The integration of Internet Protocol and Embedded Systems can enhance the communication platform. This paper describes the emerging smart technologies based on Internet of Things (IOT) and internet protocols along with embedded systems for monitoring and controlling smart devices with the help of WiFi technology and web applications. The internet protocol (IP) address has been assigned to the things to control and operate the devices via remote network that facilitates the interoperability and end-to-end communication among various devices c,onnected over a (...) network. The HTTP POST and HTTP GET command that supports the RESTful service have been used to ensure the transmission and reception of packets between the IOT Gateway and Cloud Database. The emerging smart technologies based on the Internet of Things (IoT) facilitated features like automation, controllability, interconnectivity, reliability which in turn turn paved the way for a wide range of acceptance amongst the masses. The Internet of Things (IoT) has brought in many new emerging technologies into varoius field like our daily lives, industry, agricultural sector, and many more. The world is experiencing the explosive growth with the advent of Internet of Things (IoT) these years. The potential growth of IoT is enoromous which is evidenced by all the human beings in our day to day life. (shrink)

Bio-ontologies are essential tools for accessing and analyzing the rapidly growing pool of plant genomic and phenomic data. Ontologies provide structured vocabularies to support consistent aggregation of data and a semantic framework for automated analyses and reasoning. They are a key component of the Semantic Web. This paper provides background on what bio-ontologies are, why they are relevant to botany, and the principles of ontology development. It includes an overview of ontologies and related resources that are relevant to plant science, (...) with a detailed description of the Plant Ontology (PO). We discuss the challenges of building an ontology that covers all green plants (Viridiplantae). Key results: Ontologies can advance plant science in four keys areas: 1. comparative genetics, genomics, phenomics, and development, 2. taxonomy and systematics, 3. semantic applications and 4. education. Conclusions: Bio-ontologies offer a flexible framework for comparative plant biology, based on common botanical understanding. As genomic and phenomic data become available for more species, we anticipate that the annotation of data with ontology terms will become less centralized, while at the same time, the need for cross-species queries will become more common, causing more researchers in plant science to turn to ontologies. (shrink)

The idea that technologies can change moral beliefs and practices is an old one. But how, exactly, does this happen? This paper builds on an emerging field of inquiry by developing a synoptic taxonomy of the mechanisms of techno-moral change. It argues that technology affects moral beliefs and practices in three main domains: decisional (how we make morally loaded decisions), relational (how we relate to others) and perceptual (how we perceive situations). It argues that across these three domains there are (...) six primary mechanisms of techno-moral change: (i) adding options; (ii) changing decision-making costs; (iii) enabling new relationships; (iv) changing the burdens and expectations within relationships; (v) changing the balance of power in relationships; and (vi) changing perception (information, mental models and metaphors). The paper also discusses the layered, interactive and second-order effects of these mechanisms. (shrink)

Abstract: The incorporation of Artificial Intelligence (AI) into educational technology has dramatically transformed learning through Intelligent Tutoring Systems (ITS). These systems utilize AI to offer personalized, adaptive instruction tailored to each student's needs, thereby improving learning outcomes and engagement. This paper examines the development and impact of ITS, focusing on AI technologies such as machine learning, natural language processing, and adaptive algorithms that drive their functionality. Through various case studies and applications, it illustrates how ITS have revolutionized traditional educational methods (...) and addresses the challenges and limitations, including data privacy and system biases. The discussion also covers future directions in ITS development, highlighting emerging trends and potential advancements. The goal is to provide a thorough understanding of how ITS use AI to enhance educational experiences and suggest avenues for future research in this evolving field. (shrink)

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

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

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

Abstract: Artificial Intelligence (AI) is transforming the agricultural sector, enhancing both productivity and sustainability. This paper delves into the impact of AI technologies on agriculture, emphasizing their application in precision farming, predictive analytics, and automation. AI-driven tools facilitate more efficient crop and resource management, leading to higher yields and a reduced environmental footprint. The paper explores key AI technologies, such as machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource use. Additionally, (...) it discusses challenges like data privacy, barriers to technology adoption, and the ethical implications of AI in farming. Integrating AI into agricultural practices holds the promise of greater efficiency and sustainability, paving the way for future innovations. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging represents a transformative shift in healthcare, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. This paper explores the application of AI technologies in the analysis of medical images, focusing on techniques such as convolutional neural networks (CNNs) and deep learning models. We discuss how these technologies are applied to various imaging modalities, including X-rays, MRIs, and CT scans, to enhance disease detection, image segmentation, and diagnostic support. Additionally, the (...) paper addresses the challenges faced in AI-driven medical imaging, including data quality, model interpretability, and ethical considerations. By examining recent advancements and real-world case studies, this paper provides insights into the current state of AI in medical imaging and its potential future directions. The findings highlight the ongoing evolution of AI technologies and their crucial role in advancing medical diagnostics and treatment strategies. (shrink)

Abstract: Early detection of breast cancer plays a pivotal role in improving patient prognosis and reducing mortality rates. Recent technological advancements have significantly enhanced the accuracy and effectiveness of breast cancer screening methods. This paper explores the latest innovations in early detection, including the evolution of digital mammography, the impact of 3D mammography (tomosynthesis), and the use of advanced imaging techniques such as molecular imaging and MRI. Furthermore, the integration of artificial intelligence (AI) in diagnostic tools is discussed, highlighting how (...) machine learning algorithms are refining imaging analyses and reducing diagnostic errors. Advances in genetic screening, liquid biopsies, and biomarkers are also examined, showcasing their potential in identifying high-risk individuals and enabling personalized treatment plans. This paper provides insights into the future of breast cancer detection, outlining both the opportunities and challenges that lie ahead in adopting these innovative technologies to improve patient outcomes. (shrink)

Contemporary republicanism is characterized by three main ideas: free persons, who are not subject to the arbitrary power of others; free states, which try to protect their citizens from such power without exercising it themselves; and vigilant citizenship, as a means to limit states to their protective role. This book advances an economic model of such republicanism that is ideologically centre-left. It demands an exit-oriented state interventionism, one that would require an activist government to enhance competition and resource exit from (...) dominating relationships within markets of all types, be they marital, labour, or residential. The substantial intervention and redistribution necessitated by such an approach make it leftist, while its tendency to work with rather than against the grain of markets makes it centrist. It is, in short, a contribution to progressive republican political thought, but one that is less social-democratic than market-democratic in character. (shrink)

Phenomenology gives rise to certain ontological considerations that have far-reaching implications for standard conceptions of patient autonomy in medical ethics, and, as a result, the obligations of and to patients in clinical decision-making contexts. One such consideration is the phenomenological reduction in classical phenomenology, a core feature of which is the characterisation of our primary experiences as immediately and inherently meaningful. This paper builds on and extends the analyses of the phenomenological reduction in the works of Husserl, Heidegger, and Merleau-Ponty (...) in order to identify and explain its implications for our current understanding of the principle of respect for patient autonomy and the norms of clinical decision making. (shrink)

When a computer system causes harm, who is responsible? This question has renewed significance given the proliferation of autonomous systems enabled by modern artificial intelligence techniques. At the root of this problem is a philosophical difficulty known in the literature as the responsibility gap. That is to say, because of the causal distance between the designers of autonomous systems and the eventual outcomes of those systems, the dilution of agency within the large and complex teams that design autonomous systems, and (...) the impossibility of fully predicting how autonomous systems will behave once deployed, determining who is morally responsible for harms caused by autonomous systems is unclear at a conceptual level. I review past work on this topic, criticizing prior works for suggesting workarounds rather than philosophical answers to the conceptual problem presented by the responsibility gap. The view I develop, drawing on my earlier work on vicarious moral responsibility, explains why computing professionals are ethically required to take responsibility for the systems they design, despite not being blameworthy for the harms these systems may cause. (shrink)

Jeremy Waldron has recently raised the question of whether there is anything approximating the creative self-authorship of personal autonomy in the writings of Immanuel Kant. After considering the possibility that Kantian prudential reasoning might serve as a conception of personal autonomy, I argue that the elements of a more suitable conception can be found in Kant’s Tugendlehre, or “Doctrine of Virtue”—specifically, in the imperfect duties of self-perfection and the practical love of others. This discovery is important for at least three (...) reasons: first, it elucidates the relationship among the various conceptions of autonomy employed by personal-autonomy theorists and contemporary Kantians; second, it brings to the surface previously unnoticed or undernoticed features of Kant’s moral theory; and third, it provides an essential line of defense against certain critiques of contemporary Kantian theories, especially that of John Rawls. (shrink)

Abstract: Climate change presents a critical challenge that demands advanced analytical tools to predict and mitigate its impacts. This paper explores the role of artificial intelligence (AI) in enhancing climate modeling, emphasizing how AI-driven methods are revolutionizing our understanding and response to climate change. By integrating machine learning algorithms with diverse data sources such as satellite imagery, historical climate records, and real-time sensor data, AI improves the accuracy, efficiency, and granularity of climate predictions. The paper reviews key AI techniques, including (...) neural networks and ensemble models, and their applications in simulating extreme weather events and forecasting long-term climate trends. Additionally, it examines the impact of AI-enhanced climate models on policy-making and strategic planning. Despite significant advancements, challenges such as data quality, computational demands, and model interpretability remain. This study underscores the potential of AI to transform climate modeling and highlights areas for future research and development to harness these technologies for a sustainable future. (shrink)

When we encounter extremist rhetoric, we often find it dumbfounding, incredible, or straightforwardly unintelligible. For this reason, it can be tempting to dismiss or ignore it, at least where it is safe to do so. The problem discussed in this paper is that such dismissals may be, at least in certain circumstances, epistemically unjust. Specifically, it appears that recent work on the phenomenon of hermeneutical injustice compels us to accept two unpalatable conclusions: first, that this failure of intelligibility when we (...) encounter extremist rhetoric may be a manifestation of a hermeneutical injustice; and second, that remedying this injustice requires that we ought to become more engaged with and receptive of extremist worldviews. Whilst some theorists might interpret this as a reductio of this framework of epistemic in/justice, we push back against this conclusion. First, we argue that with a suitably amended conception of hermeneutical justice—one that is sensitive to the contextual nature of our hermeneutical responsibilities, and to the difference between understanding a worldview and accepting it—we can bite the bullet and accept that certain extremists are subject to hermeneutical injustice, but without committing ourselves to any unpalatable conclusions about how we ought to remedy these injustices. Second, we argue that bringing the framework of hermeneutical in/justice to bear upon the experience of certain extremists actually provides a new and useful perspective on one of the causes of extremism, and how it might be undermined. (shrink)

Philosophy of mind and cognitive science (e.g., Clark and Chalmers 1998; Clark 2010; Palermos 2014) have recently become increasingly receptive tothe hypothesis of extended cognition, according to which external artifacts such as our laptops and smartphones can—under appropriate circumstances—feature as material realisers of a person’s cognitive processes. We argue that, to the extent that the hypothesis of extended cognition is correct, our legal and ethical theorising and practice must be updated, by broadening our conception of personal assault so as to (...) include intentional harm towards gadgets that have been appropriately integrated. We next situate the theoretical case for extended personal assault within the context of some recent ethical and legal cases and close with some critical discussion. (shrink)

Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this elusive feat. (...) Through rigorous experimentation and data-driven approaches, we seek to determine the viability of AI in the realm of lottery predictions. Our findings reveal both the promise and limitations of AI in this context, shedding light on the complexities of lottery data and the potential need for quantum computing as a last resort. (shrink)

Abstract: A corn crop typically refers to a large-scale cultivation of corn (also known as maize) for commercial purposes such as food production, animal feed, and industrial uses. Corn is one of the most widely grown crops in the world, and it is a major staple food for many cultures. Corn crops are grown in various regions of the world with different climates, soil types, and farming practices. In the United States, for example, the Midwest is known as the "Corn (...) Belt" due to its extensive corn production. Corn crops can be grown using a variety of methods, including conventional tillage, no-till farming, and the use of genetically modified crops. Due to its importance, there is a need for discovering the corn diseases and treating them. The aim of this study is to propose a deep learning model for the classification of corn leaf diseases based on Convolutional Neural Network. A model for classifying maize diseases was developed using a dataset for classifying Corn diseases that contains 4 classes of disease. The dataset was collected from Keggel website. The proposed model was trained, validated, and tested. The F1-score (99.83%), Recall (99.83%), precision (99.83%), Accuracy (99.83%). (shrink)

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

Abstract: The sinking of the RMS Titanic in 1912 remains a poignant historical event that continues to captivate our collective imagination. In this research paper, we delve into the realm of data-driven analysis by applying Artificial Neural Networks (ANNs) to predict the chances of survival for passengers aboard the Titanic. Our study leverages a comprehensive dataset encompassing passenger information, demographics, and cabin class, providing a unique opportunity to explore the complex interplay of factors influencing survival outcomes. Our ANN-based predictive model (...) achieved an accuracy rate of 78%, shedding light on the underlying patterns within the data. However, this paper underscores the multifaceted nature of survival prediction and acknowledges several challenges. These include missing data imputation, feature engineering, and the inherent noise within historical datasets. We further discuss the importance of model evaluation metrics, showcasing not only accuracy but also precision, recall, and F1-score as essential indicators of predictive performance. The research paper meticulously outlines the architecture of the ANN model, emphasizing the key hyperparameters, activation functions, and regularization techniques employed in model development. Additionally, we address ethical considerations related to data handling and potential biases within the dataset. While our achieved accuracy is a notable achievement, this study emphasizes the importance of interpreting the results with due caution. It underscores the need for a holistic perspective that considers the practical implications of false positives and false negatives, especially in the context of a disaster scenario. In conclusion, this research contributes to our understanding of survival prediction in historical events like the Titanic disaster using modern machine learning techniques. It calls for further exploration and refinement of predictive models, with a focus on enhancing the interpretability and generalizability of such models to real-world applications. (shrink)

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

Mango is an economically significant fruit crop cultivated in various tropical and subtropical regions around the world. However, the productivity and quality of mangoes can be severely impacted by a range of pests. This research paper introduces an innovative approach to identify mango pests using an expert system. The expert system integrates knowledge from entomology and plants to provide accurate identification of common mango pests. The paper outlines the development and implementation of the expert system using Clips shell, which utilizes (...) rule-based techniques to analyze symptoms associated with mango pests. By inputting observed symptoms, such as leaf spots, fruit damage, leaf mild and stem cankers the system can accurately identify the specific pest species affecting the mango tree. For each pest, the paper discusses their specific symptoms and Favorable conditions that affect the mango plant. By understanding the identification and symptoms of these pests, mango growers and agricultural professionals can implement appropriate pest management strategies to mitigate the economic losses caused by these insects. The research presented in this paper aims to contribute to the body of knowledge on mango pest management, facilitating sustainable mango production and ensuring the availability of high-quality mangoes in global markets. (shrink)

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

Water quality assessment is critical for environmental sustainability and public health. This research employs neural networks to predict water quality, utilizing a dataset of 21 diverse features, including metals, chemicals, and biological indicators. With 8000 samples, our neural network model, consisting of four layers, achieved an impressive 94.22% accuracy with an average error of 0.031. Feature importance analysis revealed arsenic, perchlorate, cadmium, and others as pivotal factors in water quality prediction. This study offers a valuable contribution to enhancing water quality (...) monitoring and decision-making for stakeholders and policymakers. (shrink)

Political theorists have long criticized policies that deny voting rights to convicted felons. However, some have recently turned to democratic theory to defend this practice, arguing that democratic self-determination justifies, or even requires, disenfranchising felons. I review these new arguments, acknowledge their force against existing criticism, and then offer a new critique of disenfranchisement that engages them on their own terms. Using democratic theory’s “all-subjected principle,” I argue that liberal democracies undermine their own legitimacy when they deny the vote to (...) felons and prisoners. I then show how this argument overcomes obstacles that cause problems for other critiques of disenfranchisement. (shrink)

heart attack analysis & prediction dataset 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 304 samples with 11 features, such as age, sex, chest pain type, Trtbps, 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, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 88% and an average error of 0078296. 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)

Exploring the intimate tie between body movement and space and time, Lee begins with the position that body movement generates space and time and explores the ethical implications of this responsibility for the situations one’s body movements generate. Whiteness theory has come to recognize the ethical responsibility for situations not of one’s own making and hence accountability for the results of more than one’s immediate personal conscious decisions. Because of our specific history, whites have developed a particular embodiment and body (...) movement that generates places that can only be characterized as more comfortable and more enabling to whites. (shrink)

As the debate between realists and empiricists in the philosophy of science drags on, one point of consensus has emerged: no one wants to be a manifestationalist. The manifestationalist is a kind of radical empiricist who argues that science provides theories that aim neither at a true picture of the entire world, nor even an empirically adequate picture that captures the world in all its observable respects. For manifestationalists, science aims only at providing theories that are true to the observed (...) aspects of reality. If the guiding idea of empiricism is that experience, and experience alone, provides us with knowledge about the world, then manifestationalism is an exceptionally strict empiricist perspective on science. Manifestationalism has primarily served within a reductio: certain empiricist views and arguments, when taken to their logical conclusion, lead to manifestationalism and so cannot be correct. The reductio works only because manifestationalism is widely agreed to be a non-starter. However, this consensus against manifestationalism is based on a single argument. We contest this assessment of manifestationalism and show that the primary argument against manifestationalism fails to hit its target. We do not intend to offer a manifesto for manifestationalism. Rather, we aim to vindicate it from a false accusation. Manifestationalism may not be the correct view of science, but the objections levied against it so far can be met. (shrink)

The research aims to identify the impact of the management requirements on operating of computerized management information systems to improve performance, and discuss the perceptions of respondents to develop the performance of employees in the Gaza Electricity Distribution Company, the researchers used the stratified sample method, (360) questionnaires were distributed on the study sample, (306) questionnaires were recoved with a percentage of (85%). The most important findings of the study: computerized MI have a positive impact on the development of performance (...) in the Gaza Electricity Distribution Company systems. The is statistical significance impact between “the physical, software supplies, human and organizational” requirements for the management and operation of computerized management information systems and performance development in the Gaza Electricity Distribution Company. -/- The research also concluded a series of recommendations: the need to strengthen the company's management interest in the use of computerized management information systems in all its components and elements of being an important variable which contributes to the effect on performance development. It's essential that we develop the infrastructure for information technology in general, and computerized management information systems, in particular for the development of performance. Increased interest in providing the material of equipment and devices used in the computerized management information system requirements. Interest in providing technical inputs to the computerized management information systems through keeping pace with technological means and modern techniques and work on the training of personnel to use those systems. And increased attention to human inputs of computerized management information systems "specialists technicians and end-users" through a variety of disciplines working in the field of computerized management information systems. (shrink)

This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed to enhance (...) model generalizability. (shrink)

Abstract. This study explores the use of convolutional neural networks (CNNs) for classifying different pineapple varieties, specifically pineapples and mini pineapples. By using a dataset of pineapple images, the research demonstrates the effectiveness of a pre-trained VGG16-based CNN model in accurately classifying these fruit categories. The model achieved over 99% accuracy on both the training and validation sets. The performance of the CNN was compared to traditional machine learning algorithms to highlight the advantages of deep learning in image classification tasks. (...) The results underscore the model’s ability to generalize well to the classification task, offering insights into feature extraction from complex image datasets. (shrink)

In this article I examine two basic questions: first, what constitutes a gifted person, and secondly, is there justification in making special educational provision for gifted children, where special provision involves spending more on their education than on the education of ‘normal’ children? I consider a hypothetical case for allocating extra resources for the gifted, and argue that gifted children are generally denied educational justice if they fail to receive an education that adequately challenges them. I further argue that an (...) adequately challenging education is essential to human flourishing, but that most children can be adequately challenged in schools in ways that promote flourishing without doing so at the expense of other children. (shrink)

The concept of interactional expertise – characterized by sociologists Harry Collins and Robert Evans as the ability to speak the language of a discipline without the corresponding ability to practice – can serve as a powerful way of breaking down expert/non-expert dichotomies and providing a role for new voices in specialist communities. However, in spite of the vast uptake of this concept and its potential to fruitfully address many important issues related to scientific expertise, there has been surprisingly little critical (...) analysis of it. We seek to remedy this situation by considering potential benefits of interactional expertise and the ways in which the current conception can – and cannot – realize those benefits. In particular, we argue that interactional expertise hasn’t reached its full potential for addressing who ought to be involved in scientific research and decision-making, largely owing to an unnecessarily restrictive way of operationalizing the concept. In its place, we offer a broader, more pluralistic account of interactional expertise – one that is in line with the original spirit of the concept, but also captures the diversity that we see as being an important aspect of interactional experts and the value they can bring to the table. (shrink)

I argue that a two-dimensional model of social objects is uniquely positioned to deliver explanations for some of the puzzling metaphysical features of words. I consider how a type-token model offers explanations for the metaphysical features of words, but I give reasons to find the model wanting. In its place, I employ an alternative model from social ontology to explain the puzzling data and questions that are generated from the metaphysical features of words. In the end I chart a new (...) path that foregrounds the agent-dependence and social conditions for word kinds. (shrink)

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

In this paper, I use a comparative analysis of mysticism in Zen and the Abrahamic faiths to formulate a phenomenological account of mysticism “as such.” I argue that, while Zen Buddhism is distinct from other forms of mystical experience in important ways, it can still be fit into a general phenomenological category of mystical experience. First, I explicate the phenomenological accounts of mysticism provided by Anthony Steinbock and Angela Bello. Second, I offer an account of Zen mysticism which both coheres (...) with and problematizes these accounts, arguing that Zen demonstrates the inadequacy of these accounts as descriptions of mysticism as a universal religious category. Lastly, I use this investigation to propose that Zen mysticism does generally cohere with the mystical experiences of other religions, but only if we devise a new formula for speaking phenomenologically about mystical experience as such which captures this phenomenon in all of its manifestations. (shrink)

In this paper we provide a defence of cosmopolitanism from a liberal perspective, examining its moral underpinnings, including moral obligations predicated on a belief in common humanity and the fundamental dignity of human people, cultural capacities that include an embrace of pluralism and a fallibilist disposition, and pragmatist resolve in finding humanitarian solutions to real problems that people face. We also scrutinise the ideal of cosmopolitanism by considering the ‘deeply religious’ as the sort of people about whom it may be (...) said that irreconcilable tensions exist between certain types of commitment and/or belonging and what the demands of cosmopolitanism involve. (shrink)

(摘要)在哲学史上，许多学者阐释了《存在与时间》的碎片化和"失败"，海德格尔本人对此也提出了三 种阐辛辛《存在与时间》因出版条件导致了偶然的不完整性，这种不完整性后来又作为存在历史的一部分而被提出。在思想(或未思)与偶然的经验意义上或存在者意义上生存着的 "作者"之间，存在着"间隙"，关于这个"间隙"的研究表明:在海德格尔的哲学生涯中，他对《存在与时间》的重要性做出的最好阐樨蕴含着一种关于&q uot;生活与著作"之间关系的理解，其中包含对生活经历的批判性理解和反思在内的理解，这种理解不同于海德格尔本人所坚持的更接近于解释学视角和阐择策略的理解。.

In a reflective and richly entertaining piece from 1979, Doug Hofstadter playfully imagined a conversation between ‘Achilles’ and an anthill (the eponymous ‘Aunt Hillary’), in which he famously explored many ideas and themes related to cognition and consciousness. For Hofstadter, the anthill is able to carry on a conversation because the ants that compose it play roughly the same role that neurons play in human languaging; unfortunately, Hofstadter’s work is notably short on detail suggesting how this magic might be achieved1. (...) Conversely in this paper - finally reifying Hofstadter’s imagination - we demonstrate how populations of simple ant-like creatures can be organised to solve complex problems; problems that involve the use of forward planning and strategy. Specifically we will demonstrate that populations of such creatures can be configured to play a strategically strong - though tactically weak - game of HeX (a complex strategic game).We subsequently demonstrate how tactical play can be improved by introducing a form of forward planning instantiated via multiple populations of agents; a technique that can be compared to the dynamics of interacting populations of social insects via the concept of meta-population. In this way although, pace Hofstadter, we do not establish that a meta-population of ants could actually hold a conversation with Achilles, we do successfully introduce Aunt Hillary to the complex, seductive charms of HeX. (shrink)

Abstract: In modern agriculture, ensuring the quality of crops plays a vital role in enhancing production and minimizing waste. This research focuses on the classification of lemon plants into two categories: good quality and bad quality, using deep learning techniques. We employ convolutional neural networks (CNN) to develop a classification model that can accurately predict plant quality based on images. Through a structured pipeline involving data collection, preprocessing, model design, and evaluation, we demonstrate the effectiveness of CNNs in performing quality (...) assessments. This paper discusses the experimental results in applying deep learning to agricultural tasks. (shrink)

In this paper, I ask whether mishpat ivri (Jewish Law) is appropriately conceived as a “legal system”. I review Menachem Elon’s use of a “Sources” Theory of Law (based on Salmond) in his account of Mishpat Ivri; the status of religious law from the viewpoint of jurisprudence itself (Bentham, Austin and Kelsen); then the use of sources (and the approach to “dogmatic error”) by halakhic authorities in discussing the problems of the agunah (“chained wife”), which I suggest points to a (...) theory more radical than the “sources” theory of law, one more akin to the ultimate phase of the thought of Kelsen (the “non-logical” Kelsen) or indeed to some form of Legal Realism (with which that phase of Kelsen’s thought has indeed been compared)? I finally juxtapose an account based on internal theological resources (a “Jurisprudence of Revelation”). Downloadable at at http://www.biu.ac.il/JS/JSIJ/jsij1.html. (shrink)

East Asians and Westerners differ in various aspects of perception and cognition. For example, visual memory for East Asians is believed to be more influenced by the contextual aspects of a scene than is the case for Westerners (Masuda & Nisbett, 2001). There are also differences in visual search: for Westerners, search for a long line among short is faster than for short among long, whereas this difference does not appear to hold for East Asians (Ueda et al., submitted). However, (...) it is unclear how these group-level differences originate. To investigate the extent to which they depend upon environment, we tested visual search and visual memory in East Asian immigrants who had lived in Canada for different amounts of time. Recent immigrants were found to exhibit no search asymmetry, unlike Westerners who had spent their lives in Canada. However, immigrants who had lived in Canada for more than two years showed performance comparable to that of Westerners. These differences could not be explained by the general analytic/holistic processing distinction believed to differentiate Westerners and East Asians, since all observers showed a strong holistic tendency for visual recognition. Results instead support the suggestion that exposure to a new environment can significantly affect the particular processes used to perceive a given stimulus. (shrink)

This chapter discusses three types of sociable robots for older adults: robotic caregivers ; robotic friends ; and sex robots. The central argument holds that society ought to make reasonable efforts to provide these types of robots and that under certain conditions, omitting such support not only harms older adults but poses threats to their dignity. The argument proceeds stepwise. First, the chapter establishes that assisting care-dependent older adults to perform activities of daily living is integral to respecting dignity. Here, (...) the argument establishes the vital role that carebots-of-the-future might play in aged societies as the supply of working age adults falls shy of demand. Next, the chapter extends this analysis to designing friendbots for socially isolated older adults. The argument holds that reasonable efforts to provide access to friendbots for socially isolated adults is also a future societal responsibility. Finally, the chapter applies similar reasoning to show that societies ought to make reasonable efforts to support sexual capabilities for older adults who want to be sexual but are bereft of sex partners. The argument draws on capability accounts of justice to show that when central human capacities, such as bodily integrity; intimate relationships; and the use of senses, imagination and thought, are at risk of falling below a threshold level, society should intercede. (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.

What is Comparative Philosophy? -/- Synkretic: Journal of Indo-pacific Philosophy.

Review of Tamar Schapiro, 'Feeling Like It'.