Results for 'Sarcasm Detection'

880 found
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  1. 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 (...)
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  2.  88
    An inferentialist account of lying.Kamil Lemanek - 2025 - Synthese 205 (2):1-13.
    The inferentialism due to Robert Brandom presents a compelling normative-deontic picture of language and discursive practices, and as such it is well positioned to address phenomena like lying. This short work outlines a simple account of how lying can be conceptualized within that framework. To that end, the basic Brandomian position is extended to include a novel type of status – namely, pseudo-commitments, which are unique in their being non-binding. The traditional definition of lying is then given a status-oriented form, (...)
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  3. Sarcasm definition and examples in literature and everyday life.Gregory Woods - manuscript
    The following articke studies the definitions of sarcasm, its usage in literature, in educational system, and its pros and cons.
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  4. Sarcasm and Taboo in the Moroccan Mediascape after the February 20 Movement.Abdelmjid Kettioui - 2020 - Journal of African Cultural Studies 1:19.
    This article aims to conceptualize the interplay between sarcasm, scatology and writing in Darija (Moroccan Arabic or Al-Maghribia) on the web in a post-uprising era. It focuses on the new Darijophone prose that emerged after 20 February 2011 protests in Goud and The Newزحيليكر‎ (The New Bumpkin). Originating with absurdist February 20 movement founder member, Mohammed Sokrat, this writing genre is realist, vulgar, profane, taboo-breaking, and borrows from the toilet space to poke fun at the schizophrenia, herd mentality and (...)
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  5. 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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  6. 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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  7.  72
    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. 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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  9. 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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  10. 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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  11. 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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  12. 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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  13. 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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  14. 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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  15. Introspection Is Signal Detection.Jorge Morales - 2024 - British Journal for the Philosophy of Science 75 (1):99-126.
    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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  16.  39
    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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  17. Deception Detection Research: Some Lessons for Epistemology.Peter Graham - 2024 - In Waldomiro J. Silva-Filho, 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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  18. 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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  19. 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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  20.  23
    Detection of Covid-19 based on convolutional neural networks using pre-processed chest X-ray images (14th edition).and Ahmed Said Badawy Arul Raj A. M., Sugumar R., Padmkala S., Jayant Giri, Naim Ahmad - 2024 - Aip Advances 14 (3):1-11.
    The global catastrophe known as COVID-19 has shattered the world’s socioeconomic structure. Effective and affordable diagnosis techniques are crucial for better COVID-19 therapy and the eradication of bogus cases. Due to the daily upsurge in cases, hospitals only have a small supply of COVID-19 test kits. The study describes a deep Convolutional Neural Network (CNN) design for categorizing chest x-ray images in the diagnosis of COVID-19. The lack of a substantial, high-quality chest x-ray picture collection made efficient and exact CNN (...)
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  21. Advancements in Early Detection of Breast Cancer: Innovations and Future Directions.Izzeddin A. Alshawwa, Hosni Qasim El-Mashharawi, Fatima M. Salman, Mohammed Naji Abu Al-Qumboz, Bassem S. Abunasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Engineering Research (IJAER) 8 (8):15-24.
    Abstract: Early detection of breast cancer plays a pivotal role in improving patient prognosis and reducing mortality rates. Recent technological advancements have significantly enhanced the accuracy and effectiveness of breast cancer screening methods. This paper explores the latest innovations in early detection, including the evolution of digital mammography, the impact of 3D mammography (tomosynthesis), and the use of advanced imaging techniques such as molecular imaging and MRI. Furthermore, the integration of artificial intelligence (AI) in diagnostic tools is discussed, (...)
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  22. 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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  23. 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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  24.  35
    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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  25. 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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  26. 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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  27. 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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  28. 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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  29.  91
    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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  30.  17
    Detecting Post-Biological and Interdimensional Civilizations: A New Framework Based on the Universal Law of Balance.Angelito Malicse - manuscript
    Detecting Post-Biological and Interdimensional Civilizations: A New Framework Based on the Universal Law of Balance -/- By: Angelito Enriquez Malicse -/- Introduction -/- The search for advanced extraterrestrial civilizations has long focused on physical evidence—radio signals, megastructures, or interstellar probes. However, if intelligence evolves beyond biological form, as suggested by AI-driven civilizations and interdimensional theories, traditional search methods may be inadequate. -/- This essay explores how the Universal Law of Balance in Nature can help predict the existence of post-biological civilizations (...)
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  31.  66
    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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  32. 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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  33.  93
    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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  34. 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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  35. Responsible Innovation in Social Epistemic Systems: The P300 Memory Detection Test and the Legal Trial.John Danaher - forthcoming - In Van den Hoven, 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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  36.  21
    Detection of Faulty Wafer by Using Machine Learning Model.Subhanshu Babbar Anshu Kumari - 2021 - International Journal of Innovative Research in Science, Engineering and Technology 10 (12):15501-15507.
    Increased, fast and tremendous growth in the semiconductor industry results in high density and IC performance in each unit area. Semiconductors are an integral part of electronicdevices that allow advances in communication, health care, computers, militarysystems, transportation, and many other systems. In most cases, however, impairmentoccurs in day-to-day production and affects the daily production of IC packages at theend of the line. Therefore, ultimately challenging the limitations of semiconductortechnology. Profile analysis and process of monitoring are critical in detecting a widerange (...)
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  37.  66
    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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  38. Multi-Layer Intrusion Detection Framework for IoT Systems Using Ensemble Machine Learning.Janet Yan - manuscript
    The proliferation of Internet of Things (IoT) devices has introduced a range of opportunities for enhanced connectivity, automation, and efficiency. However, the vast array of interconnected devices has also raised concerns regarding cybersecurity, particularly due to the limited resources and diverse nature of IoT devices. Intrusion detection systems (IDS) have emerged as critical tools for identifying and mitigating security threats. This paper proposes a Multi-Layer Intrusion Detection Framework for IoT systems, leveraging Ensemble Machine Learning (EML) techniques to improve (...)
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  39. 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 (...)
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  40.  88
    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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  41. 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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  42.  58
    Robust Human Target Detection and Acquisition.N. Sushanth - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (4):1-13.
    en the increasing importance of human target detection in various security applications, many existing systems face challenges related to cost, accuracy, and adaptability. This work presents the development of a machine learning-based system called Human Target Detection and Acquisition, designed to assist security personnel in the detection and tracking of human targets through an adaptive and cost-effective approach. In this, standard CCTV cameras provide visual data, which intelligent algorithms process to identify and track human targets, even in (...)
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  43. COVID-19 Face Mask Detection Alert System.McDonald Moyo & Cen Yuefeng - 2022 - Computer Engineering and Intelligent Systems 13 (2):1-15.
    Study shows that mask-wearing is a critical factor in stopping the COVID-19 transmission. By the time of this article, most states have mandated face masking in public space. Therefore, real-time face mask detection becomes an essential application to prevent the spread of the pandemic. This study will present a face mask detection system that can detect and monitor mask-wearing from camera feeds and alert when there is a violation. The face mask detection algorithm uses a haar cascade (...)
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  44.  43
    Breast Cancer Detection Using Machine Learning.Shifa A. M. Amrutha D. - 2024 - International Journal of Innovative Research in Science, Engineering and Technology 13 (11):19401-19406.
    Breast cancer is one of the leading causes of death among women worldwide. Early detection plays a crucial role in improving survival rates, and machine learning (ML) provides powerful tools for identifying cancerous tumors in medical imaging and diagnostic data. This paper explores various machine learning techniques used for breast cancer detection, with a particular focus on the Wisconsin Breast Cancer Dataset (WBCD). We evaluate several classification models, including Logistic Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), (...)
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  45.  42
    Speech Emotion Detection_ System using Machine Learning (12th edition).Asma Shaikh Neev Mhatre, - 2024 - International Journal of Innovative Research in Computer and Communication Engineering 12 (11):12789-12793. Translated by Neev Mhatre.
    Speech Emotion Detection (SED) refers to the identification of human emotions based on speech signals. The goal of this research is to design and implement a system that can accurately classify emotions from speech using machine learning techniques. The system can be applied in various fields such as healthcare, customer service, human-computer interaction, and mental health monitoring. The paper discusses the various stages of building such a system, from collecting and preprocessing audio data to selecting machine learning models and (...)
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  46. 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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  47. 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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  48. Preliminary Detection of GW190521 Using the Coherent Oscillation Detection and Encoding System (CODES)_ A Novel Approach to Gravitational Wave Analysis with Ongoing Validation.Devin Bostick - manuscript
    Abstract -/- We present a preliminary application of the Coherent Oscillation Detection and Encoding System (CODES), a novel method for detecting gravitational waves using prime-based encoding and phase-locking, to the GW190521 event detected by LIGO. CODES encodes strain data into a complex function C(x,t)=∑p=2,3,5,71pei(2πlog⁡(p)t+χpx) C(x,t) = \sum_{p=2,3,5,7} \frac{1}{p} e^{i(2\pi \log(p) t + \chi_p x)} C(x,t)=∑p=2,3,5,7​p1​ei(2πlog(p)t+χp​x), enhancing coherence through phase alignment to compute a Coherence Score (CCS). Using H1 detector data from GPS 1242442965.779297 to 1242442968.220459, CODES identified a peak CCS (...)
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  49.  97
    Intelligent Phishing Content Detection System Using Genetic Ranking and Dynamic Weighting Techniques.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):480-490.
    The Genetic Ranking Optimization Algorithm (GROA) is used to rank phishing content based on multiple features by optimizing the ranking system through iterative selection and weighting. Dynamic weighting further enhances the process by adjusting the weights of features based on their importance in real-time. This hybrid approach enables the model to learn from the data, improving classification over time.
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  50. 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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