Machine learning-based approach to predict ice meltdown in glaciers due to climate change and solutions.

The fate of humankind and all other life forms on earth is threatened by a foe, known as climate change. All parts of the world are affected directly or indirectly by this phenomenon. The rivers are drying up in some places and in other places, it is flooding. The global temperature is rising every year and the heat waves are taking many souls. The cloud of "extinction" is upon the majority of flora and fauna; even humans are prone to various fatal and life-shortening diseases from pollution. This is all caused by us. The so-called "development" by deforestation, releasing toxic chemicals into air and water, burning of fossil fuels in the name of industrialisation, and many others have made an irreversible cut in the heart of the environment. However, it is not too late; all of this could be healed back with the help of technology and our efforts together. As per the international climate reports, the average global temperature has increased by a little more than 1 °C since 1880s. The research is primarily focused on the use of machine learning and its algorithm to train a model that predicts the ice meltdown of a glacier, given the features using the Multivariate Linear Regression. The research strongly encourages the use of features by manipulating them to determine the feature with a major impact on the cause. The burning of coal and fossil fuels is the main source of pollution as per the study. The research focuses on the challenges to gather data that would be faced by the researchers and the requirement of the system for the development of the model. The study is aimed to spread awareness in society about the destruction we have caused and urges everyone to come forward and save the planet.

Dark Web Data Classification Using Neural Network.

There are several issues associated with Dark Web Structural Patterns mining (including many redundant and irrelevant information), which increases the numerous types of cybercrime like illegal trade, forums, terrorist activity, and illegal online shopping. Understanding online criminal behavior is challenging because the data is available in a vast amount. To require an approach for learning the criminal behavior to check the recent request for improving the labeled data as a user profiling, Dark Web Structural Patterns mining in the case of multidimensional data sets gives uncertain results. Uncertain classification results cause a problem of not being able to predict user behavior. Since data of multidimensional nature has feature mixes, it has an adverse influence on classification. The data associated with Dark Web inundation has restricted us from giving the appropriate solution according to the need. In the research design, a Fusion NN (Neural network)-S3VM for Criminal Network activity prediction model is proposed based on the neural network; NN- S3VM can improve the prediction.

Internet of Medical Things (IoMT) and Reflective Belief Design-Based Big Data Analytics with Convolution Neural Network-Metaheuristic Optimization Procedure (CNN-MOP).

In recent times, the Internet of Medical Things (IoMT) is a new loomed technology, which has been deliberated as a promising technology designed for various and broadly connected networks. In an intelligent healthcare system, the framework of IoMT observes the health circumstances of the patients dynamically and responds to backings their needs, which helps detect the symptoms of critical rare body conditions based on the data collected. Metaheuristic algorithms have proven effective, robust, and efficient in deciphering real-world optimization, clustering, forecasting, classification, and other engineering problems. The emergence of extraordinary, very large-scale data being generated from various sources such as the web, sensors, and social media has led the world to the era of big data. Big data poses a new contest to metaheuristic algorithms. So, this research work presents the metaheuristic optimization algorithm for big data analysis in the IoMT using gravitational search optimization algorithm (GSOA) and reflective belief network with convolutional neural networks (DBN-CNNs). Here the data optimization has been carried out using GSOA for the collected input data. The input data were collected for the diabetes prediction with cardiac risk prediction based on the damage in blood vessels and cardiac nerves. Collected data have been classified to predict abnormal and normal diabetes range, and based on this range, the risk for a cardiac attack has been predicted using SVM. The performance analysis is made to reveal that GSOA-DBN_CNN performs well in predicting diseases. The simulation results illustrate that the GSOA-DBN_CNN model used for prediction improves accuracy, precision, recall, F1-score, and PSNR.