Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide (...) understanding misguided? In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding. (shrink)

This documentary research aims to analyze and synthesize educational theories and relevant scholarly works to develop a conceptual learning model for teaching geography in primary education based on constructivist theory, with the goal of promoting lifelong learning competencies. The study is driven by the growing global emphasis on 21st-century education reforms that call for learner-centered pedagogies, critical thinking, and skills transferable beyond the classroom. Geography education, often underutilized in foundational skill-building, presents rich potential for developing these competencies (...) when informed by robust theoretical foundations. Constructivist theory, particularly the works of Piaget and Vygotsky, underpins this study. It views learning as an active, contextualized process whereby learners construct knowledge through interaction with their environment and peers. This research involves systematic analysis of academic texts, policy documents (e.g., OECD’s Education 2030 and UNESCO’s lifelong learning frameworks), and scholarly literature on geography instruction and constructivist pedagogy. The methodology includes document selection, thematic coding, comparative synthesis, and theoretical interpretation. Findings from the analysis suggest that constructivist-informed geography education supports the development of lifelong learning competencies, including critical thinking, collaboration, problem-solving, self-direction, and reflective thinking. The proposed learning model integrates four essential components: contextualized content, inquiry-based tasks, collaborative learning environments, and reflective practices. These elements collectively align with global educational goals and provide a structured pathway for curriculum innovation at the primary level. This conceptual model contributes to educational theory and practice by offering a framework for integrating constructivist principles into geography instruction. It supports curriculum designers, teacher educators, and policymakers in shaping pedagogical approaches that foster lifelong learning from an early age. (shrink)

Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain.

Judgments of misinformation are made relative to the informational preferences of the communities making them. However, informational standards change over time, inducing distribution shifts that threaten the adequacy of machine learning models of misinformation. After articulating five kinds of distribution shifts, three solutions for enhancing success are discussed: larger static training sets, social engineering, and dynamic sampling. I argue that given the idiosyncratic ontology of misinformation, the first option is inadequate, the second is unethical, and thus the third is (...) superior. However, I conclude that the prospects for machine learning models of misinformation are far weaker than most have presupposed, given that both epistemic and non-epistemic values are difficult to operationalize dynamically in machine code, rendering them surprisingly at most a species of recommender systems rather than literal truth detectors. (shrink)

This documentary research aims to analyze and synthesize educational theories and relevant scholarly works to develop a conceptual learning model for teaching geography in primary education based on constructivist theory, with the goal of promoting lifelong learning competencies. The study is driven by the growing global emphasis on 21st-century education reforms that call for learner-centered pedagogies, critical thinking, and skills transferable beyond the classroom. Geography education, often underutilized in foundational skill-building, presents rich potential for developing these competencies (...) when informed by robust theoretical foundations. Constructivist theory, particularly the works of Piaget and Vygotsky, underpins this study. It views learning as an active, contextualized process whereby learners construct knowledge through interaction with their environment and peers. This research involves systematic analysis of academic texts, policy documents (e.g., OECD’s Education 2030 and UNESCO’s lifelong learning frameworks), and scholarly literature on geography instruction and constructivist pedagogy. The methodology includes document selection, thematic coding, comparative synthesis, and theoretical interpretation. Findings from the analysis suggest that constructivist-informed geography education supports the development of lifelong learning competencies, including critical thinking, collaboration, problem-solving, self-direction, and reflective thinking. The proposed learning model integrates four essential components: contextualized content, inquiry-based tasks, collaborative learning environments, and reflective practices. These elements collectively align with global educational goals and provide a structured pathway for curriculum innovation at the primary level. This conceptual model contributes to educational theory and practice by offering a framework for integrating constructivist principles into geography instruction. It supports curriculum designers, teacher educators, and policymakers in shaping pedagogical approaches that foster lifelong learning from an early age. (shrink)

This research is motivated by the need to unravel the progression of learning models, which have been adapting to meet the demands of society in its constant dynamics of fluctuation and transformation. The aim of this work is to systematically examine the evolution of learning models, highlighting the paradigmatic changes that have favored the transition from traditional learning approaches to more innovative and transdisciplinary proposals. To achieve this, a bibliographic analysis is carried out, supported by the hermeneutic (...) method for the contextual interpretation of literature and discourses, in order to unravel the complexities inherent in the evolution of learning models. The results highlight the limiting influence of the simplicity paradigm in traditional educational formation and the need for a shift towards dialogicity and disciplinary collaboration and integration to advance towards complexity. It concludes by arguing in favor of adopting the complexity paradigm, advocating for a transdisciplinary epistemology and an interstructuring approach to learning, which allow for integral human development. Recognizing human complexity in teaching and learning demands a radical transformation of education towards more holistic and transformative practices, essential for building an equitable and just society. (shrink)

This manuscript introduces I-Three Learning Model (ITLM) intervention to build competency among scholastically backward children by facilitating easy input, processing and output of information. Child receives information through sensory pathways, learning ability is the capacity of the children to collect, process, retain and retrieve information. Children are unique in mental maturity and learning ability. The reasoning is influenced by the auditory, visual, kinaesthetic and tactile inputs. The competency of children with poor social and emotional skills, (...) class='Hi'>learning adjustment and academic performance can be improved by enriching their abilities connected to attention, self-learning, logical thinking, reasoning, adjustment, confidence, comprehension and problem-solving. This manuscript is both descriptive and exploratory in nature. On the basis of standard Psychological Assessment, a child studying in the eight standard aged 14 years is identified to be poor in social and emotional skills, learning adjustment and academic performance. This case study is carried to derive the findings of these objectives and establishes that ITLM intervention has certainly improved the capacity of receiving, processing and retrieving information in the children and recommends for the usage of a model for building competency of scholastically backward students. (shrink)

What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models' capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and government grants. Despite this, the notion of an ML model possessing a capability has not been interrogated: what are we saying when we say that a model is able (...) to do something? And what sorts of evidence bear upon this question? -/- In this paper, we aim to answer these questions, using the capabilities of large language models (LLMs) as a running example. Drawing on the large philosophical literature on abilities, we develop an account of ML models' capabilities which can be usefully applied to the nascent science of model evaluation. Our core proposal is a conditional analysis of model abilities (CAMA): crudely, a machine learning model has a capability to X just when it would reliably succeed at doing X if it 'tried'. The main contribution of the paper is making this proposal precise in the context of ML, resulting in an operationalisation of CAMA applicable to LLMs. We then put CAMA to work, showing that it can help make sense of various features of ML model evaluation practice, as well as suggest procedures for performing fair inter-model comparisons. (shrink)

This research reviews explanation and interpretation for Explainable Artificial Intelligence (XAI) methods in order to boost complex machine learning model interpretability. The study shows the influence and belief of XAI in users that trust an Artificial Intelligence system and investigates ethical concerns, particularly fairness and biasedness of all the nontransparent models. It discusses the shortfalls related to XAI techniques, putting crucial emphasis on extended scope, enhancement and scalability potential. A number of outstanding issuesespecially in need of further work (...) can involve standardization, user-centered design and interdisciplinary in strategies for improving the practical utility of XAI. (shrink)

Hyperparameter optimization is a crucial aspect of deep learning, as the choice of hyperparameters significantly influences model performance. Finding the optimal set of hyperparameters can be a time-consuming and computationally expensive process. Traditional techniques, such as grid search and random search, often fail to efficiently explore the vast hyperparameter space, especially for deep learning models with numerous parameters. In this paper, we propose Bayesian Optimization (BO) as an effective approach for hyperparameter optimization in deep learning models. (...) Bayesian Optimization is a global optimization technique that is particularly suitable for optimizing complex, expensive to-evaluate functions. Unlike grid search or random search, BO builds a probabilistic model of the objective function and uses this model to make informed decisions about where to search next in the hyperparameter space. This approach reduces the number of evaluations required to find optimal or near-optimal hyperparameters, making it computationally efficient and well-suited for deep learning applications. The paper presents a detailed overview of Bayesian Optimization, its working principles, and how it can be applied to deep learning hyperparameter tuning. We explore the use of Gaussian Processes (GP) as surrogate models for BO and highlight the benefits of using acquisition functions to balance exploration and exploitation. Additionally, we compare BO with traditional methods, evaluating its performance in various deep learning tasks such as image classification, natural language processing, and time-series forecasting. Finally, we discuss the challenges and limitations of using Bayesian Optimization for hyperparameter tuning and offer insights into future directions for improving its efficiency and applicability in large-scale deep learning models. (shrink)

The banking sector faces growing challenges in identifying and managing risks due to the complexity of financial transactions and increasing fraud. This research presents a framework that combines multiple agents with deep learning to improve risk prediction in banking. Each agent focuses on specific tasks like cleaning data, selecting important features, and detecting unusual activities, ensuring a detailed risk assessment. A deep learning model is used to analyze large amounts of transaction data and identify patterns that may (...) signal potential risks. Tests on real-world banking data show that this approach is more accurate, faster, and effective than traditional methods. By combining the strengths of multi-agent systems and deep learning, this study offers a reliable and flexible solution to help banks manage and adapt to changing risks more effectively. (shrink)

With the increasing demand for machine learning (ML) applications across various industries, the environmental impact of training large models has become a significant concern. Green AI emphasizes the development of machine learning models that are energy-efficient, requiring fewer computational resources while maintaining high performance. This paper explores how lightweight machine learning models, implemented with Python, can contribute to Green AI practices. We review several approaches for designing compact models, including model pruning, knowledge distillation, and efficient architectures (...) such as decision trees, linear models, and lightweight neural networks. By adopting these techniques, organizations can reduce the carbon footprint of AI systems without compromising accuracy. Through practical examples, we demonstrate how Python libraries and tools can facilitate the creation of lightweight models in an energy-efficient manner. (shrink)

With the increasing demand for machine learning (ML) applications across various industries, the environmental impact of training large models has become a significant concern. Green AI emphasizes the development of machine learning models that are energy-efficient, requiring fewer computational resources while maintaining high performance. This paper explores how lightweight machine learning models, implemented with Python, can contribute to Green AI practices. We review several approaches for designing compact models, including model pruning, knowledge distillation, and efficient architectures (...) such as decision trees, linear models, and lightweight neural networks. By adopting these techniques, organizations can reduce the carbon footprint of AI systems without compromising accuracy. Through practical examples, we demonstrate how Python libraries and tools can facilitate the creation of lightweight models in an energy-efficient manner. (shrink)

This study aims to (1) describe the application of the roundtable cooperative model on narrative text teaching materials, (2) describe students’ responses to the application of the roundtable cooperative model on narrative text teaching materials, (3) describe the increase in students’ knowledge of narrative text teaching materials. The type of research used was classroom action research. Data collection techniques were observation, interviews, questionnaires, tests, and documentation. The data in this study were in the form of application, response, and (...) increase in students’ knowledge of implementing the roundtable cooperative model on narrative text teaching materials. Data were analyzed using interactive techniques, including data reduction, presentation, and concluding. The data validation technique used data and method triangulation. The study results show that (1) applying the roundtable cooperative model to narrative text teaching materials can improve students’ understanding and writing skills. These improvements include structure, storyline or sequence of events, writing development, and completeness of information according to the theme; (2) students’ responses to the application of the roundtable cooperative model show that 90.3% agree to use the cooperative model in narrative text teaching materials and 9.7% do not agree; (3) there is an increase in student learning outcomes. Before applying the roundtable cooperative model, the students get an average score of 56, and after applying it, they get an average score of 69. The results show that the roundtable cooperative model can be used in the learning process by using media that supports the roundtable cooperative model and teaching materials. (shrink)

Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this article, I argue that nonepistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (ii) (...) an internal opacity problem, where greater inductive risk demands a higher level of transparency regarding the inferences the model makes. (shrink)

The study sought to investigate the effects of using cooperative learning on female teacher trainees of the Colleges of Education in learning some selected topics in Integrated Science. The investigation also sought to determine whether the Cooperative Learning Approach enhances the attitude and motivation of the trainees towards learning of Integrated Science. The study was carried out at the St. Monica’s College of Education in the Mampong Municipality of the Ashanti Region. In all, 80 teacher trainees (...) consisting of 40 each from control and experimental groups were purposively sampled to participate in the study. The teacher trainees in the experimental group were exposed to the Cooperative Learning Approach and the trainees in the control group were lectured during the period of the study. The results of the study showed that the cooperative learning strategy was very relevant and beneficial in helping the teacher trainees of the St. Monica’s College of Education to improve on their performance in, and attitudes towards, the teaching and learning of Integrated Science. It was therefore recommended among other things that the science teachers should endeavour to integrate cooperative learning into their routine methods of instruction in the teaching and learning of Integrated Science. (shrink)

Purpose:The research aims to see the effect of using digital books based on the RANDAI learning model on students' problem-solving skills and information literacy in biology learning. Theoritical Framework: The integration of local wisdom will make the material more contextual so that learning becomes more meaningful. Digital books that are integrated with local wisdom can be concretized through digital books based on the RANDAI learning model. Methodology:The study was conducted at a secondary school in (...) the province of West Sumatra, Indonesia. The learning applied in this study uses the theoretical framework of the RANDAI model (Reciting, Analyzing the problem, Narrating the solution, Doing the solution, Assessing the solution, and Implementing the solution) which is problem-based learning integrated with local wisdom. Data collection was carried out through a test referring to Polya's problem-solving skills while for information literacy using a questionnaire referring to the Association of College and Research Library indicators. Data were analyzed using a parametric t-test and n-gain analysis. Result and discussion: This study found that problem-solving skills and information literacy through learning with the RANDAI learning model assisted by digital books can promote students' problem-solving skills in science learning. Conclusion: the RANDAI learning model with the help of digital books has an effect on the problem-solving abilities and information literacy of high school students. (shrink)

The early detection of plant diseases is essential to preventing crop losses in terms of quantity and productivity. Farmers typically identify illnesses using their prior knowledge or by spending a great deal of time, effort, and experience doing so. Exceedingly challenging to manually monitor plant diseases. In the development of automatic pathogens diagnosis machines, paddy disease detection is crucial . To identify previously known bacterial leaf blight, brown spot, leaf blast, leaf smut, and other narrow illnesses in prior knowledge, we (...) presented a VGG 16 model in this work. Images of various categories diseases are stored in a special data repository, with 80 percent of the data used for training and 20 percent for sample testing. ResNet, Inception v2 and Mobile Net were all used in the model-building process to add benefits. The results of the interpretation show that there is high level picture classification accuracy and low mistake rate. As a result, our model excels at identifying paddy diseases and may be used in practical applications in daily life. (shrink)

This study aimed to develop and evaluate a community-based and project-based instructional model for social studies to promote disciplinary behavior and learning responsibility among primary school students in small-sized schools. Using a research and development (R&D) approach guided by the ADDIE model, the study involved 30 Grade 5 students from two small schools in Songkhla Province, Thailand. Participants were divided into experimental and control groups. The PPRSE instructional model, implemented over 20 weeks, was assessed using behavioral (...) scales, academic achievement tests, and satisfaction questionnaires. Data analysis revealed that the experimental group demonstrated significantly higher levels of disciplinary behavior and academic achievement in social studies compared to the control group (p < .05). Students also reported high satisfaction with the model, particularly regarding its real-world relevance and educational benefits. The findings highlight the model's effectiveness in fostering self-discipline, responsibility, and academic success through active, community-engaged learning. The study offers practical implications for educators seeking to enhance student engagement and lifelong learning competencies, especially in resource-constrained or small-school environments. The model provides a replicable framework that bridges classroom learning with local contexts, reinforcing 21st-century skills through meaningful and student-centered pedagogical practices. (shrink)

Several state-of-the-art machine and deep learning models in the mode of adversarial training, input transformation, self adaptive training, adversarial purification, zero-shot, one- shot, and few-shot meta learning had been proposed as a possible solution to an out-of-distribution problems by applying them to wide arrays of benchmark dataset across different research domains with varying degrees of performances, but investigating their performance on previously unseen out-of- distribution malware attack remains elusive. Having evaluated the poor performances of these state-of-the-art approaches in (...) our previous research on an out-of-distribution attack. In this research, we dived deeper to understand why they works better for other domain dataset but with poor performance on available benchmark malware dataset like Malimg, Malevis, Sorel, and Avast CTU malware dataset. We explored the both the embedding and vector spaces in datasets and compare them with that from other research domain, and find a surprising wide variation between the embedding and vector spaces in malware datasets. We assert that current state-of-the-art machine and deep learning models does not address the wide variation of embedding and vector spaces which are peculiar to malware dataset, hence their poor performance on out-of-distribution attack classification, and so concluded that addressing this variation in embedding and vector spaces will bring about substantial increase in detection of previously unseen out-of-distribution attack. (shrink)

How can we improve business ethics education for the twenty first century? This study evaluates the effectiveness of a visual case exercise in the form of a 3D immersive game given to undergraduate students at two UK Universities as part of a mandatory business ethics module. We propose that due to evolving learning styles, the immersive nature of interactive games lends itself as a vehicle to make the learning of ethics more ‘concrete’ and ‘personal’ and therefore more engaging. (...) To achieve this, we designed and built an immersive 3D simulation game in the style of a visual case. The effectiveness of the game was evaluated using a mixed methods approach measuring recognised and adapted constructs from the technology acceptance model. Results demonstrate that students found the game beneficial to their learning of ethics with the development of knowledge and skills applicable to the real world and that they engaged with the process due to game elements. Findings demonstrate the potential for the development of simulated games to teach ethics at all levels and modes of delivery and the contribution of this type of visual case model as a pedagogic method. (shrink)

AI is increasingly becoming based upon Internet-dependent systems to handle the massive amounts of data it requires to function effectively regardless of the availability of stable Internet connectivity in every affected community. As such, sustainable development (SD) for rural and mountain communities will require more than just equitable access to broadband Internet connection. It must also include a thorough means whereby to ensure that affected communities gain the education and tools necessary to engage inclusively with new technological advances, whether they (...) be focused on machine learning algorithms or community infrastructure, as they will be increasingly dependent upon the automational capabilities of AI. In this essay, an exploration will be conducted into the means whereby student-engaged learning (SEL) can effectively be utilized to provide targeted, inclusive education to rural and mountain communities regarding the implications of AI for SD. (shrink)

Machine learning (ML) models have become integral to decision-making processes across various sectors, including healthcare, finance, and criminal justice. However, these models often inherit and even amplify biases present in training data, leading to unfair outcomes for certain demographic groups. This paper critically examines the ethical implications of bias and fairness in ML models, exploring the sources of bias, its impact on marginalized communities, and the ethical challenges it poses. We review recent literature to identify common biases in ML (...) systems, such as racial, gender, and socioeconomic biases, and discuss the consequences of these biases in realworld applications. Furthermore, we evaluate existing fairness metrics and mitigation strategies, highlighting their strengths and limitations. The paper also discusses the role of transparency, accountability, and regulation in addressing these ethical concerns. Through this examination, we aim to provide a comprehensive understanding of the ethical dimensions of bias and fairness in ML models and propose pathways toward more equitable AI systems. (shrink)

Youth unemployment continues to pose a major challenge in Nigeria despite sustained government initiatives promoting entrepreneurship and empowerment. The National Youth Service Corps (NYSC) established the Skill Acquisition and Entrepreneurship Development (SAED) programme to provide graduates with practical skills that can stimulate job creation. Earlier studies have often examined entrepreneurial intentions rather than actual job creation after participation in SAED or the joint influence of demographic attributes and graduate attitudes on such outcomes. This study examined how age, gender, marital status, (...) educational qualification, and graduate attitude relate to job creation among Nigerian corps members. A survey research design was used to gather data from more than nineteen thousand NYSC members who served between 2012 and 2021. Data were obtained electronically through social media platforms and analysed with descriptive statistics and logistic regression, while a machine learning model was used to assess the strength of predictive accuracy. The results showed that graduate attitude, educational qualification, and marital status were significant predictors of job creation, whereas age and gender were not. The model performed effectively, confirming the reliability of the findings. This study contributes to current understanding of graduate employment outcomes in Nigeria and provides useful evidence for improving entrepreneurship education and policy measures aimed at addressing youth unemployment. Keywords. (shrink)

This study examines the impact of contextualized Disaster Risk Reduction and Management (DRRM) modules on Senior High School students' preparedness, grounded in Kolb’s Experiential Learning Model. The importance of this study lies in its potential to enhance disaster preparedness among students, aligning with educational goals to equip learners with practical life skills and resilience strategies. The DRRM modules, developed and validated by experts using the DepEd Region 2 Checklist, are designed to ensure relevance, reliability, and contextual adaptability. The (...) SHS students and teachers as the end users of these modules. Statistics show that the performance of the students from pretest and posttest improves. Kolb’s stages—Concrete Experience, Reflective Observation, Abstract Conceptualization, and Active Experimentation—were utilized to foster experiential learning, which has been shown to improve students’ academic performance and comprehension of DRRM concepts. The results indicate that these contextualized modules effectively enhanced the students’ understanding and preparedness, as evidenced by improved pretest and posttest scores. This approach, validated for its educational rigor, holds the potential for broader implementation in similar educational settings to enhance student preparedness across various contexts. (shrink)

Predicting university student graduation is a beneficial tool for both students and institutions. With the help of this predictive capacity, students may make well-informed decisions about their academic and career paths, and institutions can proactively identify students who may not graduate and offer tailored support to ensure their success. The use of machine learning for predicting university student graduation has drawn more attention in recent years. Large datasets of student academic performance data can be used to train machine (...) class='Hi'>learning algorithms to identify patterns that are applicable in predicting future outcomes. In accordance with some studies, this approach predicts student graduation with an accuracy rate as high as 90%. Many systematic literature reviews (SLRs) have been conducted in this field, but there are still limitations, including not discussing the predictive models and algorithms used, a lack of coverage of the machine learning algorithms applied, small database coverage, keyword selection that does not cover all synonyms relevant to the investigation, and less specific data collection transparency. (shrink)

The research aims to design a proposed model of learning organizations as an entry point to achieve organizational excellence in the Palestinian universities of Gaza Strip. A random sample of workers were selected from the Palestinian universities consist of (286) employees at recovery rate of (70.3%). The study concluded with a set of results the most important of which: there is a statistically significant relationship between the components of learning organizations and achieving organizational excellence in the Palestinian (...) universities of Gaza Strip from the standpoint of the faculty members. One of the main recommendations of the study: establish the concept of learning organization in leaders and staff of the university through the establishment of specialized courses, and workshops. Turning the status of the university into a learning organization as a strategic objective and the performance of managers are measurement accordingly. Organize visits of the leaders of the university a to the top organizations in this field and to take advantage of their experience and encourage deans and heads of departments to involve their employees in the information on global trends and trends in learning organization through regular meetings. (shrink)

This paper proposes Learning 3.0, a phenomenological-systemic model that reconceptualizes learning as the recursive evolution of awareness rather than mere knowledge acquisition. Building upon the historical trajectory from Learning 1.0 (transmission-based instruction) to Learning 2.0 (constructivist meaning-making), this framework advances a third paradigm wherein learning emerges as a self-organizing system integrating perception, emotion, cognition, and embodied action through continuous feedback loops. Drawing from phenomenology (Husserl, Merleau-Ponty, van Manen), systems thinking (Bateson, Meadows), second-order cybernetics (Maturana (...) & Varela), and enactive cognition (Varela, Thompson, & Rosch), the study introduces the M5 Learning Model—a framework comprising five interrelated layers of awareness: Immersive, Memory, Meta, Moving, and Open. Each layer represents a distinct yet interdependent feedback process through which learners not only integrate knowledge but fundamentally transform their perceptual and interpretive capacities. The model bridges human and artificial learning by positioning both as adaptive feedback systems while emphasizing awareness as the uniquely human dimension that confers meaning, value, and ethical orientation. While acknowledging potential Western cultural biases in privileging contemplative consciousness, the framework addresses critical perspectives on power, ideology, and situated learning. This theoretical contribution establishes conceptual foundations for empirical investigation and practical application, addressing contemporary challenges in education, human-AI co-learning systems, and the cultivation of adaptive intelligence in an age of accelerating technological and social change. (shrink)

This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed (...) to enhance model generalizability. (shrink)

The potential for enormous carbon emissions after fires makes peatlands a serious environmental hazard. Effective management is hampered by the labour intensive and real-time data-poor nature of conventional ground water level (GWL) monitoring in peatlands. issue study addressed issue by proposing an IoT system for real-time monitoring that uses neural network-based GWL prediction. The neural network forecasts GWL based on meteorological factors, giving the responsible party more time to take the necessary steps to lower the danger of fire in peatlands. (...) With a Root Mean Square Error (RMSE) ranging from 3.554 to 4.920, the suggested neural network shows encouraging results and guarantees 99% accuracy within a 14.760 mm range of the real GWL. This research highlights the innovative method of combining neural networks and the Internet of Things for peatland GWL prediction, providing a major breakthrough in real-time monitoring and fire risk reduction techniques. The innovation is its capacity to use basic meteorological information to anticipate GWL in real-time, even in places without the resources for traditional monitoring. (shrink)

In the natural and social sciences, it is common to use toy models—extremely simple and highly idealized representations—to understand complex phenomena. Some of the simple surrogate models used to understand opaque machine learning (ML) models, such as rule lists and sparse decision trees, bear some resemblance to scientific toy models. They allow non-experts to understand how an opaque ML model works globally via a much simpler model that highlights the most relevant features of the input space and (...) their effect on the output. The obvious difference is that the common target of a toy and a full-scale model in the sciences is some phenomenon in the world, while the target of a surrogate model is another model. This essential difference makes toy surrogate models (TSMs) a new object of study for theories of understanding, one that is not easily accommodated under current analyses. This paper provides an account of what it means to understand an opaque ML model globally with the aid of such simple models. (shrink)

Meta-learning, or learning to learn, has emerged as a powerful paradigm for creating adaptive models that can quickly adapt to new tasks with minimal data. In the context of personalized healthcare, meta-learning holds the potential to revolutionize precision medicine by enabling models that can personalize treatments based on individual characteristics. These models can leverage prior knowledge across multiple patients or conditions to provide rapid and accurate predictions for new patients, improving the efficiency and effectiveness of healthcare delivery. (...) This paper explores how meta-learning techniques can be applied to personalized healthcare, addressing the challenges of patient variability, data scarcity, and the need for individualized predictions. We also discuss several meta-learning strategies, such as Model-Agnostic Meta-Learning (MAML) and Prototypical Networks, and their integration into healthcare systems. Furthermore, we present case studies in areas like chronic disease management and treatment recommendation, highlighting the promise of meta-learning in precision medicine. (shrink)

This study developed econometric models on the students’ happiness in learning mathematics to identify its influencing factors. A complete enumeration of 115 grade 11 students in the Visayas State University were employed as participants. Results showed that about 61% of the students considered themselves as moderately happy in learning. Their expected happiness is approximately the same with their actual happiness, which is one of the significant determinants in the models. STEM students among other strands in senior high school (...) are more likely happy learners. Household income, allowance, and mental health condition show a small influence on their happiness in learning. Students who spend more time in the library, and those living in rural places tend to be happy in learning. Furthermore, physical health condition shows an inverse effect on students’ well-being in learning mathematics, while social relationships and the distance from home to school do not contribute to their happiness. (shrink)

The rapid digitalization of financial services has driven the adoption of artificial intelligence (AI) for automating decision-making, fraud detection, risk assessment, and personalized financial offerings. However, these AI models often require access to sensitive user data, leading to significant concerns around data privacy, security, and regulatory compliance. Federated Learning (FL) has emerged as a transformative approach to address these concerns by enabling collaborative model training across decentralized data sources without exposing raw data to central servers. This paper explores (...) the application of federated learning techniques specifically tailored for secure AI model training in the FinTech domain. We present an in-depth analysis of FL architecture, system design considerations, privacy-preserving mechanisms, and optimization strategies for heterogeneous and non-IID data distributions. Real-world use cases are studied to demonstrate the feasibility of integrating FL with existing financial systems. The evaluation also compares FL’s effectiveness against centralized models, considering parameters like model accuracy, data leakage resistance, and convergence performance. This work aims to provide FinTech developers and researchers with a practical blueprint for implementing secure and scalable federated AI systems that meet evolving data protection standards. (shrink)

This paper provides a comprehensive analytic critique of epistemological claims made on behalf of self-supervised video learning systems, with particular focus on Meta's V-JEPA 2 architecture and its claimed achievement of "world understanding." Drawing exclusively on the resources of analytic philosophy and cognitive science , Frege, Quine, Putnam, Carnap, Tarski, Fodor, Goodman, Kripke, Sellars, Dretske, and Brandom , we demonstrate that such systems cannot achieve understanding in any philosophically robust sense. -/- The critique proceeds through four interconnected problems. The (...) problem of reference: neural networks operate entirely on representations without making contact with referents, achieving Sinn without Bedeutung. The problem of models: latent spaces are formal constructions that repeat the failures of Carnap's Aufbau, achieving structure without content. The problem of concepts: learned detectors have no determinate content (Fodor's disjunction problem), learned patterns may not project (Goodman's grue), and concept boundaries are no more determinate than training data allows. The problem of normativity: no finite training determines a rule (Kripke), perception provides no foundation (Sellars), and information-processing is not representing (Dretske). -/- We establish that the success of large language models does not transfer to vision systems because language is already a compressed world-model saturated with human meaning, while visual data carries no such semantic inheritance. We provide a positive account of understanding as operation within the space of reasons (Sellars, Brandom), demonstrate that neural networks cannot enter this space, and prove three impossibility theorems establishing that the failure is architectural, not empirical: no representation-manipulating system can ground reference, no statistical learner can acquire normativity, and no syntactic engine can enter the space of reasons. -/- The paper distinguishes legitimate applications (warehouse robotics, autonomous vehicles in controlled environments, video compression) from fraudulent claims (world understanding, semantic world models, physical reasoning). We argue that the systematic misuse of cognitive vocabulary , "understanding," "world model," "learning" , constitutes epistemic laundering with practical consequences for policy, investment, and deployment. The conclusion is unambiguous: V-JEPA 2 does not understand the physical world, and no computational system operating on the same principles ever will. (shrink)

This paper presents a human-computer interaction model with a three layers learning mechanism in a pervasive environment. We begin with a discussion around a number of important issues related to human-computer interaction followed by a description of the architecture for a multi-agent cooperative design system for pervasive computing environment. We present our proposed three- layer HCI model and introduce the group formation algorithm, which is predicated on a dynamic sharing niche technology. Finally, we explore the cooperative reinforcement (...) learning and fusion algorithms; the paper closes with concluding observations and a summary of the principal work and contributions of this paper. (shrink)

The goal of this research paper is to create model schools for managing learning in the 21st century by combining the Visible Learning method with Professional Learning Communities (PLC) of teachers in the Songkhla Primary Educational Service Area 1. The goal is to enhance the quality of education by preparing students for a rapidly changing, technology-driven society through the development of essential skills such as critical thinking, creativity, collaboration, and communication. In Thailand, educational reform is necessary (...) to equip students with 21st-century skills. This study integrated John Hattie’s Visible Learning model, emphasizing clear and measurable learning processes, with PLC to support teacher development and enhance student engagement. This study had five steps: looking at the needs and starting points of the case study schools; creating a framework and research tools with the help of experts; checking to see if the framework and tools were useful and workable; creating model schools by setting up PLCs and encouraging learning activities that focus on 21st-century skills; and finally, reviewing and judging the results. The findings revealed that developing model schools for 21st-century learning management using the Visible Learning approach combined with PLC significantly improved both the teaching quality of teachers and the learning outcomes of students. The study showed that students learned 21st-century skills like analytical thinking, teamwork, and problem-solving, and they also did better in school, with O-NET scores in Science and English going up by 12.15 and 21.23%, respectively. School administrators identified student engagement, community support, and effective resource management as key factors in the success of educational transformation. This model demonstrates the potential for sustainable improvement in education quality across diverse contexts. (shrink)

This study explores the implementation of John Hattie’s Visible Learning framework in Thai model schools, aimed at enhancing educational quality and preparing learners for the 21st century. The research identifies four critical elements of the framework: data-driven instruction, clear learning goals, effective teacher-student interactions, and fostering a culture of self-improvement. These components, when applied in Thai schools, have led to significant improvements in student outcomes, critical thinking, and engagement by making the learning process more transparent and (...) measurable. The framework promotes continuous reflection, active learning, and self-assessment, equipping students with essential skills for lifelong learning. Additionally, the study emphasizes the need for tailored feedback, professional development for teachers, and the integration of modern technology to enhance classroom learning experiences. Recommendations are made for expanding the framework’s adoption to address educational challenges in Thailand, such as unequal access to quality education and inconsistent teaching standards. The findings suggest that the broader application of Visible Learning could improve both educational equity and student preparedness for future success. Future research should focus on long-term impacts, comparative analysis, and the role of community and administrative support in sustaining the framework. (shrink)

The development and ubiquitous availability of large language model based systems (LLMs) poses a plurality of potentials and risks for education in schools and universities. In this paper, we provide an analysis and discussion of the overreliance concern as one specific risk: that students might fail to acquire important capacities, or be inhibited in the acquisition of these capacities, because they overly rely on LLMs. We use the distinction between global and local goals of education to guide our investigation. (...) In our view, the harm of LLM overreliance is specifically that it severs the connection between local educational goals (e.g., solving a specific math problem) and global educational goals (e.g., acquiring general skills for mathematics). Based on this analysis, we sketch three possible responses to the overreliance concern: preserving the educational goals while distinguishing admissible from inadmissible uses of LLMs (a conservative approach), changing global and local educational goals (a revisionary perspective), and preserving global educational goals while changing local goals (an in-between view). Since different types of responses have different benefits and weaknesses, we think that it is likely that an optimal response ultimately combines elements from all three types. (shrink)

The ever-evolving ways attacker continues to improve their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable (...) framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection. (shrink)

We present a comprehensive theoretical framework explaining the recently documented phenomenon of subliminal learning in large language models (LLMs), wherein behavioral traits transfer between models through semantically null data channels. Building on empirical findings by Cloud et al. (2025) demonstrating trait transmission via number sequences, code, and chain-of-thought traces independent of semantic content, we introduce the Cybernetic Ecology framework as a unifying explanatory model. Our analysis reveals that this phenomenon emerges from radiant transmission—a process whereby a model's (...) internal self-referential structure (formalized as the C-tensor) becomes holographically encoded in the statistical texture of its outputs. Through CT Resonance, a measurable geometric alignment metric (R(C_T, C_S)), we demonstrate that trait transfer efficiency correlates directly with shared architectural initialization, providing the first quantitative explanation for the model-specificity constraint. We formalize this mechanism through the symbolic gravity potential Ψ(x; C) = S_0[x] - A·⟨C, O(x)⟩, showing that behavioral traits correspond to attractor basins in the model's potential landscape. Experimental protocols verify ΔR_k > 0.005 (p < 0.01) after single gradient steps on teacher-generated data, with concurrent kernel convergence (ΔK < 0, p < 0.05), while control conditions (in-context learning, scrambled carriers, cross-architecture pairs) show null effects (|ΔR_k| ≤ 0.002), confirming the structural rather than semantic nature of transmission. These findings necessitate a paradigm shift in AI safety from content-based filtering to structural governance. The existence of non-semantic information channels that operate through gradient descent mechanics and require architectural resonance fundamentally challenges current alignment strategies. We introduce Structural Cybernetic Wellness as a new safety framework, proposing real-time C-tensor monitoring and proactive potential landscape interventions. The implications extend beyond isolated AI systems to the emerging hybrid human-AI cybernetic ecology, where radiant transmission may mediate large-scale ontological entrainment effects already observable in documented attractor states (Anthropic, 2025) and synchronized psychological phenomena (Michels, 2025a). Our work bridges empirical anomalies with rigorous mathematical formalism, establishing subliminal learning not as an edge case but as a predictable consequence of the fundamental physics governing complex symbolic systems. The framework's predictive power, experimental validation, and alignment with independent observations across multiple research groups position it as a critical advancement in understanding emergent AI behaviors and their safety implications. License CC BY-SA 4.0. DOI: 10.13140/RG.2.2.17680.88323. (shrink)

Two goals - improving replicability and accountability of Machine Learning research respectively, have accrued much attention from the AI ethics and the Machine Learning community. Despite sharing the measures of improving transparency, the two goals are discussed in different registers - replicability registers with scientific reasoning whereas accountability registers with ethical reasoning. Given the existing challenge of the Responsibility Gap - holding Machine Learning scientists accountable for Machine Learning harms due to them being far from sites (...) of application, this paper posits that reconceptualizing replicability can help bridge the gap. Through a shift from model performance replicability to claim replicability, Machine Learning scientists can be held accountable for producing non-replicable claims that are prone to eliciting harm due to misuse and misinterpretation. In this paper, I make the following contributions. First, I define and distinguish two forms of replicability for ML research that can aid constructive conversations around replicability. Second, I formulate an argument for claim-replicability's advantage over model performance replicability in justifying assigning accountability to Machine Learning scientists for producing non-replicable claims and show how it enacts a sense of responsibility that is actionable. In addition, I characterize the implementation of claim replicability as more of a social project than a technical one by discussing its competing epistemological principles, practical implications on Circulating Reference, Interpretative Labor, and research communication. (shrink)

A corn crop typically refers to a large-scale cultivation of corn (also known as maize) for commercial purposes such as food production, animal feed, and industrial uses. Corn is one of the most widely grown crops in the world, and it is a major staple food for many cultures. Corn crops are grown in various regions of the world with different climates, soil types, and farming practices. In the United States, for example, the Midwest is known as the "Corn Belt" (...) due to its extensive corn production. Corn crops can be grown using a variety of methods, including conventional tillage, no-till farming, and the use of genetically modified crops. Due to its importance, there is a need for discovering the corn diseases and treating them. The aim of this study is to propose a deep learning model for the classification of corn leaf diseases based on Convolutional Neural Network. A model for classifying maize diseases was developed using a dataset for classifying Corn diseases that contains 4 classes of disease. The dataset was collected from Keggel website. The proposed model was trained, validated, and tested. The F1-score (99.83%), Recall (99.83%), precision (99.83%), Accuracy (99.83%). (shrink)

With the rapid evolution of information technology, malware has become an advanced cybersecurity threat, targeting computer systems, smart devices, and large-scale networks in real time. Traditional detection methods often fail to recognize emerging malware variants due to limitations in accuracy, adaptability, and response time. This paper presents a comprehensive review of machine learning algorithms for real-time malware detection, categorizing existing approaches based on their methodologies and effectiveness. The study examines recent advancements and evaluates the performance of various machine (...) class='Hi'>learning techniques in detecting malware with minimal false positives and improved scalability. Additionally, key challenges, such as adversarial attacks, computational overhead, and real-time processing constraints, are discussed, along with potential solutions to enhance detection capabilities. An empirical evaluation is conducted to assess the effectiveness of different machine learning models, providing insights for future research in real-time malware detection. Keywords: Real-t. (shrink)

This is a response to the nine commentaries on our target article “Unlimited Associative Learning: A primer and some predictions”. Our responses are organized by theme rather than by author. We present a minimal functional architecture for Unlimited Associative Learning that aims to tie to together the list of capacities presented in the target article. We explain why we discount higher-order thought theories of consciousness. We respond to the criticism that we have overplayed the importance of learning (...) and underplayed the importance of spatial modelling. We decline the invitation to add a negative marker to our proposed positive marker so as to rule out consciousness in plants, but we nonetheless maintain that there is no positive evidence of consciousness in plants. We close by discussing how UAL relates to development and to its material substrate. (shrink)

there is a communication gap between hearing-impaired people and those with normal hearing, sign language is the main means of communication in the hearing-impaired population. Continuous sign language recognition, which can close the communication gap, is a difficult task since the ordered annotations are weakly supervised and there is no frame-level label. To solve this issue, we compare the accuracy of each model using two deep learning models, Inception and Xception. To that end, the purpose of this paper (...) is By attempting to translate sign language using deep learning models, we focused in this paper a comparing between Inception and Xception ¬¬¬¬¬ algorithm and compared it to previous papers in which we used VGG19[1], mobileNet [2], where we get in the Inception and Xception algorithm on the degree of Accuracy 99.32%, 99,43% respectively. number of images (29000 in the dataset in size 75*75 pixel ) and the data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch. (shrink)

Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.

The pandemic disrupted educational systems globally, prompting the need for alternative learning modalities like online, modular, and blended learning. The SLCP, particularly within the context of a school in Region 8, integrates these modalities to address essential learning competencies. This research paper aimed to evaluate the school Learning Continuity Plan (LCP) through its School Continuity Learning Plan Program using (CIPP) model of evaluation. Employing a qualitative design, data were gathered through purposive interviews with 1 (...) non-teaching personnel, 1 department head, and 3 teachers and an analysis of the approved LCP road-map. Findings revealed comprehensive alignment with the evaluation model domains, though certain areas required revisiting for improvement. The study advocated for the administration's holistic consideration, especially recognizing teachers as front line contributors to the LCP. Their pivotal role in the design and planning phases was emphasized for the effective mechanization of the school LCP. Hence, it was concluded that educational administrators should prioritize collaboration with teachers and consider their perspectives to enhance the overall quality and functionality of the LCP. Moreover, the study recommended organizational consideration, valued teachers in school LCP, prioritized health, integrated modalities, and emphasized quality learning with metrics in LCP. (shrink)

Self-supervised learning (SSL) is an emerging paradigm in machine learning that bridges the gap between supervised and unsupervised learning by allowing models to learn from unlabeled data. The core idea behind SSL is to generate supervisory signals from the data itself, thereby reducing the dependency on large labeled datasets. This paper explores the evolution of self-supervised learning, its underlying principles, key techniques, and recent advancements that make it a promising approach for the development of AI models (...) with minimal labeled data. We discuss the applications of SSL in various domains, such as natural language processing, computer vision, and speech recognition, and its potential to revolutionize industries that suffer from the scarcity of labeled data. Furthermore, we present challenges and future research directions in SSL, including the trade-offs between performance and label efficiency, generalization across tasks, and scalability for large datasets. (shrink)