Results for 'pneumonia detection'

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  1.  36
    AUTOMATED PNEUMONIA DETECTION USING DEEP LEARNING AND CHEST X-RAY IMAGES.K. Mahesh - 2024 - International Journal of Engineering Innovations and Management Strategies, 1 (5):1-14.
    Pneumonia is a serious respiratory infection that poses significant health risks, particularly if not diagnosed and treated promptly. Traditional methods of pneumonia diagnosis rely on the manual interpretation of chest X-ray images by radiologists, a process that can be time-consuming, subjective, and error-prone, especially in regions with limited access to experienced medical professionals. To address these challenges, this study explores the development of an automated deep learning-based system for pneumonia detection using chest X-ray images. The results (...)
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  2. Diagnosis of Pneumonia Using Deep Learning.Alaa M. A. Barhoom & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (2):48-68.
    Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and react like humans. Some of the activities computers with artificial intelligence are designed for include, Speech, recognition, Learning, Planning and Problem solving. Deep learning is a collection of algorithms used in machine learning, It is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is a technique used (...)
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  3.  82
    A review on pneumonia in children: Clear insights.Iftear Kazim Rafi - 2024 - Mediterranean Journal of Pharmacy and Pharmaceutical Sciences 4 (4):48-57.
    In developing nations, pneumonia is the leading cause of death for young children; however, mortality can be effectively decreased with early diagnosis and care. The objectives of the review are to evaluate the significance of clinical signs and symptoms in diagnosing pneumonia, and treatment in children under the age of five, as well as to examine the precision of WHO criteria in diagnosing clinical pneumonia in general people. According to the World Health Organization's definition and the Integrated (...)
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  4. Risk Factors for Postoperative Pneumonia: A Case-Control Study.Bingbing Xiang, Shulan Jiao, Yongyu Si, Yuting Yao, Feng Yuan & Rui Chen - 2022 - Frontiers in Public Health 10:913897.
    Background: Postoperative pneumonia is a preventable complication associated with adverse outcomes, that greatly aggravates the medical expenses of patients. The goal of our study is to identify risk factors and outcomes of postoperative pneumonia.
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  5. Change Detection.Ronald A. Rensink - 2002 - Annual Review of Psychology 53 (1):245-277.
    Five aspects of visual change detection are reviewed. The first concerns the concept of change itself, in particular the ways it differs from the related notions of motion and difference. The second involves the various methodological approaches that have been developed to study change detection; it is shown that under a variety of conditions observers are often unable to see large changes directly in their field of view. Next, it is argued that this “change blindness” indicates that focused (...)
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  6. 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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  7.  63
    Detecting Introspective Errors in Consciousness Science.Andy Mckilliam - forthcoming - Ergo: An Open Access Journal of Philosophy.
    Detecting introspective errors about consciousness presents challenges that are widely supposed to be difficult, if not impossible, to overcome. This is a problem for consciousness science because many central questions turn on when and to what extent we should trust subjects’ introspective reports. This has led some authors to suggest that we should abandon introspection as a source of evidence when constructing a science of consciousness. Others have concluded that central questions in consciousness science cannot be answered via empirical investigation. (...)
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  8. True Detective: Buddhism, Pessimism or Philosophy?Finn Janning - 2014 - Journal of Philosophy of Life 4 (4).
    The aim of this paper is to raise two questions. The first question is: How is pessimism related to Buddhism (and vice versa)? The second question is: What relation does an immanent philosophy have to pessimism and Buddhism, if any? Using True Detective, an American television crime drama, as my point of departure, first I will outline some of the likenesses between Buddhism and pessimism. At the same time, I will show how the conduct of one of the main characters (...)
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  9. True Detective : Pessimism, Buddhism or Philosophy?Finn Janning - 2015 - Journal of Philosophy of Life 5 (1).
    The aim of this paper is to raise two questions. The first question is: How is pessimism related to Buddhism (and vice versa)? The second question is: What relation does an immanent philosophy have to pessimism and Buddhism, if any? Using True Detective, an American television crime drama, as my point of departure, first I will outline some of the likenesses between Buddhism and pessimism. At the same time, I will show how the conduct of one of the main characters (...)
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  10. Pedestrian detection based on hierarchical co-occurrence model for occlusion handling.Xiaowei Zhang, HaiMiao Hu, Fan Jiang & Bo Li - 2015 - Neurocomputing 10.
    In pedestrian detection, occlusions are typically treated as an unstructured source of noise and explicit models have lagged behind those for object appearance, which will result in degradation of detection performance. In this paper, a hierarchical co-occurrence model is proposed to enhance the semantic representation of a pedestrian. In our proposed hierarchical model, a latent SVM structure is employed to model the spatial co-occurrence relations among the parent–child pairs of nodes as hidden variables for handling the partial occlusions. (...)
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  11. Deepfake detection by human crowds, machines, and machine-informed crowds.Matthew Groh, Ziv Epstein, Chaz Firestone & Rosalind Picard - 2022 - Proceedings of the National Academy of Sciences 119 (1):e2110013119.
    The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s (...)
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  12. Detecting Health Problems Related to Addiction of Video Game Playing Using an Expert System.Samy S. Abu Naser & Mohran H. Al-Bayed - 2016 - World Wide Journal of Multidisciplinary Research and Development 2 (9):7-12.
    Today’s everyone normal life can include a normal rate of playing computer games or video games; but what about an excessive or compulsive use of video games that impact on our life? Our kids, who usually spend a lot of time in playing video games will likely have a trouble in paying attention to their school lessons. In this paper, we introduce an expert system to help users in getting the correct diagnosis of the health problem of video game addictions (...)
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  13. Intelligent Plagiarism Detection for Electronic Documents.Mohran H. J. Al-Bayed - 2017 - Dissertation, Al-Azhar University, Gaza
    Plagiarism detection is the process of finding similarities on electronic based documents. Recently, this process is highly required because of the large number of available documents on the internet and the ability to copy and paste the text of relevant documents with simply Control+C and Control+V commands. The proposed solution is to investigate and develop an easy, fast, and multi-language support plagiarism detector with the easy of one click to detect the document plagiarism. This process will be done with (...)
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  14. Detecting Experts Using a MiniRocket: Gaze Direction Time Series Classification of Real-Life Experts Playing the Sustainable Port.Gianluca Guglielmo, Michal Klincewicz, Elisabeth Huis in ’T. Veld & Pieter Spronck - 2025 - Gala 2024. Lecture Notes in Computer Science 15348:177–187.
    This study aimed to identify real-life experts working for a port authority and lay people (students) who played The Sustainable Port, a serious game aiming to simulate the dynamics occurring in a port area. To achieve this goal, we analyzed eye gaze data collected noninvasively using low-grade webcams from 28 participants working for the port authority of the Port of Rotterdam and 66 students. Such data were used for a classification task implemented using a MiniRocket classifier, an algorithm used for (...)
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  15. Detection of Brain Tumor Using Deep Learning.Hamza Rafiq Almadhoun & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (3):29-47.
    Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and reacts like humans, some of the computer activities with artificial intelligence are designed to include speech, recognition, learning, planning and problem solving. Deep learning is a collection of algorithms used in machine learning, it is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is used as a (...)
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  16. Failure to detect mismatches between intention and outcome in a simple decision task.Petter Johansson, Lars Hall, Sverker Sikstrom & Andreas Olsson - 2005 - Science 310 (5745):116-119.
    A fundamental assumption of theories of decision-making is that we detect mismatches between intention and outcome, adjust our behavior in the face of error, and adapt to changing circumstances. Is this always the case? We investigated the relation between intention, choice, and introspection. Participants made choices between presented face pairs on the basis of attractiveness, while we covertly manipulated the relationship between choice and outcome that they experienced. Participants failed to notice conspicuous mismatches between their intended choice and the outcome (...)
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  17. Direct Detection of Relic Neutrino Background remains impossible: A review of more recent arguments.Florentin Smarandache & Victor Christianto - manuscript
    The existence of big bang relic neutrinos—exact analogues of the big bang relic photons comprising the cosmic microwave background radiation—is a basic prediction of standard cosmology. The standard big bang theory predicts the existence of 1087 neutrinos per flavour in the visible universe. This is an enormous abundance unrivalled by any other known form of matter, falling second only to the cosmic microwave background (CMB) photon. Yet, unlike the CMB photon which boasts its first (serendipitous) detection in the 1960s (...)
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  18. Introspection Is Signal Detection.Jorge Morales - forthcoming - British Journal for the Philosophy of Science.
    Introspection is a fundamental part of our mental lives. Nevertheless, its reliability and its underlying cognitive architecture have been widely disputed. Here, I propose a principled way to model introspection. By using time-tested principles from signal detection theory (SDT) and extrapolating them from perception to introspection, I offer a new framework for an introspective signal detection theory (iSDT). In SDT, the reliability of perceptual judgments is a function of the strength of an internal perceptual response (signal- to-noise ratio) (...)
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  19. Deception Detection Research: Some Lessons for Epistemology.Peter Graham - 2024 - In Waldomiro J. Silva-Filho (ed.), Epistemology of Conversation: First essays. Cham: Springer.
    According to our folk theory of lying, liars leak observable cues of their insincerity, observable cues that make it easy to catch a liar in real time. Various prominent social epistemologists rely on the correctness of our folk theory as empirically well-confirmed when building their normative accounts of the epistemology of testimony. Deception detection research in communication studies, however, has shown that our folk-theory is mistaken. It is not empirically well-confirmed but empirically refuted. Michaelian (2010) and Shieber (2012) have (...)
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  20.  34
    Examination of Anomaly Process Detection Using Negative Selection Algorithm and Classification Techniques.Sharma Sakshi - 2020 - International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (Ijareeie) 9 (6):2526-2534.
    The examination of anomaly process detection using negative selection algorithms and classification techniques focuses on enhancing the ability to identify deviations from expected patterns within complex data sets. Negative selection algorithms, inspired by biological immune systems, offer a novel approach to anomaly detection by efficiently distinguishing between normal and anomalous data points. When combined with various classification techniques, these algorithms can improve the accuracy and robustness of anomaly detection systems. This abstract explores the integration of negative selection (...)
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  21. Fraudulent Financial Transactions Detection Using Machine Learning.Mosa M. M. Megdad, Samy S. Abu-Naser & Bassem S. Abu-Nasser - 2022 - International Journal of Academic Information Systems Research (IJAISR) 6 (3):30-39.
    It is crucial to actively detect the risks of transactions in a financial company to improve customer experience and minimize financial loss. In this study, we compare different machine learning algorithms to effectively and efficiently predict the legitimacy of financial transactions. The algorithms used in this study were: MLP Repressor, Random Forest Classifier, Complement NB, MLP Classifier, Gaussian NB, Bernoulli NB, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Bagging Classifier, Decision Tree Classifier and Deep Learning. The dataset (...)
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  22. Autism spectrum and cheaters detection.Miguel Lopez Astorga - 2014 - Dialogues in Philosophy, Mental and Neuro Sciences 7 (1):1-10.
    Rutherford and Ray think that human beings have mental mechanisms that help them to detect individuals that, deliberately, do not follow a rule. In the same way, they hold that autism is not a disorder in which these mechanisms are damaged. This idea seems contrary to the thesis, supported by some researchers, that autistic people have a theory of mind deficit. This is because of, if Rutherford and Ray are right, autistic people can detect other people’s intentions. In this paper, (...)
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  23. Fraud Detection and Analysis for Insurance Claim using Machine Learning.S. Ramasamy - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-13.
    Fraudulent activities in insurance claims have become a significant challenge for the insurance industry, leading to substantial financial losses annually. This project, titled "Fraud Detection and Analysis for Insurance Claim using Machine Learning" aims to develop a robust and an efficient system to identify and analyze fraudulent claims. The system leverages machine learning techniques to analyze patterns, anomalies, and inconsistencies in claim data, enabling early detection of potentially fraudulent activities.
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  24. Breakthroughs in Breast Cancer Detection: Emerging Technologies and Future Prospects.Ola I. A. Lafi, Rawan N. A. Albanna, Dina F. Alborno, Raja E. Altarazi, Amal Nabahin, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Health and Medical Research (IJAHMR) 8 (9):8-15.
    Abstract: Early detection of breast cancer is vital for improving patient outcomes and reducing mortality rates. Technological advancements have significantly enhanced the accuracy and efficiency of screening methods. This paper explores recent innovations in early detection, focusing on the evolution of digital mammography, the benefits of 3D mammography (tomosynthesis), and the application of advanced imaging techniques such as molecular imaging and MRI. It also examines the role of artificial intelligence (AI) in diagnostic tools, showing how machine learning algorithms (...)
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  25. Ontology-based error detection in SNOMED-CT.Werner Ceusters, Barry Smith, Anand Kumar & Christoffel Dhaen - 2004 - Proceedings of Medinfo 2004:482-6.
    Quality assurance in large terminologies is a difficult issue. We present two algorithms that can help terminology developers and users to identify potential mistakes. We demon­strate the methodology by outlining the different types of mistakes that are found when the algorithms are applied to SNOMED-CT. On the basis of the results, we argue that both formal logical and linguistic tools should be used in the development and quality-assurance process of large terminologies.
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  26. Wave detected by LIGO is not gravitational wave.Alfonso Leon Guillen Gomez - manuscript
    General Relativity defines gravity like the metric of a Lorentzian manifold. Einstein formulated spacetime as quality structural of gravity, i.e, circular definition between gravity and spacetime, also Einstein denoted "Space and time are modes by which we think, not conditions under which we live" and “We denote everything but the gravitational field as matter”, therefore, spacetime is nothing and gravity in first approximation an effect of coordinates, and definitely a geometric effect. The mathematical model generates quantitative predictions coincident in high (...)
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  27. ANN for Lung Cancer Detection.Nassar AlIbrahim & Murshidy Suheil - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 3 (3):17-21.
    In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is “survey lung (...)
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  28. Impact of Applying Fraud Detection and Prevention Instruments in Reducing Occupational Fraud: Case study: Ministry of Health (MOH) in Gaza Strip.Faris M. Abu Mouamer, Youssef M. Abu Amuna, Mohammed K. H. A. L. I. Khalil & Abedallh Aqel - 2020 - International Journal of Academic Accounting, Finance and Management Research (IJAAFMR) 4 (6):35-45.
    The study aimed to identify the effect of applying detection and prevention tools for career fraud in combating and preventing fraud and reducing its risks through an applied study on Palestinian Ministry of Health in Gaza Strip, Palestine. To achieve the objectives of the study, the researchers used the questionnaire as a main tool to collect data, and the descriptive and analytical approach to conducting the study. The study population consisted of (501) supervisory employees working at MOH in Gaza (...)
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  29.  71
    DETECTION OFTHYROIDABNORMALITY USING VISION TRANSFORMER (ViT).Veda Reddy T. - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (2):1-12.
    . Thyroid diseases represent a significant global health concern, necessitating accurate and timely diagnostic methods for effective treatment. Traditional diagnostic approaches often rely on complex blood tests and imaging techniques that can be challenging to interpret. This paper explores the application of machine learning (ML) and deep learning (DL) techniques, particularly Vision Transformers (ViT), for thyroid disease detection. We conducted a comprehensive literature survey that highlights various studies employing ML and DL models, revealing high accuracy rates but also significant (...)
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  30. Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map.Birgitta Dresp-Langley - 2021 - Symmetry 13:299.
    Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To this (...)
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  31. 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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  32.  67
    Deepfake Detection Using LSTM and RESNEXT50.Nikhil Cilivery - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (8):1-15.
    As the prevalence of deepfake videos continues to escalate, there is an urgent need for robust and efficient detection methods to mitigate the potential consequences of misinformation and manipulation. This abstract explores the application of Long Short-Term Memory (LSTM) networks in the realm of deepfake video detection. LSTM, a type of recurrent neural network (RNN), has proven to be adept at capturing temporal dependencies in sequential data, making it a promising candidate for analysing the dynamic nature of videos. (...)
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  33.  33
    Drone Detection Using Deep Learning.L. Raj Kumar - 2025 - International Journal of Engineering Innovations and Management Strategies 1 (10):1-15.
    This research focuses on developing an advanced UAV detection system using state-of-theart YOLOv8 and YOLOv9 models. By training these models on diverse datasets, we aim to enhance real-time detection accuracy and precision under various environmental conditions. The system is designed to improve UAV safety and minimize bird collisions. Through rigorous benchmarking, we demonstrate significant improvements in detection performance, accuracy, and computational efficiency compared to existing approaches. A user-friendly web interface, built using HTML, CSS, and Flask, provides real-time (...)
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  34. Lung Cancer Detection Using Artificial Neural Network.Ibrahim M. Nasser & Samy S. Abu-Naser - 2019 - International Journal of Engineering and Information Systems (IJEAIS) 3 (3):17-23.
    In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is “survey lung (...)
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  35. Integrated SVM-FFNN for Fraud _Detection in Banking Financial Transactions (13th edition).DrR. Sugumar DrR. Udayakumar, DrP. Bharath Kumar Chowdary, , DrT. Devi - 2023 - Journal of Internet Services and Information Security 13 (4):12-25. Translated by Sugumar Dr.R..
    Detecting fraud in financial transactions is crucial for guaranteeing the integrity and security of financial systems. This paper presents an integrated approach for detecting fraudulent activities that incorporates Support Vector Machines (SVM) and Feedforward Neural Networks (FFNN). The proposed methodology utilizes the strengths of SVM and FFNN to distinguish between classes and capture complex patterns and relationships, respectively. The SVM model functions as a feature extractor, supplying the FFNN with high-level representations as inputs. Through an exhaustive evaluation utilizing labeled financial (...)
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  36. Detecting Ideology in Our Understanding of Objectivity.Markus Gabriel - 2015 - In Channa van Dijk, Eva van der Graaf, Michiel den Haan, Rosa de Jong, Christiaan Roodenburg, Dyane Til & Deva Waal (eds.), Under Influence - Philosophical Festival Drift (2014). Omnia. pp. 44-63.
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  37.  66
    Network Intrusion Detection using Machine Learning.B. Ravinder Reddy - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (4):1-15.
    With the growing sophistication and frequency of cyberattacks, there is a critical need for effective systems that can detect and prevent breaches in real time. The AI/ML-based Network Intrusion Detection System (NIDS) addresses this need by analyzing traffic patterns to identify security breaches in firewalls, routers, and network infrastructures. By integrating machine learning algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest—the system is able to detect both known cyber threats and previously unseen attack vectors. Unlike traditional methods (...)
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  38. In defence of gullibility: The epistemology of testimony and the psychology of deception detection.Kourken Michaelian - 2010 - Synthese 176 (3):399-427.
    Research in the psychology of deception detection implies that Fricker, in making her case for reductionism in the epistemology of testimony, overestimates both the epistemic demerits of the antireductionist policy of trusting speakers blindly and the epistemic merits of the reductionist policy of monitoring speakers for trustworthiness: folk psychological prejudices to the contrary notwithstanding, it turns out that monitoring is on a par (in terms both of the reliability of the process and of the sensitivity of the beliefs that (...)
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  39. Robust Cyber Attack Detection with Support Vector Machines: Tackling Both Established and Novel Threats.M. Arul Selvan - 2021 - Journal of Science Technology and Research (JSTAR) 2 (1):160-165.
    The proposed IDS model is aimed at detecting network intrusions by classifying all the packet traffic in the network as benign or malicious classes. The Canadian Institute for Cyber security Intrusion Detection System (CICIDS2017) dataset has been used to train and validate the proposed model. The model has been evaluated in terms of the overall accuracy, attack detection rate, false alarm rate, and training overhead. DDOS attacks based on Canadian Institute for Cyber security Intrusion Detection System (KDD (...)
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  40. SVM-Enhanced Intrusion Detection System for Effective Cyber Attack Identification and Mitigation.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):397-403.
    The ever-evolving landscape of cyber threats necessitates robust and adaptable intrusion detection systems (IDS) capable of identifying both known and emerging attacks. Traditional IDS models often struggle with detecting novel threats, leading to significant security vulnerabilities. This paper proposes an optimized intrusion detection model using Support Vector Machine (SVM) algorithms tailored to detect known and innovative cyberattacks with high accuracy and efficiency. The model integrates feature selection and dimensionality reduction techniques to enhance detection performance while reducing computational (...)
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  41. Can Gravitons be Detected?Tony Rothman & Stephen Boughn - 2006 - Foundations of Physics 36 (12):1801-1825.
    Freeman Dyson has questioned whether any conceivable experiment in the real universe can detect a single graviton. If not, is it meaningful to talk about gravitons as physical entities? We attempt to answer Dyson’s question and find it is possible concoct an idealized thought experiment capable of detecting one graviton; however, when anything remotely resembling realistic physics is taken into account, detection becomes impossible, indicating that Dyson’s conjecture is very likely true. We also point out several mistakes in the (...)
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  42. Detecting the Factors Affecting Classroom Dialogue Quality.Chrysi Rapanta, Merce Garcia-Milà, Andrea Miralda Banda & Fabrizio Macagno - 2023 - Linguistics and Education 77:101223.
    Despite the emphasis on dialogue and argumentation in educational settings, still not much is known about how best we can support learners in their interthinking, reasoning, and metadialogic understanding. The goal of this classroom intervention study is to explore the degree of students’ dialogicity and its possible increase during a learning programme implementing dialogic and argument-based teaching goals and principles. In particular, we focus on how students from 5 to 15 years old engage with each other's ideas, and whether/how this (...)
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  43. Responsible Innovation in Social Epistemic Systems: The P300 Memory Detection Test and the Legal Trial.John Danaher - forthcoming - In Van den Hoven (ed.), Responsible Innovation Volume II: Concepts, Approaches, Applications. Springer.
    Memory Detection Tests (MDTs) are a general class of psychophysiological tests that can be used to determine whether someone remembers a particular fact or datum. The P300 MDT is a type of MDT that relies on a presumed correlation between the presence of a detectable neural signal (the P300 “brainwave”) in a test subject, and the recognition of those facts in the subject’s mind. As such, the P300 MDT belongs to a class of brain-based forensic technologies which have proved (...)
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  44.  83
    Real-Time Phishing Detection Using Genetic Algorithm-Based Ranking and Dynamic Weighting Optimization.A. Manoj Prabaharan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):491-500.
    The rapid evolution of phishing techniques necessitates more sophisticated detection and classification methods. In this paper, we propose a novel approach to phishing content classification using a Genetic Ranking Optimization Algorithm (GROA), combined with dynamic weighting, to improve the accuracy and ranking of phishing versus legitimate content. Our method leverages features such as URL structure, email content analysis, and user behavior patterns to enhance the detection system's decision-making process. The Genetic Ranking Optimization Algorithm (GROA) is used to rank (...)
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  45.  17
    Comprehensive Detection of Malware and Trojans in Power Sector Software: Safeguarding Against Cyber Threats.A. Sai Lochan - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (11):1-14.
    The increasing reliance on digital technologies within the power sector has introduced considerable cybersecurity risks, especially from malware and trojans. These threats can disrupt essential operations, manipulate grid functions, and compromise the integrity of energy systems, thereby endangering both economic stability and national security. This research aims to create a detection framework tailored to the specific challenges of the power sector. The proposed framework utilizes advanced methods such as behaviour based anomaly detection, machine learning algorithms, and both static (...)
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  46. Autism spectrum and cheaters detection.Miguel López Astorga - 2014 - Dialogues in Philosophy, Mental and Neuro Sciences 7 (1):1-10.
    Rutherford and Ray think that human beings have mental mechanisms that help them to detect individuals that, deliberately, do not follow a rule. In the same way, they hold that autism is not a disorder in which these mechanisms are damaged. This idea seems contrary to the thesis, supported by some researchers, that autistic people have a theory of mind deficit. This is because of, if Rutherford and Ray are right, autistic people can detect other people's intentions. In this paper, (...)
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  47. OPTIMIZED DRIVER DROWSINESS DETECTION USING MACHINE LEARNING TECHNIQUES.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):395-400.
    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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  48. Machine Learning-Based Cyberbullying Detection System with Enhanced Accuracy and Speed.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):421-429.
    The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify (...)
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  49.  57
    Revolutionizing Cybersecurity: Intelligent Malware Detection Through Deep Neural Networks.M. Sheik Dawood - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):655-666.
    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 (...)
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  50.  82
    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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