War is almost always conducted with various restrictions in the form of rules, rituals, and taboos. Many of the norms that regulate warfare can be found in the tradition of just war. This tradition seeks to provide a middle ground between an unrealistic (at least for politicians) pacifism that does not even allow war in self-defence and a too realistic realism that claims there is no place for ethics in war. The tradition of just war does not have the force (...) of law; it provides, above all, a vocabulary to discuss war in moral terms. At the same time, the tradition does have an impact: it forms the basis of humanitarian law and the Geneva Conventions. Just like with laws, it is recognised here that imperfect adherence to these principles does not necessarily diminish their validity. Most proponents of the tradition believe that the principles of just war, even though some date back centuries, are sufficiently general to be applicable to contemporary conflicts, such as the Russo-Ukrainian War. If we apply the norms of the just war tradition to the current situation in Ukraine, we see that Russia is waging an unjust war in an unjust manner. (shrink)

The phrase ‘social constructionism about race’ is so ambiguous that it is unable to convey anything very meaningful. I argue that the various versions of social constructionism about race are either false, overly broad, or better described as anti-realism about biological race. One of the central rhetorical purposes of social constructionism about race has been to serve as an alternative to biological racial realism. However, most versions of social constructionism about race are compatible with biological racial realism, and there are (...) some race scholars who endorse both positions. Going a step further, David Reich has recently defended both social constructionism about race and racial hereditarianism. While Reich’s defense of racial hereditarianism is unconvincing, I show that most versions of social constructionism about race are indeed compatible with racial hereditarianism. I argue that we ought to replace the social constructionist “consensus” about race with the view that there are no races, only racialized groups. (shrink)

In the debate between contextualism and relativism about predicates of taste, the challenge from disagreement (the objection that contextualism cannot account for disagreement in ordinary exchanges involving such predicates) has played a central role. This paper investigates one way of answering the challenge consisting on appeal to certain, less focused on, uses of predicates of taste. It argues that the said thread is unsatisfactory, in that it downplays certain exchanges that constitute the core disagreement data. Additionally, several arguments to the (...) effect that the exchanges in question don’t amount to disagreement are considered and rejected. (shrink)

What is the relation between meaning and use? This chapter first defends a non-reductionist understanding of Wittgenstein’s suggestion that ‘the meaning of a word is its use in the language’; facts about meaning cannot be reduced to, or explained in terms of, facts about use, characterized non-semantically. Nonetheless, it is contended, facts about meaning do supervene on non-semantic facts about use. That supervenience thesis is suggested by comments of Wittgenstein’s and is consistent with his view of meaning and rule-following. Semantic (...) supervenience is then defended against two criticisms: first, John McDowell’s suggestion that the supervenience thesis falsifies the epistemology of meaning and fails to accommodate common-sense truths about meaning; second, a series of counter-examples proposed by Stephen Kearns and Ofra Magidor, who argue that worlds may differ semantically without differing non-semantically. It is argued that neither criticism is convincing: we should accept the thesis that semantic facts supervene on non-semantic facts. (shrink)

This study aims to use the Asian knowledge model “APO” as a determinant for performance excellence in universities and identifying the most effecting factors on it. This study was applied on Al-Azhar University in Gaza strip. The result of the study showed that (APO) model is valid as a measure and there are four dimensions in the model affecting significantly more than the others (university processes, KM leadership, personnel, KM outputs). Furthermore, performance excellence produced though modernizing the means of education, (...) curriculum development, technology and flexibility in the organizational structure. The study recommends expanding the usage of (APO) model, enhancing the role of knowledge leadership, technology, organizational flexibility, sharing culture and incentive systems that encouraging innovation. (shrink)

This chapter begins with a discussion of the significance of studies of aspects of tool use in understanding causal cognition. It argues that tool use studies reveal the most basic type or causal understanding being put to use, in a way that studies that focus on learning statistical relationships between cause and effect or studies of perceptual causation do not. An overview of the subsequent chapters is also presented.

Despite an enormous philosophical literature on models in science, surprisingly little has been written about data models and how they are constructed. In this paper, I examine the case of how paleodiversity data models are constructed from the fossil data. In particular, I show how paleontologists are using various model-based techniques to correct the data. Drawing on this research, I argue for the following related theses: first, the ‘purity’ of a data model is not a measure of its epistemic reliability. (...) Instead it is the fidelity of the data that matters. Second, the fidelity of a data model in capturing the signal of interest is a matter of degree. Third, the fidelity of a data model can be improved ‘vicariously’, such as through the use of post hoc model-based correction techniques. And, fourth, data models, like theoretical models, should be assessed as adequate for particular purposes. (shrink)

Intelligent Tutoring Systems (ITS) has a wide influence on the exchange rate, education, health, training, and educational programs. In this paper we describe an intelligent tutoring system that helps student study computer networks. The current ITS provides intelligent presentation of educational content appropriate for students, such as the degree of knowledge, the desired level of detail, assessment, student level, and familiarity with the subject. Our Intelligent tutoring system was developed using ITSB authoring tool for building ITS. A preliminary evaluation of (...) the ITS was done by a group of students and teachers. The results were acceptable. (shrink)

Abstract: Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South American origin from Peru, and then spread to most countries of the world. Tomatoes contain a lot of vitamin C and mineral salts, and are recommended for people with constipation, diabetes and patients with heart and body diseases. Studies and scientific studies have proven the importance of eating tomato juice in reducing the activity of platelets in diabetics, which (...) helps in protecting them from developing deadly blood clots. A tomato classification approach is presented with a data set containing approximately 5,266 images with 7 species belonging to tomatoes. The Neural Network Algorithms (CNN), a deep learning technique applied widely in image recognition, is used for this task. (shrink)

In this chapter we take stock of some of the complexities of the sprawling field of AI ethics. We consider questions like "what is the proper scope of AI ethics?" And "who counts as an AI ethicist?" At the same time, we flag several potential uses and abuses of AI ethics. These include challenges for the AI ethicist, including what qualifications they should have; the proper place and extent of futuring and speculation in the field; and the dilemmas concerning how (...) we use our public and academic resources. (shrink)

Scientists often communicate using generic generalizations, which are unquantified generalizations such as ‘Americans overestimate social class mobility’ or ‘sound waves carry gravitational mass’. In this paper, I explain the role of such generic generalizations in science, based on a novel theory about their characteristic meaning. According to this theory, a scientific generalization of the form ‘Ks are F’ says that F is one property based on which category K qualifies as a scientific kind. Because what it takes to qualify as (...) a scientific kind varies depending on the discipline, what a generic sentence entails also varies. Based on this semantic theory, I discuss the benefits and drawbacks of using generic language in science. (shrink)

The Eleusinian Mysteries are religious rituals that include rites of initiation, purification, and revelation. The high point of these Mysteries is the moment when a priest reveals the secret of the Mysteries to the newly initiated. Plato frequently uses language and motifs from the Mysteries in his dialogues, yet Plato scholars have not paid much attention to this usage, and those who have done so have not found much philosophical significance in it. I argue that in explaining his epistemology in (...) three middle and late period dialogues Plato consciously and systematically uses Eleusinian Mystery motifs to convey the idea of a unique kind of knowledge. This immediate, direct, and incorrigible knowledge bursts upon Plato's initiates after they undergo preparatory processes such as purification through elenchus. I examine the Eleusinian Mystery motifs that Plato employs in the Ladder of Eros at Symposium 209e--212a, in the middle books of the Republic, including the Myth of the Cave at Republic 509a--518d, and in the Myth of the Soul at Phaedrus 246a--253c, and I argue that Plato finds these Mystery elements useful for two reasons. First, in many cases before an individual can come to know a form he must go through certain conditioning and transformatory processes to prepare him for it, and motifs from the Mysteries help Plato to describe these processes. Second, knowledge of a form is different from other kinds of knowledge, and the motif of the visual revelation of the epopteia helps him to express the direct, unmediated contact that constitutes knowledge of a form. ;The structure of the dissertation is as follows. In chapter one I present background information concerning the stages and events of the Mysteries. I consider the purpose and effects of initiation into the Eleusinian Mysteries, paying special attention to the preparatory rituals. In chapter two I present a general discussion of certain aspects of Plato's epistemology and show how Plato employs the five Mystery motifs of progressing through a sequence of stages, purification, being led by a mystagogos, experiencing the epopteia, and achieving eudaimonia in order to explicate these aspects. In chapter three I consider the specifics of Plato's use of Mystery terminology in the Symposium. And finally, in an Appendix, I argue that Plato has a model of knowledge by acquaintance, since I assert in chapters two and three that Plato uses the Mystery theme of an epopteia to express features of this model. (shrink)

The use of counterfactuals for considerations of algorithmic fairness and explainability is gaining prominence within the machine learning community and industry. This paper argues for more caution with the use of counterfactuals when the facts to be considered are social categories such as race or gender. We review a broad body of papers from philosophy and social sciences on social ontology and the semantics of counterfactuals, and we conclude that the counterfactual approach in machine learning fairness and social explainability can (...) require an incoherent theory of what social categories are. Our findings suggest that most often the social categories may not admit counterfactual manipulation, and hence may not appropriately satisfy the demands for evaluating the truth or falsity of counterfactuals. This is important because the wide- spread use of counterfactuals in machine learning can lead to misleading results when applied in high-stakes domains. Accordingly, we argue that even though counterfactuals play an essential part in some causal inferences, their use for questions of algorithmic fairness and social explanations can create more problems than they resolve. Our positive result is a set of tenets about using counterfactuals for fairness and explanations in machine learning. (shrink)

The rapidly evolving space tourism industry faces significant challenges in building consumer trust and balancing emotional appeal with factual accuracy—both essential for reducing uncertainty and fostering long-term public engagement in this high-risk sector. This study examines the key factors shaping individuals’ intentions to participate in space tourism, with a focus on their perceived trustworthiness, usefulness, and immersiveness of information on social media. Applying Mindsponge Theory, we explore the interplay between trust evaluation and subjective cost-benefit judegement of individuals in high-risk (...) tourism context. The findings reveal that while trustworthiness positively influences participation intention, its role diminishes when usefulness and immersiveness are particularly strong. This suggests that space tourists, who are naturally inclined toward risk-taking, prioritize experiential rewards—such as novelty, prestige, and emotional engagement—over concerns about information reliability. These insights offer practical implications for marketing in high-risk industries, highlighting the importance of immersive storytelling, exclusivity, and transformative experiences over conventional trust-building measures. Future research should investigate how personal risk tolerance, prior experience, and cognitive biases shape thought processes, decision-making, and behaviors in extreme tourism contexts. (shrink)

In the middle of the 20th century, it was a common Wittgenstein-inspired idea in philosophy that for a linguistic expression to have a meaning is for it to be governed by a rule of use. In other words, it was widely believed that meanings are to be identified with use-conditions. However, as things stand, this idea is widely taken to be vague and mysterious, inconsistent with “truth-conditional semantics”, and subject to the Frege-Geach problem. In this paper I reinvigorate the ideas (...) that meaningfulness is a matter of being governed by rules of use and that meanings are best thought of in terms of use-conditions. I will do this by sketching the Rule-Governance view of the nature of linguistic meaningfulness, showing that the view isn’t by itself subject to the two problems, and explain why the idea has had a lasting appeal to philosophers from Strawson to Kaplan and why we should find it continually attractive. (shrink)

This paper examines the potential educational uses of chat-based large language models (LLMs), moving past initial hype and skepticism. Although LLM outputs often evoke fascination and resemble human writing, they are unpredictable and must be used with discernment. Several metaphors—like calculators, cars, and drunk tutors—highlight distinct models for student interactions with LLMs, which we explore in the paper. We suggest that LLMs hold a potential in students’ learning by fostering proleptic reasoning through scaffolding, i.e., presenting a technological accompaniment in anticipating (...) and responding to potential objections to arguments. Here, the technical limitations of LLMs can be reframed as beneficial when fostering anticipatory reasoning. Whether their outputs are accurate or not, evaluating them stimulates learning. LLMs require students to critically engage, emphasizing analytical thinking over mere memorization. This interaction helps solidify knowledge. Additionally, we explore how engaging with LLMs can prepare students for constructive collective discussions and provide first steps in addressing epistemic injustices by highlighting potential research blind spots. Thus, while acknowledging the sociopolitical and ethical complexities of using LLMs in education, we suggest that when used in an informed way, they can promote critical thinking through anticipatory reasoning. (shrink)

In this chapter we use methods of corpus linguistics to investigate the ways in which mathematicians describe their work as explanatory in their research papers. We analyse use of the words explain/explanation (and various related words and expressions) in a large corpus of texts containing research papers in mathematics and in physical sciences, comparing this with their use in corpora of general, day-to-day English. We find that although mathematicians do use this family of words, such use is considerably less prevalent (...) in mathematics papers than in physics papers or in general English. Furthermore, we find that the proportion with which mathematicians use expressions related to ‘explaining why’ and ‘explaining how’ is significantly different to the equivalent proportion in physics and in general English. We discuss possible accounts for these differences. (shrink)

Accordingly, if the antennas used in nano-components are to be within this range, it should be expected that the electromagnetic waves used in the communications of these systems and devices will be around several tens of terahertz, which will include wavelengths in the infrared, visible, and ultraviolet regions. -/- .

Traditionally, logic has been the dominant formal method within philosophy. Are logical methods still dominant today, or have the types of formal methods used in philosophy changed in recent times? To address this question, we coded a sample of philosophy papers from the late 2000s and from the late 2010s for the formal methods they used. The results indicate that the proportion of papers using logical methods remained more or less constant over that time period but the proportion of papers (...) using probabilistic methods was approximately three times higher in the late 2010s than it was in the late 2000s. Further analyses explored this change by looking more closely at specific methods, specific levels of technical engagement, and specific subdisciplines within philosophy. These analyses indicate that the increasing proportion of papers using probabilistic methods was pervasive, not confined to particular probabilistic methods, levels of sophistication, or subdisciplines. (shrink)

The prohibition on using others ‘merely as means’ is one of the best-known and most influential elements of Immanuel Kant’s moral theory. But it is widely regarded as impossible to specify with precision the conditions under which this prohibition is violated. On the basis of a re-examination of Kant’s texts, the article develops a novel account of the conditions for using someone ‘merely as a means’. It is argued that this account has not only strong textual support but also significant (...) philosophical advantages over alternative conceptions. (shrink)

The main goal of this paper is to investigate whether humans are unique in using tools and artifacts. Non-human animals exhibit some impressive instances of tool and artifact-use. Chimpanzees use sticks to get termites out of a mound, beavers build dams, birds make nests, spiders create webs, bowerbirds make bowers to impress potential mates, etc. There is no doubt that some animals modify and use objects in clever and sophisticated ways. But how does this relate to the way in which (...) humans make and use objects to achieve their goals? To answer this question, this paper first presents a taxonomy of artifacts, identifying four overlapping categories, namely embodied, perceptual, cognitive, and affective artifacts. It then discusses definitions of animal tool-use, arguing that we need a more liberal approach, one that goes beyond the use of tools that are embedded in occurrent perception-action cycles. This paper ends by analysing how instances of animal tool and artifact-use can be classified according to the four identified categories, concluding that some animals use embodied, perceptual, cognitive, and affective artifacts. In this sense, humans are thus not unique in the kinds of tools and artifacts we use. What is unique, however, is our unprecedented flexibility and openness to deeply incorporate a large variety of complex tools and artifacts into our embodied, perceptual, cognitive, and affective systems. (shrink)

Integrating medical imaging with computing technologies, such as Artificial Intelligence (AI) and its subsets: Machine learning (ML) and Deep Learning (DL) has advanced into an essential facet of present-day medicine, signaling a pivotal role in diagnostic decision-making and treatment plans (Huang et al., 2023). The significance of medical imaging is escalated by its sustained growth within the realm of modern healthcare (Varoquaux and Cheplygina, 2022). Nevertheless, the ever-increasing volume of medical images compared to the availability of imaging experts. Biomedical experts (...) and radiologists have resulted in a widening disparity, causing an excess and overwhelming workload on these healthcare professionals (Chen et al., 2021). Several studies indicate that the present-day biomedical radiologist is now saddled with the daunting task of interpreting an image almost every 10 seconds to keep pace with the burgeoning clinical demands (McDonald et al., 2015; Hosny et al., 2018; Lantsman et al., 2022). This cognitive drain has invariably led to inevitable consequences such as delays in diagnosis and an amplified risk of diagnostic errors – thus, the biomedical imaging aspect is in dire need of methods that would aid accurate diagnostics and analytics for improved decision making. In this review, the importance of AI-related technologies such as ML and/or DL methods are reviewed in relation to the processing of medical or biomedical images along with their potentials, challenges, and possible suggestions for future studies in the health landscape. The focus will be on machine learning methods associated with the medical field of image classification. (shrink)

Here I defend my solution to the wrong-kind-of-reason problem against Mark Schroeder’s criticisms. In doing so, I highlight an important difference between other accounts of reasons and my own. While others understand reasons as considerations that count in favor of attitudes, I understand reasons as considerations that bear (or are taken to bear) on questions. Thus, to relate reasons to attitudes, on my account, we must consider the relation between attitudes and questions. By considering that relation, we not only solve (...) the wrong-kind-of-reason problem, but we also bring into view rational agency—the use of reasons in thought. (shrink)

Abstract: Rice is one of the most important staple crops in the world and serves as a staple food for more than half of the global population. It is a critical source of nutrition, providing carbohydrates, vitamins, and minerals to millions of people, particularly in Asia and Africa. This paper presents a study on using deep learning for the classification of different types of rice. The study focuses on five specific types of rice: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. A (...) dataset of 75,000 images was collected and annotated, consisting of 15,000 images for each of the five types of rice. The deep learning system uses convolutional neural networks (CNNs) to classify images of rice grains based on their physical characteristics. The performance of the deep learning system was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results show that the proposed deep learning system was able to achieve high accuracy in classifying the different types of rice, outperforming traditional image processing techniques. This study provides valuable insights into the potential of deep learning for classifying different types of rice and can be useful for applications such as quality control in rice processing industries and research in crop science. The large size of the dataset, 15,000 images for each type of rice, helped the deep learning system to learn the features of each type of rice, and increase the performance of the classifier. The attained overall accuracy of the proposed model (99.96%), Precision (99.96%), Recall (99.96%) and F1-score (99.96%). Thus, the proposed model proved that it leaned the five categories of rice and it can generalize these categories and it is effective. (shrink)

Animal-derived constituents are frequently used in anaesthesia and surgery, and patients are seldom informed of this. This is problematic for a growing minority of patients who may have religious or secular concerns about their use in their care. It is not currently common practice to inform patients about the use of animal-derived constituents, yet what little empirical data does exist indicates that many patients want the opportunity to give their informed consent. First, we review the nature and scale of the (...) problem by looking at the groups who may have concerns about the use of animal-derived constituents in their care. We then summarise some of the products used in anaesthesia and surgery that can contain such constituents, such as anaesthetic drugs, surgical implants and dressings. Finally, we explore the problem of animal-derived constituents and consent using Beauchamp and Childress’ four principles approach, examining issues of autonomy, beneficence, nonmaleficence and justice. Disclosing the use of animal-derived constituents in anaesthesia and surgery is warranted under Beauchamp and Childress’ four principles approach to the problem. Although there exist systemic and practical challenges to implementing this in practice, the ethical case for doing so is strong. The Montgomery ruling presents additional legal reason for disclosure because it entails that patients must be made aware of risks associated with their treatment that they attach significance to. (shrink)

Abstract: 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)

Artificial intelligence is now widely accessible and already being used by healthcare researchers throughout various stages in the research process, such as assisting with systematic reviews, supporting data collection, facilitating data analysis and drafting manuscripts for publication. The most common AI tools used are forms of generative AI such as ChatGPT, Claude and Gemini. Generative AI is a type of AI that can generate human-like text, audio, videos, code and images based on text-based prompts inputted by a human user. Generative (...) AI is trained on large amounts of data, and the outputs are sophisticated and can be indistinguishable from a response from a skilled human. In this article, we outline several AI applications that can be used in healthcare research, examining their benefits, limitations and outline best practices for maintaining research integrity and ethical standards. (shrink)

The rapid advancement of artificial intelligence (AI) and machine learning (ML) has led to significant innovations in the healthcare sector. One such development is AI-powered healthcare chatbots, which assist patients and medical professionals by providing medical guidance, symptom assessment, and appointment scheduling. This paper presents the design and implementation of an AI healthcare chatbot using machine learning techniques. The chatbot leverages natural language processing (NLP) and deep learning models to understand and respond to user queries effectively. Experimental results demonstrate the (...) chatbot's efficiency in delivering accurate and context-aware medical information. (shrink)

An organizing theme of the dissertation is the issue of how to make philosophical theories useful for scientific purposes. An argument for the contention is presented that it doesn’t suffice merely to theoretically motivate one’s theories, and make them compatible with existing data, but that philosophers having this aim should ideally contribute to identifying unique and hard to vary predictions of their theories. This methodological recommendation is applied to the ranking-theoretic approach to conditionals, which emphasizes the epistemic relevance and the (...) expression of reason relations as part of the semantics of the natural language conditional. As a first step, this approach is theoretically motivated in a comparative discussion of other alternatives in psychology of reasoning, like the suppositional theory of conditionals, and novel approaches to the problems of compositionality and accounting for the objective purport of indicative conditionals are presented. In a second step, a formal model is formulated, which allows us to derive quantitative predictions from the ranking-theoretic approach, and it is investigated which novel avenues of empirical research that this model opens up for. Finally, a treatment is given of the problem of logical omniscience as it concerns the issue of whether ranking theory (and other similar approaches) makes too idealized assumptions about rationality to allow for interesting applications in psychology of reasoning. Building on the work of Robert Brandom, a novel solution to this problem is presented, which both opens up for new perspectives in psychology of reasoning and appears to be capable of satisfying a range of constraints on bridge principles between logic and norms of reasoning, which would otherwise stand in a tension. (shrink)

It is an open question whether well-being ought to primarily be understood as a temporal concept or whether it only makes sense to talk about a person’s well-being over their whole lifetime. In this article, I argue that how this principled philosophical disagreement is settled does not have substantive practical implications for well-being science and well-being policy. Trying to measure lifetime well-being directly is extremely challenging as well as unhelpful for guiding well-being public policy, while temporal well-being is both an (...) adequate indirect measure of lifetime well-being, and an adequate focus for the purposes of improving well-being through public policy. Consequently, even if what we ought to care about is lifetime well-being, we should use temporal measures of well-being and focus on temporal well-being policies. (shrink)

In this chapter, I will discuss employing exemplars in moral and character education promoting virtue development with the involvement of embodiment. Virtue ethicists propose two phases of virtue development: early virtue habituation and later phronesis cultivation. I will overview prior research on the mechanism of habituation at the biological and neural levels to examine why embodiment is fundamental during the first phase, virtue habituation. Then, I will review recent philosophical and psychological studies about the nature of phronesis, i.e., practical wisdom, (...) to consider how educational activities cultivate it. Based on these preliminary examinations of the course of virtue development, I will examine the previous literature about the role and importance of using exemplars in moral and character education involving concrete and interactive activities with the exemplars involving embodiment during the learning process. Finally, I will draw upon the Self-determination theory about supporting students’ need for autonomy, competence, and relatedness to consider a way to improve the effectiveness of exemplar-applied education along with embodiment. After examining the relevant theoretical frameworks mentioned above, I will discuss several practical guidelines for using exemplars involving character embodiment for virtue development. (shrink)

Impostor syndrome or Impostor phenomenon is a belief that a person thinks their success is due to luck or external factors, not their abilities. This psychological trait is present in certain groups like women. In this paper, we propose a neutrosophic trait measure to represent the psychological concept of the trait-anti trait using refined neutrosophic sets. This study analysed a group of 200 undergraduate students for impostor syndrome, perfectionism, introversion and self-esteem: after the COVID pandemic break in 2021. Data labelling (...) was carried out using these neutrosophic trait measures. Machine learning models like Support Vector Machine(SVM), K-nearest neighbour (K-NN), and random forest were used to model the data; SVM provided the best accuracy of 92.15%. (shrink)

This chapter analyses the prospects of using neuroimaging methods, in particular functional magnetic resonance imaging (fMRI), for philosophical purposes. To do so, it will use two case studies from the field of emotion research: Greene et al. (2001) used fMRI to uncover the mental processes underlying moral intuitions, while Lindquist et al. (2012) used fMRI to inform the debate around the nature of a specific mental process, namely, emotion. These studies illustrate two main approaches in cognitive neuroscience: Reverse inference and (...) ontology testing, respectively. With regards to Greene et al.’s study, the use of Neurosynth (Yarkoni 2011) will show that the available formulations of reverse inference, although viable a priori, seem to be of limited use in practice. On the other hand, the discussion of Lindquist et al.’s study will present the so far neglected potential of ontology-testing approaches to inform philosophical questions. (shrink)

Alzheimer's disease (AD) poses a significant public health challenge. The hippocampus is one of the most affected brain regions and a readily accessible biomarker for diagnosis through MRI imaging in machine learning applications. However, utilizing entire MRI image slices in machine learning for AD classification has shown reduced accuracy. This study introduces the novel 'select slices' method, which involves identifying and focusing on specific landmarks within the hippocampus region in MRI images. This approach aims to improve classification accuracy by eliminating (...) irrelevant information from the analysis. Our research aims to identify which views of MRI images produce higher accuracy for AD classification. We used multiclass classification using the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, utilising Resnet50 and LeNet models to evaluate the value of three specific views (sagittal, coronal, and axial) and categories (normal, mild, and severe AD). The dataset comprised 4,500 MRI slices across these three views and categories. Our findings unequivocally demonstrate that the 'select slices' approach surpasses the use of entire slices in MRI images for AD classification. Specifically, our method elevates machine learning accuracy, with the coronal view showcasing exceptional performance. This methodology is a game-changer in improving the accuracy of machine learning models for AD classification, with results closely mirroring those of medical experts, the gold standard for AD diagnosis. Moreover, we observed that LeNet models hold great promise as effective tools for AD classification. (shrink)

Qualitative fieldwork research on sensitive topics sometimes requires that interviewees be granted confidentiality and anonymity. When qualitative researchers later publish their findings, they must ensure that any statements obtained during fieldwork interviews cannot be traced back to the interviewees. Given these protections to interviewees, the integrity of the published findings cannot usually be verified or replicated by third parties, and the scholarly community must trust the word of qualitative researchers when they publish their results. This trust is fundamentally abused, however, (...) when researchers publish articles reporting qualitative fieldwork data that they never collected. Using only publicly available information, I argue that a 2017 article in an Elsevier foreign policy and international relations journal presents anonymised fieldwork interviews that could not have occurred as described. As an exercise in post-publication peer review (PPPR), this paper examines the evidence that calls into question the reliability of the putative fieldwork quotations. I show further that the 2017 article is not a unique case. The anonymity and confidentiality protections common in some areas of research create an ethical problem: the protections necessary for obtaining research data can be used as a cover to hide substandard research practices as well as research misconduct. (shrink)

The core argument of the paper is that the modern philosophical notion of self-constitution is directed against the prospect of human beings dissolving into idleness. Arguments for self-constitution are marked by non-philosophical presuppositions about the value of usefulness. Those arguments also assume a particular conception of superior experience as conscious integration of a person’s actions within an identifiable set of chosen commitments. Exploring particular arguments by Hegel, Kant, Korsgaard and Frankfurt the paper claims that those arguments are problematic in (...) the various ways in which they suppose usefulness and explicitly or implicitly take extra-philosophical views of idleness. (shrink)

Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, History_score, score, and Risk. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model was trained and validated, achieving (...) an impressive accuracy of 100% and an average error of 0.000015, indicating its robust predictive capability. Moreover, we conducted feature importance analysis to identify the most influential features for predicting audit risk. The key features found to be critical for classifying fraudulent activities in audit risk prediction are Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, and History_score. This research contributes to the field of audit risk prediction by demonstrating the effectiveness of a neural network-based approach and highlighting the importance of specific features in detecting fraudulent activities. The findings have significant implications for auditors and organizations seeking to enhance their audit risk assessment processes, ultimately leading to improved financial transparency and fraud detection. (shrink)

The science of operations research is one of the modern sciences that have made a great revolution in all areas of life through the methods provided by it, suitable and appropriate to solve most of the problems that were facing researchers, scholars and those interested in the development of societies, and the most beneficiaries of this science were companies and institutions that are looking for scientific methods that help them manage their work so that they achieve the greatest profit and (...) the lowest cost, and one of the important methods that have been used in the management of companies we offer in this research two methods, Dynamic programming method. This method has been used in many practical matters and helped decision-makers in companies to achieve a maximum profit and less cost by formulating the reality of the state of the company and the data provided by decision-makers with a dynamic mathematical model that is solved using methods of solving dynamic models and we will provide in this research an example of this through the issue of choosing the optimal investment for the budget of a company so that it achieves a maximum profit, and the method of programming with integers: the method that provided these companies with solutions with integer values suitable for the nature of its work, through the use of the binary integer in the formulation of the appropriate mathematical model on the one hand, and on the other hand, the use of the binary integer variable helped to convert some nonlinear models that lead to some practical problems into linear models, and it should be noted here that in the previous two methods there is something indeterminable because we must make a decision in choosing or not choosing something, but the optimal solution that we will get remains A specific value because we are building the mathematical model for any realistic issue through the data provided by those responsible for the work and these data are calculated quantities and therefore they are uncertain values because their validity depends on the circumstances surrounding the work environment, they may be exposed to increase or decrease, and therefore the optimal solution on which the company will base its decision is suitable for specific values and any change in them can cause the company an uncalculated loss, so in this research we will use the concepts of neutrosophic science, the branch of science founded by the American scientist Florentin Smarandache in 1995 based on his belief that there is no absolute truth, a science that is interested in the study of ideas and concepts that are neither true nor false, but just in between, and we will take the data (calculated quantities) neutrosophic values that are specified or unspecified values are any set close to the calculated quantities, then the resulting mathematical model is a neutrosophic model and the optimal solution has neutrosophic values and thanks to the indefinite uncertainty that these values have, companies from the development of appropriate plans for all circumstances and thus achieve the greatest profit and the lowest cost, and we will clarify the above through two issues, the issue of optimal designation of a warehouse site, which we will formulate the mathematical model of using the neutrosophic integer programming method - and the issue of capital budget, which we will present in two different forms, we use in the first form the neutrosophic integer programming method and in the second the neutrosophic dynamic programming method. (shrink)

Abstract: Sign language recognition is one of the most rapidly expanding fields of study today. Many new technologies have been developed in recent years in the fields of artificial intelligence the sign language-based communication is valuable to not only deaf and dumb community, but also beneficial for individuals suffering from Autism, downs Syndrome, Apraxia of Speech for correspondence. The biggest problem faced by people with hearing disabilities is the people's lack of understanding of their requirements. In this paper we try (...) to fill this gap. By trying to translate sign language using artificial intelligence algorithms, we focused in this paper using transfer learning technique based on deep learning by utilizing a MobileNet algorithm and compared it with the previous paper results[10a], where we get in the Mobilenet algorithm on the degree of Accuracy 93,48% but the VGG16 the accuracy was 100% For the same number of images (43500 in the dataset in size 64*64 pixel ) and the same data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch . (shrink)

Abstract: Lemons are an important fruit that have a wide range of uses and benefits, from culinary to health to household and beauty applications. Deep learning techniques have shown promising results in image classification tasks, including fruit quality detection. In this paper, we propose a convolutional neural network (CNN)-based approach for detecting the quality of lemons by analysing visual features such as colour and texture. The study aims to develop and train a deep learning model to classify lemons based on (...) their quality, evaluate the model's performance, compare it to traditional machine learning approaches, and identify key factors that affect the model's performance. The dataset used in the study consists of approximately 2533 images of lemons and an empty surface, divided into training, test, and validation data, and includes images of both good and bad quality lemons under different lighting conditions. The deep learning model achieved an accuracy of 99.74% on the test dataset and outperformed traditional machine learning approaches. The developed model has the potential to improve efficiency and accuracy in lemon quality control in the agriculture and food industry. (shrink)

Despite iterative learning design being increasingly implemented, such approaches are often delineated by well-defined periods of design/implementation. However, second-order cybernetics, which suggests a participatory approach to learning design, involves responsively adapting learning environments to meet students’ needs, treating them as agentic participants in the classroom. In our mixed methods study, we investigate whether such a process can facilitate egalitarian participation and collaborative interactions in a technology-assisted classroom. We use the example of a graduate psychology class of 17 students and suggest (...) that adaptation of live-chat activities by a participant observer on the Reddit social media platform that supplemented the in-person lecture dynamically, using a network analysis and qualitative ethnography as a modelling facility mimicked the ongoing feedback loops of social media platforms, enabling students to use social media with a critical eye, and engage in productive collaboration. Our quantitative results present network graphs for weekly eigen centrality to understand the egalitarian nature of the network, and transitivity to understand the likelihood for collaboration between more than two agents. Our qualitative results elaborate selected Reddit posts, and weekly field notes to explain how redesigning the chat weekly helped augment lecture-based discussion with the instructor and critique of student presentations, spurring egalitarian participation through a space-place dialectic. Students also provided end-semester feedback that was analyzed using inductive coding, to design future courseware. (shrink)

Abstract: The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, necessitating accurate and timely forecasting of cases for effective mitigation strategies. In this research paper, we present a novel approach to predict COVID-19 cases using Artificial Neural Networks (ANNs), harnessing the power of machine learning for epidemiological forecasting. Our ANNs-based forecasting model has demonstrated remarkable efficacy, achieving an impressive accuracy rate of 97.87%. This achievement underscores the potential of ANNs in providing precise and data-driven insights into the dynamics (...) of the pandemic. However, this paper underscores the critical importance of a comprehensive evaluation beyond accuracy, including metrics such as sensitivity, specificity, and the area under the ROC curve (AUC-ROC), to assess the model's performance robustness. The research paper offers detailed insights into the architecture of the ANN model, encompassing critical hyperparameters, data preprocessing techniques, and regularization strategies employed to optimize model accuracy. Ethical considerations surrounding data privacy and potential biases within the COVID-19 dataset are also addressed. While the achieved accuracy is a significant milestone, this study underscores the dynamic and evolving nature of the pandemic, necessitating continuous model refinement and validation. Furthermore, it emphasizes the importance of considering false positives and false negatives in the context of public health decision-making. In conclusion, this research contributes to the arsenal of tools available for pandemic management by showcasing the potential of ANNs in COVID-19 case forecasting. It encourages ongoing exploration and adaptation of predictive models to enhance their applicability in real-world public health scenarios, ultimately contributing to more effective pandemic control and response efforts. (shrink)

Abstract: The aim behind analyzing Google Stock Prices dataset is to get a fair idea about the relationships between the multiple attributes a day might have, such as: the opening price for each day, the volume of trading for each day. With over a hundred thousand days of trading data, there are some patterns that can help in predicting the future prices. We proposed an Artificial Neural Network (ANN) model for predicting the closing prices for future days. The prediction is (...) based on these features (date, opening price, highest price, lowest price, volume), which were used as input variables and (closing price) as output variable for our ANN model. Our model was created, trained, and validated using data set in JNN environment, which its title is “Google-Stock-Prices”. The model evaluation showed that the ANN model is able to predict correctly 99.48% of the validation samples. (shrink)

Sign Language Recognition (SLR) aims to translate sign language into text or speech in order to improve communication between deaf-mute people and the general public. This task has a large social impact, but it is still difficult due to the complexity and wide range of hand actions. We present a novel 3D convolutional neural network (CNN) that extracts discriminative spatial-temporal features from photo datasets. This article is about classification of sign languages are not universal and are usually not mutually intelligible (...) although there are also similarities among different sign languages. They are the foundation of local Deaf cultures and have evolved into effective means of communication. Although signing is primarily used by the deaf and hard of hearing, hearing people also use it when they are unable to speak, when they have difficulty speaking due to a health condition or disability (augmentative and alternative communication), or when they have deaf family members, such as children of deaf adults. In this article we use the 43500 image in the dataset in size 64*64 pixel by use CNN Architecture and achieved 100% accuracy. (shrink)

Argument mapping is a way of diagramming the logical structure of an argument to explicitly and concisely represent reasoning. The use of argument mapping in critical thinking instruction has increased dramatically in recent decades. This paper overviews the innovation and provides a procedural approach for new teaches wanting to use argument mapping in the classroom. A brief history of argument mapping is provided at the end of this paper.

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)

A pineapple is a tropical plant with eatable leafy foods most monetarily critical plant in the family Bromeliaceous. The pineapple is native to South America, where it has been developed for a long time. The acquaintance of the pineapple with Europe in the seventeenth century made it a critical social symbol of extravagance. Since the 1820s, pineapple has been industrially filled in nurseries and numerous tropical manors. Further, it is the third most significant tropical natural product in world creation. In (...) the twentieth century, Hawaii was a prevailing maker of pineapples, particularly for the US, be that as it may, by 2016, Costa Rica, Brazil, and the Philippines represented almost 33% of the world's creation of pineapples. In this paper, machine learning based approach is presented for identifying type pineapple with a dataset that contains 1,311images use 946 images for training, 197 images for validation and 168 images for testing. A deep learning technique that extensively applied to image recognition was used. use 70% from image for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a heldout test set. (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)

To explore social media use in New Zealand, a sample of 1001 adults aged 18 and over were surveyed in November 2021. Participants were asked about the frequency of their use of different social media platforms (text message included). This report describes how often each of the nine social media sites and apps covered in the survey are used individually on a daily basis. Differences based on key demographics, i.e., age and gender, are tested for statistical significance, and findings summarised.