An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review

The repercussions of high levels of environmental pollution coupled with the low reserves and increased costs of traditional energy sources have led to the widespread adaptation of wind energy worldwide. However, the expanded use of wind energy is accompanied by major challenges for electric grid operators due to the difficulty of controlling and forecasting the production of wind energy. The development of methods for addressing these problems has therefore attracted the interest of numerous researchers. This paper presents an innovative method for assessing wind speed in different and widely spaced locations. The new method uses wind speed data from multiple sites as a single package that preserves the characteristics of the correlations among those sites. Powerful Waikato Environment for Knowledge Analysis (Weka) machine learning software has been employed for supporting data preprocessing, clustering, classification, visualization, and feature selection and for using a standard algorithm to construct decision trees according to a training set. The resultant arrangement of the sites according to likely wind energy productivity facilitates enhanced decisions related to the potential for the effective operation of wind energy farms at the sites. The proposed method is anticipated to provide network operators with an understanding of the possible productivity of each site, thus facilitating their optimal management of network operations. The results are also expected to benefit investors interested in establishing profitable projects at those locations.

A Unique Unified Wind Speed Approach to Decision-Making for Dispersed Locations

A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation

Magnetic resonance imaging (MRI) segmentation is a fundamental and significant task since it can guide subsequent clinic diagnosis and treatment. However, images are often corrupted by defects such as low-contrast, noise, intensity inhomogeneity, and so on. Therefore, a weighted level set model (WLSM) is proposed in this study to segment inhomogeneous intensity MRI destroyed by noise and weak boundaries. First, in order to segment the intertwined regions of brain tissue accurately, a weighted neighborhood information measure scheme based on local multi information and kernel function is designed. Then, the membership function of fuzzy c-means clustering is used as the spatial constraint of level set model to overcome the sensitivity of level set to initialization, and the evolution of level set function can be adaptively changed according to different tissue information. Finally, the distance regularization term in level set function is replaced by a double potential function to ensure the stability of the energy function in the evolution process. Both real and synthetic MRI images can show the effectiveness and performance of WLSM. In addition, compared with several state-of-the-art models, segmentation accuracy and Jaccard similarity coefficient obtained by WLSM are increased by 0.0586, 0.0362 and 0.1087, 0.0703, respectively.

Magnetic Resonance Imaging Segmentation via Weighted Level Set Model Based on Local Kernel Metric and Spatial Constraint.

Lumbar spine segmentation is important to help doctors diagnose lumbar disc herniation (LDH) and patients' rehabilitation treatment. In order to accurately segment the lumbar spine, a lumbar spine image segmentation algorithm based on improved Attention U-Net is proposed. The algorithm is based on Attention U-Net, the attention module based on multilevel feature map fusion is adopted, two residual modules are introduced instead of the original convolution blocks. a hybrid loss function is used for prediction during the training process, and finally, the image superposition process is realized. In this experiment, we expanded 420 lumbar MRI images of 180 patients to 1000 images and trained them by different algorithms, respectively, and accuracy, recall, and Dice similarity coefficient metrics were used to analyze these algorithms. The results show that compared with SVM, FCN, R-CNN, U-Net, and Attention U-Net models, the improved model achieved better results in all three evaluations, with 95.50%, 94.53%, and 95.01%, respectively, which proves the better performance of the proposed method for segmentation in lumbar disc and caudal vertebrae.

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Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net

BACKGROUND The automatic segmentation of brain tumour from MRI medical images is mainly covered in this review. Recently, state-of-the-art performance is provided by deep learning- based approaches in the field of image classification, segmentation, object detection, and tracking tasks. INTRODUCTION The core feature deep learning approach is the hierarchical representation of features from images, thus avoiding domain-specific handcrafted features. METHODS In this review paper, we have dealt with a review of Deep Learning Architecture and Methods for MRI Brain Tumour Segmentation. First, we have discussed the basic architecture and approaches for deep learning methods. Secondly, we have discussed the literature survey of MRI brain tumour segmentation using deep learning methods and its multimodality fusion. Then, the advantages and disadvantages of each method are analyzed and finally, it is concluded with a discussion on the merits and challenges of deep learning techniques. RESULTS The review of brain tumour identification using deep learning. CONCLUSION Techniques may help the researchers to have a better focus on it.

A Review on Deep Learning Architecture and Methods for MRI Brain Tumour Segmentation.

The categorical relationships play a crucial role for choosing of computer database. Based on it, association rules display the link among the 2 categories. Because the object oriented elegance for the computer code cultivates, the relationships among the categories. Additionally rise near the complicated computer code style. However, one will develop improved object-oriented style through right association rules among groups or categories. The present paper deals to style the proper association rules for the object-oriented databases taken from the categories. A true scenario of Electricity Bill Deposit System (EBDS) and user interested items by analyzing user behavior history in social network environment. Both applications are taken into account in Indian situation and Apriori Algorithmic rule is employed for locating the employment of recurrent information sets through the proper association rules. The association rules area unit deliberate through famous Unified Modeling Language. The current paper work is to the associate degree carrying out of Apriori algorithmic rule towards the information of EBDS.

Association Rule Modeling using UML and Apriori Algorithm

In this study, we discuss three DTN routing protocols, these are epidemic, PRoPHET and spray and wait routing protocols. A special simulator will be used; that is opportunistic network environment (ONE) to create a network environment. Spray and wait has highest delivery rate and low latency in most of the cases. Hence, spray and wait have better performance than others. This analysis of the performance of DTN protocols helps the researcher to learn better of these protocols in the different environment.,Delay-Tolerant Network (DTN) is a network designed to operate effectively over extreme distances, such as those encountered in space communications or on an interplanetary scale. In such an environment, nodes are occasional communication and are available among hubs, and determinations of the next node communications are not confirmed. In such network environment, the packet can be transferred by searching current efficient route available for a particular node. Due to the uncertainty of packet transfer route, DTN is affected by a variety of factors such as packet size, communication cost, node activity, etc.,Spray and wait have highest delivery rate and low latency in most of the cases. Hence, spray and wait have better performance than others.,The primary goal of the paper is to extend these works in an attempt to offer a better understanding of the behavior of different DTN routing protocols with delivery probability, latency and overhead ratio that depend on various amounts of network parameters such as buffer size, number of nodes, movement ratio, time to live, movement range, transmission range and message generation rate. In this study, we discuss three DTN routing protocols: these are epidemic, PRoPHET and spray and wait routing protocols. A special simulator will be used; that is opportunistic network environment (ONE) to create a network environment. Spray and wait have highest delivery rate and low latency in most of the cases. Hence, spray and wait have better performance than others. This analysis of the performance of DTN protocols helps the researcher to learn better of these protocols in the different environment.

A comparative study on delay-tolerant network routing protocols

An image is a visual representation of something which consists of several pixels. Pixel is the smallest unit which has 4 values ARGB. Each component can take a value between 0 to 255 which can be manipulated to encrypt an image. In this paper we have suggested the Cryptography algorithm that uses the XOR effect on image pixels followed by an algorithm that uses music theory as its base. The encipher and decipher algorithm based on music theory.

M Deepa

Papers

A Review on Deep Learning Architecture and Methods for MRI Brain Tumour Segmentation.

Association Rule Modeling using UML and Apriori Algorithm

A comparative study on delay-tolerant network routing protocols

IMGCRYPTO-XOR Algorithm using MUSIC Theory for secure medical data storage in cloud