In recent years, deep neural network (DNN) has been frequently used for classification. In this study, iris flowers having 3 different types are classified by using DNN which are utilized the width and length of petal and sepal features as input. Some experiments are made for the iris dataset by using different activation functions and different epoch numbers. Then, the activation function which gives the best result is determined. The classification success of the developed model is achieved 96% for the iris dataset.

A Model of Deep Neural Network for Iris Classification With Different Activation Functions

In this paper, an online identification algorithm for instrumental variable-based evolving neuro-fuzzy modeling applied to dynamic systems in noisy environment is proposed. The adopted methodology is based on neuro-fuzzy inference system with Takagi–Sugeno evolving structure, which employs an adaptive distance norm based on the maximum likelihood criterion with instrumental variable recursive parameter estimation. The application and performance analysis of the proposed algorithm is based on black-box modeling of a 2DOF Helicopter with errors in variables.

Adaptive Neuro-Fuzzy Black-Box Modeling Based on Instrumental Variable Evolving Algorithm

The main factor in fractures among seniors and women post-menopausal is osteoporosis, which decreases the density of bones. Finding a low-cost diagnostic technology to identify osteoporosis in its initial stages is imperative considering the substantial expenses of diagnosis and therapy. The simplest and most widely used imaging method for detecting bone diseases is X-ray radiography, however, it is problematic to manually examine X-rays for osteoporosis as well as to identify the essential components and choose elevated classifiers. To categorize x-ray pictures of knee joints into normal, osteopenia, and osteoporosis condition categories, authors present a process in this investigation that uses three convolutional neural networks (CNN) architectures, i.e., Inception v3, Xception, and ResNet 18, to create an ensemble-based classifier model. The suggested ensemble approach employs a fuzzy rank-based unification of classifiers by taking into account two distinct parameters on the decision scores produced by the aforementioned base classifiers. Contrary to the straightforward fusion strategies that have been mentioned in the literature, the suggested ensemble methodology finalizes predictions on the test specimens by considering the confidence in the recommendations of the base learners. A 5-fold cross-validation approach has been employed to assess the developed framework using a benchmark dataset that has been made accessible to the general population. The suggested model yields an accuracy rate of 93.5% with a loss of 0.082. Further, the AUC is observed to be 98.1, 97.9 and 97.3 for normal, osteopenia and osteoporosis, respectively. The results demonstrate the model’s usefulness by outperforming various state-of-the-art approaches. Keywords—Convolutional Neural Network; diagnosis; knee; osteoporosis; transfer learning models; X-rays

/pdf/fuzzy-rank-based-ensemble-model-for-accurate-diagnosis-of-ejcxw2tn.pdf

Fuzzy Rank-Based Ensemble Model for Accurate Diagnosis of Osteoporosis in Knee Radiographs

This paper presents an online identification algorithm based on instrumental variable evolving neuro fuzzy model applied to dynamic systems in noisy enviroment. The adopted methodology is based an online neuro-fuzzy inference system with Takagi-Sugeno evolving structure, which employs an adaptive distance norm based on the maximum likelihood criterion with instrumental variable recursive parameter estimation. The performance and application of the proposed algorithm is based on the black box modeling of a 2DOF Helicopter with errors in variables.

Online identification based on instrumental variable evolving neuro-fuzzy model for stochastic dynamic systems

In recent years, many diseases can be diagnosed in a short time with the use of deep learning models in the field of medicine. Most of the studies in this area focus on adult or pediatric patients. However, deep learning studies for the diagnosis of diseases in neonatal are not sufficient. Also, since it is known that respiratory disorders such as pneumonia have a large place among the causes of neonatal death, early and accurate diagnosis of respiratory diseases in neonates is crucial. For this reason, our study aims to detect the presence of respiratory disorders through the developed deep-learning approach using chest X-ray images of patients hospitalized in the Neonatal Intensive Care Unit. Accordingly, the enhanced version of C+EffxNet, the new hybrid deep learning model, is designed to predict respiratory disorders in neonates. In this version, the features selected by PCA are combined as 100, 200, and 300, then the binary classification process was carried out. In the study, the accuracy and kappa value were obtained as 0.965, and 0.904, respectively before feature merging, while these values were obtained as 0.977, and 0.935 after feature merging. This method, which was developed for the diagnosis of respiratory disorders in neonates, was also subsequently applied to a chest X-ray dataset that is frequently used in the literature for the diagnosis of pediatric pneumonia. For this data set, while the accuracy was 0.992, the kappa value was 0.982. The results obtained confirm the success of the proposed method for both datasets. 

A novel deep learning-based approach for prediction of neonatal respiratory disorders from chest X-ray images

Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19. Consequently, this work offers an intelligent medicare system that is an IoT-empowered, deep learning-based decision support system (DSS) for the automated detection and categorization of infectious diseases (COVID-19 and pneumothorax). The proposed DSS system was evaluated using three independent standard-based chest X-ray scans. The suggested DSS predictor has been used to identify and classify areas on whole X-ray scans with abnormalities thought to be attributable to COVID-19, reaching an identification and classification accuracy rate of 89.58% for normal images and 89.13% for COVID-19 and pneumothorax. With the suggested DSS system, a judgment depending on individual chest X-ray scans may be made in approximately 0.01 s. As a result, the DSS system described in this study can forecast at a pace of 95 frames per second (FPS) for both models, which is near to real-time.

/pdf/an-intelligent-sensor-based-decision-support-system-for-q9srdihy.pdf

An Intelligent Sensor Based Decision Support System for Diagnosing Pulmonary Ailment through Standardized Chest X-ray Scans

Various classification models exist for classifying the Iris Dataset using Neuro-Fuzzy Approach [1][2][3][4]. All had classified into three classes, named as Setosa, Virginica and Versicolour based on the parameters of flower measured in cms. The analysis of these results show a limited success as the classification has found to be non-linear. We have attempted with four parameters with neuro-fuzzy classification and have obtained the classification results with much higher accuracy.

An efficient Neuro-Fuzzy Approach for classification of Iris Dataset

In underdeveloped nations, severe lower respiratory infections are the principal reasons of infant mortality. The best treatments and early diagnosis are now being used to alleviate this issue. In developing nations, better treatment and prevention approaches are still required. Clinical, microbial, and radiographic clinical studies have a broad range of applicability within and across populations, and it much depends on the knowledge and resources that are made accessible in different situations. The most appropriate procedure is a chest radiograph (CXR), although pediatric chest X-ray techniques using machine intelligence are uncommon. A strong system is required to diagnose pediatric pneumonia. Authors provide a computer-aided diagnosis plan for the chest X-ray scans to address this. This investigation provides a deep learning-based intelligent healthcare that can reliably diagnose pediatric pneumonia. In order to improve the appearance of CXR pictures, the suggested technique also employs white balancing accompanied with contrast enhancement as a preliminary step. With an AUC of 99.1 on the testing dataset, the suggested approach outscored other state-of-the-art approaches and produced impressive results. Additionally, the suggested approach correctly classified chest X-ray scans as normal and pediatric pneumonia with a classification accuracy of 98.4%. Keywords—Contrast enhancement; convolution neural network; pediatric pneumonia; radiography; white balancing

/pdf/enhancing-image-for-cnn-based-diagnostic-of-pediatric-3epoz9t9.pdf

Enhancing Image for CNN-based Diagnostic of Pediatric Pneumonia through Chest Radiographs

Because of recent technological and interface advancements in the field, the virtual reality (VR) movement has entered a new era. Mobility is one of the most crucial behaviours in virtual reality. In this research, popular virtual reality mobility systems are compared, and it is shown that gesture control is a key technology for allowing distinctive virtual world communication paradigms. Gesture based movements are very beneficial when there are a lot of spatial restrictions. With a focus on cost-effectiveness, the current study introduces a gesture-based virtual movement (GVM) system that eradicates the obligation for expensive hardware/controllers for virtual world mobility (i.e., walk/ jump/ hold for this research) using artificial intelligence (AI). Additionally, the GVM aims to prevent users from becoming dizzy by allowing them to change the trajectory by simply turning their head in the intended direction. The GVM was assessed on its interpreted realism, presence, and spatial drift in the actual environment in comparison to the state-of-the-art techniques. The results demonstrated how the GVM outperformed the prevailing methodologies in a number of common interaction components. Additionally, the empirical analysis showed that GVM offers customers a real-time experience with a latency of ~65 milliseconds. Keywords—Artificial intelligence; dizziness; gestures; human computer interaction; user experience; virtual reality

/pdf/espousing-ai-to-enhance-cost-efficient-and-immersive-2pjecffb.pdf

Espousing AI to Enhance Cost-Efficient and Immersive Experience for Human Computer Interaction

Boosting X-Ray Scans Feature for Enriched Diagnosis of Pediatric Pneumonia using Deep Learning Models

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