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)

„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)

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)

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: 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: 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)

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.

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)

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

Abstract: Rice 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)

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)

Abstract: Pistachio nuts are a valuable source of nutrition and are widely cultivated for commercial purposes. The accurate classification of different pistachio varieties is important for quality control and market analysis. In this study, we propose a new model for the classification of different pistachio varieties using Convolutional Neural Networks (CNNs). We collected a dataset of pistachio images form Kaggle depository with two varieties (Kirmizi and Siirt). The images were then preprocessed and used to train a CNN model (...) based on VGG16. We evaluated the performance of the VGG16 on a held-out test set. Our results show that the CNN-based model VGG16 performed very well in terms of accuracy and efficiency. The VGG16 model was able to accurately classify different pistachio varieties with an accuracy of over 99.91%. Additionally, the VGG16 was able to generalize well to new and unseen data, demonstrating its ability to handle variability in the pistachio images. This study highlights the potential of CNNs for the classification of different pistachio varieties, and has important implications for the pistachio industry. The results suggest that this approach can be used to improve the quality control process and enhance market analysis. (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: 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)

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)

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.

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.

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)

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)

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)

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

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

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)

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)

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)

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: Classifying sea animals is an important problem in marine biology and ecology as it enables the accurate identification and monitoring of species populations, which is crucial for understanding and protecting marine ecosystems. This paper addresses the problem of classifying 19 different sea animals using convolutional neural networks (CNNs). The proposed solution is to use a pretrained MobileNetV2 model, which is a lightweight and efficient CNN architecture, and fine-tune it on a dataset of sea animals. The results of the study (...) show that the fine-tuned MobileNetV2 model is able to achieve an accuracy of 85% on the classification task, which is competitive with the state-of-the-art methods. Additionally, the study also found that data augmentation and regularization techniques such as dropout were important for preventing overfitting and improving the performance of the model. This study demonstrates the potential of using pretrained models and efficient CNN architectures for classifying sea animals, and it provides insights into the challenges and best practices for solving this important problem in marine biology and ecology. (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)

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

In this 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)

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)

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)

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)

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

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: 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)

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

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

Abstract: In this study, we explore the challenge of identifying and preventing diseases in apple trees, which is a popular activity but can be difficult due to the susceptibility of these trees to various diseases. To address this challenge, we propose the use of Convolutional Neural Networks, which have proven effective in automatically detecting plant diseases. To validate our approach, we use images of apple leaves, including Apple Rot Leaves, Leaf Blotch, Healthy Leaves, and Scab Leaves collected from Kaggle which (...) is part from the Plant Village dataset. We generate a comprehensive training dataset using techniques such as image filtering, compression, and generation. Our model achieves impressive accuracy scores for all classes, with an overall accuracy of 99.93% on a dataset of 10,000 labeled images . (shrink)

This chapter proceeds in five steps. First, we will describe and justify the structure of the traditional system of species classification. Second, we will discuss three formal principles governing the development of taxonomies in general. It will emerge that, in addition to these formal principles, a division of living beings must meet certain empirical constraints. In the third section, we will show that the traditional division of living beings into species best meets these constraints. Fourth, we will argue that (...) a taxonomic system based on this notion of species provides a more natural alternative to the many arbitrary classifications that are possible. Hence, the traditional classificatory system is also the most natural one. Finally, we will discuss and reject an alternative account that suggests defining species solely with a view to their evolutionary history. We will argue that taxonomic trees do not depict hereditary connections but, rather, something else. (shrink)

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

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

A pineapple is a tropical plant with eatable leafy foods most monetarily critical plant in the family Bromeliaceous. The pineapple is native to South America, where it has been developed for a long time. The acquaintance of the pineapple with Europe in the seventeenth century made it a critical social symbol of extravagance. Since the 1820s, pineapple has been industrially filled in nurseries and numerous tropical manors. Further, it is the third most significant tropical natural product in world creation. In (...) the twentieth century, Hawaii was a prevailing maker of pineapples, particularly for the US, be that as it may, by 2016, Costa Rica, Brazil, and the Philippines represented almost 33% of the world's creation of pineapples. In this paper, machine learning based approach is presented for identifying type pineapple with a dataset that contains 1,311images use 946 images for training, 197 images for validation and 168 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 heldout test set. (shrink)

The classification of a state of affairs under a legal category can be considered as a kind of con- densed decision that can be made explicit, analyzed, and assessed us- ing argumentation schemes. In this paper, the controversial conflict of opinions concerning the nature of “marriage” in Obergefell v. Hodges is analyzed pointing out the dialecti- cal strategies used for addressing the interpretive doubts. The dispute about the same-sex couples’ right to marry hides a much deeper disa- greement not (...) only about what mar- riage is, but more importantly about the dialectical rules for defining it. (shrink)

American Sign Language, or ASL as its acronym is commonly known, is a fascinating language, and many people outside of the Deaf community have begun to recognize its value and purpose. It is a visual language consisting of coordinated hand gestures, body movements, and facial expressions. Sign language is not a universal language; it varies by country and is heavily influenced by the native language and culture. The American Sign Language alphabet and the British Sign Language alphabet are completely contrary. (...) Fingerspelling is one-handed in ASL and two-handed in BSL, with the exception of the letter C. AI technologies, particularly deep learning, can play an important role in breaking down communication barriers between deaf or hearing-impaired people and other communities, significantly contributing to their social inclusion. Recent advances in sensing technologies and AI algorithms have paved the way for the development of a wide range of applications aimed at meeting the needs of the deaf and hearing-impaired communities. To that end, the purpose of this paper is By attempting to translate sign language using artificial intelligence algorithms, we focused in this paper on a transfer learning technique based on deep learning using a ResNet algorithm and compared it to previous papers in which we used VGG19[1]and mobileNet [2], where we get in the ResNet algorithm on the degree of Accuracy 93,48% e number of images (43500 in the dataset in size 64*64 pixel ) and the data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch. (shrink)

In this paper, we provide a detailed review of previous classifications of 2 × 2 games and suggest a mathematically simple way to classify the symmetric 2 × 2 games based on a decomposition of the payoff matrix into a cooperative and a zero-sum part. We argue that differences in the interaction between the parts is what makes games interesting in different ways. Our claim is supported by evolutionary computer experiments and findings in previous literature. In addition, we provide a (...) method for using a stereographic projection to create a compact 2-d representation of the game space. (shrink)