This paper examines the ways in which social scientific discourse and classification interact with the objects of social scientific investigation. I examine this interaction in the context of the traditional philosophical project of demarcating the social sciences from the natural sciences. I begin by reviewing Ian Hacking’s work on interactive classification and argue that there are additional forms of interaction that must be treated.

Comparative cognitive science often involves asking questions like ‘Do nonhumans have C?’ where C is a capacity we take humans to have. These questions frequently generate unproductive disagreements, in which one party affirms and the other denies that nonhumans have the relevant capacity on the basis of the same evidence. I argue that these questions can be productively understood as questions about natural kinds: do nonhuman capacities fall into the same natural kinds as our own? Understanding such questions in this (...) way has several advantages: it preserves the intuition that these are substantive empirical questions worth asking; it helps us to understand why they so frequently give rise to disagreements of the kind described; and it provides clues about how to diagnose and resolve them. (shrink)

Scientists represent their world, grouping and organizing phenomena into classes by means of concepts. Philosophers of science have historically been interested in the nature of these concepts, the criteria that inform their application and the nature of the kinds that the concepts individuate. They also have sought to understand whether and how different systems of classification are related and more recently, how investigative practices shape conceptual development and change. Our aim in this paper is to provide a critical overview (...) of some of the key developments in this philosophical literature and identify some interesting issues it raises about the prospects of the so-called “special sciences”, including psychiatry, psychology, and the mind-brain sciences more generally, to discover natural kinds. (shrink)

The classification system was evaluated using benchmark phishing datasets, and the results demonstrated a significant improvement in detection accuracy and reduced false positives. The proposed model outperformed traditional machine learning algorithms, showing promise for real-world deployment in phishing detection systems. We conclude with suggestions for future improvements, such as incorporating more behavioral data and deploying the system in realtime monitoring applications.

Phishing attacks remain one of the most prevalent cybersecurity threats, affecting individuals and organizations globally. The rapid evolution of phishing techniques necessitates more sophisticated detection and classification methods. In this paper, we propose a novel approach to phishing content classification using a Genetic Ranking Optimization Algorithm (GROA), combined with dynamic weighting, to improve the accuracy and ranking of phishing versus legitimate content. Our method leverages features such as URL structure, email content analysis, and user behavior patterns to enhance (...) the detection system's decision-making process. (shrink)

A Kuhnian reformulation of the recent debate in psychiatric nosography suggested that the current psychiatric classification system (the DSM) is in crisis and that a sort of paradigm shift is awaited (Aragona, 2009). Among possible revolutionary alternatives, the proposed fi ve-axes etiopathogenetic taxonomy (Charney et al., 2002) emphasizes the primacy of the genotype over the phenomenological level as the relevant basis for psychiatric nosography. Such a position is along the lines of the micro-reductionist perspective of E. Kandel (1998, 1999), (...) which sees mental disorders reducible to explanations at a fundamental epistemic level of genes and neurotransmitters. This form of micro-reductionism has been criticized as a form of genetic-molecular fundamentalism (e.g. Murphy, 2006) and a multi-level approach, in the form of the burgeoning Cognitive Neuropsychiatry, was proposed. This article focuses on multi-level mechanistic explanations, coming from Cognitive Science, as a possible alternative etiopathogenetic basis for psychiatric classification. The idea of a mechanistic approach to psychiatric taxonomy is here defended on the basis of a better conception of levels and causality. Nevertheless some critical remarks of Mechanism as a psychiatric general view are also offered. (shrink)

For Insect Classification there are many methods proposed. To find the more suitable classifier we have implemented two different methods of classification, Custom CNN and Transfer Learning. We observed the accuracy and loss parameters during training phase and validation phase on both Custom CNN and Transfer learning methods. In Transfer Learning we have created the base model from the pre-trained model MobileNetV2. This model is further trained on Imagenet Dataset which consists of 1.2M labeled images .We compared all (...) attributes of both models on this dataset. Recorded Accuracy and Loss for training and Validation. From the results we found that Custom CNN model works better than Transfer Learning for this dataset. This model can also be used for education and farming purpose. (shrink)

In contemporary healthcare, medical image categorization is essential for illness prediction, diagnosis, and therapy planning. The emergence of digital imaging technology has led to a significant increase in research into the use of machine learning (ML) techniques for the categorization of images in medical data. We provide a thorough summary of recent developments in this area in this review, using knowledge from the most recent research and cutting-edge methods.We begin by discussing the unique challenges and opportunities associated with medical image (...) classification, including the complexity of anatomical structures, variability in imaging modalities, and the need for interpretability and reliability in clinical settings. Subsequently, we survey a wide range of ML algorithms and techniques employed for medical image classification, including traditional methods such as support vector machines and k-nearest neighbors, as well as deep learning approaches like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Furthermore, we examine key considerations in dataset preparation, feature extraction, and model evaluation specific to medical image classification tasks. We highlight the importance of large, annotated datasets, transfer learning, and data augmentation techniques in enhancing model performance and generalization. (shrink)

A minimal essentialism (‘intrinsic biological essentialism’) about natural kinds is required to explain the projectability of human science terms. Human classifications that yield robust and ampliative projectable inferences refer to biological kinds. I articulate this argument with reference to an intrinsic essentialist account of HPC kinds. This account implies that human sciences (e.g., medicine, psychiatry) that aim to formulate predictive kind categories should classify biological kinds. Issues concerning psychiatric classification and pluralism are examined.

Emergence is an omnipresent phenomenon that is present in almost all subjects, including physics, other branches of natural science, languages, and even social sciences and economics. Similarities and differences can be found in these domains. We discuss the classification of emergence, which can be organized according to different rules. We also explore universal properties among different levels of emergence, and where the differences among emergence patterns in different theories come from.

Philosophers and social scientists largely agree that fake news is not just necessarily untruthful, but necessarily insincere: it’s produced either with the intention to deceive or an indifference toward its truth. Against this, I argue insincerity is neither a necessary nor obviously typical feature of fake news. The main argument proceeds in two stages. The first, methodological step develops classification criteria for identifying instances of fake news. By attending to expressed theoretical and practical interests, I observe how our (...) class='Hi'>classification practices turn on worries about fake news’s unique political-epistemic risks. From this, I argue (i) theories of fake news should capture independent mechanisms that realise these risks and (ii) the manifestation of them suffices for classifying a news story as fake news. The second step applies the classification criteria to bad science journalism. I argue the systematic epistemic faults in bad science journalism manifest the same political-epistemic risks we see in fake news, which suffices to justify classifying it as fake news. But since such faults aren’t plausibly attributed to its propagators being insincere, insincerity doesn’t function independently as a mechanism for realising fake news’s political-epistemic risks. Thus, I conclude, we should exclude insincerity from our accounts of the phenomenon. (shrink)

Aims and Methods: The general conceptual issues involved in psychiatric classification seem to be increasingly neglected in contrast to a focus on specific and empirical aspects which appear to have come to dominate the study of classification in the field. This article explores how the psychiatric field (in the UK and US) has chosen to analyse classification over time. Publication trends of articles in both The American Journal of Psychiatry and The British Journal of Psychiatry over a (...) fifty year period (1960-2010) can be viewed as indicators of the levels of interest within the psychiatric field toward classification. In an exploratory analysis, articles explicitly focusing on classification were counted and further sub-divided according to whether they focused on empirical or conceptual aspects and whether they adopted a general perspective or focused on a more specific aspect of classification. Results: Interest in classification was apparent in a minority of published articles (4.7% of all published articles). Interest in conceptual aspects dropped throughout the fifty years and was found to be considerably less than for empirical approaches which steadily increased over time. General papers about classification have been gradually on the decline and have been increasingly outnumbered by more specifically-focused articles. Clinical Implication: Classification, as a foundational endeavour within the psychiatric field, requires increased attention in the literature. This literature should address conceptual as well as empirical issues. (shrink)

In the first book of the Prior Analytics, Aristotle sets out, for the first time in Greek philosophy, a logical system. After this, Aristotle compares this method with Plato’s method of division, a procedure designed to find essences of natural kinds through systematic classification. This critical comparison in APr I.31 raises an interpretive puzzle: how can Aristotle reasonably juxtapose two methods that differ so much in their aims and approach? What can be gained by doing so? Previous interpreters have (...) failed to show how this comparison is legitimate or what important point Aristotle is making. The goal of this paper is to resolve the puzzle. In resolving this puzzle we not only learn more about the relationship between division and the syllogistic in Aristotle. We will also learn something about the motivation for the syllogistic itself, by seeing the role that it plays in his philosophy of science. (shrink)

The boundaries of social categories are frequently altered to serve normative projects, such as social reform. Griffiths and Khalidi argue that the value-driven modification of categories diminishes the epistemic value of social categories. I argue that concerns over value-modified categories stem from problematic assumptions of the value-free ideal of science. Contrary to those concerns, non-epistemic value considerations can contribute to the epistemic improvement of a scientific category. For example, the early history of the category infantile autism shows how non-epistemic value (...) considerations can contribute to delimiting and establishing infantile autism as a distinct category in mainstream psychiatry. In the case of infantile autism, non-epistemic considerations have led to a new interpretation of existing data, the expansion of research to include biology, and the creation of diagnostic criteria that further contribute to collecting relevant data. Given this case study, it can be argued that non-epistemic values can improve our knowledge of scientific categories. (shrink)

Eugenic thinking involves distinguishing between sorts or kinds of people in terms of the perceived desirable or undesirable traits that those people are likely to transmit to future generations. While eugenics itself is often thought of as an ideology that generated a social movement of global influence from roughly 1900 to 1945, eugenic thinking both pre-dates this period and continues to inform a range of contemporary debates and social policies, including those concerning prenatal screening, transhumanism, population control, and disability. Various (...) sciences, including the then-nascent sciences of psychology and psychiatry, played important roles in early twentieth-century eugenics, and their legacy is reflected in contemporary eugenic thinking and policies. Understanding the history of eugenics and the role of these early sciences of cognition in that history is useful in reflecting further on ongoing forms of eugenic thinking to which the contemporary cognitive sciences are relevant. (shrink)

Understood in their historical context, current debates about psychiatric classification, prompted by the publication of the DSM-5, open up new opportunities for improved translational research in psychiatry. In this paper, we draw lessons for translational research from three time slices of 20th century psychiatry. From the first time slice, 1913 and the publication of Jaspers’ General Psychopathology, the lesson is that translational research in psychiatry requires a pluralistic approach encompassing equally the sciences of mind (including the social (...) class='Hi'>sciences) and of brain. From the second time slice, 1953 and a conference in New York from which our present symptom-based classifications are derived, the lesson is that, while reliability remains the basis of psychiatry as an observational science, validity too is essential to effective translation. From the third time slice, 1997 and a conference on psychiatric classification in Dallas that brought together patients and carers with researchers and clinicians, the lesson is that we need to build further on collaborative models of research combining expertise-by-training with expertise-by-experience. This is important if we are to meet the specific challenges to translation presented by the complexity of the concept of mental disorder, particularly as reflected in the diversity of desired treatment outcomes. Taken together, these three lessons – a pluralistic approach, reliability and validity, and closer collaboration – provide an emerging framework for more effective translation of research into practice in 21st century psychiatry. (shrink)

Abstract -/- Rationale, aims and objectives: Irregularity limits human ability to know, understand and predict. A better understanding of irregularity may improve the reliability of knowledge. -/- Method: Irregularity and its consequences for knowledge are considered. -/- Results: Reliable predictive empirical knowledge of the physical world has always been obtained by observation of regularities, without needing science or theory. Prediction from observational knowledge can remain reliable despite some theories based on it proving false. A naïve theory of irregularity is outlined. (...) Reducing irregularity and/or increasing regularity can increase the reliability of knowledge. Beyond long experience and specialization, improvements include implementing supporting knowledge systems of libraries of appropriately classified prior cases and clinical histories and education about expertise, intuition and professional judgement. -/- Conclusions: A consequence of irregularity and complexity is that classical reductionist science cannot provide reliable predictions of the behaviour of complex systems found in nature, including of the human body. Expertise, expert judgement and their exercise appear overarching. Diagnosis involves predicting the past will recur in the current patient applying expertise and intuition from knowledge and experience of previous cases and probabilistic medical theory. Treatment decisions are an educated guess about the future (prognosis). Benefits of the improvements suggested here are likely in fields where paucity of feedback for practitioners limits development of reliable expert diagnostic intuition. Further analysis, definition and classification of irregularity is appropriate. Observing and recording irregularities are initial steps in developing irregularity theory to improve the reliability and extent of knowledge, albeit some forms of irregularity present inherent difficulties. (shrink)

Reasoning from a naturalistic perspective, viewing the mind as an evolved biological organ with a particular structure and function, a number of influential philosophers and cognitive scientists claim that science is constrained by human nature. How exactly our genetic constitution constrains scientific representations of the world remains unclear. This is problematic for two reasons. Firstly, it often leads to the unwarranted conclusion that we are cognitively closed to certain aspects or properties of the world. Secondly, it stands in the way (...) of a nuanced account of the relationship between our cognitive and perceptual wiring and scientific theory. In response, I propose a typology or classification of the different kinds of biological constraints and their sources on science. Using Boden’s notion of a conceptual space, I distinguish between constraints relating to the ease with which we can reach representations within our conceptual space and constraints causing possible representations to fall outside of our conceptual space. This last kind of constraints does not entail that some aspects or properties of the world cannot be represented by us – as argued by advocates of ‘cognitive closure’ – merely that some ways of representing the world are inaccessible to us. It relates to what Clark and Rescher have framed as ‘the alien scientist hypothesis’. The purpose of this typology is to provide some much needed clarity and structure to the debate about biological constraints on science. (shrink)

Natural Categories and Human Kinds is a recent and timely contribution to current debate on natural kinds. Because of the growing sophistication of this debate, it is necessary to make careful distinctions in order to appreciate the originality of Khalidi’s position. Khalidi’s view on natural kinds is naturalistic: if we want to know what Nature’s joints really are, we should look at the actual carving job carried out by our best scientific practices. Like LaPorte, Khalidi is a fallibilist: our best (...) scientific theories may be revised or abandoned and so can our current classifications of natural kinds. Unlike LaPorte, however, Khalidi is an anti-essentialist: he argues against the idea that membership in a natural kind is a matter of possessing an essential set of necessary and sufficient properties. Like Dupré, Khalidi is a pluralist and anti-reductionist: he does not believe that there is only one true classification system and that there are natural kinds only at the so-called "fundamental level" of microphysics. Khalidi develops his pluralistic and anti-reductionistic position on the basis of a deep appreciation for the special sciences but, unlike Dupré, he thinks that only the scientific inquiry, which is driven by epistemic purposes, discover natural kinds and that, therefore, there are important differences between scientific and folk classifications. Khalidi’s conception of natural kinds has both an epistemic and a metaphysical component. (shrink)

The topic of this dissertation is the nature of linguistic competence, the capacity to understand and produce sentences of natural language. I defend the empiricist account of linguistic competence embedded in the connectionist cognitive science. This strand of cognitive science has been opposed to the traditional symbolic cognitive science, coupled with transformational-generative grammar, which was committed to nativism due to the view that human cognition, including language capacity, should be construed in terms of symbolic representations and hardwired rules. Similarly, linguistic (...) competence in this framework was regarded as being innate, rule-governed, domain-specific, and fundamentally different from performance, i.e., idiosyncrasies and factors governing linguistic behavior. I analyze state-of-the-art connectionist, deep learning models of natural language processing, most notably large language models, to see what they can tell us about linguistic competence. Deep learning is a statistical technique for the classification of patterns through which artificial intelligence researchers train artificial neural networks containing multiple layers that crunch a gargantuan amount of textual and/or visual data. I argue that these models suggest that linguistic competence should be construed as stochastic, pattern-based, and stemming from domain-general mechanisms. Moreover, I distinguish syntactic from semantic competence, and I show for each the ramifications of the endorsement of a connectionist research program as opposed to the traditional symbolic cognitive science and transformational-generative grammar. I provide a unifying front, consisting of usage-based theories, a construction grammar approach, and an embodied approach to cognition to show that the more multimodal and diverse models are in terms of architectural features and training data, the stronger the case is for the connectionist linguistic competence. I also propose to discard the competence vs. performance distinction as theoretically inferior so that a novel and integrative account of linguistic competence originating in connectionism and empiricism that I propose and defend in the dissertation could be put forward in scientific and philosophical literature. (shrink)

Debates surrounding the nature of mental disorder have tended to divide into an objectivist camp that takes psychiatric classification to be a value-free scientific matter, and a normativist camp that takes it to be irreducibly values-based. Here we present an overlooked distinction between _status_ and _constitution_. Questions of the form “What is x?” are ambiguous between status questions (“What gives something the status of an x?”), and constitution questions (“Given that something has the status of an x, what is (...) it made of?”). We elucidate this distinction in detail, and argue that normativism is uniquely well-placed to answer status questions while objectivism provides answers, where they are available, to constitution questions. (shrink)

Over this decade the speech recognition plays an important role for speechmaker identification and identification of the various characteristics of a person involved in a particular section of the voice. The information obtained from those systems are used for interaction between the user and the machine. The emotion detection through face recognition needs to person’s face captured in the sensor for the detection of the emotion who are involved in the session. But voice recognition can be done without the knowledge (...) of the that person. The application is used in the client service, education and health care. The high accuracy system of this type can be used in the intelligence services to know the truthfulness in investigation. For this task various parameters of the audio is obtained as feature called MFCC. The GMM modelling is successfully modelled by the feature MFCC. In addition to that UBM it gives better result in emotion of the speechmaker. (shrink)

This article makes three claims concerning the concept of contingency. First, we argue that the word contingency is used in far too many ways to be useful. Its many meanings are detrimental to clarity of discussion and thought in history and the social sciences. We show how there are eight distinct uses of the word and illustrate this with numerous examples from the social sciences and history, highlighting the scope for confusion caused by the many, often contradictory uses (...) of the term. Second, we impose some order on these uses through developing a threefold classification of contingency based on assumptions about possible worlds and determinism. Finally, we discuss why we believe that one of the classes is a special use of the word without relevance to the social sciences, while the two remaining classes are nothing more than a variety of the “no hidden factors” argument in the debate on indeterminism and determinism. (shrink)

Abstract : Background: Vegetable agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon classification approach is presented with a dataset that contains approximately 2,000 (...) images belong to 3 species at a few developing phases. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results: found that CNN-driven lemon classification applications when used in farming automation have the latent to enhance crop harvest and improve output and productivity when designed properly. The trained model achieved an accuracy of 99.48% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

Abstract: Potatoes are edible tubers, available worldwide and all year long. They are relatively cheap to grow, rich in nutrients, and they can make a delicious treat. The humble potato has fallen in popularity in recent years, due to the interest in low-carb foods. However, the fiber, vitamins, minerals, and phytochemicals it provides can help ward off disease and benefit human health. They are an important staple food in many countries around the world. There are an estimated 200 varieties of (...) potatoes, which can be classified into a number of categories based on the cooked texture and ingredient functionality. Using a public dataset of 2400 images of potatoes, we trained a deep convolutional neural network to identify 4 types (Red, Red Washed, Sweet, and White).The trained model achieved an accuracy of 99.5% of test set, demonstrating the feasibility of this approach. (shrink)

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

Abstract: Rice, as a paramount staple crop worldwide, sustains billions of lives. Precise classification of rice types holds immense agricultural, nutritional, and economic significance. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing rice type classification accuracy and efficiency. This research explores rice type classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 18,188 entries and key rice grain attributes, we develop and evaluate a neural network model. Our neural (...) network, featuring a single hidden layer, achieves remarkable results—a staggering 100% accuracy and a minute average error rate of 0.000001. Beyond performance metrics, we delve into the intricacies of rice type classification through feature importance analysis. The most influential features—'id,' 'Area,' 'MajorAxisLength,' 'MinorAxisLength,' 'Eccentricity,' 'ConvexArea,' 'EquivDiameter,' 'Extent,' 'Perimeter,' 'Roundness,' and 'AspectRatio'—uncover the physiological traits underpinning accurate rice classification. This research contributes to advancing rice classification methods and highlights the potential of ANNs in optimizing agricultural practices, ensuring food safety, and bolstering global trade. (shrink)

We contend that it is possible to argue reasonably for and against arguments from classifications and definitions, provided they are seen as defeasible (subject to exceptions and critical questioning). Arguments from classification of the most common sorts are shown to be based on defeasible reasoning of various kinds represented by patterns of logical reasoning called defeasible argumentation schemes. We show how such schemes can be identified with heuristics, or short-cut solutions to a problem. We examine a variety of arguments (...) of this sort, including argument from abductive classification, argument from causal classification, argument from analogy-based classification and arguments from classification based on generalizations. (shrink)

This paper explains the importance of classifying argumentation schemes, and outlines how schemes are being used in current research in artificial intelligence and computational linguistics on argument mining. It provides a survey of the literature on scheme classification. What are so far generally taken to represent a set of the most widely useful defeasible argumentation schemes are surveyed and explained systematically, including some that are difficult to classify. A new classification system covering these centrally important schemes is built.

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

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

Over 1.5 million living animal species have been described—of which around 1 million are insects—but it has been estimated there are over 7 million animal species in total. Animals range in length from 8.5 micrometres to 33.6 metres. In this paper an Artificial Neural Network (ANN) model, was developed and tested to predict animal species. There are a number of features that influence the classification of animal species. Such as the existence of hair/ feather, if the animal gives birth (...) or spawns, it is airborne, aquatic, predator, toothed, backboned, venomous, has –fins, has-tail, cat-sized, and domestic. A model based on the Multilayer Perceptron Topology was proposed and trained, using data set what was collected from UCI Machine Learning Repository. Evaluation of the proposed model shows that the ANN model is able to correctly predict the animal category with 100% accuracy. (shrink)

A. W. F. Edwards famously named “Lewontin’s fallacy” after Richard Lewontin, the geneticist who showed that most human genetic diversity can be found within any given racialized group. “Lewontin’s fallacy” is the assumption that uncorrelated genetic data would be sufficient to classify genotypes into conventional “racial” groups. In this chapter, I argue that Lewontin does not commit the fallacy named after him, and that it is not a genuine fallacy. Furthermore, I argue that when Edwards assumes that stable classification (...) is all it takes to vindicate racial naturalism, he commits a fallacy of his own: Edwards’ fallacy. The ability to create a classificatory system, and then reliably sort things within that system, is not enough to make that system scientifically respectable. I show that Edwards’ fallacy is rife in debates about the existence of human biological races. (shrink)

In psychiatry some disorders of cognition are distinguished from instances of normal cognitive functioning and from other disorders in virtue of their surface features rather than in virtue of the underlying mechanisms responsible for their occurrence. Aetiological considerations often cannot play a significant classificatory and diagnostic role, because there is no sufficient knowledge or consensus about the causal history of many psychiatric disorders. Moreover, it is not always possible to uniquely identify a pathological behaviour as the symptom of a certain (...) disorder, as disorders that are likely to differ both in their causal histories and in their overall manifestations may give rise to very similar patterns of behaviour. -/- Consider delusions as an example. It wouldn’t be correct to define delusions as those beliefs people form as a result of a neurobiological deficit and a hypothesis-evaluation deficit (as some versions of the two-factor theory of delusions suggest), because for some delusions no neurobiological deficit may be found, and reasoning biases and motivational factors may be contributors to the formation of the delusion (e.g. McKay et al., 2005). Moreover, it would be a mistake to define delusions as symptoms of schizophrenia alone, because they occur also in other disorders, including dementia, amnesia, and delusional disorders. Thus, aetiological considerations may appear in the description and analysis of delusions, but do not feature prominently in their definition. -/- In this paper I argue that the surface features used as criteria for the classification and diagnosis of disorders of cognition are often epistemic in character. I shall offer two examples: confabulations and delusions are defined as beliefs or narratives that fail to meet standards of accuracy and justification. Although classifications and diagnoses based on features of people’s observable behaviour are necessary at these early stages of neuropsychiatric research, given the variety of conditions in which certain phenomena appear, I shall attempt to show that current epistemic accounts of confabulations and delusions have limitations. Epistemic criteria can guide both research and clinical practice, but fail to provide sufficient conditions for the identification of delusions and confabulations, and fail to demarcate pathological from non-pathological narratives or beliefs. -/- Another limitation of current epistemic accounts – which I shall not address here – is the excessive focus on epistemic faults of confabulations and delusions at the expense of their epistemically neutral or advantageous features (see Bortolotti and Cox, 2009). This may lead to a misconception of delusions and confabulations, and to an oversimplification in the assessment of the needs of people who require clinical treatment for their psychotic symptoms. (shrink)

In this paper we analyze the uses and misuses of argumentation schemes from verbal classification, and show how argument from definition supports argumentation based on argument from verbal classification. The inquiry has inevitably included the broader study of the concept of definition. The paper presents the schemes for argument from classification and for argument from definition, and shows how the latter type of argument so typically supports the former. The problem of analyzing arguments based on classification (...) is framed in a structure that reveals the crucial role it plays in the persuasion process. The survey of the literature includes the work of Hastings, Perelman, Kienpointner and Schiappa, but still finds much of value in Aristotle. Lessons drawn from Aristotle’s Topics are shown to be useful for developing new tools for assessing definitions and arguments from definition. (shrink)

In this article, I begin by giving a brief history of melanoma causation. I then discuss the current manner in which malignant melanoma is classified. In general, these systems of classification do not take account of the manner of tumour causation. Instead, they are based on phenomenological features of the tumour, such as size, spread, and morphology. I go on to suggest that misclassification of melanoma is a major problem in clinical practice. I therefore outline an alternative means of (...) classifying these tumours based on causal factors. By analogy with similar systems that have recently emerged for other cancers, I suggest that this causal classification is likely to be both workable and helpful, even in the absence of a full causal-mechanistic understanding of the aetiology of the tumour. (shrink)

In this paper, I offer one example of conceptual change. Specifically, I contend that the discovery that viruses could cause cancer represents an excellent example of branch jumping, one of Thagard’s nine forms of conceptual change. Prior to about 1960, cancer was generally regarded as a degenerative, chronic, non-infectious disease. Cancer causation was therefore usually held to be a gradual process of accumulating cellular damage, caused by relatively non-specific component causes, acting over long periods of time. Viral infections, on the (...) other hand, were generally understood to be acute processes, whereby single, specific and necessary causal agents acted alone to produce disease. However, during the 1960s and 1970s, a number of cancers were discovered to have an infectious aetiology. Of particular note were two—Burkitt’s lymphoma and cervical cancer—which I will discuss in detail later in this piece. Together, these discoveries led, in the short term, to a tentative aetiological reclassification of some types of cancer as infectious diseases and, in the longer term, to a full-blown reclassification of cancer as an aetiological disease branch in its own right. This process of reclassification forms the empirical basis for my concluding remarks on the influence of classification upon causation in medicine. Through this, I aim to demonstrate that conceptual change, far from being a purely abstract concern of the philosopher of science, is of substantial import to scientific practitioners. (shrink)

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

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

The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier eating habits.

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: Age and gender classification has been around for a long time, and efforts are still being made to improve the findings. This has been the case since the inception of social media platforms. Visible understanding has become more important in the computer vision society with the emergence of AI increase in performance and help train a model to achieve age and gender classification. Although these networks built for the mobile platform are not always as accurate as the (...) larger, more resource- intensive networks we've come to know and love, they stand out when it comes to the accuracy trade-off. Despite the importance of these attributes in our daily lives, the ability to estimate them efficiently from face images is still far from meeting the requirements of commercial applications. (shrink)

There are a number of existing classifications and staging schemes for carcinomas, one of the most frequently used being the TNM classification. Such classifications represent classes of entities which exist at various anatomical levels of granularity. We argue that in order to apply such representations to the Electronic Health Records one needs sound ontologies which take into consideration the diversity of the domains which are involved in clinical bioinformatics. Here we outline a formal theory for addressing these issues in (...) a way that the ontologies can be used to support inferences relating to entities which exist at different anatomical levels of granularity. Our case study is the colon carcinoma, one of the most common carcinomas prevalent within the European population. (shrink)

Abstract: Fast food refers to quick, convenient, and ready-to-eat meals that are usually sold at chain restaurants or take-out establishments. Fast food is often criticized for its unhealthy ingredients, such as high levels of salt, sugar, and unhealthy fats, and its contribution to the growing obesity epidemic. Despite this, fast food remains popular due to its affordability, convenience, and widespread availability. Many fast food chains have attempted to respond to these criticisms by offering healthier options, such as salads and grilled (...) chicken sandwiches, and by using more natural and locally sourced ingredients. The aim in this paper to propose a deep learning model for the classification of fast food dishes. The proposed model, Xception, was trained on natural images and was fine-tuned to make up the fast food meals. The researchers collected the Fast Food V2 dataset from Kaggle depository, the dataset, contained 20,000 images. The dataset has ten labels of food images comprised of Baked Potato, Burger, Crispy Chicken, Donut, Fries, Hot Dog, Pizza, Sandwich, Taco, and Taquito. The dataset was split into three sets: training, validation and testing. The accuracy, F1-score, recall, precision on the separate test set showed that classification of fast food was accuracy (95.13%), Precision (95.34%), Recall (95.13%), and F1-Score (95.14%). (shrink)

As a type of evidence glass can be very useful contact trace material in a wide range of offences including burglaries and robberies, hit-and-run accidents, murders, assaults, ram-raids, criminal damage and thefts of and from motor vehicles. All of that offer the potential for glass fragments to be transferred from anything made of glass which breaks, to whoever or whatever was responsible. Variation in manufacture of glass allows considerable discrimination even with tiny fragments. In this study, we worked glass (...) class='Hi'>classification and testing of artificial neural network model created by the JustNN. The aim of the study is help investigator in identifying the type of glass found in arena of the crime. The Neural Network model was trained and validated using the type of glass dataset. The accuracy of model in predicting the type of glass reached 96.7%. Thus neural network is suitable for predicating type of glasses. (shrink)

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

The aim of the paper is to improve availability classifications of components for application in construction systems. Construction production systems belong to project-based systems with serial-parallel structures with or without redundant components, and the availability function has a significant impact on the performance indicators of components and systems. The main indicators of function of the components are the availability, capacity, costs, and project time. A new approach to classification makes it possible to choose the most appropriate methodology for assessing (...) component availability in the bidding phase, and managing company’s machine park. The new classification approach was tested on a practical example. The results obtained confirmed the justification for extending the classical approach to the classification of the availability of components. (shrink)

In this paper, we investigated multiple machine learning classifiers which are, Multinomial Naive Bayes, Support Vector Machine, Decision Tree, K Nearest Neighbors, and Random Forest in a text classification problem. The data we used contains real and fake job posts. We cleaned and pre-processed our data, then we applied TF-IDF for feature extraction. After we implemented the classifiers, we trained and evaluated them. Evaluation metrics used are precision, recall, f-measure, and accuracy. For each classifier, results were summarized and compared (...) with others. (shrink)