In Emotions, Values & Agency, Christine Tappolet develops a sophisticated, perceptual theory of emotions and their role in wide range of issues in value theory and epistemology. In this paper, we raise three worries about Tappolet's proposal.

This paper explores the political campaigning strategies of Lynton Crosby, and argues that they pose a threat to democracy. In doing so, I looks to shed light on Crosby’s tactics, but also to elucidate exactly what is anti-democratic about them. I argue that there are two worrying aspects to this. The first involves Crosby’s lack of respect for voters’ beliefs, interests and values, whereas the second concerns his propensity for avoiding debate.

Boyd and Crosby's article "Zoroastrianism: Dualistic or Monotheistic?" explores various perspectives on this question. In their work, the authors delve into the dualistic and monotheistic aspects of Zoroastrianism, considering six different responses. These responses are subjected to rigorous philosophical examination, primarily focusing on how they address the challenge of evil. Ultimately, Boyd and Crosby propose a seventh response, which they find more compelling and philosophically robust than the previous six alternatives, aligning more closely with their criteria for rigorous (...) scrutiny. This paper delves into the seven versions put forth by Boyd and Crosby and evaluates their efficacy in responding to Epicurus' formulation of the problem of evil mentioned earlier. Epicurus' formulation, structured as questions demanding answers, prompts an active engagement with its inquiries. I contend that while Boyd and Crosby's version does possess certain philosophical advantages over the others, it cannot ensure humanity's salvation. This limitation arises from their perspective, which allows for the possibility of viewing the world as progressively improving, a perspective that humanity may be hesitant to relinquish entirely. In the subsequent sections, I will (1) reconstruct the seven versions as presented by Boyd and Crosby, (2) subject each of them to the questions raised by Epicurus, (3) summarize the overarching analysis, and (4) conclude with some final remarks. (shrink)

Can we build machines with which we can have interesting conversations? Observing the new optimism of AI regarding deep learning and new language models, we set ourselves an ambitious goal: We want to find out how far we can get in creating a digital replica of a philosopher. This project has two aims; one more technical, investigating of how the best model can be built. The other one, more philosophical, explores the limits and risks which are accompanied by the creation (...) of digital replicas. In cooperation with Daniel Dennett, we took his complete works to fine-tune a GPT-3. In this paper, we share our first results with some piloting models and discuss legal and ethical questions about creating and interacting with digital replicas. (shrink)

It is crucial to actively detect the risks of transactions in a financial company to improve customer experience and minimize financial loss. In this study, we compare different machine learning algorithms to effectively and efficiently predict the legitimacy of financial transactions. The algorithms used in this study were: MLP Repressor, Random Forest Classifier, Complement NB, MLP Classifier, Gaussian NB, Bernoulli NB, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Bagging Classifier, Decision Tree Classifier and Deep Learning. The dataset (...) was collected from Kaggle depository. It consists of 6362620 rows and 10 columns. The best classifier with unbalanced dataset was the Random Forest Classifier. The Accuracy 99.97%, precession 99.96%, Recall 99.97% and the F1-score 99.96%. However, the best classifier with balanced dataset was the Bagging Classifier. The Accuracy 99.96%, precession 99.95%, Recall 99.98% and the F1-score 99.96%. (shrink)

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Deep learning (...) is a technique used to generate face detection and recognize it for real or fake by using profile images and determine the differences between them. In this study, we used deep learning techniques to generate models for Real and Fake face detection. The goal is determining a suitable way to detect real and fake faces. The model was designed and implemented, including both Dataset of images: Real and Fake faces detection through the use of Deep learning algorithms based on neural networks. We have trained dataset which consists of 9,000 images for total in 150 epochs, and got the ResNet50 model to be the best model of network architectures used with 100% training accuracy, 99.18% validation accuracy, training loss 0.0003, validation loss 0.0265, and testing accuracy 99%. (shrink)

Automatic detection of diseases and anatomical landmarks in medical images by the use of computers is important and considered a challenging process that could help medical diagnosis and reduce the cost and time of investigational procedures and refine health care systems all over the world. Recently, gastrointestinal (GI) tract disease diagnosis through endoscopic image classification is an active research area in the biomedical field. Several GI tract disease classification methods based on image processing and machine learning techniques have been proposed (...) by diverse research groups in the recent past. However, yet effective and comprehensive deep ensemble neural network-based classification model with high accuracy classification results is not available in the literature. In this thesis, we review ways and mechanisms to use deep learning techniques to research on multi-disease computer-aided detection about gastrointestinal and identify these images. We re-trained five state-of-the-art neural network architectures, VGG16, ResNet, MobileNet, Inception-v3, and Xception on the Kvasir dataset to classify eight categories that include an anatomical landmark (pylorus, z-line, cecum), a diseased state (esophagitis, ulcerative colitis, polyps), or a medical procedure (dyed lifted polyps, dyed resection margins) in the Gastrointestinal Tract. Our models have showed results with a promising accuracy which is a remarkable performance with respect to the state-of-the-art approaches. The resulting accuracies achieved using VGG, ResNet, MobileNet, Inception-v3, and Xception were 98.3%, 92.3%, 97.6%, 90% and 98.2%, respectively. As it appears, the most accurate result has been achieved when retraining VGG16 and Xception neural networks with accuracy reache to 98% due to its high performance on training on ImageNet dataset and internal structure that support classification problems. (shrink)

Papaya is a tropical fruit with a green cover, yellow pulp, and a taste between mango and cantaloupe, having commercial importance because of its high nutritive and medicinal value. The process of sorting papaya fruit based on maturely is one of the processes that greatly determine the mature of papaya fruit that will be sold to consumers. The manual grading of papaya fruit based on human visual perception is time-consuming and destructive. The objective of this paper is to the status (...) classification of papaya fruits if it's mature or partially matured or unmatured. A deep learning technique that was extensively applied to image recognition was used. The trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. Classification model of VGG16 achieved a 100% accuracy and 112 seconds of training time. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Abstract: Fruits are a rich source of energy, minerals and vitamins. They also contain fiber. There are many fruits types such as: Apple and pears, Citrus, Stone fruit, Tropical and exotic, Berries, Melons, Tomatoes and avocado. Classification of fruits can be used in many applications, whether industrial or in agriculture or services, for example, it can help the cashier in the hyper mall to determine the price and type of fruit and also may help some people to determining whether a (...) certain type of fruit meets their nutritional requirement. In this paper, machine learning based approach is presented for classifying and identifying 10 different fruit with a dataset that contains 6847 images use 4793 images for training, 1027 images for validation and 1027 images for testing. A deep learning technique that extensively applied to image recognition was used. We used 70% from image for training and 15% from image for validation 15% for testing. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. (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)

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)

First paragraph: In the 1980s I went through a phase of writing limericks during idle moments when I lacked something to read. The result was a set of 27 limericks about cybernetics (Umpleby 1992). I occasionally use the limericks in class to restate a theoretical point. Limericks bring a smile and demonstrate that cybernetics can be approached in a variety of ways. Below are three limericks from this collection. The last was written by Ernst von Glasersfeld. It seems Ernst believed (...) that I was overly concerned with "reality" as opposed to perception, or at least that I had not captured the point that he was trying to make. (shrink)

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and reacts like humans, some of the computer activities with artificial intelligence are designed to include speech, recognition, learning, planning and problem solving. Deep learning is a collection of algorithms used in machine learning, it is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is used as a (...) technique to produce brain tumor detection and classification models using Magnetic Resonance Imaging (MRI) imaging for rapid and easy detection and identification of brain tumor. In this thesis, some ways and mechanisms will be reviewed to use deep learning techniques to produce a model for brain tumor detection. The goal is to find a good and effective way to detect brain tumor based on MRI to help the brain doctor in making decisions easily, accurately and rapidly. A recent report by the World Health Organization in February 2018 showed that the death rate from brain cancer or central nervous system (CNS) is the highest in the Asian continent. It is important to detect cancer early so that many of these lives can be saved. The model has been designed and implemented, including a dataset which consist of 10,000 images for brain tumor detection through the use of Deep learning algorithms based on neural networks. For testing, we have used our model, Inception, VGG16, MobileNet and ResNet models. The f-score accuracy we got for each model was as follows: Our model was 98.28, VGG16 was 99.86%, ResNet50 was 98.14%, MobileNet was 88,98%, and InceptionV3 was 99.88%. (shrink)

Abstract: Rice, as a paramount staple crop worldwide, sustains billions of lives. Precise classification of rice types holds immense agricultural, nutritional, and economic significance. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing rice type classification accuracy and efficiency. This research explores rice type classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 18,188 entries and key rice grain attributes, 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 0.000001. Beyond performance metrics, we delve into the intricacies of rice type classification through feature importance analysis. The most influential features—'id,' 'Area,' 'MajorAxisLength,' 'MinorAxisLength,' 'Eccentricity,' 'ConvexArea,' 'EquivDiameter,' 'Extent,' 'Perimeter,' 'Roundness,' and 'AspectRatio'—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 food safety, and bolstering global trade. (shrink)

Brain tumors pose significant challenges in modern healthcare, with accurate and timely diagnosis crucial for determining appropriate treatment strategies. Artificial intelligence has made significant advancements in recent years. Rule-based expert systems (if-then rule-based systems) have emerged as a promising approach for clinical decision-making in brain tumor diagnosis. In this paper, we present "A CLIPS-Based Expert System for Brain Tumor Diagnosis," which leverages a set of 14 if-then rules to diagnose brain tumors with three possible outcomes: 1) Confirm the diagnosis of (...) a brain tumor, 2) Consider the possibility of a brain tumor that has metastasized, and 3) Consider the possibility of a brain tumor. Our expert system offers a user-friendly interface, enabling users to select symptoms and receive a diagnosis based on the provided information. This paper discusses the expert system's development, implementation, and evaluation, highlighting its potential to facilitate brain tumor diagnosis and decision-making in clinical settings. Additionally, we provide a literature review that contextualizes our expert system within the broader landscape of rule-based expert systems for brain tumor diagnosis, examining their effectiveness, limitations, and challenges. (shrink)

What makes science trustworthy to the public? This chapter examines one proposed answer: the trustworthiness of science is based at least in part on its independence from the idiosyncratic values, interests, and ideas of individual scientists. That is, science is trustworthy to the extent that following the scientific process would result in the same conclusions, regardless of the particular scientists involved. We analyze this "idiosyncrasy-free ideal" for science by looking at philosophical debates about inductive risk, focusing on two recent proposals (...) which offer different methods of avoiding idiosyncrasy: the high epistemic standards proposal and the democratic values proposal. (shrink)

Mouth ulcers, also known as canker sores, are a common oral health issue affecting a significant portion of the population. Early and accurate diagnosis of mouth ulcers is crucial for effective treatment and prevention of complications. This paper presents an expert system developed using CLIPS (C Language Integrated Production System) to diagnose mouth ulcer disease. The expert system utilizes a rule-based approach, incorporating a comprehensive knowledge base consisting of symptoms, risk factors, and medical literature related to mouth ulcers. By employing (...) an inference engine, the system evaluates patient input against the rules and generates a diagnosis based on the matching symptoms and relevant factors. The system also provides detailed explanations and recommendations for each diagnosis, aiding healthcare professionals and patients in understanding the condition and selecting appropriate treatment options. The development and validation of the expert system involved a multi-step process, including knowledge acquisition, rule formulation, and testing against a diverse dataset of mouth ulcer cases. The system's performance was evaluated by comparing its diagnoses with those made by medical professionals, demonstrating a high level of accuracy and consistency. The implementation of an expert system for diagnosing mouth ulcer disease offers several advantages, including the ability to provide timely and accessible healthcare services, reduce misdiagnosis rates, and assist healthcare professionals in decision-making processes. Moreover, it empowers individuals to make informed decisions about their health and seek appropriate medical intervention. This paper contributes to the field of medical expert systems by showcasing the practical application of CLIPS in the diagnosis of mouth ulcer disease. The results demonstrate the potential of expert systems as valuable tools for healthcare professionals and patients alike, enhancing the efficiency and accuracy of diagnosis in oral health care. (shrink)

States restrict immigration on a massive scale. Governments fortify their borders with walls and fences, authorize border patrols, imprison migrants in detention centers, and deport large numbers of foreigners. Unjust Borders: Individuals and the Ethics of Immigration argues that immigration restrictions are systematically unjust and examines how individual actors should respond to this injustice. Javier Hidalgo maintains that individuals can rightfully resist immigration restrictions and often have strong moral reasons to subvert these laws. This book makes the case that unauthorized (...) migrants can permissibly evade, deceive, and use defensive force against immigration agents, that smugglers can aid migrants in crossing borders, and that citizens should disobey laws that compel them to harm immigrants. Unjust Borders is a meditation on how individuals should act in the midst of pervasive injustice. (shrink)

Diagnosing flatulence involves a thorough assessment of an individual's symptoms, medical history, and, if necessary, the use of diagnostic tests. Healthcare providers gather information about the patient's medical background and conduct a physical examination to identify any signs of gastrointestinal issues. Dietary habits are evaluated, and potential triggers are identified through an elimination diet. Diagnostic tests such as breath tests, stool analysis, or imaging studies may be performed to further investigate the underlying causes of excessive flatulence. Accurate diagnosis is crucial (...) for effective management and treatment of flatulence, enabling individuals to gain control over their symptoms and address any underlying conditions. (shrink)

By deploying the methodology of Judith Butler's notion of performativity, this article intends to understand the possibility of the concept of queerness beyond the possibilities of gender studies and queer theory and to develop a concept transcending the limits of identity. It is undeniable that Foucault's concept of disciplinarity is one of the major precursors of the notion of performativity, which is a more focused tool for what Foucault broadly devised. Both thinkers explain how the subject is a construction by (...) power. They explain how bodies are marked, assigned, and manipulated and expose the banality with which these operations take one for granted. It is through unearthing the disciplinary aspects of performativity in Butler and foreseeing the performative aspects of disciplinarity in Foucault that this article finds its methodological perspective. It is notable that disciplinarity and, as its extension, performativity ultimately exposes the underlying ontology of identity as a 'truth effect' rather than as an apriori truth. The paper critically analyses the artist's identity and the artistic discourse to unravel the queerness or an underlying plurality of aesthetic experience using performativity as a formative tool. (shrink)

I suspect the answer to the question in the title of this paper is no. But the scope of my paper will be considerably more limited: I will be concerned with whether certain types of considerations that are commonly cited in favor of dynamic semantics do in fact push us towards a dynamic semantics. Ultimately, I will argue that the evidence points to a dynamics of discourse that is best treated pragmatically, rather than as part of the semantics.

Conceptual engineering is concerned with the improvement of our concepts. The motivating thought behind many such projects is that some of our concepts are defective. But, if to use a defective concept is to do something wrong, and if to do something wrong one must be in control of what one is doing, there might be no defective concepts, since we typically are not in control of our concept use. To address this problem, this paper turns from appraising the concepts (...) we use to appraising the people who use them. First, I outline several ways in which the use of a concept can violate moral standards. Second, I discuss three accounts of moral responsibility, which I call voluntarism, rationalism, and psychologism, arguing that each allows us to find at least some cases where we are responsible for using defective concepts. Third, I answer an objection that because most of our concepts are acquired through processes for which we are not responsible, our use of defective concepts is a matter of bad luck, and not something for which we are responsible after all. Finally, I conclude by discussing some of the ways we may hold people accountable for using defective concepts. (shrink)

This paper proposes a philosophically informed decision-making methodology, inspired by Aristotle, that encourages constructive discussions amongst employers and employees; is directed towards shared higher-level goals; is consistent with planning frameworks already in place in many businesses; can be amended over time without disruptive disputes; and accounts for the particularities of each industry, enterprise, workplace, and job. It seeks to establish a more fundamental basis for discussions about remote vs. in-person office work: specifically, the purpose and nature of the work of (...) those affected. If these matters can be decided, then subsequent discussions might be focused more upon the shared outcomes to which stakeholders are committed and less upon individual preferences and ‘hunches.’. (shrink)

Research ethics committees and institutional review boards spend considerable time developing, scrutinising, and revising specific consent processes and materials for survey-based studies conducted on crowdsourcing and online recruitment platforms such as MTurk and Prolific. However, there is evidence to suggest that many users of ICT services do not read the information provided as part of the consent process and they habitually provide or refuse their consent without adequate reflection. In principle, these practices call into question the validity of their consent. (...) In this paper we argue that although the ‘no read problem’ and the routinisation of consent may apply to research participants’ consent practices for studies on crowdsourcing platforms, this is not a serious problem. Furthermore, given that the informational requirements for informed consent in these contexts are minimal, we argue that these participants are, nevertheless, sufficiently informed to give valid consent. We conclude that research ethics committees and institutional review boards should only agonise over the precise details of the informed consent process and materials in those rare cases where appreciable risks to research participants need to be managed. (shrink)

The appearance of consciousness in the universe remains one of the major mysteries unsolved by science or philosophy. Absent an agreed-upon definition of consciousness or even a convenient system to test theories of consciousness, a confusing heterogeneity of theories proliferate. In pursuit of clarifying this complicated discourse, we here interpret various frameworks for the scientific and philosophical study of consciousness through the lens of social insect evolutionary biology. To do so, we first discuss the notion of a forward test versus (...) a reverse test, analogous to the normal and revolutionary phases of the scientific process. Contemporary theories of consciousness are forward tests for consciousness, in that they strive to become a means to classify the level of consciousness of arbitrary states and systems. Yet no such theory of consciousness has earned sufficient confidence such that it might be actually used as a forward test in ambiguous settings. What is needed now is thus a legitimate reverse test for theories of consciousness, to provide internal and external calibration of different frameworks. A reverse test for consciousness would ideally look like a method for referencing theories of consciousness to a tractable model system. We introduce the Ant Colony Test as a rigorous reverse test for consciousness. We show that social insect colonies, though disaggregated collectives, fulfill many of the prerequisites for conscious awareness met by humans and honey bee workers. A long lineage of philosophically-neutral neurobehavioral, evolutionary, and ecological studies on social insect colonies can thus be redeployed for the study of consciousness in general. We suggest that the ACT can provide insight into the nature of consciousness, and highlight the ant colony as a model system for ethically performing clarifying experiments about consciousness. (shrink)

In the first chapter of his Knowledge and Lotteries, John Hawthorne argues that thinkers do not ordinarily know lottery propositions. His arguments depend on claims about the intimate connections between knowledge and assertion, epistemic possibility, practical reasoning, and theoretical reasoning. In this paper, we cast doubt on the proposed connections. We also put forward an alternative picture of belief and reasoning. In particular, we argue that assertion is governed by a Gricean constraint that makes no reference to knowledge, and that (...) practical reasoning has more to do with rational degrees of belief than with states of knowledge. (shrink)

This article criticizes radical labor republicanism on republican grounds. I show that its demand for universal workplace democracy via workers’ cooperatives conflicts with republican freedom along three different dimensions: first, freedom to choose an occupation…and not to choose one; second, freedom within the very cooperatives that workers are to democratically govern; and third, freedom within the newly proletarian state. In the conclusion, I ask whether these criticisms apply, at least in part, to the more modest, incrementalist strand of labor republicanism. (...) To the extent that they do, delaboring republicanism might be the best response. (shrink)

Abstract: Background: Hair loss is often a concern for women, men and children. It is normal to lose between 50 and 100 hairs a day, without us noticing it. Usually hair loss is a sign of a medical condition, some of them are hereditary, and some of them are temporary due to disease, stress, cancer treatment, weight loss, and iron deficiency. Objectives: The main goal of this expert system is to obtain an appropriate diagnosis of the cause of hair loss. (...) Methods: In this paper, the proposed expert system that was produced is designed to assist the physician in diagnosing hair loss disease such as seborrhea, lichen planus, ringworm, alopecia areata, thyroid disease and male-pattern baldness. The proposed expert system presents an overview about hair loss diseases are given, the cause of diseases are outlined and the treatment of disease whenever possible is given out. Clips Expert System language was used for designing and implementing the proposed expert system. Results: The proposed hair loss diseases diagnosis expert system was evaluated by Medical students and they were satisfied with its performance. Conclusions: The Proposed expert system is very useful for hair loss treatment physician, patients with hair loss problems and newly graduated physician. (shrink)

This article focuses on foundational issues in dynamic and static semantics, specifically on what is conceptually at stake between the dynamic framework and the truth-conditional framework, and consequently what kinds of evidence support each framework. The article examines two questions. First, it explores the consequences of taking the proposition as central semantic notion as characteristic of static semantics, and argues that this is not as limiting in accounting for discourse dynamics as many think. Specifically, it explores what it means for (...) a static semantics to incorporate the notion of context change potential in a dynamic pragmatics and denies that this conception of static semantics requires that all updates to the context be eliminative and distributive. Second, it argues that the central difference between the two frameworks is whether semantics or pragmatics accounts for dynamics, and explores what this means for the oft-heard claim that dynamic semantics blurs the semantics/pragmatics distinction. (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)

As sustainable tourism gains increasing attention, understanding the factors that influence travelers' motivation to engage with sustainable travel agents becomes crucial. This study investigates the moderating effect of social media usage on the relationship between the perceived value of websites and motivational factors for sustainable travel agents. The study proposes that social media usage acts as a moderator in shaping the relationship between the perceived value of websites and motivational factors. This study has utilized smart tourism. Therefore, independent variable motivation (...) in terms of informativeness gratification and gratification acted as moderating variables. This study has utilized an online questionnaire adapted from a standardized questionnaire based on the literature review. Moreover, a total of 146 managers were surveyed in which they were required to respond to 55 items that elicited the 7 variables social media use, Propensity, perceived website value, motivating factors, entertainment gratification, informativeness gratification, sustainable smart tourism. The data were analyzed by SPSS and PLS-SEM. The findings indicated that relevance and importance of social media use and sustainable smart tourism. The result revealed that 7 hypotheses had a significant relationship. 3 hypotheses did not show a moderating effect. Overall, this study extends the understanding of sustainable smart tourism. These findings will benefit travel agents’ managers in Malaysia to better understand the role of social media use and utilize it towards developing sustainable sustainable smart tourism. (shrink)

The study aimed to identify the reality of the use of social networks in the technical colleges in Palestine, where the variables of social networks were included. The analytical descriptive method was used in the study. A questionnaire consisting of (12) items was randomly distributed to college workers Technology in the Gaza Strip. The sample of the study consisted of (205) employees of these colleges. The response rate was 74.5%. The results showed a high degree of approval for the dimensions (...) of the social networks and a relative weight (74.15%) according to the perspective of the employees of the technical colleges in the Gaza Strip. The results of the study showed that there is a high level of social networking areas (site management and Website Content) in the technical colleges in the Gaza Strip. The field of site management ranked first with a relative weight of 74.91%, and second and last (Content Site) with a relative weight (73.38%). The results showed that there were differences between colleges in the use of social networks where the results showed that the most common colleges used these networks (UCAS) and the least used is (GTC). The results showed no differences between male and female employees in the use of social networks in technical colleges. The researchers suggest a number of recommendations, including: the need to raise awareness of the importance of Facebook and other social networking sites, through the holding of courses for employees in technical colleges, and to identify the ways to optimize the use of such sites, and the benefits of this use, and reflected positively on technical colleges. And the adoption of dealing with the various social networking sites as a reality, and the Palestinian and Arab technical colleges, use them in accordance with the objectives of technical colleges. Advise the Department of Technical Colleges to devote time to their presence on social networks to follow the public and respond to their queries. There is a need for the attention of decision-makers in technical colleges in social sites, because they are considered an important and effective means of communication, and the link between beneficiaries and decision-makers. There is a need to promote the use of modern electronic means of work and the need to increase the link of customers to the college through electronic services. (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)

Modern military organizations are paternalistic organizations. They typically recognize a duty of care toward military personnel and are willing to ignore or violate the consent of military personnel in order to uphold that duty of care. In this paper, we consider the case for paternalism in the military and distinguish it from the case for paternalism in medicine. We argue that one can consistently reject paternalism in medicine but uphold paternalism in the military. We consider two well-known arguments for the (...) conclusion that military organizations should not be entitled to use experimental drugs on troops without first obtaining the informed consent of those troops. We argue that both of these are unsuccessful, in the absence of an argument for the rejection of paternalism in the military altogether. The case for military paternalism is widely accepted. However, we consider three ways in which it could be challenged. (shrink)

This study identifies global factors conditioning the global problematics of the direction of social development. Global threats were evaluated and defined as dangerous processes, phenomena, and situations that cause harm to health, safety, well-being, and the lives of all humanity, and require removal. The essence of global risks was defined. These risks were defined as events or conditions that may cause a significant negative effect for several countries or spheres within a strategic period if they occur. Global problems were conceptualized. (...) These problems were defined as phenomena, matters, and situations that are not completely understandable, interesting, actual, require solving and regulation, and in addition do not have unified solutions. Current global challenges were analyzed, defined as strategic guidelines that cause transformational changes and are receiving attention from humanity. Global trends were defined as courses of social development. The composition and role of global actors were described, and defined as international organizations, leading states, regional organizations, powerful corporations, institutional investors, large cities, and well-known personalities. The directions of positive courses of social development were developed by means of using the authors’ mechanism of solving the global problematics. (shrink)

The article is devoted to the increasing role of tourism in the world economy. The dynamics of international tourism indicators is investigated. The main global innovations in the tourism industry are identified: the growth of tourism types; the application of qualitatively new solutions of scientific and methodological and applied character; growing of tourism influence on the society; the existence of synergistic effect in the tourist industry as a result of combination of subjects efforts at all management levels; changing of the (...) role of internal and external factors that encourage innovative tourism development. In the article, the interaction of global processes on tourism innovations is defined. These processes are: intellectualization, informatization, cooperation, formation of the global tourism market, liberalization of the national tourism markets, increased competition and the spread of transnationalization. (shrink)

This thesis focuses on the use of intelligent tutoring system for education and training of students specialized in electricity in the field of technical and vocational education. The use of modern systems in training and education will have a great positive impact in improving the level of students receiving training and education; this will improve the level of the local economy by producing students of professionals who are able to engage in society efficiently, especially for those who have specialized in (...) electricity. It is known that students in electrical specialties face many problems because of time and cost. Learning and training is not as easy as you imagine, so intelligence is how to overcome these limitations effectively. Motivating the Intelligent Tutorial System (ITS) for this type of training. This system consists of a student model, which in turn will contain lessons, exercises and examples that will be used by the student in addition to the user interface model which makes the teacher to be able to manage lessons, examples, and questions and make changes to the system. Expert System (ES) model representing the Artificial Intelligence (AI) here has been used to determine the level of the student and gives him the results, directions and levels. The program was implemented and was used by trainees to show the extent of its impact. The students expressed their surprise and admiration in this way of education as the system used focuses on individual education which can be used at any time and without restrictions. After this experience, the dedicated (ITS) system needs to be developed in several ways such as to be used on mobile systems and web systems in addition to linking some of the specialized programs in the field of electricity. (shrink)

Argues that choice, as a form of interpretation, is completely intertwined with the development of both sexual orientation and sexual identity. Sexual orientation is not simply a given, or determined aspect of personality.

This study presents a neural network-based model for predicting smoke potential in a specific area using a Kaggle-derived dataset with 15 environmental features and 62,631 samples. Our five-layer neural network achieved an accuracy of 89.14% and an average error of 0.000715, demonstrating its effectiveness. Key influential features, including temperature, humidity, crude ethanol, pressure, NC1.0, NC2.5, SCNT, and PM2.5, were identified, providing insights into smoke occurrence. This research aids in proactive smoke mitigation and public health protection. The model's accuracy and feature (...) analysis empower decision-makers, with potential applications in real-time smoke event monitoring and preparedness strategies. This work contributes to the field of air quality forecasting and environmental stewardship, offering a data-driven approach to address smoke-related challenges and enhance community well-being. (shrink)

One method for preventing the spread of the coronavirus and other contagious diseases is through social distancing. Therefore, creating a tool to measure and quickly discover the precise distance is necessary. In order to prevent physical contact between individuals, this study aimed to detects individuals’ physical distance, through an inaugurated battery-powered device that monitors physical distance through wireless technology. Specifically, in public or crowded areas, to lessen the spread of the virus. This study focuses on detecting people’s physical distance in (...) the region of interest utilizing an Ultrasonic and VL53L0X sensors and determining the significant difference between the two sensors in monitoring physical distance. This study employs an applied experimental research design. The efficiency of both sensors in measuring physical distance was evaluated through analysis. The Ultrasonic and VL53L0X sensors underwent 15 tests. Furthermore, the researchers effectuated a t-test to determine the significant difference between the two sensors. Data gathered revealed that the sensors’ combined mean in terms of measuring physical distance indicates that the ultrasonic sensor performed better than VL53L0X, measuring 134.7 cm, notwithstanding 99.8 cm for the VL53L0X sensor. Complementary to this, the results of the t-test show that the VL53L0X sensor and Ultrasonic sensor have a significant difference in terms of distance precision. (shrink)

This comparative examination of Nietzsche and the Islamic philosopher al-Kindi emphasizes their mutual commitment to the recovery of classical Greek and Hellenistic thought and the idea of philosophy as a way of life. Affiliating both thinkers with the Stoic lineage in particular, I examine the ways in which they appropriate common themes such as fatalism, self-cultivation via spiritual exercises, and the banishing of sorrow. Focusing primarily on their respective conceptions of self and nature, I argue that the antipodal worldviews of (...) al-Kindi and Nietzsche can be understood as a bifurcation of Stoic philosophy. (shrink)

In this paper, I offer a conceptual analysis of fake news. In essence, I suggest analysing this notion as a species of Frankfurtian bullshit. This construal, I argue, allows us to distinguish it from similar phenomena like bad or biased journalism and satire. First, I introduce four test cases. The first three are, intuitively, not cases of fake news, while the fourth one is. A correct conceptual analysis should, hence, exclude the first three while including the fourth. Next, I go (...) through some factors that could explain the difference between the first three cases and the fourth one before I offer my explanation. After that, I propose an analysis of fake news and show that it matches our intuitions about the four test cases. Finally, I consider a possible objection to my proposal. (shrink)

In diesem Aufsatz, werde ich die Frage des Naturalismus in Plessners Philosophie des organischen Lebens und seiner amerikanischen Rezeption, in besonders die philosophischen-biologischen Schriften von Marjorie Grene, untersuchen. Die amerikanische Philosophin Grene war die Hauptvertreterin Plessners im Englischen Sprachraum in 20sten Jahrhundert, die Plessners anthropologischen Argumentation in ihren Schriften zur Philosophie der Biologie aufgenommen und verwendet hat. Grene kritisierte in ihren frühen Schriften Heidegger, Sartre, und die Existenzphilosophie, die das menschliche Dasein von der Natur radikal absondert und die negative Affekte (...) wie Angst hervorhebt, in Vergleich mit Plessners weiterem Blick der Leibhaften, Sinnlichen, Tierischen Entwicklung des menschlichen Lebens. Die menschliche Existenz kann nicht dualistisch, wo „keine Brücke über den Abgrund zwischen Mensch und Tier führt,“ sondern nur biologisch-lebensphilosophisch oder ökologisch verstanden werden. Plessner und Grene setzen sich mit dem Reduktionismus des modernen Naturalismus auseinander und zeigen, wie das Natürliche weiter und ergänzt gedacht werden muss, weil der Naturalismus keine ausreichenden Begriffe für die Auslegung der Erscheinungen und Ausdrucksformen des organischen Lebens besitzt. Die Unterscheidung zwischen der "zentrischen Positionalität" des Tiers und der "exzentrischen Positionalität" des menschlichen Tiers ermöglicht eine holistisch aber differenzierte Beschreibung der Natur und Kultur, insbesondere die natürlichen Künstlichkeit, die der Kultur und das des gesellschaftlichen Lebens. Diese sind Grundlage für die geschichtliche und soziale Bildung, Aufbau, und Strukturierung der natürlichen Spontaneität, Produktivität, und Expressivität des Lebens und ermöglichen die dezentrierte, utopische, und persönliche Individuation der einzelnen Menschen im Lebenszusammenhang, was wichtige soziologische und auch ethische und politische Konsequenzen für Plessner und Grene hat: Der Mensch ist ein natürliches biologisches Individuum, das durchaus fähig ist, eine verantwortliche Person zu werden , und zwar durch die umfassende Beteiligung an (oder als ein einzigartiger Ausdruck) einer Kultur. (shrink)

I wish to defend the claim that given the content and structure of any moral theory we are likely to find palatable, there is no way of uniquely breaking down that theory into either consequentialist or deontological elements. Indeed, once we examine the actual structure of any such theory more closely, we see that it can be classified in either way arbitrarily. Hence if we ignore the metaethical pronouncements often made by adherents of the consequentialist-deontological distinction, we are quickly led (...) to the conclusion that this distinction contributes nothing of consequence to an understanding of moral theory. I will try to show that there are basically two reasons for this. First, what we mean by the terms endemic to the consequentialist-deontological distinction have no unique references to particular states of affairs in actual cases of moral decision making. Hence we may justify any such concrete moral decision by reference to typically consequentialist or deontological reasoning indifferently. Second, scrutiny of actual and viable moral theories reveals a much finer-grained structure than the consequentialist-deontological taxonomy can capture. And it is this structure, rather than simple attention to consequences or principles, that determines practical moral decision making. We would thus do better to develop the richer vocabulary of causes and constituents, goals and effects, states and events (mental, social, or physical). So in the end, the consequentialist-deontological distinction is irrelevant at the normative level of actual moral reasoning, whereas at the metaethical level it crudely schematizes two opposing types of dummy theory, neither of which is convincing, upon reflection, to any practicing moral philosopher. (shrink)