Natural language description of images using hybrid recurrent neural network

We presented a learning model that generated natural language description of images. The model utilized the connections between natural language and visual data by produced text line based contents from a given image. Our Hybrid Recurrent Neural Network model is based on the intricacies of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bi-directional Recurrent Neural Network (BRNN) models. We conducted experiments on three benchmark datasets, e.g., Flickr8K, Flickr30K, and MS COCO. Our hybrid model utilized LSTM model to encode text line or sentences independent of the object location and BRNN for word representation, this reduced the computational complexities without compromising the accuracy of the descriptor. The model produced better accuracy in retrieving natural language based description on the dataset

Hybrid deep neural network for Bangla automated image descriptor

Automated image to text generation is a computationally challenging computer vision task which requires sufficient comprehension of both syntactic and semantic meaning of an image to generate a meaningful description. Until recent times, it has been studied to a limited scope due to the lack of visual-descriptor dataset and functional models to capture intrinsic complexities involving features of an image. In this study, a novel dataset was constructed by generating Bangla textual descriptor from visual input, called Bangla Natural Language Image to Text (BNLIT), incorporating 100 classes with annotation. A deep neural network-based image captioning model was proposed to generate image description. The model employs Convolutional Neural Network (CNN) to classify the whole dataset, while Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) capture the sequential semantic representation of text-based sentences and generate pertinent description based on the modular complexities of an image. When tested on the new dataset, the model accomplishes significant enhancement of centrality execution for image semantic recovery assignment. For the experiment of that task, we implemented a hybrid image captioning model, which achieved a remarkable result for a new self-made dataset, and that task was new for the Bangladesh perspective. In brief, the model provided benchmark precision in the characteristic Bangla syntax reconstruction and comprehensive numerical analysis of the model execution results on the dataset

A deep learning approach for brain tumor detection using magnetic resonance imaging

The growth of abnormal cells in the brain’s tissue causes brain tumors. Brain tumors are considered one of the most dangerous disorders in children and adults. It develops quickly, and the patient’s survival prospects are slim if not appropriately treated. Proper treatment planning and precise diagnoses are essential to improving a patient’s life expectancy. Brain tumors are mainly diagnosed using magnetic resonance imaging (MRI). As part of a convolution neural network (CNN)-based illustration, an architecture containing five convolution layers, five max-pooling layers, a Flatten layer, and two dense layers has been proposed for detecting brain tumors from MRI images. The proposed model includes an automatic feature extractor, modified hidden layer architecture, and activation function. Several test cases were performed, and the proposed model achieved 98.6% accuracy and 97.8% precision score with a low cross-entropy rate. Compared with other approaches such as adjacent feature propagation network (AFPNet), mask region-based CNN (mask RCNN), YOLOv5, and Fourier CNN (FCNN), the proposed model has performed better in detecting brain tumors

Känslighetsanalys av modellering av väggnära turbulens för Large Eddy Simulering av inkompressibla flöden

Wall layer models are very important for the simulation of turbulent flows in complex geometries to characterize the significant flow features. For the simulation of turbulent flows, the performance of Large Eddy Simulation techniques with different wall layer models which we refer to as near-wall turbulence modeling for turbulent flows are analyzed. The wall shear stress model and Delayed Detached Eddy Simulation wall model are two options, that can be used to model the turbulent boundary layer. In this project, a wall shear stress model is used as a near-wall turbulence model in the G2 simulation technique. A sensitivity analysis of this near-wall turbulence modeling with respect to model parameters in the simulation techniques of incompressible turbulent flows is presented.  Väggmodellering är viktigt i simuleringar av turbulenta flöden ikomplexa geometrier då de mest inverkande flödesegenskaperna skakarakteriseras. Prestandan hos Large Eddy Simulation-tekniker med olikaväggmodeller analyseras för simuleringar av turbulenta flöden med höga Reynoldstal.Två alternativ som kan användas för turbulenta gränsskikt är “Wall Shear StressModel” och “Delayed Detached Eddy Simulation Wall Model”. I detta projektanvänds en wall shear stress modell för det turbulenta flödet vid väggentillsammans med G2 simuleringsmetodiken. En känslighetsanalys av denna modellmed hänsyn till modellparameterar presenteras för simuleringar avinkompressibla turbulenta flöde

Känslighetsanalys av modellering av väggnära turbulens för Large Eddy Simulering av inkompressibla flöden

Wall layer models are very important for the simulation of turbulent flows in complex geometries to characterize the significant flow features. For the simulation of turbulent flows, the performance of Large Eddy Simulation techniques with different wall layer models which we refer to as near-wall turbulence modeling for turbulent flows are analyzed. The wall shear stress model and Delayed Detached Eddy Simulation wall model are two options, that can be used to model the turbulent boundary layer. In this project, a wall shear stress model is used as a near-wall turbulence model in the G2 simulation technique. A sensitivity analysis of this near-wall turbulence modeling with respect to model parameters in the simulation techniques of incompressible turbulent flows is presented.  Väggmodellering är viktigt i simuleringar av turbulenta flöden ikomplexa geometrier då de mest inverkande flödesegenskaperna skakarakteriseras. Prestandan hos Large Eddy Simulation-tekniker med olikaväggmodeller analyseras för simuleringar av turbulenta flöden med höga Reynoldstal.Två alternativ som kan användas för turbulenta gränsskikt är “Wall Shear StressModel” och “Delayed Detached Eddy Simulation Wall Model”. I detta projektanvänds en wall shear stress modell för det turbulenta flödet vid väggentillsammans med G2 simuleringsmetodiken. En känslighetsanalys av denna modellmed hänsyn till modellparameterar presenteras för simuleringar avinkompressibla turbulenta flöde

Bangla Natural Language Image to Text (BNLIT)

We represented a new Bangla dataset with a Hybrid Recurrent Neural Network model which generated Bangla natural language description of images. This dataset achieved by a large number of images with classification and containing natural language process of images. We conducted experiments on our self-made Bangla Natural Language Image to Text (BNLIT) dataset. Our dataset contained 8,743 images. We made this dataset using Bangladesh perspective images. We used one annotation for each image. In our repository, we added two types of pre-processed data which is 224 × 224 and 500 × 375 respectively alongside annotations of full dataset. We also added CNN features file of whole dataset in our repository which is features.pkl

YOLOv3 and YOLOv5-based automated facial mask detection and recognition systems to prevent COVID-19 outbreaks

Object detection system in light of deep learning have been monstrously effective in complex item identification task images and have shown likely in an extensive variety of genuine applications counting the Coronavirus pandemic. Ensuring and enforcing the proper use of face masks is one of the main obstacles in containing and reducing the spread of the infection among the population. This paper aims to find out how the urban population of a megacity uses facial masks correctly. Using YOLOv3 and YOLOv5, we trained and validated a brand-new dataset to identify images as "with mask", "without mask", and "mask not in position". In the YOLOv3 we carried out three pre-trained models which are: YOLOv3, YOLOv3-tiny, and SPP-YOLOv3. In addition, we utilized five pre-trained models in the YOLOv5: YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. The dataset is included 6550 pictures with three classes. On mAP, the dataset achieved a commendable 95% performance accuracy. This research can be used to monitor the proper use of face masks in various public spaces through automated scanning

A deep learning approach for brain tumor detection using magnetic resonance imaging

The growth of abnormal cells in the brain’s tissue causes brain tumors. Brain tumors are considered one of the most dangerous disorders in children and adults. It develops quickly, and the patient’s survival prospects are slim if not appropriately treated. Proper treatment planning and precise diagnoses are essential to improving a patient’s life expectancy. Brain tumors are mainly diagnosed using magnetic resonance imaging (MRI). As part of a convolution neural network (CNN)-based illustration, an architecture containing five convolution layers, five max-pooling layers, a Flatten layer, and two dense layers has been proposed for detecting brain tumors from MRI images. The proposed model includes an automatic feature extractor, modified hidden layer architecture, and activation function. Several test cases were performed, and the proposed model achieved 98.6% accuracy and 97.8% precision score with a low cross-entropy rate. Compared with other approaches such as adjacent feature propagation network (AFPNet), mask region-based CNN (mask RCNN), YOLOv5, and Fourier CNN (FCNN), the proposed model has performed better in detecting brain tumors