Results for 'ml'

115 found
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  1. Translations between logical systems: a manifesto.Walter A. Carnielli & Itala Ml D'Ottaviano - 1997 - Logique Et Analyse 157:67-81.
    The main objective o f this descriptive paper is to present the general notion of translation between logical systems as studied by the GTAL research group, as well as its main results, questions, problems and indagations. Logical systems here are defined in the most general sense, as sets endowed with consequence relations; translations between logical systems are characterized as maps which preserve consequence relations (that is, as continuous functions between those sets). In this sense, logics together with translations form a (...)
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  2. Do ML models represent their targets?Emily Sullivan - forthcoming - Philosophy of Science.
    I argue that ML models used in science function as highly idealized toy models. If we treat ML models as a type of highly idealized toy model, then we can deploy standard representational and epistemic strategies from the toy model literature to explain why ML models can still provide epistemic success despite their lack of similarity to their targets.
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  3. Making Intelligence: Ethical Values in IQ and ML Benchmarks.Borhane Blili-Hamelin & Leif Hancox-Li - 2023 - Facct '23: Proceedings of the 2023 Acm Conference on Fairness, Accountability, and Transparency 23:271 - 284.
    The ML community recognizes the importance of anticipating and mitigating the potential negative impacts of benchmark research. In this position paper, we argue that more attention needs to be paid to areas of ethical risk that lie at the technical and scientific core of ML benchmarks. We identify overlooked structural similarities between human IQ and ML benchmarks. Human intelligence and ML benchmarks share similarities in setting standards for describing, evaluating and comparing performance on tasks relevant to intelligence. This enables us (...)
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  4.  41
    Development of ML Model to Assess Taste in Plants.Shailaja K. - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (3):1-13.
    Taste is an important aspect for the assessment of medicinal plants as such assessment helps to determine the therapeutic property and application of medicinal plants. Traditionally, plant taste has been assessed based on human sensory perception. This project aims at developing a machine learning (ML) model that will quantify and predict plant taste in terms of their chemical composition. Given the dataset of chemical compounds, the model will relate a specific compound to the known taste types: sweet, bitter, pungent, sour, (...)
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  5. Link Uncertainty, Implementation, and ML Opacity: A Reply to Tamir and Shech.Emily Sullivan - 2022 - In Insa Lawler, Kareem Khalifa & Elay Shech (eds.), Scientific Understanding and Representation: Modeling in the Physical Sciences. New York, NY: Routledge. pp. 341-345.
    This chapter responds to Michael Tamir and Elay Shech’s chapter “Understanding from Deep Learning Models in Context.”.
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  6. Modeling Unicorns and Dead Cats: Applying Bressan’s ML ν to the Necessary Properties of Non-existent Objects.Tyke Nunez - 2018 - Journal of Philosophical Logic 47 (1):95–121.
    Should objects count as necessarily having certain properties, despite their not having those properties when they do not exist? For example, should a cat that passes out of existence, and so no longer is a cat, nonetheless count as necessarily being a cat? In this essay I examine different ways of adapting Aldo Bressan’s MLν so that it can accommodate an affirmative answer to these questions. Anil Gupta, in The Logic of Common Nouns, creates a number of languages that have (...)
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  7. Predicting Students' end-of-term Performances using ML Techniques and Environmental Data.Ahmed Mohammed Husien, Osama Hussam Eljamala, Waleed Bahgat Alwadia & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (10):19-25.
    Abstract: This study introduces a machine learning-based model for predicting student performance using a comprehensive dataset derived from educational sources, encompassing 15 key features and comprising 62,631 student samples. Our five-layer neural network demonstrated remarkable performance, achieving an accuracy of 89.14% and an average error of 0.000715, underscoring its effectiveness in predicting student outcomes. Crucially, this research identifies pivotal determinants of student success, including factors such as socio-economic background, prior academic history, study habits, and attendance patterns, shedding light on the (...)
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  8. Widening Access to Applied Machine Learning With TinyML.Vijay Reddi, Brian Plancher, Susan Kennedy, Laurence Moroney, Pete Warden, Lara Suzuki, Anant Agarwal, Colby Banbury, Massimo Banzi, Matthew Bennett, Benjamin Brown, Sharad Chitlangia, Radhika Ghosal, Sarah Grafman, Rupert Jaeger, Srivatsan Krishnan, Maximilian Lam, Daniel Leiker, Cara Mann, Mark Mazumder, Dominic Pajak, Dhilan Ramaprasad, J. Evan Smith, Matthew Stewart & Dustin Tingley - 2022 - Harvard Data Science Review 4 (1).
    Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally (...)
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  9. Securing the Internet of Things: A Study on Machine Learning-Based Solutions for IoT Security and Privacy Challenges.Aziz Ullah Karimy & P. Chandrasekhar Reddy - 2023 - Zkg International 8 (2):30-65.
    The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular focus on (...)
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  10. An Unconventional Look at AI: Why Today’s Machine Learning Systems are not Intelligent.Nancy Salay - 2020 - In LINKs: The Art of Linking, an Annual Transdisciplinary Review, Special Edition 1, Unconventional Computing. pp. 62-67.
    Machine learning systems (MLS) that model low-level processes are the cornerstones of current AI systems. These ‘indirect’ learners are good at classifying kinds that are distinguished solely by their manifest physical properties. But the more a kind is a function of spatio-temporally extended properties — words, situation-types, social norms — the less likely an MLS will be able to track it. Systems that can interact with objects at the individual level, on the other hand, and that can sustain this interaction, (...)
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  11. Diachronic and synchronic variation in the performance of adaptive machine learning systems: the ethical challenges.Joshua Hatherley & Robert Sparrow - 2023 - Journal of the American Medical Informatics Association 30 (2):361-366.
    Objectives: Machine learning (ML) has the potential to facilitate “continual learning” in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such “adaptive” ML systems in medicine that have, thus far, been neglected in the literature. -/- Target audience: The target audiences for this tutorial are (...)
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  12. Individuality and the control of life cycles.Beckett Sterner - 2017 - In Scott Lidgard & Lynn K. Nyhart (eds.), Biological Individuality: Integrating Scientific, Philosophical, and Historical Perspectives. Chicago: University of Chicago Press. pp. 84-108.
    I will argue that MLS theory does not provide a complete, self- sufficient approach to theorizing about evolutionary transitions. As a formal, mathematical theory about evolution within a population, it presupposes but does not address the material structure of the population that realizes the model. An MLS model might tell us whether a cooperative trait could be- come fixed in a population, for example, but it won’t be able to explain how the cooperation actually works to produce an adaptive effect (...)
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  13. The contest between parsimony and likelihood.Elliott Sober - 2004 - Systematic Biology 53 (4):644-653.
    Maximum Parsimony (MP) and Maximum Likelihood (ML) are two methods for evaluating which phlogenetic tree is best supported by data on the characteristics of leaf objects (which may be species, populations, or individual organisms). MP has been criticized for assuming that evolution proceeds parsimoniously -- that if a lineage begins in state i and ends in state j, the way it got from i to j is by the smallest number of changes. MP has been criticized for needing to assume (...)
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  14. Human Induction in Machine Learning: A Survey of the Nexus.Petr Spelda & Vit Stritecky - 2021 - ACM Computing Surveys 54 (3):1-18.
    As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target environments. Yet (...)
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  15. Mapping Value Sensitive Design onto AI for Social Good Principles.Steven Umbrello & Ibo van de Poel - 2021 - AI and Ethics 1 (3):283–296.
    Value Sensitive Design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. Second, ML (...)
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  16.  39
    Correlation of vitamin D with glycemic control and body mass index in patients with type II diabetes mellitus.Fathi M. Sherif - 2022 - Mediterranean Journal of Pharmacy and Pharmaceutical Sciences 2 (1):28-36.
    Vitamin D deficiency and its effect have attracted considerable research interest due to its relation to glucose homeostasis, insulin secretion, sensitivity and synthesis. This study aimed to evaluate vitamin D levels in patients with type II diabetes mellitus aged between 35-65 years and investigate their relations with glycemic control and obesity. The study included 74 Libyan patients with a known history of type II diabetes mellitus (33 males and 41 females). Serum glucose, glycosylated hemoglobin (HbA1c) and vitamin D levels were (...)
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  17. Interpretability and Unification.Adrian Erasmus & Tyler D. P. Brunet - 2022 - Philosophy and Technology 35 (2):1-6.
    In a recent reply to our article, “What is Interpretability?,” Prasetya argues against our position that artificial neural networks are explainable. It is claimed that our indefeasibility thesis—that adding complexity to an explanation of a phenomenon does not make the phenomenon any less explainable—is false. More precisely, Prasetya argues that unificationist explanations are defeasible to increasing complexity, and thus, we may not be able to provide such explanations of highly complex AI models. The reply highlights an important lacuna in our (...)
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  18. Inductive Risk, Understanding, and Opaque Machine Learning Models.Emily Sullivan - 2022 - Philosophy of Science 89 (5):1065-1074.
    Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this article, I argue that nonepistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (ii) an internal opacity (...)
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  19. Formalising trade-offs beyond algorithmic fairness: lessons from ethical philosophy and welfare economics.Michelle Seng Ah Lee, Luciano Floridi & Jatinder Singh - 2021 - AI and Ethics 3.
    There is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many of which are in conflict with one another. However, these reductionist representations of fairness often bear little resemblance to real-life fairness considerations, which in practice are highly contextual. Moreover, fairness metrics tend to be implemented in narrow and targeted toolkits that (...)
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  20. Preemption effects in visual search: Evidence for low-level grouping.Ronald A. Rensink & James T. Enns - 1995 - Psychological Review 102 (1):101-130.
    Experiments are presented showing that visual search for Mueller-Lyer (ML) stimuli is based on complete configurations, rather than component segments. Segments easily detected in isolation were difficult to detect when embedded in a configuration, indicating preemption by low-level groups. This preemption—which caused stimulus components to become inaccessible to rapid search—was an all-or-nothing effect, and so could serve as a powerful test of grouping. It is shown that these effects are unlikely to be due to blurring by simple spatial filters at (...)
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  21. Learning to Discriminate: The Perfect Proxy Problem in Artificially Intelligent Criminal Sentencing.Benjamin Davies & Thomas Douglas - 2022 - In Jesper Ryberg & Julian V. Roberts (eds.), Sentencing and Artificial Intelligence. Oxford: OUP.
    It is often thought that traditional recidivism prediction tools used in criminal sentencing, though biased in many ways, can straightforwardly avoid one particularly pernicious type of bias: direct racial discrimination. They can avoid this by excluding race from the list of variables employed to predict recidivism. A similar approach could be taken to the design of newer, machine learning-based (ML) tools for predicting recidivism: information about race could be withheld from the ML tool during its training phase, ensuring that the (...)
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  22. Should the use of adaptive machine learning systems in medicine be classified as research?Robert Sparrow, Joshua Hatherley, Justin Oakley & Chris Bain - 2024 - American Journal of Bioethics 24 (10):58-69.
    A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called “update problem,” which concerns how regulators should approach systems whose performance and parameters continue to change even after they have received regulatory (...)
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  23. The debate on the ethics of AI in health care: a reconstruction and critical review.Jessica Morley, Caio C. V. Machado, Christopher Burr, Josh Cowls, Indra Joshi, Mariarosaria Taddeo & Luciano Floridi - manuscript
    Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data scientists argue that a key part of the solution will be ‘Artificial Intelligence’ (AI) – particularly Machine Learning (ML). This argument stems not from the belief that all healthcare needs will soon be taken care of by “robot doctors.” Instead, it is an argument that rests on the classic (...)
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  24. Proofs of valid categorical syllogisms in one diagrammatic and two symbolic axiomatic systems.Antonielly Garcia Rodrigues & Eduardo Mario Dias - manuscript
    Gottfried Leibniz embarked on a research program to prove all the Aristotelic categorical syllogisms by diagrammatic and algebraic methods. He succeeded in proving them by means of Euler diagrams, but didn’t produce a manuscript with their algebraic proofs. We demonstrate how key excerpts scattered across various Leibniz’s drafts on logic contained sufficient ingredients to prove them by an algebraic method –which we call the Leibniz-Cayley (LC) system– without having to make use of the more expressive and complex machinery of first-order (...)
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  25. Emergence of Ciprofloxacin Resistance among Pseudomonas Aeruginosa Isolated from Burn Patients [hplimg].M. R. Shakibaie, S. Adeli & Y. Nikian - 2001 - Emergence: Complexity and Organization 26 (3&4).
    Background: Increasing resistance of Pseudomonas aeruginosa to ciprofloxacin in ICU/burn units has created a problem in the treatment of infections caused by this microorganism. -/- Methods: Fifty P. aeruginosa strains were isolated from burn patients hospitalized in the Kerman Hospital during May 1999-April 2000 and were tested for in-vitro sensitivity to different antibiotics by disc diffusion breakpoint assay. The isolates were subjected to minimum inhibitory concentration (MIC) test by agar dilution method. Existence of the plasmids was also investigated in the (...)
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  26. Invisible Influence: Artificial Intelligence and the Ethics of Adaptive Choice Architectures.Daniel Susser - 2019 - Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society 1.
    For several years, scholars have (for good reason) been largely preoccupied with worries about the use of artificial intelligence and machine learning (AI/ML) tools to make decisions about us. Only recently has significant attention turned to a potentially more alarming problem: the use of AI/ML to influence our decision-making. The contexts in which we make decisions—what behavioral economists call our choice architectures—are increasingly technologically-laden. Which is to say: algorithms increasingly determine, in a wide variety of contexts, both the sets of (...)
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  27. Institutional Trust in Medicine in the Age of Artificial Intelligence.Michał Klincewicz - 2023 - In David Collins, Iris Vidmar Jovanović, Mark Alfano & Hale Demir-Doğuoğlu (eds.), The Moral Psychology of Trust. Lexington Books.
    It is easier to talk frankly to a person whom one trusts. It is also easier to agree with a scientist whom one trusts. Even though in both cases the psychological state that underlies the behavior is called ‘trust’, it is controversial whether it is a token of the same psychological type. Trust can serve an affective, epistemic, or other social function, and comes to interact with other psychological states in a variety of ways. The way that the functional role (...)
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  28. Ethical Issues in Text Mining for Mental Health.Joshua Skorburg & Phoebe Friesen - forthcoming - In Morteza Dehghani & Ryan Boyd (eds.), The Atlas of Language Analysis in Psychology. Guilford Press.
    A recent systematic review of Machine Learning (ML) approaches to health data, containing over 100 studies, found that the most investigated problem was mental health (Yin et al., 2019). Relatedly, recent estimates suggest that between 165,000 and 325,000 health and wellness apps are now commercially available, with over 10,000 of those designed specifically for mental health (Carlo et al., 2019). In light of these trends, the present chapter has three aims: (1) provide an informative overview of some of the recent (...)
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  29. Megaric Metaphysics.Dominic Bailey - 2012 - Ancient Philosophy 32 (2):303-321.
    I examine two startling claims attributed to some philosophers associated with Megara on the Isthmus of Corinth, namely: Ml. Something possesses a capacity at t if and only if it is exercising that capacity at t. M2. One can speak of a thing only by using its own proper A6yor;. In what follows, I will call the conjunction of Ml and M2 'Megaricism' .1 The lit­ erature on ancient philosophy contains several valuable discussions of Ml and M2 taken individually .2 (...)
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  30. What is it for a Machine Learning Model to Have a Capability?Jacqueline Harding & Nathaniel Sharadin - forthcoming - British Journal for the Philosophy of Science.
    What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models' capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and government grants. Despite this, the notion of an ML model possessing a capability has not been interrogated: what are we saying when we say that a model is able to do something? (...)
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  31. Vertrouwen in de geneeskunde en kunstmatige intelligentie.Lily Frank & Michal Klincewicz - 2021 - Podium Voor Bioethiek 3 (28):37-42.
    Kunstmatige intelligentie (AI) en systemen die met machine learning (ML) werken, kunnen veel onderdelen van het medische besluitvormingsproces ondersteunen of vervangen. Ook zouden ze artsen kunnen helpen bij het omgaan met klinische, morele dilemma’s. AI/ML-beslissingen kunnen zo in de plaats komen van professionele beslissingen. We betogen dat dit belangrijke gevolgen heeft voor de relatie tussen een patiënt en de medische professie als instelling, en dat dit onvermijdelijk zal leiden tot uitholling van het institutionele vertrouwen in de geneeskunde.
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  32. Effect of Trigona Honey to mRNA Expression of Interleukin-6 on Salmonella Typhi Induced of BALB/c Mice.Yuliana Syam, Rosdiana Natsir, Sutji Pratiwi Rahardjo, Andi Nilawati Usman, Ressy Dwiyanti & Mochammad Hatta - 2016 - American Journal of Microbiological Research 4 (3):77-80.
    Weak inflammatory response after Salmonella infection can cause persistent infection and facilitate the long survival of pathogens. Honey can induce key immunomodulators such as TNF-α, interleukin-6 (IL-6) and IL-1, that it can be used in the treatment of bacterial infectious diseases caused by Salmonella typhi. The purpose of this study is to determine the effect of honey on the mRNA expression of IL-6 in Salmonella enterica Typhi induced of BABL/c mice. The study used experimental pretest-posttest control design. Honey treatment was (...)
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  33. Excavating “Excavating AI”: The Elephant in the Gallery.Michael J. Lyons - 2020 - arXiv 2009:1-15.
    Two art exhibitions, “Training Humans” and “Making Faces,” and the accompanying essay “Excavating AI: The politics of images in machine learning training sets” by Kate Crawford and Trevor Paglen, are making substantial impact on discourse taking place in the social and mass media networks, and some scholarly circles. Critical scrutiny reveals, however, a self-contradictory stance regarding informed consent for the use of facial images, as well as serious flaws in their critique of ML training sets. Our analysis underlines the non-negotiability (...)
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  34. Axe the X in XAI: A Plea for Understandable AI.Andrés Páez - forthcoming - In Juan Manuel Durán & Giorgia Pozzi (eds.), Philosophy of science for machine learning: Core issues and new perspectives. Springer.
    In a recent paper, Erasmus et al. (2021) defend the idea that the ambiguity of the term “explanation” in explainable AI (XAI) can be solved by adopting any of four different extant accounts of explanation in the philosophy of science: the Deductive Nomological, Inductive Statistical, Causal Mechanical, and New Mechanist models. In this chapter, I show that the authors’ claim that these accounts can be applied to deep neural networks as they would to any natural phenomenon is mistaken. I also (...)
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  35. Epistemic virtues of harnessing rigorous machine learning systems in ethically sensitive domains.Thomas F. Burns - 2023 - Journal of Medical Ethics 49 (8):547-548.
    Some physicians, in their care of patients at risk of misusing opioids, use machine learning (ML)-based prediction drug monitoring programmes (PDMPs) to guide their decision making in the prescription of opioids. This can cause a conflict: a PDMP Score can indicate a patient is at a high risk of opioid abuse while a patient expressly reports oppositely. The prescriber is then left to balance the credibility and trust of the patient with the PDMP Score. Pozzi1 argues that a prescriber who (...)
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  36. Persons or datapoints?: Ethics, artificial intelligence, and the participatory turn in mental health research.Joshua August Skorburg, Kieran O'Doherty & Phoebe Friesen - 2024 - American Psychologist 79 (1):137-149.
    This article identifies and examines a tension in mental health researchers’ growing enthusiasm for the use of computational tools powered by advances in artificial intelligence and machine learning (AI/ML). Although there is increasing recognition of the value of participatory methods in science generally and in mental health research specifically, many AI/ML approaches, fueled by an ever-growing number of sensors collecting multimodal data, risk further distancing participants from research processes and rendering them as mere vectors or collections of data points. The (...)
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  37. The purpose of qualia: What if human thinking is not (only) information processing?Martin Korth - manuscript
    Despite recent breakthroughs in the field of artificial intelligence (AI) – or more specifically machine learning (ML) algorithms for object recognition and natural language processing – it seems to be the majority view that current AI approaches are still no real match for natural intelligence (NI). More importantly, philosophers have collected a long catalogue of features which imply that NI works differently from current AI not only in a gradual sense, but in a more substantial way: NI is closely related (...)
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  38. Effect of beverages on the disintegration time of drugs in the tablet dosage form.Fathi M. Sherif - 2024 - Mediterranean Journal of Pharmacy and Pharmaceutical Sciences 4 (2):69-74.
    Disintegration is the most important step for drug bioavailability because after, the disintegration process, the ingredients of solid dosage forms dissolve and become bioavailable. Generally, the tablets and capsules should be taken with a glass of water otherwise the manufacturer gives instructions to use the proper beverage. Several drugs are taken with different forms of beverages to ensure easy swallowing of the tablet, masking the bad taste of the drug and overcoming the drug aftertaste, these beverages can influence the disintegration (...)
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  39.  74
    An Integrated Framework for IoT Security: Combining Machine Learning and Signature-Based Approaches for Intrusion Detection.Yan Janet - manuscript
    Internet of Things (IoT) devices have transformed various industries, enabling advanced functionalities across domains such as healthcare, smart cities, and industrial automation. However, the increasing number of connected devices has raised significant concerns regarding their security. IoT networks are highly vulnerable to a wide range of cyber threats, making Intrusion Detection Systems (IDS) critical for identifying and mitigating malicious activities. This paper proposes a hybrid approach for intrusion detection in IoT networks by combining Machine Learning (ML) techniques with Signature-Based Methods. (...)
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  40. Towards Knowledge-driven Distillation and Explanation of Black-box Models.Roberto Confalonieri, Guendalina Righetti, Pietro Galliani, Nicolas Toquard, Oliver Kutz & Daniele Porello - 2021 - In Roberto Confalonieri, Guendalina Righetti, Pietro Galliani, Nicolas Toquard, Oliver Kutz & Daniele Porello (eds.), Proceedings of the Workshop on Data meets Applied Ontologies in Explainable {AI} {(DAO-XAI} 2021) part of Bratislava Knowledge September {(BAKS} 2021), Bratislava, Slovakia, September 18th to 19th, 2021. CEUR 2998.
    We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by means of two kinds of interpretable models. The first is perceptron (or threshold) connectives, which enrich knowledge representation languages such as Description Logics with linear operators that serve as a bridge between statistical learning and logical reasoning. The second is Trepan Reloaded, an ap- proach that builds post-hoc explanations of black-box classifiers in the form of decision trees enhanced by domain knowledge. Our aim is, firstly, to target (...)
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  41.  83
    Comparative assessment of Solanum melongena (Eggplant) against multi-drug-resistant Staphylococcus aureus and Pseudomonas aeruginosa.Ijeoma N. Ebenebe - 2024 - Mediterranean Journal of Pharmacy and Pharmaceutical Sciences 4 (4):33-40.
    Solanum melongena (Eggplant) is a medicinal plant belonging to the family Solanaceae. This study aimed to perform a comparative assessment of the methanol extracts of the fruit and the leaf of Solanum melongena against multi-drug-resistant Staphylococcus aureus and Pseudomonas aeruginosa. The crude extracts were obtained from the leaves and fruits of the plant using methanol. The plant extracts were tested for the presence of various phytochemical constituents qualitatively. The antibacterial assay and minimum inhibitory concentration for the crude extracts were carried (...)
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  42. Gönderim Üzerine.Bertrand Russell - 2015 - Felsefe Tartismalari 49:55-72. Translated by Alper Yavuz.
    Belirli betimlemeler, bir belirli tanımlıkla (Türkçede seslendirilmeyen ancak İngilizcede karşılığı "the" olan) başlayan "İngiltere'nin kralı", "Çin'in başkenti" gibi deyimlerdir. Russell bu yazıda belirli betimlemelerin mantıksal olarak nasıl çözümlenmesi gerektiği ile ilgili kendi betimlemeler kuramını ortaya atar. Russell'ın savı, belirli betimlemeler doğru bir biçimde çözümlenirse bir karşılığı olmayan "Fransa'nın şimdiki kralı" gibi deyimlerin yol açtığı türden birçok felsefi bilmecenin ortadan kalkacağıdır.
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  43. Understanding with Toy Surrogate Models in Machine Learning.Andrés Páez - 2024 - Minds and Machines 34 (4):45.
    In the natural and social sciences, it is common to use toy models—extremely simple and highly idealized representations—to understand complex phenomena. Some of the simple surrogate models used to understand opaque machine learning (ML) models, such as rule lists and sparse decision trees, bear some resemblance to scientific toy models. They allow non-experts to understand how an opaque ML model works globally via a much simpler model that highlights the most relevant features of the input space and their effect on (...)
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  44. Are generics and negativity about social groups common on social media? A comparative analysis of Twitter (X) data.Uwe Peters & Ignacio Ojea Quintana - 2024 - Synthese 203 (6):1-22.
    Many philosophers hold that generics (i.e., unquantified generalizations) are pervasive in communication and that when they are about social groups, this may offend and polarize people because generics gloss over variations between individuals. Generics about social groups might be particularly common on Twitter (X). This remains unexplored, however. Using machine learning (ML) techniques, we therefore developed an automatic classifier for social generics, applied it to 1.1 million tweets about people, and analyzed the tweets. While it is often suggested that generics (...)
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  45. Automated Cyberbullying Detection Framework Using NLP and Supervised Machine Learning Models.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):421-432.
    The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify cyberbullying (...)
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  46.  62
    In-Cab Smart Guidance and support system for Dragline operator.P. Lavanya - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (2):1-13.
    The proposed sensor device for dragline operator is designed to enhance environmental safety by detecting and alerting operators to climate change impacts and potential hazards. Utilizing advanced sensor technology, this device continuously monitors weather conditions, such as heavy rain, strong winds, and extreme temperatures. When a hazard is detected, the device provides real-time alerts through an intuitive interface, enabling operators to take immediate action to mitigate risks. This proactive approach ensures operators are well-informed about their surroundings, significantly reducing the risk (...)
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  47. Imagine This: Opaque DLMs are Reliable in the Context of Justification.Logan Carter - manuscript
    Artificial intelligence (AI) and machine learning (ML) models have undoubtedly become useful tools in science. In general, scientists and ML developers are optimistic – perhaps rightfully so – about the potential that these models have in facilitating scientific progress. The philosophy of AI literature carries a different mood. The attention of philosophers remains on potential epistemological issues that stem from the so-called “black box” features of ML models. For instance, Eamon Duede (2023) argues that opacity in deep learning models (DLMs) (...)
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    EduCareer: Smart AI-Based Career Guidance and Skill Development for Students.M. Sheik Dawood - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):630-640.
    The rapid advancement of artificial intelligence (AI) technologies has revolutionized various industries, including the realm of education and career guidance. This project endeavors to harness the power of AI to develop a sophisticated career guidance application that offers personalized and effective recommendations to students and job seekers. The primary objective of this project is to address the limitations of traditional career guidance methods, which often lack customization and fail to adapt to individual preferences, skills, and aspirations. Through the integration of (...)
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  49. Understanding Biology in the Age of Artificial Intelligence.Adham El Shazly, Elsa Lawerence, Srijit Seal, Chaitanya Joshi, Matthew Greening, Pietro Lio, Shantung Singh, Andreas Bender & Pietro Sormanni - manuscript
    Modern life sciences research is increasingly relying on artificial intelligence (AI) approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. As such, the interplay between these models and scientific understanding in biology is a topic with important implications for the future of scientific research, yet it (...)
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  50. The Future of Human-Artificial Intelligence Nexus and its Environmental Costs.Petr Spelda & Vit Stritecky - 2020 - Futures 117.
    The environmental costs and energy constraints have become emerging issues for the future development of Machine Learning (ML) and Artificial Intelligence (AI). So far, the discussion on environmental impacts of ML/AI lacks a perspective reaching beyond quantitative measurements of the energy-related research costs. Building on the foundations laid down by Schwartz et al., 2019 in the GreenAI initiative, our argument considers two interlinked phenomena, the gratuitous generalisation capability and the future where ML/AI performs the majority of quantifiable inductive inferences. The (...)
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