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Publisher: İstanbul Teknik Üniversitesi (Total: 1 journals) [Sort alphabetically]

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	J. of Cognitive Systems

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Journal of Cognitive Systems
Number of Followers: 0

This is an Open Access Journal

Open Access journal
ISSN (Print) 2548-0650
Published by İstanbul Teknik Üniversitesi

[1 journal]

Determination of Knowledge Levels of Nurses Working in the Emergency
Department and Intensive Care Units about Evidence-Based Practices in the
Prevention of Ventilator-Associated Pneumonia
- Authors: Leman ACUN DELEN; Serdar DERYA, Burcu KAYHAN TETİK
  Abstract: Objective:The aim of this study was to determine the level of knowledge of nurses working in the Emergency Departments (ED)and Intensive Care Units (ICU)about evidence-based practices in the prevention of ventilator-associated pneumonia(EBP-VAP).Materials and Methods:This descriptive,two-center study was conducted on nurses working in the EDs and ICUs of two public hospitals in the city center of Malatya.A total of 199 nurses who worked in the ED or ICU for at least one year were included in the study,on voluntary basis.The data were collected by using “Personal Information Form” and the “Information on Evidence-Based Practices for the Prevention of Ventilator-Associated Pneumonia Form”(EBP-VAP).Results:The mean age of the nurses was 35.92±6.54,58.8% of them were females and 6.7% were undergraduates.The mean total VAP score of the nurses was found to be 4.76±1.57.It was determined that there was a weak positive correlation between the ages of the nurses,their years in the profession,years of working in the emergency room/intensive care unit, and their average total VAP scores (p
  PubDate: Thu, 30 Dec 2021 00:00:00 +030
A COMPARATIVE STUDY FOR THE PREDICTION OF HEART ATTACK RISK AND ASSOCIATED
FACTORS USING MLP AND RBF NEURAL NETWORKS
- Authors: Rüstem YILMAZ; Fatma Hilal YAĞIN
  Abstract: — Aim: The aim of this study is to develop a predictive classification model that can identify risk factors for heart attack disease.Materials and Methods: In the study, patients with low and high probability of having a heart attack were examined. Variable importance was calculated to identify risk factors. The radial basis function and multilayer perception neural networks were used to compare the classification prediction results.Results: MLP model criteria; Accuracy 0.911, F1 score 0.918, Specificity 0.92, Sensitivity 0.903, while RBF model criteria were obtained as accuracy 0.797, F1 score 0.812, Specificity 0.84, Sensitivity 0.765. The first three most important factors that may be associated with having a heart attack were obtained as trestbps, oldpeak, and chol. Conclusion: According to the prediction results of the heart attack, it can be said that the model created with the MLP neural network has more successful predictions than the model created with the RBF neural network. In addition, estimating the importance values of the factors most associated with heart attack (obtaining the most important biomarkers that may cause heart attack) is a promising result for the diagnosis, treatment and prognosis of the disease.Keywords— Heart Attack, machine learning, neural networks, classification, variable importance.
  PubDate: Thu, 30 Dec 2021 00:00:00 +030
BREAST CANCER DIAGNOSIS BASED ON THERMOGRAPHY IMAGES USING PRE-TRAINED
NETWORKS
- Authors: Hasan UCUZAL; Muhammet BAYKARA, Zeynep KÜÇÜKAKÇALI
  Abstract: Aim: Breast cancer is the leading cause of death among women around the world. Because of its low cost and the fact that it does not emit hazardous radiation, infrared thermography has emerged as a viable approach for diagnosing the condition in young women. This study aims to create a computer-aided diagnostic system that can process thermographic breast cancer images and classify breast cancer with pre-trained networks in order to use thermography as a diagnostic method.Materials and Methods: In this study, an open-access data set consisting of thermographic breast cancer images was used for diagnostic purposes. The data set consists of 179 healthy images and 101 images from patients. The images were converted from .txt format to .jpeg format. The data set is acquired from http://visual.ic.uff.br/dmi/. In this study, various pre-trained networks were used to reduce the training time. Different metrics were employed to assess the performance of the models.Results: The images obtained during the modeling phase were used to display both breasts in the image without distinguishing the right and left breasts, that is, without fragmenting the images. According to the results of the different pre-trained network models after the data preprocessing stages, the best classification performance was achieved for the ResNet50V2 model with an accuracy value of 0.996.Conclusion: In this study, a computer-aided diagnosis system was created by developing an interface for breast cancer classification from thermographic images in addition to experimental findings. The web software based on the proposed models has provided promising predictions of breast cancer from thermographic images. The developed software can help medical and other healthcare professionals easily spot breast cancer.
  PubDate: Thu, 30 Dec 2021 00:00:00 +030
Detection of risk factors of PCOS patients with Local Interpretable
Model-agnostic Explanations (LIME) Method that an explainable artificial
intelligence model
- Authors: İpek BALIKÇI ÇİÇEK; Zeynep KÜÇÜKAKÇALI, Fatma Hilal YAĞIN
  Abstract: Aim: In this study, it is aimed to extract patient-based explanations of the contribution of important features in the decision-making process (estimation) of the Random forest (RF) model, which is difficult to interpret for PCOS disease risk, with Local Interpretable Model-Agnostic Explanations (LIME).Materials and Methods: In this study, the Local Interpretable Model-Agnostic Annotations (LIME) method was applied to the “Polycystic ovary syndrome” dataset to explain the Random Forest (RF) model, which is difficult to interpret for PCOS risk factors estimation. This dataset is available at https://www.kaggle.com/prasoonkottarathil/polycystic-ovary-syndrome-pcos.Results: Accuracy, sensitivity, specificity, positive predictive value, negative predictive value and balanced accuracy obtained from the Random Forest method were 86.03%, 86.32%, 85.37%, 93.18%, 72.92% and 85.84% respectively. According to the obtained results, the observations whose results were obtained, the values of Follicle (No) L. and Follicle (No) R. in different value ranges were positively correlated with the absence of PCOS. For the observations whose absence of PCOS results were obtained, the variables RBS(mg/dl), bmi_y, fsh_lh, TSH (mIU/L), Endometrium (mm) also played a role in obtaining the results. In addition, for the observations whose results were obtained, the values of Follicle No L and Follicle No R in different value ranges were also found to be positively correlated with PCOS. In addition, beta-HCG(mIU/mL), PRG(ng/mL), RBS(mg/dl), bmi_y, Endometrium (mm), fsh_lh variables also played a role in obtaining the results for PCOS.Conclusion: When the observations obtained from the results are examined, it can be said that the Follicle (No) L. and Follicle (No) R. variables are the most effective variables on the presence or absence of PCOS. For different value ranges of these two variables, the result of PCOS or not varies. Based on this, it can be said that different values of Follicle (No) L. and Follicle (No) R. variables for PCOS status may be effective in determining the disease.
  PubDate: Thu, 30 Dec 2021 00:00:00 +030
Heart disease classification based on performance measures using a deep
learning model
- Authors: İpek BALIKÇI ÇİÇEK; Zeynep KÜÇÜKAKÇALI
  Abstract: Heart disease, which is one of the most common diseases in the world, is expected to remain the leading cause of mortality on a global scale. Therefore the aim of this study is to classify heart disease using a deep learning approach in an open-access dataset that includes data from patients with and without heart disease.In this study, a deep learning model was applied to an open-access data set containing the data of patients with and without heart disease. The performance of the method used was evaluated with the performance criteria of specificity, sensitivity, accuracy, positive predictive value, and negative predictive value. Specificity, sensitivity, accuracy, positive predictive value and negative predictive value from the performance criteria obtained from the model were calculated as 0.946, 0.903, 0.9245, 0.9436 and 0.907, respectively.As a result of the findings obtained from the study, it was seen that the data set we discussed was successfully classified with the deep learning model used. With this obtained high classification performance, the factors associated with the disease can be revealed.
  PubDate: Thu, 30 Dec 2021 00:00:00 +030
IS TURING TEST STILL PROFICIENT AND OPERATIVE AT PRESENT STATE OF THE
ART': BEYOND TURING TEST FOR THE NEXT GENERATION AI FRAMEWORKS
- Authors: Burhan Yarkın ÇALIK
  Abstract: The problem of consciousness in terms of artificial intelligence is a difficult and big problem. With the test he put forward, the efficiency of artificial intelligence was discussed and tested. Some scientists have criticized the inability to distinguish between humans and robots with the Turing Test. Problems such as how sufficient this is and how it is possible to compare the intelligence of a human with the intelligence of a robot have been handled philosophically. The main purpose of this article is to address the adequacy of Turing testing and to question artificial intelligence tests and tools that can shed light on shaping the design of next-generation AI architectures. Searle's Chinese room experiment has been reconsidered by Turing by addressing the subjectivity-objectivity problem of Qualia philosophers and giving place to criticisms that can be directed to this test and countercriticisms that can be made to these criticisms. In addition, the role of the new generation Turing test in modeling concepts such as artificial consciousness and machine self-awareness and evaluating their performance is discussed.
  PubDate: Thu, 30 Dec 2021 00:00:00 +030