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This paper aims to present a non-linear mathematical model-based routing protocol for wireless body area networks (WBANs). Two non-linear mathematical models for WBANs are used in the proposed protocols Model 1 and Model 2. Model 1 intends to improve the data transmission rate and Model 2 intends to reduce energy consumption in the WBANs. These models are simulated for fixed deployment and priority-based data transmission, and performance of the network is analyzed under four constraints on WBANs.

Advancements in wireless technology play a vital role in several applications such as electronic health care, entertainment and games. Though WBANs are widely used in digital health care, they have restricted battery capacity which affects network stability and data transmission. Therefore, several research studies focused on reducing energy consumption and maximizing the data transmission rate in WBANs.

Simulation results of the proposed protocol exhibit superior performance in terms of four network constraints such as residual energy, the stability of the network, path loss and data transmission rate in contrast with conventional routing protocols. The performance improvement of these parameters confirms that the proposed algorithm is more reliable and consumes less energy than traditional algorithms.

The Model 1 of the proposed work provides maximum data extraction, which ensures reliable data transmission in WBANs. The Model 2 allocates minimal hop count path between the sink and the sensor nodes, which minimizes energy consumption in the WBANs.

Detecting cancer from the computed tomography (CT)images of lung nodules is very challenging for radiologists. Early detection of cancer helps to provide better treatment in advance and to enhance the recovery rate. Although a lot of research is being carried out to process clinical images, it still requires improvement to attain high reliability and accuracy. The main purpose of this paper is to achieve high accuracy in detecting and classifying the lung cancer and assisting the radiologists to detect cancer by using CT images. The CT images are collected from health-care centres and remote places through Internet of Things (IoT)-enabled platform and the image processing is carried out in the cloud servers.

IoT-based lung cancer detection is proposed to access the lung CT images from any remote place and to provide high accuracy in image processing. Here, the exact separation of lung nodule is performed by Otsu thresholding segmentation with the help of optimal characteristics and cuckoo search algorithm. The important features of the lung nodules are extracted by local binary pattern. From the extracted features, support vector machine (SVM) classifier is trained to recognize whether the lung nodule is malicious or non-malicious.

The proposed framework achieves 99.59% in accuracy, 99.31% in sensitivity and 71% in peak signal to noise ratio. The outcomes show that the proposed method has achieved high accuracy than other conventional methods in early detection of lung cancer.

The proposed algorithm is implemented and tested by using more than 500 images which are collected from public and private databases. The proposed research framework can be used to implement contextual diagnostic analysis.

The cancer nodules in CT images are precisely segmented by integrating the algorithms of cuckoo search and Otsu thresholding in order to classify malicious and non-malicious nodules.

A rotary kiln is a pyro processing device that is used to raise the temperature of materials in cement factories. Temperature monitoring is an essential process in the rotary kiln to yield high quality clinker. Temperature measurement is a challenging task in clinkering process and it is difficult to apply automation techniques. As the pyrometer gives unreliable readings, it is necessary to apply various image processing techniques on the camera images to measure the temperature inside the kiln at different zones.

In this paper, a fuzzy logic rule-based analysis is proposed to measure temperature using a burning flame image in which it considers red, green, blue (RGB) magnitude planes. The proposed method uses Mamdani fuzzy inference system for decision-making. The system takes RGB magnitude as an input fuzzified variable and generates temperature as fuzzified output.

This paper focuses on the temperature measurement obtained from the images of the camera system. The commands to the valves and actuators are controlled using the center of gravity of the control regime. The fuzzy logic controller detects the temperature of flame zones using color features of burning flame images.

Precise temperature mapping of flame images helps to control the temperature inside the rotating kiln to produce high quality clinker. The process can be viewed remotely and controlled using various control loops from anywhere.