An efficacious energy model based on Gaussian minimum shift keying (GMSK) for cooperative communication is proposed in this paper. The influence of the rate at which data are transmitted, distance, severity of fading, and quantity of participating nodes on the energy expended for communication by the proposed model is investigated through simulations. The influence of the rate at which data are transmitted and the quantity of participating nodes on the energy expended in circuitry is also studied. Through the simulations, it is apparent that the energy consumed by the circuit dominates the energy consumed for transmission for all of the transmission data rates, path loss exponents, bit error rates, and transmission distances considered.

This chapter’s significance is that it informs readers of how to increase the efficiency of solar panels equipped with indigenous solar tracking devices. If the solar panel efficiency is increased, it becomes easy to power the fish ponds using solar energy, a non-hazardous, renewable, inexhaustible energy source. The key objective is to design a hybrid maximum power tracking algorithm for solar photovoltaic systems with optimal power output to back up the electrical energy crisis for aeration systems in fish ponds. Conversely, the execution of this technique entails an exact control, which is essential to construct a sophisticated system of tracking. By merging the global positioning system (GPS), the artificial neural network (ANN) model with image processing (IP) techniques is utilized to estimate the astronomical characteristics of the sun. This GPS gives the geo-spatial information along with the images of the sun captured by an image sensor. The hybrid algorithm proposed here combines meta-heuristics and conventional ANN using backpropagation algorithm (BPA) with whale optimization perturb and observe (WOPO) and salp swarm perturb and observe (SSPO) algorithms for optimization of weights. The features collected from the sun’s image acquired by the camera utilizing IP approaches are employed as inputs for hybrid algorithms used in a decision-making AI process to distinguish between sunny and cloudy weather situations. The sun tracking system determines the approximate use of astronomical calculations based on the data acquired. Experimentation results are made available on the cloud service for coordination, allowing the proposed high-tech arrangement to be examined and validated.

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.

The core theme of this project is to assess the economic impact of climate change on Indian agriculture. Climate change is caused due to the emission of greenhouse gases like carbon dioxide (CO2), methane (CH4), and nitrous oxide from various industrial sources. Neyveli, being the source of heavy megawatt-generating stations, let out flue gases, which contain CO2, carbon monoxide, oxides of sulfur, CH4, and oxides of nitrogen. These harmful gases are responsible for depletion of the ozone layer, which has a significant effect on variation in weather and agricultural output and sometimes even produces acid rainfall. Considering the probable effects of climatic change on agriculture has motivated a vital change in the yield of agricultural products, livestock yields and also changes in the food production pattern and prices. This estimation of chlorophyll content can be done by extracting green colored pixels from the satellite images or images captured by the vision sensors and soil moisture sensor placed in the Indian agricultural area. These images are preprocessed for noise removal using edge detection technique. From the preprocessed images, feature descriptors like histogram of oriented gradients (HoG) are extracted. The HoG values are fused with the information gathered from soil moisture sensor. The extracted features are reduced using principal component analysis (PCA). The feature set is thereafter used as inputs to artificial neural networks using feed-forward structure trained with backpropagation algorithm (BPA). These estimates done using data analytics will lend a helping hand to the farmers to adapt themselves to the year within annual weather shocks. It can be inferred that the estimates, derived from short term, are capable of predicting the short- and medium-term impacts of climate change, which would direct the farmers to adapt rapidly to the changing climatic conditions. These short- and medium-term impacts of climate change are found to reduce the agricultural productivity by 4%–6% and 6%–9%, respectively. Hence it is inferred that the climate change entails significant impact on the revenue of the Indian economy until and unless the farmers can promptly identify and adjust to decreasing rainfalls and increasing atmospheric temperatures. The first challenge lies in analyzing the satellite images of the farmlands using efficient image processing algorithms to extract useful and meaningful information. This data extracted would be of a very large quantity and needs to be handled using some data analytics algorithm like BPA, whose prediction efficiency will be determined and also validated. The second challenge lies in mapping the emission of greenhouse gases with the images of the farmlands under three categories, namely, highly productive farmlands, medium productive farmlands, and less productive farmlands and correlating the yield of farmlands with respect to emission levels of greenhouse gases in particular environment under study.

Large fraction of people is affected with diabetes all across the world and early detection plays an instrumental role to improve the survival chances. We propose a preliminary preventive measure, which can be introduced just by performing some clinical laboratory tests, results of which when fed into a machine learning classifier (XGBoost) can predict the possibility of diabetes in the concerned individual with an accuracy of almost 87%. In this paper we have worked with Logistic Regression, K Nearest Neighbors, Classification Tree, Random Forest Classifier, XGBoost Classifier, Adaboost Classifier and LGBM Classifier as the different Analysis of Machine Learning Algorithms for Prediction of Diabetes machine learning algorithms to identify which one among these would have the best accuracy and fine-tuned it with 10 folds cross validation to be used as an initial screening process to identify possible individuals having diabetes.

Background: Presently, the diagnosis of coronavirus-2019 (COVID-19) is a challenging task worldwide as the disease is spreading at a very faster rate when one person with the disease comes into contact with the other. Current information denotes that several people are detected with COVID-19 and the data analyst say that the rate of spread of the disease is increasing exponentially, across many countries in the world. Novelty: This investigation has facilitated the need for diagnosing the disease within a short duration of time by using the X-ray images of the lungs. This scheme deploys artificial intelligence like deep learning algorithms to diagnose COVID-19 among the affected people by maintaining social distancing. Real-time datasets are gathered from the government hospitals for those who are affected by COVID-19 and healthy people. Further investigation can direct the patients themselves to open the smart phone app which will record the respiratory sounds. Followed by this, the features are extracted using Discrete Wavelet Transform (DWT), where a threshold is applied to extract useful coefficients that can be used to train the deep learning neural networks using Fast Recurrent Convolutional Neural Networks (F-RCNN). The respiratory audio signals are captured to detect patients affected by coronavirus by a way of noncontact, nonintrusive approach. The results reported are valued in detection of COVID-19 by using a smart phone app which is available instantly. Objectives: This approach seems to be an indigenous, noninvasive, and cost-effective approach that will relive the patients from trauma of undergoing the swab test and awaiting the laboratory reports, which incurs time delay. Experimental results are obtained from 20,000 samples of patients suffering from COVID-19 and also persons who are normal. This mobile phone app is effective in diagnosing the COVID-19 from the X-ray images of the lungs. Even low-income people can also use this technology. Methods: The effectiveness of the proposed system which uses DWT, thresholding, and deep learning algorithms resulted with a performance whose F-measure is 96–98%. The classification is carried out to classify the COVID-19-positive and COVID-19-negative cases using Fast Recurrent Convolutional Neural Networks (F-RCNN). Expected Outcome: A smart phone app will be developed to detect the COVID-19 by using a noninvasive and easily affordable technique. The forecasted results were in the range of 89–95% for the above said algorithms. It is significant from the above results that the severe impact of COVID-19 can be diagnosed using a noninvasive mobile phone app using X-ray images.

The forecast of river water temperature has been carried out based on a variety of weather reports and reports on water bodies on earth. Currently, a range of numerical modeling procedures is used for forecast of river water temperature. For this, the range of input parameters like hourly temperature of river water (Th) and atmospheric temperature (TA) relating to rivers in India is considered. Thus, to develop an indigenous river water prediction model, certain autoregressive inputs based on meteorological and hydrological models are considered. Each neural network type like feed forward neural network (FFNN) with backpropagation algorithm (BPA) and convolution neural network (CNN) is calibrated independently for 1000 iterations and the mean, median and standard deviation are computed and used for the comparison. Finally, all the models are collectively tested. The results demonstrate that CNN in majority cases outperformed the results obtained from the FFNN trained with BPA. The selection of artificial neural network (ANN) models relies on the method by which the river models are evaluated. Hence, one should consider this constraint so as to propose any other equivalent river models. The optimal results are obtained when mean, maximum and minimum daily atmospheric temperatures for the preceding days are used as inputs. The mean squared error is minimized by this collective aggregation technique by 98.2-% for specific cases, and obviously, it reduces the divergence in modeling performance obtained by various ANN models. The optimally efficient model serves as a soft sensor offering inputs to Internet of things (IoT) system. A wireless area network (WAN) is a collection of many sensor nodules, each competent of sampling, dispensation and corresponding one or more environmental parameters like hourly temperature of river water (Th) and atmospheric temperature (TA) relating to rivers in India.

In this paper, the antenna design is modified for better gain in the frequency spectrum proposed for 5G mobile applications in India. The proposed antenna can be implemented using low-cost FR-4 Epoxy substrate with εr=4.4 while maintaining good performance in terms of gain and directivity. The proposed antennas have been characterized using the commercially available software Ansoft’s HFSS (High frequency structure simulator). The performance of the designed antenna is analyzed in terms of bandwidth, gain, return loss and radiation pattern.

The objective of this investigation is to widen the robust detection schemes for the detection of breast cancer at an early stage. This is a noninvasive method called the adaptive neurofuzzy structure (ANFIS) to diagnose microcalcifications (cancerous lesions) in the mammary glands. An investigation of breast cancer by using ANFIS along with the clinical inputs from medical practioners is proposed. Outcomes of the discussions from various medical practioners and various algorithms are reviewed. Clinical images from the MIAS databased are used for testing and training of the proposed algorithm. Texture-based entropy values are also used for training and testing of features. The ANFIS-based classification is an efficient method of breast cancer diagnosis. The performance of the proposed (texture features with ANFIS) method is compared with the outputs obtained from the K-means clustering algorithm, wavelet transform, and artificial neural network-based classification using mean absolute deviation as a feature for early stage diagnosis.

Background: Cancer, the second leading cause of death worldwide is one of the most dreaded non-communicable diseases. Cancer incidence and demographic data form an important basis for cancer prevention. However, the data available through cancer registries are limited. Thus, the present study aimed to describe the epidemiological profile of cancer patients registered in our hospital.Methods: A record based retrospective study was done assessing the records of the cancer patients, admitted during the year 2017 at Coimbatore Medical College Hospital. The data collected included the epidemiological information like demographic details, place distribution and the type of cancer.Results: A total of 1192 confirmed cases of cancer were studied. Among the study subjects, 1014 (50.9%) patients were male and the age group of 50-69 had the maximum percentage of cancer cases (1121 cases, 56.3%). Most cases came from western Tamil Nadu (1138 cases, 95.5%) and majority of them belonged either to lower (560 cases, 46.6%) or upper lower class (548 cases, 45.9%). The lip, oral cavity and pharynx was the type of cancer having the maximum number of cases (429 cases, 36%) and had high proportion in comparison with world level and national statistics. Nearly 57% cases presented with regional extent of disease.Conclusions: The epidemiological factors leading to high incidence of cancer should be analysed and steps towards cancer prevention should be initiated by altering the associated risk factors.

In recent years, a large amount of heat is dissipated from various equipments such as motors, generators, compressors etc. where as a large amount of heat is evolved because of various losses mainly due to resistive heating or D.C offset effect. The heat losses dissipated is the major cause for the reduction in efficiency of the machine. But this heat can also improve the battery capacity when utilized in proper way. According to basic law of conservation of energy, ‘Energy can neither be created nor destroyed, but can be transferred from one form to another’. Thus this can be used as a weapon for the generation of electricity by using an element called Peltier sensor. By using Peltier sensor, the heat losses which are dissipated from the machinery is converted into electrical energy which can be utilized by the same load or can be used to drive other load. The power generated is transmitted by using Wireless Power Transmission technique (WPT) is adopted for transmission of generated power to the load. A wireless charging Mechanism is utilised for various machineries especially electric vehicle to detect and indicate amount of charge transferred the battery using GPRS-GSM module and Internet of Things (IOT) module.

The ZnO-CA nanocomposite film was fabricated using solution casting method. The crystalline nature of the nanofilm was characterized using x-ray diffraction technique and surface morphology was studied by using scanning electron microscopy. The factors affecting Cd (II) ion adsorption in a batch mode were studied including the solution pH, and contact time for determining removal efficiency. The adsorption isotherm could be described well with Langmuir. The maximum monolayer adsorption capacity for the removal of Cd (II) ions was found to be 104.36 mg/g. This shows that, this fabricated nanofilm is capable for using in real time application.

Railways are the most convenient mode of transport, but safety precaution is lagging. Train accidents, due to an unknown person operating the engine, will lead to the end of many lives and also loss of railway property. The optimal solution to meet this problem here proposes the effective system of "Automation of Railway Engine Pilot Security System using Multimodal Biometrics Identification" (AREPSS using MBI). Iris and Fingerprint inputs are given by engine pilot from cabin to control room using Internet of things (IoT). In control room, identifications take place by fusing the inputs and then pass the decision signal to automatically start the engine. The common unimodal biometric system can be seen in most of the places due to its popularity. Its reliability has decreased because it requires larger memory footprint, higher operational cost, and it has slower processing speed. So, we are introducing multimodal biometric identification system which uses iris and fingerprint for security reason. The major advantage of this several modality method is that as both modalities utilized the same matcher component, the reminiscence footprint of the system is reduced. High performance is achieved by integrating multiple modalities in user verification and identification causing high dependability and elevated precision. So this procedure improves the safety in engine and thus helps in saving lives and property.

Investigation of temperature measurement from the flame colour in thermal and gas turbine power plants is of enormous significance in the realm of vision machine technology. The primary objective for this work relies on detection, recognition and understanding of colour image processing for flame colour analysis. In this effort, soft computing methods using Artificial Neural Network (ANN) model with Back Propagation Algorithm (BPA) and Ant Colony Optimisation (ACO) are used for this purpose. The central theme of this work uses the fact that the colour of the flame images is dependent on the temperature. The initial move is to describe a facet quantity for each flame image together with 10 facet rudiments, which are the brightness of flame, the area of the high temperature flame, the brightness of high temperature flame, the rate of area of the high temperature flame, the flame centroid about X and Y, orientation and the two discriminant vectors correspondingly. The superiority of the images used is improved using Curvelet transform. The conception of flame detection and classification is conceded to compute the temperature from its colour. The specimen incorporates 51 flame images, a portion of which is used for trail and testing the ANN and ACO model. Ultimately, the whole specimen flame images are recognized and classified based on the temperatures corresponding to the core of the fire ball. The results are being validated by comparing with the conventional Euclidean classifier. Demonstrations establish an effective and indigenous system for flame temperature measurement. The elucidation states that the Internet of Things (IoT) with the proposed intelligent temperature sensor is connected to the embedded computing system to monitor the fluctuation in flame temperature with respect to colour changes in order to ensure complete combustion. This scheme utilizes wearable electronics technology which constantly monitors and controls the improvement of productivity in power plants. Therefore a flame tracker is deliberated in the projected model and assessed using an archetype, consisting of Arduino UNO board, intelligent temperature sensor and MATLAB with Arduino hardware support package. The realization is used for measurement of the flame temperature with respect to combustion conditions to prevent anomalous operating circumstances thereby providing a feed forward intelligent temperature controller for maximization of flame temperature to make the environment smart.

Railways are the most convenient mode of transport, but safety precaution is lagging. Train accidents due to an unknown person operating the engine will lead to the end of many lives and also loss of railway property. The golden solution to meet this problem here the proposed effective system is 'Automation of Railway Engine Pilot Security System using Multimodal Biometrics Identification' (AREPSS using MBI). Iris and fingerprint inputs are given by engine pilot from cabin to control room. In control room, identification takes place by fusing inputs, then passing the decision signal to automatically start the engine. It is the most commonly used unimodal biometric system, which can be seen in most of the places due to its popularity. Its reliability has decreased because it requires larger memory footprint and higher operational cost and it has slower processing speed. So, we are introducing Multimodal Biometric Identification System which uses iris and fingerprint for security purpose. The major advantage of this multimodal analysis is based on the template-matching phenomenon which utilizes less memory for storage as compared with footprint. User corroboration by multiple modality methods yields high output, high reliability, and high accuracy. So this technique enhances security in engine and thus saves lives and property.

In the present scenario, the world is dependent upon the ways to conserve electrical energy in an effective manner with less cost investment in India. The demand that is lagging in the year 2016–2017 is 300 GW. It is seen that day by day the demand is increasing the government is behind the generation part but conservation is very much essential to reduce the demand in an effective manner, looking over this scenario an initiative has been taken in our University to conduct electrical energy audit and management in an effective manner to reduce the demand and save 10 MW generation in 10 years. The initial work was started under the vision MGR-VISION 10 MW which was inaugurated in our University. The team has completed audit in 25 residential flats, 2 commercial building and 2 industries so far. This paper delivers a lighting layout for a commercial building in which it consist of six floor. Lighting layout of one particular floor is done with electrical energy audit and energy management. The recommendations for the benefits of implementation with breakeven chart are given to reduce the consumption in an effective manner. Recommendation for usage of renewable energy is given so as to reduce the consumption to reduce demand and save electrical utilization bills.

Analysis of combustion quality of flame images of thermal and gas turbine power plants is of great importance. In the domain of image processing the detection, recognition and understanding is the foundation for identifying the combustion condition. Soft sensors are the state of the art. So the flame temperature based on subsequent combustion quality estimation is done using Back Propagation Algorithm (BPA) and Ant Colony Optimization (ACO). The basic idea utilizes the colour information from the flame images. The first step is to define a feature vector. The 9 feature elements, from the samples of 51 flame images are used to train and test the model. Experiments prove this method to be effective. The classification of flame images based on combustion quality is dependent on the flame temperature and colour. The solution includes the Internet of Things (IoT). The intelligent sensors are embedded in the computing system to monitor the combustion quality and flame temperature. This flexible and dispensable form of environment needs continuous monitoring, controlling and behavior analysis in power plants. The prototype implementation consists of Arduino UNO board, intelligent sensors with Arduino hardware support package. The implementation is tested for monitoring the combustion quality and its subsequent flame temperature to provide a feed control for combustion quality monitoring and to make the environment smart.

This research work deals with monitoring of combustion quality in power station Boilers using Service Oriented Architecture which is used to minimize the flue gas emissions at the exit. A model of distributed industrial boilers and integrate it with the Internet through Service Oriented Architectural paradigm is designed. This strategy can be applied to monitor and control the industrial boiler process parameters such as Flame Temperature and Flame intensity. It is proposed to consider the use of service oriented architecture to program and deploy the boiler process parameters. The cost effective technique to develop an intelligent combustion monitoring system is discussed in this paper. A combination of image processing algorithm with Bayesian Classifier is used. The feature extraction was done using Image J and feature reduction was done using Support Vector Machine (SVM). The classification of the flame images based on the features was done using the Bayesian approach whose results are also validated. The combination of the two techniques proved to be beneficial so as to monitor the combustion quality at the furnace level is made possible. Moreover the flue gas emissions are minimized which reduces air pollution. The Service Oriented Architecture is designed to access boiler combustion parameter such as flame intensity as a service and implemented in such a way that for every specific requirement of the monitor/control center, the services of the boilers are invoked through a registry and the specific changes in the combustion parameters are also notified. Different boilers of a power plant can be networked together to monitor different combustion process parameters, and they have been integrated with Internet by registering them as services; hence a complete distributed integration environment is exploited. The aim of this paper is to construct a model of distributed industrial sensors and integrate it with the Internet through Service Oriented Architectural paradigm. This strategy can be applied to monitor and control the industrial process parameters such as Temperature, Pressure, the level of CO, CO2, NOx, and Combustion Quality. It is proposed to consider the use of service oriented architecture to program and deploy the sensed process and pollution parameters. The Service Oriented Architecture for sensor network has been extended to Cloud, to access sensor as a service and implemented in such a way that for every specific requirement of the monitor/control center, the assimilation regulator invoke the services of the sensors through a registry and the specific changes in the sensed parameters are also notified as auditable event using push interaction pattern of SOA. The sensed parameters and the combustion quality level can be viewed through mobile, using appropriate authentication.