The proposed online-based malnutrition-induced anemia detection smart phone app is built, to remotely measure and monitor the anemia and malnutrition in humans by using a non-invasive method. This painless method enables user-friendly measurements of human blood stream parameters like hemoglobin (Hb), iron, folic acid, and vitamin B12 by embedding intelligent image processing algorithms which will process the photos of the fingernails captured by the camera in the smart phone. This smart phone app extracts the color and shape of the fingernails, will classify the anemic and vitamin B12 deficiencies as onset, medieval, and chronic stage with specific and accurate measurements instantly. On the other dimension, this novel technology will place an end to the challenge involved in the disposal of biomedical waste, thereby offering a contactless measurement system during this pandemic Covid-19 situation.

Screening of nanosized pancreatic tumors, both benign and malignant, is a very important issue in the medical field because it is directly influences the digestive system, which has an effect on human health. Nanobiosensor is the application of nanotechnology in the medical field. This is a multidisciplinary field that currently involves nanotechnology and biomedical applications. The malfunctioning of the pancreas is a health concern because it helps maintain the blood glucose level at a nominal value. Day-to-day food habits have driven the need to develop rapid, responsive, and reliable methods to detect pancreatic tumors. The rapid development of nanosensors that have an advantage to detect variations in the texture of the pancreas in nanometers has paved way to diagnose a malfunctioning pancreas at the onset stage linking nanosensors with modern Information and Communication Technologies (ICTs) enabling novel and online ways of detection accompanied with high accuracy. Various types of nanosensors are being developed to meet different requirements in the field of medicine for detection of various abnormalities related to the organs of the human body. Detection of nanosized pancreatic tumors is the focus of this work. The existence of nanosized pancreatic tumors in the patient leads to early diagnosis. If the tumor is identified in the chronic stage, the chances of survival of the patient are very less. Detection of nanosized tumors will enhance the analysis, diagnosis, and prognosis at the onset stage leading to suitable and timely medication. Currently, this work depends on feature analysis of Magnetic Resonance Imaging (MRI) images obtained from the database to identify the nanosized pancreatic tumors at the onset stage. A distinct diagnosis method is proposed for identification of pancreatic tumors using image texture characters, which were statistically evaluated using MATLAB. Diagnosis was done using Deep Wavelet Neural Networks (DWNN). Using DWNN method, combined with intelligent and pattern recognition algorithms, nearly 99% of sensitivity in the detection of nanosized pancreatic tumors is achieved.

Scrutiny of combustion quality and its equivalent NOx emissions from flame images in thermal and gas turbine power plants is of immense significance in the realm of climate change. A remote monitoring scheme using image processing, Artificial Intelligence (AI) and Internet of Things (IoT) to efficiently minimize the flue gas emissions can be carried out. The principal goal is in detection, recognition and understanding of combustion conditions in power plants ensuring low green house or flue gas emissions which contribute to climate change. In this work, smart sensors using feed forward neural network with Ant Colony Optimisation (ACO) and Particle Swarm Optimization (PSO) are used for estimation of various flue emissions. This scheme uses the information from the colour of the flame images in the combustion chamber at power plants, which is the foundation for obtaining high combustion quality and low flue gas emissions. The initial gait is to describe a facet vector for each flame image including 10 feature elements. Image Enhancement is done to obtain distinctive attributes from the captured images. The perception of object (flame feature) recognition and classification of the flame image is conceded out to measure the combustion quality and flue gas emissions from the flame colour. The samples including some flame images, parts of which are used to train and test the model. Finally, the entire samples are recognized and classified. Experiments prove that flame image classification to be an effective monitoring scheme for reducing the flue gas emissions.

In a hospital, a system by which a patient’s physical condition and physiological parameters can be continuously monitored is essential. For example, parameters such as blood pressure (BP), cardiac rate, and fetal movements need to be taken in order to properly manage their condition. This chapter focuses on a scheme that has the ability to monitor physiological parameters from various patients. In the planned scheme, a director node is attached to the surface of the patient’s skin to gather information from the unwired sensors and transmit it to the ground station. This scheme can sense anomalous conditions and can produce a corresponding alert signal for the patient. At the same time it can pass on a message to the mobile service or send an e-mail to the patient’s general practitioner. In addition, the planned scheme contains a number of reliable, unwired relay nodes used to transmit the information passed on by the director node to the ground station. The key benefit achieved during assessment is a decrease in the energy used to extend the system’s existence, speed up and the extent to which the communication exposure to boost the liberty for increasing the patient’s life. Thus a multi-user design for infirmary healthcare has been developed and its performance weighed against existing accessible networks. It is supported by a multi-hop relay node in line with exposure to energy usage and speed.

Power is utilized as the prime fuel for hybrid and module electric vehicles in order to build the productivity of commercial vehicles. This paper forecasts the emission factors utilizing discrete Fourier transform, artificial neural networks and hybridization of back propagation algorithm. The DFT facilitates the extraction of the performance indicators which are otherwise called the features. The coefficients of the power spectrum denote the performance indicators. The ANN learns the pattern for emissions from HEVs using these performance indicators. This ANN based strategy offers an optimal control action to detect and reduce the exhaust gas emissions which are hazardous. These vehicles are provided with automated highway traffic Jam assist. Hence the forecast of these emissions offers increased efficiency of 90% to 100% thereby ensuring optimal operating condition for the hybrid vehicles.