The Knowledge Norm or Knowledge Account of Assertion (KAA) has received added support recently from data on prompting assertion (Turri 2010) and from a refinement suggesting that assertions ought to express knowledge (Turri 2011). This paper adds another argument from parenthetical positioning, and then argues that KAA’s unified explanation of some of the earliest data (from Moorean conjunctions) adduced in its favor recommends KAA over its rivals.

One of the predominant debates in AI Ethics is the worry and necessity to create fair, transparent and accountable algorithms that do not perpetuate current social inequities. I offer a critical analysis of Reuben Binns’s argument in which he suggests using public reason to address the potential bias of the outcomes of machine learning algorithms. In contrast to him, I argue that ultimately what is needed is not public reason per se, but an audit of the implicit moral assumptions of (...) societies within which algorithms are built and applied. Public justification is appealing since it offers us the possibility to align the decision-making outcomes of the algorithm with the core moral values of stakeholders within a constitutional democratic society. My concern is that the common moral principles that form the foundation of public reason are not necessarily neutral, as they still express specific moral ideals and normative standards even if there is moral agreement by society as a whole, or among different stakeholders within society. Appealing to such normative standards may thus still lead to algorithmic outcomes being biased as common moral values may very well still be discriminatory even though they are formed from a consensus, and even if public reason is applied as a kind of filter for potential algorithmic outcomes. Hence, I argue that these implicit moral norms within society that we take as a given in public reasoning, need to be audited from generation to generation in order to effectively mitigate potential algorithmic bias. (shrink)

Surprisingly little has been written about hedged assertion. Linguists often focus on semantic or syntactic theorizing about, for example, grammatical evidentials or epistemic modals, but pay far less attention to what hedging does at the level of action. By contrast, philosophers have focused extensively on normative issues regarding what epistemic position is required for proper assertion, yet they have almost exclusively considered unqualified declaratives. This essay considers the linguistic and normative issues side-by-side. We aim to bring some order and clarity (...) to thinking about hedging, so as to illuminate aspects of interest to both linguists and philosophers. In particular, we consider three broad questions. 1) The structural question: when one hedges, what is the speaker’s commitment weakened from? 2) The functional question: what is the best way to understand how a hedge weakens? And 3) the taxonomic question: are hedged assertions genuine assertions, another speech act, or what? (shrink)

Recent epistemology has focused almost exclusively on propositional knowledge. This paper considers an underexplored area of epistemology, namely knowledge of persons: if propositional knowledge is a state of mind, consisting in a subject's attitude to a (true) proposition, the account developed here thinks of interpersonal knowledge as a state of minds, involving a subject's attitude to another (existing) subject. This kind of knowledge is distinct from propositional knowledge, but it exhibits a gradability characteristic of context-sensitivity, and admits of shifty thresholds. (...) It is supported by a wide range of unexplored linguistic data and intuitive cases; and it promises to illuminate debates within epistemology, philosophy of religion, and ethics. (shrink)

Recent epistemology offers an account of what it is to know other persons. Such views hold promise for illuminating several issues in philosophy of religion, and for advancing a distinctive approach to religious epistemology. This paper develops an account of interpersonal knowledge, and clarifies its relation to propositional and qualitative knowledge. I then turn to our knowledge of God and God's knowledge of us, and compare my account of interpersonal knowledge with important work by Eleonore Stump on "Franciscan" knowledge. I (...) examine how interpersonal knowledge may figure in liturgical practice, in diffusing the problem of divine hiddenness, and in motivating a novel understanding of divine love. I also explore the possibility of epistemic injustice arising from dismissal or neglect of our religious testimony to one another, or of divine testimony to humanity, focusing specifically on the import of interpersonal knowledge. (shrink)

Hope is an attitude with a distinctive epistemological dimension: it is incompatible with knowledge. This chapter examines hope as it relates to knowledge but also to probability and inductive considerations. Such epistemic constraints can make hope either impossible, or, when hope remains possible, they affect how one’s epistemic situation can make hope rational rather than irrational. Such issues are especially relevant to when hopefulness may permissibly figure in practical deliberation over a course of action. So I consider cases of second-order (...) inductive reflection on when one should, or should not, be hopeful for an outcome with which one has a long record of experience: in other words, what is the epistemology behind when one should, if ever, stop hoping for outcomes which have failed one many times in the past? (shrink)

Traditional definitions of lying require that a speaker believe that what she asserts is false. Sam Fox Krauss seeks to jettison the traditional belief requirement in favour of a necessary condition given in a credence-accuracy framework, on which the liar expects to impose the risk of increased inaccuracy on the hearer. He argues that this necessary condition importantly captures nearby cases as lies which the traditional view neglects. I argue, however, that Krauss's own account suffers from an identical drawback of (...) being unable to explain nearby cases; and even worse, that account fails to distinguish cases of telling lies from cases of telling the truth. (shrink)

This essay expands upon the suggestion that Lessing's infamous ‘ditch’ is actually three ditches: temporal, metaphysical, and existential gaps. It examines the complex problems these ditches raise, and then proposes that Kierkegaard's Fragments and Postscript exhibit a similar triadic organizational structure, which may signal a deliberate attempt to engage and respond to Lessing's three gaps. Viewing the Climacean project in this way offers an enhanced understanding of the intricacies of Lessing's rationalist approach to both religion and historical truth, and illuminates (...) Climacus's subjective response to Lessing. (shrink)

Some philosophers oppose recent arguments for the Knowledge Norm of Assertion by claiming that assertion, being an act much like any other, will be subject to norms governing acts generally, such as those articulated by Grice for the purpose of successful, cooperative endeavours. But in fact, Grice is a traitor to their cause; or rather, they are his dissenters, not his disciples. Drawing on Grice's unpublished papers, I show that he thought of asserting as a special linguistic act in need (...) of its own norm, and he tied his maxim of Quality to knowledge. I also develop a simple Gricean-inspired argument showing that the Quality maxim is not dependent on the Cooperative Principle. If it is not thus dependent, then the Cooperative Principle cannot be the explanation of, or source of normativity for, the Quality maxim. Thus, leveraging the insights informing the maxim of Quality actually provides the resources for a distinctive positive case that knowledge is the constitutive norm of assertion. (shrink)

Linda Zagzebski's "Epistemic Authority" (Oxford University Press, 2012) brings together issues in social epistemology with topics in moral and political philosophy as well as philosophy of religion. In this paper I criticize her discussion of self-trust and rationality, which sets up the main argument of the book; I consider how her view of authority relates to some issues of epistemic authority in testimony; and I raise some concerns about her treatment of religious epistemology and religious authority in particular.

Epistemologists focus primarily on cases of knowledge, belief, or credence where the evidence which one possesses, or on which one is relying, plays a fundamental role in the epistemic or normative status of one's doxastic state. Recent work in epistemology goes beyond the evidence one possesses to consider the relevance for such statuses of evidence which one does not possess, particularly when there is a sense in which one should have had some evidence. I focus here on Sanford Goldberg's approach (...) ("Should Have Known," Synthese, forthcoming; and "On the Epistemic Significance of Evidence You Should Have Had," Episteme 2016, this issue); but the discussion will interest anyone working on epistemic defeat. (shrink)

Pascal Engel (2008) has insisted that a number of notable strategies for rejecting the knowledge norm of assertion are put forward on the basis of the wrong kinds of reasons. A central aim of this paper will be to establish the contrast point: I argue that one very familiar strategy for defending the knowledge norm of assertion—viz., that it is claimed to do better in various respects than its competitors (e.g. the justification and the truth norms)— relies on a presupposition (...) that is shown to be ultimately under motivated. That presupposition is the uniqueness thesis—that there is a unique epistemic rule for assertion, and that such a rule will govern assertions uniformly. In particular, the strategy I shall take here will be to challenge the sufficiency leg of the knowledge norm in a way that at the same time counts against Williamson’s (2000) own rationale for the uniqueness thesis. However, rather than to challenge the sufficiency leg of the knowledge norm via the familiar style of ‘expert opinion’ and, more generally, ‘second-hand knowledge’ cases (e.g. Lackey (2008)), a strategy that has recently been called into question by Benton (2014), I’ll instead advance a very different line of argument against the sufficiency thesis, one which turns on a phenomenon I call epistemic hypocrisy. (shrink)

The Knowledge Account of Assertion (KAA) says that knowledge is the norm of assertion: you may assert a proposition only if you know that it’s true. The primary support for KAA is an explanatory inference from a broad range of linguistic data. The more data that KAA well explains, the stronger the case for it, and the more difficult it is for the competition to keep pace. In this paper we critically assess a purported new linguistic datum, which, it has (...) been argued, KAA well explains. We argue that KAA does not well explain it. (shrink)

Assertion is governed by an epistemic norm requiring knowledge. This idea has been hotly debated in recent years, garnering attention in epistemology, philosophy of language, and linguistics. This chapter presents and extends the main arguments in favor of the knowledge norm, from faulty conjunctions, several conversational patterns, judgments of permission, excuse, and blame, and from showing how. (Paired with a chapter by Peter J. Graham and Nikolaj J. L. L. Pedersen, "Knowledge is Not Our Norm of Assertion.").

This chapter covers contemporary work on disagreement, detailing both the conceptual and normative issues in play in the debates in mainstream analytic epistemology, and how these relate to religious diversity and disagreement. §1 examines several sorts of disagreement, and considers several epistemological issues: in particular, what range of attitudes a body of evidence can support, how to understand higher-order evidence, and who counts as an epistemic “peer”. §2 considers how these questions surface when considering disagreements over religion, including debates over (...) the nature of evidence and truth in religion, epistemic humility, concerns about irrelevant influences and about divine hiddenness, and arguments over exclusivism, inclusivism, and pluralism. Finally, §3 summarizes the contributors’ essays in this volume. (shrink)

Assertion is widely regarded as an act associated with an epistemic position. To assert is to represent oneself as occupying this position and/or to be required to occupy this position. Within this approach, the most common view is that assertion is strong: the associated position is knowledge or certainty. But recent challenges to this common view present new data that are argued to be better explained by assertion being weak. Old data widely taken to support assertion being strong has also (...) been challenged. This paper examines such challenges and finds them wanting. Far from diminishing the case for strong assertion, carefully considering new and old data reveals that assertion is as strong as ever. (shrink)

Hope, in its propositional construction "I hope that p," is compatible with a stated chance for the speaker that not-p. On fallibilist construals of knowledge, knowledge is compatible with a chance of being wrong, such that one can know that p even though there is an epistemic chance for one that not-p. But self-ascriptions of propositional hope that p seem to be incompatible, in some sense, with self-ascriptions of knowing whether p. Data from conjoining hope self-ascription with outright assertions, with (...) first- and third-person knowledge ascriptions, and with factive predicates suggest a problem: when combined with a plausible principle on the rationality of hope, they suggest that fallibilism is false. By contrast, the infallibilist about knowledge can straightforwardly explain why knowledge would be incompatible with hope, and can offer a simple and unified explanation of all the linguistic data introduced here. This suggests that fallibilists bear an explanatory burden which has been hitherto overlooked. (shrink)

John N. Williams (1994) and Matthew Weiner (2005) invoke predictions in order to undermine the normative relevance of knowledge for assertions; in particular, Weiner argues, predictions are important counterexamples to the Knowledge Account of Assertion (KAA). I argue here that they are not true counterexamples at all, a point that can be agreed upon even by those who reject KAA.

What is it to know someone? Epistemologists rarely take up this question, though recent developments make such inquiry possible and desirable. This paper advances an account of how such interpersonal knowledge goes beyond mere propositional and qualitative knowledge about someone, giving a central place to second-personal treatment. It examines what such knowledge requires, and what makes it distinctive within epistemology as well as socially. It assesses its theoretic value for several issues in moral psychology, epistemic injustice, and philosophy of mind. (...) And it offers an account of the complex content in play if interpersonal knowledge is to be understood in terms of its mental states and their functions. (shrink)

The problem of evil is the most prominent argument against the existence of God. Skeptical theists contend that it is not a good argument. Their reasons for this contention vary widely, involving such notions as CORNEA, epistemic appearances, 'gratuitous' evils, 'levering' evidence, and the representativeness of goods. We aim to dispel some confusions about these notions, in particular by clarifying their roles within a probabilistic epistemology. In addition, we develop new responses to the problem of evil from both the phenomenal (...) conception of evidence and the knowledge-first view of evidence. (shrink)

Expert testimony figures in recent debates over how best to understand the norm of assertion and the domain-specific epistemic expectations placed on testifiers. Cases of experts asserting with only isolated second-hand knowledge (Lackey 2011, 2013) have been used to shed light on whether knowledge is sufficient for epistemically permissible assertion. I argue that relying on such cases of expert testimony introduces several problems concerning how we understand expert knowledge, and the sharing of such knowledge through testimony. Refinements are needed to (...) clarify exactly what principles are being tested by such cases; but once refined, such cases raise more questions than they answer. (shrink)

This book examines a main theme in religious epistemology, namely, the possibility of knowledge of God. Most often philosophers consider the rationality or justification of propositional belief about God, particularly beliefs about the existence and nature of God; and they will assess the conditions under which, if there is a God, such propositional beliefs would be knowledge, particularly in light of counter-evidence or the availability of religious disagreement. This book surveys such familiar areas, then turns toward newer and less-developed terrain: (...) interpersonal epistemology, namely what it is to know another person. It then explores the prospects for understanding what it might take to know God relationally, the contours of which are significant for many theistic traditions. (shrink)

Epistemologists have shown increased interest in the epistemic significance of disagreement, and in particular, in whether there is a rational requirement concerning belief revision in the face of peer disagreement. This article examines some of the general issues discussed by epistemologists, and then considers how they may or may not apply to the case of religious disagreement, both within religious traditions and between religious (and non-religious) views.

The knowledge account of assertion - roughly: one should not assert what one does not know - can explain a variety of Moorean conjunctions, a fact often cited as evidence in its favor. David Sosa ("Dubious Assertions," Phil Studies, 2009) has objected that the account does not generalize satisfactorily, since it cannot explain the infelicity of certain iterated conjunctions without appealing to the controversial "KK" principle. This essay responds by showing how the knowledge account can handle such conjunctions without use (...) of the KK principle. (shrink)

The lottery and preface paradoxes pose puzzles in epistemology concerning how to think about the norms of reasonable or permissible belief. Contextualists in epistemology have focused on knowledge ascriptions, attempting to capture a set of judgments about knowledge ascriptions and denials in a variety of contexts (including those involving lottery beliefs and the principles of closure). This article surveys some contextualist approaches to handling issues raised by the lottery and preface, while also considering some of the difficulties encountered by those (...) approaches. (shrink)

Many epistemologists are enamored with a defeat condition on knowledge. In this paper we present some implementation problems for defeatism, understood along either internalist or externalist lines. We then propose that one who accepts a knowledge norm of belief, according to which one ought to believe only what one knows, can explain away much of the motivation for defeatism. This is an important result, because on the one hand it respects the plausibility of the intuitions about defeat shared by many (...) in epistemology; but on the other hand, it obviates the need to provide a unified account of defeat which plays well with the most plausible views of how knowledge fits with evidential probability. (shrink)

Abstract: Smoke detectors are critical devices for early fire detection and life-saving interventions. This research paper explores the application of Artificial Neural Networks (ANNs) in smoke detection systems. The study aims to develop a robust and accurate smoke detection model using ANNs. Surprisingly, the results indicate a 100% accuracy rate, suggesting promising potential for ANNs in enhancing smoke detection technology. However, this paper acknowledges the need for a comprehensive evaluation beyond accuracy. It discusses potential challenges, such as overfitting, dataset size, (...) and class imbalance, and presents strategies employed to address these issues. Additionally, the paper emphasizes the importance of alternative evaluation metrics, including precision, recall, and F1-score, to assess the model's performance comprehensively. Detailed information regarding the model architecture, data preprocessing, and hyperparameter tuning is provided to enhance the reproducibility of the research. The study also discusses ethical considerations and the implications of false positives and false negatives in smoke detection applications. While achieving 100% accuracy is a notable accomplishment, this paper calls for cautious interpretation and further validation in real-world settings. It concludes by highlighting areas for future research and improvement in smoke detection technology, emphasizing the need for practical deployment and the reduction of false alarms. (shrink)

Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass '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,' with a total of 774 samples. Our proposed neural network architecture, consisting of three (...) layers (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (shrink)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary classification, identifying the (...) presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

Abstract: In recent years, the outbreak of various poultry diseases has posed a significant threat to the global poultry industry. Therefore, the accurate and timely detection of chicken diseases is critical to reduce economic losses and prevent the spread of diseases. In this study, we propose a method for classifying chicken diseases using a convolutional neural network (CNN). The proposed method involves preprocessing the chicken images, building and training a CNN model, and evaluating the performance of the model. The dataset (...) used in this study comprises chicken images of three different diseases: Newcastle disease, avian influenza, and infectious bursal disease. The proposed method achieved an overall F1-score of 99.04% in classifying the chicken diseases. The results demonstrate the effectiveness of using CNN in classifying chicken diseases and provide a promising approach for early detection and diagnosis of poultry diseases. (shrink)

This paper critically engages Philip Mirowki's essay, "The scientific dimensions of social knowledge and their distant echoes in 20th-century American philosophy of science." It argues that although the cold war context of anti-democratic elitism best suited for making decisions about engaging in nuclear war may seem to be politically and ideologically motivated, in fact we need to carefully consider the arguments underlying the new rational choice based political philosophies of the post-WWII era typified by Arrow's impossibility theorem. A distrust of (...) democratic decision-making principles may be developed by social scientists whose leanings may be toward the left or right side of the spectrum of political practices. (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)

Abstract: Context: grapevine leaves are an important agricultural product that is used in many Middle Eastern dishes. The species from which the grapevine leaf originates can differ in terms of both taste and price. Method: In this study, we build a deep learning model to tackle the problem of grape leaf classification. 500 images were used (100 for each species) that were then increased to 10,000 using data augmentation methods. Convolutional Neural Network (CNN) algorithms were applied to build this model (...) specifically using the pre-trained model on top of the VGG16 architecture. Then, dense layers were added to classify the output of the Convolutional layers and classify outputs to the five classes (species) the leaf belonged to. Results: It was found that feature extraction without fine-tuning the convolutional layers yielded poor results, about 86% accuracy, while training the whole network along with some data preprocessing gave the best results, about 99.45% accuracy on the testing dataset. Conclusions: The proposed CNN model is an effective one for the problem of classification of grape leaf species. (shrink)

The study aimed at identifying the personal variables and their effect in promoting job creation in Gaza Strip through business incubators. The researchers used the descriptive analytical approach to achieve the study objectives. The study population consisted of 92 of the pilot projects benefiting from the three business incubators in Gaza Strip (Palestinian Information Technology Incubator, UCAS Technology Incubator and Business and Technology Incubator). The study reached a number of results, the most important of which are the existence of statistically (...) significant differences on entrepreneurship attributed to each age as most of them are between the ages of 22-30, Gender for males, business incubator, scientific qualification for the specialties of information technology and engineering, and years of experience. Based on the findings, the researchers recommend focusing on university students in guiding them towards entrepreneurship and helping new graduates to start entrepreneurship. And to guide students to scientific disciplines that help them in entrepreneurship after graduation, whether starting a small business or self-employment, support females to entrepreneurship as most of the entrepreneurs are male, in addition to stimulating males as well. (shrink)

Abstract: The Knowledge-Based System for Diagnosing Breast Cancer aims to support medical students in enhancing their education regarding diagnosis and counseling. The system facilitates the analysis of biopsy images under a microscope, determination of tumor type, selection of appropriate treatment methods, and identification of disease-related questions. According to the Ministry of Health's annual report in Gaza, there were 7,069 cases of breast cancer between 2009 and 2014, with 1,502 cases reported in 2014. In an era dominated by visual information, where (...) 65% of the population are visual learners, the Knowledge-Based System serves as an effective tool to aid long-term information retention by utilizing visual and textual resources. The system features a user-friendly interface, enabling students to diagnose the disease and expand their understanding. Furthermore, it can be accessed via smartphones. The Knowledge-Based System comprises three primary components: Firstly, it provides specific patient-oriented questions to familiarize students with the questioning process. Secondly, it facilitates the analysis of biopsy sample images to determine their integrity and identify potential infections. Lastly, the system incorporates animated videos illustrating various approaches for treating breast cancer. (shrink)

The purpose of this paper is to introduce and study the characteristic function of a neutrosophic set. After given the fundamental definitions of neutrosophic set operations generated by the characteristic function of a neutrosophic set ( Ng for short), we obtain several properties, and discussed the relationship between neutrosophic sets generated by Ng and others. Finally, we introduce the neutrosophic topological spaces generated by Ng . Possible application to GIS topology rules are touched upon.

Abstract: Dates are the fruit of date palm trees, and it is one of the fruits famous for its high nutritional value. It is a summer fruit spread in the Arab world. In the past, the Arabs relied on it in their daily lives. Dates take an oval shape and vary in size from 20 to 60 mm in length and 8 to 30 mm in diameter. The ripe fruit consists of a hard core surrounded by a papery cover called (...) the tartar that separates the core from the fleshy part that is eaten. Historians disagreed about the place of its origin, so some historians expressed their belief that it had originated around the Arabian Gulf, and some of them say that the oldest known information about palm trees was in Babylon 4 thousand years ago BC. It is known about the ancient Egyptians using dates in wine. Dates contain a high nutritional value and are considered a basic food for man since ancient times. The fruits of dates are considered the highest fruits in containing sugars. These components vary according to the nature of the fruit, whether it is wet, semi-dry or dry, as well as according to the environmental conditions surrounding the trees. The components of the fruits also differ in different varieties and increase the percentage of sugars in dates is 70-78% of the fruit’s components. These sugars are characterized by their rapid absorption and transfer into the blood directly, digestion and burning. 10 dates (about 100 grams) per day provide a person with all his daily needs of magnesium, manganese, copper, sulfur and half of his needs of calcium and potassium. In this paper we presented a system that recognize the nine types of dates based on deep learning using python on Colab, and classifying using a dataset contain 1350 images. Our trained model achieved an accuracy of 99.44% on a proven test set. (shrink)

Abstract: Heart attacks, or myocardial infarctions, are a leading cause of mortality worldwide. Early prediction and accurate analysis of potential risk factors play a crucial role in preventing heart attacks and improving patient outcomes. In this study, we conduct a comprehensive review of datasets related to heart attack analysis and prediction. We begin by examining the various types of datasets available for heart attack research, encompassing clinical, demographic, and physiological data. These datasets originate from diverse sources, including hospitals, research institutions, (...) and public health agencies. Our analysis aims to identify common features, data quality, and potential biases in these datasets. Next, we explore the predictive modeling techniques employed in heart attack prediction. Machine learning algorithms, such as decision trees, support vector machines, and deep neural networks, have gained prominence in predicting heart attacks. We discuss the strengths and limitations of these methods and highlight recent advancements in predictive modeling. Furthermore, we delve into the critical risk factors associated with heart attacks. Factors such as age, gender, hypertension, diabetes, and cholesterol levels are examined for their significance in predicting cardiac events. We also investigate the role of lifestyle factors, including smoking, diet, and physical activity, in heart attack risk assessment. Additionally, this review addresses the importance of data preprocessing and feature engineering in improving prediction accuracy. Feature selection methods, missing data handling, and data scaling techniques are discussed to enhance the robustness of heart attack prediction models. In conclusion, this comprehensive dataset review provides valuable insights into the state of heart attack analysis and prediction. It serves as a resource for researchers and healthcare professionals seeking to better understand the datasets available for heart attack research and the methods employed for accurate prediction. Ultimately, our efforts in dataset analysis and predictive modeling contribute to the advancement of preventive cardiology and the reduction of heart attack-related morbidity and mortality. (shrink)

In this book I argue that school integration is not a proxy for educational justice. I demonstrate that the evidence consistently shows the opposite is more typically the case. I then articulate and defend the idea of voluntary separation, which describes the effort to redefine, reclaim and redirect what it means to educate under preexisting conditions of segregation. In doing so, I further demonstrate how voluntary separation is consistent with the liberal democratic requirements of equality and citizenship. The position I (...) defend is not opposed to integration but rather is a justified response to the daily experience of frustration and disappointment with a system that has failed members of marginalized groups for too long. I argue that most voluntary separation experiments in education, far from being motivated by a sense of racial, cultural or religious exclusion, are in fact driven among other things by a desire for a quality education, not to mention community membership and self respect. As such, voluntary separation represents a morally robust pragmatic strategy that is able to answer liberal challenges concerning involuntary stratification, ethnocentrism and democratic deliberation. (shrink)

This study investigates the dynamics of brick-and-mortar versus click-and-mortar microbusinesses, focusing on the strengths, weaknesses, opportunities, and threats (SWOT) within the local context of Balayan. Ten purposively sampled microbusiness entrepreneurs were examined using the SWOT method. The findings underscore the significance of product assessment, communication, and customer experiences in cultivating trust. Sensory experiences and competitive pricing emerge as strengths, while challenges such as poor sales necessitate strategic interventions. External factors, particularly technological advancements, exert influence on the retail landscape. Key strategies (...) include competitive pricing, flexibility, and an understanding of customer behavior. The study highlights growth potential in tailored experiences but acknowledges the risks posed by e-commerce competition. Additionally, a SWOT strategic plan is developed to leverage internal and external strengths and opportunities while mitigating weaknesses and threats. (shrink)

This study aims to identify the digital reputation at the University of Palestine: an analytical perspective of the employees ’point of view, where the researchers used the descriptive and analytical approach, through a questionnaire distributed to a sample of employees at the University of Palestine, where the size of the study population is (234) employees, and the size of The sample is (117) employees, of whom (90) employees responded. The study provided a theoretical framework for what the writers and researchers (...) presented about the study variables, as well as a practical analytical framework for the opinions of employees at the University of Palestine. The digital reputation was expressed as an independent variable through its four indicators (feeling about the organization (satisfaction), admiration and respect, and trust, and social responsibility). The study found a set of results, the most important of which are: the existence of a high degree of digital reputation at the University of Palestine from the viewpoint of employees, and the absence of differences in the opinions of the study sample about digital reputation according to demographic variables. The study presented a set of recommendations, the most important of which are: The need for universities to enhance their digital reputation and raise its level. (shrink)

Life expectancy prediction, a pivotal facet of public health and policy formulation, has witnessed remarkable advancements owing to the integration of neural network models and comprehensive datasets. In this research, we present an innovative approach to forecasting life expectancy in diverse countries. Leveraging a neural network architecture, our model was trained on a dataset comprising 22 distinct features, acquired from Kaggle, and encompassing key health indicators, socioeconomic metrics, and cultural attributes. The model demonstrated exceptional predictive accuracy, attaining an impressive 99.27% (...) and an average error of 0.0034, underscoring the potency of deep learning in tackling complex, real-world challenges. Furthermore, our study delved into feature importance analysis, identifying critical determinants such as HIV/AIDS prevalence, income composition of resources, and socioeconomic status that significantly influence life expectancy. These findings provide actionable insights for healthcare policymakers and practitioners, emphasizing the importance of addressing health disparities, promoting economic development, and implementing targeted interventions. This research bridges the gap between data-driven methodologies and global health, offering a robust predictive tool and enriching our understanding of the multifaceted dynamics that shape life expectancy across nations. (shrink)

Abstract: In this study, we explore the possibility of classifying the plant species. We collected the plant species from Kaggle website. This dataset encompasses 544 samples, encompassing 136 distinct plant species. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing plant Species classification accuracy and efficiency. This research explores plant Species classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 544 entries, we develop and evaluate a neural network model. Our neural network, (...) featuring a single hidden layer, achieves remarkable results—a staggering 100% accuracy and a minute average error rate of 0.002. Beyond performance metrics, we delve into the intricacies of plant Species classification through feature importance analysis. The most influential features— Vegsout, durflow, semiros, pdias, begflow, wind, leafy, autopoll and insects— uncover the physiological traits underpinning accurate rice classification. This research contributes to advancing rice classification methods and highlights the potential of ANNs in optimizing agricultural practices, ensuring plant safety, and bolstering global trade. (shrink)

This essay represents a novel contribution to Nietzschean studies by combining an assessment of Friedrich Nietzsche’s challenging uses of “truth” and the “eternal return” with his insights drawn from Indian philosophies. Specifically, drawing on Martin Heidegger’s Nietzsche, I argue that Nietzsche’s critique of a static philosophy of being underpinning conceptual truth is best understood in line with the Theravada Buddhist critique of “self ” and “ego” as transitory. In conclusion, I find that Nietzsche’s “eternal return” can be understood as a (...) direct inversion of “nirvana”: Nietzsche celebrates profound attachment to each and every moment, independent from its pleasurable or distasteful registry. (shrink)

The aim behind analyzing the Goodreads dataset is to get a fair idea about the relationships between the multiple attributes a book might have, such as: the aggregate rating of each book, the trend of the authors over the years and books with numerous languages. With over a hundred thousand ratings, there are books which just tend to become popular as each day seems to pass. We proposed an Artificial Neural Network (ANN) model for predicting the overall rating of books. (...) The prediction is based on these features (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which were used as input variables and (average_rating) as output variable for our ANN model. Our model were created, trained, and validated using data set in JNN environment, which its title is “Goodreads-books”. Model evaluation showed that the ANN model is able to predict correctly 99.78% of the validation samples. (shrink)

Abstract: Forests are areas with a high density of trees, and they play a vital role in the health of the planet. They provide a habitat for a wide variety of plant and animal species, and they help to regulate the climate by absorbing carbon dioxide from the atmosphere. While in 2010, the world had 3.92Gha of forest cover, covering 30% of its land area, in 2019, there was a loss of forest cover of 24.2Mha according to the Global Forest (...) Watch institute. Discovery and classification depend on human experience and effort, so the error in the results of this process can lead to forest fires and disasters. Therefore, deep learning algorithms from artificial intelligence and machine learning sciences have been applied to help specialists avoid false or inaccurate diagnoses when detecting Forest fires in images using a pre-trained convolutional neural network called VGG16. The model was customized to fit the Forest fires classification and then applied to a dataset consisting of (14,000) of the Forests collected from the Kaggle depository. We trained, validated, and tested the modified VGG16 model. The proposed VGG16 model obtained Precision (99.96%), Recall (99.96%), and F1-Score (99.96%). (shrink)

Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in dvance regarding: on-going construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. In (...) our proposed system we propose a new forecasting method that uses a Convolutional Neural Network monthly rainfall for a selected location. In our proposed system we are going to forecast the rainfall result based on the mean square error, mean absolute error and root mean square error, which we get in train and test of the dataset based deep learning technique. (shrink)

In this paper we aim to demonstrate the enormous ethical complexity that is prevalent in child obesity cases. This complexity, we argue, favors a cautious approach. Against those perhaps inclined to blame neglectful parents, we argue that laying the blame for child obesity at the feet of parents is simplistic once the broader context is taken into account. We also show that parents not only enjoy important relational prerogatives worth defending, but that children, too, are beneficiaries of that relationship in (...) ways difficult to match elsewhere. Finally, against the backdrop of growing public concern and pressure to intervene earlier in the life cycle, we examine the perhaps unintended stigmatizing effects that labeling and intervention can have and consider a number of risks and potential harms occasioned by state interventions in these cases. (shrink)