Hybridization of Machine Learning Techniques for WSN Optimal Cluster Head Selection

Analysis of intracranial neoplasm using multimodal MR images requires accurate and automatic segmentation. However, manually classifying tumors with similar structures or appearances in magnetic resonance imaging (MRI) with similar anatomy or appearances is more challenging, requiring experience to detect brain tumors. Precise segmentation of brain tumors gives clinicians with a foundation for surgical planning and treatment. Due to its capacity to segment brain tumor images automatically, Deep Neural Networks (DNN) have been widely used in image segmentation applications. To classify, segment and marking the occurrence of the brain tumor area accurately, we present custom Deep Convolution Neural Network (CNN) based Residual block U-Net (RB-ResUnet) architecture. Our technique is tested on publicly available Kaggle datasets utilizing quantitative metrics. Comparative results demonstrate that the custom CNN-based RB-ResUnet model can more reliably identify tumor locations and give accurate segmentation masks to tumor locations that are defined by bounding boxes. The findings of the experiment reveal that our proposed model RB-ResUnet can effectively aid in the identification, toxicological evaluation of the brain tumor and has clinical research as well as practical application.

MRI Intracranial Neoplasm Localization Using Convolution Neural Network Based Residual Block ResUnet: A Systematic Approach

Murthy,

Joshi,

et al. 2023

A Fuzzy Logic Based Cluster Head Election Technique for Energy Consumption Reduction in Wireless Sensor Networks

Onyango,

Lang’at,

Konditi

2023

Wireless sensor networks deploy sensor nodes to different areas for data collection. The small size of these sensor nodes allows limited energy storage capacity, and most applications of the networks do not support recharging the batteries once their energy is depleted. Research on energy efficiency in wireless sensor networks is thus an active area that seeks to minimize energy consumption so that the sensor nodes can live longer. Clustering, one of the energy consumption optimization techniques, is employed in this research. It splits the network into smaller groups for data collection and forwards the data to the base station via appointed cluster heads. A fuzzy-based cluster head election strategy is proposed here to improve energy efficiency in wireless sensor networks. The input parameters of the fuzzy inference system are chosen as the residual energy, the node centrality, and the mobility factor. The system generates an output of the chance of a node being selected as a cluster head based on the combination of the values of the given inputs. The simulation results show that the proposed model reduces the network’s overall energy consumption and extends the sensor nodes’ lifetime.

A Novel Buffer Packet Delivery Strategy for High Throughput and Better Health (HTBH) Method in Wireless Sensor Networks

V S,

Siddaraju

2023

There is massive call for the Packet Sender Device Network (PSDN) primarily based on tracking of areas, figuring out the consequences of climate, detection of enemy vehicles. The PSDN could have useful Packet Sender Devices (PSD’s) which can study the vicinity and then send the data from initial to receiver PSD. There are numerous constraints which have restrictions on the feature like battery, memory, and range. There is hierarchical community wherein the PSDs are spread on more than one area with every vicinity having their very own PSD’s whilst communique has to manifest among PSDs of various areas then it requires chief PSD in every vicinity which have to be elected primarily based on higher battery degree, distance to base station in addition to mobility of the PSD over a duration of time. LEACH suffers from side-to-side propagation among base station and PSDs. LEACH elects the pinnacle PSD primarily based on random possibility which has consequences of Good-Bad Ratio due to the fact there are possibilities that the PSD with low battery degree be selected as a head PSD. The proposed (HTBH) method will select the head PSD based on battery level, distance between base station and PSD, the mobility of PSD. In order to deliver the data to the processing lab PSDs of the area along with head PSD are used. The packets are classified into high and low priority so that preferences can be used during the data delivery process. Along with sending the current data packets even the data packets reside within the PSD which have to send towards the processing centre. Two policies namely single threshold and dual threshold are used to evict the packets within the PSD and then moving them towards processing centre. This study proposes a higher choice of head PSD with the aid of using considering computation of element which takes into consideration battery, distance, and mobility. The communique among the detection vicinity to receiver vicinity takes place with the assist of PSD’s and head PSD there with the aid of using decreasing range of hops. HTBH method is compared to LEACH and E-LEACH with respect various performance parameters.