The use of multimodal magnetic resonance imaging (MRI) to autonomously segment brain tumors and subregions is critical for accurate and consistent tumor measurement, which can help with detection, care planning, and evaluation. This research is a contribution to the neuroscience research. In the present work, we provide a completely automated brain tumor segmentation method based on a mathematical model and deep neural networks (DNNs). Each slice of the 3D picture is enhanced by the suggested mathematical model, which is then sent through the 3D attention U-Net to provide a tumor segmented output. The study includes a detailed mathematical model for tumor pixel enhancement as well as a 3D attention U-Net to appropriately separate the pixels. On the BraTS 2019 dataset, the suggested system is tested and verified. This proposed work will definitely help for the treatment of the brain tumor patient. The pixel level accuracy for tumor pixel segmentation is 98.90%. The suggested system architecture's outcomes are compared to those of current system designs. This study also examines the suggested system architecture's time complexity on various processing units with neuroscience approach.

https://downloads.hindawi.com/journals/cin/2022/4271711.pdf

Deep Neural Network-Based Novel Mathematical Model for 3D Brain Tumor Segmentation

Dynamic Architecture Based Deep Learning Approach for Glioblastoma Brain Tumor Survival Prediction

Multimedia-based emerging technologies and data analytics for Neuroscience as a Service (NaaS)

Pneumonia, an infectious disease caused by a bacterium in the lungs' alveoli, is frequently the result of pollution. A lung infection causes pus to build up in the affected tissue. Professionals conduct bodily examinations and diagnose their patients using a chest X-ray, ultrasound, or lung biopsy to determine if they have certain conditions. Misdiagnosis, incorrect treatment, and failure to recognize the disease will result in a patient's inability to lead a normal life. Deep learning's advancement helps specialists make better decisions when diagnosing patients with certain diseases. The research provides a flexible and efficient deep learning technique that uses the CNN model to predict and detect a patient who is unaffected. Using a chest X-ray photograph, the study applies a flexible and effective deep learning technique of using the CNN model in predicting and detecting a patient unaffected and affected by the illness. To demonstrate the overall performance of the CNN model being trained, the researchers used an amassed dataset of 20,000 photographs and a 224x224 photograph decision with 32 batch lengths. At some point throughout the total performance training, the trained version produced a 95 percent accuracy charge. The research study may detect and predict COVID-19, bacterial, and viral pneumonia illnesses based solely on chest X-ray photographs, according to the results of the testing.

Pneumonia Detection and Classification using CNN and VGG16

The design of Hadoop has ability to elimination of fault tolerance, which consists of rescheduling the task on the defective nodes to run on other devices in the system. However, this strategy is ineffective if an error arises after most of the task has been completed. As a result, it is essential to make an early detection of the problem at the node to ensure that the resumption of the work will not result in a significant loss of both time and productivity. The ability to predict these problems provides us with the required time to move the workload onto different nodes, which helps to avoid data loss or processing time. In this paper, we propose an identification of faulty nodes from a large Hadoop distributed environment using machine learning techniques. Initially, we deployed one controller node and numerous data nodes as virtual machines in a distributed manner. The execution performs when the end-user submits a specific job to the controller node. The master node is the middleware controller that continuously communicates with other data nodes and assigns a task to each data node accordingly. However, this conventional process of HDFS that can generate data leakage or high computation whenever the specific node is heated or straggler. In our approach, we initially collect the log history of each data node and apply some statistical and a few machine learning algorithms to identify nodes' status. According to the achieved outcome of each node, we can decide to eliminate the specific node for task execution. We applied five machine learning algorithms in the extensive experimental analysis, including a Support Vector Machine (SVM). The SVM obtains 96.7% higher accuracy over the conventional machine learning classifiers for the entire execution.

Faulty Node Detection in HDFS Using Machine Learning Techniques

An efficient way of text-based emotion analysis from social media using LRA-DNN

The number of people living in the world is continuously rising, which means that there must be an increase in crop production. The estimation of agricultural yields as well as the monitoring of the growth of crops are very significant for the overall economic development of a nation. The prediction of crop production is highly difficult since it is dependent on a wide variety of factors, including the genotype of the crop, environmental factors, management strategies, and the relationships between these elements. Deep learning is gaining relevance in environmental monitoring, crop type segmentation, and crop yield estimation applications as a result of recent advancements in image classification achieved by the utilization of deep Convolutional Neural Networks. Convolutional neural networks, or CNNs, are a type of deep learning approach that has shown remarkable performance in picture classification tasks. In this study, CNNs are utilized to construct a model for crop production prediction.

G. Yogapriya

Papers

An efficient way of text-based emotion analysis from social media using LRA-DNN

Crop Yield Identification Using CNN