Results for 'deep time'

974 found
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  1. Different researchers’ opinion based survey: On the insights and the beliefs’ regarding the existence of God in various religions to the atheistic belief with ‘no presence of God at all’.Deep Bhattacharjee - manuscript
    If this can be seen as a long way from the beginning of the ancient history, where humans have envisioned different new things and then invented them to make their life’s working smoother and easier, then it can be found that they have attributed their discoveries to various aspects and names of Gods and tried to signify their belief in the form of portraying the God’s powers through the nature of their discovery. Rather, in much modern times, when humans have (...)
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  2. Situated and distributed cognition in artifact negotiation and trade-specific skills: A cognitive ethnography of Kashmiri carpet weaving practice.Gagan Deep Kaur - 2018 - Theory and Psychology 28 (4):451-475.
    This article describes various ways actors in Kashmiri carpet weaving practice deploy a range of artifacts, from symbolic, to material, to hybrid, in order to achieve diverse cognitive accomplishments in their particular task domains: information representation, inter and intra-domain communication, distribution of cognitive labor across people and time, coordination of team activities, and carrying of cultural heritage. In this repertoire, some artifacts position themselves as naïve tools in the actors’ environment to the point of being ignored; however, their usage-in-context (...)
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  3.  31
    Deep Neural Networks for Real-Time Plant Disease Diagnosis and Productivity Optimization.K. Usharani - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):645-652.
    The health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high (...)
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  4. Predictive Analysis of Lottery Outcomes Using Deep Learning and Time Series Analysis.Asil Mustafa Alghoul & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (10):1-6.
    Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this (...)
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  5. Wandering in intersectional time: subjectivity and identity in Richard Flanagan’s The Narrow Road to the Deep North.Victoria Reeve - 2016 - Text 34.
    Using hokku poet Basho’s aesthetics of wandering, as defined by Thomas Heyd, I argue that, by detailing the excruciating pointlessness of work undertaken according to commands that take little or no account of their feasibility, Richard Flanagan’s novel, The Narrow Road to the Deep North (which takes its title from Basho's work) transforms the features of this aesthetics into the lived experience of prisoners of war on the ‘line’. In doing so, Flanagan transfers Basho’s aesthetics into a represented actuality (...)
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  6.  27
    Deep Learning - Driven Data Leakage Detection for Secure Cloud Computing.Yoheswari S. - 2024 - International Journal of Engineering Innovations and Management Strategies 5 (1):1-4.
    Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short- Term Memory (LSTM) and Convolutional Neural (...)
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  7. Time and Modality.Samuele Iaquinto - forthcoming - In Nina Emery (ed.), The Routledge Companion to Philosophy of Time. Routledge.
    Time and modality show remarkable similarities. Each of the most discussed theories in philosophy of time finds an analogous counterpart in modal metaphysics, suggesting that the parallel between the two notions is metaphysically deep. This chapter offers a brief overview of their analogies. Section 1 addresses the analogy between presentism and actualism. Section 2 explores the analogy between non-presentist theories and possibilism. Section 3 discusses the analogy between temporal and modal persistence.
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  8. Using Deep Learning to Classify Eight Tea Leaf Diseases.Mai R. Ibaid & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):89-96.
    Abstract: People all over the world have been drinking tea for thousands of centuries, and for good reason. Many types of teas can help you stay healthy by boosting your immune system, reducing inflammation, and even preventing cancer and heart disease. There is sufficient material to show that regularly consuming tea can improve your health over the long term. A deep learning model that categorizes tea disorders has been completed. When focusing on the tea, we must also focus on (...)
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  9. The self in deep ecology: A response to Watson.Joshua Anderson - 2020 - Asian Philosophy 30 (1):30-39.
    Richard Watson maintains that deep ecology suffers from an internal contradiction and should therefore be rejected. Watson contends that deep ecology claims to be non-anthropocentric while at the same time is committed to setting humans apart from nature, which is inherently anthropocentric. I argue that Watson’s objection arises out of a fundamental misunderstanding of how deep ecologist’s conceive of the ‘Self.’ Drawing on resources from Buddhism, I offer an understanding of the ‘Self’ that is fully consistent (...)
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  10. High hopes for “Deep Medicine”? AI, economics, and the future of care.Robert Sparrow & Joshua Hatherley - 2020 - Hastings Center Report 50 (1):14-17.
    In Deep Medicine, Eric Topol argues that the development of artificial intelligence (AI) for healthcare will lead to a dramatic shift in the culture and practice of medicine. Topol claims that, rather than replacing physicians, AI could function alongside of them in order to allow them to devote more of their time to face-to-face patient care. Unfortunately, these high hopes for AI-enhanced medicine fail to appreciate a number of factors that, we believe, suggest a radically different picture for (...)
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  11. Attuning to the deep. On the opportunities of thinking with art for an ethics of the deep sea.Kristien Hens, Christina Stadlbauer & Bart Vandeput - manuscript
    Seabed mining, the extraction of minerals from the deep-sea floor, is hotly contested. Policymakers have agreed on the need for a regulatory framework. However, traditional ethical theories and principles are not well equipped for the ethics of the alien deep sea. Engaging with the sea means engaging with something abstract that we can only access indirectly. We argue that this invisibility and alienness of the sea and its inhabitants can give new insights into how ethics are done. Rather (...)
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  12.  45
    Deep Learning - Driven Data Leakage Detection for Secure Cloud Computing.Yoheswari S. - 2025 - International Journal of Engineering Innovations and Management Strategies 1 (1):1-4.
    Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (...)
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  13.  44
    Revolutionizing Agriculture with Deep Learning-Based Plant Health Monitoring.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):655-666.
    By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. Additionally, (...)
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  14. Classifications of Pineapple using Deep Learning.Amjad H. Alfarra, Lamis F. Samhan, Yasmin E. Aslem, Marah M. Almasawabe & Samy S. Abu-Naser - 2021 - International Journal of Academic Information Systems Research (IJAISR) 5 (12):37-41.
    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. (...)
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  15. Papaya Maturity Classifications using Deep Convolutional Neural Networks.Marah M. Al-Masawabe, Lamis F. Samhan, Amjad H. AlFarra, Yasmeen E. Aslem & Samy S. Abu-Naser - 2021 - International Journal of Engineering and Information Systems (IJEAIS) 5 (12):60-67.
    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 (...)
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  16. (1 other version)Paleontology: Outrunning Time.John E. Huss - 2017 - Boston Studies in the Philosophy and History of Science 326:211-235.
    In this paper, I discuss several temporal aspects of paleontology from a philosophical perspective. I begin by presenting the general problem of “taming” deep time to make it comprehensible at a human scale, starting with the traditional geologic time scale: an event-based, relative time scale consisting of a hierarchy of chronological units. Not only does the relative timescale provide a basis for reconstructing many of the general features of the history of life, but it is also (...)
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  17. The Moral Obligation to Prioritize Research Into Deep Brain Stimulation Over Brain Lesioning Procedures for Severe Enduring Anorexia Nervosa.Jonathan Pugh, Jacinta Tan, Tipu Aziz & Rebecca J. Park - 2018 - Frontiers in Psychiatry 9:523.
    Deep Brain Stimulation is currently being investigated as an experimental treatment for patients suffering from treatment-refractory AN, with an increasing number of case reports and small-scale trials published. Although still at an exploratory and experimental stage, initial results have been promising. Despite the risks associated with an invasive neurosurgical procedure and the long-term implantation of a foreign body, DBS has a number of advantageous features for patients with SE-AN. Stimulation can be fine-tuned to the specific needs of the particular (...)
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  18. RAINFALL DETECTION USING DEEP LEARNING TECHNIQUE.M. Arul Selvan & S. Miruna Joe Amali - 2024 - Journal of Science Technology and Research 5 (1):37-42.
    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 (...)
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  19. Deep Learning Techniques for Comprehensive Emotion Recognition and Behavioral Regulation.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):383-389.
    Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies (...)
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  20. Deep Indeterminacy in Physics and Fiction.George Darby, Martin Pickup & Jon Robson - 2017 - In Otávio Bueno, Steven French, George Darby & Dean Rickles (eds.), Thinking About Science, Reflecting on Art: Bringing Aesthetics and Philosophy of Science Together. New York: Routledge.
    Indeterminacy in its various forms has been the focus of a great deal of philosophical attention in recent years. Much of this discussion has focused on the status of vague predicates such as ‘tall’, ‘bald’, and ‘heap’. It is determinately the case that a seven-foot person is tall and that a five-foot person is not tall. However, it seems difficult to pick out any determinate height at which someone becomes tall. How best to account for this phenomenon is, of course, (...)
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  21. The Threefold Emergence of Time unravels Physics'Reality.Guido J. M. Verstraeten & Willem W. Verstraeten - 2013 - Pensée 75 (12):136-142.
    Time as the key to a theory of everything became recently a renewed topic in scientific literature. Social constructivism applied to physics abandons the inevitable essentials of nature. It adopts uncertainty in the scope of the existential activity of scientific research. We have enlightened the deep role of social constructivism of the predetermined Newtonian time and space notions in natural sciences. Despite its incompatibility with determinism governing the Newtonian mechanics, randomness and entropy are inevitable when negative localized (...)
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  22. Sarcasm Detection in Headline News using Machine and Deep Learning Algorithms.Alaa Barhoom, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (4):66-73.
    Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...)
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  23. Time in Cosmology.C. D. McCoy & Craig Callender - 2022 - In Eleanor Knox & Alastair Wilson (eds.), The Routledge Companion to Philosophy of Physics. London, UK: Routledge. pp. 707–718.
    Readers familiar with the workhorse of cosmology, the hot big bang model, may think that cosmology raises little of interest about time. As cosmological models are just relativistic spacetimes, time is understood just as it is in relativity theory, and all cosmology adds is a few bells and whistles such as inflation and the big bang and no more. The aim of this chapter is to show that this opinion is not completely right...and may well be dead wrong. (...)
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  24. The Deep Structure of Lives.Michael Kubovy - 2015 - Philosophia Scientiae 19:153-176.
    La psychologie a toujours traité le comportement et l’expérience comme étant enchâssés dans un flux temporel unidimensionnel, « le courant du comportement » dans lequel les événements et les actions occupent des intervalles de temps qui ne se chevauchent pas. Pourtant, une analyse phénoménologique révèle que la structure de nos vies est bien plus riche et intéressante. En utilisant la notion de « quasidécomposabilité » de Herbert Simon, je décris cette structure comme un assemblage d’épisodes quasi-indépendants se réalisant de façon (...)
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  25. Vegetable Classification Using Deep Learning.Mostafa El-Ghoul & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):105-112.
    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 (...)
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  26. AI-Completeness: Using Deep Learning to Eliminate the Human Factor.Kristina Šekrst - 2020 - In Sandro Skansi (ed.), Guide to Deep Learning Basics. Springer. pp. 117-130.
    Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time (...)
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  27. Time for Pragmatism.Huw Price - forthcoming - In Josh Gert (ed.), Neopragmatism.
    Are the distinctions between past, present and future, and the apparent ‘passage’ of time, features of the world in itself, or manifestations of the human perspective? Questions of this kind have been at the heart of metaphysics of time since antiquity. The latter view has much in common with pragmatism, though few in these debates are aware of that connection, and few of the view’s proponents think of themselves as pragmatists. For their part, pragmatists are often unaware of (...)
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  28.  28
    Advanced Deep Learning Models for Proactive Malware Detection in Cybersecurity Systems.A. Manoj Prabharan - 2023 - Journal of Science Technology and Research (JSTAR) 5 (1):666-676.
    By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further (...)
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  29.  87
    ADVANCED EMOTION RECOGNITION AND REGULATION UTILIZING DEEP LEARNING TECHNIQUES.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):383-388.
    Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies (...)
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  30. Classification of Dates Using Deep Learning.Raed Z. Sababa & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):18-25.
    Abstract: Dates are the fruit of date palm trees, and it is one of the fruits famous for its high nutritional value. It is a summer fruit spread in the Arab world. In the past, the Arabs relied on it in their daily lives. Dates take an oval shape and vary in size from 20 to 60 mm in length and 8 to 30 mm in diameter. The ripe fruit consists of a hard core surrounded by a papery cover called (...)
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  31. EXISTENCE(s) – Short deep-forage Chapters.István Király V. - 2017 - Saarbrucken, Germany: Lambert Academic Publishing.
    The chapters of the book are seemingly short, but deep explorations on the various fields and possibilities of human being and existence. Such explorations of course reorder and reformulate the timely and essential possibilities of philosophy and philosophizing. These together convey the true weight and stakes of things. For it is indeed so that: „Philosophy is destined to deal with the Deepest and most disturbing questions. It would hardly survive, if they were definitively solved.”.
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  32. Classification of Anomalies in Gastrointestinal Tract Using Deep Learning.Ibtesam M. Dheir & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (3):15-28.
    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 (...)
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  33. Awareness without Time.Akiko Frischhut - forthcoming - Philosophical Quarterly.
    Recently, philosophers with an interest in consciousness have turned their attention towards “fringe states of consciousness”. Examples include dreams, trances, and meditative states. Teetering between wakefulness and non-consciousness, fringe states illuminate the limits and boundaries of consciousness. This paper aims to give a coherent conceptualisation of deep meditative states, focussing in particular on phenomenal temporality during meditation. Advanced meditators overwhelmingly describe deep states of meditation as atemporal and timeless; however, they also report being continuously alert while meditating. I (...)
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  34. PREDICTION OF EDUCATIONAL DATA USING DEEP CONVOLUTIONAL NEURAL NETWORK.K. Vijayalakshmi - 2022 - Journal of Science Technology and Research (JSTAR) 3 (1):93-111.
    : 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 (...)
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  35. Time’s entanglements: Beauvoir and Fanon on reductive temporalities.Marilyn Stendera - 2022 - Continental Philosophy Review 56 (1):1-20.
    Simone de Beauvoir and Frantz Fanon both argue that oppression fundamentally constrains the subject’s relationship to and embodied experience of time, yet their accounts of temporality are rarely brought together. This paper will explore what we might learn about the operation of different types of reductive temporality if we read Beauvoir and Fanon alongside each other, focusing primarily on the early works that arguably lay out the central concerns of their respective temporal frameworks. At first glance, it seems that (...)
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  36.  44
    A Novel Deep Learning-Based Framework for Intelligent Malware Detection in Cybersecurity.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):666-669.
    With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...)
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  37. Tomato Leaf Diseases Classification using Deep Learning.Mohammed F. El-Habibi & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):73-80.
    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 (...)
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  38. On Breaking Up Time, or, Perennialism as Philosophy of History.Bennett Gilbert - 2016 - Joirnal of the Philosophy of History 12 (1):5-26.
    Current and recent philosophy of history contemplates a deep change in fundamental notions of the presence of the past. This is called breaking up time. The chief value for this change is enhancing the moral reach of historical research and writing. However, the materialist view of reality that most historians hold cannot support this approach. The origin of the notion in the thought of Walter Benjamin is suggested. I propose a neo-idealist approach called perennialism, centered on recurrent moral (...)
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  39.  24
    Intelligent Malware Detection Empowered by Deep Learning for Cybersecurity Enhancement.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):625-635.
    With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...)
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  40. "Death is a Disease": Cryopreservation, Neoliberalism, and Temporal Commodification in the U.S.Taylor R. Genovese - 2018 - Technology in Society 54:52-56.
    In this article, I will be focusing specifically on cryopreservation and two of the American biotechnomedical tenets introduced by Robbie Davis-Floyd and Gloria St. John in their technocratic model of medicine: the “body as machine” and “death as defeat.” These axioms are embraced by both the biotechnomedical establishment as well as the cryopreservation communities when they discuss the future of humankind. In particular, I will be focusing on the political economy of cryopreservation as an embodiment of American neoliberalism—as well as (...)
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  41. Are We in a Sixth Mass Extinction? The Challenges of Answering and Value of Asking.Federica Bocchi, Alisa Bokulich, Leticia Castillo Brache, Gloria Grand-Pierre & Aja Watkins - forthcoming - British Journal for the Philosophy of Science.
    In both scientific and popular circles it is often said that we are in the midst of a sixth mass extinction. Although the urgency of our present environmental crises is not in doubt, such claims of a present mass extinction are highly controversial scientifically. Our aims are, first, to get to the bottom of this scientific debate by shedding philosophical light on the many conceptual and methodological challenges involved in answering this scientific question, and, second, to offer new philosophical perspectives (...)
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  42. Recurrent Neural Network Based Speech emotion detection using Deep Learning.P. Pavithra - 2022 - Journal of Science Technology and Research (JSTAR) 3 (1):65-77.
    In modern days, person-computer communication systems have gradually penetrated our lives. One of the crucial technologies in person-computer communication systems, Speech Emotion Recognition (SER) technology, permits machines to correctly recognize emotions and greater understand users' intent and human-computer interlinkage. The main objective of the SER is to improve the human-machine interface. It is also used to observe a person's psychological condition by lie detectors. Automatic Speech Emotion Recognition(SER) is vital in the person-computer interface, but SER has challenges for accurate recognition. (...)
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  43. The time asymmetry of quantum mechanics and concepts of physical directionality of time Part 1.Andrew Thomas Holster - manuscript
    This is Part 1 of a four part paper, intended to redress some of the most fundamental confusions in the subject of physical time directionality, and represent the concepts accurately. There are widespread fallacies in the subject that need to be corrected in introductory courses for physics students and philosophers. We start in Part 1 by analysing the time reversal symmetry of quantum probability laws. Time reversal symmetry is defined as the property of invariance under the (...) reversal transformation, T: t --> -t. It is shown that quantum mechanics (classical or relativistic) is strongly time asymmetric in its probability laws. This contradicts the orthodox analysis, found throughout the conventional literature on physical time, which claims that quantum mechanics is time symmetric or reversible. This is widely claimed as settled scientific fact, and large philosophical and scientific conclusions are drawn from it. But it is an error. The fact is that while quantum mechanics is widely claimed to be reversible on the basis of two formal mathematical properties (that it does have), these properties do not represent invariance under the time reversal transformation. A recent experiment (Batalhão at alia, 2015) showing irreversibility of quantum thermodynamics is discussed as an illustration of this result. Most physicists remain unaware of the errors, decades after they were first demonstrated. Orthodox specialists in the philosophy of time who are aware of the error continue to refer to the ‘time symmetry’ or ‘reversibility’ of quantum mechanics anyway – and exploit the ambiguity to claim false implications about physical time reversal symmetry in nature. The excuse for perpetrating the confusion is that, since it is has now become customary to refer to the formal properties of quantum mechanics as ‘reversibility’ or ‘time reversal symmetry’, we should just keep referring to them by this name, even though they are not time reversal symmetry. This causes endless confusion, in attempts to explain the physical irreversibility of our universe, and in philosophical discussions of implications of physics for the nature of time. The failure of genuine time reversal symmetry in quantum mechanics changes the interpretation of modern physics in a deep way. It changes the problem of explaining the real irreversibility found throughout nature. (shrink)
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  44. 'Next Time Try Looking it up in your Gut!!': Tolerance, Civility, and Healthy Conflict in a Tea Party Era.Jason A. Springs - 2011 - Soundings: An Interdisciplinary Journal 94 (3-4):325-358.
    In this paper I critically explore the possibility that the hope for engaging in democratic discourse and coalition-building across deep— potentially irreconcilable— moral, religious divisions in current U.S. public life depends less upon further calls for “more tolerance,” and instead in thinking creatively and transformatively about how to democratize and constructively utilize conflict and intolerance. Is it possible to distinguish between constructive and destructive forms of intolerance? If so, what are the prospects for re-orienting analysis of democratic practices and (...)
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  45. Classification of Chicken Diseases Using Deep Learning.Mohammed Al Qatrawi & Samy S. Abu-Naser - 2024 - Information Journal of Academic Information Systems Research (Ijaisr) 8 (4):9-17.
    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 (...)
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  46.  25
    Efficient Plant Disease Identification through Advanced Deep Learning Techniques.A. Manoj Prabaharan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):645-655.
    The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. Additionally, the system is designed to provide real-time feedback to farmers, helping them take immediate corrective action. This automated approach eliminates the need for expert human intervention and can be deployed on (...)
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  47. Your Self is Deeper Than You Think: A Deep Self View of Moral Responsibility.Ke Zhang - 2023 - Dissertation, University of Arizona
    This dissertation is a collection of standalone papers about a novel version of the deep self view of moral responsibility. Taken on its own, each chapter deals with a different thesis. But as the title of my dissertation reveals, taken together, the three chapters in it constitute the groundwork for my deep self view of moral responsibility. In Chapter 1, I develop and defend the thesis of responsibility for the deep self. In Chapter 2, I argue for (...)
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  48. AI-Driven Emotion Recognition and Regulation Using Advanced Deep Learning Models.S. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):383-389.
    Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies (...)
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  49. Predicting Whether Student will continue to Attend College or not using Deep Learning.Samy S. Abu-Naser, Qasem M. M. Zarandah, Moshera M. Elgohary, Zakaria K. D. AlKayyali, Bassem S. Abu-Nasser & Ashraf M. Taha - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (6):33-45.
    According to the literature review, there is much room for improvement of college student retention. The aim of this research is to evaluate the possibility of using deep and machine learning algorithms to predict whether students continue to attend college or will stop attending college. In this research a feature assessment is done on the dataset available from Kaggle depository. The performance of 20 learning supervised machine learning algorithms and one deep learning algorithm is evaluated. The algorithms are (...)
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  50.  45
    Empowering Cybersecurity with Intelligent Malware Detection Using Deep Learning Techniques.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):655-665.
    With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...)
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