In 1993, biologist Margie Profet captured the attention of the popular press with the publication of her radical thesis: menstruation has a function. Traditional theories, she claims, typically view menstruation as a functionless by-product of cyclic flux. The details of Profet's functional account are similarly radical: she argues that menstruation has been naturally selected to defend the female reproductive tract from sperm-borne pathogens. There are a number of weaknesses in Profet's evolutionary analysis. However, I focus on a set of pragmatic (...) problems that arise prior to any details of her evolutionary account. In arguing for the importance of pragmatic considerations, I draw from the linguistic analyses of Nelson Goodman. I conclude that critical investigation of the evolutionary details of Profet's pathogen defense account will be more feasible if and when biologists more frequently feature the female system of pathogen defense in their inductive generalisations. The system needs to be better entrenched before its functional components, such as menstruation, can be thoroughly investigated. (shrink)

: The relationship between facts and values—in particular, naturalism and normativity—poses an ongoing challenge for feminist science studies. Some have argued that the fact/value holism of W.V. Quine's naturalized epistemology holds promise. I argue that Quinean epistemology, while appropriately naturalized, might weaken the normative force of feminist claims. I then show that Quinean epistemic themes are unnecessary for feminist science studies. The empirical nature of our work provides us with all the naturalized normativity we need.

While feminist epistemologists have made important contributions to the deconstruction of the traditional representationalist model, some elements of the Cartesian legacy remain. For example, relativism continues to play a role in the underdetermination thesis used by Longino and Keller. Both argue that because scientific theories are underdetermined by evidence, theory choice must be relative to interpretive frameworks. Utilizing Davidson's philosophy of language, I offer a nonrepresentationalist alternative to suggest how relativism can be more fully avoided.

Feminist theorists have shown that knowledge is embodied in ways that make a difference in science. Intemann properly endorses feminist standpoint theory over Longino’s empiricism, insofar as the former better addresses embodiment. I argue that a pragmatist analysis further improves standpoint theory: Pragmatism avoids the radical subjectivity that otherwise leaves us unable to account for our ability to share scientific knowledge across bodies of different kinds; and it allows us to argue for the inclusion, not just of the knowledge produced (...) from marginalised bodies, but of the marginalised themselves. (shrink)

This chapter’s main topic revolves around Davidson’s account of radical interpretation and the concept of triangulation as a necessary feature of communication and the formation of beliefs. There are two important implications of this model of belief formation for feminists studying the effects of social location on knowledge production generally, and the production of scientific knowledge in particular. The first is Davidson’s argument that whatever there is to the meaning of any of our beliefs must be available from the radical (...) interpreter’s external, third-person perspective. The second important implication of triangulation is that Davidson’s model is a holistic one that shows that there is no substantive difference in the triangulation process by which we form beliefs concerning basic descriptive features of the world and beliefs concerning evaluative features of the world. (shrink)

In assessing the appropriateness of a scientific community's research effort, Solomon considers a number of "decision vectors," divided into the empirical and non-empirical. Value judgments get sorted as non-empirical vectors. By way of contrast, I introduce Anderson's discussion of the evidential role of value judgments. Like Anderson, I argue that value judgments are empirical in the relevant sense. I argue further that Solomon's decision matrix needs to be reconceptualized: the distinction should not be between the empirical vs. non-empirical, but between (...) the relevant vs. irrelevant. Whether particular value judgments are relevant or not is an empirical question, to be decided on a case-by-case basis. (shrink)

The works of the later Wittgenstein resonate with aspects of the pragmatist tradition in American philosophy. Davidson’s work is similarly informed. We argue that because of their association with the pragmatist tradition, their work can be put to use by philosophers interested in social justice issues, including, for example, feminism, and critical race theory. Philosophers concerned with social justice continue to struggle between the extremes of an untenable foundationalism and a radical relativism. Given their holistic understanding of knowledge, meaning and (...) communication, the work of Wittgenstein and Davidson is particularly suited to dissolving the foundationalist/relativist dichotomy. We explore how this and other features of their work facilitates philosophy for social change. (shrink)

In lieu of an abstract here is the first paragraph: -/- I mean the subtitle of my essay both as praise for the clarity with which Elizabeth Anderson writes about what is at stake in debates about values in science, and as a promise to outline an even more direct route to the heart of the matter. I begin with a quick review of the steps in Anderson’s argument that seem necessary and, indeed, laudable, followed by a brief discussion of (...) those steps in her argument that we might fruitfully skip. The hope is that we will arrive at roughly the same location but with a firmer foothold. (shrink)

I have argued that political values are beliefs informed, more or less well, by the evidence of experience and that, where relevant and well-supported by evidence, the inclusion of political values in scientific theorizing can increase the objectivity of research. The position I endorse has been called the “values-as-evidence” approach. In this essay I respond to three kinds of resistance to this approach, using examples of feminist political values. Solomon questions whether values are beliefs that can be tested, Alcoff argues (...) that even if our values are beliefs that can be tested, testing them might not be desirable because doing so assigns these important values a contingency that weakens their normative force, and Yap argues that the approach is too idealistic in its articulation of the role of evidence in our political deliberations. In response, I discuss the ways that values can be tested, I analyze the evidential strength of feminist values in science, and I argue that the evidence-based nature of these values is neither a weakness nor an idealization. Problems with political values affecting science properly concern the dogmatic ways that evaluative beliefs are sometimes held—a problem that arises with dogmatism toward descriptive beliefs as well. I conclude that scientists, as with the rest of us, ought to adopt a pragmatically-inclined appreciation of the fallible, inductive process by which we gather evidence in support of any of our beliefs, whether they are described as evaluative or descriptive. (shrink)

In Clough’s reply paper to me (http://wp.me/p1Bfg0-1aN), she laments how feminist calls for diversity within scientific communities are inadvertently sidelined by our shared feminist empiricist prescriptions. She offers a novel justification for diversity within epistemic communities and challenges me to accept this addendum to my prior prescriptions for biomedical research communities (Goldenberg 2013) on the grounds that they are consistent with the epistemic commitments that I already endorse. In this response, I evaluate and accept her challenge.

While most of healthcare research and practice fully endorses evidence-based healthcare, a minority view borrows popular themes from philosophy of science like underdetermination and value-ladenness to question the legitimacy of the evidence-based movement’s philosophical underpinnings. While the feminist origins go unacknowledged, those critics adopt a feminist reading of the “gap argument” to challenge the perceived objectivism of evidence-based practice. From there, the critics seem to despair over the “subjective elements” that values introduce to clinical reasoning, demonstrating that they do not (...) subscribe to feminist science studies’ normative program——where contextual values can enable good science and justified decisions. In this paper, I investigate why it is that the critics of evidence-based medicine adopt feminist science’s characterization of the problem but resist the productive solutions offered by those same theorists. I suggest that the common feminist empiricist appeal to idealized epistemic communities is impractical for those working within the current biomedical context and instead offer an alternate stream of feminist research into the empirical content of values (found in the work of Elizabeth Anderson and Sharyn Clough) as a more helpful recourse for facilitating the important task of legitimate and justified clinical decision-making. I use a case study on clinical decision-making to illustrate the fruitfulness of the latter feminist empiricist framework. -/- See response by Sharyn Clough: http://wp.me/p1Bfg0-1aN See reply by Maya Goldenberg: http://wp.me/p1Bfg0-1oY. (shrink)

Racist beliefs express value judgments. According to an influential view, value judgments are subjective, and not amenable to rational adjudication. In contrast, we argue that the value judgments expressed in, for example, racist beliefs, are false and objectively so. Our account combines a naturalized, philosophical account of meaning inspired by Donald Davidson, with a prominent social-psychological theory of values pioneered by the social-psychologist Milton Rokeach. We use this interdisciplinary approach to show that, just as with beliefs expressing descriptive judgments, beliefs (...) expressing value judgments have empirical content, or can be inferentially linked to beliefs that do; the truth or falsity of that content can be objectively assigned; and that assignment is amenable to rational assessment. While versions of this objective view of value judgments have been defended by moral realists of various metaphysical stripes, our argument has the virtue of appealing, instead, to accounts that are as naturalistically informed as possible. And, unlike the influential subjective view of value judgments, and racist beliefs more particularly, our arguments are better able to account for instances where rational, persuasive strategies have been effective in reducing the ubiquity of racism in American culture. (shrink)

Fact/value holism has become commonplace in philosophy of science, especially in feminist literature. However, that facts are bearers of empirical content, while values are not, remains a firmly-held distinction. I support a more thorough-going holism: both facts and values can function as empirical claims, related in a seamless, semantic web. I address a counterexample from Kourany where facts and values seem importantly discontinuous, namely, the simultaneous support by the Nazis of scientifically sound cancer research and morally unsound political policies. I (...) conclude that even by the criteria available at the time, Nazi cancer research was empirically weak, and the weaknesses in their research are continuous with their moral failures in just the ways predicted by the holism I support. (shrink)

The hygiene hypothesis offers an explanation for the correlation, well-established in the industrialized nations of North and West, between increased hygiene and sanitation, and increased rates of asthma and allergies. Recent studies have added to the scope of the hypothesis, showing a link between decreased exposure to certain bacteria and parasitic worms, and increased rates of depression and intestinal auto- immune disorders, respectively. What remains less often discussed in the research on these links is that women have higher rates than (...) men of asthma and allergies, as well as many auto-immune disorders, and also depression. The current paper introduces a feminist understanding of gender socialization to the epidemiological and immunological picture. That standards of cleanliness are generally higher for girls than boys, especially under the age of five when children are more likely to be under close adult supervision, is a robust phenomenon in industrialized nations, and some research points to a cross- cultural pattern. I conclude that, insofar as the hygiene hypothesis successfully identifies standards of hygiene and sanitation as mediators of immune health, then attention to the relevant patterns of gender socialization is important. The review also makes clear that adding a feminist analysis of gender socialization to the hygiene hypothesis helps explain variation in morbidity rates not addressed by other sources and responds to a number of outstanding puzzles in current research. Alternative explanations for the sex differences in the relevant morbidity rates are also discussed (e.g., the effects of estrogens). Finally, new sources of evidence for the hygiene hypothesis are suggested in the form of cross-cultural and other natural experiments. (shrink)

Glucagon-like peptide-1 (GLP-1) receptor agonists, such as semaglutide, and dual GLP-1 and glucose-dependent insulinotropic polypeptide (GIP) receptor agonists, such as tirzepatide, have been found to be effective for treating obesity and diabetes, significantly reducing weight and the risk or predicted risk of adverse cardiovascular events. There is a global shortage of these medications that could last several years and raises questions about how limited supplies should be allocated. We propose a fair-allocation framework that enables evaluation of the ethics of current (...) practices and could guide governments, professional societies, and physicians in allocation decisions. (shrink)

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

Abstract: Fish are important for both nutritional and economic reasons. They are a good source of protein, vitamins, and minerals and play a significant role in human diets, especially in coastal and island communities. In addition, fishing and fish farming are major industries that provide employment and income for millions of people worldwide. Moreover, fish play a critical role in marine ecosystems, serving as prey for larger predators and helping to maintain the balance of aquatic food chains. Overall, fish play (...) a vital role in supporting human well-being and the health of our planet. . Fishes have a lots of types such that: Red Mullet, Sea Bass, Striped Red Mullet and Shrimp. Each of them has its own shape and characteristics that differ from other types. We proposed a system that recognize nine types of fishes using deep learning. We trained the model with a dataset that contain 9000 images that were slit into 6300 images for training, 1350 for validation and 1350 for testing. The proposed model achieved accuracy (99.68%), precision (99.69%), recall (99.68%), and f1-score (99.68%). This indicates that our proposed model can effectively predicate and classify different types of fish with very high accuracy. (shrink)

The truth of every human being is the end his life with death, and this leads to leaving assets and funds for those after him and can lead to hate between the heirs, it has made a point of Islamic law on all aspects of life, including the subject of the inheritance of the deceased. The main problem is how to get the knowledge of the basics of inheritance. This paper reviews work done in the use of expert system software (...) to calculate inheritance in Islam. A proposed expert system was designed and developed using CLIPS language to calculate the inheritance in Islam. (shrink)

Abstract: Ensuring the proactive detection of transaction risks is paramount for financial institutions, particularly in the context of managing credit scores. In this study, we compare different machine learning algorithms to effectively and efficiently. The algorithms used in this study were: MLogisticRegressionCV, ExtraTreeClassifier,LGBMClassifier,AdaBoostClassifier, GradientBoostingClassifier,Perceptron,RandomForestClassifier,KNeighborsClassifier,BaggingClassifier, DecisionTreeClassifier, CalibratedClassifierCV, LabelPropagation, Deep Learning. The dataset was collected from Kaggle depository. It consists of 164 rows and 8 columns. The best classifier with unbalanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%,Recall100.0% and the F1-score (...) 100.0%. However, the best classifier with balanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%, Recall 100.0% and the F1-score 100.0%. (shrink)

Abstract: Classifying sea animals is an important problem in marine biology and ecology as it enables the accurate identification and monitoring of species populations, which is crucial for understanding and protecting marine ecosystems. This paper addresses the problem of classifying 19 different sea animals using convolutional neural networks (CNNs). The proposed solution is to use a pretrained MobileNetV2 model, which is a lightweight and efficient CNN architecture, and fine-tune it on a dataset of sea animals. The results of the study (...) show that the fine-tuned MobileNetV2 model is able to achieve an accuracy of 85% on the classification task, which is competitive with the state-of-the-art methods. Additionally, the study also found that data augmentation and regularization techniques such as dropout were important for preventing overfitting and improving the performance of the model. This study demonstrates the potential of using pretrained models and efficient CNN architectures for classifying sea animals, and it provides insights into the challenges and best practices for solving this important problem in marine biology and ecology. (shrink)

Abstract: In this study, we explore the challenge of identifying and preventing diseases in apple trees, which is a popular activity but can be difficult due to the susceptibility of these trees to various diseases. To address this challenge, we propose the use of Convolutional Neural Networks, which have proven effective in automatically detecting plant diseases. To validate our approach, we use images of apple leaves, including Apple Rot Leaves, Leaf Blotch, Healthy Leaves, and Scab Leaves collected from Kaggle which (...) is part from the Plant Village dataset. We generate a comprehensive training dataset using techniques such as image filtering, compression, and generation. Our model achieves impressive accuracy scores for all classes, with an overall accuracy of 99.93% on a dataset of 10,000 labeled images . (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)

Academic excellence and intelligence are commonly lauded as commendable attributes synonymous with success. However, a disconcerting trend has surfaced within educational institutions, challenging the prevailing narrative of scholastic accomplishment—smart shaming. This research delves into the increasing concern of smart shaming within educational settings, particularly at Immaculate Conception College of Balayan, Inc., questioning the predominant emphasis on academic excellence and intelligence. A qualitative case study design, along with judgmental sampling, was employed to examine fifteen (15) student leaders who had experienced smart (...) shaming on campus. Thematic analysis revealed that sarcastic remarks including "edi wow," "Okay ikaw na matalino," "bidabida," and "sipsip" were a prevalent manifestation of smart shaming, adversely affecting emotional well-being, academic and leadership prowess, mental health, and self-perception. Coping mechanisms, predominantly centered around ignoring and focusing on goals, were identified. The findings underscore the imperative for educators to remain vigilant regarding instances of smart shaming in classrooms, promoting open communication to ensure student well-being. Additionally, guidance counselors play a pivotal role in aiding students in effectively navigating the repercussions of smart shaming. This study advocates for proactive programs and interventions to address and alleviate the deleterious effects of smart shaming on students. (shrink)

Abstract: Artificial Intelligence (AI) is revolutionizing the agricultural sector by enhancing productivity and sustainability. This paper explores the transformative impact of AI technologies on agriculture, focusing on their applications in precision farming, predictive analytics, and automation. AI-driven tools enable more efficient management of crops and resources, leading to improved yields and reduced environmental impact. The paper examines key AI technologies, including machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource use. Additionally, (...) it addresses challenges such as data privacy, technology adoption barriers, and the ethical implications of AI in farming. By integrating AI into agricultural practices, the industry can achieve greater efficiency and sustainability, paving the way for future advancements. (shrink)

The co-evolutionary concept of Three-modal stable evolutionary strategy of Homo sapiens is developed. The concept based on the principle of evolutionary complementarity of anthropogenesis: value of evolutionary risk and evolutionary path of human evolution are defined by descriptive (evolutionary efficiency) and creative-teleological (evolutionary correctly) parameters simultaneously, that cannot be instrumental reduced to others ones. Resulting volume of both parameters define the trends of biological, social, cultural and techno-rationalistic human evolution by two gear mechanism ˗ gene-cultural co-evolution and techno- humanitarian balance. (...) The resultant each of them can estimated by the ratio of socio-psychological predispositions of humanization/dehumanization in mentality. Explanatory model and methodology of evaluation of creatively teleological evolutionary risk component of NBIC technological complex is proposed. Integral part of the model is evolutionary semantics (time-varying semantic code, the compliance of the biological, socio-cultural and techno-rationalist adaptive modules of human stable evolutionary strategy). (shrink)

The study aimed at identifying responsive flexibility and its role in improving service quality, from the point of view of the internal beneficiary in non-governmental hospitals in Gaza Strip. The study relied on the descriptive and analytical approach, and the questionnaire was designed as a tool to collect data, and the researchers used the comprehensive survey method, and the number of the study population was (536) single, where (434) questionnaires were retrieved, and the recovery rate was (80.97%). The study showed (...) many results, the most important of which were: The presence of moderate degree of approval by the study sample on the responsive flexibility, as it was evident through the response flexibility field obtaining a relative weight (60.50%), and the results of the study indicated that there is a high degree of approval by the members of the study sample. On the quality of service, as it was evident through the field of service quality obtaining a relative weight (79.90%). The results of the study revealed a statistically significant relationship between responsive flexibility and service quality in non-governmental hospitals in Gaza Strip, with a correlation coefficient of 0.418, and the results of the study revealed a statistically significant effect between responsive flexibility and service quality in non-governmental hospitals in Gaza Strip. The study reached many recommendations, the most important of which were: Finding mechanisms by which stored information can be used to enhance the decision-making process, and setting up an effective system to receive patients' complaints in a manner that ensures rapid response and treatment, to achieve continuous communication between patients and the hospital management, and to notify patients of abuse With the complaints that they submit, and working to provide all medical and health specialties in the hospitals under study, by making use of the medical delegations that visit Gaza Strip, involving them in the treatment processes, bringing in doctors and specialists from abroad, and updating the standards related to measuring the services provided to patients continuously, based on On patients' suggestions and complaints, developing facilities in hospitals, as well as updating medical devices and equipment used in hospitals periodically. (shrink)

Abstract: Artificial Intelligence (AI) has rapidly evolved, offering transformative capabilities for business decision-making. This paper explores how AI can be leveraged to enhance strategic decision-making in business contexts. It examines the integration of AI-driven analytics, predictive modeling, and automation to improve decision accuracy and operational efficiency. By analyzing current applications and case studies, the paper highlights the opportunities AI presents, including enhanced data insights, risk management, and personalized customer experiences. Additionally, it addresses the challenges businesses face in adopting AI, such (...) as data privacy concerns, integration issues, and the need for skilled personnel. The paper concludes with recommendations for effectively implementing AI solutions to maximize their benefits while navigating potential pitfalls. (shrink)

E-money systems have revolutionized global business transactions through digital payment methods. This quantitative correlational study aimed to assess the awareness and acceptance of e-money among individuals in Balayan, Batangas. Employing quota and purposive sampling, 100 participants aged 21 to 70 completed a survey questionnaire. Statistical analysis revealed that consumers were aware of e-money but lacked comprehensive knowledge. They acknowledged the convenience of e-money for online shopping and expense tracking. Age significantly influenced acceptance, while gender did not exhibit a similar effect. (...) Significant differences were observed in awareness and acceptance levels. Recommendations for improving acceptance include administrator-led campaigns, business incentives, consumer education, financial institution partnerships, and integration into student curricula. The study underscores the need for targeted strategies to translate e-money knowledge into practical use. Future research should explore adoption trends over time, barriers, and the impact of regulations on e-money utilization. (shrink)

To improve oneself and grow professionally, career development has been found to be crucial, as it serves as a roadmap for the professional growth of employees. However, a barrier, known as the glass ceiling, hinders the progress of employees, especially those in the LGBTQIA+ community. This study explores the impact of the glass ceiling on the career development of selected LGBTQIA+ individuals, shedding light on the barriers faced by this community in the workplace. The researchers used a qualitative multiple-case study (...) and selected ten (10) LGBTQIA+ participants from large enterprises via the judgmental sampling technique. Through thematic analysis, psychological impact, workplace discomfort, lack of recognition, and career progress challenges emerged as the impacts of the glass ceiling on the participants. Strategies for overcoming glass ceiling barriers, such as resilience, self-reflection, and community support, are identified. Thus, the study found that glass ceilings are really evident in the context of businesses. With this, the proposed advocacy plan in the study suggests interventions like workplace inclusion training, mentorship networks, and seminars on employee rights. Furthermore, it emphasizes the call for ongoing research and advocacy efforts to dismantle barriers and cultivate workplaces that not only embrace diversity but also provide equitable opportunities for career growth. (shrink)

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

De burgemeester van Etterbeek liet tijdens de afgelopen periode van vrieskou daklozen van straat halen. Soms tegen hun wil in. Omdat het soms nodig is om mensen tegen zichzelf te beschermen.

Abstract: Vegetables are an essential component of a healthy diet and play a critical role in promoting overall health and well- being. Vegetables are rich in important vitamins and minerals, including vitamin C, folate, potassium, and iron. They also provide fiber, which helps maintain digestive health and prevent chronic diseases. We are proposing a deep learning model for the classification of vegetables. A dataset was collected from Kaggle depository for Vegetable with 15000 images for 15 different classes. The data was (...) preprocessed, normalized and split into three sets (train, valid, test). The proposed model was trained and validated using the train and valid sets and accuracy of both training and validation was very high. For the evaluation of the proposed model we utilized these metrics: accuracy, F1-score, precision, Recall and time required for testing. We then tested the proposed model using the test set. The result of the testing was accuracy (99.95%), F1-score (99.95%), precision (99.95%), Recall (99.95%) and time required for testing the test set was 1.38 seconds. (shrink)

A evolução da seleção genética de aves e suínos proporcionou aumento no crescimento muscular e no desempenho produtivo, bem como na produção intensiva. Os premixes proporcionam ingredientes, como vitaminas e minerais que são essenciais para o crescimento muscular, redução do estresse, eficiência de diversos metabolismos e o bem-estar animal, contribuindo para uma produção mais sustentável. Deste modo, os premixes desempenham um papel fundamental na otimização do desempenho produtivo e reprodutivo de aves, equinos e suínos. Para formular uma dieta balanceada é (...) necessária a inclusão de vitaminas e minerais, os quais são inseridos em baixas concentrações, em função disto é necessário incluí-los por meio de premix para garantir uma boa homogeneidade dos microingredientes na ração. Os premixes podem ser classificados como: ➢ Vitamínicos, ➢ Minerais e ➢ Minerais-vitamínicos ➢ e conter aminoácidos, antibióticos ou enzimas. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into educational technology has revolutionized learning through Intelligent Tutoring Systems (ITS). These systems harness AI to deliver personalized, adaptive instruction that caters to individual student needs, thereby enhancing learning outcomes and engagement. This paper explores the evolution and impact of ITS, highlighting key AI technologies such as machine learning, natural language processing, and adaptive algorithms that underpin their functionality. By examining various case studies and applications, the paper illustrates how ITS have transformed traditional (...) educational practices and identifies the challenges and limitations associated with their implementation, including data privacy concerns and system biases. The discussion also extends to future directions in ITS development, emphasizing emerging trends and potential advancements. Ultimately, this paper aims to provide a comprehensive understanding of how ITS leverage AI to enhance educational experiences and propose pathways for future research in this dynamic field. (shrink)

Identification of factors that predict recurrent antisocial behavior is integral to the social sciences, criminal justice procedures, and the effective treatment of high-risk individuals. Here we show that error-related brain activity elicited during performance of an in- hibitory task prospectively predicted subsequent rearrest among adult offenders within 4 y of release (N =96). The odds that an offender with relatively low anterior cingulate activity would be rearrested were approximately double that of an offender with high activity in this region, holding (...) constant other observed risk factors. These results suggest a potential neurocognitive biomarker for persistent antisocial behavior. (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)

Abstract: As artificial intelligence (AI) technologies become increasingly integrated into various sectors, ethical considerations surrounding their development and deployment have become paramount. This paper explores the multifaceted ethical landscape of AI, focusing on key challenges such as bias, transparency, privacy, and accountability. It examines how these issues manifest in AI systems and their impact on society. The paper also evaluates current approaches and solutions aimed at mitigating these ethical concerns, including regulatory frameworks, ethical guidelines, and best practices for AI design. (...) By providing a comprehensive analysis of these challenges and solutions, this paper aims to contribute to the ongoing discourse on responsible AI development and implementation, advocating for a balance between technological innovation and ethical integrity. (shrink)

The study determined the chemical composition and acceptability of organic bath soap among students in Yaba College of Technology, Yaba, Lagos State, Nigeria. The study was guided by two research questions. Mixed method research design was used for the study. Population was 46 which comprised all Home Economics students from the Department of Vocational Education. In-home use questionnaire was used for data collection. The study produced two samples of turmeric bath soap. Findings showed that the chemical compositions of the turmeric (...) soap samples are in accordance with the specifications of the Standard Organization of Nigeria (SON) with toilet soap specifications of pH ranging from 9 to 11, total fatty matter ranging from 60-75 gm and total free caustic soda ranging from 0.05 to 0.08 %. Findings showed that the sensory evaluation attributes for colour, smell, consistency foam ability and skin sensitivity were accepted after use for three months as the samples were skin friendly, safe to use without side effects. It was recommended that turmeric body soap and turmeric face gel can be produced in large quantities for commercial purpose as a form of income generation for unemployed graduates. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into leadership practices is transforming organizational dynamics and This decision-making processes. paper explores how AI can enhance leadership effectiveness by providing data-driven insights, optimizing decision-making, and automating routine tasks. It also examines the challenges leaders face in adopting AI, including ethical considerations, potential biases in AI systems, and the need for upskilling. By analyzing current applications of AI in leadership and discussing future trends, this study aims to provide a comprehensive overview of the (...) opportunities AI presents for effective leadership and the strategies required to address its associated challenges. (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)

Abstract: Artificial Intelligence (AI) is revolutionizing organizational dynamics by reshaping both structures and processes. This paper explores how AI-driven innovations are transforming organizational frameworks, from hierarchical adjustments to decentralized decision-making models. It examines the impact of AI on various processes, including workflow automation, data analysis, and enhanced decision support systems. Through case studies and empirical research, the paper highlights the benefits of AI in improving efficiency, driving innovation, and fostering agility within organizations. Additionally, it addresses the challenges associated with AI (...) integration, such as resistance to change, ethical concerns, and data security issues. The paper concludes by offering insights into future trends and implications for organizations navigating the evolving landscape of AI-driven change. (shrink)

This study employs a neural network to analyze the connection between smoking, drinking, and various health-related factors using a dataset of 5148 samples. Achieving an impressive 99.94% accuracy and an average training error of 0.0016, the model identifies influential factors such as serum aminotransferases, serum creatinine, sex, weight, and triglyceride levels. These findings enhance our understanding of lifestyle choices and their impact on health. This research underscores the potential of machine learning in studying complex health phenomena.

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)

This essay argues that the exploration of scepticism and its implications in the work of Stanley Cavell and David Hume bears more similarities than is commonly acknowledged, especially along the lines of what I wish to call “sceptical naturalism.” These lines of similarity are described through the way each philosopher relates the “natural” and “nature” to the universal, the necessary, and the conventional.