institution would try to look deeply into the consideration of embedding emotional intelligence as part of the (especially on Emotional Literacy and Emotional Quotient Outcomes of the students) college curriculum. It can be done if the school shall have an effective Emotional Intelligence framework or program manned by qualified and competent teachers, guidance counselors in different colleges in its implementation.

Keywords: academic performance, emotional intelligence, college students, academic success

Procedia PDF Downloads 355

Authors: Nashwah Alnajjar

Abstract:

This research paper provides an overview of Replika, an AI chatbot application that uses natural language processing technology to engage in conversations with users. The app was developed to provide users with a virtual AI friend who can converse with them on various topics, including mental health. This study explores the experiences of Replika users using quantitative research methodology. A survey was conducted with 12 participants to collect data on their demographics, usage patterns, and experiences with the Replika app. The results showed that Replika has the potential to play a role in mental health support and well-being.

Keywords: Replika, chatbot, mental health, artificial intelligence, natural language processing

Procedia PDF Downloads 59

Authors: Basem Kamal Abasakhiroun Farag

Abstract:

Fashion, like many other areas of design, has undergone numerous developments over the centuries. The aim of the article is to recognize and evaluate the importance of advanced technologies in fashion design and to examine how they are transforming the role of contemporary fashion designers by transforming the creative process. It also discusses how contemporary culture is involved in such developments and how it influences fashion design in terms of conceptualization and production. The methodology used is based on examining various examples of the use of technology in fashion design and drawing parallels between what was feasible then and what is feasible today. Comparison of case studies, examples of existing fashion designs and experiences with craft methods; We therefore observe patterns that help us predict the direction of future developments in this area. Discussing the technological elements in fashion design helps us understand the driving force behind the trend. The research presented in the article shows that there is a trend towards significantly increasing interest and progress in the field of fashion technology, leading to the emergence of hybrid artisanal methods. In summary, as fashion technologies advance, their role in clothing production is becoming increasingly important, extending far beyond the humble sewing machine.

Keywords: fashion, identity, such, textiles ambient intelligence, proximity sensors, shape memory materials, sound sensing garments, wearable technology bio textiles, fashion trends, nano textiles, new materials, smart textiles, techno textiles fashion design, functional aesthetics, 3D printing.

Procedia PDF Downloads 29

Authors: Andrew Shugyo Daijo Bonnici

Abstract:

Core-Self Intelligence (CSI) is an absolutely still, non-verbal, non-cerebral intelligence. Our still core-self intelligence is felt at our body's center point of gravity, just an inch below our navel, deep within our lower abdomen. The still sub-quantum depth of core-Self remains untouched by the conditioning influences of family, society, culture, religion, and spiritual views that shape our personalities and ego-self identities. As core-Self intelligence is inborn and unconditioned, it exists within all human beings regardless of age, race, color, creed, mental acuity, or national origin. Our core-self intelligence functions as a wise and compassionate guide that advances our health and well-being, our mental clarity and emotional resiliency, our fearless peace and behavioral wisdom, and our ever-deepening compassion for self and others. Although our core-Self, with its absolutely still non-judgmental intelligence, operates far beneath the functioning of our ego-self identity and our thinking mind, it effectively coexists with our passing thoughts, all of our figuring and thinking, our logical and rational way of knowing, the ebb and flow of our feelings, and the natural or triggered emergence of our emotions. When we allow our whole inner somatic awareness to gently sink into the intelligent center point of gravity within our lower abdomen, the felt arising of our core- Self’s inborn stillness has a serene and relaxing effect on our ego-self and thinking mind. It naturally slows down the speedy passage of our involuntary thoughts, diminishes our ego-self's defensive and reactive functioning, and decreases narcissistic reflections on I, me, and mine. All of these healthy cognitive benefits advance our innate wisdom and compassion, facilitate our personal and interpersonal growth, and liberate the ever-fresh wonder and curiosity of our beginner's heartmind. In conclusion, by studying, exploring, and researching our core-Self intelligence, psychologists and psychotherapists can unlock new avenues for advancing the farther reaches of our mental, emotional, and spiritual health and well-being, our innate behavioral wisdom and boundless empathy, our lucid compassion for self and others, and our unwavering confidence in the still guiding light of our core-Self that exists at the abdominal center point of all human beings.

Keywords: intelligence, transpersonal, beginner’s heartmind, compassionate wisdom

Procedia PDF Downloads 37

Authors: Joshua Harrell, Gideon Osei Bonsu, Susan Garza, Clarence Conner, Da’Neisha Harris, Emma Bukoswki, Zohreh Safari

Abstract:

The Social Vulnerability Index (SVI) stands as a pivotal tool for gauging community resilience amidst diverse stressors, including pandemics like COVID-19. This paper synthesizes recent research and underscores the significance of SVI in elucidating the differential impacts of crises on communities. Drawing on studies by Fox et al. (2023) and Mah et al. (2023), we delve into the application of SVI alongside emerging data sources to uncover nuanced insights into community vulnerability. Specifically, we explore the utilization of SVI in conjunction with mobility data from platforms like SafeGraph to probe the intricate relationship between social vulnerability and mobility dynamics during the COVID-19 pandemic. By leveraging 16 community variables derived from the American Community Survey, including socioeconomic status and demographic characteristics, SVI offers actionable intelligence for guiding targeted interventions and resource allocation. Building upon recent advancements, this paper contributes to the discourse on harnessing AI techniques to mitigate health disparities and fortify public health resilience in the face of pandemics and other crises.

Keywords: social vulnerability index, mobility dynamics, data analytics, health equity, pandemic preparedness, targeted interventions, data integration

Procedia PDF Downloads 39

Authors: Naila Naseer

Abstract:

Emotional Intelligence (EI) concept is not new yet unique and interesting. EI is a person’s ability to be aware of his/her own emotions and to manage, handle and communicate emotions with others effectively. The present study was conducted to assess the relationship between emotional intelligence and age of graduate level students at Allama Iqbal Open University (AIOU). Population consisted of Allama Iqbal Open University students (B.Ed 3rd Semester, Autumn 2007) from Rawalpindi and Islamabad regions. Total number of sample consisted of 469 participants was randomly drawn out by using table of random numbers. Bar-On EQ-i was administered on the participants through personal contact. The instrument was also validated through pilot study on a random sample of 50 participants (B.Ed students Spring 2006), who had completed their B.Ed degree successfully. Data was analyzed and tabulated in percentages, frequencies, mean, standard deviation, correlation, and scatter gram in SPSS (version 16.0 for windows). The results revealed that students with higher age group had scored low on the scale (Bar-On EQ-i). Moreover, the students in low age groups exhibited higher levels of EI as compared with old age students.

Keywords: emotional intelligence, age level, learning, emotion-related feelings

Procedia PDF Downloads 302

Authors: George Kyriakopoulos, Photini Pazartzis, Anthi Koskina, Crystalie Bourcha

Abstract:

The need for securing safe access to and return from outer space, as well as ensuring the viability of outer space operations, maintains vivid the debate over the promotion of organization of space traffic through a Space Traffic Management System (STM). The proliferation of outer space activities in recent years as well as the dynamic emergence of the private sector has gradually resulted in a diverse universe of actors operating in outer space. The said developments created an increased adverse impact on outer space sustainability as the case of the growing number of space debris clearly demonstrates. The above landscape sustains considerable threats to outer space environment and its operators that need to be addressed by a combination of scientific-technological measures and regulatory interventions. In this context, recourse to recent technological advancements and, in particular, to Artificial Intelligence (AI) and machine learning systems, could achieve exponential results in promoting space traffic management with respect to collision avoidance as well as launch and re-entry procedures/phases. New technologies can support the prospects of a successful space traffic management system at an international scale by enabling, inter alia, timely, accurate and analytical processing of large data sets and rapid decision-making, more precise space debris identification and tracking and overall minimization of collision risks and reduction of operational costs. What is more, a significant part of space activities (i.e. launch and/or re-entry phase) takes place in airspace rather than in outer space, hence the overall discussion also involves the highly developed, both technically and legally, international (and national) Air Traffic Management System (ATM). Nonetheless, from a regulatory perspective, the use of AI for the purposes of space traffic management puts forward implications that merit particular attention. Key issues in this regard include the delimitation of AI-based activities as space activities, the designation of the applicable legal regime (international space or air law, national law), the assessment of the nature and extent of international legal obligations regarding space traffic coordination, as well as the appropriate liability regime applicable to AI-based technologies when operating for space traffic coordination, taking into particular consideration the dense regulatory developments at EU level. In addition, the prospects of institutionalizing international cooperation and promoting an international governance system, together with the challenges of establishment of a comprehensive international STM regime are revisited in the light of intervention of AI technologies. This paper aims at examining regulatory implications advanced by the use of AI technology in the context of space traffic management operations and its key correlating concepts (SSA, space debris mitigation) drawing in particular on international and regional considerations in the field of STM (e.g. UNCOPUOS, International Academy of Astronautics, European Space Agency, among other actors), the promising advancements of the EU approach to AI regulation and, last but not least, national approaches regarding the use of AI in the context of space traffic management, in toto. Acknowledgment: The present work was co-funded by the European Union and Greek national funds through the Operational Program "Human Resources Development, Education and Lifelong Learning " (NSRF 2014-2020), under the call "Supporting Researchers with an Emphasis on Young Researchers – Cycle B" (MIS: 5048145).

Keywords: artificial intelligence, space traffic management, space situational awareness, space debris

Procedia PDF Downloads 222

Authors: Aisha Adam

Abstract:

This study critically examines the transformative impact of automated decision-making (ADM) and artificial intelligence (AI) systems on South African labour law. As AI technologies increasingly infiltrate workplaces, existing liability frameworks face challenges in addressing the unique complexities presented by these innovations. This article explores the necessity of redefining liability to accommodate the nuanced landscape of ADM and AI within South African labour law. It emphasises the importance of ensuring responsible deployment and safeguarding the rights of workers amid evolving technological dynamics. This research investigates the central concern of fairness, bias, and discrimination in ADM and AI decision-making. Focusing on algorithmic bias and discriminatory outcomes, the paper advocates for the integration of mechanisms within the South African legal framework, particularly under the Promotion of Equality and Prevention of Unfair Discrimination Act (PEPUDA) and the Employment Equity Act (EEA). The study scrutinises the shifting dynamics of the employment relationship, calling for clear guidelines on the responsibilities and liabilities of employers, employees, and technology providers. Furthermore, the article analyses legal and policy responses to ADM and AI within South African labour law, exploring potential amendments to legislation, guidelines, and codes of practice. It assesses the role of regulatory bodies, specifically the Commission for Conciliation, Mediation, and Arbitration (CCMA), in overseeing and enforcing responsible practices in the workplace. Lastly, the research evaluates the impact of ADM and AI on human and social rights in the South African context. Emphasising the protection of constitutional rights, including fair labour practices, privacy, and equality, the study proposes remedies and safeguards. It advocates for a multidisciplinary approach involving legal, technological, and ethical considerations to redefine liability in South African labour law effectively. The article contends that a shift from accountability to responsibility is crucial for promoting fairness, antidiscrimination, and the protection of human and social rights in the age of automated decision-making. It calls for collaborative efforts among stakeholders to shape responsible practices and redefine liability in this evolving technological landscape.

Keywords: automated decision-making, artificial intelligence, labour law, vicarious liability

Procedia PDF Downloads 50

Authors: M. Hassani, Y. Hassani, N. Ajudanioskooei, N. N. Benvid

Abstract:

Laser Forming process as a non-contact thermal forming process is widely used to forming and bending of metallic and non-metallic sheets. In this process, according to laser irradiation along a specific path, sheet is bent. One of the most important output parameters in laser forming is bending angle that depends on process parameters such as physical and mechanical properties of materials, laser power, laser travel speed and the number of scan passes. In this paper, Artificial Neural Network and Fuzzy Logic System were used to predict of bending angle in laser forming process. Inputs to these models were laser travel speed and laser power. The comparison between artificial neural network and fuzzy logic models with experimental results has been shown both of these models have high ability to prediction of bending angles with minimum errors.

Keywords: artificial neural network, bending angle, fuzzy logic, laser forming

Procedia PDF Downloads 563

Authors: Fatima Faiza Ahmed, Syed Farrukh Hussain

Abstract:

The last few decades have seen a large proportion of our population bending towards e-learning technologies, starting from learning tools used in primary and elementary schools to competency based e-learning systems specifically designed for applications like finance and marketing. The huge diversity in this crowd brings about a large number of challenges for the designers of these e-learning systems, one of which is the adaptability of such systems. This paper focuses on adaptability in the learning material in an e-learning course and how artificial intelligence and the semantic web can be used as an effective tool for this purpose. The study proved that the semantic web, still a hot topic in the area of computer science can prove to be a powerful tool in designing and implementing adaptable e-learning systems.

Keywords: adaptable e-learning, HTMLParser, information extraction, semantic web

Procedia PDF Downloads 295

Authors: Swasti Singh Ghai

Abstract:

Creativity is understood as an intersubjective phenomenon shaped by socio-cultural values and economic forces. Creativity as a means to achieve progress is a very modern concept, driven by a global capitalist market economy. The dominant urban, often first-world articulations of creativity, overshadow the rural, local and cultural notions of people in the developing nations. Artisanal practices of making grounded in preindustrial and pre-capitalist contexts hold varying cultural and region-specific concepts and standards for ascribing creativity to a person or product, or process. These notions reflect the underlying philosophy that constitutes their worldview. The process of colonization through western education has blurred or overlapped some of these key philosophical concepts. This article adopts a post-colonial stance to understand the perceptions of skill, intelligence and creativity among tradition rooted textile craft practitioners of Kutch, Gujarat in India. The artisans, while negotiating their space in the contemporary markets, are making efforts to include the modern categories of art, craft, and design in their worldview. The paper will first review theories of creativity that throw light on the link between skill, intelligence and creativity. Then the paper will use secondary research and data from interviews to share crafts person notions of skill, creativity and intelligence and their interrelationship.

Keywords: traditional craft, textile, creativity, skill, intelligence

Procedia PDF Downloads 92

Authors: Nisha Harry

Abstract:

The study discussed in this article sought to assess whether a sense of professional efficacy mediates the relationship between personal factors and career adaptability. A quantitative cross-sectional survey approach was followed. A non–probability sample of (N = 409) of which predominantly early career and permanently employed black females in call centres in Africa participated in this study. In order to assess personal factors, the participants completed sense of meaningfulness and emotional intelligence measures. Measures of professional efficacy and career adaptability were also completed. The results of the mediational analysis revealed that professional efficacy significantly mediates the meaningfulness (sense of coherence) and career adaptability relationship, but not the emotional intelligence–career adaptability relationship. Call centre agents with professional efficacy are likely to be more work engaged as a result of their sense of meaningfulness and emotional intelligence.

Keywords: call centre, professional efficacy, career adaptability, emotional intelligence

Procedia PDF Downloads 332

Authors: Rooma Rani

Abstract:

The education is really important for the improvement of physical and mental well-being of the school students. It is used to express inner potential, acquire knowledge, develop skills, shape habits, attitudes, values, belief, etc. along with providing strengths and resilience to people to changing situations and allowing them to develop all those capacities which will enable individual to control surrounding environment. Education has a significant effect on the behavior of individuals which helps us in the new situations of everyday life. Educating the child is directing the child’s capacities, attitudes interest, urges, and needs into the most desirable channels. We are the part of 21st century and now a day emotional intelligence is considered more important than intelligence in the success of a person. Success depends on several intelligences and on the control of emotions too. Emotional Intelligence, like general intelligence is the product of one’s heredity and its interaction with his environmental forces. There are certain methods evolved in modern researches. Keeping in view the nature and purpose of the study, the descriptive survey method is preferred. This method is one of the important methods in education research because it describes the current position of the phenomenon under study. The term descriptive survey is generally used for the type of research which proposes to condition of practices of the present time. In the present study, a systematically random sampling method was used to select a representative sample. 50 students were selected from 2 schools. Out of 50 students, 25 were boys and 25 were girls. In the study, a) it has been found a significant difference in the level of adjustment between male and female students; b) it has been found a non-significant difference in the level of emotional intelligence between male and female students; c) it has been found a non-significant relationship between adjustment and emotional intelligence among male students; d) it has been found a significant relationship between adjustment and emotional intelligence among male students. The results of the study indicated that amongst the students those who possess high scores on emotional intelligence tests are high in level of adjustment. Measures should be adopted to improve and sustain the emotional intelligence level of students throughout their studies. Adolescent students are prone to many problems like physical, social and psychological. They need a congenial home atmosphere so that they grow into full-fledged citizens of our country. After understanding these, it helps in the development of personality which leads to a better learning situation and better thinking capacities, in turn, enhances adjustment and achievement along with a better perception of self.

Keywords: adjustment, education, emotional intelligence, students

Procedia PDF Downloads 110

Authors: Dehini Rachid, Ferdi Brahim

Abstract:

The shunt active power filter (SAPF) is not destined only to improve the power factor, but also to compensate the unwanted harmonic currents produced by nonlinear loads. This paper presents a SAPF with identification and control method based on artificial neural network (ANN). To identify harmonics, many techniques are used, among them the conventional p-q theory and the relatively recent one the artificial neural network method. It is difficult to get satisfied identification and control characteristics by using a normal (ANN) due to the nonlinearity of the system (SAPF + fast nonlinear load variations). This work is an attempt to undertake a systematic study of the problem to equip the (SAPF) with the harmonics identification and DC link voltage control method based on (ANN). The latter has been applied to the (SAPF) with fast nonlinear load variations. The results of computer simulations and experiments are given, which can confirm the feasibility of the proposed active power filter.

Keywords: artificial neural networks (ANN), p-q theory, harmonics, total harmonic distortion

Procedia PDF Downloads 357

Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

Abstract:

Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotive EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: brain computer interface, electroencephalogram, EEGLab, BCILab, emotive, emotions, interval features, spectral features, artificial neural network, control applications

Procedia PDF Downloads 299

Authors: Raid R. O. Al-Nima, S. S. Dlay, W. L. Woo

Abstract:

A relationship between face and signature biometrics is established in this paper. A new approach is developed to predict faces from signatures by using artificial intelligence. A multilayer perceptron (MLP) neural network is used to generate face details from features extracted from signatures, here face is the physical biometric and signatures is the behavioural biometric. The new method establishes a relationship between the two biometrics and regenerates a visible face image from the signature features. Furthermore, the performance efficiencies of our new technique are demonstrated in terms of minimum error rates compared to published work.

Keywords: behavioural biometric, face biometric, neural network, physical biometric, signature biometric

Procedia PDF Downloads 455

Authors: Kerols Seif Said Botros

Abstract:

The relationship between development and human rights has been debated for a long time. Various principles, from the right to development to development-based human rights, are applied to understand the dynamics between these two concepts. Despite the measures calculated, the connection between enhancement and human rights remains vague. Despite, the connection between these two opinions and the need to strengthen human rights have increased in recent years. It will then be examined whether the right to sustainable development is acceptable or not. In various human rights instruments and this is a good vibe to the request cited above. The book then cites domestic and international human rights treaties, as well as jurisprudence and regulations defining human rights institutions, to support this view.

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

Procedia PDF Downloads 24

Authors: Neeta Kumari, Gopal Pathak

Abstract:

Artificial neural network has been proved to be an efficient tool for non-parametric modeling of data in various applications where output is non-linearly associated with input. It is a preferred tool for many predictive data mining applications because of its power , flexibility, and ease of use. A standard feed forward networks (FFN) is used to predict the groundwater fluoride content. The ANN model is trained using back propagated algorithm, Tansig and Logsig activation function having varying number of neurons. The models are evaluated on the basis of statistical performance criteria like Root Mean Squarred Error (RMSE) and Regression coefficient (R2), bias (mean error), Coefficient of variation (CV), Nash-Sutcliffe efficiency (NSE), and the index of agreement (IOA). The results of the study indicate that Artificial neural network (ANN) can be used for groundwater fluoride prediction in the limited data situation in the hard rock region like western parts of Jharkhand with sufficiently good accuracy.

Keywords: Artificial neural network (ANN), FFN (Feed-forward network), backpropagation algorithm, Levenberg-Marquardt algorithm, groundwater fluoride contamination

Procedia PDF Downloads 515

Authors: Elham Jafari

Abstract:

The aim of this study was to evaluate the effectiveness of emotional intelligence training on reducing marital conflict and marital adjustment in married students of the University of Tehran. This research is an applied type in terms of purpose and a semi-experimental design of pre-test-post-test type with the control group and with follow-up test in terms of the data collection method. The statistical population of the present study consisted of all married students of the University of Tehran. In this study, 30 married students of the University of Tehran were selected by convenience sampling method as a sample that 15 people in the experimental group and 15 people in the control group were randomly selected. The method of data collection in this research was field and library. The data collection tool in the field section was two questionnaires of marital conflict and marital adjustment. To analyze the collected data, first at the descriptive level, using statistical indicators, the demographic characteristics of the sample were described by SPSS software. In inferential statistics, the statistical method used was the test of analysis of covariance. The results showed that the effect of the independent variable of emotional intelligence on the reduction of marital conflicts is statistically significant. And it can be inferred that emotional intelligence training has reduced the marital conflicts of married students of the University of Tehran in the experimental group compared to the control group. Also, the effect of the independent variable of emotional intelligence on marital adjustment was statistically significant. It can be inferred that emotional intelligence training has adjusted the marital adjustment of married students of the University of Tehran in the experimental group compared to the control group.

Keywords: emotional intelligence, marital conflicts, marital compatibility, married students

Procedia PDF Downloads 229

Authors: Elabed Saud

Abstract:

This paper introduces the new ability of Artificial Neural Networks (ANNs) in estimating speed and controlling the separately excited DC motor. The neural control scheme consists of two parts. One is the neural estimator which is used to estimate the motor speed. The other is the neural controller which is used to generate a control signal for a converter. These two neutrals are training by Levenberg-Marquardt back-propagation algorithm. ANNs are the standard three layers feed-forward neural network with sigmoid activation functions in the input and hidden layers and purelin in the output layer. Simulation results are presented to demonstrate the effectiveness of this neural and advantage of the control system DC motor with ANNs in comparison with the conventional scheme without ANNs.

Keywords: Artificial Neural Network (ANNs), excited DC motor, convenional controller, speed Controller

Procedia PDF Downloads 696

Authors: Maria Poulou

Abstract:

The study investigated how teachers’ self-rated Emotional Intelligence (EI), competence in implementing Social and Emotional Learning (SEL) skills and teaching efficacy relate to teacher-student relationships and students’ emotional and behavioral difficulties. Participants were 98 elementary teachers from public schools in central Greece. They completed the Self-Rated Emotional Intelligence Scale (SREIS), the Teacher SEL Beliefs Scale, the Teachers’ Sense of Efficacy Scale (TSES), the Student-Teacher Relationships Scale-Short Form (STRS-SF) and the Strengths and Difficulties Questionnaire (SDQ) for 617 of their students, aged 6-11 years old. Structural equation modeling was used to examine an exploratory model of the variables. It was demonstrated that teachers’ emotional intelligence, SEL beliefs and teaching efficacy were significantly related to teacher-student relationships, but they were not related to students’ emotional and behavioral difficulties. Rather, teachers’ perceptions of teacher-students relationships were significantly related to these difficulties. These findings and their implications for research and practice are discussed.

Keywords: emotional intelligence, social and emotional learning, teacher-student relationships, teaching efficacy

Procedia PDF Downloads 414

Authors: Annalisa Gaetani, Anna Nora Tassetti, Gianna Fabi

Abstract:

The hydroacoustic technology is an efficient tool to study the sea environment: the most recent advancement in artificial habitat mapping involves acoustic systems to investigate fish abundance, distribution and behavior in specific areas. Along with a detailed high-coverage bathymetric mapping of the seabed, the high-frequency Multibeam Echosounder (MBES) offers the potential of detecting fine-scale distribution of fish aggregation, combining its ability to detect at the same time the seafloor and the water column. Surveying fish schools distribution around artificial structures, MBES allows to evaluate how their presence modifies the biological natural habitat overtime in terms of fish attraction and abundance. In the last years, artificial habitat mapping experiences have been carried out by CNR-ISMAR in the Adriatic sea: fish assemblages aggregating at offshore gas platforms and artificial reefs have been systematically monitored employing different kinds of methodologies. This work focuses on two case studies: a gas extraction platform founded at 80 meters of depth in the central Adriatic sea, 30 miles far from the coast of Ancona, and the concrete and steel artificial reef of Senigallia, deployed by CNR-ISMAR about 1.2 miles offshore at a depth of 11.2 m . Relating the MBES data (metrical dimensions of fish assemblages, shape, depth, density etc.) with the results coming from other methodologies, such as experimental fishing surveys and underwater video camera, it has been possible to investigate the biological assemblage attracted by artificial structures hypothesizing which species populate the investigated area and their spatial dislocation from these artificial structures. Processing MBES bathymetric and water column data, 3D virtual scenes of the artificial habitats have been created, receiving an intuitive-looking depiction of their state and allowing overtime to evaluate their change in terms of dimensional characteristics and depth fish schools’ disposition. These MBES surveys play a leading part in the general multi-year programs carried out by CNR-ISMAR with the aim to assess potential biological changes linked to human activities on.

Keywords: artificial habitat mapping, fish assemblages, hydroacustic technology, multibeam echosounder

Procedia PDF Downloads 236

Authors: Lina Zhong, Sunny Sun, Rob Law

Abstract:

Artificial intelligence has been developing rapidly. Previous studies have evaluated hotel technology either from an employee or consumer perspective. However, impacts, which mainly include the social and economic impacts of hotel robots, are unknown as they are newly introduced. To bridge the aforementioned research gap, this study evaluates hotel robots from contextual, diagnostic, evaluative, and strategic aspects using framework analysis as a basis to assist hotel managers in real-time hotel marketing strategy management, adjustment and revenue achievement. Findings show that, from a consumer perspective, the overall acceptance of hotel robots is low. The main implication is that the cost of hotel robots should be carefully estimated, and the investment should be made based on phases.

Keywords: application, evaluation, framework analysis, hotel robot

Procedia PDF Downloads 152

Authors: Raed Alsufyani, Hissam Tawfik, Victor Chang, Muthu Ramachandran

Abstract:

This study investigates the factors that influence the decision by players in the healthcare sector to embrace Cloud Business Intelligence Technology with a focus on healthcare organizations in Saudi Arabia. To bring this matter into perspective, this study primarily considers the Technology-Organization-Environment (TOE) framework and the Human Organization-Technology (HOT) fit model. A survey was hypothetically designed based on literature review and was carried out online. Quantitative data obtained was processed from descriptive and one-way frequency statistics to inferential and regression analysis. Data were analysed to establish factors that influence the decision to adopt Cloud Business intelligence technology in the healthcare sector. The implication of the identified factors was measured, and all assumptions were tested. 66.70% of participants in healthcare organization backed the intention to adopt cloud business intelligence system. 99.4% of these participants considered security concerns and privacy risk have been the most significant factors in the adoption of cloud Business Intelligence (CBI) system. Through regression analysis hypothesis testing point that usefulness, service quality, relative advantage, IT infrastructure preparedness, organization structure; vendor support, perceived technical competence, government support, and top management support positively and significantly influence the adoption of (CBI) system. The paper presents quantitative phase that is a part of an on-going project. The project will be based on the consequences learned from this study.

Keywords: cloud computing, business intelligence, HOT-fit model, TOE, healthcare and innovation adoption

Procedia PDF Downloads 145

Authors: Hamed Zolfaghari, Mojtaba Kord

Abstract:

After feasibility study the design phase is started and the rest of other phases are highly dependent on this phase. forecasting the duration of design phase could do a miracle and would save a lot of time. This study provides a fast and accurate Machine learning (ML) and optimization framework, which allows a quick duration estimation of project design phase, hence improving operational efficiency and competitiveness of a design construction company. 3 data sets of three years composed of daily time spent for different design projects are used to train and validate the ML models to perform multiple projects. Our study concluded that Artificial Neural Network (ANN) performed an accuracy of 0.94.

Keywords: time estimation, machine learning, Artificial neural network, project design phase

Procedia PDF Downloads 60

Authors: Yu-Hsiu Lin, Wen-Chun Lin, Yen-Chang Cheng, Chia-Ju Yeh, Yu-Chuan Chen, Tai-You Li

Abstract:

Demand-Side Management (DSM) has the potential to reduce electricity costs and carbon emission, which are associated with electricity used in the modern society. A home Energy Management System (EMS) commonly used by residential consumers in a down-stream sector of a smart grid to monitor, control, and optimize energy efficiency to domestic appliances is a system of computer-aided functionalities as an energy audit for residential DSM. Implementing fault detection and classification to domestic appliances monitored, controlled, and optimized is one of the most important steps to realize preventive maintenance, such as residential air conditioning and heating preventative maintenance in residential/industrial DSM. In this study, a cloud intelligence-based green EMS that comes up with an Internet of Things (IoT) technology stack for residential DSM is developed. In the EMS, Arduino MEGA Ethernet communication-based smart sockets that module a Real Time Clock chip to keep track of current time as timestamps via Network Time Protocol are designed and implemented for readings of load phenomena reflecting on voltage and current signals sensed. Also, a Network-Attached Storage providing data access to a heterogeneous group of IoT clients via Hypertext Transfer Protocol (HTTP) methods is configured to data stores of parsed sensor readings. Lastly, a desktop computer with a WAMP software bundle (the Microsoft® Windows operating system, Apache HTTP Server, MySQL relational database management system, and PHP programming language) serves as a data science analytics engine for dynamic Web APP/REpresentational State Transfer-ful web service of the residential DSM having globally-Advanced Internet of Artificial Intelligence (AI)/Computational Intelligence. Where, an abstract computing machine, Java Virtual Machine, enables the desktop computer to run Java programs, and a mash-up of Java, R language, and Python is well-suited and -configured for AI in this study. Having the ability of sending real-time push notifications to IoT clients, the desktop computer implements Google-maintained Firebase Cloud Messaging to engage IoT clients across Android/iOS devices and provide mobile notification service to residential/industrial DSM. In this study, in order to realize edge intelligence that edge devices avoiding network latency and much-needed connectivity of Internet connections for Internet of Services can support secure access to data stores and provide immediate analytical and real-time actionable insights at the edge of the network, we upgrade the designed and implemented smart sockets to be embedded AI Arduino ones (called embedded AIduino). With the realization of edge analytics by the proposed embedded AIduino for data analytics, an Arduino Ethernet shield WizNet W5100 having a micro SD card connector is conducted and used. The SD library is included for reading parsed data from and writing parsed data to an SD card. And, an Artificial Neural Network library, ArduinoANN, for Arduino MEGA is imported and used for locally-embedded AI implementation. The embedded AIduino in this study can be developed for further applications in manufacturing industry energy management and sustainable energy management, wherein in sustainable energy management rotating machinery diagnostics works to identify energy loss from gross misalignment and unbalance of rotating machines in power plants as an example.

Keywords: demand-side management, edge intelligence, energy management system, fault detection and classification

Procedia PDF Downloads 229

Authors: S. M. Anowarul Haque, Md. Asiful Islam

Abstract:

Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.

Keywords: load forecasting, artificial neural network, particle swarm optimization

Procedia PDF Downloads 149

Authors: Seyedabdolali Mortazavi Kamalabadi, Faegheh Taheran

Abstract:

By giving an overview of previous arguments and findings concerned with the concept of competitive advantage, first, we define the overall concept of competitive advantage and discuss nuances of understanding such an important and strategic idea. Finally, by considering the major concerns of marketing academia, including globalization, AI-based technologies, consumer well-being, and internal coopetition between a firm’s units, fruitful avenues to be explored by future studies are presented in the form of research propositions. In the end, relevant gaps mentioned by numerous studies that are worth investigating are demonstrated.

Keywords: artificial intelligence, competitive advantage, consumer well-being, coopetition, globalization, literature review, temporary competitive advantage

Procedia PDF Downloads 96

Authors: Manal Bouasria, Mohammed-Hichem Benzaama, Valérie Pralong, Yassine El Mendili

Abstract:

Reducing the quantity of cement in cementitious composites can help to reduce the environmental effect of construction materials. By-products such as ferronickel slags (FNS), fly ash (FA), and Crepidula fornicata (CR) are promising options for cement replacement. In this work, we investigated the relevance of substituting cement with FNS-CR and FA-CR on the mechanical properties of mortar and on the thermal properties of concrete. Foraging intervals ranging from 2 to 28 days, the mechanical properties are obtained by 3-point bending and compression tests. The chosen mix is used to construct a prototype in order to study the material’s hygrothermal performance. The data collected by the sensors placed on the prototype was utilized to build an artificial neural network.

Keywords: artificial neural network, cement, circular economy, concrete, by products

Procedia PDF Downloads 93

Authors: Mina Sadat Orooje, Mohammad Mehdi Latifi, Behnam Fereydooni Eftekhari

Abstract:

The integration of cutting-edge Internet of Things (IoT) technologies with advanced Artificial Intelligence (AI) systems is revolutionizing healthcare facility management. However, the current landscape of hospital building maintenance suffers from slow, repetitive, and disjointed processes, leading to significant financial, resource, and time losses. Additionally, the potential of Building Information Modeling (BIM) in facility maintenance is hindered by a lack of data within digital models of built environments, necessitating a more streamlined data collection process. This paper presents a robust framework that harmonizes AI with BIM-IoT technology to elevate healthcare Facility Maintenance Management (FMM) and address these pressing challenges. The methodology begins with a thorough literature review and requirements analysis, providing insights into existing technological landscapes and associated obstacles. Extensive data collection and analysis efforts follow to deepen understanding of hospital infrastructure and maintenance records. Critical AI algorithms are identified to address predictive maintenance, anomaly detection, and optimization needs alongside integration strategies for BIM and IoT technologies, enabling real-time data collection and analysis. The framework outlines protocols for data processing, analysis, and decision-making. A prototype implementation is executed to showcase the framework's functionality, followed by a rigorous validation process to evaluate its efficacy and gather user feedback. Refinement and optimization steps are then undertaken based on evaluation outcomes. Emphasis is placed on the scalability of the framework in real-world scenarios and its potential applications across diverse healthcare facility contexts. Finally, the findings are meticulously documented and shared within the healthcare and facility management communities. This framework aims to significantly boost maintenance efficiency, cut costs, provide decision support, enable real-time monitoring, offer data-driven insights, and ultimately enhance patient safety and satisfaction. By tackling current challenges in healthcare facility maintenance management it paves the way for the adoption of smarter and more efficient maintenance practices in healthcare facilities.

Keywords: artificial intelligence, building information modeling, healthcare facility maintenance, internet of things integration, maintenance efficiency

Procedia PDF Downloads 28