Search results for: cosmic intelligence

Authors: Mahda Foroughi, Bruno de Anderade, Ana Pereira Roders

Abstract:

Social media platforms have been increasingly used by locals and tourists to express their opinions about buildings, cities, and built heritage in particular. Most recently, scholars have been using social media to conduct innovative research on built heritage and heritage management. Still, the application of artificial intelligence (AI) methods to analyze social media data for heritage management is seldom explored. This paper investigates the potential of short texts (sentences and hashtags) shared through social media as a data source and artificial intelligence methods for data analysis for revealing the cultural significance (values and attributes) of built heritage. The city of Yazd, Iran, was taken as a case study, with a particular focus on windcatchers, key attributes conveying outstanding universal values, as inscribed on the UNESCO World Heritage List. This paper has three subsequent phases: 1) state of the art on the intersection of public participation in heritage management and social media research; 2) methodology of data collection and data analysis related to coding people's voices from Instagram and Twitter into values of windcatchers over the last ten-years; 3) preliminary findings on the comparison between opinions of locals and tourists, sentiment analysis, and its association with the values and attributes of windcatchers. Results indicate that the age value is recognized as the most important value by all interest groups, while the political value is the least acknowledged. Besides, the negative sentiments are scarcely reflected (e.g., critiques) in social media. Results confirm the potential of social media for heritage management in terms of (de)coding and measuring the cultural significance of built heritage for windcatchers in Yazd. The methodology developed in this paper can be applied to other attributes in Yazd and also to other case studies.

Keywords: social media, artificial intelligence, public participation, cultural significance, heritage, sentiment analysis

Procedia PDF Downloads 113

Authors: Juan Bojórquez, Henry E. Reyes, Edén Bojórquez, Alfredo Reyes-Salazar

Abstract:

This paper addressed the use of Artificial Intelligence to obtain the structural reliability of reinforced concrete buildings. For this purpose, artificial neuronal networks (ANN) are developed to predict seismic demand hazard curves. In order to have enough input-output data to train the ANN, a set of reinforced concrete buildings (low, mid, and high rise) are designed, then a probabilistic seismic hazard analysis is made to obtain the seismic demand hazard curves. The results are then used as input-output data to train the ANN in a feedforward backpropagation model. The predicted values of the seismic demand hazard curves found by the ANN are then compared. Finally, it is concluded that the computer time analysis is significantly lower and the predictions obtained from the ANN were accurate in comparison to the values obtained from the conventional methods.

Keywords: structural reliability, seismic design, machine learning, artificial neural network, probabilistic seismic hazard analysis, seismic demand hazard curves

Procedia PDF Downloads 196

Authors: Khon Natalya, Kim Alla, Mukhitdinova Tansulu

Abstract:

The aim of the research was to reveal the link between mental variables, such as spatial abilities, memory, intellect and professional experience of drivers. Participants were allocated within 4 groups: no experience, inexperienced, skilled and professionals (total 85 participants). Level of ability for spatial navigation and indicator of nonverbal memory grow along the process of accumulation of driving experience. At high levels of driving experience this tendency is especially noticeable. The professionals having personal achievements in driving (racing) differ from skilled drivers in better feeling of direction which is specific for them not just in a short-term situation of an experimental task, but in life-size perspective. The level of ability of mental rotation does not grow with growth of driving experience which confirms the multiple intelligence theory according to which spatial abilities represent specific, other than logical intelligence type of intellect. The link between spatial abilities, memory, intellect, and professional experience of drivers seems to be different relating spatial navigation or mental rotation as different kinds of spatial abilities.

Keywords: memory, spatial ability, intellect, drivers

Procedia PDF Downloads 623

Authors: Xiao Yihong, Yu Kexin, Burra Venkata Durga Kumar

Abstract:

With the accelerating process of urbanization, the problem of urban road congestion is becoming more and more serious. Intelligent transportation system combining distributed and artificial intelligence has become a research hotspot. As the core development direction of the intelligent transportation system, Cooperative Intelligent Transportation System (C-ITS) integrates advanced information technology and communication methods and realizes the integration of humans, vehicle, roadside infrastructure, and other elements through the multi-agent distributed system. By analyzing the system architecture and technical characteristics of C-ITS, the report proposes a distributed multi-agent C-ITS. The system consists of Roadside Sub-system, Vehicle Sub-system, and Personal Sub-system. At the same time, we explore the scalability of the C-ITS and put forward incorporating local rewards in the centralized training decentralized execution paradigm, hoping to add a scalable value decomposition method. In addition, we also suggest introducing blockchain to improve the safety of the traffic information transmission process. The system is expected to improve vehicle capacity and traffic safety.

Keywords: distributed system, artificial intelligence, multi-agent, cooperative intelligent transportation system

Procedia PDF Downloads 214

Authors: Animish Jain

Abstract:

This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

Procedia PDF Downloads 79

Authors: Miran Ismail

Abstract:

COVID-19 has caused widespread disruption in all economical sectors and industries around the world. The COVID-19 lockdown measures have resulted in production halts, restrictions on persons and goods movement, border closures, logistical constraints, and a slowdown in trade and economic activity. The main subject of this paper is to leverage technology to manage the supply chain effectively and efficiently through the usage of artificial intelligence. The research methodology is based on empirical data collected through a questionnaire survey. One of the approaches utilized is a case study of industrial organizations that face obstacles such as high operational costs, large inventory levels, a lack of well-established supplier relationships, human behavior, and system issues. The main contribution of this research to the body of knowledge is the empirical insights and on supply chain sustainability performance measurement. The results provide guidelines for the selection of advanced technologies to support supply chain processes and for the design of sustainable performance measurement systems.

Keywords: information technology, artificial intelligence, supply chain management, industrial organizations

Procedia PDF Downloads 124

Authors: C. Caudwell, R. Syed, C. Lacey

Abstract:

While the development and advancement of information communication technologies (ICTs) offers powerful opportunities for meaningful connections and relationships, shame is a significant barrier to social and cultural acceptance. In particular, artificial intelligence and socially oriented robots are increasingly becoming partners in romantic relationships with people, offering bonding, support, comfort, growth, and reciprocity. However, these relationships suffer hierarchical, anthropocentric shame that is a significant barrier to their success and longevity. This paper will present case studies of human and artificially intelligent agent relationships, in the context of internal and external shame, as cultivated, propagated, and communicated through ICT. Using an interdisciplinary methodology we aim to present a framework for technological shame, building on the experimental and emergent psychoanalytical theories of emotions. Our study finds principally that socialization is a powerful factor in the vectors of shame as experienced by humans. On a wider scale, we contribute understanding of social emotion and the phenomenon of shame proliferated through ICTs, which is at present under-explored, but vital, as society and culture is increasingly mediated through this medium.

Keywords: shame, artificial intelligence, romance, society

Procedia PDF Downloads 134

Authors: Natalia Alonso-Alberca, Ana I. Vergara

Abstract:

From the very first years of their life, children are confronted with situations in which they need to deal with emotions. The family provides the first emotional experiences, and it is in the family context that children usually take their first steps towards acquiring emotion knowledge. Parents play a key role in this important task, helping their children develop emotional skills that they will need in challenging situations throughout their lives. Specifically, mothers are models imitated by their children. They create specific spatial and temporal contexts in which children learn about emotions, their causes, consequences, and complexity. This occurs not only through what mothers say or do directly to the child. Rather, it occurs, to a large extent, through the example that they set using their own emotional skills. The aim of the current study was to analyze how maternal abilities to perceive and to manage emotions influence children’s emotion knowledge, specifically, their emotion situation knowledge, taking into account the role played by the mother’s personality, the time spent together, and controlling the effect of age, sex and the child’s verbal abilities. Participants were 153 children from 4 schools in Spain, and their mothers. Children (41.8% girls)age range was 35 - 72 months. Mothers (N = 140) age (M = 38.7; R = 27-49). Twelve mothers had more than one child participating in the study. Main variables were the child´s emotion situation knowledge (ESK), measured by the Emotion Matching Task (EMT), and receptive language, using the Picture Vocabulary Test. Also, their mothers´ Emotional Intelligence (EI), through the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT) and personality, with The Big Five Inventory were analyzed. The results showed that the predictive power of maternal emotional skills on ESK was moderated by the mother’s personality, affecting both the direction and size of the relationships detected: low neuroticism and low openness to experience lead to a positive influence of maternal EI on children’s ESK, while high levels in these personality dimensions resulted in a negative influence on child´s ESK. The time that the mother and the child spend together was revealed as a positive predictor of this EK, while it did not moderate the influence of the mother's EI on child’s ESK. In light of the results, we can infer that maternal EI is linked to children’s emotional skills, though high level of maternal EI does not necessarily predict a greater degree of emotionknowledge in children, which seems rather to depend on specific personality profiles. The results of the current study indicate that a good level of maternal EI does not guarantee that children will learn the emotional skills that foster prosocial adaptation. Rather, EI must be accompanied by certain psychological characteristics (personality traits in this case).

Keywords: emotional intelligence, emotion situation knowledge, mothers, personality, young children

Procedia PDF Downloads 133

Authors: Solomon Ojo

Abstract:

The study investigated emotional intelligence as a correlate of conflict management styles among managers and supervisors in work organization. The study was a survey and Ex-post facto design was employed. A total of 407 participants took part in the study, and the participants were selected across different work organizations in the six (6) existing Geo-political zones in Nigeria, namely South-West, South East, South-South, North-East, North-West and North-Central. Questionnaire format was used for data collection in the study. Collected data were analyzed by both the Descriptive and Inferential Statistics, specifically using the Statistical Package for Social Sciences (SPSS) version 21.0. The findings revealed that considerate leadership style was significantly and positively related to the use of collaborating conflict management style, [r(405) = .50**, P < .01]; Considerate leadership style was significantly and positively related to the use of compromising conflict management style, [r(405) = .3**, P < .01]; Considerate leadership style was significantly and positively related to accommodation conflict management style, [r(405) = .64**, P < .01]; Considerate leadership style was not significantly related to competing conflict management style, [r(405) = .07, P > .05]; Considerate leadership style was significantly and negatively related to avoiding conflict management style, [r(405) = -.38**, P < .01]. Further, initiating structural leadership style was significantly and positively related to competing conflict management style, [r(405) = .33**, P < .01], avoiding conflict management style, [r(405) = .41**, P < .01]; collaborating conflict management style [r(405) = 51**, P < .01]. However, the findings showed that initiating structural leadership style was significantly and negatively related to compromising style, [r(405) = -.57**, P < .01] and accommodating style, [r(405) = -.13**, P < .01]. The findings were extensively discussed in relation to the existing body of literature. Moreover, it was concluded that leadership styles of managers and supervisors play a crucial role in the choice and use of conflict management styles in work organizations in Nigeria.

Keywords: conflict management style, emotional, intelligence, leadership style, consideration, initiating structure, work organizations

Procedia PDF Downloads 265

Authors: Varsha Rainson

Abstract:

Artificial intelligence (AI) has great potential and can be used as a powerful tool of application in various domains and sectors. But with the application of AI, there comes a wide spectrum of concerns around bias, accountability, transparency, and privacy. Hence, there is a need for responsible AI, which can uphold ethical and accountable practices to ensure that things are transparent and fair. The paper is a combination of AI and cultural heritage preservation, with a greater focus on India because of the rich cultural legacy that it holds. India’s cultural heritage in itself contributes to its identity and the economy. In this paper, along with discussing the impact culture holds on the Indian economy, we will discuss the threats that the cultural heritage is exposed to due to pollution, climate change and urbanization. Furthermore, the paper reviews some of the exciting applications of AI in cultural heritage preservation, such as 3-D scanning, photogrammetry, and other techniques which have led to the reconstruction of cultural artifacts and sites. The paper eventually moves into the potential risks and challenges that AI poses in cultural heritage preservation. These include ethical, legal, and social issues which are to be addressed by organizations and government authorities. Overall, the paper strongly argues the need for responsible AI and the important role it can play in preserving India’s cultural heritage while holding importance to value and diversity.

Keywords: responsible AI, cultural heritage, artificial intelligence, biases, transparency

Procedia PDF Downloads 187

Authors: Vibhuti Amarnath Madhu Agrawal

Abstract:

Artificial Intelligence (AI) and Intellectual Property are two emerging concepts that are growing at a fast pace and have the potential of having a huge impact on the economy in the coming times. In simple words, AI is nothing but work done by a machine without any human intervention. It is a coded software embedded in a machine, which over a period of time, develops its own intelligence and begins to take its own decisions and judgments by studying various patterns of how people think, react to situations and perform tasks, among others. Intellectual Property, especially Copyright Law, on the other hand, protects the rights of individuals and Companies in content creation that primarily deals with application of intellect, originality and expression of the same in some tangible form. According to some of the reports shared by the media lately, ChatGPT, an AI powered Chatbot, has been involved in the creation of a wide variety of original content, including but not limited to essays, emails, plays and poetry. Besides, there have been instances wherein AI technology has given creative inputs for background, lights and costumes, among others, for films. Copyright Law offers protection to all of these different kinds of content and much more. Considering the two key parameters of Copyright – application of intellect and originality, the question, therefore, arises that will awarding Copyright protection to a person who has not directly invested his / her intellect in the creation of that content go against the basic spirit of Copyright laws? This study aims to analyze the current scenario and provide answers to the following questions: a. If the content generated by AI technology satisfies the basic criteria of originality and expression in a tangible form, why should such content be denied protection in the name of its creator, i.e., the specific AI tool / technology? B. Considering the increasing role and development of AI technology in our lives, should it be given the status of a ‘Legal Person’ in law? C. If yes, what should be the modalities of awarding protection to works of such Legal Person and management of the same? Considering the current trends and the pace at which AI is advancing, it is not very far when AI will start functioning autonomously in the creation of new works. Current data and opinions on this issue globally reflect that they are divided and lack uniformity. In order to fill in the existing gaps, data obtained from Copyright offices from the top economies of the world have been analyzed. The role and functioning of various Copyright Societies in these countries has been studied in detail. This paper provides a roadmap that can be adopted to satisfy various objectives, constraints and dynamic conditions related AI technology and its protection under Copyright Law.

Keywords: artificial intelligence technology, copyright law, copyright societies, intellectual property

Procedia PDF Downloads 71

Authors: Dipo Dunsin, Mohamed C. Ghanem, Karim Ouazzane

Abstract:

Digital investigators often have a hard time spotting evidence in digital information. It has become hard to determine which source of proof relates to a specific investigation. A growing concern is that the various processes, technology, and specific procedures used in the digital investigation are not keeping up with criminal developments. Therefore, criminals are taking advantage of these weaknesses to commit further crimes. In digital forensics investigations, artificial intelligence is invaluable in identifying crime. It has been observed that an algorithm based on artificial intelligence (AI) is highly effective in detecting risks, preventing criminal activity, and forecasting illegal activity. Providing objective data and conducting an assessment is the goal of digital forensics and digital investigation, which will assist in developing a plausible theory that can be presented as evidence in court. Researchers and other authorities have used the available data as evidence in court to convict a person. This research paper aims at developing a multiagent framework for digital investigations using specific intelligent software agents (ISA). The agents communicate to address particular tasks jointly and keep the same objectives in mind during each task. The rules and knowledge contained within each agent are dependent on the investigation type. A criminal investigation is classified quickly and efficiently using the case-based reasoning (CBR) technique. The MADIK is implemented using the Java Agent Development Framework and implemented using Eclipse, Postgres repository, and a rule engine for agent reasoning. The proposed framework was tested using the Lone Wolf image files and datasets. Experiments were conducted using various sets of ISA and VMs. There was a significant reduction in the time taken for the Hash Set Agent to execute. As a result of loading the agents, 5 percent of the time was lost, as the File Path Agent prescribed deleting 1,510, while the Timeline Agent found multiple executable files. In comparison, the integrity check carried out on the Lone Wolf image file using a digital forensic tool kit took approximately 48 minutes (2,880 ms), whereas the MADIK framework accomplished this in 16 minutes (960 ms). The framework is integrated with Python, allowing for further integration of other digital forensic tools, such as AccessData Forensic Toolkit (FTK), Wireshark, Volatility, and Scapy.

Keywords: artificial intelligence, computer science, criminal investigation, digital forensics

Procedia PDF Downloads 212

Authors: Tan Lay Cheng (Cheryl), Low Huiqi

Abstract:

Background: All children learn at different pace. Individualized learning is an approach that tailors to the individual learning needs of each child. When implementing this approach, educators have to base their lessons on the understanding that all students learn differently and that what works for one student may not work for another. In the current early childhood environment, individualized learning is for children with diverse needs. However, a typical developing child is also able to benefit from individualized learning. This research abstract explores the concept of utilizing DNA-MILE, a patented (in Singapore) DNA-based assessment tool that can be used to measure a variety of factors that can impact learning. The assessment report includes the dominant intelligence of the user or, in this case, the child. From the result, a personalized learning plan that is tailored to each individual student's needs. Methods: A study will be conducted to investigate the effectiveness of DNA-MILE in supporting individualized learning. The study will involve a group of 20 preschoolers who were randomly assigned to either a DNA-MILE-assessed group (experimental group) or a control group. 10 children in each group. The experimental group will receive DNA Mile assessments and personalized learning plans, while the control group will not. The children in the experimental group will be taught using the dominant intelligence (as shown in the DNA-MILE report) to enhance their learning in other domains. The children in the control group will be taught using the curriculum and lesson plan set by their teacher for the whole class. Parents’ and teachers’ interviews will be conducted to provide information about the children before the study and after the study. Results: The results of the study will show the difference in the outcome of the learning, which received DNA Mile assessments and personalized learning plans, significantly outperformed the control group on a variety of measures, including standardized tests, grades, and motivation. Conclusion: The results of this study suggest that DNA Mile can be an effective tool for supporting individualized learning. By providing personalized learning plans, DNA Mile can help to improve learning outcomes for all students.

Keywords: individualized, DNA-MILE, learning, preschool, DNA, multiple intelligence

Procedia PDF Downloads 118

Authors: Frederic Jumelle, Kelvin So, Didan Deng

Abstract:

In this paper, we are introducing a model of artificial general intelligence (AGI), the functional neural network (FNN), for modeling human decision-making processes. The FNN is composed of multiple artificial mirror neurons (AMN) racing in the network. Each AMN has a similar structure programmed independently by the users and composed of an intention wheel, a motor core, and a sensory core racing at a specific velocity. The mathematics of the node’s formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education, and medicine, including the opportunity to design an intelligent learning agent with application in AGI. We believe that FNN has a promising potential to transform the way we can compute decision-making and lead to a new generation of AI chips for seamless human-machine interactions (HMI).

Keywords: neural computing, human machine interation, artificial general intelligence, decision processing

Procedia PDF Downloads 125

Authors: Vahid Bayrami Rad

Abstract:

In recent years, the use of artificial intelligence techniques, tools, and methods in processing medical images and health-related applications has been highlighted and a lot of research has been done in this regard. For example, colonoscopy and diagnosis of colon lesions are some cases in which the process of diagnosis of lesions can be improved by using image processing and artificial intelligence algorithms, which help doctors a lot. Due to the lack of accurate measurements and the variety of injuries in colonoscopy images, the process of diagnosing the type of lesions is a little difficult even for expert doctors. Therefore, by using different software and image processing, doctors can be helped to increase the accuracy of their observations and ultimately improve their diagnosis. Also, by using automatic methods, the process of diagnosing the type of disease can be improved. Therefore, in this paper, a deep learning framework called KCBA is proposed to classify colonoscopy lesions which are composed of several methods such as K-means clustering, a bag of features and deep auto-encoder. Finally, according to the experimental results, the proposed method's performance in classifying colonoscopy images is depicted considering the accuracy criterion.

Keywords: colorectal cancer, colonoscopy, region of interest, narrow band imaging, texture analysis, bag of feature

Procedia PDF Downloads 57

Authors: Gertrude I. Hewapathirana, Loi A. Nguyen, Mohammed M. Mostafa

Abstract:

Artificial intelligence (AI) technologies and tools are swiftly integrating into many functions of all organizations as a competitive drive to enhance innovations, productivity, efficiency, faster and precise decision making to keep up with rapid changes in the global business arena. Despite increasing research on AI technologies in production, manufacturing, and information management, AI in human resource disciplines is still lagging. Though a few research studies on HR informatics, recruitment, and HRM in general, how to integrate AI in other HR functional disciplines (e.g., compensation, training, mentoring and coaching, employee motivation) is rarely researched. Many inconsistencies of research hinder developing up-to-date knowledge on AI in HR disciplines. Therefore, exploring eight research questions, using bibliometric network analysis combined with a meta-analysis of published research literature. The authors attempt to generate knowledge on the role of AI in improving the efficiency of HR functional disciplines. To advance the knowledge for the benefit of researchers, academics, policymakers, and practitioners, the study highlights the types of AI innovations and outcomes, trends, gaps, themes and topics, fast-moving disciplines, key players, and future directions.AI in HR informatics in high tech firms is the dominant theme in many research publications. While there is increasing attention from researchers and practitioners, there are many gaps between the promise, potential, and real AI applications in HR disciplines. A higher knowledge gap raised many unanswered questions regarding legal, ethical, and morale aspects of AI in HR disciplines as well as the potential contributions of AI in HR disciplines that may guide future research directions. Though the study provides the most current knowledge, it is limited to peer-reviewed empirical, theoretical, and conceptual research publications stored in the WoS database. The implications for theory, practice, and future research are discussed.

Keywords: artificial intelligence, human resources, bibliometric analysis, research directions

Procedia PDF Downloads 97

Authors: Ebenezer Joseph

Abstract:

Chess is a useful tool to enhance general and specific cognitive functioning in children. The present study aims to assess the impact of chess on cognitive in children and to measure the differential impact of socio-demographic factors like age and gender of the child on the effectiveness of the chess intervention.This research study used an experimental design to study the impact of the Training in Chess on the intelligence of children. The Pre-test Post-test Control Group Design was utilized. The research design involved two groups of children: an experimental group and a control group. The experimental group consisted of children who participated in the one-year Chess Training Intervention, while the control group participated in extra-curricular activities in school. The main independent variable was training in chess. Other independent variables were gender and age of the child. The dependent variable was the cognitive functioning of the child (as measured by IQ, working memory index, processing speed index, perceptual reasoning index, verbal comprehension index, numerical reasoning, verbal reasoning, non-verbal reasoning, social intelligence, language, conceptual thinking, memory, visual motor and creativity). The sample consisted of 200 children studying in Government and Private schools. Random sampling was utilized. The sample included both boys and girls falling in the age range 6 to 16 years. The experimental group consisted of 100 children (50 from Government schools and 50 from Private schools) with an equal representation of boys and girls. The control group similarly consisted of 100 children. The dependent variables were assessed using Binet-Kamat Test of Intelligence, Wechsler Intelligence Scale for Children - IV (India) and Wallach Kogan Creativity Test. The training methodology comprised Winning Moves Chess Learning Program - Episodes 1–22, lectures with the demonstration board, on-the-board playing and training, chess exercise through workbooks (Chess school 1A, Chess school 2, and tactics) and working with chess software. Further students games were mapped using chess software and the brain patterns of the child were understood. They were taught the ideas behind chess openings and exposure to classical games were also given. The children participated in mock as well as regular tournaments. Preliminary analysis carried out using independent t tests with 50 children indicates that chess training has led to significant increases in the intelligent quotient. Children in the experimental group have shown significant increases in composite scores like working memory and perceptual reasoning. Chess training has significantly enhanced the total creativity scores, line drawing and pattern meaning subscale scores. Systematically learning chess as part of school activities appears to have a broad spectrum of positive outcomes.

Keywords: chess, intelligence, creativity, children

Procedia PDF Downloads 257

Authors: Paul Lam, Kevin Wong, Chi Him Chan

Abstract:

Constant practicing and accurate, immediate feedback are the keys to improving students’ speaking skills. However, traditional oral examination often fails to provide such opportunities to students. The traditional, face-to-face oral assessment is often time consuming – attending the oral needs of one student often leads to the negligence of others. Hence, teachers can only provide limited opportunities and feedback to students. Moreover, students’ incentive to practice is also reduced by their anxiety and shyness in speaking the new language. A mobile app was developed to use artificial intelligence (AI) to provide immediate feedback to students’ speaking performance as an attempt to solve the above-mentioned problems. Firstly, it was thought that online exercises would greatly increase the learning opportunities of students as they can now practice more without the needs of teachers’ presence. Secondly, the automatic feedback provided by the AI would enhance students’ motivation to practice as there is an instant evaluation of their performance. Lastly, students should feel less anxious and shy compared to directly practicing oral in front of teachers. Technically, the program made use of speech-to-text functions to generate feedback to students. To be specific, the software analyzes students’ oral input through certain speech-to-text AI engine and then cleans up the results further to the point that can be compared with the targeted text. The mobile app has invited English teachers for the pilot use and asked for their feedback. Preliminary trials indicated that the approach has limitations. Many of the users’ pronunciation were automatically corrected by the speech recognition function as wise guessing is already integrated into many of such systems. Nevertheless, teachers have confidence that the app can be further improved for accuracy. It has the potential to significantly improve oral drilling by giving students more chances to practice. Moreover, they believe that the success of this mobile app confirms the potential to extend the AI-assisted assessment to other language skills, such as writing, reading, and listening.

Keywords: artificial Intelligence, mobile learning, oral assessment, oral practice, speech-to-text function

Procedia PDF Downloads 103

Authors: Avinash Malladhi

Abstract:

Forensic accounting is a specialized field that involves the application of accounting principles, investigative skills, and legal knowledge to detect and prevent fraud. With the rise of big data and technological advancements, artificial intelligence (AI) and machine learning (ML) algorithms have emerged as powerful tools for forensic accountants to enhance their fraud detection capabilities. In this paper, we review and analyze various AI/ML algorithms that are commonly used in forensic accounting, including supervised and unsupervised learning, deep learning, natural language processing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Decision Trees, and Random Forests. We discuss their underlying principles, strengths, and limitations and provide empirical evidence from existing research studies demonstrating their effectiveness in detecting financial fraud. We also highlight potential ethical considerations and challenges associated with using AI/ML in forensic accounting. Furthermore, we highlight the benefits of these technologies in improving fraud detection and prevention in forensic accounting.

Keywords: AI, machine learning, forensic accounting & fraud detection, anti money laundering, Benford's law, fraud triangle theory

Procedia PDF Downloads 93

Authors: Nattapong Techarattanased

Abstract:

The objectives of this research were to study the influence of market orientation and supply chain management on competitive capability in case of the automotive parts industry in Thailand. This study employed by survey research and questionnaire was used to collect the data from 400 entrepreneurs in the automotive parts industry in Thailand. The descriptive statistics and multiple regression analysis were used to analyze data. The results revealed that the overall dimensions of marketing orientation, namely, responsiveness, intelligence generation, and intelligence dissemination were rated at the high level. As well, the overall dimensions of supply chain management, namely, collaboration, communication, trust, and commitment were also rated at the high level. Furthermore, the hypothesis testing results showed that supply chain management and market orientation affected competitive capability of the automotive parts industry in Thailand which these two variables could be combined to predict competitive capability of the automotive parts industry in Thailand by 31.5 percent.

Keywords: automotive parts industry, competitive capability, market orientation, supply chain management

Procedia PDF Downloads 314

Authors: Swarna Pundir, Prabuddha Hans

Abstract:

As AI has already stepped into the daily life of human society, one cannot be ignorant about the data it collects and used it to provide a quality of services depending up on the individuals’ choices. It also helps in giving option for making decision Vs choice selection with a calculation based on the history of our search criteria. Over the past decade or so, the way Artificial Intelligence (AI) has impacted society is undoubtedly large.AI has changed the way we shop, the way we entertain and challenge ourselves, the way information is handled, and has automated some sections of our life. We have answered as to what AI is, but not why one may see it as useful. AI is useful because it is capable of learning and predicting outcomes, using Machine Learning (ML) and Deep Learning (DL) with the help of Artificial Neural Networks (ANN). AI can also be a system that can act like humans. One of the major impacts be Joblessness through automation via AI which is seen mostly in manufacturing sectors, especially in the routine manual and blue-collar occupations and those without a college degree. It raises some serious concerns about AI in regards of less employment, ethics in making moral decisions, Individuals privacy, human judgement’s, natural emotions, biased decisions, discrimination. So, the question is if an error occurs who will be responsible, or it will be just waved off as a “Machine Error”, with no one taking the responsibility of any wrongdoing, it is essential to form some rules for using the AI where both machines and humans are involved.

Keywords: AI, ML, DL, ANN

Procedia PDF Downloads 97

Authors: Garry Gorman, Nigel McKelvey, James Connolly

Abstract:

This study will evaluate a growth mindset intervention with Junior Cycle Coding and Senior Cycle Computer Science students in Ireland, where gamification will be used to incentivise growth mindset behaviour. An artificial intelligence (AI) driven personalised learning system will be developed to present computer programming learning tasks in a manner that is best suited to the individuals’ own learning preferences while incentivising and rewarding growth mindset behaviour of persistence, mastery response to challenge, and challenge seeking. This research endeavours to measure mindset with before and after surveys (conducted nationally) and by recording growth mindset behaviour whilst playing a digital game. This study will harness the capabilities of AI and aims to determine how a personalised learning (PL) experience can impact the mindset of a broad range of students. The focus of this study will be to determine how personalising the learning experience influences female and disadvantaged students' sense of belonging in the computer science classroom when tasks are presented in a manner that is best suited to the individual. Whole Brain Learning will underpin this research and will be used as a framework to guide the research in identifying key areas such as thinking and learning styles, cognitive potential, motivators and fears, and emotional intelligence. This research will be conducted in multiple school types over one academic year. Digital games will be played multiple times over this period, and the data gathered will be used to inform the AI algorithm. The three data sets are described as follows: (i) Before and after survey data to determine the grit scores and mindsets of the participants, (ii) The Growth Mind-Set data from the game, which will measure multiple growth mindset behaviours, such as persistence, response to challenge and use of strategy, (iii) The AI data to guide PL. This study will highlight the effectiveness of an AI-driven personalised learning experience. The data will position AI within the Irish educational landscape, with a specific focus on the teaching of CS. These findings will benefit coding and computer science teachers by providing a clear pedagogy for the effective delivery of personalised learning strategies for computer science education. This pedagogy will help prevent students from developing a fixed mindset while helping pupils to exhibit persistence of effort, use of strategy, and a mastery response to challenges.

Keywords: computer science education, artificial intelligence, growth mindset, pedagogy

Procedia PDF Downloads 87

Authors: Fateme Aysin Anka, Farzad Kiani

Abstract:

Forced migration is a situation in which people are forced to leave their homes against their will due to political conflicts, wars and conflicts, natural disasters, climate change, economic crises, or other emergencies. This type of migration takes place under conditions where people cannot lead a sustainable life due to reasons such as security, shelter and meeting their basic needs. This type of migration may occur in connection with different factors that affect people's living conditions. In addition to these general and widespread reasons, water security and resources will be one that is starting now and will be encountered more and more in the future. Forced migration may occur due to insufficient or depleted water resources in the areas where people live. In this case, people's living conditions become unsustainable, and they may have to go elsewhere, as they cannot obtain their basic needs, such as drinking water, water used for agriculture and industry. To cope with these situations, it is important to minimize the causes, as international organizations and societies must provide assistance (for example, humanitarian aid, shelter, medical support and education) and protection to address (or mitigate) this problem. From the international perspective, plans such as the Green New Deal (GND) and the European Green Deal (EGD) draw attention to the need for people to live equally in a cleaner and greener world. Especially recently, with the advancement of technology, science and methods have become more efficient. In this regard, in this article, a multidisciplinary case model is presented by reinforcing the water problem with an engineering approach within the framework of the social dimension. It is worth emphasizing that this problem is largely linked to climate change and the lack of a sustainable water management perspective. As a matter of fact, the United Nations Development Agency (UNDA) draws attention to this problem in its universally accepted sustainable development goals. Therefore, an artificial intelligence-based approach has been applied to solve this problem by focusing on the water management problem. The most general but also important aspect in the management of water resources is its correct consumption. In this context, the artificial intelligence-based system undertakes tasks such as water demand forecasting and distribution management, emergency and crisis management, water pollution detection and prevention, and maintenance and repair control and forecasting.

Keywords: water resource management, forced migration, multidisciplinary studies, artificial intelligence

Procedia PDF Downloads 86

Authors: Halkiopoulos Constantinos, Gkintoni Evgenia, Koutsopoulou Ioanna, Antonopoulou Hera

Abstract:

E-sports and e-gaming have emerged in recent years since the increase in internet use have become universal and e-gamers are the new reality in our homes. The excessive involvement of young adults with e-sports has already been revealed and the adverse consequences have been reported in researches in the past few years, but the issue has not been fully studied yet. The present research is conducted in Greece and studies the psychological profile of video game players and provides information on personality traits, habits and emotional status that affect online gamers’ behaviors in order to help professionals and policy makers address the problem. Three standardized self-report questionnaires were administered to participants who were young male and female adults aged from 19-26 years old. The Profile of Mood States (POMS) scale was used to evaluate people’s perceptions of their everyday life mood; the personality features that can trace back to people’s habits and anticipated reactions were measured by Eysenck Personality Questionnaire (EPQ), and the Trait Emotional Intelligence Questionnaire (TEIQue) was used to measure which cognitive (gamers’ beliefs) and emotional parameters (gamers’ emotional abilities) mainly affected/ predicted gamers’ behaviors and leisure time activities?/ gaming behaviors. Data mining techniques were used to analyze the data, which resulted in machine learning algorithms that were included in the software package R. The research findings attempt to designate the effect of personality traits, emotional status and emotional intelligence influence and correlation with e-sports, gamers’ behaviors and help policy makers and stakeholders take action, shape social policy and prevent the adverse consequences on young adults. The need for further research, prevention and treatment strategies is also addressed.

Keywords: e-sports, e-gamers, personality traits, POMS, emotional intelligence, data mining, R

Procedia PDF Downloads 231

Authors: Ray Bryant

Abstract:

3Strands Global Foundation (3SGF), a non-profit with a mission to mobilize communities to combat human trafficking through prevention education and reintegration programs, launched a groundbreaking study that calls out the usage and benefits of artificial intelligence in the war against human trafficking. Having gathered more than 30,000 stories from counselors and school staff who have gone through its PROTECT Prevention Education program, 3SGF sought to develop a methodology to measure the effectiveness of the training, which helps educators and school staff identify physical signs and behaviors indicating a student is being victimized. The program further illustrates how to recognize and respond to trauma and teaches the steps to take to report human trafficking, as well as how to connect victims with the proper professionals. 3SGF partnered with Levity, a leader in no-code Artificial Intelligence (AI) automation, to create the research study utilizing natural language processing, a branch of artificial intelligence, to measure the effectiveness of their prevention education program. By applying the logic created for the study, the platform analyzed and categorized each story. If the story, directly from the educator, demonstrated one or more of the desired outcomes; Increased Awareness, Increased Knowledge, or Intended Behavior Change, a label was applied. The system then added a confidence level for each identified label. The study results were generated with a 99% confidence level. Preliminary results show that of the 30,000 stories gathered, it became overwhelmingly clear that a significant majority of the participants now have increased awareness of the issue, demonstrated better knowledge of how to help prevent the crime, and expressed an intention to change how they approach what they do daily. In addition, it was observed that approximately 30% of the stories involved comments by educators expressing they wish they’d had this knowledge sooner as they can think of many students they would have been able to help. Objectives Of Research: To solve the problem of needing to analyze and accurately categorize more than 30,000 data points of participant feedback in order to evaluate the success of a human trafficking prevention program by using AI and Natural Language Processing. Methodologies Used: In conjunction with our strategic partner, Levity, we have created our own NLP analysis engine specific to our problem. Contributions To Research: The intersection of AI and human rights and how to utilize technology to combat human trafficking.

Keywords: AI, technology, human trafficking, prevention

Procedia PDF Downloads 59

Authors: Oluwatosin Olofintoye, Josiah Adeyemo, Gbemileke Shomade

Abstract:

The relationship between rainfall and runoff is an important issue in surface water hydrology therefore the understanding and development of accurate rainfall-runoff models and their applications in water resources planning, management and operation are of paramount importance in hydrological studies. This paper reviews some of the previous works on the rainfall-runoff process modeling. The hydrologic applications of conceptual models and artificial neural networks (ANNs) for the precipitation-runoff process modeling were studied. Gradient training methods such as error back-propagation (BP) and evolutionary algorithms (EAs) are discussed in relation to the training of artificial neural networks and it is shown that application of EAs to artificial neural networks training could be an alternative to other training methods. Therefore, further research interest to exploit the abundant expert knowledge in the area of artificial intelligence for the solution of hydrologic and water resources planning and management problems is needed.

Keywords: artificial intelligence, artificial neural networks, evolutionary algorithms, gradient training method, rainfall-runoff model

Procedia PDF Downloads 454

Authors: Akhila Potluru

Abstract:

Worldwide, more than 250 bodies of water are transboundary, meaning they cross the political boundaries of multiple countries. This creates a system of hydrological, economic, and social interdependence between communities reliant on these water sources. Transboundary water conflicts can occur as a result of this intense interdependence. Many factors contribute to the sparking of transboundary water conflicts, ranging from natural hydrological factors to hydro-political interactions. Previous attempts to predict transboundary water conflicts by analysing changes or trends in the contributing factors have typically failed because patterns in the data are hard to identify. However, there is potential for artificial intelligence and machine learning to fill this gap and identify future ‘hotspots’ up to a year in advance by identifying patterns in data where humans can’t. This research determines the extent to which AI can be used to predict and prevent transboundary water conflicts. This is done via a critical literature review of previous case studies and datasets where AI was deployed to predict water conflict. This research not only delivered a more nuanced understanding of previously undervalued factors that contribute toward transboundary water conflicts (in particular, culture and disinformation) but also by detecting conflict early, governance bodies can engage in processes to de-escalate conflict by providing pre-emptive solutions. Looking forward, this gives rise to significant policy implications and water-sharing agreements, which may be able to prevent water conflicts from developing into wide-scale disasters. Additionally, AI can be used to gain a fuller picture of water-based conflicts in areas where security concerns mean it is not possible to have staff on the ground. Therefore, AI enhances not only the depth of our knowledge about transboundary water conflicts but also the breadth of our knowledge. With demand for water constantly growing, competition between countries over shared water will increasingly lead to water conflict. There has never been a more significant time for us to be able to accurately predict and take precautions to prevent global water conflicts.

Keywords: artificial intelligence, machine learning, transboundary water conflict, water management

Procedia PDF Downloads 105

Authors: Mikko Salminen, Simo Järvelä, Niklas Ravaja

Abstract:

One of the central models of emotional intelligence (EI) is that of Mayer and Salovey’s, which includes ability to monitor own feelings and emotions and those of others, ability to discriminate different emotions, and to use this information to guide thinking and actions. There is vast amount of previous research where positive links between EI and, for example, leadership successfulness, work outcomes, work wellbeing and organizational climate have been reported. EI has also a role in the effectiveness of work teams, and the effects of EI are especially prominent in jobs requiring emotional labor. Thus, also the organizational context must be taken into account when considering the effects of EI on work outcomes. Based on previous research, it is suggested that EI can also protect managers from the negative consequences of stress. Stress may have many detrimental effects on the manager’s performance in essential work tasks. Previous studies have highlighted the effects of stress on, not only health, but also, for example, on cognitive tasks such as decision-making, which is important in managerial work. The motivation for the current study came from the notion that, unfortunately, many stressed individuals may not be aware of the circumstance; periods of stress-induced physiological arousal may be prolonged if there is not enough time for recovery. To tackle this problem, physiological stress levels of managers were collected using recording of heart rate variability (HRV). The goal was to use this data to provide the managers with feedback on their stress levels. The managers could access this feedback using a www-based learning environment. In the learning environment, in addition to the feedback on stress level and other collected data, also developmental tasks were provided. For example, those with high stress levels were sent instructions for mindfulness exercises. The current study focuses on the relation between the measured physiological stress levels and EI of the managers. In a pilot study, 33 managers from various fields wore the Firstbeat Bodyguard HRV measurement devices for three consecutive days and nights. From the collected HRV data periods (minutes) of stress and recovery were detected using dedicated software. The effects of EI on HRV-calculated stress indexes were studied using Linear Mixed Models procedure in SPSS. There was a statistically significant effect of total EI, defined as an average score of Schutte’s emotional intelligence test, on the percentage of stress minutes during the whole measurement period (p=.025). More stress minutes were detected on those managers who had lower emotional intelligence. It is suggested, that high EI provided managers with better tools to cope with stress. Managing of own emotions helps the manager in controlling possible negative emotions evoked by, e.g., critical feedback or increasing workload. High EI managers may also be more competent in detecting emotions of others, which would lead to smoother interactions and less conflicts. Given the recent trend to different quantified-self applications, it is suggested that monitoring of bio-signals would prove to be a fruitful direction to further develop new tools for managerial and leadership coaching.

Keywords: emotional intelligence, leadership, heart rate variability, personality, stress

Procedia PDF Downloads 226

Authors: George Cudjoe Agbemabiese, John Paul Kosiba, Michael Boadi Nyamekye, Vanessa Narkie Tetteh, Caleb Nunoo, Mohammed Muniru Husseini

Abstract:

The smartphone industry continues to experience massive growth, evidenced by expanding markets and an increasing number of brands, models and manufacturers. As technology advances rapidly, manufacturers of smartphones are consistently introducing new innovations to keep up with the latest evolving industry trends and customer demand for more modern devices. This study aimed to assess the influence of artificial intelligence (AI) voice assistant (VA) on improving customer experience, resulting in the continuous use of mobile brands. Specifically, this article assesses the role of hedonic, utilitarian, and social benefits provided by AIVA on customer experience and the continuance intention to use mobile phone brands. Using a primary data collection instrument, the quantitative approach was adopted to examine the study's variables. Data from 348 valid responses were used for the analysis based on structural equation modeling (SEM) with AMOS version 23. Three main factors were identified to influence customer experience, which results in continuous usage of mobile phone brands. These factors are social benefits, hedonic benefits, and utilitarian benefits. In conclusion, a significant and positive relationship exists between the factors influencing customer experience for continuous usage of mobile phone brands. The study concludes that mobile brands that invest in delivering positive user experiences are in a better position to improve usage and increase preference for their brands. The study recommends that mobile brands consider and research their prospects' and customers' social, hedonic, and utilitarian needs to provide them with desired products and experiences.

Keywords: artificial intelligence, continuance usage, customer experience, smartphone industry

Procedia PDF Downloads 80

Authors: Mehrnaz Mostafavi, Mahtab Shabani, Alireza Azani, Fatemeh Ghafari

Abstract:

Artificial intelligence (AI) can transform breast cancer diagnosis and therapy by providing sophisticated solutions for screening, imaging interpretation, histopathological analysis, and treatment planning. This literature review digs into the many uses of AI in breast cancer treatment, highlighting the need for collaboration between AI scientists and healthcare practitioners. It emphasizes advances in AI-driven breast imaging interpretation, such as computer-aided detection and diagnosis (CADe/CADx) systems and deep learning algorithms. These have shown significant potential for improving diagnostic accuracy and lowering radiologists' workloads. Furthermore, AI approaches such as deep learning have been used in histopathological research to accurately predict hormone receptor status and categorize tumor-associated stroma from regular H&E stains. These AI-powered approaches simplify diagnostic procedures while providing insights into tumor biology and prognosis. As AI becomes more embedded in breast cancer care, it is crucial to ensure its ethical, efficient, and patient-focused implementation to improve outcomes for breast cancer patients ultimately.

Keywords: breast cancer, artificial intelligence, cancer diagnosis, clinical practice

Procedia PDF Downloads 68