Results for 'Birth Weight, ANN, Predictive Model'

997 found
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  1. 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 (...) cases in hospitals. The evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the birth weight with 100% accuracy. (shrink)
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  2. 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 (...)
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  3. 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 (...) cases in hospitals. The evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the birth weight with 100% accuracy. (shrink)
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  4. ANN for Predicting Animals Category.Nassar Ibraheem & AlKahlout Mohammad - 2020 - International Journal of Academic and Applied Research (IJAAR) 3 (2):18-23.
    In this paper an Artificial Neural Network (ANN) model, was developed and tested for predicting the category of an animal. There is a number of factors that influence the classification of animals. Such as the existence of hair/ feather, if the animal gives birth or spawns, it is airborne, aquatic, predator, toothed, backboned, venomous, has –fins, has-tail, cat-sized, and domestic. They were then used as input variables for the ANN model. A model based on the Multilayer (...)
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  5.  96
    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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  6. Artificial Neural Network for Predicting Animals Category.Ibrahim M. Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic and Applied Research (IJAAR) 3 (2):18-24.
    Abstract: In this paper an Artificial Neural Network (ANN) model, was developed and tested for predicting the category of an animal. There is a number of factors that influence the classification of animals. Such as the existence of hair/ feather, if the animal gives birth or spawns, it is airborne, aquatic, predator, toothed, backboned, venomous, has –fins, has-tail, cat-sized, and domestic. They were then used as input variables for the ANN model. A model based on the (...)
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  7. ANN Model for Predicting Protein Localization Sites in Cells.Mohammed Nafez Abu Samra, Bilal Ezz El-Din Abed, Hossam Abdel Nasser Zaqout & Samy S. Abu-Naser - 2020 - International Journal of Academic and Applied Research (IJAAR) 4 (9):43-50.
    To automate examination of massive amounts of sequence data for biological function, it is important to computerize interpretation based on empirical knowledge of sequence-function relationships. For this purpose, we have been constructing an Artificial Neural Network (ANN) by organizing various experimental and computational observations as a collection ANN models. Here we propose an ANN model which utilizes the Dataset for UCI Machine Learning Repository, for predicting localization sites of proteins. We collected data for 336 proteins with known localization sites (...)
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  8.  73
    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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  9.  45
    Development of Keyword Trend Prediction Models for Obesity Before and After the COVID-19 Pandemic Using RNN and LSTM: Analyzing the News Big Data of South Korea.Gayeong Eom & Haewon Byeon - 2022 - Frontiers in Public Health 10:894266.
    The Korea National Health and Nutrition Examination Survey (2020) reported that the prevalence of obesity (≥19 years old) was 31.4% in 2011, but it increased to 33.8% in 2019 and 38.3% in 2020, which confirmed that it increased rapidly after the outbreak of COVID-19. Obesity increases not only the risk of infection with COVID-19 but also severity and fatality rate after being infected with COVID-19 compared to people with normal weight or underweight. Therefore, identifying the difference in potential factors for (...)
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  10. 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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  11. Measuring the World: Olfaction as a Process Model of Perception.Ann-Sophie Barwich - 2018 - In John A. Dupre & Daniel Nicholson (eds.), Everything Flows: Towards a Processual Philosophy of Biology. 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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  12. 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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  13. 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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  14.  46
    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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  15.  77
    ANN for Predicting Overall Car Performance.Mubayyed Osamma & Gazaz Ahmed - 2020 - International Journal of Academic and Applied Research (IJAAR) 1 (3):1-4.
    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 99.62 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study (...)
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  16.  41
    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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  17. Predicting Overall Car Performance Using Artificial Neural Network.Osama M. Al-Mubayyed, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic and Applied Research (IJAAR) 3 (1):1-5.
    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 99.62 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study (...)
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  18. Prediction of Whether Mushroom is Edible or Poisonous Using Back-Propagation Neural Network.Eyad Sameh Alkronz, Khaled A. Moghayer, Mohamad Meimeh, Mohannad Gazzaz, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic and Applied Research (IJAAR) 3 (2):1-8.
    Abstract: Predication is an application of Artificial Neural Network (ANN). It is a supervised learning due to predefined input and output attributes. Multi-Layer ANN model is used for training, validating, and testing of the data. In this paper, Multi-Layer ANN model was used to train and test the mushroom dataset to predict whether it is edible or poisonous. The Mushrooms dataset was prepared for training, 8124 instances were used for the training. JustNN software was used to training and (...)
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  19. Artificial Neural Network for Forecasting Car Mileage Per Gallon in the City.Mohsen Afana, Jomana Ahmed, Bayan Harb, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2018 - International Journal of Advanced Science and Technology 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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  20. 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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  21. 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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  22. Predicting the Age of Abalone From Physical Measurements Using Artificial Neural Network.Ghaida Riyad Mohammed, Jaffa Riad Abu Shbikah, Mohammed Majid Al-Zamili, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2020 - International Journal of Academic and Applied Research (IJAAR) 4 (11):7-12.
    Abalones have long been a valuable food source for humans in every area of the world where a species is abundant. Predicting the age of abalone is done using physical measurements. The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task. Other measurements, which are easier to obtain, are used to predict the age of abalone is using Artificial Neural Network (...)
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  23.  39
    Modelling Competing Legal Arguments Using Bayesian Model Comparison and Averaging.Martin Neil, Norman Fenton, David Lagnado & Richard David Gill - 2019 - Artificial Intelligence and Law 27 (4):403-430.
    Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt to make them (...)
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  24. 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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  25. Presence of Amphibian Species Prediction Using Features Obtained From GIS and Satellite Images.Nadia Shaker Habib, Omar Kamal Abu Maghasib, Ahmed Rashad Al-Ghazali, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2020 - International Journal of Academic and Applied Research (IJAAR) 4 (11):13-22.
    The establishment of the transport infrastructure is usually preceded by an EIA procedure, which should determine amphibian breeding sites and migration routes. However, evaluation is very difficult due to the large number of habitats spread over a vast area and the limited time available for field work. An artificial Neural Network (ANN) is proposed for predicting the presence of amphibians species near the water reservoirs based on features obtained from GIS systems and satellite images. The dataset collected from UCI Machine (...)
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  26. 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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  27. 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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  28.  49
    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 inhibition of (...)
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  29. 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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  30. Sonopelvimetry: An Innovative Method for Early Prediction of Obstructed Labour.Yinon Gilboa - 2014 - Open Journal of Obstetrics and Gynecology 4:757-765.
    To evaluate an innovative sonopelvimetry method for early prediction of obstructed labour. Methods: A prospective study was conducted in two centers.GPS-based sonopelvimetry, laborProTM (Trig Medical Inc., Yoqneam Ilit, Israel) devise, was used prior to labour in nulliparous women at 39 - 42 weeks gestation remote from labor. Maternal pelvic parameters, including inter-iliac transverse diameter, obstetric conjugate and interspinous diameter were evaluated. Fetal parameters included head station, biparietal diameter and occipitofrontal diameter. Data on delivery and outcome were collected from the electronic (...)
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  31. 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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  32.  54
    Does Temperature Shocks Affect Birth Weight in Vietnam?My Nguyen, Kien Le, Huong T. T. Hoang, Hang Khanh, Khoi Duc & Thuy Trang - 2017
    This pаpеr invеstigаtеs thе еxtеnt tо which in-utеrо еxpоsurе tо tеmpеrаturе shоcks аffеcts birth wеight оutcоmеs in Viеtnаm. Еxplоiting thе vаriаtiоns аcrоss districts аnd cоncеptiоn timing within districts, wе shоw thаt а оnе stаndаrd dеviаtiоn incrеаsе in tеmpеrаturе rеlаtivе tо thе lоcаl nоrm (аpprоximаtеly 0.52 dеgrее Cеlsius) during thе first trimеstеr оf prеgnаncy rеducеs thе child’s wеight аt birth by 67 grаms оr 2.2%. Оur hеtеrоgеnеity аnаlysis suggеsts thаt infаnts living in rurаl аrеаs, bоrn tо pооr аnd lоw-еducаtеd (...)
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  33.  41
    Does Rainfall Affect Birth Weight in Vietnam?Huong T. T. Hoang, Kien Le, Thuy Trang, Hang Khanh, My Nguyen & Khoi Duc - 2017
    This pаpеr invеstigаtеs thе lеss discеrniblе cоst оf rаinfаll shоcks tо birth wеight оutcоmеs within thе cоntеxt оf Viеtnаm. Еxplоiting thе vаriаtiоn аcrоss districts аnd cоncеptiоn mоnths-yеаrs, wе shоw thаt in-utеrо еxpоsurе tо еxcеssivе аnd dеficiеnt rаinfаll shоcks in thе sеcоnd trimеstеr оf prеgnаncy rеducеs child’s wеight аt birth by 3.5 аnd 3.1%, rеspеctivеly. Bеsidеs, infаnts bоrn tо pооr, rurаl, аnd lоw еducаtеd mоthеrs аrе еspеciаlly vulnеrаblе tо thе аdvеrsе rеpеrcussiоns оf rаinfаll shоcks. Sincе pооr infаnt hеаlth cаn (...)
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  34.  35
    Conflicts and Birth Weight.Hang Khanh, My Nguyen, Thuy Trang, Khoi Duc, Kien Le & Huong T. T. Hoang - 2016
    This pаpеr invеstigаtеs thе hiddеn yеt pеrsistеnt cоst оf cоnflict tо birth wеight оutcоmеs fоr 53 dеvеlоping cоuntriеs еxpеriеncing cоnflict in thе pаst thrее dеcаdеs (1990-2018). Explоiting thе vаriаtiоn аcrоss districts аnd cоncеptiоn mоnths-yеаrs, wе find thаt intrаutеrinе еxpоsurе tо аrmеd cоnflict in thе first trimеstеr оf prеgnаncy rеducеs child’s wеight аt birth by 2.8% аnd rаisеs thе incidеncе оf lоw birth wеight by 3.2 pеrcеntаgе pоints. Infаnts bоrn tо pооr аnd lоw еducаtеd mоthеrs аrе еspеciаlly vulnеrаblе (...)
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  35.  78
    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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  36. Predictive Processing and the Phenomenology of Time Consciousness: A Hierarchical Extension of Rick Grush’s Trajectory Estimation Model.Wanja Wiese - 2017 - Philosophy and Predictive Processing.
    This chapter explores to what extent some core ideas of predictive processing can be applied to the phenomenology of time consciousness. The focus is on the experienced continuity of consciously perceived, temporally extended phenomena (such as enduring processes and successions of events). The main claim is that the hierarchy of representations posited by hierarchical predictive processing models can contribute to a deepened understanding of the continuity of consciousness. Computationally, such models show that sequences of events can be represented (...)
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  37. Prediction and Topological Models in Neuroscience.Bryce Gessell, Matthew Stanley, Benjamin Geib & Felipe De Brigard - forthcoming - In Fabrizio Calzavarini & Marco Viola (eds.), Neural Mechanisms: New challenges in the philosophy of neuroscience. Springer.
    In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we argue that (...)
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  38. The Risk GP Model: The Standard Model of Prediction in Medicine.Jonathan Fuller & Luis J. Flores - 2015 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 54:49-61.
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  39.  36
    Standardised Predictive Linear Models of Managerial Processes and the Sustainability of Graduate Programmes (SGPs) in Universities: A Case Study.Valentine Joseph Owan & Oni Enene Offu - 2021 - Contemporary Mathematics and Science Education 2 (1):Article ep21006.
    The exploration of the literature indicated that much studies abound in related areas. Much seems yet to be known about the nature of the relationship that exists between managerial variables and the sustainability of graduate programmes. To bridge this gap, we utilized a standardised multiple regression approach to build up linear models that examine three managerial processes (strategic planning, staff and information/communication management) and how they affect three proxies of the sustainability of graduate programmes (availability of funds and facilities, as (...)
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  40.  43
    Cognitive Biases and the Predictable Perils of the Patient‐Centric Free‐Market Model of Medicine.Michael J. Shaffer - forthcoming - Metaphilosophy.
    This paper addresses the recent rise of the use of alternative medicine in Western countries and it offers a novel explanation of that phenomenon in terms of cognitive and economic factors related to the free-market and patient-centric approach to medicine that is currently in place in those countries, in contrast to some alternative explanations of this phenomenon. Moreover, the paper addresses this troubling trend in terms of the serious harms associated with the use of alternative medical modalities. The explanatory theory (...)
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  41. Medical Models of Addiction.Harold Kincaid & Jacqueline Anne Sullivan - 2010 - In Kincaid Ross (ed.), What is Addiction?
    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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  42. Predictive Coding and Representationalism.Paweł Gładziejewski - 2016 - Synthese 193 (2).
    According to the predictive coding theory of cognition , brains are predictive machines that use perception and action to minimize prediction error, i.e. the discrepancy between bottom–up, externally-generated sensory signals and top–down, internally-generated sensory predictions. Many consider PCT to have an explanatory scope that is unparalleled in contemporary cognitive science and see in it a framework that could potentially provide us with a unified account of cognition. It is also commonly assumed that PCT is a representational theory of (...)
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  43. ANN for Parkinson’s Disease Prediction.Salah Sadek, Abdul Mohammed, Abdul Karim Abunbehan, Majed Abdul Ghattas & Mohamed Badawi - 2020 - International Journal of Academic Health and Medical Research (IJAHMR) 3 (1):1-7.
    Parkinson's Disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Doctors do not know what causes it and finds difficulty in early diagnosing the presence of Parkinson’s disease. An artificial neural network system with back propagation algorithm is presented in this paper for helping doctors in identifying (...)
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  44. A Comparative Analysis of Data Mining Techniques on Breast Cancer Diagnosis Data Using WEKA Toolbox.Majdah Alshammari & Mohammad Mezher - 2020 - (IJACSA) International Journal of Advanced Computer Science and Applications 8:224-229.
    Abstract—Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer dataset is used to perform a (...)
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  45. 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 - 2018 - In Otávio Bueno, George Darby, Steven French & Dean Rickles (eds.), Thinking About Science, Reflecting on Art: Bringing Aesthetics and Philosophy of Science Together. London, UK:
    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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  46.  28
    Predictive Path Modelling of Indicators of Secondary School Instructors’ Affective, Continuance and Normative Job Commitment.Valentine Joseph Owan - 2021 - Journal of International Cooperation and Development 4 (2):86-108.
    There is a growing body of literature investigating the impact of retraining and motivation on employee work efficiency. However, little seems to be understood about the effects of employee placement on the commitment of teachers to their jobs. To the best of the researcher's awareness, the partial and composite impact of staff placement, retraining, and motivation on the three aspects of job commitment (affective, continuance and normative) among secondary educators have scarcely been examined. This research was intended to fill this (...)
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  47.  30
    IS THERE A LINK BETWEEN POLLUTION AND HEALTH?Silvia Kuswandari - 2020
    Based on data from densely populated counties, this article assesses the effects of air pollution on newborn death rates. Unlike earlier studies in this field, these figures are based on a well-defined behavioral model of health production that was calculated using suitable simultaneous equations techniques. The findings show that sulfur dioxide is the most important air contaminant in terms of infant survival. There is additional evidence that a rise in sulfur dioxide affects the newborn death rate by increasing the (...)
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  48. Extending the Argument From Unconceived Alternatives: Observations, Models, Predictions, Explanations, Methods, Instruments, Experiments, and Values.Darrell Patrick Rowbottom - 2016 - Synthese (10).
    Stanford’s argument against scientific realism focuses on theories, just as many earlier arguments from inconceivability have. However, there are possible arguments against scientific realism involving unconceived (or inconceivable) entities of different types: observations, models, predictions, explanations, methods, instruments, experiments, and values. This paper charts such arguments. In combination, they present the strongest challenge yet to scientific realism.
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  49.  67
    ANN for Predicting DNA Lung Cancer.Wajeeh Abu Kashf, Nedal Okasha, Ashraf Sahyoun, Emal El-Rabi & Bastami Bashhar - 2017 - International Journal of Academic Pedagogical Research (IJAPR) 10 (2):6-13.
    Abstract: Lung cancer is the top reason of cancer-associated deaths globally. Surgery is the typical treatment for early-stage non-small cell lung cancer (NSCLC). Advancement in the knowledge of the biology of non-small cell lung cancer has shown molecular evidence used for systemic cancer therapy aiming metastatic disease, with a significant impact on patients’ overall survival (OS) and eminence of life. Though, a biopsy of overt metastases is an invasive technique restricted to assured positions and not effortlessly satisfactory in the clinic. (...)
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  50.  98
    Prediction of Heart Disease Using a Collection of Machine and Deep Learning Algorithms.Ali M. A. Barhoom, Abdelbaset Almasri, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (4):1-13.
    Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with (...)
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