Formulating the nature of things is one of the oldest subjects addressed in the world and all over the world, because ancient peoples were high-intellect enough to be aware of the issue. -/- To me it’s about resolving indefinite- -dynamic theory potentialities into best effort coherent, true-to-life sense. -/- Ancient Athens addressed the issue. Plato settled on a formulation- -system. It took hold in much of the Western world. It was informed by intellect of the primal senses premises and his (...) ideas about matter as “the four elements”. -/- So he had to theorize Father Supernatural, emanating life and consciousness to matter. -/- Today we are aware of quantum dynamics as a tension of ‘forces’. This tension expresses in the macro world as life and consciousness. -/- This then generated the problem that Father Supernatural and Mother Nature did not cohere. -/- It never made satisfactory sense to specialists as a coherent, true to life system. So, they kept writing for 2,400 years. -/- E-PS emerged informed by high intellect and modern ideas about matter: Effect, through featuring indefinite- -dynamic ‘forces’; one emerges through- -as tension of featuring ‘forces’. -/- So it is quite a contrast to Plato’s categoricalism…. and a coherent, true-to-life world can be shown to develop from that principle … or law of Nature … an indefinite- -dynamic world. -/- For the uninitiated I believe E-PS would serve as a rigorous frame of reference with which to engage with the thoughts of Buddha, Plato and Oriental Sage Wisdom, etc. and to test how coherent and true-to-life the system featuring in your culture is. -/- If one is impacted by a system without a frame of reference, it is likely to imprint. As children in the home and as young adults in ‘the academy’, this happens. Many never transcend it. -/- However, now such transcending is done and is presented here. -/- Socrates: "The unexamined life is not worth living". -/- Also, through E-PS, convolutions in Physics are corrected! (shrink)

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to (...) a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain. (shrink)

The core objective of this project is to recognize and reconstruct distorted facial images, particularly in the context of accidents. This involves using deep learning techniques to analyze the features of a distorted face and regenerate it into a recognizable form. Deep learning models are wellsuited for this task due to their ability to learn complex patterns and representations from data the input data consists of distorted facial images, typically obtained from MRI scans of accident victims. These images may contain (...) various types of distortions such as swelling, bruising, or other injuries that affect facial appearance. (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)

Focusing principally on the once-feted philosophical movement of object-oriented ontology (OOO), this article examines the ways in which this movement fits into a broader “speculative turn,” which seeks to reverse the purportedly wrongheaded emphasis of post-Kantian critical philosophy upon the finitude of the subject and to once again unleash the fecund potentialities of speculative thought. Identifying several incongruities and tensions that traverse this project, it is argued that OOO exemplifies the difficulties faced when attempting to articulate a decidedly pre-critical metaphysics.

: One of the most active study fields in natural language processing, web mining, and text mining is sentiment analysis. Big data is an important research component in education that is used to advance the value of education by watching students' performance and understanding their learning habits. Real-time student feedback will enable teachers and students to understand teaching and learning challenges in the most user-friendly manner for students. By linking learning analytics to grounded theory, the proposed Deep Convolutional Neural Network (...) (DCNN) analyses students' sentiments and emotions through feedback using a Lexicon-based emotional analysis approach. The sentiment analysis approach is a computer procedure that identifies and classifies subjective information from the source material as good, negative, or neutral. (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)

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)

The accuracy levels of VGG19 and 3D CNN are compared using the performance metrics. This comparison helps in identifying which model performs better in the task of facial reconstruction from distorted images. Visualizing the results in the form of a graph provides a clear and concise way to understand the comparative performance of the algorithms. The ultimate goal of this project is to develop a system that can accurately reconstruct distorted faces, which can be invaluable in identifying accident victims or (...) assisting in medical treatments. By providing a reliable method for facial reconstruction, this technology can potentially save lives and improve outcomes for individuals involved in accidents. (shrink)

. The accuracy levels of VGG19 and 3D CNN are compared using the performance metrics. This comparison helps in identifying which model performs better in the task of facial reconstruction from distorted images. Visualizing the results in the form of a graph provides a clear and concise way to understand the comparative performance of the algorithms. The ultimate goal of this project is to develop a system that can accurately reconstruct distorted faces, which can be invaluable in identifying accident victims (...) or assisting in medical treatments. By providing a reliable method for facial reconstruction, this technology can potentially save lives and improve outcomes for individuals involved in accidents. (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)

Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in dvance regarding: on-going construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. In (...) our proposed system we propose a new forecasting method that uses a Convolutional Neural Network monthly rainfall for a selected location. In our proposed system we are going to forecast the rainfall result based on the mean square error, mean absolute error and root mean square error, which we get in train and test of the dataset based deep learning technique. (shrink)

The representations of deep convolutional neural networks (CNNs) are formed from generalizing similarities and abstracting from differences in the manner of the empiricist theory of abstraction (Buckner, Synthese 195:5339–5372, 2018). The empiricist theory of abstraction is well understood to entail infinite regress and circularity in content constitution (Husserl, Logical Investigations. Routledge, 2001). This paper argues these entailments hold a fortiori for deep CNNs. Two theses result: deep CNNs require supplementation by Quine’s “apparatus of identity and quantification” in order to (1) (...) achieve concepts, and (2) represent objects, as opposed to “half-entities” corresponding to similarity amalgams (Quine, Quintessence, Cambridge, 2004, p. 107). Similarity amalgams are also called “approximate meaning[s]” (Marcus & Davis, Rebooting AI, Pantheon, 2019, p. 132). Although Husserl inferred the “complete abandonment of the empiricist theory of abstraction” (a fortiori deep CNNs) due to the infinite regress and circularity arguments examined in this paper, I argue that the statistical learning of deep CNNs may be incorporated into a Fodorian hybrid account that supports Quine’s “sortal predicates, negation, plurals, identity, pronouns, and quantifiers” which are representationally necessary to overcome the regress/circularity in content constitution and achieve objective (as opposed to similarity-subjective) representation (Burge, Origins of Objectivity. Oxford, 2010, p. 238). I base myself initially on Yoshimi’s (New Frontiers in Psychology, 2011) attempt to explain Husserlian phenomenology with neural networks but depart from him due to the arguments and consequently propose a two-system view which converges with Weiskopf’s proposal (“Observational Concepts.” The Conceptual Mind. MIT, 2015. 223–248). (shrink)

It has been argued that the transition from classical to quantum mechanics is an example of a Kuhnian scientific revolution, in which there is a shift from the simple, intuitive, straightforward classical paradigm, to the quantum, convoluted, counterintuitive, amazing new quantum paradigm. In this paper, after having clarified what these quantum paradigms are supposed to be, I analyze whether they constitute a radical departure from the classical paradigm. Contrary to what is commonly maintained, I argue that, in addition to radical (...) quantum paradigms, there are also legitimate ways of understanding the quantum world that do not require any substantial change to the classical paradigm. (shrink)

In a convoluted and a frail sense there might be external reasons. One cannot just precipitate an external reason on demand. Their emergence is feasible only via a posteriori rationalization---effectively the effects of future mental events. This paper attempts to specify pre-conditions for an (internal) reason for action in the past to transform into an external reason later. Most fundamentally one must not know something about one's reason at the time of action .

In the world according to Hume, people are complicated creatures, with convoluted, often contradictory characters. Consider, for example, Hume's controversial assessment of Charles I: "The character of this prince, as that of most men, if not of all men, was mixed .... To consider him in the most favourable light, it may be affirmed, that his dignity was free from pride, his humanity from weakness, his bravery from rashness, his temperance from austerity, his frugality from avarice .... To speak the (...) most harshly of him, we may affirm, that many of his good qualities were attended with some latent frailty, which, though seemingly inconsiderable, was able, when seconded by the extreme malevolence of his fortune, to disappoint them of all their influence: His beneficent disposition was clouded by a manner not very gracious; his virtue was tinctured with superstition; his good sense was disfigured by a deference to persons of a capacity inferior to his own; and his moderate temper exempted him not from hasty and precipitate resolutions." This sketch shows Charles in all his complexities, with his virtues, near virtues, and contradicting virtues. I have quoted it at length because it is hard to summarize without losing the subtleties that lie within it. Hume's moral theory is based fundamentally on judgments of character, 2 so those subtleties are important to his view. The character sketches that pervade the.. (shrink)

The inoffensive title of Section 1.4.7 of Hume’s Treatise of Human Nature, ‘Conclusion of this Book’, belies the convoluted treatment of scepticism contained within. It is notoriously difficult to decipher Hume’s considered response to scepticism in this section, or whether he even has one. In recent years, however, one line of interpretation has gained popularity in the literature. The ‘usefulness and agreeableness reading’ (henceforth U&A) interprets Hume as arguing in THN 1.4.7 that our beliefs and/or epistemic policies are justified via (...) their usefulness and agreeableness to the self and others; proponents include Ardal (in Livingston & King (eds.) Hume: a re-evaluation, 1976), Kail (in: Frasca-Spada & Kail (eds.) Impressions of Hume, 2005), McCormick (Hume Stud 31:1, 2005), Owen (Hume’s reason, 1999), and Ridge (Hume Stud 29:2, 2003), while Schafer (Philosophers, forthcoming) also defends an interpretation along these lines. In this paper, I will argue that although U&A has textual merit, it struggles to maintain a substantive distinction between epistemic and moral justification—a distinction that Hume insists on. I then attempt to carve out the logical space for there being a distinctly epistemic notion of justification founded on usefulness and agreeableness. However, I find that such an account is problematic for two reasons: first, it cannot take advantage of the textual support for U&A; secondly, it is incompatible with other features of the text. (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)

The World Health Organization has declared the outbreak of the novel coronavirus, Covid-19 as pandemic across the world. With its alarming surge of affected cases throughout the world, lockdown, and awareness (social distancing, use of masks etc.) among people are found to be the only means for restricting the community transmission. In a densely populated country like India, it is very difficult to prevent the community transmission even during lockdown without social awareness and precautionary measures taken by the people. Recently, (...) several containment zones had been identified throughout the country and divided into red, orange and green zones, respectively. The red zones indicate the infection hotspots, orange zones denote some infection and green zones indicate an area with no infection. Contact tracing is known to be an effective technique for detecting and monitoring persons who may have been exposed to individuals infected with any communicable disease. While a good number of contact tracing schemas are existent today (eg., in-person and phone interviews, paper forms, email and web based questionnaires and smartphone apps), they often require active user participation and might miss certain COVID-19 transmission. The Android application updates the locations of the areas in a Google map which are identified to be the containment zones. The application also notifies the users if they have entered a containment zone and to achieve all these functionalities, many tools, and APIs from Google like Firebase and Geofencing API are used in this application. Therefore, this application can be used as a tool for creating further social awareness about the arising need of precautionary measures to be taken by the people of India. (shrink)

In this essay we focus on our vast web of beliefs that serves us as a rough and ready map of reality, generated more to give us comfort and confidence in an intimidating world than to be accurate. Maps of reality can never be accurate in any ultimate sense since reality itself is a convoluted entity that can only be accessed in never- ending layers. Our repertoire of beliefs, generated compulsively in the mind, span a huge spectrum in respect of (...) ties to affect and emotions on the one hand and of their credibility in depicting reality on the other. The interaction between our affect-based psychological needs, our need for having a sound comprehension of reality, and the infinitely tangled reality itself, leads to the generation of a large diversity of types of beliefs, where these get revised under the impact of reality by overcoming the restrain- ing effect of emotions in diverse ways and over diverse time scales—interestingly, the ‘reality’ includes the complex repertoire of affect and emotions itself that may, in cer- tain cases, generate the impact for our beliefs to change. Beliefs represented by small clusters in our belief network (see below) are commonly prone to frequent revision in a more or less random manner, though there are beliefs acquired in early life that are intransigent to change because of overriding ties to emotions. Beliefs represented by larger clusters are comparatively durable, and their revision has a systematic aspect to it because of a greater role of evidence-based factors—however,the evidence may, once again, be tied with emotions and beliefs oriented away from reason. Finally, there are prepotent beliefs that are resistant to change even over a lifetime. Often, these beliefs get revised only if we engage with our own selves, looking at conflicting forces within ourselves. In this context, we refer to narratives that are self-reinforcing and exercise almost a mesmerizing influence on us. In all this, there is a dual role of ‘rationality’ that needs being attended to in describing and explaining belief revision—rationality is not exclusively based on evidence and reason, but includes the implicit operation of affect and emotions too. (shrink)

Society is a composite of interacting people and groups. These groups play a significant role in maintaining social status, establishing group identity and social identity, and enforcing norms. As such, groups are essential for understanding human behavior. Nevertheless, the study of groups in everyday group life yields many diverse and sometimes contradicting theories of group behavior, and researchers tend to agree that we have yet to understand the emergence of groups out of aggregates of individuals. The current paper aims to (...) shed new light on the convoluted interrelation between groups and individuals by focusing on individuals’ social identities and group categorization. It does so by exploring the dynamic nature of the self and its implications on identity and group membership, and introducing a framework recognizing the fluidity of groups and group categorization. Incorporating historical insights with contemporary theories, this paper argues for a flexible understanding of group dynamics that surpasses rigid in-group and out-group classifications, proposing instead that group affiliations exist along a continuum that reflects the ever-changing social landscape. (shrink)

The dominant legal and regulatory approach to protecting information privacy is a form of mandated disclosure commonly known as “notice-and-consent.” Many have criticized this approach, arguing that privacy decisions are too complicated, and privacy disclosures too convoluted, for individuals to make meaningful consent decisions about privacy choices—decisions that often require us to waive important rights. While I agree with these criticisms, I argue that they only meaningfully call into question the “consent” part of notice-and-consent, and that they say little about (...) the value of notice. We ought to decouple notice from consent, and imagine notice serving other normative ends besides readying people to make informed consent decisions. (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)

I have two main goals in this paper. The first is to argue for the thesis that Kant gave up on his highest good argument for the existence of God around 1800. The second is to revive a dialogue about this thesis that died out in the 1960s. The paper is divided into three sections. In the first, I reconstruct Kant’s highest good argument. In the second, I turn to the post-1800 convolutes of Kant’s Opus postumum to discuss his repeated (...) claim that there is only one way to argue for the existence of God, a way which resembles the highest good argument only in taking the moral law as its starting point. In the third, I explain why I do not find the counterarguments to my thesis introduced in the 1960s persuasive. (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)

This paper aims to offer a new view of the role of connectionist models in the study of human cognition through the conceptualization of the history of connectionism – from the simplest perceptrons to convolutional neural nets based on deep learning techniques, as well as through the interpretation of criticism coming from symbolic cognitive science. Namely, the connectionist approach in cognitive science was the target of sharp criticism from the symbolists, which on several occasions caused its marginalization and almost complete (...) abandonment of its assumptions in the study of cognition. Criticisms have mostly pointed to its explanatory inadequacy as a theory of cognition or to its biological implausibility as a theory of implementation, and critics often focused on specific shortcomings of some connectionist models and argued that they apply to connectionism in general. In this paper, we want to show that both types of critique are based on the assumption that the only valid explanations in cognitive science are instances of homuncular functionalism and that by removing this assumption and by adopting an alternative methodology – exploratory mechanistic strategy, we can reject most objections to connectionism as irrelevant, explain the progress of connectionist models despite their shortcomings and sketch the trajectory of their future development. By adopting mechanistic explanations and by criticizing functionalism, we will reject the objections of explanatory inadequacy, by characterizing connectionist models as generic rather than concrete mechanisms, we will reject the objections of biological implausibility, and by attributing the exploratory character to connectionist models we will show that practice of generalizing current to general failures of connectionism is unjustified. (shrink)

PUTNAM has made highly regarded contributions to mathematics, to philosophy of logic and to philosophy of science, and in this book he brings his ideas in these three areas to bear on the traditional philosophic problem of materialism versus (objective) idealism. The book assumes that contemporary science (mathematical and physical) is largely correct as far as it goes, or at least that it is rational to believe in it. The main thesis of the book is that consistent acceptance of contemporary (...) science requires the acceptance of some sort of Platonistic idealism affirming the existence of abstract, non-temporal, non-material, non-mental entities (numbers, scientific laws, mathematical formulas, etc.). The author is thus in direct opposition to the extreme materialism which had dominated philosophy of science in the first three quarters of this century. The book can be recommended to the scientifically literate, general reader whose acquaintance with these areas is limited to the literature of the 1950’s and before, when it had been assumed that empiricistic materialism was the only philosophy compatible with a scientific outlook. To this group the book presents an eye-opening challenge fulfilling the author’s intention of “shaking up preconceptions and stimulating further discussion”. QUINE’S book is not easy to read, partly because the level of sophistication fluctuates at high frequency between remote extremes and partly because of convoluted English prose style and devilish terminology. Almost all of the minor but troublesome technical errata in the first printing have been corrected [see reviews, e.g., the reviewer, Philos. Sci. 39 (1972), no. 1, 97–99]. In the opinion of the reviewer the book is not suitable for undergraduate instruction, and without external motivation few mathematicians are likely to have the patience to appreciate it. Nevertheless, a careful study of the book will more than repay the effort and one should expect to find frequent references to this book in coming years. (shrink)

Quilty-Dunn et al. argue that deep convolutional neural networks (DCNNs) optimized for image classification exemplify structural disanalogies to human vision. A different kind of artificial vision – found in reinforcement-learning agents navigating artificial three-dimensional environments – can be expected to be more human-like. Recent work suggests that language-like representations substantially improves these agents’ performance, lending some indirect support to the language-of-thought hypothesis (LoTH).

The proposed model in this paper employs different integrated detectors, such as heat, smoke, and flame. The signals from those detectors go through the system algorithm to check the fire's potentiality and then broadcast the predicted result to various parties using GSM modem associated with the GSM network system. The system uses various sensors to detect fire, smoke, and gas, then transmits the message using GSM module. After the message, send by the module the help arrives in 15 minutes. The (...) system is associated with water pump and a sprinkler. The Water system is associated with a water tank and a sprinkler. The water tank uses a Ultrasonic sensor to monitor the level of water level in the tank for refilling the water tank. Once fire is detected, it automatically sends the emergency message and sprinkles the water. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging represents a transformative shift in healthcare, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. This paper explores the application of AI technologies in the analysis of medical images, focusing on techniques such as convolutional neural networks (CNNs) and deep learning models. We discuss how these technologies are applied to various imaging modalities, including X-rays, MRIs, and CT scans, to enhance disease detection, image segmentation, and diagnostic support. Additionally, the (...) paper addresses the challenges faced in AI-driven medical imaging, including data quality, model interpretability, and ethical considerations. By examining recent advancements and real-world case studies, this paper provides insights into the current state of AI in medical imaging and its potential future directions. The findings highlight the ongoing evolution of AI technologies and their crucial role in advancing medical diagnostics and treatment strategies. (shrink)

: I offer a new approach to the increasingly convoluted debate between the A- and B-theories of time, the ‘tensed’ and ‘tenseless’ theories. It is often assumed that the B-theory faces more difficulties than the A-theory in explaining the apparently tensed features of temporal experience. I argue that the A-theory cannot explain these features at all, because on any physicalist or supervenience theory of the mind, in which the nature of experience is fixed by the physical state of the world, (...) the tensed properties of time posited by the A-theory could play no role in shaping temporal experience. It follows that the A-theory is false; even a priori arguments for it fail, because they still require the tensed vocabulary which is used to describe temporal experience. (shrink)

The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge data set, (...) the cybersecurity warning systems index system is first constructed, then the index factors are picked and measured, and finally, the situation evaluation is done.Numerous bio-inspired techniques were used to enhance the productivity of an IDS by lowering the data dimensionality and deleting unnecessary and noisy input. The Grey Wolf Optimization algorithm (GWO) is a developed bio-inspired algorithm that improves the efficacy of the IDS in detecting both regular and abnormal congestion in the network. The smart initialization step integrates the different pre-processing strategies to make sure that informative features are incorporated in the early development stages, has been improved. Researchers pick multi-source material in a big data environment for the identification and verification of index components and present a parallel reduction approach based on the classification significance matrix to decrease data underlying data characteristics. For the simulation of this situation, grey wolf optimization and whale optimization were combined to detect the attack prevention and the deep learning approach was presented. Utilizing system software plagiarism, the TensorFlow deep neural network is intended to classify stolen software. To reduce the noise from the signal and to zoom the significance of each word in the perspective of open-source plagiarism, the tokenization and weighting feature approaches are utilized. Malware specimens have been collected from the Mailing database for testing purposes. The experimental findings show that the suggested technique for measuring cyber security hazards in IoT has superior classification results to existing methods. Hence to detect the attack prevention in IoT process Whale with Grey wolf optimization (WGWO) and deep convolution network is used. (shrink)

Probably the leading exponent of Wittgenstein’s ideas on the language games of inner and outer (the ‘Two Selves’ operation of our personality or intentionality or EP etc.) the prolific Daniel Hutto’s (DH) approach is called ‘Radical Enactivism’ and is well explained in numerous recent books and papers. It is a development of or version of the Embodied Mind ideas now current and, cleansed of its jargon, it is a straightforward extension of W’s 2nd and 3rd period writings (though Hutto seems (...) only intermittently aware of this). -/- The basic idea of the Embodied Mind or Enactivism is that much of behavior is automated and does not involve representations (basically S2 dispositions-see Hutto’s lovely dissection of the ‘representation rats nest’ in his online papers ). To me this is just another way of stating the fact that System 1 precedes the operation of System 2 which is a standard feature of contemporary psychology, which I have explained above and in further detail in my reviews of Wittgenstein (who was the first to see this and explored it in great detail) and Searle (who called it The Phenomenological Illusion in his superb essay of this name in his book Philosophy in a New Century which I have also reviewed). Since these are basic incontrovertible facts of animal behavior and I have already discussed them I won’t dwell on it here. -/- This book is a sustained argument against other similar ways of describing behavior which he calls CEC and CIC in favor of REC (Radical Embodied Cognition), which he characterizes as “the strongest reading of the embodiment thesis—one that uncompromisingly maintains that basic cognition is literally constituted by, and to be understood in terms of concrete patterns of environmental situated organismic activity, nothing more or less” (p11). This is clear as a bell if you understand the two systems view explained above but likely opaque if you don’t. Much clearer is Fodor’s characterization which he quotes as “abilities are prior to theories”, that “competence is prior to content” and that “knowing how is the paradigm cognitive state and it is prior to knowing that” (p10). That is, the unconscious automatisms of S1 are evolutionarily and behaviorally prior to the slow conscious dispositions of S2. -/- This is classic Hutto high level philosophical dialog, which is quite elegant, but somewhat too dense and a tad pretentious for the rest of us. I have not before encountered his coauthor Myin so can’t say how much of this text is really due to him. It is clear from this and the rest of Hutto’s work that (like everyone else) he has not quite kept up with the latest work in psychology nor really grasped the full power of W or S, even though he is one of the top Wittgensteinians alive and as bright as anyone in the field. His discussions of the language games of “information” and “representation” in his other papers and books (and much else including his deconstructions of Dennett and Fodor) should be required reading for anyone interested in behavior. So, I have the greatest respect for him, but one hopes that he will mellow with time and write descriptions of behavior (i.e., all we can really do as philosophers according to W) in more mundane prose such as this lovely summation on p15. “Hence, REC is nothing less than a fundamental rethinking of the very foundations of standard approaches to cognitive science and philosophy of mind.” Yes and what a pity that this great Wittgensteinian (and everyone else) does not realize that W laid it all out with great (and unmatched) clarity in his third period works over 60 years ago. -/- I have much less sympathy for the extended and scaffolded minds of Chap 7. I don’t see how one can lay the burden of explaining how the mind works at Searle’s door, nor how the convoluted prose about “decoupled contentful activities” etc., helps at all. Why not just say that automated unconscious prelinguistic S1 feeds deliberate, conscious linguistic S2, which is axiomatically extended by public language into the myriad wonders of culture? Beginning and end of story. -/- Their last chapter is about “regaining consciousness,” but I would say that if one has understood Wittgenstein and Searle, one has never lost it. And, though this is an excellent book by two of the brightest and the best, I suggest mulling over my thoughts in this and other reviews and reading Johnston and the latest from Searle, along of course with as much of 3rd period W as feasible, is an even better filter for folly. In sum an excellent book with various faults which I try to correct. -/- Those wishing a comprehensive up to date framework for human behavior from the modern two systems view may consult my article The Logical Structure of Philosophy, Psychology, Mind and Language as Revealed in Wittgenstein and Searle 59p(2016). For all my articles on Wittgenstein and Searle see my e-book ‘The Logical Structure of Philosophy, Psychology, Mind and Language in Wittgenstein and Searle 367p (2016). Those interested in all my writings in their most recent versions may consult my e-book Philosophy, Human Nature and the Collapse of Civilization - Articles and Reviews 2006-2016 662p (2016). -/- All of my papers and books have now been published in revised versions both in ebooks and in printed books. -/- Talking Monkeys: Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet - Articles and Reviews 2006-2017 (2017) https://www.amazon.com/dp/B071HVC7YP. -/- The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle--Articles and Reviews 2006-2016 (2017) https://www.amazon.com/dp/B071P1RP1B. -/- Suicidal Utopian Delusions in the 21st century: Philosophy, Human Nature and the Collapse of Civilization - Articles and Reviews 2006-2017 (2017) https://www.amazon.com/dp/B0711R5LGX . (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)

Probably the leading exponent of W’s ideas on the language games of inner and outer (the ‘Two Selves’ operation of our personality or intentionality or EP etc. ) the prolific Daniel Hutto’s (DH) approach is called ‘Radical Enactivism’ and is well explained in numerous recent books and papers. It is a development of or version of the Embodied Mind ideas now current and, cleansed of its jargon, it is a straightforward extension of W’s 2nd and 3rd period writings (though Hutto (...) seems only intermittently aware of this). -/- The basic idea of the Embodied Mind or Enactivism is that much of behavior is automated and does not involve representations (basically S2 dispositions-see Hutto’s lovely dissection of the ‘representation rats nest’ in his online papers ). To me this is just another way of stating the fact that System 1 precedes the operation of System 2 which is a standard feature of contemporary psychology, which I have explained above and in further detail in my reviews of Wittgenstein (who was the first to see this and explored it in great detail) and Searle (who called it The Phenomenological Illusion in his superb essay of this name in his book Philosophy in a New Century which I have also reviewed). Since these are basic incontrovertible facts of animal behavior and I have already discussed them I won’t dwell on it here. -/- This book is a sustained argument against other similar ways of describing behavior which he calls CEC and CIC in favor of REC (Radical Embodied Cognition), which he characterizes as “the strongest reading of the embodiment thesis—one that uncompromisingly maintains that basic cognition is literally constituted by, and to be understood in terms of concrete patterns of environmental situated organismic activity, nothing more or less” (p11). This is clear as a bell if you understand the two systems view explained above but likely opaque if you don’t. Much clearer is Fodor’s characterization which he quotes as “abilities are prior to theories”, that “competence is prior to content” and that “knowing how is the paradigm cognitive state and it is prior to knowing that” (p10). That is, the unconscious automatisms of S1 are evolutionarily and behaviorally prior to the slow conscious dispositions of S2. -/- This is classic Hutto high level philosophical dialog, which is quite elegant, but somewhat too dense and a tad pretentious for the rest of us. I have not before encountered his coauthor Myin so can’t say how much of this text is really due to him. It is clear from this and the rest of Hutto’s work that (like everyone else) he has not quite kept up with the latest work in psychology nor really grasped the full power of W or S, even though he is one of the top Wittgensteinians alive and as bright as anyone in the field. His discussions of the language games of “information” and “representation” in his other papers and books (and much else including his deconstructions of Dennett and Fodor) should be required reading for anyone interested in behavior. So, I have the greatest respect for him, but one hopes that he will mellow with time and write descriptions of behavior (i.e., all we can really do as philosophers according to W) in more mundane prose such as this lovely summation on p15. “Hence, REC is nothing less than a fundamental rethinking of the very foundations of standard approaches to cognitive science and philosophy of mind.” Yes and what a pity that this great Wittgensteinian (and everyone else) does not realize that W laid it all out with great (and unmatched) clarity in his third period works over 60 years ago. -/- I have much less sympathy for the extended and scaffolded minds of Chap 7. I don’t see how one can lay the burden of explaining how the mind works at Searle’s door, nor how the convoluted prose about “decoupled contentful activities” etc helps at all. Why not just say that automated unconscious prelinguistic S1 feeds deliberate, conscious linguistic S2, which is axiomatically extended by public language into the myriad wonders of S3 culture? Beginning and end of story. -/- Their last chapter is about “regaining consciousness,” but I would say that if one has understood Wittgenstein and Searle, one has never lost it. And, though this is an excellent book by two of the brightest and the best, I suggest mulling over my thoughts in this and other reviews and reading Johnston and the latest from Searle, along of course with as much of 3rd period W as feasible, is an even better filter for folly. In sum an excellent book with various faults which I try to correct. -/- Those wishing a comprehensive up to date framework for human behavior from the modern two systems view may consult my book ‘The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle’ 2nd ed (2019). Those interested in more of my writings may see ‘Talking Monkeys--Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet--Articles and Reviews 2006-2019 3rd ed (2019), The Logical Structure of Human Behavior (2019), and Suicidal Utopian Delusions in the 21st Century 4th ed (2019). (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)

There has been no shortage of such conceptual analyses and no shortage of counterexamples to all of them. The counterexamples exploit, at least partly, situations in which we are presumed to have clear intuitions about what causes what, but which intuitions are not being respected by the suggested philosophical analysis. The counterexamples typically lead to a battery of sophisticated attempts to revise or amend the philosophical analysis so that it is saved from refutation. These attempts, typically, either deny the intuitions (...) on which the counterexamples are based or accommodate the problematic cases within the theory by adding further clauses to the original philosophical analysis. The result of all this is that where the original philosophical theory rested on a simple, forceful and intuitively plausible idea (e.g., that causation consists in a relation of counterfactual dependence between discrete events), the modified philosophical theory becomes very convoluted, somewhat ad hoc and implausible. (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)

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)

Abstract: Lemons are an important fruit that have a wide range of uses and benefits, from culinary to health to household and beauty applications. Deep learning techniques have shown promising results in image classification tasks, including fruit quality detection. In this paper, we propose a convolutional neural network (CNN)-based approach for detecting the quality of lemons by analysing visual features such as colour and texture. The study aims to develop and train a deep learning model to classify lemons based on (...) their quality, evaluate the model's performance, compare it to traditional machine learning approaches, and identify key factors that affect the model's performance. The dataset used in the study consists of approximately 2533 images of lemons and an empty surface, divided into training, test, and validation data, and includes images of both good and bad quality lemons under different lighting conditions. The deep learning model achieved an accuracy of 99.74% on the test dataset and outperformed traditional machine learning approaches. The developed model has the potential to improve efficiency and accuracy in lemon quality control in the agriculture and food industry. (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)

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: Forests are areas with a high density of trees, and they play a vital role in the health of the planet. They provide a habitat for a wide variety of plant and animal species, and they help to regulate the climate by absorbing carbon dioxide from the atmosphere. While in 2010, the world had 3.92Gha of forest cover, covering 30% of its land area, in 2019, there was a loss of forest cover of 24.2Mha according to the Global Forest (...) Watch institute. Discovery and classification depend on human experience and effort, so the error in the results of this process can lead to forest fires and disasters. Therefore, deep learning algorithms from artificial intelligence and machine learning sciences have been applied to help specialists avoid false or inaccurate diagnoses when detecting Forest fires in images using a pre-trained convolutional neural network called VGG16. The model was customized to fit the Forest fires classification and then applied to a dataset consisting of (14,000) of the Forests collected from the Kaggle depository. We trained, validated, and tested the modified VGG16 model. The proposed VGG16 model obtained Precision (99.96%), Recall (99.96%), and F1-Score (99.96%). (shrink)

Abstract: A corn crop typically refers to a large-scale cultivation of corn (also known as maize) for commercial purposes such as food production, animal feed, and industrial uses. Corn is one of the most widely grown crops in the world, and it is a major staple food for many cultures. Corn crops are grown in various regions of the world with different climates, soil types, and farming practices. In the United States, for example, the Midwest is known as the "Corn (...) Belt" due to its extensive corn production. Corn crops can be grown using a variety of methods, including conventional tillage, no-till farming, and the use of genetically modified crops. Due to its importance, there is a need for discovering the corn diseases and treating them. The aim of this study is to propose a deep learning model for the classification of corn leaf diseases based on Convolutional Neural Network. A model for classifying maize diseases was developed using a dataset for classifying Corn diseases that contains 4 classes of disease. The dataset was collected from Keggel website. The proposed model was trained, validated, and tested. The F1-score (99.83%), Recall (99.83%), precision (99.83%), Accuracy (99.83%). (shrink)

We can attend to the logic of Anselm's ontological argument, and amuse ourselves for a few hours unraveling its convoluted word-play, or we can seek to look beyond the flawed logic, to the search for God it expresses. From the perspective of this second approach the Ontological Argument might be seen as more than a mere argument - indeed, as something of a contemplative exercise. One can see in the argument a tantalizing attempt to capture in logical form the devotee’s (...) experience of the presence of God in the contemplation of God. It is a peculiarity of the argument that it can seem hopelessly silly or richly evocative depending upon which of these approaches one takes. In this essay I examine the flawed logic of the Ontological Argument, but then attempt to reflect upon the contemplative experience that may underlie it. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging marks a transformative advancement in healthcare, significantly enhancing diagnostic accuracy, efficiency, and patient outcomes. This paper delves into the application of AI technologies in medical image analysis, with a particular focus on techniques such as convolutional neural networks (CNNs) and deep learning models. We examine how these technologies are employed across various imaging modalities, including X-rays, MRIs, and CT scans, to improve disease detection, image segmentation, and diagnostic support. Furthermore, the (...) paper discusses the challenges associated with AI-driven medical imaging, such as data quality, model interpretability, and ethical considerations. By analyzing recent advancements and real-world case studies, this paper offers insights into the current landscape of AI in medical imaging and explores its potential future directions. The findings underscore the ongoing evolution of AI technologies and their pivotal role in advancing medical diagnostics and treatment strategies. (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)

The incremental risk charge (IRC) is a new regulatory requirement from the Basel Committee in response to the recent financial crisis. Notably few models for IRC have been developed in the literature. This paper proposes a methodology consisting of two Monte Carlo simulations. The first Monte Carlo simulation simulates default, migration, and concentration in an integrated way. Combining with full re-valuation, the loss distribution at the first liquidity horizon for a subportfolio can be generated. The second Monte Carlo simulation is (...) the random draws based on the constant level of risk assumption. It convolutes the copies of the single loss distribution to produce one year loss distribution. The aggregation of different subportfolios with different liquidity horizons is addressed. Moreover, the methodology for equity is also included, even though it is optional in IRC. (shrink)

For one to simply think, philosophy as a rational investigation of truths and principles of knowledge, being, and conduct, that is, philosophy as a "science," is not required. For thinking, what requisite is a reason, a human endowment constitutive of one's intelligence. One only needs a mind to be able to think. But something more is exigent for one to think twice, is to think again, to reconsider and see something from a different perspective. To think things twice, one needs (...) a conscious apprehension of the notion of truth, a deliberate and critical engagement with principles, and a learned competency of habits of thinking that discloses the eternal freshness of reality. These are the elements that constitute philosophy not simply as a way of life, but as an academic discipline with methods and theories. Here lies philosophy's vocational relevance. Thinking things twice is not the mental disorder of indecisiveness or the unfortunate product of capriciousness. To think things twice is the stubborn instinct of human intelligence which remains restless in entrenched patterns of thought. The apprehension of the possibility of thinking things twice is the initial promise of liberation, the first step towards unshackling the mind and its powers from mental scripts that do not give birth to creativity. This explains why it is said that in the domain of philosophical discourses, questions always outlive their answers. Indeed, though philosophy, in its many faces and guises, has offered opinions, beliefs and sometimes divergent truths to the fundamental questions of life. The more important are the questions asked than the answers proffered. The convoluted transformations and shifting grounds in the history of ideas show to us how, philosophically considered, answers have no finality. (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)