Results for 'convolution'

78 found
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  1.  46
    3D Convolutional Neural Networks for Accurate Reconstruction of Distorted Faces.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (4):560-570.
    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 (...)
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  2. Empiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks.Cameron Buckner - 2018 - Synthese (12):1-34.
    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 (...)
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  3. (1 other version)Effectuationism (Feb. 2023): Correcting Convolutions in Philosophy and Physics.Peter Kinane - 2023 - Tipperary: O’Bhríd Press, c/o Peter Kinane.
    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 (...)
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  4. Pistachio Variety Classification using Convolutional Neural Networks.Ahmed S. Sabah & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):113-119.
    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 (...)
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  5.  45
    Optimized Face Reconstruction Using 3D Convolutional Neural Networks.A. Manoj Prabaharan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):509-520.
    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 (...)
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  6. 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 Neural Network (...)
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  7. The Contortions and Convolutions of the “Speculative Turn”.Thomas Sutherland - 2021 - Diacritics 49 (1):108-126.
    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.
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  8. Classification of Alzheimer's Disease Using Convolutional Neural Networks.Lamis F. Samhan, Amjad H. Alfarra & Samy S. Abu-Naser - 2022 - International Journal of Academic Information Systems Research (IJAISR) 6 (3):18-23.
    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 (...)
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  9.  40
    Facial Distortion Reconstruction with 3D Convolutional Neural Networks.M. Sheik Dawood - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):575-590.
    . 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 (...)
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  10. 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 status (...)
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  11. Potato Classification Using Deep Learning.Abeer A. Elsharif, Ibtesam M. Dheir, Alaa Soliman Abu Mettleq & Samy S. Abu-Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):1-8.
    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 (...)
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  12. 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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  13.  20
    Evaluating Advanced Deep Learning Methods for Regional Air Quality Index Forecasting.M. Sheik Dawood - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):600-620.
    We investigate the application of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model for forecasting air pollution levels based on historical data. Our experimental setup uses real-world air quality datasets from multiple regions, containing measurements of pollutants like PM2.5, PM10, CO, NO2, and SO2, alongside meteorological data such as temperature, humidity, and wind speed. The models are trained, validated, and tested using a split dataset, and their accuracy is evaluated using performance metrics like Mean (...)
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  14.  16
    Comparing LSTM, GRU, and CNN Approaches in Air Quality Prediction Models.A. Manoj Prabharan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):576-585.
    The results show that the hybrid CNN-LSTM model outperforms the individual models in terms of prediction accuracy and robustness, suggesting that combining convolutional layers with recurrent units is beneficial for capturing both spatial and temporal patterns in air quality data. This study demonstrates the potential of deep learning methods to offer real-time, accurate air quality forecasting systems, which can aid policymakers and urban planners in managing air pollution more effectively.
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  15. Can Deep CNNs Avoid Infinite Regress/Circularity in Content Constitution?Jesse Lopes - 2023 - Minds and Machines 33 (3):507-524.
    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) (...)
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  16.  35
    Internal Camouflage of External Reasons.Morteza Shahram - manuscript
    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 .
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  17. Kant’s post-1800 Disavowal of the Highest Good Argument for the Existence of God.Samuel Kahn - 2018 - Kant Yearbook 10 (1):63-83.
    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 (...)
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  18. Causal Pluralism.Stathos Psillos - 2009 - In Robrecht Vanderbeeken & Bart D'Hooghe (eds.), Worldviews, Science and Us: Studies of Analytical Metaphysics. World Scientific.
    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 (...)
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  19. CORCORAN's THUMBNAIL REVIEWS OF OPPOSING PHILOSOPHY OF LOGIC BOOKS.John Corcoran - 1978-9 - MATHEMATICAL REVIEWS 56:98-9.
    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 (...)
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  20. Hume on the Characters of Virtue.Richard H. Dees - 1997 - Journal of the History of Philosophy 35 (1):45-64.
    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 (...)
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  21. Notice After Notice-and-Consent: Why Privacy Disclosures Are Valuable Even If Consent Frameworks Aren’t.Daniel Susser - 2019 - Journal of Information Policy 9:37-62.
    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 (...)
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  22.  66
    Harnessing Artificial Intelligence to Enhance Medical Image Analysis.Malak S. Hamad, Mohammed H. Aldeeb, Mohammed M. Almzainy, Shahd J. Albadrasawi, Musleh M. Musleh, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Health and Medical Research (IJAHMR) 8 (9):1-7.
    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 (...)
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  23. Quantum Mechanics and Paradigm Shifts.Valia Allori - 2015 - Topoi 34 (2):313-323.
    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 (...)
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  24. Hume’s practically epistemic conclusions?Hsueh Qu - 2014 - Philosophical Studies 170 (3):501-524.
    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 (...)
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  25.  14
    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 improve (...)
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  26. Lemon Classification Using Deep Learning.Jawad Yousif AlZamily & Samy Salim Abu Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):16-20.
    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 (...)
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  27.  66
    Intelligent Driver Drowsiness Detection System Using Optimized Machine Learning Models.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):397-405.
    : Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as Genetic (...)
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  28. The Exploratory Status of Postconnectionist Models.Miljana Milojevic & Vanja Subotić - 2020 - Theoria: Beograd 2 (63):135-164.
    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 (...)
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  29. Classification of Alzheimer’s Disease Using Traditional Classifiers with Pre-Trained CNN.Husam R. Almadhoun & Samy S. Abu-Naser - 2021 - International Journal of Academic Health and Medical Research (IJAHMR) 5 (4):17-21.
    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 (...)
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  30. A new problem for the A-theory of time.Simon Prosser - 2000 - Philosophical Quarterly 50 (201):494-498.
    : 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, (...)
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  31. 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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  32. (1 other version) FIRE MANAGEMENT SYSTEM FOR INDUTRIAL SAFETY APPLICATIONS.M. Arul Selvan - 2023 - Journal of Science Technology and Research (JSTAR) 4 (1):247-259.
    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 (...)
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  33. Advancements in AI for Medical Imaging: Transforming Diagnosis and Treatment.Zakaria K. D. Alkayyali, Ashraf M. H. Taha, Qasem M. M. Zarandah, Bassem S. Abunasser, Alaa M. Barhoom & Samy S. Abu-Naser - 2024 - International Journal of Academic Engineering Research(Ijaer) 8 (8):8-15.
    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 (...)
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  34. Classification of Rice Using Deep Learning.Mohammed H. S. Abueleiwa & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):26-36.
    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 (...)
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  35. Using Deep Learning to Classify Corn Diseases.Mohanad H. Al-Qadi & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems (Ijaisr) 8 (4):81-88.
    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 (...)
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  36. Classification of Apple Diseases Using Deep Learning.Ola I. A. Lafi & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):1-9.
    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 (...)
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  37.  64
    Advanced Driver Drowsiness Detection Model Using Optimized Machine Learning Algorithms.S. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):396-402.
    Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as Genetic Algorithms (...)
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  38. Toward biologically plausible artificial vision.Mason Westfall - 2023 - Behavioral and Brain Sciences 46:e290.
    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).
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  39. In Search of the Ontological Argument.Richard Oxenberg - manuscript
    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 (...)
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  40. Fine-tuning MobileNetV2 for Sea Animal Classification.Mohammed Marouf & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):44-50.
    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 (...)
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  41. Medical Image Classification with Machine Learning Classifier.Destiny Agboro - forthcoming - Journal of Computer Science.
    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 (...)
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  42. Using Deep Learning to Detect the Quality of Lemons.Mohammed B. Karaja & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):97-104.
    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 (...)
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  43. Forest Fire Detection using Deep Leaning.Mosa M. M. Megdad & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):59-65.
    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 (...)
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  44. Cantaloupe Classifications using Deep Learning.Basel El-Habil & Samy S. Abu-Naser - 2021 - International Journal of Academic Engineering Research (IJAER) 5 (12):7-17.
    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 (...)
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  45. Grape Leaf Species Classification Using CNN.Mohammed M. Almassri & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):66-72.
    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 (...)
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  46.  80
    (1 other version)A PBL REPORT FOR CONTAINMENT ZONE ALERTING APPLICATION.M. Arul Selvan - 2023 - Journal of Science Technology and Research (JSTAR) 4 (1):233-246.
    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, (...)
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  47.  83
    Beliefs: Our Map of the World.Avijit Lahiri - manuscript
    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 (...)
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  48.  71
    Beyond binary group categorization: towards a dynamic view of human groups.Kati Kish Bar-On - 2024 - Philosophical Psychology:1–28.
    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 (...)
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  49. Classification of Sign-language Using VGG16.Tanseem N. Abu-Jamie & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (6):36-46.
    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 (...)
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  50. Incremental Risk Charge Methodology.Tim Xiao - manuscript
    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 (...)
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