Segmenting brain tumors automatically using MR data is crucial for disease investigation and monitoring. Due to the aggressive nature and diversity of gliomas, well-organized and exact segmentation methods are used to classify tumors intra-tumorally. The proposed technique uses a Gray Level Co-occurrence matrix extraction of features approach to strip out unwanted details from the images. In comparison with the current state of the art, the accuracy of brain tumor segmentation was significantly improved using Convolutional Neural Networks, which are frequently used in the field of biomedical image segmentation. By merging the results of two separate segmentation networks, the proposed method demonstrates a major but simple combinatorial strategy that, as a direct consequence, yields much more precise and complete estimates. A U-Net and a Three-Dimensional Convolutional Neural Network. These networks are used to break up images into their component parts. Following that, the prediction was constructed using two distinct models that were combined in a number of ways. In comparison to existing state-of-the-art designs, the proposed method achieves the mean accuracy (%) of 99.40, 98.46, 98.29, precision (%) of 99.41, 98.51, 98.35, F-Score (%) of 99.4, 98.29, 98.46 and sensitivity (%) of 99.39, 98.41, 98.25 for the whole tumor, enhanced tumor, tumor core on the validation set, respectively.

Fashion has always been an essential feature in our daily routine. It also plays a significant role in everyone’s lives. In this research, convolutional neural networks (CNN) were used to train images of different fashion styles, which were attempted to be predicted with a high success rate. Deep learning has been widely applied in a variety of fields recently. A CNN is a deep neural network that delivers the most accurate answers when tackling real-world situations. Apparel manufacturers have employed CNN to tackle various difficulties on their e-commerce sites, including clothing recognition, search, and suggestion. A set of photos from the Fashion-MNIST dataset is used to train a series of CNN-based deep learning architectures to distinguish between photographs. CNN design, batch normalization, and residual skip connections reduce the time it takes to learn. The CNN model’s findings are evaluated using the Fashion-MNIST datasets. In this paper, classification is done with a convolutional layer, filter size, and ultimately connected layers. Experiments are run with different activation functions, optimizers, learning rates, dropout rates, and batch sizes. The results showed that the choice of activation function, optimizer, and dropout rate impacts the correctness of the results.

Wireless networks with a large number of peer nodes are known as mobile ad hoc networks (MANETs). In MANETs, the mobility of nodes causes a number of challenges, including path preservation, battery life, safety, dependability, and unexpected connection characteristics. As a result, the network’s quality of service (QoS) would be compromised (QoS). For the discovery and maintenance of pathways in MANETs, the routing protocol is critical. By implementing the multicast routing protocol, the MANET network’s reliability may be improved significantly. Evaluation of multicast routing for quality of service (QoS) is the primary goal of this study. In multicasting, data packets from one node are transmitted to a set of receiver nodes at a time, simultaneously. Multicasting reduces transmission costs. Cluster head selection is one of the challenges in MANET. This proposed research paper optimal route selection (ORS) provides the cluster head selection and alternate cluster head selection for avoiding the failure of the cluster head, generation of the optimal path between the cluster head and member node based on reliability pair factor and node’s energy, and establishment of the path based on maximum energy and number of hops between the nodes (minimum number of hops). In comparison to existing methods, ORS is more effective in the energy-efficient path between base station and cluster head, and member node is provided by an ORS route. Results show that the proposed ORSMAN has higher throughput, minimum latency, minimum jitter, and maximum packet delivery ratio, when compared to the existing methodologies.

Osteoporosis is a word used to describe a condition in which bone density has been diminished as a result of inadequate bone tissue development to counteract the elimination of old bone tissue. Osteoporosis diagnosis is made possible by the use of medical imaging technologies such as CT scans, dual X-ray, and X-ray images. In practice, there are various osteoporosis diagnostic methods that may be performed with a single imaging modality to aid in the diagnosis of the disease. The proposed study is to develop a framework, that is, to aid in the diagnosis of osteoporosis which agrees to all of these CT scans, X-ray, and dual X-ray imaging modalities. The framework will be implemented in the near future. The proposed work, CBTCNNOD, is the integration of 3 functional modules. The functional modules are a bilinear filter, grey-level zone length matrix, and CB-CNN. It is constructed in a manner that can provide crisp osteoporosis diagnostic reports based on the images that are fed into the system. All 3 modules work together to improve the performance of the proposed approach, CBTCNNOD, in terms of accuracy by 10.38, 10.16, 7.86, and 14.32; precision by 11.09, 9.08, 10.01, and 16.51; sensitivity by 9.77, 10.74, 6.20, and 12.78; and specificity by 11.01, 9.52, 9.5, and 15.84, while requiring less processing time of 33.52, 17.79, 23.34, and 10.86, when compared to the existing techniques of RCETA, BMCOFA, BACBCT, and XSFCV, respectively.