Recommender system: prediction/diagnosis of breast cancer using hybrid machine learning algorithm

Multimedia Tools and Applications - Breast cancer is the second popular cause of the women’s death. There are some existing techniques for identifying the breast cancer and one of them is...     

QoS aware cross layer paradigm for urban development applications in IoT

Wireless Networks - Large number of services and heterogeneity of the objects have made Internet of Things (IoT) a complex paradigm. It is necessary to placate the requirements of quality of...     

Prediction of the mortality rate and framework for remote monitoring of pregnant women based on IoT

Multimedia Tools and Applications - To save the risk during the pregnancies in remote areas (where women cannot approach the doctors in the urban areas for proper check-ups), the authors have...     

Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications

Energy is vital parameter for communication in Internet of Things (IoT) applications via Wireless Sensor Networks (WSN). Genetic algorithms with dynamic clustering approach are supposed to be very effective technique in conserving energy during the process of network planning and designing for IoT. Dynamic clustering recognizes the cluster head (CH) with higher energy for the data transmission in the network. In this paper, various applications, like smart transportation, smart grid, and smart cities, are discussed to establish that implementation of dynamic clustering computing-based IoT can support real-world applications in an efficient way. In the proposed approach, the dynamic clustering-based methodology and frame relay nodes (RN) are improved to elect the most preferred sensor node (SN) amidst the nodes in cluster. For this purpose, a Genetic Analysis approach is used. The simulations demonstrate that the proposed technique overcomes the dynamic clustering relay node (DCRN) clustering algorithm in terms of slot utilization, throughput and standard deviation in data transmission.

     

A genetic load balancing algorithm to improve the QoS metrics for software defined networking for multimedia applications

Multimedia Tools and Applications - With the increasing growth in the network and latest technologies by which people communicates via voice or data and modifies the radio devices easily and cost...     

A hybrid approach for the optimization of quality of service metrics of WSN

The core objective behind this research paper is to implement a hybrid optimization technique along with proactive routing algorithm to enhance the network lifetime of wireless sensor networks (WSN). The combination of two soft computing techniques viz. genetic algorithm (GA) and bacteria foraging optimization (BFO) techniques are applied individually on destination sequence distance vector (DSDV) routing protocol and after that the hybridization of GA and BFO is applied on the same routing protocol. The various simulation parameters used in the research are: throughput, end to end delay, congestion, packet delivery ratio, bit error rate and routing overhead. The bits are processed at a data rate of 512 bytes/s. The packet size for data transmission is 100 bytes. The data transmission time taken by the packets is 200 s i.e. the simulation time for each simulation scenario. Network is composed of 60 nodes. Simulation results clearly demonstrates that the hybrid approach along with DSDV outperforms over ordinary DSDV routing protocol and it is best suitable under smaller size of WSN.