Abstract: In worldwide, there are several hundred cultivars of mango. Depending on the cultivar, mango fruit varies in size, shape, sweetness, skin color, and flesh color which may be pale yellow, gold, or orange. Where there are more than 15 types of manga. In this paper, two types Mango classification approach is presented with a dataset that contains approximately 1200 images. 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 Mango classification applications when used in classification automation it enables people to know the type of mango properly. The trained model achieved an accuracy of 100% on test set, demonstrating the feasibility of this approach. (shrink)

A classification of the global catastrophic risks of AI is presented, along with a comprehensive list of previously identified risks. This classification allows the identification of several new risks. We show that at each level of AI’s intelligence power, separate types of possible catastrophes dominate. Our classification demonstrates that the field of AI risks is diverse, and includes many scenarios beyond the commonly discussed cases of a paperclip maximizer or robot-caused unemployment. Global catastrophic failure could happen at (...) various levels of AI development, namely, before it starts self-improvement, during its takeoff, when it uses various instruments to escape its initial confinement, or after it successfully takes over the world and starts to implement its goal system, which could be plainly unaligned, or feature-flawed friendliness. AI could also halt at later stages of its development either due to technical glitches or ontological problems. Overall, we identified around several dozen scenarios of AI-driven global catastrophe. The extent of this list illustrates that there is no one simple solution to the problem of AI safety, and that AI safety theory is complex and must be customized for each AI development level. (shrink)

Abstract: In recent years email has become one of the fastest and most economical means of communication. However increase of email users has resulted in the dramatic increase of spam emails during the past few years. Data mining -classification algorithms are used to categorize the email as spam or non-spam. Numerous email spam messages are marketable in nature but might similarly encompass camouflaged links that seem to be for acquainted websites but actually lead to phishing web sites or sites (...) that are holding malware. Spam email might likewise comprise malware as scripts or other executable file attachments. Spammers use spam bots to generate email distribution lists. A spammer naturally sends an email to millions of email addresses. The address and identity of the sender are concealed Mass Mailing with the expectation that only a small number will respond or interact with the message and Spam mails might be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spamming is rising at a quick speed as sending a deluge of mails is simple and for free. Spam mails interrupt the one calmness, spending much time and put away various resources like memory and networks bandwidth. In this study, we present a method for spam filtering using Artificial Neural Network to predict whether an email is spam or not. (shrink)

„Errors are the greatest obstacles to the progress of science; to correct such errors is of more practical value than to achieve new knowledge,“ asserted Eugen Bleuler. Basic error of several prevailing classification schemes of pathological conditions, as for example ICD-10, lies in confusing and mixing symptoms with diseases, what makes them unscientific. Considering the need to bring order into the chaos and light into terminological obscureness, I introduce the Causal classification of diseases originating from the notion of (...) bodily wholeness and causality of its loss. (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)

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: Banana, fruit of the genus Musa, of the family Musaceae, one of the most important fruit crops of the world. The banana is grown in the tropics, and, though it is most widely consumed in those regions, it is valued worldwide for its flavour, nutritional value, and availability throughout the year. Cavendish, or dessert, bananas are most commonly eaten fresh, though they may be fried or mashed and chilled in pies or puddings. They may also be used to flavour (...) muffins, cakes, or breads. Cooking varieties, or plantains, are starchy rather than sweet and are grown extensively as a staple food source in tropical regions; they are cooked when ripe or immature. A ripe fruit contains as much as 22 percent of carbohydrate and is high in dietary fibre, potassium, manganese, and vitamins B6 and C.. In this paper, machine learning based approach is presented for identifying type Apple with a dataset that contains 8,554 images use 4,488 images for training, 1,928 images for validation and 2,138 images for testing. A deep learning technique that extensively applied to image recognition was used. use 70% from image for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. (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: Small Blue Round Cell Tumors (SBRCTs) are a heterogeneous group of tumors that are difficult to diagnose because of overlapping morphologic, immunehistochemical, and clinical features. About two-thirds of EWSR1-negative SBRCTs are associated with CIC-DUX4-related fusions, whereas another small subset shows BCOR-CCNB3 X-chromosomal par acentric inversion. In this paper, we propose an ANN model to Classify and Predict SBRCTs Cancers. The accuracy of the classification reached 100%.

Avocado is the fruit of the avocado tree, scientifically known as Persia Americana. This fruit is prized for its high nutrient value and is added to various dishes due to its good flavor and rich texture. It is the main ingredient in guacamole. These days, the avocado has become an incredibly popular food among health-conscious individuals. It’s often referred to as a superfood, which is not surprising given its health properties. Using a public dataset of 1,234 images of Avocado collected (...) under controlled conditions, we trained a deep convolutional neural network to identify tow type of avocado. The trained model achieved an accuracy of 99.84% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets present a clear path toward types of avocado. (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: Apple is a plant species that follows the apple genus, which is a fruit because it contains seeds of the pink family. It is one of the most fruit trees in terms of agriculture. The apple tree is small in length from 3 to 12 meters. Several recent studies have shown many health benefits of apples. It helps with the strengthening of the brain, heart, and stomach. It is used in the treatment of joint pain and limberness. It is (...) opposite. It stops vomiting. It goes to dyspnea. It corrects the liver, purifies the blood of toxins, strengthens the heart muscle, and kills the abdominal dentures. Which are richer in cell nutrition and development, bone strengthening and neuronal regeneration is an important source of detoxification, viruses, bacteria, and microbes in the body. In this paper, machine learning based approach is presented for identifying type of Apple with a dataset that contains 8,554 images. We used 4,488 images for training, 1,928 images for validation and 2,138 images for testing. A deep learning technique that extensively applied to image recognition was used. 70% from images were used for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. (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: Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South American origin from Peru, and then spread to most countries of the world. Tomatoes contain a lot of vitamin C and mineral salts, and are recommended for people with constipation, diabetes and patients with heart and body diseases. Studies and scientific studies have proven the importance of eating tomato juice in reducing the activity of platelets in diabetics, which (...) helps in protecting them from developing deadly blood clots. A tomato classification approach is presented with a data set containing approximately 5,266 images with 7 species belonging to tomatoes. The Neural Network Algorithms (CNN), a deep learning technique applied widely in image recognition, is used for this task. (shrink)

Agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. 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. Preceding instrument vision methods established for selective weeding have confronted with major challenges for trustworthy and precise weed recognition. In this paper, (...) plant seedlings classification approach is presented with a dataset that contains approximately 5,000 images with 960 unique plants that belong to 12 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 seedling 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)

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.

Abstract: 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 neural network exploits the non-linearity of a problem to define a set of desired inputs. Neural networks are important in realizing a better way for classification in machine learning and finds application in various fields such as data mining, pattern recognition, forensics etc. In this paper, our focus is to classify of patient records obtained from clinical data. Feature selection is a supervised method that attempts to select a subset of the predictor features based on the information gain. (...) The Lymphography dataset comprises of 18 attributes and 148 instances with the class label having four distinct values. This paper highlights the accuracy of EasyNN backbrapagation calssification algorithm in classifying predictor attributes and highlights its performance on Lymphography dataset. The accuracy we have reached is 97.78 percent in classification accuracy with the predictor feature. (shrink)

Fruit has been recognized as a good source of vitamins and minerals, and for their role in preventing vitamin C and vitamin A deficiencies. People who eat fruit as part of an overall healthy diet generally have a reduced risk of chronic diseases. Fruit are important sources of many nutrients, including potassium, fiber, vitamin C and folate (folic acid). One of important types of fruit is Grapefruit . Grapefruit is a tropical citrus fruit known its sweet and somewhat sour taste. (...) It's rich in nutrients, antioxidants and fiber, making it one of the healthiest citrus fruits you can eat. Research shows that it may have some powerful health benefits, including weight loss and a reduced risk of heart disease. In this paper we presented a system that recognize the two types of Grapefruit Pink and white based on deep learning using python on colab editor . This system may help people to automate their factories , restaurants or anything else need to classify these two types for different use . (shrink)

In this chapter, I provide an overview of phenomenological approaches to psychiatric classification. My aim is to encourage and facilitate philosophical debate over the best ways to classify psychiatric disorders. First, I articulate phenomenological critiques of the dominant approach to classification and diagnosis—i.e., the operational approach employed in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the International Classification of Diseases (ICD-10). Second, I describe the type or typification approach to psychiatric classification, which I (...) distinguish into three different versions: ideal types, essential types, and prototypes. I argue that despite their occasional conflation in the contemporary literature, there are important distinctions among these approaches. Third, I outline a new phenomenological-dimensional approach. I show how this approach, which starts from basic dimensions of human existence, allows us to investigate the full range of psychopathological conditions without accepting the validity of current diagnostic categories. (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)

Although delusion is one of the central concepts of psychopathology, it stills eludes precise conceptualization. In this paper, I present certain basic issues concerning the classification and definition of delusion, as well as its ontological status. By examining these issues, I aim to shed light on the ambiguity of the clinical term ‘delusion’ and its extension, as well as provide clues as to why philosophers are increasingly joining the ranks of psychiatrists, psychologists, and neuroscientists in the effort to come (...) to a comprehensive understanding of delusion. (shrink)

Abstract: Tumor is a group of diseases that involve abnormal increases in the number of cells, with the potential to invade or spread to other parts of the body. Not all tumors or lumps are cancerous; benign tumors are not classified as being cancer because they do not spread to other parts of the body. There are over 100 different known Tumors that affect humans. Tumors are often described by the body part that they originated in. However, some body parts (...) contain multiple types of tissue, so for greater precision, tumors are additionally classified by the type of cell that the tumor cells originated from. The aim of this study is to propose an Artificial Neural Network model for the classification of tumor types. Some of important features in the classification of the tumors are age, sex, histologic-type, degree-of-diffe, status of bone, bone-marrow, lung, pleura, peritoneum, liver, brain, skin, neck, supraclavicular, axillar, mediastinum, and abdominal. They were used as input variables for the ANN model. A model based on the Multilayer Perceptron topology was created and trained using “primary tumor” dataset which was collected from the University Medical Centre, Institute of Oncology, Ljubljana, Evaluation of the ANN model showed that the ANN model is able to correctly classify the tumor type with 79.65 % accuracy rate. (shrink)

Abstract: A grape is a fruit, botanically a berry, of the deciduous woody vines of the flowering plant genus Vitis. it can be eaten fresh or they can be used for making jam, grape juice, jelly, grape seed extract, raisins, and grape seed oil. Grapes are a nonclimacteric type of fruit, generally occurring in clusters. Grapes are a type of fruit that grow in clusters of 15 to 300, and can be crimson, black, dark blue, yellow, green, orange, and pink. (...) "White" grapes are actually green in color, and are evolutionarily derived from the purple grape. Mutations in two regulatory genes of white grapes turn off production of anthocyanins, which are responsible for the color of purple grapes. Grapes are typically an ellipsoid shape resembling a prolate spheroid. In this paper, machine learning based approach is presented for identifying type Grapes with a dataset that contains 4,565 images use 2,393 images for training, 1,026 images for validation and 1,146 images for testing. A deep learning technique that extensively applied to image recognition was used. use 70% from image for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

This paper addresses philosophical issues concerning whether mental disorders are natural kinds and how the DSM should classify mental disorders. I argue that some mental disorders (e.g., schizophrenia, depression) are natural kinds in the sense that they are natural classes constituted by a set of stable biological mechanisms. I subsequently argue that a theoretical and causal approach to classification would provide a superior method for classifying natural kinds than the purely descriptive approach adopted by the DSM since DSM-III. My (...) argument suggests that the DSM should classify natural kinds in order to provide predictively useful (i.e., projectable) diagnostic categories and that a causal approach to classification would provide a more promising method for formulating valid diagnostic categories. (shrink)

Abstract: Peach, (Prunus persica), fruit tree of the rose family (Rosaceae), grown throughout the warmer temperate regions of both the Northern and Southern hemispheres. Peaches are widely eaten fresh and are also baked in pies and cobblers; canned peaches are a staple commodity in many regions. Yellow-fleshed varieties are especially rich in vitamin A. Peach trees are relatively short-lived as compared with some other fruit trees. In some regions orchards are replanted after 8 to 10 years, while in others trees (...) may produce satisfactorily for 20 to 25 years or more, depending upon their resistance to diseases, pests, and winter damage. in the body.[1] In this paper, machine learning based approach is presented for identifying type peach with a dataset that contains 2,306 images use 1,212 images for training, 520 images for validation and 574 images for testing. A deep learning technique that extensively applied to image recognition was used. use 70% from image for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

The scheme for classifications of concepts is introduced. It has founded on the triplet model of concepts. In this model a concept is depicted by means of three kinds of knowledge: a concept base, a concept representing part and the linkage between them. The idea of triplet classifications of concepts is connected with a usage of various specifications of these knowledge kinds as classification criteria.

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)

Abstract: A grape is a fruit, botanically a berry, of the deciduous woody vines of the flowering plant genus Vitis. it can be eaten fresh or they can be used for making jam, grape juice, jelly, grape seed extract, raisins, and grape seed oil. Grapes are a nonclimacteric type of fruit, generally occurring in clusters. Grapes are a type of fruit that grow in clusters of 15 to 300, and can be crimson, black, dark blue, yellow, green, orange, and pink. (...) "White" grapes are actually green in color, and are evolutionarily derived from the purple grape. Mutations in two regulatory genes of white grapes turn off production of anthocyanins, which are responsible for the color of purple grapes. Grapes are typically an ellipsoid shape resembling a prolate spheroid. In this paper, machine learning based approach is presented for identifying type Grapes with a dataset that contains 4,565 images use 2,393 images for training, 1,026 images for validation and 1,146 images for testing. A deep learning technique that extensively applied to image recognition was used. use 70% from image for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

In Chapter 3 of Book I of Psychology from an Empirical Standpoint, Brentano articulates what he takes to be the four most basic and central tasks of psychology. One of them is to discover the ‘fundamental classification’ of mental phenomena. Brentano attends to this task in Chapters 5-9 of Book II of the Psychology, reprinted (with appendices) in 1911 as a standalone book (Brentano 1911a). The classification is further developed in an essay entitled “A Survey of So-Called Sensory (...) and Noetic Objects of Inner Perception,” published posthumously in Brentano 1928/1981b, as well as a 1907 dictation entitled “Loving and Hating,” reprinted in Brentano 1969. (shrink)

In a world of ever growing specialization, the idea of a unity of science is commonly discarded, but cooperative work involving cross-disciplinary points of view is encouraged. The aim of this paper is to show with some textual support that Charles S. Peirce not only identified this paradoxical situation a century ago, but he also mapped out some paths for reaching a successful solution. A particular attention is paid to Peirce's classification of the sciences and to his conception of (...) science as a collective and cooperative activity of all those whose lives are animated by the desire to discover the truth. -/- . (shrink)

This article uses the case study of ethnobiological classification to develop a positive and a negative thesis about the state of natural kind debates. On the one hand, I argue that current accounts of natural kinds can be integrated in a multidimensional framework that advances understanding of classificatory practices in ethnobiology. On the other hand, I argue that such a multidimensional framework does not leave any substantial work for the notion “natural kind” and that attempts to formulate a general (...) account of naturalness have become an obstacle to understanding classificatory practices. (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)

that can serve as a foundation for more refined ontologies in the field of proteomics. Standard data sources classify proteins in terms of just one or two specific aspects. Thus SCOP (Structural Classification of Proteins) is described as classifying proteins on the basis of structural features; SWISSPROT annotates proteins on the basis of their structure and of parameters like post-translational modifications. Such data sources are connected to each other by pairwise term-to-term mappings. However, there are obstacles which stand in (...) the way of combining them together to form a robust meta-classification of the needed sort. We discuss some formal ontological principles which should be taken into account within the existing datasources in order to make such a metaclassification possible, taking into account also the Gene Ontology (GO) and its application to the annotation of proteins. (shrink)

Abstract. Death seems to be a permanent event, but there is no actual proof of its irreversibility. Here we list all known ways to resurrect the dead that do not contradict our current scientific understanding of the world. While no method is currently possible, many of those listed here may become feasible with future technological development, and it may even be possible to act now to increase their probability. The most well-known such approach to technological resurrection is cryonics. Another method (...) is indirect mind uploading, or digital immortality, namely the preservation of data about a person to allow for future reconstruction by powerful AI. More speculative ways to immortality include combinations of future superintelligence on a galactic scale, which could use simulation to resurrect all possible people, and new physical laws, which may include time-travel or obtaining information from the past. Acausal trade with parallel worlds could help combine random resurrection and reconstruction based on known data without loss of share of worlds where I exist (known as existence measure). Quantum immortality could help to increase the probability of success for cryonics and digital immortality. There many possible approaches to technological resurrection and thus if large-scale future technological development occurs, some form of resurrection is inevitable. (shrink)

Ethnobiology has a long tradition of metaphysical debates about the “naturalness,” “objectivity”, “reality”, and “universality” of classifications. Especially the work of Brent Berlin has been influential in developing a “convergence metaphysics” that explains cross-cultural similarities of knowledge systems through shared recognition of objective discontinuities in nature. Despite its influence on the development of the field, convergence metaphysics has largely fallen out of favor as contemporary ethnobiologists tend to emphasize the locality and diversity of classificatory practices. The aim of this article (...) is twofold: First, I provide a historical account of the rise and fall of convergence metaphysics in ethnobiology. I show how convergence metaphysics emerged as an innovative theoretical program in the wake of the “cognitive revolution” and the “modern evolutionary synthesis” but failed to incorporate both theoretical insights and political concerns that gained prominence in the 1980s and 1990s. Second, I develop a positive proposal of how to engage with metaphysical issues in ethnobiology. By integrating traditional research on convergence of classifications with more nuanced accounts of distinctly local categories, a revamped metaphysics of ethnobiological classification can make substantial contributions to debates about ontological difference in anthropology and about the relation between applied and theoretical ethnobiology. (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)

A large part of the controversy surrounding the publication of DSM-5 stems from the possibility of replacing the purely descriptive approach to classification favored by the DSM since 1980. This paper examines the question of how mental disorders should be classified, focusing on the issue of whether the DSM should adopt a purely descriptive or theoretical approach. I argue that the DSM should replace its purely descriptive approach with a theoretical approach that integrates causal information into the DSM’s descriptive (...) diagnostic categories. The paper proceeds in three sections. In the first section, I examine the goals (viz., guiding treatment, facilitating research, and improving communication) associated with the DSM’s purely descriptive approach. In the second section, I suggest that the DSM’s purely descriptive approach is best suited for improving communication among mental health professionals; however, theoretical approaches would be superior for purposes of treatment and research. In the third section, I outline steps required to move the DSM towards a hybrid system of classification that can accommodate the benefits of descriptive and theoretical approaches, and I discuss how the DSM’s descriptive categories could be revised to incorporate theoretical information regarding the causes of disorders. I argue that the DSM should reconceive of its goals more narrowly such that it functions primarily as an epistemic hub that mediates among various contexts of use in which definitions of mental disorders appear. My analysis emphasizes the importance of pluralism as a methodological means for avoiding theoretical dogmatism and ensuring that the DSM is a reflexive and self-correcting manual. (shrink)

Abstract Recently, some philosophers of psychiatry (viz., Rachel Cooper and Dominic Murphy) have analyzed the issue of psychiatric classification. This paper expands upon these analyses and seeks to demonstrate that a consideration of the history of the Diagnostic and Statistical Manual of Mental Disorders (DSM) can provide a rich and informative philosophical perspective for critically examining the issue of psychiatric classification. This case is intended to demonstrate the importance of history for philosophy of psychiatry, and more generally, the (...) potential benefits of historically-informed approaches to philosophy of science. (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)

Locke is often cited as a precursor to contemporary natural kind realism. However, careful attention to Locke’s arguments show that he was unequivocally a conventionalist about natural kinds. To the extent that contemporary natural kind realists see themselves as following Locke, they misunderstand what he was trying to do. Locke argues that natural kinds require either dubious metaphysical commitments (e.g., to substantial forms or universals), or a question-begging version of essentialism. Contemporary natural kind realists face a similar dilemma, and should (...) not appeal to Locke in their defense. (shrink)

In recent years email has become one of the fastest and most economical means of communication. However increase of email users has resulted in the dramatic increase of spam emails during the past few years. Data mining -classification algorithms are used to categorize the email as spam or non-spam. Numerous email spam messages are marketable in nature but might similarly encompass camouflaged links that seem to be for acquainted websites but actually lead to phishing web sites or sites that (...) are holding malware. Spam email might likewise comprise malware as scripts or other executable file attachments. Spammers use spam bots to generate email distribution lists. A spammer naturally sends an email to millions of email addresses. The address and identity of the sender are concealed Mass Mailing with the expectation that only a small number will respond or interact with the message and Spam mails might be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spamming is rising at a quick speed as sending a deluge of mails is simple and for free. Spam mails interrupt the one calmness, spending much time and put away various resources like memory and networks bandwidth. In this study, we present a method for spam filtering using Artificial Neural Network to predict whether an email is spam or not. (shrink)

In this essay several challenges are raised to the project of classifying Śāntideva’s ethical reasoning given in his Bodhicaryāvatāra, or Guide to the Way of the Bodhisattva, as a species of ethical theory such as consequentialism or virtue ethics. One set of difficulties highlighted here arises because Śāntideva wrote this text to act as a manual of psychological transformation, and it is therefore often difficult to determine when his statements indicate his own ethical views. Further, even assuming we can identify (...) a set of statements that accurately portray the moral position of Śāntideva, it is argued that these statements underdetermine which foundational normative theory should be ascribed to him. (shrink)

Contemporary psychiatry finds itself in the midst of a crisis of classification. The developments begun in the 1980s—with the third edition of the Diagnostic and Statistical Manual of Mental Disorders —successfully increased inter-rater reliability. However, these developments have done little to increase the predictive validity of our categories of disorder. A diagnosis based on DSM categories and criteria often fails to accurately anticipate course of illness or treatment response. In addition, there is little evidence that the DSM categories link (...) up with genetic findings, and even less evidence that they... (shrink)

Identifying the necessary and sufficient conditions for individuating and classifying diseases is a matter of great importance in the fields of law, ethics, epidemiology, and of course, medicine. In this paper, I first propose a means of achieving this goal, ensuring that no two distinct disease-types could correctly be ascribed to the same disease-token. I then posit a metaphysical ontology of diseases—that is, I give an account of what a disease is. This is essential to providing the most effective means (...) of interfering with disease processes. Following existing work in the philosophy of medicine and epidemiology (primarily Christopher Boorse; Caroline Whitbeck; Alexander Broadbent), philosophy of biology (Joseph LaPorte; D.L. Hull), conditional analyses of causation (J.L. Mackie; David Lewis), and recent literature on dispositional essentialism (Stephen Mumford and Rani Anjum; Alexander Bird), I endorse a dispositional conception of disease. Following discussion of various conceptions of disease-identity, their relations to the clinical and pathological effects of the diseases in question, and how diseases are treated, I conclude (i) that diseases should be individuated by their causes, and (ii) that diseases are causal processes best seen as simultaneously acting sequences of mutually manifesting dispositions. (shrink)

It is widely understood that protein functions can be exhaustively described in terms of no single parameter, whether this be amino acid sequence or the three-dimensional structure of the underlying protein molecule. This means that a number of different attributes must be used to create an ontology of protein functions. Certainly much of the required information is already stored in databases such as Swiss-Prot, Protein Data Bank, SCOP and MIPS. But the latter have been developed for different purposes and the (...) separate data-structures which they employ are not conducive to the needed data integration. When we attempt to classify the entities in the domain of proteins, we find ourselves faced with a number of cross-cutting principles of classification. Our question here is: how can we bring together these separate taxonomies in order to describe protein functions? Our proposed answer is: via a careful top-level ontological analysis of the relevant principles of classification, combined with a new framework for the simultaneous manipulation of classifications constructed for different purposes. (shrink)

The aim of this paper is to analyze, from a philosophical perspective, the scientific robustness of the construct of psychopathy as measured by the Psychopathy Checklist Revised that was developed by Robert Hare (1991; 2003). The scientific robustness and validity of classifications are topics of many debates in philosophy of science and philosophy of psychiatry more specifically. The main problem consists in establishing whether scientific classifications reflect natural kinds where the concept of a natural kind refers to the existence of (...) some objective divisions in nature that do not depend exclusively on subjective judgments of the classifier. The construct of psychopathy is especially interesting since the diagnosis of psychopathy has substantial social consequences. In the light of the recent debates regarding the problem of natural kinds in philosophy of psychiatry, we advocate the following distinction between two types of scientific classifications: natural and practical kinds. Natural kinds refer to those categories that are united by common causal mechanisms or properties. Practical kinds refer to categories that fulfill some practical classificatory goals such as prediction. We argue that the construct of psychopathy can fulfill the role of a practical kind. In addition, we contend that our current scientific knowledge about psychopathy does not allow us to conclude that this category is a natural kind. (shrink)

This article presents some results of a research on computational strategies for the visualization of sign classification structures and sign processes. The focus of this research is the various classifications of signs described by Peirce. Two models are presented. One of them concerns specifically the 10-fold classification as described in the 1903 Syllabus (MS 540, EP 2: 289–299), while the other deals with the deep structure of Peirce’s various trichotomic classifications. The first is 10cubes, an interactive 3-D model (...) of Peirce’s 10-fold classification, as described in the Syllabus. The second is 3N3, a computer program that builds equivalent diagrams for any n-trichotomic classification of signs. We are specially interested in how a graphic design methodology, associated with computer graphic resources and techniques, can contribute to the construction of interactive models that serve as tools for the investigation of C. S. Peirce’s theory of signs. (shrink)

Abstract: Alzheimer's disease (AD) is one of the most common types of dementia. Symptoms appear gradually and end with severe brain damage. People with Alzheimer's disease lose the abilities of knowledge, memory, language and learning. Recently, the classification and diagnosis of diseases using deep learning has emerged as an active topic covering a wide range of applications. This paper proposes examining abnormalities in brain structures and detecting cases of Alzheimer's disease especially in the early stages, using features derived from (...) medical images. The entire brain image was passed on through the transmission of Xception learning architectures. The Convolutional Neural Network (CNN) was constructed with the help of separable convolution layers that It can automatically learn general features from imaging data for classification. (shrink)