In this paper, a special type of polysemantic words, that is, words with multiple meanings for the same part of speech, are analyzed under the name of neutrosophic words. These words represent the most dif cult cases for the disambiguation algorithms as they represent the most ambiguous natural language utterances. For approximate their meanings, we developed a semantic representation framework made by means of concepts from neutrosophic theory and entropy measure in which we incorporate sense related data. We show the (...) advantages of the proposed framework in a sentiment classification task. (shrink)

Hume describes “mental geography” as the endeavor to know “the different operations of the mind, to separate them from each other, to class them under their proper heads, and to correct all that seeming disorder, in which they lie involved, when made the object of reflection and enquiry.” While much has been written about his geography of thought in Treatise Book 1, relatively little has been written about his geography of feeling in Books 2 and 3, with the result that (...) there has been considerable puzzlement and disagreement about the meaning and grounds of many of his central theses about the passions, action, and morals. My endeavor in this chapter is to remove some common sources of perplexity about his classification of the operations of feeling “under their proper heads” in that work. I begin by explaining his three highest-level distinctions bearing on this terrain: that between impressions and ideas; that between original impressions and secondary impressions; and that between the passions and the other emotions. In order to understand this third distinction, I explain his three different senses of the term ‘emotion’ and the relations among them. I ill then examine five different kinds of secondary impressions that he recognizes. These are: (1) sensible agitations; (2) feelings of or from mental operations; (3) volitions; (4) the passions (including motives); and (5) sentiments of taste. The broad outlines of the resulting geography are mapped out in a chart at the end. (shrink)

This is a brief review of the Rasa theory of Indian aesthetics and the works I have done on the same. A major source of the Indian system of classification of emotional states comes from the ‘Natyasastra’, the ancient Indian treatise on the performing arts, which dates back to the 2nd Century AD (or much earlier, pg. LXXXVI: Natyasastra, Ghosh, 1951). The ‘Natyasastra’ speaks about ‘sentiments’ or ‘Rasas’ (pg.102: Natyasastra, Ghosh, 1951) which are produced when certain ‘dominant states’ (sthayi (...) Bhava), ‘transitory states’ (vyabhicari Bhava) and ‘temperamental states’ (sattvika Bhava) of emotions come together (pgs.102, 105: Natyasastra, Ghosh, 1951). This Rasa theory, which is still widely followed in classical Indian performing arts, classifies eight Rasas or sentiments which are: Sringara (erotic), Hasya (comic), Karuna (pathetic), Raudra (furious), Vira (heroic), Bhayanaka (terrible), Bibhatsa (odious) and Adbhuta (marvellous). There was a later addition of the ninth sentiment or Rasa called Santa (peace) in later Sanskrit poetics (pg.102: Natyasastra, Ghosh, 1951). According to ancient Indian aesthetics (especially in the context of Bharatas’ ‘Natyasastra’, Anandavardhana’s ‘Dhvanyaloka’ and Abhinavagupta’s ‘Abhinavabharati’), ‘Rasa’ is the relishable state of elemental human emotions called ‘Bhavas’. Bharata’s ‘Natyasastra’ originally spoke of eight Rasas. The concept of the 9th Rasa was a later interpolation by the Kashmiri Shaivist Abhinavagupta (10th Century AD) and also his predecessor Anandavardhana (9th Century AD). Abhinavagupta extends the eight Rasas by adding the concept of the Santa Rasa which he regards as the essence of all Rasas. It is this 9th Rasa which according to Abhinavagupta lets the Rasika attain the aesthetic detachment and savour the essences of all other Rasas and therefore the true aesthetic delight. The introduction of 9th Rasa integrates the concepts of Bharata’s Rasasutra and Patanjali’s Yoga theory – the detachment necessary to introspect inwards into the inherent state of freedom and bliss (aesthetic consciousness). (shrink)

The need to act in networks and to function in a society where we relate in complex fashions has fostered the use of data in accordance with distributed models. In a big data context, the individual is often overcome with the sentiment that one’s life does not matter, nor does it make a difference. What are sought are trends, and since there is no detached observer, those change things as much as they measure them. Where can we find a (...) vantage point, a ‘topsight’ necessary before the conception of any program? One could seek it in the direction of a collective figure, somewhat like the Leviathan of Hobbes, made up in its gigantism of smaller figures. Those that have been schooled in the classical tradition, or philosophia perennis, might think that we are abandoning solid realism, which seeks a conformation of statements, propositions, and language to what there is. They might criticize the reign of opinion. But what we entered into is not, upon closer inspection, some overarching paradigm of truth as success. It is rather a rationality of the likely, and it requires assessing the truths that can seem distributed between alternating or competing positions. But then, to seek decisions despite this, is to erase from our world this ‘esprit de finesse’ which Pascal deemed necessary to balance the illusions of a procedural mathematical rationality claiming that its end-products are simply in correspondence with reality. When we gather information, we frame it within an ontology for classification purposes, but this does not import with itself the immanence of this information as endogenous power to harmonise and give form. Feeling, as Whitehead construed it, seeks to take within it a receptivity to the world in order to turn it into an internalised representation. For this, it needs to be possible for a part of the world to reflect in itself the whole. Yet, this reflection is not a totalisation in an immaterial data range, it is rather a grounding in the mystery of the flesh, which is found here as a signpost on the way, if we are to heal dreams of complete mastery. (shrink)

According to a historically popular view, emotions are normative experiences that ground moral knowledge much as perceptual experiences ground empirical knowledge. Given the analogy it draws between emotion and perception, sentimental perceptualism constitutes a promising, naturalist-friendly alternative to classical rationalist accounts of moral knowledge. In this paper, we consider an important but underappreciated objection to the view, namely that in contrast with perception, emotions depend for their occurrence on prior representational states, with the result that emotions cannot give perceptual-like access (...) to normative properties. We argue that underlying this objection are several specific problems, rooted in the different types of mental states to which emotions may respond, that the sentimental perceptualist must tackle for her view to be successful. We argue, moreover, that the problems can be answered by filling out the theory with several independently motivated yet highly controversial commitments, which we carefully catalogue. The plausibility of sentimental perceptualism, as a result, hinges on further claims sentimental perceptualists should not ignore. (shrink)

According to sentimental perceptualism, affect grounds evaluative or normative knowledge in a similar way to the way perception grounds much of descriptive knowledge. In this paper, we present a novel challenge to sentimental perceptualism. At the centre of the challenge is the assumption that if affect is to ground knowledge in the same way as perception does, it should have a function to accurately represent evaluative properties, and if it has that function, it should also have it in its future-directed (...) imaginative use. As the data on affective forecasting errors indicates, however, the affect system does not have that function. As a result, it is doubtful if affect can do the kind of knowledge-grounding work that sentimental perceptualism assumes it does. (shrink)

Brain-related diseases are among the most difficult diseases due to their sensitivity, the difficulty of performing operations, and their high costs. In contrast, the operation is not necessary to succeed, as the results of the operation may be unsuccessful. One of the most common diseases that affect the brain is Alzheimer’s disease, which affects adults, a disease that leads to memory loss and forgetting information in varying degrees. According to the condition of each patient. For these reasons, it is important (...) to classify memory loss and to know the patient at what level and his assessment of Alzheimer's disease through CT scans of the brain. In this thesis, we review ways and techniques to use deep learning classification to classifying the Alzheimer's Disease The proposed method used to improve patient care, reduce costs, and allow fast and reliable analysis in large studies. The model will be designed using Python language for implementing the system, which is very useful for doctors, classifying the Alzheimer's Disease, was used. The model used 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. (shrink)

Abstract: Fruits are a rich source of energy, minerals and vitamins. They also contain fiber. There are many fruits types such as: Apple and pears, Citrus, Stone fruit, Tropical and exotic, Berries, Melons, Tomatoes and avocado. Classification of fruits can be used in many applications, whether industrial or in agriculture or services, for example, it can help the cashier in the hyper mall to determine the price and type of fruit and also may help some people to determining whether (...) a certain type of fruit meets their nutritional requirement. In this paper, machine learning based approach is presented for classifying and identifying 10 different fruit with a dataset that contains 6847 images use 4793 images for training, 1027 images for validation and 1027 images for testing. A deep learning technique that extensively applied to image recognition was used. We used 70% from image for training and 15% from image for validation 15% for testing. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. (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)

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 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)

A large amount of data is maintained in every Social networking sites.The total data constantly gathered on these sites make it difficult for methods like use of field agents, clipping services and ad-hoc research to maintain social media data. This paper discusses the previous research on sentiment analysis.

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)

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)

„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)

For many people, among the first experiences they have of things as being valuable are experiences of things as possessing sentimental value. Such is the case in childhood where treasured objects are often among the first things we experience as valuable. In everyday life, we frequently experi- ence apparent sentimental value belonging to particular garments, books, cards, and places. Philosophers, however, have seldom discussed sentimental value and have also tended to think about value generally in a way that makes it (...) difficult for sentimental value to be a real kind of value. (shrink)

This study uncovered the sentiments of Filipino parents on the mandatory Reserve Officers' Training Corps (ROTC) in senior high school. The study employed the qualitative approach using text mining. The data used in this study were the selected 100 Facebook comments of parents on the online news articles posted on Facebook, headlining the mandatory ROTC in senior high school. The orange software was used to determine the most frequently used words in parents' sentiments on the mandatory ROTC in old high (...) school and to analyze the parents’ sentiments. Meanwhile, thematic analysis was utilized to formulate the definitions of parents' sentiments on the issue. The study revealed that the most frequently used words found on the sentiments of parents on the mandatory ROTC in senior high school as generated by Word Cloud in Orange software are ROTC, train, discipline, yes, mandatory, military, good, student, youth, country, and need. The study further revealed that Filipino parents believed that ROTC inculcates values, trains students to prepare for war, and is suitable for the country. They also thought that the Filipino youth needs discipline nowadays, that a highly qualified official should handle ROTC, and that it should be mandatory for all, except for the physically incapable. It was concluded that most Filipino parents favored implementing the compulsory ROTC in senior high school as the benefits that students and the country would derive outweigh the issues or oppositions against the proposed program. (shrink)

In this book, Russell examines Hume's notion of free will and moral responsibility. It is widely held that Hume presents us with a classic statement of a compatibilist position--that freedom and responsibility can be reconciled with causation and, indeed, actually require it. Russell argues that this is a distortion of Hume's view, because it overlooks the crucial role of moral sentiment in Hume's picture of human nature. Hume was concerned to describe the regular mechanisms which generate moral sentiments such (...) as responsibility, and Russell argues that his conception of free will must be interprted within this naturalistic framework. He goes on to discuss Hume's views about the nature and character of moral sentiment; the extent to which we have control over our moral character; and the justification of punishment. Throughout, Russell argues that the naturalistic avenue of interpretation of Hume's thought, far from draining it of its contemporary interest and significance, reveals it to be of great relevance to the ongoing contemporary debate - particuarly in relation to the views of P.F. Strawson and his influential paper "Freedom and Resentment". (shrink)

In this paper, I challenge standard views of the moral badness of sentimentality defended by art critics and philosophers. Accounts based on untruthfulness and self-indulgence lack the resources to both explain the badness of bad sentimentality and to allow that there are benign instances. We are sometimes permitted to be sentimental even though it is self-serving. A non-moralistic account should allow for this. To provide such an account, I first outline a substantive view of the ideal of unsentimentality by turning (...) to Rainer Maria Rilke’s Letters on Cézanne. Rilke celebrates the artist for his unsentimental love of his subjects and “untiring objective wakefulness” to them. I then turn to Iris Murdoch’s ethics of attention (itself influenced by Rilke) to explain why this ideal is so difficult to live up to in practice, the various ways that we fall short, and what our failures mean. What Murdoch contributes is both a sense that the lovingly attentive attitude that Rilke describes is morally—and not just artistically—important and a compassionate account of the human egocentrism that inhibits it. By thinking of sentimentality as a failure of moral attention, we can appreciate how particular instances differ from one another. Sentimentality can console us in difficult times or can gratify our egos; it can be a form of escapism or can rigidly enclose reality; and it can be harmlessly self-serving or a selfish failure to morally attend to the other when it counts. I conclude by outlining some ways that being seen with Cézanne-like moral attention can matter to us. (shrink)

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)

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

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)

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)

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)

Automatic detection of diseases and anatomical landmarks in medical images by the use of computers is important and considered a challenging process that could help medical diagnosis and reduce the cost and time of investigational procedures and refine health care systems all over the world. Recently, gastrointestinal (GI) tract disease diagnosis through endoscopic image classification is an active research area in the biomedical field. Several GI tract disease classification methods based on image processing and machine learning techniques have (...) been proposed by diverse research groups in the recent past. However, yet effective and comprehensive deep ensemble neural network-based classification model with high accuracy classification results is not available in the literature. In this thesis, we review ways and mechanisms to use deep learning techniques to research on multi-disease computer-aided detection about gastrointestinal and identify these images. We re-trained five state-of-the-art neural network architectures, VGG16, ResNet, MobileNet, Inception-v3, and Xception on the Kvasir dataset to classify eight categories that include an anatomical landmark (pylorus, z-line, cecum), a diseased state (esophagitis, ulcerative colitis, polyps), or a medical procedure (dyed lifted polyps, dyed resection margins) in the Gastrointestinal Tract. Our models have showed results with a promising accuracy which is a remarkable performance with respect to the state-of-the-art approaches. The resulting accuracies achieved using VGG, ResNet, MobileNet, Inception-v3, and Xception were 98.3%, 92.3%, 97.6%, 90% and 98.2%, respectively. As it appears, the most accurate result has been achieved when retraining VGG16 and Xception neural networks with accuracy reache to 98% due to its high performance on training on ImageNet dataset and internal structure that support classification problems. (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: Rice is one of the most important staple crops in the world and serves as a staple food for more than half of the global population. It is a critical source of nutrition, providing carbohydrates, vitamins, and minerals to millions of people, particularly in Asia and Africa. This paper presents a study on using deep learning for the classification of different types of rice. The study focuses on five specific types of rice: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. (...) A dataset of 75,000 images was collected and annotated, consisting of 15,000 images for each of the five types of rice. The deep learning system uses convolutional neural networks (CNNs) to classify images of rice grains based on their physical characteristics. The performance of the deep learning system was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results show that the proposed deep learning system was able to achieve high accuracy in classifying the different types of rice, outperforming traditional image processing techniques. This study provides valuable insights into the potential of deep learning for classifying different types of rice and can be useful for applications such as quality control in rice processing industries and research in crop science. The large size of the dataset, 15,000 images for each type of rice, helped the deep learning system to learn the features of each type of rice, and increase the performance of the classifier. The attained overall accuracy of the proposed model (99.96%), Precision (99.96%), Recall (99.96%) and F1-score (99.96%). Thus, the proposed model proved that it leaned the five categories of rice and it can generalize these categories and it is effective. (shrink)

This study aimed to unveil the sentiments of college students' feedback on their teacher's teaching performance during online classes. The research utilized a qualitative approach, explicitly employing text mining. Orange software determined the most frequently used words illustrating teachers' teaching performance during online courses. Moreover, Latent Semantic Indexing (LSI) was utilized to reveal how those frequently used words were structured. Meanwhile, the study showed that the words "teacher," "class," "us," "student," "online," "teaching," "happy," "learning," "understand," and "good" were most frequently (...) used in college students' sentiments regarding their teacher's performance during online classes. The study also indicated that most students have positive feedback on their teachers' teaching performance in online courses. Therefore, the findings suggest that teachers have the abilities and knowledge to successfully impart lessons to their students despite the enormous dilemma the pandemic has brought about in every sector, especially in education. (shrink)

This qualitative study employing the sentiment analysis method uncovered the people's sentiments on the appointment of Vice-President Sara Z Duterte as the Secretary of the Department of Education. The comments from the Facebook online news platforms were used as the corpora of this study. Results revealed that the appointment of Vice-President Sara Z. Duterte as the Department of Education Secretary received 79% positive sentiments, 11% negative sentiments, and 10% neutral sentiments from the people. This indicates that most people responded (...) favorably to the appointment of Vice President Sara Z. Duterte to the position of Secretary of the Department of Education. This suggests that people believed Vice President Duterte's ability to lead the Education Department and bring about significant changes within the department. (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.

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)

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)

Age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Even Nevertheless, contrast to the large performance improvements recently reported for the closely related task of audio. In this research, we show that performance on these tasks can be significantly improved by learning representations using deep convolutional neural networks (CNN). where we get in the ResNet the training accuracy was 98% ,validation accuracy 95%, testing accuracy 96% (...) .Testing dataset organized into one folder (age-gender-test) and contains 1676 audio files related to 39 of age and gender categories and 40 epoch. (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.

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)

Despite its apparent ubiquity, philosophers have not talked much about sentimental value. One exception is Anthony Hatzimoysis (The Philosophical Quarterly 53:373–379, 2003). Those who wish to take sentimental value seriously are likely to make use of Christine Korsgaard’s ideas on two distinctions in value. In this paper I show that Hatzimoysis has misrendered Korsgaard’s insight in his discussion of sentimental value. I begin by briefly summarising Korsgaard’s idea before showing how Hatzimoysis’ treatment of it is mistaken.

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.

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)

Sign Language Recognition (SLR) aims to translate sign language into text or speech in order to improve communication between deaf-mute people and the general public. This task has a large social impact, but it is still difficult due to the complexity and wide range of hand actions. We present a novel 3D convolutional neural network (CNN) that extracts discriminative spatial-temporal features from photo datasets. This article is about classification of sign languages are not universal and are usually not mutually (...) intelligible although there are also similarities among different sign languages. They are the foundation of local Deaf cultures and have evolved into effective means of communication. Although signing is primarily used by the deaf and hard of hearing, hearing people also use it when they are unable to speak, when they have difficulty speaking due to a health condition or disability (augmentative and alternative communication), or when they have deaf family members, such as children of deaf adults. In this article we use the 43500 image in the dataset in size 64*64 pixel by use CNN Architecture and achieved 100% accuracy. (shrink)

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

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)

How to distinguish emotions such as envy, disgust, and shame from sentiments such as love, hate, and adoration? While the standard approach argues that emotions and sentiments differ in terms of their temporal structures (e.g., Ben-ze’ev, 2000; Deonna & Teroni, 2012; Frijda et al., 1991), this paper sketches an alternative approach according to which each of these states exhibits a distinctive intentional structure. More precisely, this paper argues that emotions and sentiments exhibit distinct forms of affective intentionality. The paper begins (...) by examining the temporal criteria of duration, etiology, and phenomenology widely employed to distinguish between both states. It demonstrates that none of them provides a clear-cut distinction between emotions and sentiments. Next, it presents the intentional approach as an alternative. To this end, it discusses what I call the axiological account (De Monticelli’s 2006; 2020), before introducing my version of the intentional approach according to which emotions and sentiments exhibit different forms of affective intentionality. The main findings are summarized in the conclusion. (shrink)

We seem to be able to acquire evaluative knowledge by mere reflection, or “from the armchair.” But how? This question is especially pressing for proponents of sentimental perceptualism, which is the view that our evaluative knowledge is rooted in affective experiences in much the way that everyday empirical knowledge is rooted in perception. While such empirical knowledge seems partially explained by causal relations between perceptions and properties in the world, in armchair evaluative inquiry, the relevant evaluative properties are typically not (...) even present. The paper shows how sentimental perceptualists can ultimately provide a broadly causal explanation of our reflective evaluative knowledge. (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 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)

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

This chapter examines philosophical issues surrounding the classification of mental disorders by the Diagnostic and Statistical Manual of Mental Disorders (DSM). In particular, the chapter focuses on issues concerning the relative merits of descriptive versus theoretical approaches to psychiatric classification and whether the DSM should classify natural kinds. These issues are presented with reference to the history of the DSM, which has been published regularly by the American Psychiatric Association since 1952 and is currently in its fifth edition. (...) While the first two editions of the DSM adopted a theoretical (psychoanalytic) and etiological approach to classification, subsequent editions of the DSM have adopted an atheoretical and purely descriptive (“neo-Kraepelinian”) approach. It is argued that largest problem with the DSM at present—viz., its failure to provide valid diagnostic categories—is directly related to the purely descriptive methodology championed by the DSM since the third edition of the DSM. In light of this problem, the chapter discusses the prospects of a theoretical and causal approach to psychiatric classification and critically examines the assumption that the DSM should classify natural kinds. (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)

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Deep learning (...) is a technique used to generate face detection and recognize it for real or fake by using profile images and determine the differences between them. In this study, we used deep learning techniques to generate models for Real and Fake face detection. The goal is determining a suitable way to detect real and fake faces. The model was designed and implemented, including both Dataset of images: Real and Fake faces detection through the use of Deep learning algorithms based on neural networks. We have trained dataset which consists of 9,000 images for total in 150 epochs, and got the ResNet50 model to be the best model of network architectures used with 100% training accuracy, 99.18% validation accuracy, training loss 0.0003, validation loss 0.0265, and testing accuracy 99%. (shrink)

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)