In On the Plurality of Worlds (Lewis 1986), David Lewis imposes a condition on realist theories of modality which he calls ‘plenitude’. Lewis apparently assigns this condition considerable importance, and uses it to motivate his Humean principle of recombination, but he never says exactly what plenitude amounts to. This chapter first sets aside some obvious ways of reconstructing the plenitude criterion which do not fit with the textual evidence. An objection to modal realism due to John Divers and Joseph Melia (...) (Divers and Melia 2002) is diagnosed as equivocating between an overly-demanding plenitude constraint and a weaker constraint which fails to establish their conclusion. An alternative deflationary interpretation of the plenitude condition has it following from an application of standard theoretical virtues to a modal realist’s total theory; Lewis’ correspondence provides new evidence in support of this interpretation. The deflationary plenitude criterion also has broader application, beyond Lewisian modal realism. (shrink)

My topic in this paper is the relationships of metaphysical priority which might hold between the different alethic modal statuses—necessity, contingency, possibility and impossibility. In particular, I am interested in exploring the view that the necessity of necessities is ungrounded while the contingency of contingencies is grounded—a scenario I call ‘necessity first’. I will explicate and scrutinize the contrast between necessity first and its ‘contingency first’ contrary, and then compare both views with ‘multimodal’ and ‘amodal’ alternatives, drawing on David Lewis’s (...) modal realism and Barbara Vetter’s potentialism as example cases. I will then defend the necessity-first point of view from a reversed version of Blackburn’s classic dilemma against theories of the source of necessity. (shrink)

What explains the outcomes of chance processes? We claim that their setups do. Chances, we think, mediate these explanations of outcome by setup but do not feature in them. Facts about chances do feature in explanations of a different kind: higher-order explanations, which explain how and why setups explain their outcomes. In this paper, we elucidate this 'mediator view' of chancy explanation and defend it from a series of objections. We then show how it changes the playing field in four (...) metaphysical disputes concerning chance. First, it makes it more plausible that even low chances can have explanatory power. Second, it undercuts a circularity objection against reductionist theories of chance. Third, it redirects the debate about a prominent argument against epistemic theories of chance. Finally, it sheds light on potential chancy explanations of the Universe's origin. (shrink)

Niels Jørgen Cappelørn, Alastair Hannay, Bruce H Kirmmse, David D Possen, Joel D S Rasmussen, and Vanessa Rumble working with the Princeton University Press and the Søren Kierkegaard Research Center at the University of Copenhagen have produced this huge work with facsimiles etc. The review comments on Kierkegaard's shrewd observations which are applicable today in the New Media World of information skews in a COVID 19 world. Further; Kierkegaard's attack against mediocrity is commented on. This review finds Kierkegaard on (...) St Augustine of Hippo as immature. It is another thing that Kierkegaard's observations and this reviewer's extrapolations from them are not yet part of the New Media Studies's mostly platitude-filled webinars. How long will we go on about Marshall McLuhan? At least let us now learn from that most sharp of ironists: that the Press will be the Press and its mostly about optics. One wonders still how, Kierkegaard misread so much Augustine? Well, no man is an island and Kierkegaard tried to be an anonymous Christian. An impossibility. (shrink)

In her short story “Stable Strategies for Middle Management,” Eileen Gunn imagines a future in which Margaret, an office worker, seeks radical genetic enhancements intended to help her secure the middle-management job she wants. One source of the story’s tension and dark humor is dramatic irony: readers can see that the enhancements Margaret buys stand little chance of making her life go better for her; enhancing is, for Margaret, probably a prudential mistake. This paper argues that our positions in (...) the real world are sufficiently similar to Margaret’s position in Gunn’s fictional world that we should take this story seriously as grounding an argument from analogy for the conclusion that radical genetic enhancements are, for us, probably a prudential mistake. The paper then defends this method. When the question at hand is one of speculative ethics, there is no method better fit to the purpose than argument from analogy to speculative fiction. (shrink)

Sheep production is the most representative livestock activities in Brazil and in the world. However, the reproductive performance of these animals is determined by factors genetic, physical environment, management and, especially, nutritional. Thirty half-breed lambs from Dorper × Santa Inês were used, with initial weight and age of 31.87 ± 0.5 kg and 157 days, respectively. These animals were prepared on a diet with three food levels (ad libitum, 30% and 70%). Morphometric measurements were measured at intervals of 16 to (...) 20 days in 56 experimental days. The measurements performed were scrotal circumference (CE), chest circumference (CT), body condition score (ECC), withers height (AC) and scrotal bag thickness (EBE), body weight (CP) and semen collection. Interaction occurrence for biometric measurements between food levels and age (p <0.05). The TC and ECC values were higher in animals fed ad libitum (p <0.001). EBE had higher values in animals ad libitum and at 211 days of age (p <0.001). The CP was increasing for the animals ad libitum and (p <0.05) throughout the experiment, while the T30 was only in the initial period. T70 animals did not grow during the whole experiment. CE was influenced by diet (p <0.001) and age (p <0.001) independently. So that animals under food restriction had less development and was higher in older animals. The AC values were influenced only by age (p <0.001), demonstrating the effect of age growth. We conclude that it is not advantageous to submit lambs, it corrects food reproduction. Although it was not possible to assess whether initial malnutrition would bring future problems for the animal's seminal quality, it proved to be immensely negative for the onset of puberty and the development of reproductive structures. -/- REFERÊNCIAS BIBLIOGRÁFICAS AKUMBUGU, F. E., et al. 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Performance and adaptability of the Dorper sheep breed under Hungarian and Romanian rearing conditions. Scientific Papers Animal Science and Biotechnologies, v. 46, n. 1, p. 344-349, 2013. CARNEIRO, P. L. S. et al. Desenvolvimento ponderal e diversidade fenotípica entre cruzamentos de ovinos Dorper com raças locais. Pesquisa Agropecuária Brasileira, v. 42, n. 7, p. 991-998, 2007. CARRIJO JUNIOR, O. A. et al. Morphological evaluation of the testicles of young santa Inês rams submitted to different regimes of protein supplementation and drenching. Ciência animal brasileira, Goiânia, v. 9, n. 2, p. 433-441, abr./jun. 2008. CASTRO, F.A.B. de et al. Desempenho de cordeiros Santa Inês do nascimento ao desmame filhos de ovelhas alimentadas com diferentes níveis de energia. Semina: Ciências Agrárias, v. 33, n. 6 supl 2, p. 3379-3388, 2012 CEZAR, M. F. et al. Avaliação de parâmetros fisiológicos de ovinos Dorper, Santa Inês e seus mestiços perante condições climáticas do trópico semi-árido nordestino. Ciência e Agrotecnologia, v. 28, n. 3, p. 614-620, 2004. CHACÓN, L. et al. Características de la pubertad en corderos de pelo y sus cruces en Colombia en condiciones de baja altitud. Revista MVZ Cordoba, v. 24, n. 1, p. 7099-7103, 2019. CHEMINEAU, P. et al. Training manual on artificial insemination in sheep and goats. Rome: FAO; 1991. DA SILVA, Emanuel Isaque Cordeiro. Comportamento Sexual dos Animais Domésticos. 2020. DA SILVA, Emanuel Isaque Cordeiro. Fisiologia do ciclo estral dos animais domésticos. Emanuel Isaque Cordeiro da Silva, 2021. Da Silva EIC. Formulação de ração para ovinos. 1ª ed. Belo Jardim, PE: IPA, 2021. DA SILVA, Emanuel Isaque Cordeiro. Puberdade e Estacionalidade Reprodutiva dos Animais. 2020. FAO. Animal Production and Health Paper, v. 83. ELMAZ, Ö. et al. Prediction of postpubertal reproductive potential according to prepubertal body weight, testicular size, and testosterone concentration using multiple regression analysis in Kıvırcık ram lambs. Turkish journal of veterinary and animal sciences, v. 32, n. 5, p. 335-343, 2008. JUCÁ, A. F. et al. Performance of the Santa Ines breed raised on pasture in semiarid tropical regions and factors that explain trait variation. Tropical animal health and production, v. 46, n. 7, p. 1249-1256, 2014. FOURIE, P. J. et al. Scrotal, testicular and semen characteristics of young Dorper rams managed under intensive and extensive conditions. Small Ruminant Research, v. 54, n. 1-2, p. 53-59, 2004. GUNN, R. G.; SIM, D. A.; HUNTER, E. A. Effects of nutrition in utero and in early life on the subsequent lifetime reproductive performance of Scottish Blackface ewes in two management systems. Animal Science, v. 60, n. 2, p. 223-230, 1995. HÖTZEL, M. J. et al. Morphometric and endocrine analyses of the effects of nutrition on the testis of mature Merino rams. Reproduction, v. 113, n. 2, p. 217-230, 1998. Instituto Brasileiro de Geografia e Estatística (IBGE). Produção Pecuária municipal. Rio de Janeiro, v. 46, p.1-8, 2018. KENYON, P. R.; MALONEY, S. K.; BLACHE, D. Review of sheep body condition score in relation to production characteristics. New Zealand Journal of Agricultural Research, v. 57, n. 1, p. 38-64, 2014. KHERADMAND, A.; BABAEI, H.; ALI BATAVANI, R. Effect of improved diet on semen quality and scrotal circumference in the ram. Veterinarski arhiv, v. 76, n. 4, p. 333-341, 2006. KORKMAZ, M. K.; EMSEN, E. Growth and reproductive traits of purebred and crossbred Romanov lambs in Eastern Anatolia. Anim. Reprod., v. 13, n. 1, p. 3-6, 2016. KRIDLI, R. T. et al. Age at puberty and some biological parameters of Awassi and its first crosses with Charollais and Romanov rams. Italian journal of animal science, v. 5, n. 2, p. 193-202, 2006. LOUW, D. F. J.; JOUBERT, D. M. Puberty in the male Dorper sheep and Boer goat. S. Afr. J. Agric. Sci, v. 7, p. 509-520, 1964. MAIA, A. S. C.; SILVA, R. G.; ANDRADE, P. C. Efeitos da temperatura e da movimentação do ar sobre o isolamento térmico do velo de ovinos em câmara climática. Revista Brasileira de Zootecnia, v. 38, n. 1, p. 104-108, 2009. MARTINS, R. D. et al. Avaliação da sazonalidade reprodutiva de carneiros Santa Inês criados no Distrito Federal. R. Bras. Zootec., Viçosa, v. 32, n. 6, supl. 1, p. 1594-1603, Dec. 2003. MENDES, A. M. P. et al. Zoneamento bioclimático para a raça ovina Dorper no Estado de Pernambuco. Pesquisa Agropecuária Brasileira, v. 49, n. 12, p. 986-993, 2014. MOULLA, F. et al. Characterization of the onset of puberty in Tazegzawt lambs, an endangered Algerian sheep: Body weight, thoracic perimeter, testicular growth, and seminal parameters. Veterinary world, v. 11, n. 7, p. 889, 2018. NATIONAL RESEARCH COUNCIL (NRC). Nutrient Requirements of Small Ruminants: Sheep, Goats, Cervids, and New World Camelids. Washington, D.C.: National Academy Press, 2007. NOTTER, D. R.; LUCAS, J. R.; MCCLAUGHERTY, F. S. Accuracy of estimation of testis weight from in situ testis measures in ram lambs. Theriogenology, v. 15, n. 2, p. 227-234, 1981. SEJIAN, V. et al. Effect of body condition score on the allometric measurements and reproductive performance of Garole X Malpura ewes under hot semi-arid environment. Journal of Dairy, Veterinary & Animal Research, v. 2, n. 6, p. 1-4, 2015. PACHECO, A.; MADELLA-OLIVEIRA, A. F.; QUIRINO, C. R. Biometria e formas dos testículos em cordeiros da raça Santa Inês explorados em regime de manejo intensivo. Revista Brasileira de Ciências Agrárias, v. 5, n. 1, p. 123-128, 2010. REGO NETO, A. A. et al. Population genetic structure of Santa Inês sheep in Brazil. Tropical animal health and production, v. 50, n. 3, p. 503-508, 2018. RHIND, S. M. Effects of maternal nutrition on fetal and neonatal reproductive development and function. Animal Reproduction Science, v. 82, p. 169-181, 2004. RIBEIRO, E. L. A.; GONZÁLEZ-GARCÍA, E. Indigenous sheep breeds in Brazil: potential role for contributing to the sustainability of production systems. Tropical animal health and production, v. 48, n. 7, p. 1305-1313, 2016. RIBEIRO, M. S. et al. Repeatability and effect of age on reproductive characteristics in Santa Ines rams. Archivos de zootecnia, v. 67, n. 257, p. 143-147, 2018. RUSSEL, A. J. F.; DONEY, J. M.; GUNN, R. G. Subjective assessment of body fat in live sheep. The Journal of Agricultural Science, v. 72, n. 3, p. 451-454, 1969. SILVA, N. V. et al. Medidas in vivo e da carcaça e constituintes não carcaça de ovinos alimentados com diferentes níveis do subproduto agroindustrial da goiaba. Revista Brasileira de Saúde e Produção Animal, v. 17, n. 1, p. 101-115, 2016. SILVA, P. et al. Influence of placentally mediated fetal growth restriction on the onset of puberty in male and female lambs. Reproduction, v. 122, n. 3, p. 375-383, 2001. SIQUEIRA FILHO, E. R. Influência dos níveis protéicos fornecidos na dieta sobre o sistema reprodutivo de carneiros. 2007. 75 f. Dissertação (mestrado) - Universidade Estadual Paulista, Faculdade de Medicina Veterinária e Zootecnia de Botucatu, 2007. SOUZA, J. S da et al. Biometric measurements of Santa Inês meat sheep reared on Brachiaria brizantha pastures in Northeast Brazil. Plos one, v. 14, n. 7, p. e0219343, 2019. (shrink)

The advocates of moral perception claim that we can literally perceive moral properties such as goodness and badness. One of the objections to the thesis of moral perception is that since we are not able to causally interact with moral properties and these properties are causally inert, thus they do not fall into the scope of our perception. In reply, the advocates propose different solutions: 1) moral properties supervene on natural properties, 2) moral properties are secondary natural properties, and 3) (...) moral properties are non-secondary natural properties. Each of these proposals aims to attack a different premise of the objection. In this paper, I am going to propose the thesis that assuming that moral properties are grounded in natural properties, Wilsonian grounding can be a new reply to this objection. I will try to explain how Alastair Wilson’s account of grounding, in addition to a grounding thesis about the relation between moral and natural properties, can prevent accidence without setting moral properties aside from perceptual scope. (shrink)

This article is by design a response to Alastair M. Taylor's "For Philosophers and Scientists: A General Systems Paradigm." That work is an advance over stage theories. But its focus on modernization tacitly accepts marginalization. Its focus on an undifferentiated evolving human species disregards intra- and intersocietal conflicts. Its uncritical talk of societal energy shifts obscures the reality of conquest and exploitation. If general systems theory is to be truly objective, it should take into account world-around system imbalance and (...) the relevance of Newton's Third Law. (Publisher omitted title of this article and used only its subtitle.). (shrink)

Distinctions in fundamentality between different levels of description are central to the viability of contemporary decoherence-based Everettian quantum mechanics (EQM). This approach to quantum theory characteristically combines a determinate fundamental reality (one universal wave function) with an indeterminate emergent reality (multiple decoherent worlds). In this chapter I explore how the Everettian appeal to fundamentality and emergence can be understood within existing metaphysical frameworks, identify grounding and concept fundamentality as promising theoretical tools, and use them to characterize a system of explanatory (...) levels (with associated laws of nature) for EQM. This Everettian level structure encompasses and extends the ‘classical’ levels structure. The ‘classical’ levels of physics, chemistry, biology, etc. are recovered, but they are emergent in character and potentially variable across Everett worlds. EQM invokes an additional fundamental level, not present in the classical levels picture, and a novel potential role for self-location in interlevel metaphysics. When given a modal realist interpretation, EQM also makes trouble for supervenience-based approaches to levels. (shrink)

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

Abstract: Artificial Intelligence (AI) is rapidly becoming a cornerstone of modern healthcare, offering unprecedented capabilities in diagnostics, treatment planning, patient care, and healthcare management. This paper explores the transformative impact of AI on the healthcare sector, examining how it enhances patient outcomes, improves the efficiency of medical practices, and introduces new ethical and operational challenges. By analyzing current applications such as AI-driven diagnostic tools, personalized medicine, and hospital management systems, this paper highlights the significant advancements AI has brought to the (...) field. However, the integration of AI also raises concerns related to data privacy, algorithmic bias, and the displacement of healthcare professionals. This study provides a comprehensive overview of these developments, offering insights into the future potential of AI in revolutionizing healthcare while addressing the ethical considerations that must accompany its adoption. The findings suggest that while AI holds the promise of transforming healthcare delivery, careful consideration of its implications is essential to ensure equitable and effective implementation. (shrink)

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

Abstract: 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: Artificial Intelligence (AI) has rapidly advanced, offering significant potential to transform business decision-making. This paper delves into how AI can be harnessed to enhance strategic decision-making within business contexts. It investigates the integration of AI-driven analytics, predictive modeling, and automation, emphasizing their role in improving decision accuracy and operational efficiency. By examining current applications and case studies, the paper underscores the opportunities AI offers, including improved data insights, risk management, and personalized customer experiences. It also addresses the challenges businesses (...) face when adopting AI, such as data privacy concerns, integration difficulties, and the need for skilled professionals. The paper concludes by offering recommendations for successfully implementing AI solutions to maximize benefits while mitigating potential challenges. (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: The integration of artificial intelligence (AI) into drug discovery is revolutionizing the pharmaceutical industry by accelerating the development of novel therapeutics. AI-powered tools enable researchers to process vast datasets, identify drug candidates, and predict their efficacy and safety with unprecedented speed and accuracy. This paper explores the transformative impact of AI on drug discovery, highlighting key advancements in machine learning algorithms, deep learning, and predictive modeling. Additionally, it addresses the challenges associated with AI implementation, including data quality, regulatory hurdles, (...) and ethical considerations. By analyzing case studies of AI-driven pharmaceutical breakthroughs, this paper underscores AI's potential to streamline drug development, reduce costs, and address unmet medical needs. The future of drug discovery is poised to shift dramatically as AI continues to advance, offering promising solutions for improving human health. (shrink)

Making Things Up, by Karen Bennett. Oxford: Oxford University Press, 2017. Pp. xi + 260.

Abstract: The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, necessitating accurate and timely forecasting of cases for effective mitigation strategies. In this research paper, we present a novel approach to predict COVID-19 cases using Artificial Neural Networks (ANNs), harnessing the power of machine learning for epidemiological forecasting. Our ANNs-based forecasting model has demonstrated remarkable efficacy, achieving an impressive accuracy rate of 97.87%. This achievement underscores the potential of ANNs in providing precise and data-driven insights into the dynamics (...) of the pandemic. However, this paper underscores the critical importance of a comprehensive evaluation beyond accuracy, including metrics such as sensitivity, specificity, and the area under the ROC curve (AUC-ROC), to assess the model's performance robustness. The research paper offers detailed insights into the architecture of the ANN model, encompassing critical hyperparameters, data preprocessing techniques, and regularization strategies employed to optimize model accuracy. Ethical considerations surrounding data privacy and potential biases within the COVID-19 dataset are also addressed. While the achieved accuracy is a significant milestone, this study underscores the dynamic and evolving nature of the pandemic, necessitating continuous model refinement and validation. Furthermore, it emphasizes the importance of considering false positives and false negatives in the context of public health decision-making. In conclusion, this research contributes to the arsenal of tools available for pandemic management by showcasing the potential of ANNs in COVID-19 case forecasting. It encourages ongoing exploration and adaptation of predictive models to enhance their applicability in real-world public health scenarios, ultimately contributing to more effective pandemic control and response efforts. (shrink)

This study explores experiences of ex-incels—men who have withdrawn from incel communities—through eleven qualitative interviews analysed using R.W. Connell’s hegemonic masculinity (HM) framework. Findings reveal some ex-incels adopt flexible masculinities, while others struggle with prescriptive norms perpetuated by the anti-feminist ‘manosphere’. Findings spotlight identity reconstructions, where men both reject and remain influenced by rigid archetypes, performing hybrid masculinities. This study deepens understanding of incel ideology, its impact on identity, and interplay between inceldom and masculinities via contributing to hybrid masculinities theorising. (...) Insights presents applications for gender theory and inform further research on HM’s influence within unique cultural contexts. (shrink)

Abstract: Colon cancer is a prevalent and life-threatening disease, necessitating accurate and timely diagnosis for effective treatment and improved patient outcomes. This research paper presents the development of a knowledge-based system for diagnosing colon cancer using the CLIPS language. Knowledge-based systems offer the potential to assist healthcare professionals in making informed diagnoses by leveraging expert knowledge and reasoning mechanisms. The methodology involves acquiring and structuring medical knowledge specific to colon cancer, followed by the implementation of a knowledge- based system using (...) CLIPS. The system's design integrates relevant medical databases and employs advanced reasoning mechanisms to facilitate accurate diagnosis. Evaluation results demonstrate the system's effectiveness, with high accuracy and sensitivity rates compared to existing methods. The findings highlight the potential of knowledge-based systems in improving clinical practice and patient care. The research contributes to the growing field of medical informatics by demonstrating the feasibility and effectiveness of CLIPS-based knowledge systems for colon cancer diagnosis. Future research can focus on enhancing system performance and expanding its scope to encompass other types of cancer diagnoses. (shrink)

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

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)

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: 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)

Abstract: The integration of Artificial Intelligence (AI) into leadership practices is rapidly transforming organizational dynamics and decision-making processes. This paper explores the ways in which AI enhances leadership effectiveness by providing data- driven insights, optimizing decision-making, and automating routine tasks. Additionally, it examines the challenges leaders face when 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 offer a (...) comprehensive overview of the opportunities AI presents for effective leadership, as well as the strategies required to address its associated challenges. (shrink)

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

This research paper presents the development and evaluation of an expert system for diagnosing kidney diseases. The expert system utilizes a decision-making tree approach and is implemented using the CLIPS and Delphi frameworks. The system's accuracy in diagnosing kidney diseases and user satisfaction were evaluated. The results demonstrate the effectiveness of the expert system in providing accurate diagnoses and high user satisfaction.

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)

Vertigo is a common symptom that can result from various underlying diseases and conditions, ranging from benign to severe. Accurate and timely diagnosis of the cause of vertigo is crucial for appropriate management and treatment. In this research, we propose the development of an expert system for vertigo diseases diagnosis, utilizing artificial intelligence (AI) and the proposed Expert System which was produced to help assist healthcare professionals in diagnosing the cause of vertigo based on a patient's symptoms, medical history, and (...) other relevant clinical information. The proposed expert system presents an overview about vertigo diseases are given, the cause of disease is outlined and the treatment or recommendation of disease whenever possible is given out. CLIPS language was used for designing and implementing the proposed expert system. The potential of the proposed expert system lies in its ability to enhance the accuracy and efficiency of vertigo diagnosis, as well as assist in the proper referral and management of patients. (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)

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)

Sensory substitution devices (SSDs) aim to compensate for the loss of a sensory modality, typically vision, by converting information from the lost modality into stimuli in a remaining modality. “The vOICe” is a visual-to-auditory SSD which encodes images taken by a camera worn by the user into “soundscapes” such that experienced users can extract information about their surroundings. Here we investigated how much detail was resolvable during the early induction stages by testing the acuity of blindfolded sighted, naïve vOICe users. (...) Initial performance was well above chance. Participants who took the test twice as a form of minimal training showed a marked improvement on the second test. Acuity was slightly but not significantly impaired when participants wore a camera and judged letter orientations “live”. A positive correlation was found between participants' musical training and their acuity. The relationship between auditory expertise via musical training and the lack of a relationship with visual imagery, suggests that early use of a SSD draws primarily on the mechanisms of the sensory modality being used rather than the one being substituted. If vision is lost, audition represents the sensory channel of highest bandwidth of those remaining. The level of acuity found here, and the fact it was achieved with very little experience in sensory substitution by naïve users is promising. (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)

Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including 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. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model was trained and validated, achieving (...) an impressive accuracy of 100% and an average error of 0.000015, indicating its robust predictive capability. Moreover, we conducted feature importance analysis to identify the most influential features for predicting audit risk. The key features found to be critical for classifying fraudulent activities in audit risk prediction are Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, and History_score. This research contributes to the field of audit risk prediction by demonstrating the effectiveness of a neural network-based approach and highlighting the importance of specific features in detecting fraudulent activities. The findings have significant implications for auditors and organizations seeking to enhance their audit risk assessment processes, ultimately leading to improved financial transparency and fraud detection. (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)

Abstract: The aim behind analyzing Google Stock Prices dataset is to get a fair idea about the relationships between the multiple attributes a day might have, such as: the opening price for each day, the volume of trading for each day. With over a hundred thousand days of trading data, there are some patterns that can help in predicting the future prices. We proposed an Artificial Neural Network (ANN) model for predicting the closing prices for future days. The prediction is (...) based on these features (date, opening price, highest price, lowest price, volume), which were used as input variables and (closing price) as output variable for our ANN model. Our model was created, trained, and validated using data set in JNN environment, which its title is “Google-Stock-Prices”. The model evaluation showed that the ANN model is able to predict correctly 99.48% of the validation samples. (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)

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)

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

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)

Accurate assessment and comprehension of smoking behavior are pivotal for elucidating associated health risks and formulating effective public health strategies. In this study, we introduce an innovative approach to predict and analyze smoking prevalence using an artificial neural network (ANN) model. Leveraging a comprehensive dataset spanning multiple years and geographic regions, our model incorporates various features, including demographic data, economic indicators, and tobacco control policies. This research investigates smoking trends with a specific focus on gender-based analyses. These findings are pivotal (...) for enhancing public health initiatives aimed at mitigating tobacco use, assessing associated health risks, and mitigating smoking-related diseases. (shrink)

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

The problem of other minds has a distinguished philosophical history stretching back more than two hundred years. Taken at face value, it is an epistemological question: it concerns how we can have knowledge of, or at least justified belief in, the existence of minds other than our own. In recent decades, philosophers, psychologists, neuroscientists, anthropologists and primatologists have debated a related question: how we actually go about attributing mental states to others (regardless of whether we ever achieve knowledge or rational (...) justification in this domain). Until the mid-nineties, the latter debate – which sometimes goes under the name of the “mindreading” debate – was characterized by a fairly clear-cut opposition between two theoretical outlooks: “theory-theory” (TT) and “simulation theory” (ST). Theory-theorists typically argued that we attribute mental states to others on the basis of a “theory of mind” that is either constructed in early infancy and subsequently revised and modified (Gopnik 1996), or else is the result of maturation of innate mindreading “modules” (Baron-Cohen 1995). (shrink)

In philosophy, psychiatry, and cognitive science, definitions of clinical delusions are not based on the mechanisms responsible for the formation of delusions, since there is no consensus yet on what causes delusions. Some of the defining features of delusions are epistemic and focus on whether delusions are true, justified, or rational, as in the definition of delusions as fixed beliefs that are badly supported by evidence. Other defining features of delusions are psychological and focus on whether delusions are harmful, as (...) in the definition of delusions as beliefs that disrupt good functioning. Even if the epistemic features go some way towards capturing what otherwise different instances of clinical delusions have in common, they do not succeed in distinguishing delusions as a clinical phenomenon from everyday irrational beliefs. Focusing on the psychological features is a more promising way to mark the difference between clinical and non-clinical irrational beliefs, but there is wide variability in the extent to which delusions are psychologically harmful, and some everyday irrational beliefs can affect functioning in similarly negative ways. In this chapter we consider three types of belief that share similar epistemic features and exhibit variation with respect to how psychologically harmful they are: (1) delusions of thought insertion, (2) alien abduction beliefs, and (3) self-enhancing beliefs. In the light of the similarities and differences among these cases, we highlight the difficulty in providing an answer to what makes an irrational belief delusional. (shrink)

Stretching is one of the important things to do before doing a physical activity. Stretching helps you to avoid possible injuries, muscle soreness and even improve overall performance. The researcher came up with an idea to create a modified stretching routine to assess if our created modified stretching is better than the traditional stretching. This study aims to assess the impact or the effect of the created modified stretching routine to the performance of students. This study utilizes the quasi-experimental research (...) design. In analyzing the data, descriptive statistics and multiple linear regression are being utilized. Overall, based on the results, utilizing modified stretching routine has a positive impact to the student’s performance. There is a significant difference on the student’s performance when they use the modified stretching routine than the traditional stretching routine. From the data collected, it shows that students who uses the modified stretching routine definitely improve their flexibility, cardiovascular endurance and muscular endurance. (shrink)

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

Abstract: Sign language recognition is one of the most rapidly expanding fields of study today. Many new technologies have been developed in recent years in the fields of artificial intelligence the sign language-based communication is valuable to not only deaf and dumb community, but also beneficial for individuals suffering from Autism, downs Syndrome, Apraxia of Speech for correspondence. The biggest problem faced by people with hearing disabilities is the people's lack of understanding of their requirements. In this paper we try (...) to fill this gap. By trying to translate sign language using artificial intelligence algorithms, we focused in this paper using transfer learning technique based on deep learning by utilizing a MobileNet algorithm and compared it with the previous paper results[10a], where we get in the Mobilenet algorithm on the degree of Accuracy 93,48% but the VGG16 the accuracy was 100% For the same number of images (43500 in the dataset in size 64*64 pixel ) and the same data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch . (shrink)