Investigating the acute effect of low and moderate intensity aerobic exercise on whole-body task learning and cognition in young adults.

Recent studies have shown that a single bout of exercise has acute improvements on various forms of memory, including procedural motor learning, through mechanisms such as the plasticity-promoting effect. This study aimed to examine (1) the acute effects of timing and intensity of aerobic exercise on the acquisition and retention of motor learning in healthy adults, (2) the effect of sleep quality of the night before and after acquisition on motor learning, and (3) the acute effects of low and moderate-intensity aerobic exercise on cognitive functions. Seventy-five healthy adults were divided into five groups: Two groups performed low or moderate intensity aerobic exercise before motor practice; two groups performed low or moderate intensity aerobic exercise after motor practice; the control group only did motor practice. Low- and moderate-intensity exercises consisted of 30 min of running at 57%-63% and 64%-76% of the maximum heart rate, respectively. Motor learning was assessed using a golf putting task. The sleep quality of the night before and after the acquisition was evaluated using the Richard Campbell Sleep Questionnaire. Cognitive function was assessed before and after aerobic exercise using the Paced Auditory Serial Acquisition Task test. Results indicated that all groups demonstrated acquisition, 1-day and 7-day retention at a similar level (p > 0.05). Regression analysis revealed no significant relationship between sleep quality on the night before the experimental day and total acquisition (p > 0.05). However, a positive correlation was found between the sleep quality on the night of the experimental day and both 1-day and 7-day retention (p < 0.05). A single bout of low or moderate acute exercise did not modify motor skill acquisition and retention. Other results showed the importance of night sleep quality on the retention and proved that a single bout of moderate intensity exercise was associated with improved cognitive function.

The effects of single education session on physiotherapy students' knowledge levels and thoughts on accreditation.

BACKGROUND: and Purpose: In the global landscape, quality assurance is paramount for educational institutions to adapt and thrive. The accreditation process involves evaluating an institution's quality according to standards established by experts and officially documenting its level of quality. This study aimed to assess the impact of a single educational session on physiotherapy and rehabilitation students' awareness and understanding of accreditation processes, recognizing their vital role in quality assurance. METHODS: A pretest-posttest design was employed with 211 students from a physiotherapy and rehabilitation department. Data were collected using a questionnaire assessing demographic information, knowledge about accreditation, and thoughts regarding accreditation. The educational session focused on accreditation criteria and processes, involving presentations and interactive discussions. McNemar's analysis was used to compare the response rates given by the students pre-and post-session. RESULTS: Analysis after the education session revealed a significant increase in students' knowledge of accreditation concepts (p < 0.05). Positive attitudes towards accreditation were reinforced, with students recognizing its importance in education quality. Despite pre-existing positive attitudes, the educational intervention enhanced students' understanding and engagement in accreditation processes with a significant increase in three of the eight questions on thoughts about accreditation (p < 0.05). DISCUSSION: This study underscores the efficacy of educational interventions in fostering student engagement and awareness of accreditation. Findings suggest the need for integrating accreditation education into curricula and advocating its significance through seminars and literature support, ultimately enhancing student participation in quality assurance processes.

Advanced convolutional neural network modeling for fuel cell system optimization and efficiency in methane, methanol, and diesel reforming.

Fuel cell systems (FCSs) have been widely used for niche applications in the market. Furthermore, the research community has worked on using FCSs for different sectors, such as transportation, stationary power generation, marine and maritime, aerospace, military and defense, telecommunications, and material handling. The reformation of various fuels, such as methanol, methane, and diesel can be utilized to generate hydrogen for FCSs. This study introduces an advanced convolutional neural network (CNN) model designed to accurately forecast hydrogen yield and carbon monoxide volume percentages during the reformation processes of methane, methanol, and diesel. Moreover, the CNN model has been tailored to accurately estimate methane conversion rates in methane reforming processes. The proposed CNN models are created by combining the 3D-CNN and 2D-CNN models. The Keras Tuner approach in Python is employed in this study to find the ideal values for different hyperparameters such as batch size, learning rate, time steps, and optimization method selection. The accuracy of the proposed CNN model is evaluated by using the root mean square error (RMSE), mean absolute percentage error (MAE), mean absolute error (MAE), and R2. The results indicate that the proposed CNN model is better than other artificial intelligence (AI) techniques and standard CNN for performance estimation of reforming processes of methane, diesel, and methanol. The results also show that the suggested CNN model can be used to accurately estimate critical output parameters for reforming various fuels. The proposed method performs better in CO prediction than the support vector machine (SVM), with an R2 of 0.9989 against 0.9827. This novel methodology not only improves performance estimation for reforming processes but also provides a valuable tool for accurately estimating output parameters across various fuel types.

Relationship between sensation and balance and gait in multiple sclerosis patients with mild disability.

BACKGROUND: Patients with Multiple Sclerosis (PwMS) often experience sensory, balance, and gait problems. Impairment in any sensation may increase imbalance and gait disorder in PwMS. This study aimed to (1) compare foot plantar sensations, knee position sense, balance, and gait in PwMS compared to Healthy Individuals (HI) and (2) examine the relationship between plantar sensations, knee position sense, balance, and gait in PwMS. METHODS: Thirty PwMS with mild disability and 10 HI participated in this study. Light touch threshold, two-point discrimination, vibration duration, and knee position sense were examined on the Dominant Side (DS) and Non-Dominant Side (NDS). Balance and spatio-temporal gait analysis were evaluated in all participants. RESULTS: PwMS had higher postural sway with eyes closed on the foam surface, longer swing phase of DS, longer single support phase of NDS, and shorter double support phase of DS compared to HI (p < 0.05). The results of regression analysis showed that the light touch thresholds of the 1st and 5th toes of the DS were associated with postural sway in different sensory conditions (p < 0.05). In contrast, the light touch thresholds of the 1st and 5th toes, two-point discrimination of the heel, vibration duration of the 1st metatarsal head and knee position sense of the NDS, and light touch threshold in the medial arch of both sides were associated with the gait parameters (p < 0.05). CONCLUSION: PwMS, even with mild disabilities needs neurorehabilitation to improve plantar sensation and knee position sense.

Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases.

It is a known fact that gastrointestinal diseases are extremely common among the public. The most common of these diseases are gastritis, reflux, and dyspepsia. Since the symptoms of these diseases are similar, diagnosis can often be confused. Therefore, it is of great importance to make these diagnoses faster and more accurate by using computer-aided systems. Therefore, in this article, a new artificial intelligence-based hybrid method was developed to classify images with high accuracy of anatomical landmarks that cause gastrointestinal diseases, pathological findings and polyps removed during endoscopy, which usually cause cancer. In the proposed method, firstly trained InceptionV3 and MobileNetV2 architectures are used and feature extraction is performed with these two architectures. Then, the features obtained from InceptionV3 and MobileNetV2 architectures are merged. Thanks to this merging process, different features belonging to the same images were brought together. However, these features contain irrelevant and redundant features that may have a negative impact on classification performance. Therefore, Dandelion Optimizer (DO), one of the most recent metaheuristic optimization algorithms, was used as a feature selector to select the appropriate features to improve the classification performance and support vector machine (SVM) was used as a classifier. In the experimental study, the proposed method was also compared with different convolutional neural network (CNN) models and it was found that the proposed method achieved better results. The accuracy value obtained in the proposed model is 93.88%.

Fast 19 F spectroscopic imaging with pseudo-spiral k-space sampling.

Fluorine MRI is finding wider acceptance in theranostics applications where imaging of 19 F hotspots of fluorinated contrast material is central. The essence of such applications is to capture ghosting-artifact-free images of the inherently low MR response under clinically viable conditions. To serve this purpose, this work introduces the balanced spiral spectroscopic imaging (BaSSI) sequence, which is implemented on a 3.0 T clinical scanner and is capable of generating 19 F hotspot images in an efficient manner. The sequence utilizes an all-phase-encoded pseudo-spiral k-space trajectory, enabling the acquisition of broadband (80 ppm) fluorine spectra free from chemical shift ghosting. BaSSI can acquire a 64 × 64 image with 1 mm × 1 mm voxels in just 14 s, significantly outperforming typical MRSI sequences used in 1 H or 31 P imaging. The study employed in silico characterization to verify essential design choices such as the excitation pulse, as well as to identify the boundaries of the parameter space explored for optimization. BaSSI's performance was further benchmarked against the 3D ultrashort-echo-time balanced steady-state free precession (3D UTE BSSFP) sequence, a well established method used in 19 F MRI, in vitro. Both sequences underwent extensive optimization through exploration of a wide parameter space on a small phantom containing 10 µL of non-diluted bulk perfluorooctylbromide (PFOB) prior to comparative experiments. Subsequent to optimization, BaSSI and 3D UTE BSSFP were employed to capture images of small non-diluted bulk PFOB samples (0.10 and 0.05 µL), with variations in the number of signal averages, and thus the total scan time, in order to assess the detection sensitivities of the sequences. In these experiments, the detection sensitivity was evaluated using the Rose criterion (Rc ), which provides a quantitative metric for assessing object visibility. The study further demonstrated BaSSI's utility as a (pre)clinical tool through postmortem imaging of polymer microspheres filled with PFOB in a BALB/c mouse. Anatomic localization of 19 F hotspots was achieved by denoising raw data obtained with BaSSI using a filter based on the Rose criterion. These data were then successfully registered to 1 H anatomical images. BaSSI demonstrated superior detection sensitivity in the benchmarking analysis, achieving Rc values approximately twice as high as those obtained with the 3D UTE BSSFP method. The technique successfully facilitated imaging and precise localization of 19 F hotspots in postmortem experiments. However, it is important to highlight that imaging 10 mM PFOB in small mice postmortem, utilizing a 48 × 48 × 48 3D scan, demanded a substantial scan time of 1 h and 45 min. Further studies will explore accelerated imaging techniques, such as compressed sensing, to enhance BaSSI's clinical utility.

Automatic classification of kidney CT images with relief based novel hybrid deep model.

One of the most crucial organs in the human body is the kidney. Usually, the patient does not realize the serious problems that arise in the kidneys in the early stages of the disease. Many kidney diseases can be detected and diagnosed by specialists with the help of routine computer tomography (CT) images. Early detection of kidney diseases is extremely important for the success of the treatment of the disease and for the prevention of other serious diseases. In this study, CT images of kidneys containing stones, tumors, and cysts were classified using the proposed hybrid model. Results were also obtained using pre-trained models that had been acknowledged in the literature to evaluate the effectiveness of the suggested model. The proposed model consists of 29 layers. While classifying kidney CT images, feature maps were obtained from the convolution 6 and convolution 7 layers of the proposed model, and these feature maps were combined after optimizing with the Relief method. The wide neural network classifier then classifies the optimized feature map. While the highest accuracy value obtained in eight different pre-trained models was 87.75%, this accuracy value was 99.37% in the proposed model. In addition, different performance evaluation metrics were used to measure the performance of the model. These values show that the proposed model has reached high-performance values. Therefore, the proposed approach seems promising in order to automatically and effectively classify kidney CT images.

Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model.

Urine sediment examination is one of the main tests used in the diagnosis of many diseases. Thanks to this test, many diseases can be detected in advance. Examining the results of this test is an intensive and time-consuming process. Therefore, it is very important to automatically interpret the urine sediment test results using computer-aided systems. In this study, a data set consisting of eight classes was used. The data set used in the study consists of 8509 particle images obtained by examining the particles in the urine sediment. A hybrid model based on textural and Convolutional Neural Networks (CNN) was developed to classify the images in the related data set. The features obtained using textural-based methods and the features obtained from CNN-based architectures were combined after optimizing using the Minimum Redundancy Maximum Relevance (mRMR) method. In this way, we aimed to extract different features of the same image. This increased the performance of the proposed model. The CNN-based ResNet50 architecture and textural-based Local Binary Pattern (LBP) method were used for feature extraction. Finally, the optimized and combined feature map was classified at different machine learning classifiers. In order to compare the performance of the model proposed in the study, results were also obtained from different CNN architectures. A high accuracy value of 96.0% was obtained in the proposed model.

Comparison of Computed Tomography-Based Artificial Intelligence Modeling and Magnetic Resonance Imaging in Diagnosis of Cholesteatoma.

BACKGROUND: In this study, we aimed to compare the success rates of computed tomography image-based artificial intelligence models and magnetic resonance imaging in the diagnosis of preoperative cholesteatoma. METHODS: The files of 75 patients who underwent tympanomastoid surgery with the diagnosis of chronic otitis media between January 2010 and January 2021 in our clinic were reviewed retrospectively. The patients were classified into the chronic otitis group without cholesteatoma (n=34) and the chronic otitis group with cholesteatoma (n=41) according to the presence of cholesteatoma at surgery. A dataset was created from the preoperative computed tomography images of the patients. In this dataset, the success rates of artificial intelligence in the diagnosis of cholesteatoma were determined by using the most frequently used artificial intelligence models in the literature. In addition, preoperative MRI were evaluated and the success rates were compared. RESULTS: Among the artificial intelligence architectures used in the paper, the lowest result was obtained in MobileNetV2 with an accuracy of 83.30%, while the highest result was obtained in DenseNet201 with an accuracy of 90.99%. In our paper, the specificity of preoperative magnetic resonance imaging in the diagnosis of cholesteatoma was 88.23% and the sensitivity was 87.80%. CONCLUSION: In this study, we showed that artificial intelligence can be used with similar reliability to magnetic resonance imaging in the diagnosis of cholesteatoma. This is the first study that, to our knowledge, compares magnetic resonance imaging with artificial intelligence models for the purpose of identifying preoperative cholesteatomas.

Multi-feature fusion and improved BO and IGWO metaheuristics based models for automatically diagnosing the sleep disorders from sleep sounds.

A night of regular and quality sleep is vital in human life. Sleep quality has a great impact on the daily life of people and those around them. Sounds such as snoring reduce not only the sleep quality of the person but also reduce the sleep quality of the partner. Sleep disorders can be eliminated by examining the sounds that people make at night. It is a very difficult process to follow and treat this process by experts. Therefore, this study, it is aimed to diagnose sleep disorders using computer-aided systems. In the study, the used data set contains seven hundred sound data which has seven different sound class such as cough, farting, laugh, scream, sneeze, sniffle, and snore. In the model proposed in the study, firstly, the feature maps of the sound signals in the data set were extracted. Three different methods were used in the feature extraction process. These methods are MFCC, Mel-spectrogram, and Chroma. The features extracted in these three methods are combined. Thanks to this method, the features of the same sound signal extracted in three different methods are used. This increases the performance of the proposed model. Later, the combined feature maps were analyzed using the proposed New Improved Gray Wolf Optimization (NI-GWO), which is the improved version of the Improved Gray Wolf Optimization (I-GWO) algorithm, and the proposed Improved Bonobo Optimizer (IBO) algorithm, which is the improved version of the Bonobo Optimizer (BO). In this way, it is aimed to run the models faster, reduce the number of features, and obtain the most optimum result. Finally, Support Vector Machine (SVM) and k-nearest neighbors (KNN) supervised shallow machine learning methods were used to calculate the metaheuristic algorithms' fitness values. Different types of metrics such as accuracy, sensitivity, F1 etc., were used for the performance comparison. Using the feature maps optimized by the proposed NI-GWO and IBO algorithms, the highest accuracy value was obtained from the SVM classifier with 99.28% for both metaheuristic algorithms.

An Explainable Deep Learning Model to Prediction Dental Caries Using Panoramic Radiograph Images.

Dental caries is the most frequent dental health issue in the general population. Dental caries can result in extreme pain or infections, lowering people's quality of life. Applying machine learning models to automatically identify dental caries can lead to earlier treatment. However, physicians frequently find the model results unsatisfactory due to a lack of explainability. Our study attempts to address this issue with an explainable deep learning model for detecting dental caries. We tested three prominent pre-trained models, EfficientNet-B0, DenseNet-121, and ResNet-50, to determine which is best for the caries detection task. These models take panoramic images as the input, producing a caries-non-caries classification result and a heat map, which visualizes areas of interest on the tooth. The model performance was evaluated using whole panoramic images of 562 subjects. All three models produced remarkably similar results. However, the ResNet-50 model exhibited a slightly better performance when compared to EfficientNet-B0 and DenseNet-121. This model obtained an accuracy of 92.00%, a sensitivity of 87.33%, and an F1-score of 91.61%. Visual inspection showed us that the heat maps were also located in the areas with caries. The proposed explainable deep learning model diagnosed dental caries with high accuracy and reliability. The heat maps help to explain the classification results by indicating a region of suspected caries on the teeth. Dentists could use these heat maps to validate the classification results and reduce misclassification.

Functional Performance, Leg Muscle Strength, and Core Muscle Endurance in Multiple Sclerosis Patients With Mild Disability: A Cross-Sectional Study.

This study aimed to investigate the relationship of sit-to-stand and walking performance with leg muscle strength and core muscle endurance in people with multiple sclerosis (PwMS) with mild disabilities. In this study, 49 PwMS (Expanded Disability Status Scale score = 1.59 ± 0.79) and 26 healthy controls were enrolled. The functional performances, including sit-to-stand and walking performances, were evaluated with the five-repetition sit-to-stand test, timed up and go test, and 6-min walking test. The PwMS finished significantly slower five-repetition sit-to-stand, timed up and go, and 6-min walking test than the healthy controls. In addition, the significant contributors were the weakest trunk lateral flexor endurance for five-repetition sit-to-stand; the Expanded Disability Status Scale score, and the weakest hip adductor muscle for timed up and go; the weakest hip extensor muscles strength for 6-min walking test. The functional performances in PwMS, even with mild disabilities, were lower compared with healthy controls. Decreases in both leg muscle strength and core muscle endurance are associated with lower functional performance in PwMS.

Identification of metabolic correlates of mild cognitive impairment in Parkinson's disease using magnetic resonance spectroscopic imaging and machine learning.

OBJECTIVE: To investigate metabolic changes of mild cognitive impairment in Parkinson's disease (PD-MCI) using proton magnetic resonance spectroscopic imaging (1H-MRSI). METHODS: Sixteen healthy controls (HC), 26 cognitively normal Parkinson's disease (PD-CN) patients, and 34 PD-MCI patients were scanned in this prospective study. Neuropsychological tests were performed, and three-dimensional 1H-MRSI was obtained at 3 T. Metabolic parameters and neuropsychological test scores were compared between PD-MCI, PD-CN, and HC. The correlations between neuropsychological test scores and metabolic intensities were also assessed. Supervised machine learning algorithms were applied to classify HC, PD-CN, and PD-MCI groups based on metabolite levels. RESULTS: PD-MCI had a lower corrected total N-acetylaspartate over total creatine ratio (tNAA/tCr) in the right precentral gyrus, corresponding to the sensorimotor network (p = 0.01), and a lower tNAA over myoinositol ratio (tNAA/mI) at a part of the default mode network, corresponding to the retrosplenial cortex (p = 0.04) than PD-CN. The HC and PD-MCI patients were classified with an accuracy of 86.4% (sensitivity = 72.7% and specificity = 81.8%) using bagged trees. CONCLUSION: 1H-MRSI revealed metabolic changes in the default mode, ventral attention/salience, and sensorimotor networks of PD-MCI patients, which could be summarized mainly as 'posterior cortical metabolic changes' related with cognitive dysfunction.

Treatment Results and Postoperative Complications of Single-Stage Tracheal Resection in Adolescent Patients with Post-Intubation Tracheal Stenosis, Compared to Adults.

Objective: The purpose of this study was to analyze the treatment outcomes and postoperative complications of tracheal resection in patients under the age of 19 years with post-intubation tracheal stenosis, and to compare the results with those of adults. Methods: Data were retrospectively retrieved from the medical records, including demographic characteristics, perioperative features, any postoperative complications and follow-up statuses of the patients. Treatment results and postoperative complications were compared between adolescent and adult groups. Results: Overall, anastomotic and non-anastomotic complication rates in the adolescent group and the adult group were 40%, 40%, 10% and 63%, 44.4%, 33.3%, respectively. Overall treatment success rates based on tracheostomy tube and tracheal stent free status were 90% and 92.6% in adolescent and adults, respectively. Conclusion: Treatment success rates and incidence of anastomotic complications were found similar in patients under the age of 19 years and adult patients who underwent single-stage tracheal resection and end to end anastomosis for treatment of post-intubation tracheal stenosis.

Is it useful to use computerized tomography image-based artificial intelligence modelling in the differential diagnosis of chronic otitis media with and without cholesteatoma?

OBJECTIVE: Cholesteatoma is an aggressive form of chronic otitis media (COM). For this reason, it is important to distinguish between COM with and without cholesteatoma. In this study, the role of artificial intelligence modelling in differentiating COM with and without cholesteatoma on computed tomography images was evaluated. METHODS: The files of 200 patients who underwent mastoidectomy and/or tympanoplasty for COM in our clinic between January 2016 and January 2021 were retrospectively reviewed. According to the presence of cholesteatoma, the patients were divided into two groups as chronic otitis with cholesteatoma (n = 100) and chronic otitis without cholesteatoma (n = 100). The control group (n = 100) consisted of patients who did not have any previous ear disease and did not have any active complaints about the ear. Temporal bone computed tomography (CT) images of all patients were analyzed. The distinction between cholesteatoma and COM was evaluated by using 80% of the CT images obtained for the training of artificial intelligence modelling and the remaining 20% for testing purposes. RESULTS: The accuracy rate obtained in the hybrid model we used in our study was 95.4%. The proposed model correctly predicted 2952 out of 3093 CT images, while it predicted 141 incorrectly. It correctly predicted 936 (93.78%) of 998 images in the COM group with cholesteatoma, 835 (92.77%) of 900 images in the COM group without cholesteatoma, and 1181 (98.82%) of 1195 images in the normal group. CONCLUSION: In our study, it has been shown that the differentiation of COM with and without cholesteatoma with artificial intelligence modelling can be made with highly accurate diagnosis rates by using CT images. With the deep learning modelling we proposed, the highest correct diagnosis rate in the literature was obtained. According to the results of our study, we think that with the use of artificial intelligence in practice, the diagnosis of cholesteatoma can be made earlier, it will help in the selection of the most appropriate treatment approach, and the complications can be reduced.

Role of institutional, cultural and economic factors in the effectiveness of lockdown measures.

OBJECTIVE: This study considered the role of institutional, cultural and economic factors in the effectivemess of lockdown measures during the coronavirus pandemic. Earlier studies focusing on cross-sectional data found an association between low case numbers and a higher level of cultural tightness. Meanwhile, institutional strength and income levels revealed a puzzling negative relationship with the number of cases and deaths. METHODS: Data available at the end of September 2021 were used to analyse the dynamic impact of these factors on the effectiveness of lockdowns. The cross-sectional dimension of country-level data was combined with the time-series dimension of pandemic-related measures, using econometric techniques dealing with panel data. FINDINGS: Greater stringency of lockdown measures was associated with fewer cases. Institutional strength enhanced this negative relationship. Countries with well-defined and established laws performed better for a given set of lockdown measures compared with countries with weaker institutional structures. Cultural tightness reduced the effectiveness of lockdowns, in contrast to previous findings at cross-sectional level. CONCLUSION: Institutional strength plays a greater role than cultural and economic factors in enhancing the performance of lockdowns. These results underline the importance of strengthening institutions for pandemic control.

Diagnosis and grading of vesicoureteral reflux on voiding cystourethrography images in children using a deep hybrid model.

BACKGROUND AND OBJECTIVE: Vesicoureteral reflux is the leakage of urine from the bladder into the ureter. As a result, urinary tract infections and kidney scarring can occur in children. Voiding cystourethrography is the primary radiological imaging method used to diagnose vesicoureteral reflux in children with a history of recurrent urinary tract infection. Besides the diagnosis of reflux, it is graded with voiding cystourethrography. In this study, we aimed to diagnose and grade vesicoureteral reflux in Voiding cystourethrography images using hybrid CNN in deep learning methods. METHODS: Images of pediatric patients diagnosed with VUR between 2016 and 2021 in our hospital (Firat University Hospital) were graded according to the international vesicoureteral reflux radiographic grading system. VCUG images of 236 normal and 992 with vesicoureteral reflux pediatric patients were available. A total of 6 classes were created as normal and graded 1-5 patients. RESULTS: In this study, a hybrid-based mRMR (Minimum Redundancy Maximum Relevance) using CNN (Convolutional Neural Networks) model is developed for the diagnosis and grading of vesicoureteral reflux on voiding cystourethrography images. Googlenet, MobilenetV2, and Densenet201 models are used as a part of the hybrid architecture. The obtained features from these architectures are examined in concatenating process. Then, these features are classified in machine learning classifiers after optimizing with the mRMR method. Among the models used in the study, the highest accuracy value was obtained in the proposed model with an accuracy rate of 96.9%. CONCLUSIONS: It shows that the hybrid model developed according to the findings of our study can be used in the diagnosis and grading of vesicoureteral reflux in voiding cystourethrography images.

Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR.

Early diagnosis of breast lesions and differentiation of malignant lesions from benign lesions are important for the prognosis of breast cancer. In the diagnosis of this disease ultrasound is an extremely important radiological imaging method because it enables biopsy as well as lesion characterization. Since ultrasonographic diagnosis depends on the expert, the knowledge level and experience of the user is very important. In addition, the contribution of computer aided systems is quite high, as these systems can reduce the workload of radiologists and reinforce their knowledge and experience when considered together with a dense patient population in hospital conditions. In this paper, a hybrid based CNN system is developed for diagnosing breast cancer lesions with respect to benign, malignant and normal. Alexnet, MobilenetV2, and Resnet50 models are used as the base for the Hybrid structure. The features of these models used are obtained and concatenated separately. Thus, the number of features used are increased. Later, the most valuable of these features are selected by the mRMR (Minimum Redundancy Maximum Relevance) feature selection method and classified with machine learning classifiers such as SVM, KNN. The highest rate is obtained in the SVM classifier with 95.6% in accuracy.

Productive Ecosystems and the arrow of development.

Economic growth is associated with the diversification of economic activities, which can be observed via the evolution of product export baskets. Exporting a new product is dependent on having, and acquiring, a specific set of capabilities, making the diversification process path-dependent. Taking an agnostic view on the identity of the capabilities, here we derive a probabilistic model for the directed dynamical process of capability accumulation and product diversification of countries. Using international trade data, we identify the set of pre-existing products, the product Ecosystem, that enables a product to be exported competitively. We construct a directed network of products, the Eco Space, where the edge weight corresponds to capability overlap. We uncover a modular structure, and show that low- and middle-income countries move from product communities dominated by small Ecosystem products to advanced (large Ecosystem) product clusters over time. Finally, we show that our network model is predictive of product appearances.

Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture.

Brain tumor is one of the dangerous and deadly cancer types seen in adults and children. Early and accurate diagnosis of brain tumor is important for the treatment process. It is an important step for specialists to detect the brain tumor using computer aided systems. These systems allow specialists to perform tumor detection more easily. However, mistakes made with traditional methods are also prevented. In this paper, it is aimed to diagnose the brain tumor using MRI images. CNN models, one of the deep learning networks, are used for the diagnosis process. Resnet50 architecture, one of the CNN models, is used as the base. The last 5 layers of the Resnet50 model have been removed and added 8 new layers. With this model, 97.2% accuracy value is obtained. Also, results are obtained with Alexnet, Resnet50, Densenet201, InceptionV3 and Googlenet models. Of all these models, the model developed with the highest performance has classified the brain tumor images. As a result, when analyzed in other studies in the literature, it is concluded that the developed method is effective and can be used in computer-aided systems to detect brain tumor.