Intuitive Nature Research Articles

Empirical study on the psychological mechanism of consumers in the context of live streaming seafood sales: evidence from China

The advent of the 5G era signifies China’s official entry into the digital economy. This technological shift has attracted countless merchants to the market via e-commerce platforms, seeking to capitalize on new opportunities. Concurrently, it offers a novel avenue for expanding seafood sales channels in China. The interactive and intuitive nature of e-commerce live streaming has subtly but significantly influenced and shaped consumers’ purchasing behaviors. In light of this, this study, based on 330 valid survey responses and grounded in the theories of perceived value and consumer trust, explores the psychological mechanisms affecting consumers in the context of e-commerce live streaming for aquatic products. The findings reveal that: (1) Consumer trust positively impacts purchase intention in the context of live-streamed seafood e-commerce. (2) Perceived entertainment value positively influences consumer purchase intention. (3) Perceived utilitarian value also positively influences consumer purchase intention. This paper contributes theoretically by broadening the application of perceived value theory and enhancing the understanding of consumer behavior concerning seafood. Additionally, it may offer practical insights into the expansion of seafood sales channels and market scale in other countries and regions.

Inclusive elementary school musicking practice for Japanese students with special needs: A narrative inquiry

Musicking is an old, yet new concept of music-making. The term, first proposed by anthropologist Christopher Small, intended to reinterpret music as not merely an act but also as an object. While musicking, anyone can participate in musical activities, which include singing, dancing, improvisation, and listening to musicking sessions. This study examines the use of musicking strategies in a Japanese elementary school with special needs students. The initial participants comprised 10 students from the first to sixth grades from a public elementary school in the northern part of Okayama Prefecture, Japan. Dr. A, the lead researcher of this study, is a violinist–teacher–researcher with professional orchestra experience in the United States. His career as a guest music teacher spans more than 20 years. In addition, two homeroom teachers and two university students supported the research project as participant observers. Based on Dr. A’s related practice in this area, this study explored musicking with a special emphasis on student inclusion. By applying a narrative inquiry and multivocal ethnography, this study investigated various events that were experienced in the musicking practice. Finally, the participants co-constructed a research-based narrative, focusing on creating a learning environment enabling all participants to build a musical community together. By synthesizing the participants’ thoughts, providing insights into musicking practice, exploring unexpected events, and utilizing the intuitive nature of music, the participants identified an approach for a comprehensive model for musicking.

Trusting Our Instincts: Evaluating the Evolutionary and Rational Foundations of Moral Intuitions

This paper delves into the nature and reliability of moral intuitions, building upon Jonathan Haidt’s social intuitionist model, which suggests that moral judgments stem from instinctive reactions followed by rational justifications. By examining Haidt’s experiments, which reveal the primacy of intuitive responses over rational deliberation, this paper seeks to address whether we have valid reasons to trust these moral intuitions. Drawing from evolutionary psychology, it argues that moral intuitions have historically contributed to the survival and success of the human species, thereby providing them with normative value. However, given the rapid advancements in technology and societal changes, these intuitions may sometimes lead to nonoptimal or disastrous outcomes. The paper proposes a rational test to determine the reliability of moral intuitions: we should trust an intuition if its generalized application benefits the human species. Potential counterarguments, such as the historical misuse of evolutionary logic in eugenics, are addressed to underscore the validity of this test. The conclusion emphasizes that while moral intuitions are generally trustworthy due to their evolutionary roots, rational thought must intervene when intuitions become outdated in the face of contemporary challenges.

PD-Explain: A Unified Python-Native Framework for Query Explanations Over DataFrames

Interfaces that rely on the Python programming language have become a popular tool for data analysis and exploration. In particular, the Pandas library allows users to query, manipulate, and visualize data in an easy and intuitive manner. However, users who perform such manipulations over the data in the exploratory process may struggle to justify their results, or understand which part (if any) of the obtained results is interesting and why. To handle such scenarios we developed PD-Explain, a Python library that adapts multiple prevalent query explanation approaches from the literature, and makes them accessible to Pandas users. PD-Explain is seamlessly integrated with Pandas and contains explanation functions that users can employ to choose the explanation approach they wish to use along with the necessary parameters in order to get the explanation in the suitable form. PD-Explain further allows users to automatically detect the interesting parts of a query result and get a visualization of the explanation accompanied by a Natural Language description. Our demonstration will include four different types of query result explanations and three real-world datasets with appropriate analysis tasks that will highlight the intuitive nature and usefulness of PD-Explain in data exploration tasks.

Storytelling based on generative AI to promote the inclusion of people with disabilities

This article presents the comprehensive design and evaluation of a digital storytelling system tailored forchildren aged 4 to 6 in Latin America. This system is based on generative artificial intelligence.Tests were conducted encompassing the system’s functionality, content diversity, generation times,voice evaluation, intonation, speed, and pronunciation quality. The results confirm the system’s correctoperation and intuitive nature. The generated stories exhibit a high degree of diversity, as evidenced bythe calculation of Jaccard indices, with a maximum value of 0.2 observed in the evaluations of 30 analyzedstories. As anticipated, generation times increase with the length of the stories. It was determined thatthe voice best suited for storytelling is Onyx from OpenAI’s TTS. However, pronunciation errors werenoted in all TTS model voices. According to the conducted analysis, the system produces diverse stories that promote values among Spanish-speaking children, thus emphasizing the importance of inclusivity for individuals with different abilities. It is noteworthy that no inappropriate content for children was found in any of the stories

Challenging trophic position assessments in complex ecosystems: Calculation method, choice of baseline, trophic enrichment factors, season and feeding guild do matter: A case study from Marquesas Islands coral reefs.

Assessments of ecosystem functioning are a fundamental ecological challenge and an essential foundation for ecosystem-based management. Species trophic position (TP) is essential to characterize food web architecture. However, despite the intuitive nature of the concept, empirically estimating TP is a challenging task due to the complexity of trophic interaction networks. Various methods are proposed to assess TPs, including using different sources of organic matter at the base of the food web (the 'baseline'). However, it is often not clear which methodological approach and which baseline choices are the most reliable. Using an ecosystem-wide assessment of a tropical reef (Marquesas Islands, with available data for 70 coral reef invertebrate and fish species), we tested whether different commonly used TP estimation methods yield similar results and, if not, whether it is possible to identify the most reliable method. We found significant differences in TP estimates of up to 1.7 TPs for the same species, depending on the method and the baseline used. When using bulk stable isotope data, the choice of the baseline significantly impacted TP values. Indeed, while nitrogen stable isotope (δ15N) values of macroalgae led to consistent TP estimates, those using phytoplankton generated unrealistically low TP estimates. The use of a conventional enrichment factor (i.e. 3.4‰) or a 'variable' enrichment factor (i.e. according to feeding guilds) also produced clear discrepancies between TP estimates. TPs obtained with δ15N values of source amino acids (compound-specific isotope analysis) were close to those assessed with macroalgae. An opposite seasonal pattern was found, with significantly lower TPs in winter than in summer for most species, with particularly pronounced differences for lower TP species. We use the observed differences to discuss possible drivers of the diverging TP estimates and the potential ecological implications.

Offline robot programming assisted by task demonstration: an AutomationML interoperable solution for glass adhesive application and welding

ABSTRACT Robots have been successfully deployed in both traditional and novel manufacturing processes. However, they are still difficult to program by non-experts, which limits their accessibility to a wider range of potential users. Programming robots requires expertise in both robotics and the specific manufacturing process in which they are applied. Robot programs created offline often lack parameters that represent relevant manufacturing skills when executing a specific task. These skills encompass aspects like robot orientation and velocity. This paper introduces an intuitive robot programming system designed to capture manufacturing skills from task demonstrations performed by skilled workers. Demonstration data, including orientations and velocities of the working paths, are acquired using a magnetic tracking system fixed to the tools used by the worker. Positional data are extracted from CAD/CAM. Robot path poses are transformed into Cartesian space and validated in simulation, subsequently leading to the generation of robot programs. PathML, an AutomationML-based syntax, integrates robot and manufacturing data across the heterogeneous elements and stages of the manufacturing systems considered. Experiments conducted on the glass adhesive application and welding processes showcased the intuitive nature of the system, with path errors falling within the functional tolerance range.

Nonlinear Matlab/Simulink-Based Mathematical Modeling of Solar Photovoltaic Modules for Power Generation

Solar photovoltaic (PV) arrays comprised of modules are the most important power conversion elements of solar PV-generating systems. Because of the nonlinear characteristics of the solar PV array, determining its operating curves under various operating conditions is a laborious and expensive process. To overcome these barriers, engineers have updated multiple engineering software platforms, including Matlab and Simulink, to incorporate standardized and simplified solar panel designs. Nevertheless, these models are unsuitable for implementation in hybrid energy systems due to their intuitive nature and the need to manually adjust specific system parameters. Consequently, this article outlines a systematic process for simulating photovoltaic cells, modules, and arrays utilizing Matlab and Simulink. The reference model utilised is a 100-watt solar panel. Additionally, the operational characteristics of PV arrays under a broad spectrum of physical parameters and operating conditions are investigated. The simulation investigation is conducted for three distinct weather scenario conditions: cloudless days, days with moderate clouds, and days with heavy overcast conditions. When solar irradiation falls from 1 KW/m2 to 100 W/m2, the resulting voltage, current, and output power all decrease. The output power and voltage increase slightly as the temperature decreases, but the output current from solar PV panels remains approximately constant. The I-V and P-V curves of the solar photovoltaic module are significantly influenced when the shunt resistance changes from 1000 Ω to 0.1 Ω, resulting in a noticeable decrease in power output.

Democratising complex system modelling

In the contemporary context of an acute need for sustainability and swift response to imminent crises such as global warming, pandemics and economic system disruptions, the focus on responsible decision making, ethical risk assessment and mitigation at all organizational levels is an overarching goal. Our aim is to introduce a deterministic method for investigating the stability of complex systems, in order to find the most important elements of such systems and their impact on different scenarios. The novelty of the current approach lies in its compact format and intuitive nature, designed to accommodate a limited amount of computational resources. The proposed modelling method involves the mapping of complex systems from a diversity of disciplines (economic markets, resource management domain and the community impact of suburbanisation) onto a sequence of chemical reactions and involving a subsequent mathematical analysis. Mapping the results back onto the use cases shows that one can retrieve a considerable amount of detail, making the modelling strategy general enough to be adaptable and scalable while also detailed enough to provide valuable insights for practical scenarios.

Role of Intuition in Philosophical Inquiry

Purpose: This study sought to examine the role of intuition in philosophical inquiry. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the role of intuition in philosophical inquiry. Preliminary empirical review revealed that philosophers frequently relied on intuition as a heuristic tool for generating hypotheses and evaluating arguments. The study also highlighted significant cross-cultural variations in the conceptualization and utilization of intuition in philosophical reasoning. Moreover, neuroscientific methods revealed the neural mechanisms underlying intuitive processing during philosophical tasks. Overall, the study deepened our understanding of the complexities of philosophical inquiry and identified avenues for future research to enhance the quality and rigor of philosophical research. Unique Contribution to Theory, Practice and Policy: The Reflective Equilibrium theory, Foundationalism and Coherentism may be used to anchor future studies on the role of intuition in philosophical inquiry. The study made several recommendations, contributing to theory, practice, and policy. It suggested further exploration into the nature of intuition, including its cognitive mechanisms and neural correlates, and recommended integrating empirical methods into philosophical research. Additionally, the study emphasized the importance of cultivating metacognitive awareness among philosophers and promoting ethical guidelines for the responsible use of intuition in research and dissemination. It also advocated for the integration of diverse perspectives and voices in philosophical inquiry to foster inclusivity and enrich discourse. Overall, these recommendations aimed to advance theoretical understanding, inform practical interventions, and guide policy initiatives in the field of philosophical inquiry. Keywords: Intuition, Philosophical Inquiry, Metacognitive Awareness, Ethical Guidelines, Cognitive Mechanisms, Neural Correlates, Interdisciplinary, Diversity, Inclusivity, Discourse, Responsible, Advancement

Sprayable Diacetylene-Containing Amphiphile Coatings for Visual Detection of Gas-Phase Hydrogen Peroxide

Colorimetric chemical sensing of target gases, such as hydrogen peroxide vapors, is an evolving area of research that implements responsive materials that undergo molecule-specific interaction, resulting in a visible color change. Due to the intuitive nature of an observable color change, such sensing systems are particularly desirable as they can be widely deployed at low cost and without the need for complex analytical instrumentation. In this work, we describe our development of a new spray-on sensing material that can provide a colorimetric response to the presence of a gas-phase target, specifically hydrogen peroxide vapor. By providing a cumulative response over time, we identified that part per million concentrations of hydrogen peroxide vapor can be detected. Specifically, we make use of iron chloride-containing formulations to enable the catalysis of hydrogen peroxide to hydroxyl radicals that serve to initiate polymerization of the diacetylene-containing amphiphile, resulting in a white to blue color transition. Due to the irreversible nature of the color change mechanism, the cumulative exposure to hydrogen peroxide over time is demonstrated, enabling longitudinal assessment of target exposure with the same coatings. The versatility of this approach in generating a colorimetric response to hydrogen peroxide vapor may find practical applications for environmental monitoring, diagnostics, or even industrial safety.

Social intuition: behavioral and neurobiological considerations.

Social intuition is instrumental in bringing about successful human interactions, yet its behavioral and neural underpinnings are still poorly understood. We focus in this article on the automatic, involuntary, nature of social intuition, rather than on higher-level cognitive and explicit Theory-of-Mind processes (which contribute to rendering social intuition meaningful in real-life situations). We argue that social-affective implicit learning plays a crucial role in establishing automatic social intuition. These implicit learning processes involve associations between the perception of other's bodily articulations, concurrent events, and the consequences or outcomes in terms of subsequent actions, affective valences and visceral states. The traditional non-social implicit learning paradigms do not allow one to draw conclusions about the role of implicit learning processes in social intuition, as they lack these vital characteristics typically associated with human actions. We introduce a new implicit learning paradigm, which aims to fill these gaps. It targets agile, rapid, social-affective learning processes, involving cue contingencies with a relatively simple structure, unlike the very complex structures that underpin the traditional tasks. The paradigm features matching social and non-social versions, allowing direct comparison. Preliminary data suggest equal performance of TD (typically-developed) and ASC (autism spectrum conditions) groups on the non-social version, but impaired implicit learning in ASC on the social version. We hypothesize that this reflects an anomalous use of implicitly learned affective information in ASC when judging other people. We further argue that the mirror neuron mechanism (MNM), which is part of the Action Observation Network, forms an integral part of the neural substrate for social intuition. In particular as there are indications that the MNM supports action anticipation, and that implicitly learned information can trigger MNM activation, which both seem vital to a social intuition ability. The insights that can be derived from comparing the performances of TD and ASC individuals on (non)social implicit learning tasks, and the implications for the role of MNM activation, are discussed.

Automation of MBES noise reduction: An approach based on seafloor bathymetry features derived from manual editing procedures

This study introduces an innovative technique specifically designed to effectively mitigate multibeam sonar noise in intricate scenarios where conventional denoising methods have typically faltered. These traditional methods have historically struggled to maintain a delicate balance between sensitivity, specificity, and interpretability, leading many cartographers and research professionals to resort to laborious and time-consuming manual processing techniques. In response to this prevalent challenge, we meticulously formulated a mathematical model to represent each individual step involved in the manual editing process. The approach involves parameterization of cartographers’ perceptual manipulation, resulting in a novel noise rejection methodology. A notable attribute of this method is its simplicity and intuitive nature; users are required to set only a handful of fundamental parameters to obtain highly accurate processing results. Importantly, this technique spares the data from exposure to linear filters such as lowpass or average filters, enhancing the fidelity and reliability of the results. Furthermore, our proposed method retains the interpretability of the processed seafloor bathymetry as it abstains from implementing opaque methodologies such as Neural Networks. This aspect of the technique makes it an ideal choice for researchers and cartographers who prioritize understanding the underlying processes affecting their data. In the discussion segment of this paper, we present a quantitative comparison of the data processed by manual edit, the commercially mature algorithm, and our newly proposed method. This comparison underscores the superior performance and effectiveness of our technique, further reinforcing its potential applicability in real-world scenarios.

La intuición como fuente de evidencia en filosofía: La visión mínima

In the past two decades, there has been a sudden increase of inquiry within the branch of analytic philosophy on the nature and role of intuition in philosophy. Philosophers began to investigate what intuition is, how it should be defined, what role it plays in philosophy, what its epistemic status is and many more. There is also a growing number of philosophers arguing that the whole debate rests on a mistake: intuition in philosophy plays no role whatsoever and philosophers do not use it as (a source of) evidence for their philosophical claims. This strategy is often conducted by differentiating among the two senses in which intuition is supposed to play an evidential role in philosophy. Intuition, thus, can be understood as a state of intuiting something or a propositional content that is intuited. Intuition in the first sense, the argument goes, cannot be treated as evidence as it would present the risk of psychologizing evidence in philosophy. If, however, we take intuition as evidence in the second sense, there is nothing distinctive about it: ultimately, all evidence in philosophy is of propositional nature, regardless of the intuitiveness of a given proposition. In my paper, I argue that this strategy fails and propose, instead, the view on intuition that, firstly, explains why the aforementioned distinction does not render the intuitiveness of the content irrelevant to its epistemic status, secondly, is in accord with the current findings in psychology, and, thirdly, is minimal enough to allow the different views of intuitions to be incorporated under that umbrella. In particular, I argue that it is an intuitive judgment, characterized by its non-inferentiality and defeasibility, that serves as evidence for particular philosophical claims, while its source is an intuition understood as a state of non-propositional character that can be examined empirically.

Reflections on the English Teaching Mode in Colleges and Universities under the Environment of Multimedia Network Technology

Abstract With the advancement of educational technologies, multimedia and network technologies have significantly enhanced classroom teaching efficiency and quality through their dynamic and intuitive nature. This study introduces a flipped classroom teaching model for college-level English, utilizing the SPOC platform, grounded in constructivist theory and task-based language teaching methodologies. Conducted in a university in City H, this research involved two classes. It employed a paired-sample t-test and principal component factor analysis to evaluate students’ English performance and learning interest pre- and post-implementation. Additionally, a Student Satisfaction Scale was developed to assess the model’s reliability and effectiveness. Results indicated a 1.39-fold increase in learning interest, with independent learning needs’ mean value rising from 10.69 to 16.78 and a notable improvement of 12.49 points in English grades. The factor analysis confirmed the model’s robustness, with all variable influence coefficients surpassing 0.75 and fitness values exceeding standard benchmarks. The SPOC-based English flipped classroom model has proven effective in promoting independent learning, fostering mutual learning spirit, and enhancing academic performance.

Exploring Factors for Implementation of EPAs in Pediatric Subspecialty Fellowships: A Qualitative Study of Program Directors.

To understand fellowship program directors' (FPDs) perspectives on facilitators and barriers to using entrustable professional activities (EPAs) in pediatric subspecialty training. We performed a qualitative study of FPDs, balancing subspecialty, program size, geographic region and current uses of EPAs. A study coordinator conducted 1-on-1 interviews using a semistructured approach to explore EPA use or nonuse and factors supporting or preventing their use. Investigators independently coded transcribed interviews using an inductive approach and the constant comparative method. Group discussion informed code structure development and refinement. Iterative data collection and analysis continued until theoretical sufficiency was achieved, yielding a thematic analysis. Twenty-eight FPDs representing 11 pediatric subspecialties were interviewed, of whom 16 (57%) reported current EPA use. Five major themes emerged: (1) facilitators including the intuitive nature and simple wording of EPAs; (2) barriers such as workload burden and lack of a regulatory requirement; (2) variable knowledge and training surrounding EPAs, leading to differing levels of understanding; (3) limited current use of EPAs, even among self-reported users; and (4) complementary nature of EPAs and milestones. FPDs acknowledged the differing strengths of both EPAs and milestones but sought additional knowledge about the value added by EPAs for assessing trainees, including the impact on outcomes. Identified themes can inform effective and meaningful EPA implementation strategies: Supporting and educating FPDs, ongoing assessment of the value of EPAs in training, and practical integration with current workflow. Generating additional data and engaging stakeholders is critical for successful implementation for the pediatric subspecialties.

Construction of Remote Sensing Indices Knowledge Graph (RSIKG) Based on Semantic Hierarchical Graph

Remote sensing indices are widely used in various fields of geoscience research. However, there are limits to how effectively the knowledge of indices can be managed or analyzed. One of the main problems is the lack of ontology models and research on indices, which makes it difficult to acquire and update knowledge in this area. Additionally, there is a lack of techniques to analyze the mathematical semantics of indices, making it difficult to directly manage and analyze their mathematical semantics. This study utilizes an ontology and mathematical semantics integration method to offer a novel knowledge graph for a remote sensing index knowledge graph (RSIKG) so as to address these issues. The proposed semantic hierarchical graph structure represents the indices of knowledge with an entity-relationship layer and a mathematical semantic layer. Specifically, ontologies in the entity-relationship layer are constructed to model concepts and relationships among indices. In the mathematical semantics layer, index formulas are represented using mathematical semantic graphs. A method for calculating similarity for index formulas is also proposed. The article describes the entire process of building RSIKG, including the extraction, storage, analysis, and inference of remote sensing index knowledge. Experiments provided in this article demonstrate the intuitive and practical nature of RSIKG for analyzing indices knowledge. Overall, the proposed methods can be useful for knowledge queries and the analysis of indices. And the present study lays the groundwork for future research on analysis techniques and knowledge processing related to remote sensing indices.

The Mediterranean Diet: From the pyramid to the circular model

The Mediterranean Diet (MD) is a millennial example of healthy and sustainable nutrition for humankind and the planet. It bases its principles on the limited and responsible use of resources, encouraging their reuse, whenever possible, and preventing over-exploitation. The MD is also a healthy food model through the prevention of many chronic non-transmissible diseases, safeguarding human health of present and future generations. The advancement of health through healthy eating has made it necessary to disseminate nutritional recommendations in line with scientific guidelines. In order to enhance the accessibility of these recommendations to the public, it has become widely adopted to employ graphic models that are both expressive and easily understandable, like the pyramid model. The intuitive nature and simplicity of interpreting the food pyramid have made it an effective educational tool for circulating nutritional recommendations. Over the years, the pyramidal representations have been enriched in an attempt to provide increasingly more information, while at the same time becoming more difficult to read. Circular graphic models are rapidly gaining popularity as a means of representing nutritional recommendations, alongside traditional pyramid models. These circular models not only hold cultural relevance but also strike a chord with the collective imagination, making them highly effective tools for conveying dietary guidelines. The goal of our study is to propose a new representation of the Mediterranean Diet based on circularity. Our representation aims to highlight both the importance of the various components/categories within the model, as well as the inherent cyclical nature that characterizes human life, the seasonality of food, the production chain, and the concealed circular economy behind each product. The choice of a circular model therefore represents the concept that virtuous choices can feed a “virtuous cycle” and reestablish a healthy balance among people, environment, and society.

Level and Dynamics of Selected Measures of Research and Development Activity in Poland

ABSTRACT Research and development (R&D) activity is an important source of knowledge for innovative processes in business entities. In practice, this activity is accompanied by numerous dysfunctions that reduce the efficiency and universality of the functioning of the R&D sphere. One of them is random, intuitive management. The aim of the publication is to attempt to analyse the level and dynamics of selected measures of R&D activity, to assess the prevalence of this activity in Polish business entities and to propose directions for improvements in the management of the R&D sphere. The article was prepared using the following research methods: cognitive-critical analysis of selected literature on the subject to identify the research problem; descriptive-comparative method for the presentation of a research problem; statistical method to determine the percentage shares of selected measures; and the projection method to propose the concept of rational management of R&D&I activities. The time range of the study covers the years 2018–2021. The subject scope concerns the level and dynamics of selected measures of R&D activity in Poland. The source of empirical data for the analysis of the phenomenon are publications of the Central Statistical Office. The analysis of the source material indicates a relatively low level of adopted measures of R&D activity and their dynamics on a national scale, and in the adopted cross-sections of analysis, as well as the random, intuitive nature of R&D management.

Optimal fused feature selection with ensemble learning foratrial fibrillation detection using ECG with enhanced average and subtraction-based optimizer

Most common asymptomatic arrhythmia that significantly leads to death and morbidity is Atrial Fibrillation (AF). It has the ability to extract valuable features is necessary for AF identification. Still, many existing studies have relied on weak frequencies that, are Time-Frequency Energy (TFE) and shallow time features. It requires lengthy ECG data to confine the information and is unable to confine the slight variation affected by the previous AF. The interfering noise signals focus primarily on separating AF from signals with a Sinus Rhythm (SR). Thus, this study would explore the detection of AF with heuristic-assisted deep learning approaches. Initially, the ECG Signals are gathered from the standard resources. Next, these gathered signals are pre-processed to perform denoising and artifact removal for enhancing the quality of data for further processes. Then, the deep feature extraction is done in two phases. In the first phase, the RR interval is extracted from the pre-processing ECG signals and the deep features are removed utilizing a Convolutional Neural Network (CNN). In contrast, deep features are employed to extract the features from the pre-processed ECG signals using the same CNN in the second phase. Then, these gathered in-depth features are fused and fed to the newly suggested heuristic algorithm called Enhanced Average and Subtraction-Based Optimizer (E-ASBO) for selecting the optimal fused features for reducing the redundancy in the signals. Finally, the chosen optimal fused features are fed to the new Adaptive Ensemble Neural Network (AENN) with heuristic adoption with the techniques such as Elma Neural Network, Deep Neural Network (DNN), and Recurrent Neural Network (RNN). This model focuses on increasing the accuracy of detecting AF. These proposed networks have more significant potential in future AF screening or clinical computer-aided AF diagnosis in wearable devices. It has superior performance and intuitive nature compared to the existing works.