Results for 'oxygen consumption, ANN, Predictive Model'

949 found
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  1. Effect of Oxygen Consumption of Thylakoid Membranes (Chloroplasts) From Spinach after Inhibition Using JNN.Hisham Ziad Belbeisi, Youssef Samir Al-Awadi, Muhammad Munir Abbas & Samy S. Abu-Naser - 2020 - International Journal of Academic Health and Medical Research (IJAHMR) 4 (11):1-7.
    Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict effect of oxygen consumption of thylakoid membranes (chloroplasts) from spinach after inhibition. A number of factors were identified that may affect of oxygen consumption of thylakoid membranes from spinach. Factors such as curve, herbicide, dose, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some (...)
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  2. Predicting urban Heat Island in European cities: A comparative study of GRU, DNN, and ANN models using urban morphological variables.Alireza Attarhay Tehrani, Omid Veisi, Kambiz Kia, Yasin Delavar, Sasan Bahrami, Saeideh Sobhaninia & Asma Mehan - 2024 - Urban Climate 56 (102061):1-27.
    Continued urbanization, along with anthropogenic global warming, has and will increase land surface temperature and air temperature anomalies in urban areas when compared to their rural surroundings, leading to Urban Heat Islands (UHI). UHI poses environmental and health risks, affecting both psychological and physiological aspects of human health. Thus, using a deep learning approach that considers morphological variables, this study predicts UHI intensity in 69 European cities from 2007 to 2021 and projects UHI impacts for 2050 and 2080. The research (...)
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  3. Shopping for Meaning: Tracing the Ontologies of Food Consumption in Latvia.Anne Sauka - 2022 - Letonica 44 (1):169-190.
    Researchers of different calibres from phenomenology to posthumanism and beyond have outlined the processuality of the body and the environment (Alaimo 2010; Gendlin 2017), stressing the importance of changing the ontological presuppositions of the body-environment bond (Schoeller and Duanetz 2018: 131), since the existing models facilitate the alienation and intangibility of the environment, thus, leading to reduced societal awareness of the importance of environmental issues (Neimanis, Åsberg, Hedrén 2015: 73–74). In this article, I argue that in questions relating to food, (...)
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  4. Measuring the World: Olfaction as a Process Model of Perception.Ann-Sophie Barwich - 2018 - In Daniel J. Nicholson & John Dupré (eds.), Everything Flows: Towards a Processual Philosophy of Biology. Oxford, United Kingdom: Oxford University Press. pp. 337-356.
    How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities (...)
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  5. Distance education students’ indulgence in six sharp practices: General linear modelling of predictive parameters.Valentine Joseph Owan, Onyinye Chuktu, Ann E. Dijeh, Abderrazak Zaafour, Julius U. Ukah, Margaret U. Chukwurah, Denis A. Ube, Michael Ekpenyong Asuquo, Uwase Uwase Esuong, Udida Joseph Udida & Cyprian Oba Ojong - 2023 - Turkish Online Journal of Distance Education 24 (3):71-92.
    This study examined the degree to which students indulge in six prominent misconducts in Distance Education Institutions (DEIs). The study also quantified how class size, instructional delivery and institutional policies predict students’ indulgence in sharp practices using a general linear modelling approach. A sample of 871 participants was drawn from 1,742 final-year students across two DEIs in Nigeria. A structured questionnaire was used for data collection. The questionnaire had acceptable psychometric estimates of dimensionality, content and construct validity, as well as (...)
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  6. ANN for Predicting Temperature and Humidity in the Surrounding Environment.Abd Al-Rahman Shawwa, Saji Al-Absi, Khaled Hassanein & Bastami Bashhar - 2017 - International Journal of Academic Pedagogical Research (IJAPR) 9 (2):1-5.
    Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict temperature in the surrounding environment. A number of factors were identified that may affect temperature or humidity. Factors such as the nature of the surrounding place, proximity or distance from water surfaces, the influence of vegetation, and the level of rise or fall below sea level, among others, as input variables for the ANN model. A model based on multi-layer concept topology (...)
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  7. Prediction Heart Attack using Artificial Neural Networks (ANN).Ibrahim Younis, Mohammed S. Abu Nasser, Mohammed A. Hasaballah & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (10):36-41.
    Abstract Heart Attack is the Cardiovascular Disease (CVD) which causes the most deaths among CVDs. We collected a dataset from Kaggle website. In this paper, we propose an ANN model for the predicting whether a patient has a heart attack or not that. The dataset set consists of 9 features with 1000 samples. We split the dataset into training, validation, and testing. After training and validating the proposed model, we tested it with testing dataset. The proposed model (...)
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  8. ANN for Predicting Medical Expenses.Khaled Salah & Ahmed Altalla - 2016 - International Journal of Engineering and Information Systems (IJEAIS) 2 (10):11-16.
    Abstract: In this research, the Artificial Neural Network (ANN) model was developed and tested to predict the rate of treatment expenditure on an individual or family in a country. A number of factors have been identified that may affect treatment expenses. Factors such as age, grade level such as primary, preparatory, secondary or college, sex, size of disability, social status, and annual medical expenses in fixed dollars excluding dental and outpatient clinics among others, as input variables for the ANN (...)
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  9. ANN for Predicting Birth Weight.Shawwah Mohammad & Murshidy Suheil - 2020 - International Journal of Academic Health and Medical Research (IJAHMR) 1 (3):9-12.
    In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in (...)
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  10. ANN Car Mileage per Gallon Prediction.Jomana Ahmed, Bayan Harb, Bassem S. Abu, Mohsen Afana & Rafiq Madhoun - 2017 - International Journal of Advanced Science and Technology 124:51-58.
    In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict (...)
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  11. ANN for English Alphabet Prediction.Hamza H. Heriz, Sharief M. Salah, Mohammad Abu Abdu & Qassas Randa - 2016 - International Journal of Academic Pedagogical Research (IJAPR) 11 (2):8-13.
    Abstract: In this paper an Artificial Neural Network (ANN) model, for predicting the Letters from twenty dissimilar fonts for each letter. The character images were, initially, based on twenty dissimilar fonts and each letter inside these twenty fonts was arbitrarily distorted to yield a file of 20,000 distinctive stimuli. Every stimulus was transformed into 16 simple numerical attributes (arithmetical moments and edge amounts) which were then ascended to be suitable into a range of numeral values from 0 to 15. (...)
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  12. ANN for Predicting Antibiotic Susceptibility.Maaruf Ahmed & Qassas Randa - 2016 - International Journal of Academic Pedagogical Research (IJAPR) 10 (2):1-4.
    Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict efficiency of antibiotics in treating various bacteria types. Attributes that were taken in account are: organism name, specimen type, and antibiotic name as input and susceptibility as an output. A model based on one input, one hidden, and one output layers concept topology was developed and trained using a data from Queensland government's website. The evaluation shows that the ANN model is (...)
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  13. Low Birth Weight Prediction Using JNN.Osama Salah El-Din Al-Madhoun, Afnan Omar Abu Hasira, Soha Ahmed Hegazy & Samy S. Abu-Naser - 2020 - International Journal of Academic Health and Medical Research (IJAHMR) 4 (11):8-14.
    Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases (...)
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  14. Predicting Birth Weight Using Artificial Neural Network.Mohammed Al-Shawwa & Samy S. Abu-Naser - 2019 - International Journal of Academic Health and Medical Research (IJAHMR) 3 (1):9-14.
    In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in (...)
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  15. Diabetes Prediction Using Artificial Neural Network.Nesreen Samer El_Jerjawi & Samy S. Abu-Naser - 2018 - International Journal of Advanced Science and Technology 121:54-64.
    Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to (...)
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  16. Energy Efficiency Prediction using Artificial Neural Network.Ahmed J. Khalil, Alaa M. Barhoom, Bassem S. Abu-Nasser, Musleh M. Musleh & Samy S. Abu-Naser - 2019 - International Journal of Academic Pedagogical Research (IJAPR) 3 (9):1-7.
    Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on (...)
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  17. Forecasting COVID-19 cases Using ANN.Ibrahim Sufyan Al-Baghdadi & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (10):22-31.
    Abstract: The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, necessitating accurate and timely forecasting of cases for effective mitigation strategies. In this research paper, we present a novel approach to predict COVID-19 cases using Artificial Neural Networks (ANNs), harnessing the power of machine learning for epidemiological forecasting. Our ANNs-based forecasting model has demonstrated remarkable efficacy, achieving an impressive accuracy rate of 97.87%. This achievement underscores the potential of ANNs in providing precise and data-driven insights into the (...)
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  18. Artificial Neural Network for Predicting COVID 19 Using JNN.Walaa Hasan, Mohammed S. Abu Nasser, Mohammed A. Hasaballah & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (10):41-47.
    Abstract: The emergence of the novel coronavirus (COVID-19) in 2019 has presented the world with an unprecedented global health crisis. The rapid and widespread transmission of the virus has strained healthcare systems, disrupted economies, and challenged societies. In response to this monumental challenge, the intersection of technology and healthcare has become a focal point for innovation. This research endeavors to leverage the capabilities of Artificial Neural Networks (ANNs) to develop an advanced predictive model for forecasting the spread of (...)
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  19. Chances of Survival in the Titanic using ANN.Udai Hamed Saeed Al-Hayik & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (10):17-21.
    Abstract: The sinking of the RMS Titanic in 1912 remains a poignant historical event that continues to captivate our collective imagination. In this research paper, we delve into the realm of data-driven analysis by applying Artificial Neural Networks (ANNs) to predict the chances of survival for passengers aboard the Titanic. Our study leverages a comprehensive dataset encompassing passenger information, demographics, and cabin class, providing a unique opportunity to explore the complex interplay of factors influencing survival outcomes. Our ANN-based predictive (...)
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  20. 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, consisting of three (...)
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  21. Predicting Tumor Category Using Artificial Neural Networks.Ibrahim M. Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic Health and Medical Research (IJAHMR) 3 (2):1-7.
    In this paper an Artificial Neural Network (ANN) model, for predicting the category of a tumor was developed and tested. Taking patients’ tests, a number of information gained that influence the classification of the tumor. Such information as age, sex, histologic-type, degree-of-diffe, status of bone, bone-marrow, lung, pleura, peritoneum, liver, brain, skin, neck, supraclavicular, axillar, mediastinum, and abdominal. They were used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was established (...)
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  22. 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 develop a (...)
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  23. Predicting Life Expectancy in Diverse Countries Using Neural Networks: Insights and Implications.Alaa Mohammed Dawoud & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):45-54.
    Life expectancy prediction, a pivotal facet of public health and policy formulation, has witnessed remarkable advancements owing to the integration of neural network models and comprehensive datasets. In this research, we present an innovative approach to forecasting life expectancy in diverse countries. Leveraging a neural network architecture, our model was trained on a dataset comprising 22 distinct features, acquired from Kaggle, and encompassing key health indicators, socioeconomic metrics, and cultural attributes. The model demonstrated exceptional predictive accuracy, attaining (...)
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  24. Predicting Whether a Couple is Going to Get Divorced or Not Using Artificial Neural Networks.Ibrahim M. Nasser - 2019 - International Journal of Engineering and Information Systems (IJEAIS) 3 (10):49-55.
    In this paper, an artificial neural network (ANN) model was developed and validated to predict whether a couple is going to get divorced or not. Prediction is done based on some questions that the couple answered, answers of those questions were used as the input to the ANN. The model went through multiple learning-validation cycles until it got 100% accuracy.
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  25. Breast Cancer Diagnosis and Survival Prediction Using JNN.Mohammed Ziyad Abu Shawarib, Ahmed Essam Abdel Latif, Bashir Essam El-Din Al-Zatmah & Samy S. Abu-Naser - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 4 (10):23-30.
    Abstract: Breast cancer is reported to be the most common cancer type among women worldwide and it is the second highest women fatality rate amongst all cancer types. Notwithstanding all the progresses made in prevention and early intervention, early prognosis and survival prediction rates are still not sufficient. In this paper, we propose an ANN model which outperforms all the previous supervised learning methods by reaching 99.57 in terms of accuracy in Wisconsin Breast Cancer dataset. Experimental results on Haberman’s (...)
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  26. Unlocking Literary Insights: Predicting Book Ratings with Neural Networks.Mahmoud Harara & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (10):22-27.
    Abstract: This research delves into the utilization of Artificial Neural Networks (ANNs) as a powerful tool for predicting the overall ratings of books by leveraging a diverse set of attributes. To achieve this, we employ a comprehensive dataset sourced from Goodreads, enabling us to thoroughly examine the intricate connections between the different attributes of books and the ratings they receive from readers. In our investigation, we meticulously scrutinize how attributes such as genre, author, page count, publication year, and reader reviews (...)
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  27. Google Stock Price Prediction Using Just Neural Network.Mohammed Mkhaimar AbuSada, Ahmed Mohammed Ulian & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (10):10-16.
    Abstract: The aim behind analyzing Google Stock Prices dataset is to get a fair idea about the relationships between the multiple attributes a day might have, such as: the opening price for each day, the volume of trading for each day. With over a hundred thousand days of trading data, there are some patterns that can help in predicting the future prices. We proposed an Artificial Neural Network (ANN) model for predicting the closing prices for future days. The prediction (...)
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  28. Artificial Neural Network for Predicting Car Performance Using JNN.Awni Ahmed Al-Mobayed, Youssef Mahmoud Al-Madhoun, Mohammed Nasser Al-Shuwaikh & Samy S. Abu-Naser - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 4 (9):139-145.
    In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study (...)
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  29. Climate Change temperature Prediction Using Just Neural Network.Saja Kh Abu Safiah & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):35-45.
    Climate change temperature prediction plays a crucial role in effective environmental planning. This study introduces an innovative approach that harnesses the power of Artificial Neural Networks (ANNs) within the Just Neural Network (JustNN) framework to enhance temperature forecasting in the context of climate change. By leveraging historical climate data, our model achieves exceptional accuracy, redefining the landscape of temperature prediction without intricate preprocessing. This model sets a new standard for precise temperature forecasting in the context of climate change. (...)
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  30. Effective energy consumption parameters in residential buildings using Building Information Modeling.Nima Amani & Abdulamir Rezasoroush - 2020 - Global Journal of Environmental Science and Management (Gjesm) 6 (4):467–480.
    Building information modeling can help in predicting the energy efficiency in future based on dynamic patterns obtained by visualization of data. The aim of this study was to investigate the effective parameters of energy consumption using BIM technology which can evaluate the buildings energy performance. First, three forms of general states in the building were modeled to evaluate the proposed designs in Autodesk Revit Software. Then, the main building form for energy modeling and analysis was selected. Autodesk Revit 2020 software (...)
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  31. Books’ Rating Prediction Using Just Neural Network.Alaa Mazen Maghari, Iman Ali Al-Najjar, Said Jamil Al-Laqtah & Samy S. Abu-Naser - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 4 (10):17-22.
    Abstract: The aim behind analyzing the Goodreads dataset is to get a fair idea about the relationships between the multiple attributes a book might have, such as: the aggregate rating of each book, the trend of the authors over the years and books with numerous languages. With over a hundred thousand ratings, there are books which just tend to become popular as each day seems to pass. We proposed an Artificial Neural Network (ANN) model for predicting the overall rating (...)
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  32. ANN for Diagnosing Hepatitis Virus.Fathi Metwally, Khaled AbuSharekh & Bastami Bashhar - 2017 - International Journal of Academic Pedagogical Research (IJAPR) 11 (2):1-6.
    Abstract: This paper presents an artificial neural network based approach for the diagnosis of hepatitis virus. A number of factors that may possibly influence the performance of patients were outlined. Such factors as age, sex, Steroid, Antivirals, Fatigue, Malaise, Anorexia, Liver Big, Liver Firm Splean Palpable, Spiders, Ascites, Varices, Bilirubin, Alk Phosphate, SGOT, Albumin, Protine and Histology, were then used as input variables for the ANN model . Test data evaluation shows that the ANN model is able to (...)
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  33. Respiratory rhythms of the predictive mind.Micah Allen, Somogy Varga & Detlef H. Heck - 2022 - Psychological Review (4):1066-1080.
    Respiratory rhythms sustain biological life, governing the homeostatic exchange of oxygen and carbon dioxide. Until recently, however, the influence of breathing on the brain has largely been overlooked. Yet new evidence demonstrates that the act of breathing exerts a substantive, rhythmic influence on perception, emotion, and cognition, largely through the direct modulation of neural oscillations. Here, we synthesize these findings to motivate a new predictive coding model of respiratory brain coupling, in which breathing rhythmically modulates both local (...)
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  34. Predictive Modeling of Breast Cancer Diagnosis Using Neural Networks:A Kaggle Dataset Analysis.Anas Bachir Abu Sultan & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):1-9.
    Breast cancer remains a significant health concern worldwide, necessitating the development of effective diagnostic tools. In this study, we employ a neural network-based approach to analyze the Wisconsin Breast Cancer dataset, sourced from Kaggle, comprising 570 samples and 30 features. Our proposed model features six layers (1 input, 1 hidden, 1 output), and through rigorous training and validation, we achieve a remarkable accuracy rate of 99.57% and an average error of 0.000170 as shown in the image below. Furthermore, our (...)
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  35. Predicting Car Mileage per Gallon.Mohsen Afana, Jomana Ahmed, Bayan Harb, Basem Nasser & Rafiq Madhoun - 2015 - International Journal of Advanced Science and Technology 124 (124):51-59.
    In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict (...)
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  36. Machine Learning Application to Predict The Quality of Watermelon Using JustNN.Ibrahim M. Nasser - 2019 - International Journal of Engineering and Information Systems (IJEAIS) 3 (10):1-8.
    In this paper, a predictive artificial neural network (ANN) model was developed and validated for the purpose of prediction whether a watermelon is good or bad, the model was developed using JUSTNN software environment. Prediction is done based on some watermelon attributes that are chosen to be input data to the ANN. Attributes like color, density, sugar rate, and some others. The model went through multiple learning-validation cycles until the error is zero, so the model (...)
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  37. Physiology of long pranayamic breathing: Neural respiratory elements may provide a mechanism that explains how slow deep breathing shifts the autonomic nervous system.Jerath Ravinder, James W. Edry, Vernon A. Barnes & Vandna Jerath - 2006 - Medical Hypotheses 67 (3):566-571.
    Pranayamic breathing, defined as a manipulation of breath movement, has been shown to contribute to a physiologic response characterized by the presence of decreased oxygen consumption, decreased heart rate, and decreased blood pressure, as well as increased theta wave amplitude in EEG recordings, increased parasympathetic activity accompanied by the experience of alertness and reinvigoration. The mechanism of how pranayamic breathing interacts with the nervous system affecting metabolism and autonomic functions remains to be clearly understood. It is our hypothesis that (...)
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  38. Comparing Artificial Neural Networks with Multiple Linear Regression for Forecasting Heavy Metal Content.Rachid El Chaal & Moulay Othman Aboutafail - 2022 - Acadlore Transactions on Geosciences 1 (1):2-11.
    This paper adopts two modeling tools, namely, multiple linear regression (MLR) and artificial neural networks (ANNs), to predict the concentrations of heavy metals (zinc, boron, and manganese) in surface waters of the Oued Inaouen watershed flowing towards Inaouen, using a set of physical-chemical parameters. XLStat was employed to perform multiple linear and nonlinear regressions, and Statista 10 was chosen to construct neural networks for modeling and prediction. The effectiveness of the ANN- and MLR-based stochastic models was assessed by the determination (...)
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  39. Forecasting Stock Prices using Artificial Neural Network.Ahmed Munther Abdel Hadi & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (10):42-50.
    Abstract: Accurate stock price prediction is essential for informed investment decisions and financial planning. In this research, we introduce an innovative approach to forecast stock prices using an Artificial Neural Network (ANN). Our dataset, consisting of 5582 samples and 6 features, including historical price data and technical indicators, was sourced from Yahoo Finance. The proposed ANN model, composed of four layers (1 input, 1 hidden, 1 output), underwent rigorous training and validation, yielding remarkable results with an accuracy of 99.84% (...)
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  40. When are Purely Predictive Models Best?Robert Northcott - 2017 - Disputatio 9 (47):631-656.
    Can purely predictive models be useful in investigating causal systems? I argue ‘yes’. Moreover, in many cases not only are they useful, they are essential. The alternative is to stick to models or mechanisms drawn from well-understood theory. But a necessary condition for explanation is empirical success, and in many cases in social and field sciences such success can only be achieved by purely predictive models, not by ones drawn from theory. Alas, the attempt to use theory to (...)
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  41.  97
    Going in, moral, circles: A data-driven exploration of moral circle predictors and prediction models.Hyemin Han & Marja Graham - manuscript
    Moral circles help define the boundaries of one’s moral consideration. One’s moral circle may provide insight into how one perceives or treats other entities. A data-driven model exploration was conducted to explore predictors and prediction models. Candidate predictors were built upon past research using moral foundations and political orientation. Moreover, we also employed additional moral psychological indicators, i.e., moral reasoning, moral identity, and empathy, based on prior research in moral development and education. We used model exploration methods, i.e., (...)
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  42. Is Captain Kirk a natural blonde? Do X-ray crystallographers dream of electron clouds? Comparing model-based inferences in science with fiction.Ann-Sophie Barwich - 2017 - In Otávio Bueno, Steven French, George Darby & Dean Rickles (eds.), Thinking About Science, Reflecting on Art: Bringing Aesthetics and Philosophy of Science Together. New York: Routledge.
    Scientific models share one central characteristic with fiction: their relation to the physical world is ambiguous. It is often unclear whether an element in a model represents something in the world or presents an artifact of model building. Fiction, too, can resemble our world to varying degrees. However, we assign a different epistemic function to scientific representations. As artifacts of human activity, how are scientific representations allowing us to make inferences about real phenomena? In reply to this concern, (...)
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  43. Medical Models of Addiction.Harold Kincaid & Jacqueline Anne Sullivan - 2010 - In Don Ross, Harold Kincaid & David Spurrett (eds.), What Is Addiction? The MIT Press.
    Biomedical science has been remarkably successful in explaining illness by categorizing diseases and then by identifying localizable lesions such as a virus and neoplasm in the body that cause those diseases. Not surprisingly, researchers have aspired to apply this powerful paradigm to addiction. So, for example, in a review of the neuroscience of addiction literature, Hyman and Malenka (2001, p. 695) acknowledge a general consensus among addiction researchers that “[a]ddiction can appropriately be considered as a chronic medical illness.” Like other (...)
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  44.  77
    Interpretable and accurate prediction models for metagenomics data.Edi Prifti, Antoine Danchin, Jean-Daniel Zucker & Eugeni Belda - 2020 - Gigascience 9 (3):giaa010.
    Background: Microbiome biomarker discovery for patient diagnosis, prognosis, and risk evaluation is attracting broad interest. Selected groups of microbial features provide signatures that characterize host disease states such as cancer or cardio-metabolic diseases. Yet, the current predictive models stemming from machine learning still behave as black boxes and seldom generalize well. Their interpretation is challenging for physicians and biologists, which makes them difficult to trust and use routinely in the physician-patient decision-making process. Novel methods that provide interpretability and biological (...)
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  45. Virtue, self-mastery, and the autocracy of practical reason.Anne Margaret Baxley - 2015 - In Lara Denis & Oliver Sensen (eds.), Kant's Lectures on Ethics: A Critical Guide. New York: Cambridge University Press. pp. 223-238.
    As analysis of Kant’s account of virtue in the Lectures on Ethics shows that Kant thinks of virtue as a form of moral self-mastery or self-command that represents a model of self-governance he compares to an autocracy. In light of the fact that the very concept of virtue presupposes struggle and conflict, Kant insists that virtue is distinct from holiness and that any ideal of moral perfection that overlooks the fact that morality is always difficult for us fails to (...)
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  46. Celebrity Admiration and Its Relationship to the Self-Esteem of Filipino Male Teenagers.Ann Jesamine P. Dianito, Jayfree A. Chavez, Rhanarie Angela Ranis, Brent Oliver Cinco, Trizhia Mae Alvez, Nhasus D. Ilano, Amor Artiola, Wenifreda Templonuevo & Jhoselle Tus - 2023 - Psychology and Education: A Multidisciplinary Journal 7 (1):305-313.
    Fan culture has grown immensely over the past few years. People are constantly looking up to celebrities and personalities as role models for their fashion, identity, and success. During the stage of adolescence, it is normal for teenagers to admire well- known people and form fan attachments as part of their identity formation. However, this admiration of a specific media figure can be associated with one's personality, cognitive processes, and psychological well-being. Thus, the current study aims to investigate the correlation (...)
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  47. Slippery Slope Arguments.Anneli Jefferson - 2014 - Philosophy Compass 9 (10):672-680.
    Slippery slope arguments are frequently dismissed as fallacious or weak arguments but are nevertheless commonly used in political and bioethical debates. This paper gives an overview of different variants of the argument commonly found in the literature and addresses their argumentative strength and the interrelations between them. The most common variant, the empirical slippery slope argument, predicts that if we do A, at some point the highly undesirable B will follow. I discuss both the question which factors affect likelihood of (...)
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  48. Against reductivist character realism.Anne Jeffrey & Alina Beary - 2022 - Philosophical Psychology 36 (1):186-213.
    It seems like people have character traits that explain a good deal of their behavior. Call a theory character realism just in case it vindicates this folk assumption. Recently, Christian Miller has argued that the way to reconcile character realism with decades of psychological research is to adopt metaphysical reductivism about character traits. Some contemporary psychological theories of character and virtue seem to implicitly endorse such reductivism; others resist reduction of traits to finer-grained mental components or processes; and still others (...)
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  49. A Bite of the Forbidden Fruit: The Abject of Food and Affirmative Environmental Ethics.Anne Sauka - 2022 - Open Philosophy 5 (1):281-295.
    This article explores the negative framing of environmental concern in the context of food procurement and consumption, through the lens of the myth of Eden considering the ontological and genealogical aspects of the experienced exile from nature. The article first considers the theoretical context of the negative framing of food ethics. Demonstrating the consequences of the experience of food as abject, the article then goes on to discuss the exile from Eden as an explanatory myth for the perceptual inbetweenness of (...)
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  50. Socratic Meditation and Emotional Self-Regulation: Human Dignity in a Technological Age.Anne-Marie Schultz & Paul E. Carron - 2013 - Journal of Interdisciplinary Studies 25 (1-2):137-160.
    This essay proposes that Socrates practiced various spiritual exercises, including meditation, and that this Socratic practice of meditation was habitual, aimed at cultivating emotional self-control and existential preparedness. Contemporary research in neurobiology supports the view that intentional mental actions, including meditation, have a profound impact on brain activity, neuroplasticity, and help engender emotional self-control. This impact on brain activity is confirmed via technological developments, a prime example of how technology benefits humanity. Socrates attains the balanced emotional self-control that Alcibiades describes (...)
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