For so long, We have been suffering, violated and victimised, For things, we cannot give name to, All we knew was, It’s against our tradition. -/- Tradition and culture hold South African queers captive. South Africa, with perhaps one of the most progressive constitutions in the world is, in Gastrow’s (1992) view, acknowledged for fostering ideas of democracy and equality across the continent. However, perhaps South Africa is not as free as it seems; perhaps, it is arguably only free because (...) there is no alternative way to conceptualise the phase we are in as a country. It is questionable that marginal identities within the South African context are constantly subjected to violence and victimisation aggravated by how queerness is framed culturally and traditionally as taboo. Such stigmatisation of queer subjects is informed by hegemonic masculinity. Furthermore, queer visibility in the traditional landscape becomes a trap, as it is held as offensive to normative, traditional ways of being. (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)

Abstract: Tomatoes are among the most popular vegetables in the world due to their frequent use in many dishes, which fall into many varieties in common and traditional foods, and due to their rich ingredients such as vitamins and minerals, so they are frequently used on a daily basis, When we focus our attention on this vegetable, we must also focus and take into consideration the diseases that affect this vegetable, a deep learning model that classifies tomato diseases has been (...) proposed. The aim of this paper is to diagnose tomato diseases based on a dataset containing 11000 picture and 11 classes. The model gave the final accuracy test with Accuracy (99.87%), F1-score (99.87%), Recall (99.87%), and Precision (99.88%) in an estimated time 1.36 second, as with these results it proved its effectiveness and good ability in the classification of the testing data. (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)

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)

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: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and react like humans. Some of the activities computers with artificial intelligence are designed for 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 a technique used (...) to produce Pneumonia detection and classification models using x-ray imaging for rapid and easy detection and identification of pneumonia. In this thesis, we review ways and mechanisms to use deep learning techniques to produce a model for Pneumonia detection. The goal is find a good and effective way to detect pneumonia based on X-rays to help the chest doctor in decision-making easily and accuracy and speed. The model will be designed and implemented, including both Dataset of image and Pneumonia detection through the use of Deep learning algorithms based on neural networks. The test and evaluation will be applied to a range of chest x-ray images and the results will be presented in detail and discussed. This thesis uses deep learning to detect pneumonia and its classification. (shrink)

Heart failure is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 918 samples with 11 features, such as age, sex, chest pain type, resting blood pressure, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and HeartDisease. A neural (...) network model with four layers (1 input and 1 output) was trained on the dataset and achieved an accuracy of 90% and an average error of 0.009. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (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)

In computer science, Artificial Intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Deep Learning is a new field of research. One of the branches of Artificial Intelligence Science deals with the creation of theories and algorithms that (...) allow the machine to learn by simulating neurons in the human body. Most in-depth learning research focuses on finding high-level methods. The strippers analyze a large data set using linear and nonlinear transformations. The method of deep learning is used in the detection of several diseases including blood cell diseases and their classification using the radiography of blood cells to help decision makers to know the type of blood cell and its associated diseases and the results will be presented in detail and discussed. This thesis is using python language and deep learning to detect blood cell diseases and their classifications. The proposed deep learning model was trained, validated and the tested. The accuracy of proposed model was 98.00%. (shrink)

All parties involved in researching, developing, manufacturing, and distributing COVID-19 vaccines need guidance on their ethical obligations. We focus on pharmaceutical companies' obligations because their capacities to research, develop, manufacture, and distribute vaccines make them uniquely placed for stemming the pandemic. We argue that an ethical approach to COVID-19 vaccine production and distribution should satisfy four uncontroversial principles: optimising vaccine production, including development, testing, and manufacturing; fair distribution; sustainability; and accountability. All parties' obligations should be coordinated and mutually consistent. For (...) instance, companies should not be obligated to provide host countries with additional booster shots at the expense of fulfilling bilateral contracts with countries in which there are surges. Finally, any satisfactory approach should include mechanisms for assurance that all parties are honouring their obligations. This assurance enables countries, pharmaceutical companies, global organisations, and others to verify compliance with the chosen approach and protect ethically compliant stakeholders from being unfairly exploited by unethical behaviour of others. (shrink)

Abstract: Online book review platforms generate vast user data, making accurate rating prediction crucial for personalized recommendations. This research explores neural networks as simple models for predicting book ratings without complex algorithms. Our novel approach uses neural networks to predict ratings solely from user-book interactions, eliminating manual feature engineering. The model processes data, learns patterns, and predicts ratings. We discuss data preprocessing, neural network design, and training techniques. Real-world data experiments show the model's effectiveness, surpassing traditional methods. This research can (...) enhance user experience, book catalog organization, and aid publishers, simplifying recommendation processes and providing tailored suggestions based on user preferences. (shrink)

In fact, people get neck problems due to something such as sports or woke and Wrong sleep habits. In this paper an expert system was designed to help users to correctly diagnose neck problems world (muscle spasm, Muscle aches, Meningitis, herniated cervical disc, Fibromyalgia, Cervical spondylosis, Trigger points) with some information about the disease and self-care. Java language was used to design and implement this expert system.

Abstract cantaloupe and honeydew melons are part of the muskmelon family, which originated in the Middle East. When picking either cantaloupe or honeydew melons to eat, you should choose a firm fruit that is heavy for its size, with no obvious signs of bruising. They can be stored at room temperature until you cut them, after which they should be kept in the refrigerator in an airtight container for up to five days. You should always wash and scrub the rind (...) of your melon before you cut it to remove any dirt or bacteria on the outside. In this paper, cantaloupe classification approach is presented with a dataset that contains approximately 1,312 of Cantaloupes and honeydews. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results found that CNN-driven classification applications when used in farming automation have the latent to enhance crop harvest and improve output and productivity when designed properly. The trained model achieved an accuracy of 99.74% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

This paper has three main goals. First, to motivate a puzzle about how ignorance-expressing terms like maybe and if interact: they iterate, and when they do they exhibit scopelessness. Second, to argue that there is an ambiguity in our theoretical toolbox, and that exposing that opens the door to a solution to the puzzle. And third, to explore the reach of that solution. Along the way, the paper highlights a number of pleasing properties of two elegant semantic theories, explores some (...) meta-theoretic properties of dynamic notions of meaning, dips its toe into some hazardous waters, and offers characterization theorems for the space of meanings an indicative conditional can have. (shrink)

As these opening quotes acknowledge, the Prisoner’s Dilemma (PD) represents a core puzzle within the formal mathematics of game theory.3 Its rise in conspicuity is evident figure 2.1 above demonstrating a relatively steady rise in incidences of the phrase’s usage between 1960 to 1995, with a stable presence persisting into the twenty first century. This famous two-person “game,” with a stock narrative cast in terms of two prisoners who each independently must choose whether to remain silent or speak, each advancing (...) self-interest at the expense of the other and thereby achieving a mutually suboptimal outcome, mires any social interaction it is applied to into perplexity. The logic of this game proves the inverse of Adam Smith’s invisible hand: individuals acting on self interest will achieve a mutually suboptimal outcome. However, as this chapter illuminates, the assumptions underlying game theory drive this conclusion. (shrink)

Heart palpitations, while often benign, can sometimes be indicative of severe underlying conditions requiring immediate intervention. Accurate and swift diagnosis thus remains a clinical priority. "A CLIPS-Based Expert System for Heart Palpitations Diagnosis" represents a novel approach to addressing this challenge, harnessing the power of artificial intelligence and rule-based expert systems. Specifically, this system applies a suite of 7 if-then rules to evaluate potential heart palpitations causes and assign one of three outcomes: 1) A confirmed diagnosis of heart palpitations, 2) (...) A suspected link to cardiovascular diseases, and 3) A possible association with anxiety or stress disorders. The expert system offers an intuitive user interface, allowing for seamless symptom input and instant diagnosis based on user-provided information. This paper explores the various phases of this expert system's lifecycle, including design, implementation, and evaluation. Furthermore, the study situates the system within the broader discourse on rule-based expert systems for heart palpitations diagnosis, critically analyzing their efficiency, potential pitfalls, and ongoing challenges. Through this research, the value of integrating rule-based expert systems in clinical diagnostic processes is highlighted, illustrating its capacity to enhance diagnostic accuracy and patient outcomes. (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)

The theoretical foundations of climate science have received little attention from philosophers thus far, despite a number of outstanding issues. We provide a brief, non-technical overview of several of these issues – related to theorizing about climates, climate change, internal variability and more – and attempt to make preliminary progress in addressing some of them. In doing so, we hope to open a new thread of discussion in the emerging area of philosophy of climate science, focused on theoretical foundations.

Vocal fry is a phonation, or voicing, in which an individual drops their voice below its natural register and consequently emits a low, growly, creaky tone of voice. Media outlets have widely acknowledged it as a generational vocal style characteristic of millennial women. Critics of vocal fry often claim that it is an exclusively female vocal pattern, and some say that the voicing is so distracting that they cannot understand what is being said under the phonation. Claiming that a phonation (...) is so distracting as to prevent uptake of the semantic content of an utterance associated with it is an extreme reaction, especially when accompanied by demands for women to change their phonation. We argue that this reaction limits women's communicative autonomy. We analyze the extreme reaction to female vocal fry, which we characterize as a non-content-based response, from the perspectives of philosophy of language, feminist epistemology, and linguistics. We argue that when fry is heard as annoying and distracting, it is because the hearer interprets the speaker as echoing an utterance from a position of authority to which she is not entitled. We show that this reaction encodes conscious or unconscious sexist attitudes toward women's voices. (shrink)

Contemporary republicans have adopted a less-than-charitable attitude toward private beneficence, especially when it is directed to the poor, worrying that rich patrons may be in a position to exercise arbitrary power over their impoverished clients. These concerns have led them to support impartial public provision by way of state welfare programs, including an unconditional basic income (UBI). In contrast to this administrative model of public welfare, I will propose a competitive model in which the state regulates and subsidizes a decentralized (...) and nonstatist provision of support for the poor. This model will fix the historically objectionable features of private provision by having the state prevent collusion among private charities, deliver information to recipients about alternative sources of assistance, and give substantial grants to charities as well as tax incentives and vouchers to donors. I will contend that such an approach would do a better job of minimizing domination of the poor than traditional welfare states and may prove more politically feasible than a UBI, at least in the near term in certain national contexts. (shrink)

Current COVID-19 pandemic and previous SARS/MERS outbreaks have caused a series of major crises to global public health. We must integrate the large and exponentially growing amount of heterogeneous coronavirus data to better understand coronaviruses and associated disease mechanisms, in the interest of developing effective and safe vaccines and drugs. Ontologies have emerged to play an important role in standard knowledge and data representation, integration, sharing, and analysis. We have initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO). (...) As an Open Biomedical Ontology (OBO) library ontology, CIDO is an open source and interoperable with other existing OBO ontologies. In this article, the general architecture and the design patterns of the CIDO are introduced, CIDO representation of coronaviruses, phenotypes, anti-coronavirus drugs and medical devices (e.g. ventilators) are illustrated, and an application of CIDO implemented to identify repurposable drug candidates for effective and safe COVID-19 treatment is presented. (shrink)

The article entitled “Cultural evolution in Vietnam’s early 20th century: A Bayesian networks analysis of Hanoi Franco-Chinese house designs” investigates how diverse cultures and religious creeds influenced the architecture of Hanoi in the early 20th century.

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)

One critique of the early Daoist texts associated with Laozi and Zhuangzi is that they neglect the human and lack a proper sense of ethical personhood in maintaining the primacy of an impersonal dehumanizing “way.” This article offers a reconsideration of the appropriateness of such negative evaluations by exploring whether and to what extent the ethical sensibility unfolded in the Zhuangzi is aporetic, naturalistic, and/or religious. As an ethos of cultivating life and free and easy wandering by performatively enacting openness (...) and responsiveness to things in an immanent this-worldly context, the Zhuangzi is oriented toward the relational attunement of disposition and practice rather than toward metaphysics or religion in a transcendent sense. It consequently suggests an immanent anarchic ethics without principles while neither forgetting nor reifying the sacred and the mundane in its playful illumination of the biospiritual dynamics of cultivating life. (shrink)

The critique of philosophical foundations of neoclassical economics is significant, because of its hegemony on economic education and research programs in Iran and worldwide academies. Due to an epistemological fallacy, methodological individualism plays a prominent role in the philosophy of economic; since the ontological aspects of economy are reduced to methodological considerations. Accordingly, critique of methodological individualism is regarded as the main entry for philosophical analysis of neoclassical economics. This article aims to analyze and appraise the methodological individualism from critical (...) realism’s point of view. Critical realism is taken as a stand for criticizing methodological individualism, because critical realism and neoclassical economics as a positive approach to economics, both share a realistic worldview. The critical realism’ critique of methodological individualism is bases on analyzing the concepts of formalism, syllogism, social atomism, isolation and reduction. From critical realism’ point of view because of unrealistic postulations of methodological individualism, neoclassical economics is turned to an abstract and deductive science such as mathematics with a low potentiality of explanation. (shrink)

The Axis II Work Group of the Task Force on Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) has expressed concern that antisocial personality disorder (APD) criteria are too long and cumbersome and that they focus on antisocial behaviors rather than personality traits central to traditional conceptions of psychopathy and to international criteria. R. D. Hare et al describe an alternative to the approach taken in the DSM-III—Revised (DSM-III—R; American Psychiatric Association, 1987), namely, the revised Psychopathy Checklist. The authors also (...) discuss the multisite APD field trials designed to evaluate and compare 4 criteria sets: the DSM-III—R criteria, a shortened list of these criteria, the criteria for dyssocial personality disorder from the 10th edition of the International Classification of Diseases (World Health Organization, 1990), and a 10-item criteria set for psychopathic personality disorder derived from the revised Psychopathy Checklist. (PsycINFO Database Record (c) 2015 APA, all rights reserved). (shrink)

The aim of this critical commentary is to distinguish and analytically discuss some important variations in which legal moralism is defined in the literature. As such, the aim is not to evaluate the most plausible version of legal moralism, but to find the most plausible definition of legal moralism. As a theory of criminalization, i.e. a theory that aims to justify the criminal law we should retain, legal moralism can be, and has been, defined as follows: the immorality of an (...) act of type A is a sufficient reason for the criminalization of A, even if A does not cause someone to be harmed. In what follows, I critically examine some of the key definitions and proposals that have, unfortunately, not always been carefully distinguished. Finally, I propose a definition that seems to capture the essence of what many philosophers refer to when they talk about legal moralism, while also providing more clarity. (shrink)

Five arguments are presented in favour of the proposal that people who opt in as organ donors should receive a tax break. These arguments appeal to welfare, autonomy, fairness, distributive justice and self-ownership, respectively. Eight worries about the proposal are considered in this paper. These objections focus upon no-effect and counter-productiveness, the Titmuss concern about social meaning, exploitation of the poor, commodification, inequality and unequal status, the notion that there are better alternatives, unacceptable expense, and concerns about the veto of (...) relatives. The paper argues that none of the objections to the proposal is very telling. (shrink)

The study zeroed in on the perception of college students who are exposed to sights of poverty in their immediate environment. The student-participants were asked to provide their perception, understanding, and behaviour towards poverty using the photographs that they took on their own. In qualitative research practice, this methodology is called photo elicitation. It was revealed, among others, that the participants have shown negative perceptions about poverty. They strongly felt bad about each photograph that they took and what these images (...) portrayed. The participants visualized their community and thereby weaved analyses and opinions that encroached on various philosophical and sociological theories. There are four interrelated dominant images that were drawn: exteriority image, dirt-poor image, victim-blaming image, and hand-to-mouth image. (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)

The paper argues that the moral philosophy of Thomas Hobbes is unified by a complex conception of reason that imposes consistency norms of both rationality and reasonableness. Hobbes’s conceptions of rightness as reciprocity, and moral goodness as sociability belong to an original and attractive moral theory that is neither teleological nor classically deontological, nor as interpreters have variously argued, subjectivist, contractarian, egoist, or dependent on divine command.

Public health care interventions—regarding vaccination, obesity, and HIV, for example—standardly take the form of information dissemination across a community. But information networks can vary importantly between different ethnic communities, as can levels of trust in information from different sources. We use data from the Greater Pittsburgh Random Household Health Survey to construct models of information networks for White and Black communities--models which reflect the degree of information contact between individuals, with degrees of trust in information from various sources correlated with (...) positions in that social network. With simple assumptions regarding belief change and social reinforcement, we use those modeled networks to build dynamic agent-based models of how information can be expected to flow and how beliefs can be expected to change across each community. With contrasting information from governmental and religious sources, the results show importantly different dynamic patterns of belief polarization within the two communities. (shrink)

Francesco Guala has written an important book proposing a new account of social institutions and criticizing existing ones. We focus on Guala’s critique of collective acceptance theories of institutions, widely discussed in the literature of collective intentionality. Guala argues that at least some of the collective acceptance theories commit their proponents to antinaturalist methodology of social science. What is at stake here is what kind of philosophizing is relevant for the social sciences. We argue that a Searlean version of collective (...) acceptance theory can be defended against Guala’s critique and question the sufficiency of Guala’s account of the ontology of the social world. (shrink)

An estimated 700,000 people in the United States have "long COVID," that is, symptoms of COVID-19 persisting beyond three weeks. COVID-19 and its long-term sequelae are strongly influenced by social determinants such as poverty and by structural inequalities such as racism and discrimination. Primary care providers are in a unique position to provide and coordinate care for vulnerable patients with long COVID. Policy measures should include strengthening primary care, optimizing data quality, and addressing the multiple nested domains of inequity.

There are many eye diseases but the most two common retinal diseases are Age-Related Macular Degeneration (AMD), which the sharp, central vision and a leading cause of vision loss among people age 50 and older, there are two types of AMD are wet AMD and DRUSEN. Diabetic Macular Edema (DME), which is a complication of diabetes caused by fluid accumulation in the macula that can affect the fovea. If it is left untreated it may cause vision loss. Therefore, early detection (...) of diseases is a critical importance. Our main goal is to help doctors detect these diseases quickly before reaching a late stage of the disease. In ophthalmology, optical coherence tomography (OCT) is critical for evaluating retinal conditions. OCT is an imaging technique used to capture high-resolution cross-sections of the retinas of patient. In this thesis, we review ways and techniques to use deep learning classification of the optical coherence tomography images of diseases from which a Retinal is suffering. The models used to improve patient care are (VGG-16, MobileNet, ResNet-50, Inception V3, and Xception) to reduce costs and allow fast and reliable analysis in large studies. The obtained results are encouraging, since the best model ResNet-50 reaching 96.21% of testing accuracy, which is very useful for doctors, to diagnose retinal diseases. (shrink)

This review essay of Economics Rules situates Dani Rodrik’s contribution with respect to the 2007–2008 global economic crisis. This financial meltdown, which the eurozone did not fully recover from before the Covid-19 pandemic, led to soul- searching among economists as well as a call for heterodox economic approaches. Yet, over the past decade, instead the economics profession has maintained its orthodoxy. Rodrik’s Economics Rules offers a critique of the economics profession that is castigating but mild. It calls for economists to (...) use more and diverse models without becoming wedded to any single model or an overarching vision. Yet Rodrik ratifies many of the benchmark models standard to orthodox economics and provides little ground for a fundamental rethinking of the discipline. This essay analyses the conservatism underlying Rodrik’s approach, which upholds general equilibrium theory and rational expectations underlying the efficient market hypothesis. It argues that the economics discipline’s scope-creep to maintain its applicability to all human decision-making, and its acceptance of all-inclusive utility functions, crowds out moral sentiments and civic virtue. Thus. it argues that rather than urging economists simply to be more cautious in their application of models to address particular social concerns, instead economists must recognise their discipline’s inherent limitations. (shrink)

This paper analyses the National Populist Challenges to Europe’s Center Right. It assesses the cases of the UK, Germany and France. It poses three questions for Europe: How will political integration be achieved and maintained? What policies will foster economic inclusion in the Eurozone? And, third, what are the best means to achieve economic solvency and growth. The paper make a case that neoliberal economic policies over the past decades have undermined some nations' public sector and have also contributed to (...) tensions between the geographical east and west of Europe. (shrink)

Looks into evaluation of information provision probability from different sources, based on use of linguistic variables. Formation of functions appurtenant for its unclear variables provides for adoption of decisions by the decision maker, in conditions of nonprobabilistic equivocation. The development of market relations in Ukraine increases the independence and responsibility of enterprises in justifying and making management decisions that ensure their effective, competitive activities. As a result of the analysis, it is determined that the condition of economic facilities can be (...) described and determined by the decision-maker, in the presence of the necessary information. The confidence of the decision-maker in the information received is different and the decisions made have a correspondingly different level of information risk. It is important to substantiate the procedure for assessing the numerical extent of information risk in decision-making based on the information obtained in conditions of uncertainty. The use of a linguistic variable in the processing of expert data presented in the form of a matrix of binary relations of values of the membership function, which allowed to move to further processing of knowledge to support decision-making in the management of industrial, commercial, financial and other activities. As a mathematical model for estimating the numerical measure of information risk when making decisions based on the information obtained in conditions of non-stochastic uncertainty, a model has been developed to model natural language uncertainties, which differs from existing ones by formalizing knowledge taking into account uncertainty of input information. Making such a clear decision in a fuzzy environment has appropriate values of effectiveness and risk. The paper proposes all the functions and accessories of indicators of both quantitative nature and qualitative nature to bring their values in the field of definition to one scale. Then the indicator of the effectiveness of decision-making will be a measure of the clarity of the cross-section of fuzzy subsets, which correspond to the introduced indicators of information risk. The condition of economic facilities can be described and determined by the decision-maker, if the necessary information is available. Decision-making on thenumerical measure of information risk must be determined by a set of basic indicators, which can be both quantitative and qualitative in nature. Predictive values of indicators should be determined in conditions of nonstochastic uncertainty. In this case, the indicators of a quantitative nature can be determined by fuzzy triangular numbers, which implement a high level of confidence in the subjective judgments of experts. Indicators of qualitative nature should be presented in linguistic variables. The values of the indicators of qualitative nature that are predicted must be considered for all fuzzy variable terms-sets of linguistic variables introduced into consideration. For any fuzzy variable, the introduction to the consideration of a clear set of values as carriers of the α-level of its membership function allows to reduce to a single interpretation of the predicted values of indicators of quantitative and qualitative nature in terms of non-stochastic uncertainty. (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)

Abstract: The research also aims at identifying the differences between respondents as attributed to their professional and personal traits of age, gender, educational level, distribution of department, dispute resolution experience and representation party. Researchers adopted mixed data collection methods; quantitative and qualitative. For qualitative, a semi-structured interview was conducted with (9) officials including management, staff unions and expert external mediators. (63) Questionnaires were distributed to a systematic random sample population; (56) were received, with a response rate 88.9%. The results proved (...) that there are no significant differences among respondents attributed to personal and professional traits of age, academic background, gender, department, years’ of experience, educational degree and representation party. The research has presented some recommendations concerning the factors affecting the mediation outcomes of workplace disputes resolution. The recommendations include: Selecting expert mediators to deal with multi-culture groups. Ensuring equal gender representation in working groups, teamwork and committees and encouraging female participation in mediation processes to resolve dispute at work place. Developing the mediation schema in a way that empowers the mediator to increase her/his ability to lead and establish compromise solutions for workplace disputes. (shrink)

Abstract— Anatomy of the anterior compartment includes the tibia and the fibula. It also includes the tibialis anterior tendon, the extensor hallucis longus tendon and the extensor digitorum longus tendons. The anterior tibial artery and the deep peroneal nerve and finally the superior and inferior extensor retinaculum. In this paper an expert system was designed to help users to correctly diagnose ankle problems. There are many structures present at the anterior aspect of the ankle; these structures are often susceptible to (...) injury common injuries and conditions around the anterior ankle. Clips expert system language was used to design and implement this expert system. (shrink)

The study aimed to identify the level of organizational performance in ceramic companies in the Tenth of Ramadan City, where the researchers used the descriptive analytical method, through a questionnaire distributed. The study reached a set of results, the most important of which are: the presence of a clear consensus of the study sample from the members of the senior management that all dimensions of the balanced scorecard variable are largely present in the ceramic companies under study, and the results (...) showed the approval of the study sample of workers in an average way on the application of the financial dimension and that it is applied Well, the approval of the study sample of the employees in an average way that tends to decline on the degree of application of the customer dimension and that it is applied by the senior management in an inappropriate way, the approval of the study sample of the employees in a large way on the degree of application of the internal operations dimension and that it is applied by the senior management in a large and appropriate way, approval The study sample of workers applying the learning and growth dimension is moderately low and it is applied inappropriately by the senior management. The study presented a set of recommendations, the most important of which are: working on clarifying and translating the vision and strategy of the institution, working on clarifying the strategy and linking it to the goals, planning and setting goals and arranging strategic initiatives, promoting strategic feedback. (shrink)

Abstract: The study aimed to identify the level of increased achievement motivation among Palestinian police workers in Gaza Strip in light of the Corona pandemic, and to achieve the objectives of the study, the researchers used the descriptive approach in its analytical method, using a questionnaire applied to the police personnel at the Central Governorate Police Station, whose number is (113) individuals, who were chosen In a stratified, random manner, the study resulted in a set of results, the most important (...) of which is that the level of increase in achievement motivation among workers in the Palestinian police in Gaza Strip under the outbreak of the Corona epidemic came with a relative weight (82.40%), which is to a large extent, there are no statistically significant differences between the averages of the estimates. Individuals of the study sample on the increase in achievement motivation in the Palestinian police in Gaza Strip due to a variable, age group, academic qualification, scientific specialization, scientific experience, and in light of these results the researchers suggested a set of recommendations, the most important of which are: Increasing interest in preparing and organizing specialized training courses and programs in the field of dealing management with all the different segments, in order to protect the lives of people from the Corona epidemic. (shrink)

This article considers the significance of the Blessed Isles in Nietzsche’s Thus Spoke Zarathustra. They are the isolated locale to which Zarathustra and his fellow creators retreat in the Second Part of the book. I trace Zarathustra’s Blessed Isles back to the ancient Greek paradisiacal afterlife of the makarōn nēsoi and frame them against Nietzsche’s Platonic conception of philosophers as “commanders and legislators,” but I argue that they represent something more like a modern Epicurean Garden. Ultimately, I suggest that Zarathustra’s (...) Epicurean impulse toward withdrawal (whether into a sequestered friendship community or mountain solitude) undermines his Platonic attempts at great politics. (shrink)