Search results for: social learning

Authors: Daniel Dor

Abstract:

The research presents a new general theory of language as a socially-constructed communication technology, designed by cultural evolution for a very specific function: the instruction of imagination. As opposed to all the other systems of intentional communication, which provide materials for the interlocutors to experience, language allows speakers to instruct their interlocutors in the process of imagining the intended meaning-instead of experiencing it. It is thus the only system that bridges the experiential gaps between speakers. This is the key to its enormous success.

Keywords: experience, general theory of language, imagination, language as technology, social essence of language

Procedia PDF Downloads 590

Authors: Morteza Pourazar, Fatemeh Mirakhori, Fazlolah Bagherzadeh, Rasool Hemayattalab

Abstract:

The purpose of study was to examine the effects of external and internal focus of attention in the motor learning of children with cerebral palsy. The study involved 30 boys (7 to 12 years old) with CP type 1 who practiced throwing beanbags. The participants were randomly assigned to the internal focus, external focus, and control groups, and performed six blocks of 10-trial with attentional focus reminders during a practice phase and no reminders during retention and transfer tests. Analysis of variance (ANOVA) with repeated measures on the last factor was used. The results show that significant main effects were found for time and group. However, the interaction of time and group was not significant. Retention scores were significantly higher for the external focus group. The external focus group performed better than other groups; however, the internal focus and control groups’ performance did not differ. The study concluded that motor skills in Spastic Hemiparetic Cerebral Palsy (SHCP) children could be enhanced by external attention.

Keywords: cerebral palsy, external attention, internal attention, throwing task

Procedia PDF Downloads 318

Authors: Al Romano, Jeryl C. Hije, Mechaela Marie O. Tabiolo

Abstract:

The decline in water quality is a major factor in aquatic disease outbreaks and can lead to significant mortality among aquatic organisms. Understanding the relationship between dissolved oxygen (DO) and water temperature is crucial, as these variables directly impact the health, behavior, and survival of fish populations. This study investigated how DO levels, water temperature, and atmospheric temperature interact in Sampaloc Lake to assess the risk of fish mortality. By employing a combination of linear regression models and machine learning techniques, researchers developed predictive models to forecast DO concentrations at various depths. The results indicate that while DO levels generally decrease with depth, the predicted concentrations are sufficient to support the survival of common fish species in Sampaloc Lake during March, April, and May 2025.

Keywords: aquaculture, dissolved oxygen, water temperature, regression analysis, machine learning, fish mass mortality, early warning system

Procedia PDF Downloads 42

Authors: Orit Shamir Balderman, Shamir Michal

Abstract:

Israel often deals with crisis situations, but most have been characterized as security crises (e.g., war). This is the first time that the Israel has dealt with a health and social emergency as part of a global crisis. The crisis began in January 2020 with the emergence of the novel coronavirus (Covid-19), which was defined as a pandemic (World Health Organization, 2020) and arrived in Israel in early March 2020. This study examined how single mothers by choice (SMBC) experience state anxiety (SA), social support, work–family conflict (WFC), and happiness. This group has not been studied in the context of crises in general or a global crisis. Using a snowball sample, 386 SMBCanswered an online questionnaire. The findings show a negative relationship between income and level of state anxiety. State anxiety was also negatively associated with social support, level of happiness, and WFC. Finally, a stepwise regression analysis indicated that happiness explained 34% of the variance in SA. We also found that most of the women did not turn to formal support agencies such as social workers, other Government Ministries, or municipal welfare. A positive and strong correlations was also found between SA and WFC. The findings of the study reinforce the understanding that although these women made a conscious and informed decision regarding the choice of their family cell, their situation is more complex in the absence of a spouse support. Therefore, this study, as other future studies in the field of SMBC, may contribute to the improvement of their social status and the understanding that they are a unique group. Although SMBC are a growing sector of society in the past few years, there are still special needs and special attention that is needed from the formal and informal supports systems. A comparative study of these two groups and in different countries would shed light on SA among mothers in general, regardless of their relationship status and location.Researchers should expand this study by comparing mothers in relationships and exploring how SMBC coped in other countries. In summary, the findings of the study contribute knowledge on three levels: (a) knowledge about SMBC in general and during crisis situations; (b) examination of social support using tools assessing receipt of assistance and support, some of which were developed for the present study; and (c) insights regarding counseling, accompaniment, and guidance of welfare mechanisms.

Keywords: single mothers by choice, state anxiety, social support, happiness, work–family conflict

Procedia PDF Downloads 89

Authors: Mark Williams

Abstract:

The digital revolution has radically transformed our social environment creating vast opportunities for interconnectivity and social interaction. This revolution, however, has also changed the reach and impact of hate crime, with social media providing a new platform to victimize and harass users in their homes. In this way, developments in the information and communication technologies have exacerbated and facilitated the commission of hate crime, increasing its prevalence and impact. Unfortunately, legislators, policymakers and criminal justice professionals have struggled to keep pace with these technological developments, reducing their ability to intervene in, regulate and govern the commission of hate crimes online. This work is further complicated by the global nature of this crime due to the tendency for offenders and victims to reside in multiple different jurisdictions, as well as the need for criminal justice professionals to obtain the cooperation of private companies to access information required for prosecution. Drawing on in-depth interviews with key criminal justice professionals and policymakers with detailed knowledge in this area, this paper examines the specific challenges the police and prosecution services face as they attempt to intervene in and prosecute the commission of hate crimes online. It is argued that any attempt to reduce online othering, such as the commission of hate crimes online, must be multifaceted, collaborative and involve both innovative technological solutions as well as internationally agreed ethical and legal frameworks.

Keywords: cybercrime, digital policing, hate crime, social media

Procedia PDF Downloads 230

Authors: Zachary Pan

Abstract:

Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy.

Keywords: body part, healthcare, machine learning, neural networks

Procedia PDF Downloads 112

Authors: Pimpisa Rattanadilok Na Phuket

Abstract:

This research study aims to examine: 1) teaching competency of the native English-speaking teacher (NEST) 2) the English language learning achievement of Thai students, and 3) students’ perceptions toward their NEST. The population considered in this research was a group of 39 undergraduate students of the academic year 2013. The tools consisted of a questionnaire employed to measure the level of competency of NEST, pre-test and post-test used to examine the students’ achievement on English pronunciation, and an interview used to discover how participants perceived their NEST. The data was statistically analysed as percentage, mean, standard deviation and One-sample-t-test. In addition, the data collected by interviews was qualitatively analyzed. The research study found that the level of teaching competency of native speaker teachers of English was mostly low, the English pronunciation achievement of students had increased significantly at the level of 0.5, and the students’ perception toward NEST is combined. The students perceived their NEST as an English expertise, but they felt that NEST had not recognized students' linguistic difficulty and cultural differences.

Keywords: competency, native English-speaking teacher (NET), English teaching, learning achievement

Procedia PDF Downloads 378

Authors: Selim M. Khan, Aaron A. Goodarzi, Joshua M. Taron, Tryggve Rönnqvist

Abstract:

Indoor air quality, a prime determinant of health, is strongly influenced by the presence of hazardous radon gas within the built environment. As a health issue, dangerously high indoor radon arose within the 20th century to become the 2nd leading cause of lung cancer. While the 21st century building metrics and human behaviors have captured, contained, and concentrated radon to yet higher and more hazardous levels, the issue is rapidly worsening in Canada. It is established that Canadians in the Prairies are the 2nd highest radon-exposed population in the world, with 1 in 6 residences experiencing 0.2-6.5 millisieverts (mSv) radiation per week, whereas the Canadian Nuclear Safety Commission sets maximum 5-year occupational limits for atomic workplace exposure at only 20 mSv. This situation is also deteriorating over time within newer housing stocks containing higher levels of radon. Deep machine learning (LSTM) algorithms were applied to analyze multiple quantitative and qualitative features, determine the most important contributory factors, and predicted radon levels in the known past (1990-2020) and projected future (2021-2050). The findings showed gradual downwards patterns in Sweden, whereas it would continue to go from high to higher levels in Canada over time. The contributory factors found to be the basement porosity, roof insulation depthness, R-factor, and air dynamics of the indoor environment related to human window opening behaviour. Building codes must consider including these factors to ensure adequate indoor ventilation and healthy living that can prevent lung cancer in non-smokers.

Keywords: radon, building metrics, deep learning, LSTM prediction model, lung cancer, canada, sweden

Procedia PDF Downloads 117

Authors: Mirjana Dragas, Marli Gonan Bozac, Morena Paulisic

Abstract:

Corporate social responsibility (CSR) is a balance between strategic and financial goals of companies, as well as social needs. The integration of competitive strategy and CSR in food and beverage industry has allowed companies to find new sources of competitive advantage. The paper discusses the fact that socially responsible companies encourage co-operation with socially responsible suppliers in order to strengthen market competitiveness. In addition to the descriptive interpretation of the results obtained by a questionnaire, factor analysis was used, while principal components analysis was applied as a factor extraction method. The research results based on two multiple regression analyses show that: (1) selecting the CSR supplier explains a statistically significant part of the variance of the results on the scale of financial aspects of competitiveness (as much as 44.7% of the explained variance); and (2) selecting the CSR supplier is a significant predictor of non-financial aspects of competitiveness (explains 43.9% of the variance of the results on the scale of non-financial aspects of competitiveness). A successful competitive strategy must ultimately support the growth strategy. This implies an analytical approach to finding factors that influence competitiveness through socially sustainable solutions and satisfactory top management decisions.

Keywords: competitiveness, corporate social responsibility, food and beverage industry, supply chain decision making

Procedia PDF Downloads 364

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 69

Authors: Ndidi Ofole

Abstract:

Ongoing Boko Haram crisis and instability in North-Eastern Nigeria has placed additional stress on academic resilience of female undergraduates who are already challenged by gender discrimination in educational opportunities. Students without resilience lack stress hardiness to cope with academic challenges. There is a limited study on academic resilience targeting this disadvantaged population in Nigeria. Consequently, survey research design was employed to investigate the contextual and personal factors that could predict academic resilience among female undergraduates in Boko Haram Neighbourhood in North-Eastern, Nigeria. Five hundred and thirty female students with age range of 18 to 24 years ( = 19.2; SD=6.9) were randomly drawn from 3 Universities in North-Eastern Nigeria. They responded to five instruments, namely; Academic Resilience scale (r=0.72); Social Support questionnaire (r=0. 64); Social Connectedness questionnaire (r=0.75); Self-Efficacy scale (r=0. 68) and Emotional Regulation questionnaire (r=78). Results showed that there was significant positive relationship between the four independent variables and academic resilience. The variables jointly contributed 5.9% variance in the prediction of academic resilience. In terms of magnitude, social support was most potent while self-efficacy was the least. It concluded that the factors considered in this study are academic resilience facilitators. The outcomes of the study have both theoretical and practical implications.

Keywords: academic resilience, emotional regulation, school connectedness, self-efficacy , social support

Procedia PDF Downloads 212

Authors: M. Verde, G. Falzarano

Abstract:

The paper examines the connection between corporate social responsibility (CSR), capability approach and poverty reduction; in particular, the local employment development (LED) by way of CSR initiatives. The joint action of LED/CSR results in a win-win situation, not only for the enterprises but also for all the stakeholders involved; in this regard, subsidiarity and coordination between national and regional/local authorities are central to a socially-oriented market economy. In the first section, the CSR is analysed on the basis of its social function in the fight against poverty, as a 'capabilities deprivation'. In the central part, the attention is focused on the relationship between CSR and LED; ergo, on the role of the enterprises in fostering capabilities development (the employment). Besides, all the potential solutions are presented, stressing the possible combinations, in the last part. The benchmark is the enterprise as an economic and a social institution: the business should not be combined with profit merely, paying more attention to its sustainable impact and social contribution. In which way could it be possible? The answer is the CSR. The impact of CSR on poverty reduction is still little explored. The companies help to reduce poverty through economic contribution, human rights and social inclusion; hence, the business becomes an 'agent of development' in order to fight against 'inequality'. The starting point is the pyramid of social responsibility, where ethic and philanthropic responsibilities involve programmes and actions aimed at personal development of the individuals, improving human standard of living in all forms, including poverty, when people do not have a choice between different 'life options', ranging from level of education to employment. At this point, CSR comes into play and works on two dimensions: poverty reduction and poverty prevention, by means of a series of initiatives: first of all, job creation and precarious work reduction. Empowerment of the local actors, financial support and combination of top down and bottom up initiatives are some of CSR areas of activity. Several positive effects occur on individual levels of educations, access to capital, individual health status, empowerment of youth and woman, access to social networks and it was observed that these effects depend on the type of CSR strategy. Indeed, CSR programmes should take into account fundamental criteria, such as the transparency, the information about benefits, a coordination unit among institutions and more clear guidelines. In this way, the advantages to the corporate reputation and to the community translate into a better job matching on the labour market, inter alia. It is important to underline that the success depends on the specific measures of the areas in question, by adapting them to the local needs, in light of general principles and index; therefore, the concrete commitment of the all stakeholders involved is decisive in order to achieve the goals. The enterprise would represent a concrete contribution for the pursuit of sustainable development and for the dissemination of a social and well being awareness.

Keywords: capability approach, local employment development, poverty, social inclusion

Procedia PDF Downloads 145

Authors: Zoya Khan, Amina Muzaffar

Abstract:

Social media based brand communities are communities that are developed around a brand. In the highly globalized world of today, Facebook is undoubtedly being regarded and has been widely recognized as a trendy and well-accepted medium of marketing. By means of a Facebook fan page, organizations can effectually create, enhance, and sustain customer-brand relationship. In this article, we explore whether brand communities based on social media (a special type of online brand communities) have positive effects on the main community elements and value creation practices in the communities as well as on brand trust and brand loyalty. A survey was conducted and 201 valid responses were used for analysis. The results of structural equation modeling show that brand communities established on social media have positive effects on value creation practices. Brand use, impression management practices and brand identification has an impact on brand trust and this brand trust then further leads to brand loyalty.

Keywords: relationship quality, impression management practices, brand identification, brand trust, brand loyalty

Procedia PDF Downloads 480

Authors: Matteo Mazzucato, Elena Faccio, Antonio Iudici

Abstract:

Prison is a social reality constructed by everyday interactions between an inmate, other social actors (cellmates, prison officers, educationalists and psychologists or other detainees) and the external world which participates in this complex construction through the social discourses on prison reality and its problems. Being a detainee means performing a self dealing with processes of stereotypization, attribution of a social role and prejudices assigned by various interlocutors and depending on what kind of crime one has been convicted of. Among all inmates, sex offenders are the ones who risk more to be socially condemned beyond a legal sentence since they have committed one of the most hated and disapproved crime. Regarding this, prison has to be considered as a critical context in which all community expectations and beliefs are converged: for common sense, rapists and child molesters are dangerous people who have to be stigmatized, punished and isolated. Furthermore, other detainees share a code of conduct by which the ‘sex offender’ is collocated at the lowest level of the social hierarchy of the prison. The penitentiary administration too defines this kind of detainee as a ‘vulnerable person to protect’ while prison staff considers him as a particular inmate who has to be treated and definitely changed. Considering all the complexities connected with being imprisoned as a sex offender, our research aimed at exploring how people convicted of sex crimes are called upon to manage all these hetero-narrations about their selves. Set this goal, textual data retrieved from this qualitative research show that sex offenders tend to not face the stigma assigned to them. They are rather used to minimize the story telling about their selves and costruct alternative biographies to be shared with other inmates. Managing narrations about their selves in this way permits to distance them from all the threats perceived living together with other detainees but it blocks sex offenders’ ri-signification of their offences during prison treatment. Given these results, prison administration should develop activities in order to create fields of interaction between detainees where experiencing new versions of their selves spendable even in external social situations. Regarding this it’s important to re-consider prison as part of the community and the sex offenders as a member of it.

Keywords: interactions, qualitative research, prison reality, sex offender

Procedia PDF Downloads 224

Authors: Joshua van Staden, Dane Brown, Karen Bradshaw

Abstract:

Gun violence is a significant problem in modern-day society. Early detection of carried handguns through closed-circuit television (CCTV) can aid in preventing potential gun violence. However, CCTV operators have a limited attention span. Machine learning approaches to automating the detection of dangerous gun carriers provide a way to aid CCTV operators in identifying these individuals. This study provides insight into the relationship between human key points extracted using human pose estimation (HPE) and their intention to fire a weapon. We examine the feature importance of each keypoint and their correlations. We use principal component analysis (PCA) to reduce the feature space and optimize detection. Finally, we run a set of classifiers to determine what form of classifier performs well on this data. We find that hips, shoulders, and knees tend to be crucial aspects of the human pose when making these predictions. Furthermore, the horizontal position plays a larger role than the vertical position. Of the 66 key points, nine principal components could be used to make nonlinear classifications with 86% accuracy. Furthermore, linear classifications could be done with 85% accuracy, showing that there is a degree of linearity in the data.

Keywords: feature engineering, human pose, machine learning, security

Procedia PDF Downloads 95

Authors: Zhi Liu, Xian Peng, Monika Domanska, Lingyun Kang, Sannyuya Liu

Abstract:

Massive Open education has become increasingly popular among worldwide learners. An increasing number of course reviews are being generated in Massive Open Online Course (MOOC) platform, which offers an interactive feedback channel for learners to express opinions and feelings in learning. These reviews typically contain subjective emotion and topic information towards the courses. However, it is time-consuming to artificially detect these opinions. In this paper, we propose an emotion-oriented topic detection model to automatically detect the students’ opinioned aspects in course reviews. The known overall emotion orientation and emotional words in each review are used to guide the joint probabilistic modeling of emotion and aspects in reviews. Through the experiment on real-life review data, it is verified that the distribution of course-emotion-aspect can be calculated to capture the most significant opinioned topics in each course unit. This proposed technique helps in conducting intelligent learning analytics for teachers to improve pedagogies and for developers to promote user experiences.

Keywords: Massive Open Online Course (MOOC), course reviews, topic model, emotion recognition, topical aspects

Procedia PDF Downloads 264

Authors: Elham A. Alsadoon, Hamadah B. Alsadoon

Abstract:

Educators try to design learning environments that are preferred by their students. With the wide-spread adoption of cell phones surpassing any other technology, educators should not fail to invest in the power of such technology. This study aimed to explore the current use of cell phones in education among Saudi students in Saudi universities and how students perceive such use. Data was collected from 237 students at King Saud University. Descriptive analysis was used to analyze the data. A T-test for independent groups was used to examine whether there was a significant difference between males and females in their perception of using cell phones in education. Findings suggested that students have a positive attitude toward the use of cell phones in education. The most accepted use was for sending notification to students, which has already been experienced through the Twasel system provided by King Saud University. This electronic system allows instructors to easily send any SMS or email to their students. The use of cell phone applications came in the second rank of using cell phones in education. Students have already experienced the benefits of having these applications handy wherever they go. On the other hand, they did not perceive using cell phones for assessment as practical educational usage. No gender difference was detected in terms of students’ perceptions toward using cell phones in education.

Keywords: cell phone, mobile learning, educational sciences, education

Procedia PDF Downloads 416

Authors: Miramar Damanhouri

Abstract:

This study examines the relationship between language use in computer-mediated communication and the social identity of the user. The data were collected by surveying 298 Saudi bilingual speakers who are familiar with Arabizi, a blend of Latin characters and Arabic numerals to transliterate Arabic sounds, and then analyzed quantitatively by running tests for statistical confidence in order to determine differences in perceptions between young adults (ages 15-25 years) and middle-aged adults (ages 26-50 years). According to the findings of this study, English is the dominant language among most of the young adults surveyed, and when they do use Arabic, they use Arabizi because of its flexibility, compatibility with modern technology, and its acceptance among people of their age and sociocultural backgrounds. On the other hand, most middle-aged adults surveyed here tend to use Arabic, as they believe that they should show their loyalty to their origin. The results of the study demonstrate a mutual relationship between language use in computer-mediated communication and the user’s social identity, as language is used both to reflect and construct that identity.

Keywords: Arabizi, computer mediated communication, digital communication, language use

Procedia PDF Downloads 138

Authors: Khairani Binti Supyan, Fatimah Khalid, Mas Rina Mustaffa, Azreen Bin Azman, Amirul Azuani Romle

Abstract:

Perennial plant classification plays a significant role in various agricultural and environmental applications, assisting in plant identification, disease detection, and biodiversity monitoring. Nevertheless, attaining high accuracy in perennial plant image classification remains challenging due to the complex variations in plant appearance, the diverse range of environmental conditions under which images are captured, and the inherent variability in image quality stemming from various factors such as lighting conditions, camera settings, and focus. This paper proposes an adaptation approach to optimize perennial plant image classification by fine-tuning the pre-trained DNNs model. This paper explores the efficacy of fine-tuning prevalent architectures, namely VGG16, ResNet50, and InceptionV3, leveraging transfer learning to tailor the models to the specific characteristics of perennial plant datasets. A subset of the MYLPHerbs dataset consisted of 6 perennial plant species of 13481 images under various environmental conditions that were used in the experiments. Different strategies for fine-tuning, including adjusting learning rates, training set sizes, data augmentation, and architectural modifications, were investigated. The experimental outcomes underscore the effectiveness of fine-tuning deep neural networks for perennial plant image classification, with ResNet50 showcasing the highest accuracy of 99.78%. Despite ResNet50's superior performance, both VGG16 and InceptionV3 achieved commendable accuracy of 99.67% and 99.37%, respectively. The overall outcomes reaffirm the robustness of the fine-tuning approach across different deep neural network architectures, offering insights into strategies for optimizing model performance in the domain of perennial plant image classification.

Keywords: perennial plants, image classification, deep neural networks, fine-tuning, transfer learning, VGG16, ResNet50, InceptionV3

Procedia PDF Downloads 72

Authors: Maurice Ntahobari, Levin Kuhlmann, Mario Boley, Zhinoos Razavi Hesabi

Abstract:

For machine learning based epileptic seizure prediction, it is important for the model to be implemented in small implantable or wearable devices that can be used to monitor epilepsy patients; however, current state-of-the-art methods are complex and computationally intensive. We use Shapley Additive Explanation (SHAP) to find relevant intracranial electroencephalogram (iEEG) features and improve the computational efficiency of a state-of-the-art seizure prediction method based on the extra trees classifier while maintaining prediction performance. Results for a small contest dataset and a much larger dataset with continuous recordings of up to 3 years per patient from 15 patients yield better than chance prediction performance (p < 0.004). Moreover, while the performance of the SHAP-based model is comparable to that of the benchmark, the overall training and prediction time of the model has been reduced by a factor of 1.83. It can also be noted that the feature called zero crossing value is the best EEG feature for seizure prediction. These results suggest state-of-the-art seizure prediction performance can be achieved using efficient methods based on optimal feature selection.

Keywords: machine learning, seizure prediction, extra tree classifier, SHAP, epilepsy

Procedia PDF Downloads 118

Authors: Yaser Mohammed Altameemi

Abstract:

This research looks at the representation of the social media for the Saudi Government decrees regarding the developmental projects of the Saudi 2030 vision. The paper analyses a television interview with the Crown Prince Mohammed Bin Salman who talks about the progress of the Saudi vision of 2030, and how the government had acted as response to the COVID-19 pandemic. The interview was on 28/4/2021. The paper analyses the tweets on Twitter that cover the interview for the purpose of investigating the development of concepts and meanings regarding the Saudi peoples’ orientations towards the Saudi projects. The data include all related tweets from the day of the interview and the following seven days after the interview. The finding of the collocation analysis suggests that nationalism notion is explicitly expressed by users in Twitter. The main finding of this paper suggests the importance of further analyses for the concordance lines. However, the collocation network suggests that there is a clear highlight for nationalism.

Keywords: social media, twitter, political interview, prince Mohammed Bin Salman, Saudi vision 2030

Procedia PDF Downloads 196

Authors: N. D'Amico, E. Grossi, B. Colombo, F. Rigiroli, M. Buscema, D. Fazzini, G. Cornalba, S. Papa

Abstract:

Purpose: To characterize, through a radiomic approach, the nature of nodules considered non-specific by expert radiologists, recognized in magnetic resonance mammography (MRm) with T1-weighted (T1w) sequences with paramagnetic contrast. Material and Methods: 47 cases out of 1200 undergoing MRm, in which the MRm assessment gave uncertain classification (non-specific nodules), were admitted to the study. The clinical outcome of the non-specific nodules was later found through follow-up or further exams (biopsy), finding 35 benign and 12 malignant. All MR Images were acquired at 1.5T, a first basal T1w sequence and then four T1w acquisitions after the paramagnetic contrast injection. After a manual segmentation of the lesions, done by a radiologist, and the extraction of 150 radiomic features (30 features per 5 subsequent times) a machine learning (ML) approach was used. An evolutionary algorithm (TWIST system based on KNN algorithm) was used to subdivide the dataset into training and validation test and to select features yielding the maximal amount of information. After this pre-processing, different machine learning systems were applied to develop a predictive model based on a training-testing crossover procedure. 10 cases with a benign nodule (follow-up older than 5 years) and 18 with an evident malignant tumor (clear malignant histological exam) were added to the dataset in order to allow the ML system to better learn from data. Results: NaiveBayes algorithm working on 79 features selected by a TWIST system, resulted to be the best performing ML system with a sensitivity of 96% and a specificity of 78% and a global accuracy of 87% (average values of two training-testing procedures ab-ba). The results showed that in the subset of 47 non-specific nodules, the algorithm predicted the outcome of 45 nodules which an expert radiologist could not identify. Conclusion: In this pilot study we identified a radiomic approach allowing ML systems to perform well in the diagnosis of a non-specific nodule at MR mammography. This algorithm could be a great support for the early diagnosis of malignant breast tumor, in the event the radiologist is not able to identify the kind of lesion and reduces the necessity for long follow-up. Clinical Relevance: This machine learning algorithm could be essential to support the radiologist in early diagnosis of non-specific nodules, in order to avoid strenuous follow-up and painful biopsy for the patient.

Keywords: breast, machine learning, MRI, radiomics

Procedia PDF Downloads 271

Authors: Reagan Rodriguez

Abstract:

With a growing divide between political echo chambers in the United States, exacerbated by race and income inequality, it might seem to be unfathomable to draw connections that tie working class in an industrial city and a U.S. territory. Yet, in regions where either the economy has been hit due to dwindling job infrastructure or natural disasters have left indelible marks on an island already once marked by colonial imperialism, a larger social shared identity is at play. Fracking has long been an intergenerational and stable work opportunity for many in the Pittsburg PA, yet the rising severity of global climate change may soon impact the policy and even presidential elections which could result in the reduction of jobs in the industry. Cock-fighting, considered a cultural mainstay within the island of Puerto Rico, has already had legislation banning activity and thus cutting out one of the most lucrative aspects of a severely injured economy. Insecurity, infringement, and isolation while being tied to a working-class bracket with no other opportunities in proximity have left both groups expressing similar frustration and while another larger shared identity politic is giving little other options to develop social mobility. This paper utilizes a thematic analysis and compares convergent and divergent themes on internet forums amongst unionized fracking workers in Pittsburg and cockfighters in Puerto Rico. This research examines how group identity in relation to job and cultural identity is most strong and at which points its most malleable; when intergenerational job identity becomes a part of one’s cultural identity, its override may be strongest when it is perceived as threatened. Final findings and limitations were comprehensively outlined.

Keywords: identity threat, social psychology, group identity, culture and social mobility

Procedia PDF Downloads 154

Authors: Guru Prakash, Revanth Dugalam

Abstract:

A road’s condition often deteriorates due to harsh loading such as overload due to trucks, and severe environmental conditions such as heavy rain, snow load, and cyclic loading. In absence of proper maintenance planning, this results in potholes, wide cracks, bumps, and increased roughness of roads. In this paper, a data-driven model will be developed to detect these damages using vibration and image signals. The key idea of the proposed methodology is that the road anomaly manifests in these signals, which can be detected by training a machine learning algorithm. The use of various machine learning techniques such as the support vector machine and Radom Forest method will be investigated. The proposed model will first be trained and tested with artificially simulated data, and the model architecture will be finalized by comparing the accuracies of various models. Once a model is fixed, the field study will be performed, and data will be collected. The field data will be used to validate the proposed model and to predict the future road’s health condition. The proposed will help to automate the road condition monitoring process, repair cost estimation, and maintenance planning process.

Keywords: SVM, data-driven, road health monitoring, pot-hole

Procedia PDF Downloads 89

Authors: Lisa Graham, Kathleen Grant

Abstract:

South Africa is a multicultural country with eleven official languages, yet most of the formal education at institutions of higher education in the country is in English. It is well known that many students, irrespective of their home language, struggle to grasp difficult scientific concepts and the same is true for students enrolled in the Extended Curriculum Programme at the Cape Peninsula University of Technology (CPUT), studying biomedical sciences. Today we are fortunate in that there is a plethora of resources available to students to research and better understand subject matter online. For example, the students often use YouTube videos to supplement the formal education provided in our course. Unfortunately, most of this material is presented in English. The rationale behind this project lies in that it is well documented that students think and grasp concepts easier in their home language and addresses the fact that the lingua franca of instruction in the field of biomedical science is English. A project aimed at addressing the lack of available resources in most of the South African languages is planned, where students studying Bachelor of Health Science in Medical Laboratory Science will collaborate with those studying Film and Video Technology to create educational videos, explaining scientific concepts in their home languages. These videos will then be published on our own YouTube channel, thereby making them accessible to fellow students, future students and anybody with interest in the subject. Research will be conducted to determine the benefit of the project as well as the published videos to the student community. It is suspected that the students engaged in making the videos will benefit in such a way as to gain further understanding of their course content, a broader appreciation of the discipline, an enhanced sense of civic responsibility, as well as greater respect for the different languages and cultures in our classes. Indeed, an increase in student engagement has been shown to play a central role in student success, and it is well noted that deeper learning and more innovative solutions take place in collaborative groups. We aim to make a meaningful contribution towards the production and repository of knowledge in multilingual teaching and learning for the benefit of the diverse student population and staff. This would strengthen language development, multilingualism, and multiculturalism at CPUT and empower and promote African languages as languages of science and education at CPUT, in other institutions of higher learning, and in South Africa as a whole.

Keywords: educational videos, multiculturalism, multilingualism, student engagement

Procedia PDF Downloads 159

Authors: Eva Šmelová, Alena Berčíková

Abstract:

The issue of school maturity and readiness of a child for a successful start of compulsory education is one of the long-term monitored areas, especially in the context of education and psychology. In the context of the curricular reform in the Czech Republic, the issue has recently gained importance. Analyses of research in this area suggest a lack of a broader overview of indicators informing about the current level of children’s school maturity and school readiness. Instead, various studies address partial issues. Between 2009 and 2013 a research study was performed at the Faculty of Education, Palacký University Olomouc (Czech Republic) focusing on children’s maturity and readiness for compulsory education. In this study, social skills were of marginal interest; the main focus was on the mental area. This previous research is smoothly linked with the present study, the objective of which is to identify the level of school maturity and school readiness in selected characteristics of social skills as part of the adaptation process after enrolment in compulsory education. In this context, the following research question has been formulated: During the process of adaptation to the school environment, which social skills are weakened? The method applied was observation, for the purposes of which the authors developed a research tool – record sheet with 11 items – social skills that a child should have by the end of preschool education. The items were assessed by first-grade teachers at the beginning of the school year. The degree of achievement and intensity of the skills were assessed for each child using an assessment scale. In the research, the authors monitored a total of three independent variables (gender, postponement of school attendance, participation in inclusive education). The effect of these independent variables was monitored using 11 dependent variables. These variables are represented by the results achieved in selected social skills. Statistical data processing was assisted by the Computer Centre of Palacký University Olomouc. Statistical calculations were performed using SPSS v. 12.0 for Windows and STATISTICA: StatSoft STATISTICA CR, Cz (software system for data analysis). The research sample comprised 115 children. In their paper, the authors present the results of the research and at the same time point to possible areas of further investigation. They also highlight possible risks associated with weakened social skills.

Keywords: compulsory education, curricular reform, educational diagnostics, pupil, school curriculum, school maturity, school readiness, social skills

Procedia PDF Downloads 255

Authors: Wenqi Liu, Reginald Bailey

Abstract:

This study proposes a comprehensive and effective approach to business-to-business (B2B) sales forecasting by integrating advanced machine learning models with a rule-based decision-making framework. The methodology addresses the critical challenge of optimizing sales pipeline performance and improving conversion rates through predictive analytics and actionable insights. The first component involves developing a classification model to predict the likelihood of conversion, aiming to outperform traditional methods such as logistic regression in terms of accuracy, precision, recall, and F1 score. Feature importance analysis highlights key predictive factors, such as client revenue size and sales velocity, providing valuable insights into conversion dynamics. The second component focuses on forecasting sales value using a regression model, designed to achieve superior performance compared to linear regression by minimizing mean absolute error (MAE), mean squared error (MSE), and maximizing R-squared metrics. The regression analysis identifies primary drivers of sales value, further informing data-driven strategies. To bridge the gap between predictive modeling and actionable outcomes, a rule-based decision framework is introduced. This model categorizes leads into high, medium, and low priorities based on thresholds for conversion probability and predicted sales value. By combining classification and regression outputs, this framework enables sales teams to allocate resources effectively, focus on high-value opportunities, and streamline lead management processes. The integrated approach significantly enhances lead prioritization, increases conversion rates, and drives revenue generation, offering a robust solution to the declining pipeline conversion rates faced by many B2B organizations. Our findings demonstrate the practical benefits of blending machine learning with decision-making frameworks, providing a scalable, data-driven solution for strategic sales optimization. This study underscores the potential of predictive analytics to transform B2B sales operations, enabling more informed decision-making and improved organizational outcomes in competitive markets.

Keywords: machine learning, XGBoost, regression, decision making framework, system engineering

Procedia PDF Downloads 33

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 75

Authors: Mirza Waseem Abbas, Syed Danish Raza

Abstract:

For many countries agriculture is still the major force driving the economy and a critically important socioeconomic sector, despite exceptional industrial development across the globe. In countries like Pakistan, this sector is considered the backbone of the economy, and most of the economic decision making revolves around agricultural outputs and data. Timely and accurate facts and figures about this vital sector hold immense significance and have serious implications for the long-term development of the economy. Therefore, any significant improvements in the statistics and other forms of data regarding agriculture sector are considered important by all policymakers. This is especially true for decision making for the betterment of crops and the agriculture sector in general. Provincial and federal agricultural departments collect data for all cash and non-cash crops and the sector, in general, every year. Traditional data collection for such a large sector i.e. agriculture, being time-consuming, prone to human error and labor-intensive, is slowly but gradually being replaced by remote sensing techniques. For this study, remotely sensed data were used for change detection (machine learning, supervised & unsupervised classification) to assess the increase or decrease in area under agriculture over the last fifteen years due to urbanization. Detailed Landsat Images for the selected agricultural districts were acquired for the year 2000 and compared to images of the same area acquired for the year 2016. Observed differences validated through detailed analysis of the areas show that there was a considerable decrease in vegetation during the last fifteen years in four major agricultural districts of the Punjab province due to urbanization (housing societies).

Keywords: change detection, area estimation, machine learning, urbanization, remote sensing

Procedia PDF Downloads 255

Authors: Andreas Johansson

Abstract:

Conceptual understanding needs to be grounded on observation of physical phenomena, experiences or metaphors. Observation of physical phenomena using demonstration experiments has a long tradition within physics education and students need to develop mental models to relate the observations to concepts from scientific theories. This study investigates how live and video experiments involving an acoustic trap to visualize particle-field interaction, field properties and particle properties can help develop students' mental models and how they can be used differently to realize their potential as teaching tools. Initially, they were treated as analogs and the lesson designs were kept identical. With a design-based approach, the experimental and video designs, as well as best practices for a respective teaching tool, were then developed in iterations. Variation theory was used as a theoretical framework to analyze the planned respective realized pattern of variation and invariance in order to explain learning outcomes as measured by a pre-posttest consisting of conceptual multiple-choice questions inspired by the Force Concept Inventory and the Force and Motion Conceptual Evaluation. Interviews with students and teachers were used to inform the design of experiments and videos in each iteration. The lesson designs and the live and video experiments has been developed to help teachers improve student learning and make school physics more interesting by involving experimental setups that usually are out of reach and to bridge the gap between what happens in classrooms and in science research. As students’ conceptual knowledge also rises their interest in physics the aim is to increase their chances of pursuing careers within science, technology, engineering or mathematics.

Keywords: acoustic trap, design-based research, experiments, variation theory

Procedia PDF Downloads 86