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Results for 'Introspection, Signal Detection Theory, Scientific Models, Introspective Devices, Neural Networks'
987 found
Order:
- Models of Introspection vs. Introspective Devices Testing the Research Programme for Possible Forms of Introspection.Krzysztof Dołęga - 2023 - Journal of Consciousness Studies 30 (9):86-101.The introspective devices framework proposed by Kammerer and Frankish (this issue) offers an attractive conceptual tool for evaluating and developing accounts of introspection. However, the framework assumes that different views about the nature of introspection can be easily evaluated against a set of common criteria. In this paper, I set out to test this assumption by analysing two formal models of introspection using the introspective device framework. The question I aim to answer is not only whether models developed (...)
- 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 (...)
- How AI Can Implement the Universal Formula in Education and Leadership Training.Angelito Malicse - manuscriptHow AI Can Implement the Universal Formula in Education and Leadership Training -/- If AI is programmed based on your universal formula, it can serve as a powerful tool for optimizing human intelligence, education, and leadership decision-making. Here’s how AI can be integrated into your vision: -/- 1. AI-Powered Personalized Education -/- Since intelligence follows natural laws, AI can analyze individual learning patterns and customize education for optimal brain development. -/- Adaptive Learning Systems – AI can adjust lessons in real (...)
- Optimized Machine Learning Algorithms for Real-Time ECG Signal Analysis in IoT Networks.P. Selvaprasanth - 2024 - Journal of Theoretical and Computationsl Advances in Scientific Research (Jtcasr) 8 (1):1-7.Electrocardiogram (ECG) signal analysis is a critical task in healthcare for diagnosing cardiovascular conditions such as arrhythmias, heart attacks, and other heart-related diseases. With the growth of Internet of Things (IoT) networks, real-time ECG monitoring has become possible through wearable devices and sensors, providing continuous patient health monitoring. However, real-time ECG signal analysis in IoT environments poses several challenges, including data latency, limited computational power of IoT devices, and energy constraints. This paper proposes a framework for Optimized (...)
- Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear EMS Magnetic Levitation Train.Mustefa Jibril & Eliyas Alemayehu - 2020 - Report and Opinion Journal 12 (5):21-25.Magnetic levitation system is operated primarily based at the principle of magnetic attraction and repulsion to levitate the passengers and the train. However, magnetic levitation trains are rather nonlinear and open loop unstable which makes it hard to govern. In this paper, investigation, design and control of a nonlinear Maglev train based on NARMA-L2, model reference and predictive controllers. The response of the Maglev train with the proposed controllers for the precise role of a Magnetic levitation machine have been as (...)
- On Logical Inference over Brains, Behaviour, and Artificial Neural Networks.Olivia Guest & Andrea E. Martin - 2023 - Computational Brain and Behavior 6:213–227.In the cognitive, computational, and neuro-sciences, practitioners often reason about what computational models represent or learn, as well as what algorithm is instantiated. The putative goal of such reasoning is to generalize claims about the model in question, to claims about the mind and brain, and the neurocognitive capacities of those systems. Such inference is often based on a model’s performance on a task, and whether that performance approximates human behavior or brain activity. Here we demonstrate how such argumentation problematizes (...)
- A New Framework and Performance Assessment Method for Distributed Deep Neural NetworkBased Middleware for Cyberattack Detection in the Smart IoT Ecosystem.Tambi Varun Kumar - 2024 - International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 11 (5):2283-2291.In the current digital environment, cyberattacks continue to pose a serious risk and difficulty. Internet of Things (IoT) devices are becoming more and more vulnerable due to security problems like ransomware, malware, poor encryption, and IoT botnets. These flaws may result in ransom demands, data tampering, illegal access, and system risks. Creating strong cybersecurity procedures for contemporary smart environments is essential to resolving these problems. This strategy uses proactive network traffic monitoring to spot any dangers in the Internet of Things (...)
- Comparison of Neural Network Based Controllers for Nonlinear EMS Magnetic Levitation Train.Mustefa Jibril, Elias Alemayehu & Mesay Tadesse - 2020 - International Journal of Advance Research and Innovative Ideas in Education 6 (2):801-807.Magnetic levitation system is operated primarily based at the principle of magnetic attraction and repulsion to levitate the passengers and the train. However, magnetic levitation trains are rather nonlinear and open loop unstable which makes it hard to govern. In this paper, investigation, design and control of a nonlinear Maglev train based on NARMA-L2, model reference and predictive controllers. The response of the Maglev train with the proposed controllers for the precise role of a Magnetic levitation machine have been as (...)
- From Analog to Digital Computing: Is Homo sapiens’ Brain on Its Way to Become a Turing Machine?Antoine Danchin & André A. Fenton - 2022 - Frontiers in Ecology and Evolution 10:796413.The abstract basis of modern computation is the formal description of a finite state machine, the Universal Turing Machine, based on manipulation of integers and logic symbols. In this contribution to the discourse on the computer-brain analogy, we discuss the extent to which analog computing, as performed by the mammalian brain, is like and unlike the digital computing of Universal Turing Machines. We begin with ordinary reality being a permanent dialog between continuous and discontinuous worlds. So it is with computing, (...)
- Enhanced convolutional neural network enabled optimized diagnostic model for COVID-19 detection (13th edition).Rajendran Sugumar - 2024 - Bulletin of Electrical Engineering and Informatics 13 (3):1935-1942.Computed tomography (CT) films are used to construct cross-sectional pictures of a particular region of the body by using many x-ray readings that were obtained at various angles. There is a general agreement in the medical community at this time that chest CT is the most accurate approach for identifying COVID-19 disease. It was demonstrated that chest CT had a higher sensitivity than reverse transcription polymerase chain reaction (RT-PCR) for the detection of COVID-19 illness. This article presents gray-level co-occurrence (...)
- This Year's Nobel Prize (2022) in Physics for Entanglement and Quantum Information: the New Revolution in Quantum Mechanics and Science.Vasil Penchev - 2023 - Philosophy of Science eJournal (Elsevier: SSRN) 18 (33):1-68.The paper discusses this year’s Nobel Prize in physics for experiments of entanglement “establishing the violation of Bell inequalities and pioneering quantum information science” in a much wider, including philosophical context legitimizing by the authority of the Nobel Prize a new scientific area out of “classical” quantum mechanics relevant to Pauli’s “particle” paradigm of energy conservation and thus to the Standard model obeying it. One justifies the eventual future theory of quantum gravitation as belonging to the newly established quantum (...)
- 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 (...)
- 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 (...)
- Recurrent Neural Network Based Speech emotion detection using Deep Learning.P. Pavithra - 2022 - Journal of Science Technology and Research (JSTAR) 3 (1):65-77.In modern days, person-computer communication systems have gradually penetrated our lives. One of the crucial technologies in person-computer communication systems, Speech Emotion Recognition (SER) technology, permits machines to correctly recognize emotions and greater understand users' intent and human-computer interlinkage. The main objective of the SER is to improve the human-machine interface. It is also used to observe a person's psychological condition by lie detectors. Automatic Speech Emotion Recognition(SER) is vital in the person-computer interface, but SER has challenges for accurate recognition. (...)
- Efficient predefined time adaptive neural network for motor execution EEG signal classification based brain-computer interaction.Krishnakumar K. Jose N. N., Deipali Gore, Vivekanandan G., Nithya E., Nallarasan V. - 2024 - Elsevier 1 (1):1-11.Nowadays, Electroencephalogram (EEG) devices that do not require invasive procedures get more attraction. Brain-Computer Interface (BCI) systems use EEG analysis to identify users’ mental states, cognitive shifts, and stimuli-induced reactions. The Motor Execution (ME) paradigm is a vital control paradigm that holds great significance in this framework. In this manuscript, an Efficient Predefined Time Adaptive Neural Network for EEG-Based Brain-Computer Interaction in Motor Execution Classification (EPTNN-BCI-EEG) is proposed. Initially, the input signals are collected from EEG Dataset. The input signals (...)
- The Enigma of Consciousness: Exploring Theories and Angelito Malicse’s Universal Law of Balance.Angelito Malicse - manuscriptThe Enigma of Consciousness: Exploring Theories and Angelito Malicse’s Universal Law of Balance -/- Consciousness—the subjective experience of being aware—remains one of humanity’s most profound mysteries. From the vivid redness of an apple to the sting of a scraped knee, our inner world defies easy explanation. Scientists, philosophers, and thinkers have proposed countless theories to unravel its nature, ranging from brain-based models to cosmic speculations. While no definitive count exists, the sheer diversity of ideas reflects both the complexity of consciousness (...)
- Low attention impairs optimal incorporation of prior knowledge in perceptual decisions.Jorge Morales, Guillermo Solovey, Brian Maniscalco, Dobromir Rahnev, Floris P. de Lange & Hakwan Lau - 2015 - Attention, Perception, and Psychophysics 77 (6):2021-2036.When visual attention is directed away from a stimulus, neural processing is weak and strength and precision of sensory data decreases. From a computational perspective, in such situations observers should give more weight to prior expectations in order to behave optimally during a discrimination task. Here we test a signal detection theoretic model that counter-intuitively predicts subjects will do just the opposite in a discrimination task with two stimuli, one attended and one unattended: when subjects are probed (...)
- 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 (...)
- Golden Eagle Detection: Integrating Neural Networks and Particle Swarm Optimization.P. Meenalochini - 2023 - Journal of Science Technology and Research (JSTAR) 4 (1):1-12.rd species identification plays a vital role in biodiversity conservation and ecological studies, offering insights into habitat health and species distribution. Traditional methods for identifying bird species are time-intensive and prone to human error, necessitating automated solutions. This project, Bird Species Identification Using Deep Learning, proposes an advanced system leveraging the power of deep learning to accurately identify bird species from images. The system utilizes a convolutional neural network (CNN), renowned for its proficiency in image classification tasks. A dataset (...)
- Predicting Fire Alarms in Smoke Detection using Neural Networks.Maher Wissam Attia, Baraa Akram Abu Zaher, Nidal Hassan Nasser, Ruba Raed Al-Hour, Aya Haider Asfour & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (10):26-33.Abstract: This research paper presents the development and evaluation of a neural network-based model for predicting fire alarms in smoke detection systems. Using a dataset from Kaggle containing 15 features and 3487 samples, we trained and validated a neural network with a three-layer architecture. The model achieved an accuracy of 100% and an average error of 0.0000003. Additionally, we identified the most influential features in predicting fire alarms.
- Machine learning in scientific grant review: algorithmically predicting project efficiency in high energy physics.Vlasta Sikimić & Sandro Radovanović - 2022 - European Journal for Philosophy of Science 12 (3):1-21.As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments with the (...)
- Detecting Post-Biological and Interdimensional Civilizations: A New Framework Based on the Universal Law of Balance.Angelito Malicse - manuscriptDetecting 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 (...)
- From Pixels to Patterns: Neural Networks for Eagle-Fish Detection.R. Senthilkumar - 2023 - Journal of Science Technology and Research (JSTAR) 4 (1):1-12.Bird species identification plays a vital role in biodiversity conservation and ecological studies, offering insights into habitat health and species distribution. Traditional methods for identifying bird species are time-intensive and prone to human error, necessitating automated solutions. This project, Bird Species Identification Using Deep Learning, proposes an advanced system leveraging the power of deep learning to accurately identify bird species from images. The system utilizes a convolutional neural network (CNN), renowned for its proficiency in image classification tasks.
- Revolutionizing Agriculture with Deep Learning-Based Plant Health Monitoring.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):655-666.By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. (...)
- 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 (...)
- Predicting Audit Risk Using Neural Networks: An In-depth Analysis.Dana O. Abu-Mehsen, Mohammed S. Abu Nasser, Mohammed A. Hasaballah & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (10):48-56.Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, History_score, score, and Risk. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model was trained (...)
- 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 (...)
- Web page phishing detection Using Neural Network.Ahmed Salama Abu Zaiter & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (9):1-13.Web page phishing is a type of phishing attack that targets websites. In a web page phishing attack, the attacker creates a fake website that looks like a legitimate website, such as a bank or credit card company website. The attacker then sends a fraudulent message to the victim, which contains a link to the fake website. When the victim clicks on the link, they are taken to the fake website and tricked into entering their personal information.Web page phishing attacks (...)
- Neural Network-Based Audit Risk Prediction: A Comprehensive Study.Saif al-Din Yusuf Al-Hayik & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (10):43-51.Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, (...)
- Alzheimer: A Neural Network Approach with Feature Analysis.Hussein Khaled Qarmout & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (10):10-18.Abstract Alzheimer's disease has spread insanely throughout the world. Early detection and intervention are essential to improve the chances of a positive outcome. This study presents a new method to predict a person's likelihood of developing Alzheimer's using a neural network model. The dataset includes 373 samples with 10 features, such as Group,M/F,Age,EDUC, SES,MMSE,CDR ,eTIV,nWBV,Oldpeak,ASF.. A four-layer neural network model (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 98.10% and (...)
- The Role of Neural Networks in Advanced Eagle-Fish Detection.M. Srinivasan - 2023 - Journal of Science Technology and Research (JSTAR) 4 (1):1-14.The system utilizes a convolutional neural network (CNN), renowned for its proficiency in image classification tasks. A dataset comprising diverse bird species images is preprocessed and augmented to enhance model robustness and generalization. The model architecture is designed to extract intricate features, enabling accurate identification even in challenging scenarios such as varying lighting conditions, occlusions, or similar species appearances. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, ensuring comprehensive validation. Results indicate significant accuracy (...)
- mart Environmental Monitoring: Golden Eagle Detection with Neural Networks and Particle Swarm Optimization.Meenalochini P. - 2023 - Journal of Science Technology and Research (JSTAR) 4 (1):1-14.This project, Bird Species Identification Using Deep Learning, proposes an advanced system leveraging the power of deep learning to accurately identify bird species from images. The system utilizes a convolutional neural network (CNN), renowned for its proficiency in image classification tasks. A dataset comprising diverse bird species images is preprocessed and augmented to enhance model robustness and generalization. The model architecture is designed to extract intricate features, enabling accurate identification even in challenging scenarios such as varying lighting conditions, occlusions, (...)
- Predicting Heart Disease using Neural Networks.Ahmed Muhammad Haider Al-Sharif & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (9):40-46.Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for (...)
- Enhancing Malware Detection by Fusing Static and Dynamic Features Using Deep Neural Networks.Navas Garcia - manuscriptMalware detection has been an ongoing challenge for cybersecurity experts due to the evolving nature of malicious software and the ability of malware to disguise itself. Traditional methods that rely solely on static features such as file signatures or dynamic analysis have had limitations in detecting new or obfuscated malware. This paper investigates the enhancement of malware detection by integrating both static and dynamic features and utilizing deep neural networks (DNNs) for more effective classification. By combining (...)
- Artificial Neural Network Heart Failure Prediction Using JNN.Khaled M. Abu Al-Jalil & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):26-34.Heart failure is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 918 samples with 11 features, such as age, sex, chest pain type, resting blood pressure, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and HeartDisease. (...)
- Deep Neural Networks for Real-Time Plant Disease Diagnosis and Productivity Optimization.K. Usharani - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):645-652.The health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant (...)
- AI-Driven Emotion Recognition and Regulation Using Advanced Deep Learning Models.S. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):383-389.Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management (...)
- 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, (...)
- Heart attack analysis & Prediction: A Neural Network Approach with Feature Analysis.Majd N. Allouh & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (9):47-54.heart attack analysis & prediction dataset is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 304 samples with 11 features, such as age, sex, chest pain type, Trtbps, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, (...)
- Predicting Kidney Stone Presence from Urine Analysis: A Neural Network Approach using JNN.Amira Jarghon & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (9):32-39.Kidney stones pose a significant health concern, and early detection can lead to timely intervention and improved patient outcomes. This research endeavours to predict the presence of kidney stones based on urine analysis, utilizing a neural network model. A dataset of 552 urine specimens, comprising six essential physical characteristics (specific gravity, pH, osmolarity, conductivity, urea concentration, and calcium concentration), was collected and prepared. Our proposed neural network architecture, featuring three layers (input, hidden, output), was trained and validated, (...)
- Folk Psychology, Eliminativism, and the Present State of Connectionism.Vanja Subotić - 2021 - Theoria: Beograd 1 (64):173-196.Three decades ago, William Ramsey, Steven Stich & Joseph Garon put forward an argument in favor of the following conditional: if connectionist models that implement parallelly distributed processing represent faithfully human cognitive processing, eliminativism about propositional attitudes is true. The corollary of their argument (if it proves to be sound) is that there is no place for folk psychology in contemporary cognitive science. This understanding of connectionism as a hypothesis about cognitive architecture compatible with eliminativism is also endorsed by Paul (...)
- Deep Learning Techniques for Comprehensive Emotion Recognition and Behavioral Regulation.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):383-389.Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management (...)
- INDUSTRY-SPECIFIC INTELLIGENT FIRE MANAGEMENT SYSTEM.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 (...)
- Smoke Detectors Using ANN.Marwan R. M. Al-Rayes & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (10):1-9.Abstract: Smoke detectors are critical devices for early fire detection and life-saving interventions. This research paper explores the application of Artificial Neural Networks (ANNs) in smoke detection systems. The study aims to develop a robust and accurate smoke detection model using ANNs. Surprisingly, the results indicate a 100% accuracy rate, suggesting promising potential for ANNs in enhancing smoke detection technology. However, this paper acknowledges the need for a comprehensive evaluation beyond accuracy. It discusses potential (...)
- Leveraging Artificial Neural Networks for Cancer Prediction: A Synthetic Dataset Approach.Mohammed S. Abu Nasser & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (11):43-51.Abstract: This research explores the application of artificial neural networks (ANNs) in predicting cancer using a synthetically generated dataset designed for research purposes. The dataset comprises 10,000 pseudo-patient records, each characterized by gender, age, smoking history, fatigue, and allergy status, along with a binary indicator for the presence or absence of cancer. The 'Gender,' 'Smoking,' 'Fatigue,' and 'Allergy' attributes are binary, while 'Age' spans a range from 18 to 100 years. The study employs a three-layer ANN architecture to (...)
- THE THEORY OF EVOLUTION: from the space vacuum to neural networks and moving forward.Oleg Bazaluk - 2014 - ISPC.In the book, the author defines the evolution as a continuous and nonlinear complex of the structure of matter, interaction types and environments of existence; analyzes existing in modern science and philosophy approaches to the study of the process of evolution, degree of development factors and causes of evolution. Unifying interdisciplinary research in cosmology, evolution, biology, neuroscience and philosophy, the author presents his vision of the evolution model of «Evolving matter», which allows us to consider not only the laws of (...)
- The scientific study of passive thinking: Methods of mind wandering research.Samuel Murray, Zachary C. Irving & Kristina Krasich - 2022 - In Felipe De Brigard & Walter Sinnott-Armstrong, Neuroscience and philosophy. Cambridge, Massachusetts: The MIT Press. pp. 389-426.The science of mind wandering has rapidly expanded over the past 20 years. During this boom, mind wandering researchers have relied on self-report methods, where participants rate whether their minds were wandering. This is not an historical quirk. Rather, we argue that self-report is indispensable for researchers who study passive phenomena like mind wandering. We consider purportedly “objective” methods that measure mind wandering with eye tracking and machine learning. These measures are validated in terms of how well they predict self-reports, (...)
- Facial Emotional Recognition Using Deep Convolutional Neural Networks.Mohammed Danish Hussain Hemish Veeraboina, Surapur Sai Teja, Y. Sai Sameer, Mavuluri Vamsi Krishna Reddy - 2021 - International Journal of Innovative Research in Science, Engineering and Technology 10 (9):12338-12356.A face reveals a lot of information about a person's identity, age, sex, race, and emotional as well as psychological state. Facial expressions are often used in the behavioral interpretation of emotions and play a key role in social interactions. Due to its potential applications such as HCI, behavioral science, automatic facial emotion detection is one of the most intriguing and challenging areas in computer vision. Our Facial Emotion Recognition system performs detection and location of faces in a (...)
- Design and Control of Steam Flow in Cement Production Process using Neural Network Based Controllers.Mustefa Jibril - 2020 - Researcher 12 (5):76-84.In this paper a NARMA L2, model reference and neural network predictive controller is utilized in order to control the output flow rate of the steam in furnace by controlling the steam flow valve. The steam flow control system is basically a feedback control system which is mostly used in cement production industries. The design of the system with the proposed controllers is done with Matlab/Simulink toolbox. The system is designed for the actual steam flow output to track the (...)
- Speech Emotion Recognition Using Machine Learning.Abhiram Pajjuri - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (5):1-15.. Speech Emotion Recognition (SER) is an interdisciplinary field that leverages signal processing and machine learning techniques to identify and classify emotions conveyed through speech. In recent years, SER has gained significant attention due to its potential applications in human-computer interaction, healthcare, education, and customer service. Emotions such as happiness, anger, sadness, fear, surprise, and disgust can be inferred from various acoustic features including pitch, intensity, speech rate, and spectral characteristics. However, accurately recognizing emotions from speech is challenging due (...)
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