Search

Now days, the human being are affecting by so many diseases in all over the world. If we will consider about the tumor in the encephalon, then detection of tumor often involves radiological imaging. Radiological Imaging is used to check the spread of tumor and progress of treatment and also used to monitor cancer. The manual detection of the Encephalon tumor becomes a meticulous and chaotic task for the clinical experts. If the doctors or various departmental specialists want to eliminate the spoiled cancer areas from Magnetic Resonance Images, then it becomes very hectic and takes more time. The main aim of this work is to detect the encephalon tumor regions. Once the brain is going to be scan then it offers proficient and quick identification of encephalon tumor. To modernize the performance and abbreviate the intricacy involved in the image detection process in the proposed system as well as the Fuzzy CMean predicated image segmentation processes are used. Furthermore, to expand the variance of different images, particularly when the data are worn in image is signified beside different variance of images and then another technique has been implemented in the paper that is Histogram Equalization. The proposed method is completely automatic and tested on different kinds of brain MR images and proved as robust.

Wireless networks (WSN) are sometimes inaccessible to humans and can be found in deep forests, diverse risky fields, high-mountainous areas and even underwater environments at least. Owing to continuous or instant changes in environmental parameters, failures in sensor networks are unavoidable. A fault can lead to a misread, which can harm the environment economically and physically. In several primary applications, wireless sensor networks (WSNs), including battlefield control, environmental monitoring and forestry fire monitoring are widely used. Research is being conducted to reduce energy usage, improve the longevity and life of the WSN network. This paper provides an ANFIS method for the summation, based on the neuro-fuzzy optimization model estimator of defect-tolerant WSNs. For WSNs, the scheme proposes the use of an intra-cluster and inter-cluster failure detection estimator adaptive neuro-fuzzy inference system (ANFIS). Traditional detection strategies for malfunction include a centralized mechanism for avoiding failure detection when the principal defect detector fails. In typical methods, the fault nodes are discarded. To solve these problems, we propose the identification and classification of distributed fault nodes by means of adaptive neuro-fuzzy inference method.

Nowadays, so many people are living in world. If so many people are living, then the diseases are also increasing day by day due to adulterated and chemical content food. The people may suffer either from a small disease such as cold and cough or from a big disease such as cancer. In this work, we have discussed on the encephalon tumor or cancer which is a big problem nowadays. If we will consider about the whole world, then there are deficiency of clinical experts or doctors as compared to the encephalon tumor affected person. So, here, we have used an automatic classification of tumor by the help of particle swarm optimization (PSO)-based extreme learning machine (ELM) technique with the segmentation process by the help of improved fast and robust fuzzy C mean (IFRFCM) algorithm and most commonly feature reduction method used gray level co-occurrence matrix (GLCM) that may helpful to the clinical experts. Here, we have used the BraTs (“Multimodal Brain Tumor Segmentation Challenge 2020”) dataset for both the training and testing purpose. It has been monitored that our system has given better classification accuracy as an approximation of 99.47% which can be observed as a good outcome.

The recent development of IOT emphasizes on the connection of most of the devices by means of smarter and intelligence way. These devices communicate with each other via internet without or/very less communication link of human being. The network of IoT is highly heterogeneous, uncontrolled, broad and perspicacious. All the data centric activities are done by 3 vital steps i.e sensing, analyzing and processing. The Internet of Things (IoT) is a cooperative system which connects a large number of computing devices which includes sensors, actuators, processors to a single IP address and the computing devices of IoT are provided with Unique Identifiers (UIDs). The major goal of IoT is to create a real world environment where frequently used agile objects coherently linked with internet. The growth rate of IoT devices has been increasing at a very high speed, so IOT can find its application in every field. All most all areas are using IoT to improve efficiency, customer service and value of the business, but in all respect IOT devices are still requires security and privacy optimization for future sustainable development.

Petroleum is an essential commodity in today’s world for the survival of mankind. The petroleum reserve in the world is limited and it is expected to get exhausted in the coming future, and the world is witnessing the crisis of this oil. The prediction of petroleum crisis in the world is a challenging problem to deal with. In this paper, we are using a novel and suitable time series prediction approach based on artificial neural network (ANN) for the forecast of future instances of a time series data. The prediction of petroleum crisis in the near future is obtained by a nonlinear and multistep method known as nonlinear autoregressive network (NARnet). The data set is obtained from different government sources of ten countries (like USA, Middle East countries, China and India, etc.) for over a period of more than 30 years and contains three features, viz. population, petroleum production, and petroleum consumption. The NARnet model requires known instances which are employed for the dynamic multistep prediction for the ahead time where one can predict as many numbers of future data instances as looked-for. The normalized mean square error (NMSE) and T-test of our prediction method, i.e., NARnet, have been verified by different standard predictive methods to handle the data set and found to be better in its performance. It has been forecasted that by 2050, hydrogen fuel (which can reduce pollution and boost the fuel efficiency at lower cost) could be thought of as an appropriate replacement for petroleum.

Petroleum is being consumed at a rapid pace all over world, but the amount of petroleum is constant in earth crust and production to consumption requirement is not up to mark. It is expected that a day may come when this world will witness the crisis of this oil. For this our paper addresses the prediction of petroleum crisis in two most populated country of the world i.e. India and China using novel Artificial Neural Network (ANN) based approach. The set of observation comprising three features like population, petroleum production and petroleum consumption are being considered to design the predictive model. Our work shows that petroleum production over consumption with respect to sharp increase of population, leads to a decisive issue in production of an alternative fuel like Hydrogen fuel. In our analysis, we used the data provided by different government sources over a period of more than 30 years and then simulated by a multistep ahead prediction methodology, i.e. nonlinear autoregressive Network (NARnet) to predict petroleum crisis in near future. The results of present study reveals that for India, the Normalized Mean Square Error (NMSE) values found for population petroleum production and consumption are 0.000046, 0.2233 and 0.0041 respectively. Similarly for China the corresponding values are 0.0011, 0.0126 and 0.0041 respectively, which validates the accuracy of the proposed model. The study forecasts that by 2050 hydrogen fuel may be a suitable replacement for petroleum, and will not only reduce pollution, but also enhance the fuel efficiency at lower cost as compared to that of petroleum.